[NMusers] March Webinar: From data driven to theoretical: improving preclinical decision making with modeling

[Rebecca Baillie]

March Webinar:  From data driven to theoretical: improving preclinical decision 
making with modeling

Amy Moody, PhD
Senior Principal Scientist, Pfizer, Cambridge, MA

March 13, 2024 12:00-1:00 PM EST
Register at https://rosaandco.com/webinars

At Pfizer, modeling in the preclinical space is used in numerous ways to place 
programs in the appropriate quantitative context and supports key decision 
making on the path to clinical development. In this presentation, we will give 
two examples where modeling was at the center of decisions to progress or 
terminate early programs.

Tafamidis is a small molecule TTR stabilizer and was the first treatment 
approved for amyloid cardiomyopathy (ATTR-CM). While tafamidis delays disease 
progression and provides substantial clinical benefit, it does not completely 
arrest disease progression and Pfizer was interested in whether a more potent 
molecule could provide additional clinical benefit. We will describe analysis 
of clinical and preclinical data that concluded tafamidis captures greater than 
90% of the horsepower of this mechanism which led to the decision to terminate 
a follow-on program.

Three mechanisms all aim to treat sickle cell disease by preventing 
polymerization of mutated hemoglobin (HbS). We will describe a model that was 
developed to predict the required level of target modulation by each mechanism 
alone and in combination. This model helped identify programs with the highest 
likelihood of success as well as faster paths to the clinic through combination 
strategies.

These examples show how very different approaches (patient data driven vs. 
theoretical model) can be applied to preclinical programs to assess confidence 
in the target, set clear goals for program progression, and chart more 
efficient paths towards clinical success.

[NMusers] February Modeling Webinar: The Long Road from Discovery to Design

[Rebecca Baillie]

The Long Road from Discovery to Design

Brian Topp, Senior Director, Merck, Rahway, NJ

Feb 21, 2024 12:00-1:00 PM EST

Registration is free: https://rosaandco.com/webinars

Abstract: Lockheed Martin does not discovery fighter jets.  Ferrari dose not 
discover F1 race cars.  Increasingly, the pharmaceutical industry does not 
discover drugs.  Novel therapies are engineered.  Target selection is dependent 
on reverse engineering of complex physiologic systems.  Molecules are designed 
to display desired characteristics via knowledge of  3D structural chemistry.  
Patient selection, trial design, and combination therapies are increasingly 
dependent on causal (physiologic-based) mathematical models.  Today I will 
provide some insights gained during my 20 years as a systems modeler then 
describe some recent finding from our systems oncology group focusing on 
lesion-to-lesion heterogeneity in patients with advanced cancer.

[NMusers] January webinar: Clinical Trials For Patients Who Are Not Average

[Rebecca Baillie]

Clinical Trials For Patients Who Are Not Average
By Tom Yankeelov, Ph.D.
W.A. "Tex" Moncrief Chair of Computational Oncology; Director, Center for 
Computational Oncology, Oden Institute for Computational Engineering and 
Sciences; Director, Cancer Imaging Research, Livestrong Cancer Institutes; 
Co-leader, Quantitative Oncology Research Program, Livestrong Cancer 
Institutes; Adjunct Professor of Imaging Physics, MD Anderson Cancer Center; 
Professor of Biomedical Engineering, Diagnostic Medicine, Oncology. U.Texas, 
Austin, Texas
Wednesday, January 17, 2024, 12:00-1:00 PM EST
Register at https://rosaandco.com/webinars
Abstract:
Our lab is focused on developing tumor forecasting methods by integrating 
advanced imaging technologies with mathematical models to predict tumor growth 
and treatment response. In this presentation, we will focus on how quantitative 
magnetic resonance imaging (MRI) data can be employed to calibrate mathematical 
models built on first-order effects related to well-established "hallmarks" of 
cancer including proliferation, migration/invasion, vascular status, and 
drug-related tumor growth inhibition and cell death. In particular, we will 
present some of our recent results through four vignettes focusing on breast 
and brain cancer: 1) incorporating patient-specific data into mechanism-based 
mathematical models, 2) predicting and optimizing outcomes via patient-specific 
digital twins, 3) guiding interventions through applications of optimal control 
theory, and 4) updating predictions through data assimilation. The long-term 
goal of this set of studies is to provide a rigorous methodology that is 
practical enough for predicting--and optimizing--therapeutic interventions on a 
patient-specific basis.

[NMusers] Dec. Webinar: The Q-ATN Model of Alzheimer’s Disease: A Work in Progress

[Rebecca Baillie]

The Q-ATN Model of Alzheimer’s Disease: A Work in Progress
Norman A. Mazer, M.D., Ph.D., Founder and Disease Modeling Consultant, NAM 
Consulting, Basel, Switzerland

Wednesday, December 13, 2023, 12:00-1:00 PM EST

Register at https://rosaandco.com/webinars

Abstract:
The Q-ATN model is a quantitative, semi-mechanistic model of Alzheimer’s 
Disease (AD) based on the amyloid (A)/tau (T)/neurodegeneration (N) framework 
proposed by Clifford Jack Jr. and colleagues [1, 2]. It describes both the 
natural history of AD as well as the effect of anti-amyloid therapy on A/T/N 
biomarkers and the clinical outcome measure, CDR-SB. A full description of the 
Q-ATN model was published one year ago [3] and an update, based on the phase 3 
results reported for gantenerumab [4] and lecanemab [5] was presented at the 
AAIC 2023 meeting [6].

The objectives of this presentation will be to:

Describe the 4 sequential linkages between the A/T/N biomarkers and CDR-SB 
that are hypothesized in the Q-ATN model and the data that informed them.
Explain how treatment with anti-amyloid antibodies can “bend” the 
trajectory of AD relative to natural history or placebo groups.
Compare Q-ATN simulations with the available phase 3 trial data on 
anti-amyloid antibodies including the recent results on Donanemab [7].
Discuss the current strengths and weaknesses of the Q-ATN model and the 
potential for further development.

References:
Jack CR Jr, et al. A/T/N: an unbiased descriptive classification scheme for 
Alzheimer disease biomarkers. Neurology. 2016; 87: 539-547.
Jack CR Jr, et al. NIA-AA Research Framework: toward a biological definition of 
Alzheimer's disease. Alzheimer’s & Dementia. 2018; 14: 535-562.
Mazer NA, et al. Development of a quantitative semi‐mechanistic model of 
Alzheimer's disease based on the amyloid/tau/neurodegeneration framework (the 
Q‐ATN model). Alzheimer’s & Dementia. 2023 Jun;19(6):2287-97. (First published 
10 December 2022; https://doi.org/10.1002/alz.12877)
Bateman RJ, et al. GRADUATE I and II: Topline Results of Two Global, Phase III, 
Randomized, Placebo-Controlled Studies Assessing the Efficacy and Safety of 
Subcutaneous Gantenerumab in Early Alzheimer’s Disease. Presented at CTAD 2022, 
San Francisco, CA, USA.
Bateman RJ, et al. Imaging, Plasma, and CSF Biomarkers Assessments from Clarity 
AD. Presented at CTAD 2022, San Francisco, CA, USA.
Boess F. et al. Re-estimation of drug-specific amyloid removal parameters and 
the rate constant for pathogenic tau turnover brings the Q-ATN model into 
better alignment with recent phase 3 data from gantenerumab and lecanemab. 
Presented at AAIC 2023, Amsterdam, The Netherlands.
Sims JR, et al. Donanemab in early symptomatic Alzheimer disease: the 
TRAILBLAZER-ALZ 2 randomized clinical trial. JAMA. 2023 Aug 8;330(6):512-27.

[NMusers] Nov. Webinar: Understand Tumor Response Heterogeneity in Colorectal Cancer: Share the similarities, celebrate the differences.

[Rebecca Baillie]

Understand Tumor Response Heterogeneity in Colorectal Cancer: Share the 
similarities, celebrate the differences.
Jiawei Zhou, PhD, Pharmacometrician, Pfizer Inc., New York
Wednesday, November 15, 2023, 12:00-1:00 PM EST
Register at https://rosaandco.com/webinars
Abstract:
Achieving systemic tumor control across metastases is vital for long-term 
patient survival but remains intractable in many patients. High lesion-level 
response heterogeneity persists, conferring many dissociated responses across 
metastatic lesions. Most studies of metastatic disease focus on tumor molecular 
and cellular features, which are crucial to elucidating the mechanisms 
underlying lesion-level variability. However, our understanding of 
lesion-specific heterogeneity on the macroscopic level, such as lesion dynamics 
in growth, response, and progression during treatment, remains rudimentary.
We integrated statistical methods, pharmacometrics models, and machine learning 
algorithms to analyze inter-lesion heterogeneity in metastatic colorectal 
cancer patients. The lesion response and progression differences were 
associated with treatment efficacy and patient survival. The lesion response 
heterogeneity across metastases informed drug efficacy and patient survival, 
which could improve the current methods for treatment evaluation and patient 
prognosis. In conclusion, our study provides insights into lesion-specific 
response and progression heterogeneity in mCRC and creates impetus for 
metastasis-specific therapeutics.

[NMusers] Oct. Webinar: PK/PD modelling of eflornithine: Assessment of an oral treatment for the fatal parasitic disease human African trypanosomiasis

[Rebecca Baillie]

Mikael Boberg, PhD, Senior Scientist, AstraZeneca R, Gothenburg, Sweden
Wednesday, October 11, 2023, 9:00 to 10:00 am PDT
Register (free) at https://rosaandco.com/webinars
Abstract: Human African trypanosomiasis, also known as sleeping sickness, is a 
fatal parasitic disease if not treated. It is a vector-borne disease endemic in 
countries in sub-Saharan Africa.
Eflornithine is a drug used to treat gambiense human African trypanosomiasis 
(g-HAT) in the later disease stage, i.e., when the parasites have invaded the 
central nervous system. Eflornithine is currently dosed as a racemic mixture of 
D- and L-eflornithine via repeated intravenous infusions, which comes with 
several disadvantages in terms of both logistics and drug dosing in hospitals.
 In this talk, the potential for an oral eflornithine treatment are discussed 
with an outline that spans from lab-based in vitro efficacy studies via in vivo 
pharmacokinetic (PK) studies to clinical modelling & simulation where 
assessment for a potentially efficacious clinical dosing regimens for 
eflornithine are made with a modelling & simulation approach.

[NMusers] Sept. Webinar: A Pharmacodynamic Model for the Assessment and Optimization of PROTACs

[Rebecca Baillie]

A Pharmacodynamic Model for the Assessment and Optimization of PROTACs


Robin T.U. Haid, MSc, PhD Student, Bayer AG, Preclinical Development, Drug 
Metabolism and Pharmacokinetics, Preclinical Modeling and Simulation / ETH 
Zurich, Institute of Pharmaceutical Sciences, Biopharmacy

Wednesday, September 13, 2023, 9:00 to 10:00 am PDT

Register 
for
 free at https://rosaandco.com/webinars.


Abstract:
Small molecule proteolysis targeting chimeras (PROTACs) represent an exciting 
new therapeutic modality as they produce a knockdown phenotype while also 
carrying the potential for oral administration. Turning conventional inhibitors 
into PROTACs has proven to constitute a viable strategy for increasing potency 
and generating long-lasting pharmacological effects. However, many of the 
learnings from optimizing inhibitors are not directly applicable to PROTACs due 
to their fundamentally different mechanism of action.

Here, we examine this topic using a new comprehensive mathematical model that 
follows the four pillars of translational pharmacology up to biomarker readout. 
The model is applied to a variety of PROTAC compounds, both from literature as 
well as from in-house projects, yielding several key insights. First, we 
demonstrate how target exposure is translated to target engagement and then 
target degradation, pointing out the most critical parameters. Based on that 
mechanistic understanding, the basic Emax model is adapted to account for the 
peculiarities of the PROTAC concentration-response profile. Target degradation 
is further linked to biomarker readouts and the contribution of target 
inhibition to overall efficacy is evaluated. Finally, we discuss implications 
of our findings for drug discovery and we derive actionable strategies for the 
characterization and optimization of PROTACs.

The overarching aim of this talk is to allow researchers to tailor their 
experimental work and to arrive at a better understanding of their results, 
ultimately leading to more successful PROTAC discovery.

[NMusers] Webinar: Proteomics-Informed PBPK Modeling to Predict Systemic and Tissue Drug Concentrations in Rats

[Rebecca Baillie]

Proteomics-Informed PBPK Modeling to Predict Systemic and Tissue Drug 
Concentrations in Rats
Sheena Sharma, PhD, Research Investigator, PBPK, Clinical Pharmacology, & 
Pharmacometrics, Bristol Myers Squibb, Lawrenceville, NJ
Wednesday, August 16, 2023, 9:00 to 10:00 am PDT
Register for free at https://rosaandco.com/webinars
Abstract:
The majority of drugs fail in clinical trials due to limited efficacy or 
safety, partly because of the inability to measure drug concentrations in 
target tissues where efficacy or toxicity occurs. Moreover, translating 
preclinical data to humans is often challenging when a drug undergoes 
substantial metabolism and transport, given the differences in the abundance of 
drug-metabolizing enzyme and transporter (DMET) proteins.

The recent bill to implement Food and Drug Administration (FDA) Modernization 
Act 2.0 advocates for alternative methods for testing drug efficacy and safety. 
Physiologically based pharmacokinetic (PBPK) modeling is a reliable alternative 
to predict tissue and systemic drug concentrations, utilizing in vitro 
laboratory assays, DMET protein abundance, and physiology. Regulatory agencies 
such as the FDA and European Medicines Agency, also encourage to use PBPK 
modeling to support the drug development lifecycle and regulatory 
decision-making.

This webinar presents a quantitative map of clinically relevant DMET proteins 
in the liver and intestinal segments of rats using quantitative global 
(untargeted) and targeted proteomics approaches and its integration in PBPK 
modeling. The session showcases the successful application of proteomics 
informed PBPK modeling to reliably predict systemic and tissue concentrations 
of digoxin as a case study.

[NMusers] August Webinar: Proteomics-Informed PBPK Modeling to Predict Systemic and Tissue Drug Concentrations in Rats

[Rebecca Baillie]

Proteomics-Informed PBPK Modeling to Predict Systemic and Tissue Drug 
Concentrations in Rats

Sheena Sharma, PhD, Research Investigator, PBPK, Clinical Pharmacology, & 
Pharmacometrics, Bristol Myers Squibb, Lawrenceville, NJ

Wednesday, August 16, 2023, 9:00 to 10:00 am PDT

Register for free at https://rosaandco.com/webinars

Abstract:
The majority of drugs fail in clinical trials due to limited efficacy or 
safety, partly because of the inability to measure drug concentrations in 
target tissues where efficacy or toxicity occurs. Moreover, translating 
preclinical data to humans is often challenging when a drug undergoes 
substantial metabolism and transport, given the differences in the abundance of 
drug-metabolizing enzyme and transporter (DMET) proteins.

The recent bill to implement Food and Drug Administration (FDA) Modernization 
Act 2.0 advocates for alternative methods for testing drug efficacy and safety. 
Physiologically based pharmacokinetic (PBPK) modeling is a reliable alternative 
to predict tissue and systemic drug concentrations, utilizing in vitro 
laboratory assays, DMET protein abundance, and physiology. Regulatory agencies 
such as the FDA and European Medicines Agency, also encourage to use PBPK 
modeling to support the drug development lifecycle and regulatory 
decision-making.

This webinar presents a quantitative map of clinically relevant DMET proteins 
in the liver and intestinal segments of rats using quantitative global 
(untargeted) and targeted proteomics approaches and its integration in PBPK 
modeling. The session showcases the successful application of proteomics 
informed PBPK modeling to reliably predict systemic and tissue concentrations 
of digoxin as a case study.

[NMusers] Webinar on Wednesday: Modeling IFNα signaling

[Rebecca Baillie]

Mechanistic insights into sensitization/desensitization of IFNα signaling and 
its effect on patient treatment strategies.


Dr. Marcus Rosenblatt, PhD
University of Freiburg, Freiburg, Germany

July 12, 2023, 12:00-1:00 PM EDT


Register for free at 
https://register.gotowebinar.com/register/8578933309977451096


Abstract: Interferon alpha (IFNα) orchestrates the antiviral response in 
hepatocytes as a key component of the innate immune system. The IFNα signal 
transduction pathway is known to desensitize upon activation, which constitutes 
a significant problem for using IFNα as a treatment against chronic viral 
infections or as an anti-tumor drug. However, the mechanisms that lead to this 
desensitization remain poorly understood.

In collaboration with the German Cancer Research Center, we developed an ODE 
model that describes the biochemical reaction network of IFNα signaling in 
different hepatoma cell lines and primary human hepatocytes.

This talk will present how we learned from the model that, in addition to a 
dose-dependent desensitization mediated by the negative feedback components 
SOCS1 and USP18 acting at the receptor level, the IFNα signaling pathway can 
also show (hyper-)sensitization/priming due to upregulation of the 
intra-cellular components IRF9 and STAT2.

In addition, we will discuss how the findings from this research help us to 
understand and predict the dynamics of the production of Interferon Stimulated 
Genes (ISGs), which exert numerous antiviral and anti-inflammatory effector 
functions, and how this fosters patient-individual optimization of IFNα 
treatment strategies.

[NMusers] July Webinar: Modeling IFNα signaling and optimizing treatment strategies

[Rebecca Baillie]

Mechanistic insights into sensitization/desensitization of IFNα signaling and 
its effect on patient treatment strategies.

Dr. Marcus Rosenblatt, PhD
University of Freiburg, Freiburg, Germany
July 12, 2023, 12:00-1:00 PM EDT

Register for free at 
https://register.gotowebinar.com/register/8578933309977451096

Abstract: Interferon alpha (IFNα) orchestrates the antiviral response in 
hepatocytes as a key component of the innate immune system. The IFNα signal 
transduction pathway is known to desensitize upon activation, which constitutes 
a significant problem for using IFNα as a treatment against chronic viral 
infections or as an anti-tumor drug. However, the mechanisms that lead to this 
desensitization remain poorly understood.

In collaboration with the German Cancer Research Center, we developed an ODE 
model that describes the biochemical reaction network of IFNα signaling in 
different hepatoma cell lines and primary human hepatocytes.

This talk will present how we learned from the model that, in addition to a 
dose-dependent desensitization mediated by the negative feedback components 
SOCS1 and USP18 acting at the receptor level, the IFNα signaling pathway can 
also show (hyper-)sensitization/priming due to upregulation of the 
intra-cellular components IRF9 and STAT2.

In addition, we will discuss how the findings from this research help us to 
understand and predict the dynamics of the production of Interferon Stimulated 
Genes (ISGs), which exert numerous antiviral and anti-inflammatory effector 
functions, and how this fosters patient-individual optimization of IFNα 
treatment strategies.

[NMusers] June Webinar: A QSP Model to Understand Clinical Cytokine Dynamics Following Bispecific Dosing in Solid Tumors

[Rebecca Baillie]

June Webinar: A QSP Model to Understand Clinical Cytokine Dynamics Following 
Bispecific Dosing in Solid Tumors

Jared Weddell, PhD, Senior Manager, Clinical Pharmacology and Exploratory 
Development
Astellas Pharma US, Northbrook, IL

Wednesday, June 14, 2023, 9:00 to 10:00 am PDT
Register for free at www.rosaandco.com/webinars

Abstract:
Cytokine release syndrome (CRS) is a common clinical adverse effect observed 
following CD3-based bispecific dosing. However, the pathophysiology of CRS is 
not fully understood, and no computational model mechanistically describing 
clinical cytokine dynamics following bispecific dosing in solid tumors exists. 
A quantitative systems pharmacology (QSP) model describing peripheral clinical 
cytokine dynamics following bispecific dosing in solid tumors is presented. 
Using tebentafusp as a case study, a CD3-bispecific approved for uveal 
melanoma, the QSP model captures biological phenomena such as cytokine 
attenuation using step-up dosing regimens and the importance of on-target 
off-tumor binding towards CRS. The QSP model additionally serves as a platform 
for other CD3-based bispecifics or tumor types, supporting applications 
including dose selection, candidate nomination, and disease area selection.

[NMusers] May Webinar: Leveraging machine learning strategies for nonlinear mixed effects model selection

[Rebecca Baillie]

Leveraging machine learning strategies for nonlinear mixed effects model 
selection (using pyDarwin)
By Robert R. Bies, Pharm.D. Ph.D. FISoP
Professor of Pharmaceutical Sciences, Member Institute for Artificial 
Intelligence and Data Science. University at Buffalo, Buffalo NY
Wednesday, May 17, 2023 at 9 am -10 am PDT
Register for free at 
https://attendee.gotowebinar.com/register/8634611478568290907 or see 
https://rosaandco.com/webinars
Abstract: The application of machine learning approaches to pharmacokinetic and 
pharmacodynamic measurements is becoming more widespread.  Most of these 
approaches are used to predict these measurements without providing inferential 
insights.  This presentation focuses on the application of machine learning for 
nonlinear mixed effects PK model selection.  It was recognized almost three 
decades ago that there are significant interactions between structural, 
statistical and covariate models that result in very different inferences with 
respect to model structure, covariate and random effects (Wade 1994).  
Classical stepwise approaches to model development are particularly susceptible 
to these interactions.  Previous applications of a genetic algorithm-based 
model search strategy illustrated superiority of this approach to stepwise 
model evaluation based on typical model fitness considerations (Bies 2006, 
Sherer 2012, Sale 2015).  The machine learning strategies described in this 
talk present an alternative means of evaluating the model search space that may 
provide greater insight into these interactions while optimizing the numerical 
characteristics of the solutions obtained.  This is illustrated using the 
implementation of these techniques (Random Forest, Gaussian Process/Bayesian 
Optimization, Genetic Algorithm among others) in the open source pyDarwin 
software.
Wade, J.R., Beal, S.L. & Sambol, N.C. Interaction between structural, 
statistical, and covariate models in population pharmacokinetic analysis. 
Journal of Pharmacokinetics and Biopharmaceutics 22, 165-177 (1994). 
https://doi.org/10.1007/BF02353542

Sale M, Sherer EA. A genetic algorithm based global search strategy for 
population pharmacokinetic/pharmacodynamic model selection. Br J Clin 
Pharmacol. 2015 Jan;79(1):28-39. doi: 10./bcp.12179. PMID: 23772792; PMCID: 
PMC4294074.

Sherer EA, Sale ME, Pollock BG, Belani CP, Egorin MJ, Ivy PS, Lieberman JA, 
Manuck SB, Marder SR, Muldoon MF, Scher HI, Solit DB, Bies RR. Application of a 
single-objective, hybrid genetic algorithm approach to pharmacokinetic model 
building. J Pharmacokinet Pharmacodyn. 2012 Aug;39(4):393-414. doi: 
10.1007/s10928-012-9258-0. Epub 2012 Jul 6. PMID: 22767341; PMCID: PMC3400037.

Bies RR, Muldoon MF, Pollock BG, Manuck S, Smith G, Sale ME. A genetic 
algorithm-based, hybrid machine learning approach to model selection. J 
Pharmacokinet Pharmacodyn. 2006 Apr;33(2):195-221. doi: 
10.1007/s10928-006-9004-6. Epub 2006 Mar 28. PMID: 16565924.

[NMusers] Webinar: QSP Modeling to Support a New Dose Application

[Rebecca Baillie]

Demonstrating Efficacy When Clinical Trials Are Impossible: QSP Modeling to 
Support a 505(b)(2) Application
Rebecca Baillie, PhD, Christina Friedrich, PhD, Jake Nichols, PharmD, MBA
Principal Scientist, Chief Engineer, Rosa & Co. San Carlos, CA; Director of 
Medical Affairs, US WorldMeds, Louisville, KY

Wednesday, April 19, 2023, 9:00 to 10:00 am PDT

Register at https://rosaandco.com/webinars

Abstract:
For over 50 years, fentanyl has been used in anesthesia and pain control, with 
naloxone as a counter agent to reverse overdoses. The increasing abuse of 
synthetic opioids has necessitated expanding the use of naloxone from hospitals 
and clinics to community use. The need for increased naloxone dosing, speed of 
onset, and ease of use has led to the development of intramuscular (IM) dosing 
devices for community use. Because overdose clinical trials are problematic to 
implement and require consent after the opioid has been administered, the FDA 
has recommended modeling of opioid receptor displacement of potent opioids with 
naloxone as an alternative method to guide dose recommendations.
This webinar discusses the development of a mu-opioid receptor binding 
quantitative systems pharmacology (QSP) model with competing drug treatments 
(naloxone, fentanyl). The model was used to support regulatory approval of a 
higher clinical dose of naloxone in an IM device to address the unmet medical 
need. Simulations using the model demonstrated the added utility of higher 
naloxone doses in displacing fentanyl from the mu receptor. QSP modeling 
demonstrated that higher doses of naloxone lead to a greater displacement of 
fentanyl from the mu receptor and reduce the time it takes for fentanyl binding 
to dip below a critical threshold for overdose reversal. The modeling increased 
confidence in the added utility of the higher-dose naloxone product and 
supported approval by the FDA.

[NMusers] Present an Early Career Investigator Webinar

[Rebecca Baillie]

Reminder to send in an abstract for

Rosa’s Early Career Investigator Webinars

Rosa is seeking PhD students, postdoctoral scholars, and early researchers in 
academia & industry to present as part of the Rosa World Wide Webinar series. 
These webinars will support outstanding early career investigators by providing 
an opportunity for them to show their research to a broad audience. We are 
looking for presenters whose research advances the adoption of modeling and 
simulation in drug development.

If you are an early career investigator with an interesting research 
presentation, please send a title, abstract, and a short bio to 
webi...@rosaandco.com.

Rosa has been hosting the monthly “Impact” webinar series since 2011 and each 
Webinar draws several hundred attendees from a mix of drug development 
disciplines. The speakers and audience come from both industry and academia. 
Each Webinar includes a slide presentation followed by a brief discussion, 
lasting about an hour total. After the webinar, a recording of the webinar and 
slides from the webinar are posted on the webinar archive site. For your 
reference, you can see archived sessions and the upcoming Webinar schedule 
here: http://www.rosaandco.com/webinars.

Deadline to apply to present as an early career investigator is April 1, 2023

RE: [NMusers] March Modeling Webinar: Pharmacokinetic modeling for antivenom development

[Rebecca Baillie]

This is a great example for the use of PK/PD and should be of interest to 
anyone developing venom-based drugs or antibody/nanobody drugs.

Defining Design Rules for Next-Generation Snakebite Antivenoms
Natalie Morris
University of Bristol, Bristol, UK
Wednesday, March 15th at 9:00 PDT
Register for free at https://rosaandco.com/webinars
Abstract
Snakebite is a neglected tropical disease which causes over 100,000 deaths and 
400,000 cases of disability each year. Snakebite is treated using antivenoms, 
which are currently produced by hyper-immunising horses against a venom and 
harvesting their toxin-neutralising antibodies. There is an urgent need to 
improve the way that we design and produce antivenoms, owing to limitations in 
their cost, efficacy, and safety. In recent years, in vitro antibody selection 
has made new antivenom scaffolds accessible for researchers. There is currently 
no consensus as to the pharmacokinetic properties of an optimised antivenom, 
and whether these change depending on the type of venom being treated. To 
address this question computationally, we built a compartmental model of 
snakebite envenomation and treatment. The model tracks the movement of venom, 
antivenom, and neutralised venom through blood and tissue. The model was 
parameterised with experimental data from rabbits. It enables user-control of 
several treatment scenario parameters and antivenom design parameters 
(antivenom size, dose, affinity, and valency).

We have applied our model to explore the impact of different antivenom design 
features on treatment outcome. We simulated treatment of two model venoms with 
a set of theoretical antivenoms, across a range of treatment time delays. 
Global parameter optimisation and global sensitivity analysis show kon to 
primarily mediate treatment efficacy. While molecular weight has a negligible 
direct impact on treatment outcome, low molecular weight scaffolds can be more 
easily designed for optimised treatment, particularly when treatment is 
delayed. The same underlying trends are seen for both venom types tested. This 
approach can be used to elucidate the dynamics of envenomation-treatment 
systems, and inform the development of next-generation antivenoms.

[NMusers] Early career investigators wanted to give a webinar

[Rebecca Baillie]

Apply to present in Rosa's Early Career Investigator Webinars
Rosa is seeking PhD students, postdoctoral scholars, and early researchers in 
academia & industry to present as part of the Rosa World Wide Webinar series. 
These webinars will support outstanding early career investigators by providing 
an opportunity for them to show their research to a broad audience. We are 
looking for presenters whose research advances the adoption of modeling and 
simulation in drug development.

If you are an early career investigator with an interesting research 
presentation, please send a title, abstract, and a short bio to 
webi...@rosaandco.com.

Rosa has been hosting the monthly "Impact" webinar series since 2011 and each 
Webinar draws several hundred attendees from a mix of drug development 
disciplines. The speakers and audience come from both industry and academia. 
Each Webinar includes a slide presentation followed by a brief discussion, 
lasting about an hour total. After the webinar, a recording of the webinar and 
slides from the webinar are posted on the webinar archive site. For your 
reference, you can see archived sessions and the upcoming Webinar schedule 
here: http://www.rosaandco.com/webinars.

Deadline to apply to present as an early career investigator is April 1, 2023

[NMusers] March Modeling Webinar: Pharmacokinetic modeling for antivenom development

[Rebecca Baillie]

Defining Design Rules for Next-Generation Snakebite Antivenoms
Natalie Morris
University of Bristol, Bristol, UK
Wednesday, March 15th at 9:00 PDT
Register for free at https://rosaandco.com/webinars
Abstract
Snakebite is a neglected tropical disease which causes over 100,000 deaths and 
400,000 cases of disability each year. Snakebite is treated using antivenoms, 
which are currently produced by hyper-immunising horses against a venom and 
harvesting their toxin-neutralising antibodies. There is an urgent need to 
improve the way that we design and produce antivenoms, owing to limitations in 
their cost, efficacy, and safety. In recent years, in vitro antibody selection 
has made new antivenom scaffolds accessible for researchers. There is currently 
no consensus as to the pharmacokinetic properties of an optimised antivenom, 
and whether these change depending on the type of venom being treated. To 
address this question computationally, we built a compartmental model of 
snakebite envenomation and treatment. The model tracks the movement of venom, 
antivenom, and neutralised venom through blood and tissue. The model was 
parameterised with experimental data from rabbits. It enables user-control of 
several treatment scenario parameters and antivenom design parameters 
(antivenom size, dose, affinity, and valency).

We have applied our model to explore the impact of different antivenom design 
features on treatment outcome. We simulated treatment of two model venoms with 
a set of theoretical antivenoms, across a range of treatment time delays. 
Global parameter optimisation and global sensitivity analysis show kon to 
primarily mediate treatment efficacy. While molecular weight has a negligible 
direct impact on treatment outcome, low molecular weight scaffolds can be more 
easily designed for optimised treatment, particularly when treatment is 
delayed. The same underlying trends are seen for both venom types tested. This 
approach can be used to elucidate the dynamics of envenomation-treatment 
systems, and inform the development of next-generation antivenoms.

[NMusers] Webinar: A case‐study of model‐informed drug development

[Rebecca Baillie]

A case‐study of model‐informed drug development of a novel PCSK9 anti sense 
oligonucleotide
Presented by Dr Dinko Rekic, PhD and Dr Jane Knöchel, PhD
Global Product Leader – ticagrelor; Clinical Pharmacometrician, AstraZeneca, 
Gothenburg, Sweden
February 8, 2023, 9:00-10:00 AM PST
Register at https://rosaandco.com/webinars
Abstract:
This talk focuses on model‐informed drug development (MIDD) of a novel 
antisense oligonucleotide, targeting PCSK9 for treatment of hypocholesteremia. 
The case study exemplifies use of MIDD to analyze emerging data from an ongoing 
first‐in‐human study, utility of the US Food and Drug Administration MIDD pilot 
program to accelerate timelines, innovative use of competitor data to set 
biomarker targets, and use of MIDD to optimize sample size and dose selection, 
as well as to accelerate and de‐risk a phase IIb study. The focus of the 
case‐study is on the cross‐functional collaboration and other key MIDD enablers 
that are critical to maximize the value of MIDD, rather than the technical 
application of MIDD.

[NMusers] Vincent Lemaire and Fei Hua webinar: Perception of the Use and Impact of QSP in immuno-oncology

[Rebecca Baillie]

"From Cold to Hot: Perception of the Use and Impact of QSP in immuno-oncology – 
A Survey of the Community and Stakeholders".

Vincent Lemaire, Distinguished Scientist Modeling and Simulation, Genentech, 
South San Francisco, CA
Fei Hua, VP, Head of modeling and simulation services, Applied BioMath Concord, 
MA

January 18th at 9 am PST

Register for free at https://rosaandco.com/webinars

Abstract
Immuno-oncology (IO) is a fast-expanding field due to recent successes in 
treating cancer. It is also a challenging field where therapeutics have to 
leverage the complex interactions between a tumor and the immune system. 
Approaches that can make sense of this complexity are required to better inform 
IO therapy research and development. Quantitative systems pharmacology (QSP) 
modeling has the potential to address some of the challenges in the IO field, 
by representing biological mechanisms of disease and the mode of action of 
drugs with mathematical equations. To assess the perspectives of the community 
on the impact of QSP modeling in IO drug development and to understand current 
applications and challenges, the IO QSP working group-under the QSP Special 
Interest Group (SIG) of the International Society of Pharmacometrics 
(ISoP)-conducted a survey among QSP modelers, non-QSP modelers, and 
non-modeling IO program stakeholders. The survey results are presented here 
with discussions on how to address some of the key findings.

[NMusers] Webinar: Variability and uncertainty: interpretation and usage of pharmacometric simulations and intervals

[Rebecca Baillie]

Variability and uncertainty: interpretation and usage of pharmacometric 
simulations and intervals
Chuanpu Hu, PhD. Senior Scientific Director and Fellow, Janssen R, LLC
Wednesday, November 16, 2022, 9:00 to 10:00 am PST
Register at https://rosaandco.com/webinars
Abstract:
Pharmacometric modeling and simulation are being used increasingly in all 
stages of drug development. The simulations can produce a variety of 
probabilistic intervals representing different combinations of variability and 
uncertainty. Pharmacometricians are often unclear on when would be best to use 
which interval. The problem is exacerbated by often-unrealized confusions on 
the concepts of confidence interval and prediction interval among 
pharmacometricians. Consequently, pharmacometric intervals may often be 
inappropriately constructed or misinterpreted, and drug development decisions 
may be jeopardized.
This talk aims to clarify some of the important underlying concepts and the 
appropriate pharmacometric simulation focus, develop a classification of common 
simulation applications and properties, and provide a practically convenient 
guideline on simulation conduct and interval interpretation. Some common 
misconceptions and misusages will also be discussed.

[NMusers] Webinar by Dr. Krzyzanski, Pharmacodynamic age-structured population model for cell trafficking

[Rebecca Baillie]

Wojciech Krzyzanski PhD, MA
Associate Professor, University at Buffalo
Pharmacodynamic age structured population model for cell trafficking
October 19, 2022, 9:00-10:00 AM PDT

Registration 
(Free):
 https://rosaandco.com/webinars
Abstract
Cell trafficking encompasses movement of the immune system cells (e.g., 
granulocytes, lymphocytes) between the blood and the extravascular tissues 
(e.g., lymph nodes). Basophils are effector cells responsible for inflammatory 
reactions during the immune challenge. Basophils are used as biomarkers of 
inflammatory responses. Corticosteroids are known to suppress cell trafficking. 
Existing models of cell trafficking employ the methodology of compartmental 
systems where the cells transfer between two compartments at first-order rates. 
Such an approach limits the model ability to account for prolonged times most 
of the immune cells spend outside the vasculature before recirculating to the 
blood.

The age-structured population models introduce the transit time as a structure 
that allows to quantify the distribution of times the immune cells spend in the 
blood and the extravascular tissues. The key tools are the hazards of transfer 
between the tissues and hazards of cell death. The hazard can depend on the 
cell age (e.g., the time it spends in the tissue) and the time (e.g., through 
the time-dependent drug effect).

This webinar will show how to apply the well-known McKendrick age-structured 
population model to describe drug effects on cell trafficking between blood 
cells and cells in the extravascular space. The model was validated using 
published data on corticosteroid inhibition of the basophil counts in healthy 
volunteers using mixed effects modeling. The corticosteroid effect decreases 
the hazard of cell recirculation from the extravascular tissues. We will show 
that the age structure is essential to explain the rebound observed in the 
blood count response to a single dose drug. We will also provide insights on 
how to use age-structure population models to describe pharmacodynamics of 
other cell populations.

[NMusers] Webinar: AI-powered modeling approaches to support the development of new therapies for autoimmune diseases

[Rebecca Baillie]

AI-powered modeling approaches to support the development of new therapies for 
autoimmune diseases
Philippe Moingeon, PhD, MBA, Head of Immuno-inflammation Portfolio, Servier
Wednesday, September 21, 2022, 9:00 to 10:00 am PDT
Register for free at  
www.rosaandco.com/webinars
Abstract:
Artificial Intelligence (AI) can support decision-making during drug 
development to select the right target, drug, dosing regimen and patient. AI 
and machine learning (ML) are useful to model disease heterogeneity, identify 
therapeutic targets within dysregulated molecular pathways, design and optimize 
drug-candidates, and evaluate clinical efficacy in silico. By creating 
predictive models on both the patient specificities and drug candidate 
properties, AI fosters the emergence of Computational Precision Medicine to 
better tailor therapies to the characteristics of individual patients in terms 
of their physiology, the pathophysiology of their disease and their 
susceptibilities to genetic and environmental risks.
This webinar will illustrate how, from the perspective of the pharmaceutical 
industry, various computational modeling strategies are being used to support 
the development of new treatments for primary Sjögren Syndrome (pSS) and 
Systemic Lupus Erythematosus (SLE), two autoimmune diseases with significant 
unmet medical needs. Multiomics profiling data of whole blood samples from 
hundreds of pSS patients and matched controls from the PRECISESADs IMI cohort 
were integrated to stratify patients by hierarchical and k-means clustering. A 
parallel modeling of pSS based on Artificial Neural Networks (ANN) data mining 
was undertaken by network computational analyses of transcriptomics data in 
blood and in salivary glands to identify therapeutic targets. In collaboration 
with ROSA, a quantitative system pharmacology (QSP) model of SLE was 
successfully developed to predict in silico the efficacy of the 
pan-neutralizing anti-interferon alpha S95021 monoclonal antibody.
Collectively, these various predictive models emerge as very powerful tools to 
inform drug development and support precision medicine strategies. They also 
provide supportive data to document drug efficacy and increase significantly 
the probability of success in future confirmatory real-world clinical studies.

[NMusers] Webinar: Injecting Reality into The Commercial Due Diligence Process for In-Licensing, Partnering, or Purchasing Pharmaceutical Assets in Development.

[Rebecca Baillie]

Injecting Reality into The Commercial Due Diligence Process for In-Licensing, 
Partnering, or Purchasing Pharmaceutical Assets in Development.

Bill Brastow, Ph.D., CTO, Market Modeling, Rosa & Co LLC, San Carlos, CA
Wednesday, August 17, 2022, 9:00 to 10:00 am PDT

Register for free at https://www.rosaandco.com/webinars
Abstract:
When performing due diligence for in-licensing, partnering, or purchasing 
pharmaceutical assets in development, pharmaceutical and biotech companies 
evaluate the asset based on factors including the scientific data available, 
intellectual property of the asset, clinical development plan, competitive 
analysis of the commercial opportunity for the asset and a financial analysis 
related to revenue projections.

Companies may attempt to complete this effort on their own or they may choose 
to use outside consulting firms to assist with components of the due diligence 
process.

This webinar will focus on how pharmaceutical and biotech companies can inject 
reality into the commercial opportunity analysis by measuring expected 
physician demand for the drug to inform revenue projections and decisions about 
in-licensing, partnering, or purchasing these assets.

[NMusers] Virtual Patients Webinar Roundtable

[Rebecca Baillie]

VPs... You got to know when to hold them, know when to remove them, know when 
to resample, know when to run.
Date: July 13th (14th in NZ). 8am NZST/ 1pm PST/ 4pm EST
Register for free at https://rosaandco.com/webinars

Abstract:

Typically, the workflow of a QSP model includes the creation, simulation, and 
analysis of Virtual Patients (VPs). VPs are parametric variations of a QSP 
model, and are used to explore the systemic implications of the uncertainty and 
variability of the model's parameters. Simulation of VPs provides an 
understanding of the mechanistic drivers of clinical variability and predict 
the range of responses to a novel therapy. Special ensembles of VPs, termed 
Virtual Populations (VPops), predict the distribution of responses to novel 
protocols or therapies. Over the past decade, sophisticated techniques have 
emerged to automate the creation of VPs and VPops.

However, how confident are we that Vpops generate plausible clinical responses 
to therapeutics? Like the mechanistic physiology they represent, QSP models are 
complex and non-linear. Might these features lead to either unexpected or 
non-physiological solutions? What risk does this pose for use of QSP in 
decision-making? Is emergent behavior, that might not match our intuition, a 
strength or a weakness of QSP models?

In this webinar, we will explore these questions with a panel of QSP experts. 
Drs. Stephen Duffull, Ted Rieger, and Christina Friedrich will share their 
perspectives and participate in a panel discussion moderated by Dr. Kapil 
Gadkar.
Speakers:

  *   Christina Friedrich, PhD, Chief Engineer, Rosa & Co, San Carlos, CA
  *   Stephen Duffull, PhD, Professor, (1) Senior Scientific Advisor, Certara, 
(2) Otago Pharmacometrics Group, School of Pharmacy, University of Otago, 
Dunedin, New Zealand
  *   Theodore Rieger, PhD, Associate Research Fellow, Pfizer Inc, Cambridge, MA

[NMusers] Webinar: Analyzing Outcome Score Data by Chuanpu Hu, Janssen R

[Rebecca Baillie]

Analyzing Outcome Score Data

Chuanpu Hu, PhD
Senior Scientific Director and Fellow, Janssen R, LLC
Wednesday, May 25, 2022 12:00 to 1:00 pm EDT

Register for free at https://www.rosaandco.com/webinars

Abstract:
Clinical trial endpoints are often bounded outcome scores which take restricted 
values on finite intervals and achieve boundary values. Such end points are 
conceptually ordered categorical variables, but often analyzed as continuous 
data due to the large number of possible values. The continuous analysis 
approach may predict the data outside of the natural range and may be biased 
with skewed data. Several dedicated approaches have been proposed but many 
confusions exist on their validity, effectiveness, and comparisons. This talk 
will clarify the confusions, present the most recent advance, and show how to 
select the appropriate analysis method in practice.

[NMusers] Dr. Joga Gobburu presents 'Predictive Healthcare Analytics'

[Rebecca Baillie]

Predictive Healthcare Analytics

A webinar by Dr. Joga Gobburu

Professor, University of Maryland
Wednesday April 20, 2022, 12:00 to 1:00 pm EDT
Register for free at https://www.rosaandco.com/webinars
Abstract:
We started our journey as clinical pharmacologists with pharmacokinetics (PK), 
physiologic-based PK (PBPK) and gradually expanded to time course of 
pharmacologic effect (pharmacodynamics). Pharmacometrics, as we know it, 
encompasses disease, drug and trial modeling to support drug development and 
regulatory decisions. The more mechanistic pharmacometrics approaches such as 
PBPK and Quantitative Systems Pharmacology (QSP) have had an increased role in 
drug development to support drug-drug interaction projections, and discovering 
novel targets. Quantitative clinical pharmacologists have made great strides 
into drug development, regulatory science, and patient care. The access to 
digital data, that was previously unavailable, opens new avenues for us. We are 
at the cusp of a great revolution that can propel us into predictive analytics. 
A vision for investing in Predictive Healthcare Analytics will be presented to 
generate a discussion.

[NMusers] March Webinar: Nick Holford

[Rebecca Baillie]

Concentration Guided Dosing

Nick Holford, MB, ChB, MSc, FRACP,

Professor Emeritus Clinical Pharmacology, Department of Pharmacology and 
Clinical Pharmacology, University of Auckland, New Zealand

Wednesday March 16, 2022, 12:00 to 1:00 pm EDT

Register for free at 
https://www.rosaandco.com/webinars/2022/concentration-guided-dosing

Abstract:

Individualized dosing for treatment of human disease takes many forms. Dosing 
based on pharmacological principles recognizes that drug effects are caused by 
drug concentration not dose. Concentration guided dosing (CGD) encompasses a 
variety of approaches that are based on pharmacokinetic and pharmacodynamics 
concepts even though these concepts may not be explicitly recognized. CGD may 
be based on similar patient information including measured patient responses 
such drug concentrations but approaches for using these responses to determine 
an individualized dose have different clinical outcomes.

The two most common approaches are therapeutic drug monitoring (TDM) and target 
concentration intervention (TCI). TDM is based on achieving a response within a 
range (the therapeutic window) while TCI is based on achieving a single target 
response. Clinical studies have shown that TCI can improve clinical outcome 
while TDM does not (Holford, Ma, Metz 2020).

The webinar will explain the concept of concentration guided dosing and 
illustrate the superiority of the TCI approach over the TDM approach.

Holford N, Ma G, Metz D. TDM is dead. Long live TCI! Br J Clin Pharmacol. 
2020;Early View(doi:10./bcp.14434).

[NMusers] Webinar: Using Machine Learning Surrogate Modeling for Faster QSP VP-Cohort Generation

[Rebecca Baillie]

Using Machine Learning Surrogate Modeling for Faster QSP VP-Cohort Generation

Christina Friedrich, PhD; Jérémy Huard
Chief Engineer, PhysioPD, Rosa & Co, LLC; Senior Application Engineer, MathWorks

Wednesday February 16, 2022, 12:00 to 1:00 pm EST
Register for free at https://www.rosaandco.com/webinars

Abstract:
Virtual patients (VPs) are widely used within QSP modeling to explore the 
impact of variability and uncertainty on clinical response. In one method of 
generating VPs, parameters are sampled from a distribution, protocols are 
simulated, and the possible VP is either accepted or rejected based on 
constraints on model output behavior, such as achieving reasonable responses to 
clinical protocols. The approach works but can be inefficient, i.e., the vast 
majority of model runs typically do not result in valid VPs.

Machine learning (ML) surrogate models offer an opportunity to greatly improve 
the efficiency of VP creation. Surrogate models are trained using the full QSP 
model to discriminate between parameter combinations that result in feasible 
VPs vs. those that do not. Once the surrogate models are developed, parameter 
combinations can be pre-screened rapidly, and the overwhelming majority of 
pre-vetted combinations result in valid VPs when tested in the original QSP 
model.

In this webinar, Rosa and MathWorks will present this novel workflow and give a 
case study example using a psoriasis disease QSP model from the Rosa 
PhysioPD(tm) practice and the MATLAB® Regression Learner app to select and 
optimize the surrogate models. The VPs generated by the surrogate modeling 
approach are statistically similar to VPs generated using only the original QSP 
model. We conclude with comparisons of the relative efficiency of the methods, 
and ideas for expansion of the use of this and other ML methods in QSP modeling.

[NMusers] Webinar: A mechanistic model-based analysis of Fn14 - NFκB dysregulation in glioblastoma multiforme

[Rebecca Baillie]

A mechanistic model-based analysis of Fn14 - NFκB dysregulation in glioblastoma 
multiforme
Dr. Dipak Barua, Research Principal Scientist at Takeda

Wednesday December 8, 2021, 12:00 to 1:00 pm EST

Register for free at https://www.rosaandco.com/webinars

Abstract:
Fn14 is a transmembrane receptor protein of the tumor necrosis factor receptor 
(TNFR) superfamily. The protein is found overexpressed in solid tumor cells and 
its elevated expression is often linked to the progression of glioma patients. 
The signaling pathway downstream of Fn14 shares many molecular interactions 
with TNF-α receptor (TNFαR), which is a more well-characterized member of the 
TNFR family. Nonetheless, reports indicate that these two receptor proteins 
display considerably distinct response characteristics when stimulated. 
Crosslinking of Fn14 by its extracellular ligand TWEAK induces prolonged 
activation of transcription factor NF-κB that sustains for a long period after 
the ligand exposure. In contrast, TNF-αR engagement of TNFα leads to transient 
NF-κB activation. This study was directed to understand the molecular 
mechanisms underlying this distinctive response behavior in Fn14 signaling. A 
mechanistic model was developed to characterize specific features of the Fn14 
pathway that could explain its divergence from TNFαR signaling leading to its 
elevated expression in glioblastoma. Analysis using the model revealed highly 
non-linear dynamics in Fn14 signaling, including stable limit cycles and 
bistable responses depending on the dose and duration of the TWEAK signal. 
Systematic interrogations using the model identified a positive feedback loop 
in the Fn14 pathway that may play a crucial role in the prolonged activation of 
NF-κB and elevated Fn14 expression under specific circumstances. The 
model-based analyses revealed potential targets for interventions to 
effectively counteract Fn14 overexpression in tumor progression.

[NMusers] Webinar: Using Machine Intelligence in Building PK/PD and Disease Progression Models.

[Rebecca Baillie]

James Lu, PhD. Principal AI Scientist, Genentech

Using Machine Intelligence in Building PK/PD and Disease Progression Models.

Nov 17, 2021, 9:00-10:00 AM PST.

Register for free at https://www.rosaandco.com/webinars

Abstract: The analyses of time course data from clinical trials are currently 
performed using pharmacometrics or QSP methodologies based on differential 
equations. These require considerable human expertise and trial-and-error in 
devising the appropriate model equations. However, recent advancements in Deep 
Learning have led to the development of the neural ordinary differential 
equation (Neural-ODE) methodology, thereby opening up a new paradigm whereby 
the governing equations can be generated from clinical data via machine 
intelligence. In this presentation, we show how this methodology in conjunction 
with key pharmacological principles, can enable the discovery of Neural-PK/PD 
models from data [Lu et al, Nature Machine Intelligence (2021)]. Furthermore, 
we show how the Neural-ODE methodology with an encoder-decoder architecture can 
be utilized for Disease Progression Modeling as well. We envisage that machine 
intelligence has the potential to transform modeling and discuss the associated 
opportunities and challenges.

[NMusers] Webinar with Dr. Palmer on modeling combination cancer therapy

[Rebecca Baillie]

Models to understand and predict the clinical efficacy of combination cancer 
therapy


Dr. Adam Palmer
Assistant Professor of Pharmacology, University of North Carolina at Chapel Hill

Wednesday October 20, 2021, 12:00 to 1:00 pm EDT

Register for free at https://www.rosaandco.com/webinars


Abstract:

Developing optimal drug combinations is one of the central challenges of cancer 
treatment research: drug combinations are used to treat most types of cancer, 
and are almost exclusively responsible for cures of advanced cancers. However, 
historically successful combination therapies were developed empirically, and 
the mechanistic basis for their efficacy has been largely speculative. I will 
present experiments, models, and computational analyses of clinical trial data, 
to investigate the mechanistic basis of clinically successful combination 
therapies across 12 types of cancer and 30 different therapies. These studies 
consistently identify the control of cancer heterogeneity between-patients 
(inter-tumor) and within-patients (intra-tumor) by independently active drugs 
as critical contributors to the efficacy of combination therapies in human 
patients. The key approaches for data analysis and modeling in these studies 
consist of adapting classical pharmacological concepts to the complex situation 
of populations of cancers with heterogeneous drug sensitivity. We find that 
supra-additive drug interactions are uncommon in humans among approved 
combination therapies, and multiple curative regimens are consistent with drug 
additivity in both experimental measurements and in clinical outcomes. 
Mathematical descriptions of heterogeneity in cellular or patient populations, 
and quantitative experimental measurements of how drug combinations address 
heterogeneity, lead to accurate predictions of clinical trial results for a 
diverse range of combination therapies, including those with immune checkpoint 
inhibitors (correlation between observed and expected Progression Free Survival 
in 14 trials, Pearson r = 0.98, P < 10^-8) and curative chemotherapy regimens 
for hematological cancers (correlation between observed and expected response 
rates and cure rates in childhood ALL, Pearson r = 0.99, P < 10^-10). These 
results have broad significance for the treatment of cancers, for the 
interpretation of clinical trials, and point to new opportunities to use 
combination therapies with greater precision.

[NMusers] Webinar: Probing antibody-target interactions in living systems

[Rebecca Baillie]

Probing antibody-target interactions in living systems
Yanguang (Carter) Cao, PhD., Assistant Professor, University of North Carolina 
at Chapel Hill
September 15, 2021, 9:00-10:00 AM PDT
Register for free at https://www.rosaandco.com/webinars

Abstract:
Antibodies have become an attractive class of therapeutic agents for multiple 
types of diseases, mainly owing to their high target selectivity and target 
affinity. The target binding properties are critical for antibodies' efficacy 
and toxicity. However, we still heavily rely on the static in vitro system to 
characterize antibody's target binding properties, and their behaviors in vivo 
- the actual sites of actions - remain undefined. Our group has developed a 
bioluminescence resonance energy transfer (BRET) imaging approach that directly 
supports the measurement of the binding dynamics between antibodies and their 
targets in the native tissue environment. Built upon the imaging data, we have 
developed a spatially resolved computational model analyzing the longitudinal 
BRET imaging data to explore the kinetics of antibody-target binding. Our work 
has yielded insights into the physiological factors affecting antibody-target 
interactions in their sites of action, which are critical for improving 
antibody-based therapeutics.

[NMusers] Webinar: Integrating Quantitative Systems Pharmacology and Machine learning, why bother?

[Rebecca Baillie]

Integrating Quantitative Systems Pharmacology and Machine learning, why bother?
Tongli Zhang, PhD, Assistant Professor, University of Cincinnati
Wednesday July 14, 2021, 12:00 to 1:00 pm EDT
Register for free at https://www.rosaandco.com/webinars
Abstract:
It is challenging to understand complex biomedical systems that include 
interacting feedback and feedforward loops. What is more, the biomedical 
control system is only partially revealed, and it can differ dramatically 
between different individuals. In this talk, I will share my vision of how an 
integration between Quantitative Systems Pharmacology and Machine learning 
could utilize the strength of each method and overcome their limitation, aid us 
to cope with the above mentioned challenges, better understand biomedical 
systems, and eventually lead to the design of optimized treatments.

[NMusers] BAPKPD Network Special Topics Webinar: Dr. Giacomini, High Throughput Screening and Real World Biomarkers to Predict Drug-Drug and Drug-Nutrient Interactions: Implications to polypharmacy as

[Rebecca Baillie]

Bay Area PKPD Network

Special Topics Webinar

Thursday, June 10, 2021 from 12:00pm-1:00pm PDT

Location: Online

Register: for free at https://tinyurl.com/BAPKPD-webinar2021 or see 
http://www.bapkpd.org/upevent.html for information



Kathy Giacomini, Ph.D.

Professor, Department of Bioengineering and Therapeutic Sciences UCSF Schools 
of Pharmacy and Medicine



High Throughput Screening and Real World Biomarkers to Predict Drug-Drug and 
Drug-Nutrient Interactions:
Implications to polypharmacy associated with COVID treatment

Individuals with serious COVID infections often have pre-existing 
co-morbidities and are generally older.  Thus, in addition to being prescribed 
drugs for the treatment of COVID19 and its sequelae, many of these individuals 
are taking a myriad of other drugs.  In this presentation, I will describe a 
screening study of 25 small molecule drugs in clinical trials for COVID19 
against 11 drug transporters, which are targets for clinically relevant 
drug-drug interactions (DDIs).Our in vitro studies revealed that 20 of the 
25 drugs met the criteria suggested by FDA DDI guidance to consider a clinical 
DDI study.  Further, I will describe the analyses of real world transporter 
biomarkers in data from electronic health records, which suggested that several 
of the drugs actually do cause transporter-mediated DDIs clinically.  I will 
end with a discussion of the propensity for many anti-microbial drugs to 
perpetrate clinical DDIs and drug-nutrient interactions, and the use of various 
biomarkers for predicting DDIs in pre- and post-marketing settings.

[NMusers] Webinar: Dr. Siokis from Sanofi: An agent-based simulation platform studying the immunological synapse dynamics

[Rebecca Baillie]

An agent-based simulation platform studying the immunological synapse dynamics
Anastasios Siokis, PhD, Postdoc in Translational Disease Modeling, Sanofi, 
Germany
June 9, 2021
12:00-1:00 PM EDT

Registration (Free): https://www.rosaandco.com/webinars
Abstract: During immunological synapse (IS) formation, T cell receptor (TCR) 
signaling complexes, integrins, as well as costimulatory and inhibitory 
molecules exhibit characteristic spatial localization. The IS is built around a 
TCR-peptide-major histocompatibility complex (pMHC) core, and is surrounded by 
an integrin ring (Monks, et al., 1998). Small immunoglobulin superfamily 
(sIGSF) adhesion complexes form a corolla of microdomains outside the integrin 
ring, which is shown to recruit and retain the major costimulatory and 
checkpoint complexes that regulate the responses to TCR engagement (Demetriou, 
et al., 2020). The positioning of these molecules drives T cell signaling and 
fate decision, making forces that govern IS formation of particular interest.
To gain insights into the mechanisms underlying molecular reorganization and 
characteristic pattern formation during IS formation, we developed a general 
agent-based simulation platform able to test different hypotheses. The 
simulations identify several key biological interactions.
This work establishes a general simulation framework that can recapitulate 
complex pattern formation processes observed in cell-bilayer and cell-cell 
interfaces. The presented results have implications for the understanding of T 
cell activation and fate decision

[NMusers] Upcoming webinar: Predicting subjective or complex clinical outcomes in QSP models

[Rebecca Baillie]

Predicting subjective or complex clinical outcomes in QSP models: challenges 
and approaches
Vincent Hurez, DVM, PHD, Senior Scientist, Rosa & Co LLC
Wednesday May 12, 2021, 12:00 pm to 1:00 pm EDT
Register for free at: https://www.rosaandco.com/webinars
Abstract:
Many clinical trials use complex disease activity scores to assess patient 
response, and the connections between biological components and these scores 
are often unclear. We explore how QSP modeling supports elucidation of disease 
pathophysiology and better-informed extrapolation between biological components 
and disease scores to facilitate prediction of clinical outcomes. Disease 
scores can be modeled by (1) identifying the components of each disease 
activity score, (2) formulating a biological rationale for associating specific 
biomarkers with each score component, and (3) calibrating the proposed function 
using clinical data from existing therapies. QSP models are valuable tools to 
integrate existing mechanistic and clinical data. The ability to integrate and 
generate plausible predictions of standard clinical disease scores in response 
to novel interventions improves the clinical acceptance and usability of QSP 
models.

[NMusers] Webinar: The Ravaging Respiratory Infection: Fighting Influenza Using Mechanistic Models

[Rebecca Baillie]

The Ravaging Respiratory Infection: Fighting Influenza Using Mechanistic Models
Amber M. Smith, PhD
Assistant Professor, University of Tennessee Health Science Center
Wednesday March 17, 2021, 11:00 am to 12:00 pm EDT
Register for free at https://www.rosaandco.com/webinars
Abstract:
Respiratory viruses, including influenza virus, cause a significant number of 
infections each year and are traditionally difficult to treat. This is in part 
due to the short window where antivirals are efficacious. But, if we can 
effectively forecast the tit-for-tat between the pathogen and the host, we may 
be able to identify new preventative and therapeutic regimens that are more 
effective.
In this webinar, I will show how we can use an integrative model-experiment 
exchange to establish the dynamical connections between virus spread in the 
lung, control by host immune responses, lung damage inflicted throughout the 
infection, and how these relate to disease severity.
I will describe the mathematical models and analyses and how experiments can be 
designed to validate the model predictions and gain confidence in our ability 
to predict disease progression,  potential complications, and therapeutic 
options and efficacy.

[NMusers] Webinar: Modeling Physician Decision-Making Behavior Using Discrete Choice

[Rebecca Baillie]

Out of the Lab - Into the Market: Modeling Physician Decision-Making Behavior 
Using Discrete Choice
Bill Brastow, PhD
Principal and CTO Rosa Market Modeling, Rosa & Co

Tuesday February 16, 2021, 12:00 to 1:00 pm EDT
Register for this free webinar at https://www.rosaandco.com/webinars

Abstract:
Modeling drugs and diseases is undoubtedly an important step in product 
development, but it is only a part of the equation. Even if a product, or a 
diagnostic, makes it to market, will it be prescribed by physicians?
Traditional market research can generate some answers, but like many other 
aspects in product development, when this is combined with modeling, it becomes 
more powerful.
In this webinar, we will talk about the problem of "Discrete Choice" and the 
kinds of commercialization decisions that mathematical models of discrete 
choice can support
We will show how, given a finite collection of competing drugs or diagnostic 
products, we assign each of a group of decision-makers a probability of 
choosing each product. These probabilities are a function of these products' 
characteristics and physicians' attitudes toward these characteristics.
We will describe the mathematical underpinnings that define these models, the 
methods used to estimate model parameters, and how choice probabilities can be 
derived from them.
We will also address the real-world practice of model construction and show 
where in the process of moving a drug or diagnostic from the lab into the 
marketplace, the discrete choice problem fits.

[NMusers] Webinar on Complex Drug-Protein Interactions

[Rebecca Baillie]

Building Kinetic Models with Complex Drug-Protein Interactions: application to 
the targeted inhibition of MAPK signaling in cancer
Luca Gerosa, PhD, Postdoctoral Fellow, Laboratory of Systems Pharmacology, 
Harvard Medical School
Wednesday January 20, 2021, 12:00 to 1:00 pm EDT
Register at https://www.rosaandco.com/webinars
Abstract:
A key goal in the field of Quantitative Systems Pharmacology (QSP) is the 
construction of mechanistic models able to predict drug efficacy. A major 
challenge in building such models is the necessity to properly describe highly 
cooperative drug-protein and protein-protein interactions that govern the 
functioning of biochemical networks. In this seminar, I will show how Ordinary 
Differential Equations (ODEs) models comprising large numbers of drug-protein 
and protein-protein interactions can be efficiently built using rule-based 
modelling and energy-based descriptions of molecular cooperativity.
The modelling framework I will present is based on an extension of the Python 
Systems Biology (PySB) toolbox to incorporate energy-based specifications 
supported by BioNetGen (eBNG). The resulting framework allows modelers to write 
large ODEs models as compact Python programs in which molecular cooperativity 
is specified as free energy contributions and detailed balance is satisfied by 
construction. As a case study, I will show that the framework allows the 
accurate description of high-order cooperativity interactions between 
components of the MAPK signaling pathway and targeted kinase inhibitors and 
that the inclusion of such interactions predicts clinically-relevant drug 
resistance mechanisms in skin and colorectal cancers.

[NMusers] Upcoming webinar by Paul Watkins on QST

[Rebecca Baillie]

QST and the Transformation in Drug Safety Assessment

Paul B. Watkins, M.D. FAASLD
Director, Institute for Drug Safety Sciences , University of North Carolina in 
Chapel Hill

Wednesday December 16, 2020, 12:00 to 1:00 pm EDT

Register for free at https://www.rosaandco.com/webinars

Abstract:
Establishing the safety of new drug candidates is a major hurdle to drug 
development as standard preclinical toxicology does not reliably predict human 
adverse drug events. Liver toxicity is a potentially fatal adverse event that 
has been particularly challenging to predict from preclinical studies. 
Moreover, abnormalities in serum liver chemistries are commonly observed in 
clinical trials raising suspicion of liver safety liability that can currently 
only be removed with very large clinical trials. This talk will focus on the 
progress of a public-private partnership (the DILI-sim Initiative) that for the 
last decade has been developing a Quantitative Systems Toxicology (QST) model 
(DILIsym(r)) to improve mechanistic understanding and therefore prediction of 
liver safety liabilities of new drug candidates.

The DILIsym model uses PBPK and other available data to determine the 
concentration of parent drug and major metabolites inside the hepatocyte during 
various dosing regimens. Also fed into the model are the exposure dependent 
effects of parent drug and major metabolites on oxidative stress, bile acid 
homeostasis, and mitochondrial function as measured in in vitro or cellular 
systems. Parameters in the model have been varied to reflect genetic and 
non-genetic variability to create a virtual healthy human population as well as 
disease-specific populations. With the data inputs, DILIsym will predict the 
incidence and severity of liver injury that will be observed in a simulated 
patient population as a function of dosing regimen. Results of DILIsym modeling 
are increasingly used in decision making within Pharma and have also been 
helpful in interactions with regulators.

DILIsym provides an example of how increased application of QST modeling should 
transform the safety assessment of new drug candidates as well as risk 
management in clinical trials and post-approval.

[NMusers] Upcoming webinar: Using QSP to predict cardiotoxicity caused by cancer drugs

[Rebecca Baillie]

Using QSP to predict cardiotoxicity caused by cancer drugs
Eric Sobie, PhD
Professor, Pharmacological Sciences at Icahn School of Medicine at Mount Sinai

November 18, 2020 at 12:00-1:00 PM EST

Register for free at https://www.rosaandco.com/webinars

Abstract
Tyrosine kinase inhibitor drugs, or TKIs, have been highly effective at 
treating several types of cancer, yet many TKIs are associated with various 
forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse 
events remain poorly understood.
We are exploring potential mechanisms of TKI-induced cardiotoxicity using a 
strategy that integrates several complementary approaches. The pipeline 
involves: (1) transcriptomics to quantify drug-induced changes in gene 
expression in stem cell-derived myocytes (iPSC-CMs); (2) mechanistic QSP 
modeling to predict subsequent changes in physiological dynamics; and (3) 
physiological measurements to confirm or refute model predictions. This QSP 
approach successfully predicted individual-specific TKI susceptibility whereby 
particular drugs were tolerated in one cell line but disrupted dynamics in 
another cell line.
Overall, the work offers new insight into cardiotoxicity caused by TKIs and 
illustrates a novel approach for integrating transcriptomic measurements and 
QSP models to generate experimentally testable, individual-specific predictions.

[NMusers] Webinar: Using QSP to predict cardiotoxicity caused by cancer drugs

[Rebecca Baillie]

Using QSP to predict cardiotoxicity caused by cancer drugs
Eric Sobie, PhD
Professor, Pharmacological Sciences at
Icahn School of Medicine at Mount Sinai, NY

Wednesday November 18, 2020, 12:00 to 1:00 pm EDT

Register for free at https://www.rosaandco.com/webinars

Abstract:
Tyrosine kinase inhibitor drugs, or TKIs, have been highly effective at 
treating several types of cancer, yet many TKIs are associated with various 
forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse 
events remain poorly understood.

We are exploring potential mechanisms of TKI-induced cardiotoxicity using a 
strategy that integrates several complementary approaches. The pipeline 
involves: (1) transcriptomics to quantify drug-induced changes in gene 
expression in stem cell-derived myocytes (iPSC-CMs); (2) mechanistic QSP 
modeling to predict subsequent changes in physiological dynamics; and (3) 
physiological measurements to confirm or refute model predictions. This QSP 
approach successfully predicted individual-specific TKI susceptibility whereby 
particular drugs were tolerated in one cell line but disrupted dynamics in 
another cell line.

Overall, the work offers new insight into cardiotoxicity caused by TKIs and 
illustrates a novel approach for integrating transcriptomic measurements and 
QSP models to generate experimentally testable, individual-specific predictions.

[NMusers] Webinar: Do you need a life scientist for QSP modeling?

[Rebecca Baillie]

Do you need a life scientist for QSP modeling?
Katherine Kudrycki, PhD, Principal Scientist at Rosa & Co. LLC
On October 14, 2020 at 12:00-1:00 PM EST
Register for free at https://www.rosaandco.com/webinars

Abstract:
While QSP models are defined as comprehensive models of biological mechanisms, 
the inclusion and role of life scientists and disease area experts in model 
development are often omitted or overlooked. This webinar addresses how 
including dedicated life scientists on the QSP modeling team can improve model 
quality and enhance research results.
QSP modeling begins with scoping of the biological system and progresses to 
include equations and parameters consistent with physical and biochemical 
principles. Each decision in the development and qualification of QSP models 
requires scientific judgment. Life scientists with expertise and experience in 
the appropriate therapeutic area or biological field can provide essential 
input for the design of the model. Life scientists apply expert judgment to 
make scope decisions, assess data, inform assumptions where knowledge gaps 
exist and provide a biologically relevant interpretation of model simulation 
results. Without dedicated expert life scientist input, a team may run the risk 
of building models that "work" but are not fit for purpose or are not 
biologically sound. For example, a model may show good agreement with data on 
previously measured outcomes but have unrealistic parameter values or 
qualitative behaviors that are clear to a biologist.
Life scientists' interpretation of simulation results can improve the 
likelihood of meaningful and actionable conclusions. The life scientists can 
also help the team to communicate these results in a way relevant to the 
project's stakeholders. Having dedicated life scientists on the team leads to 
more efficient model building, qualification, and interpretation of the 
results, and can help ensure impact.

[NMusers] Stacey Tannenbaum: Making the 'Moster' of Your Poster Webinar tomorrow

[Rebecca Baillie]

Making the 'Moster' of Your Poster
Stacey Tannenbaum, PhD, FISoP
Senior Director, Pharmacometrics US Clinical Pharmacology and Exploratory 
Development, Astellas Pharma, Northbrook, IL
September 9, 2020
12:00-1:00 PM EDT
Register for free at https://www.rosaandco.com/webinars
An excerpt from a nightmare: I walk into a giant hotel ballroom, with the air 
conditioning either off or at subzero temperatures. Before me stretches row 
after (endless) row of dense text in small font, lengthy tables, detailed 
equations, technobabble, and busy graphs, which I am expected to fully absorb 
while being constantly bumped into and distracted by the buzz of 300 
conversations. Students in crisp suits stand in front of their posters, 
tentatively smiling in desperate hope that I will stop by and listen to their 
15 minute detailed walk through. But I still have to eat lunch and visit a few 
exhibitors and network during this limited time. And the coffee urn is down to 
the dregs, and they are out of Splenda! N!
It's no wonder that poster sessions are something I generally dread! But I also 
know poster sessions can provide exciting science, innovative ideas, 
collaboration opportunities, productive conversations, and a fantastic 
opportunity to learn and engage. In this webinar, I will share tips on 
preparing, perfecting, presenting, and pitching a perfect poster. Guidelines on 
how to streamline your poster content and crisply focus the messaging will be 
presented, along with good layout and graphics principles that will make your 
poster stand out in the crowd. We will discuss how to manage the various types 
of poster visitors (like "The Browser," "The Lurker," and "The Never Ending 
Questioner") so you maximize your exposure and focus on the best collaboration 
opportunities. And in today's environment where virtual or "e-poster" 
presentations will become more common, we will learn how to prepare a succinct 
but high impact summary presentation using good scientific communication 
practices.

Please help me turn my poster session nightmare into a dream by joining me for 
this webinar, and share your own stories, tips, tricks, and strategies for 
making the 'moster' ... of your poster!

[NMusers] Webinar: An Integrated Machine Learning Framework for Novel Small Molecule Drug Design

[Rebecca Baillie]

An Integrated Machine Learning Framework for Novel Small Molecule Drug Design
Dr. Jonathan E. Allen, Informatics Thrust Leader, Biosecurity Center at ATOM 
Consortium
Wednesday July 15, 2020, 12:00 to 1:00 pm EDT
Register for free at https://www.rosaandco.com/webinars
Abstract:
The drug discovery process is costly, slow, and failure prone. It takes an 
average of 5.5 years to get to the clinical testing stage, and in this time 
millions of molecules are tested, thousands are made, and most fail. The ATOM 
Consortium (atomscience.org), comprised of LLNL, GSK, Frederick National Lab, 
and UCSF, is working to increase efficiencies in the drug discovery process 
through improved integration of machine learning earlier in the drug design and 
discovery process by evaluating multiple properties needed to make a viable 
drug. A combination of safety, pharmacokinetic and efficacy properties are 
considered simultaneously in the early drug design phase with an aim to 
ultimately show that these molecules will have better success rates with 
subsequent pre-clinical and clinical testing.
The purpose of this webinar will be to introduce key components of the ATOM 
computational framework, highlight ongoing challenges and opportunities for 
improvement. The presentation will begin with a description of AMPL, the open 
source framework developed to build machine learning models that generate key 
safety and pharmacokinetics parameters, used for molecule evaluation and as 
input to anticipated Quantitative System Pharmacology and Toxicology models. 
The end-to-end pipeline handles data curation, feature extraction, model 
building, prediction generation, and data visualization.
Next, we'll describe how the best-performing models are integrated into an 
active learning loop (with code in the process of being open sourced) to guide 
the search for de novo compounds, with plans to integrate an in-house PBPK 
model to predict in-vivo behavior. The active learning loop includes a 
computational search through chemical space for candidate small molecules with 
opportunities for proposed molecules to be evaluated experimentally for model 
validation and re-training. Discussion of the active learning pipeline will 
include an examination of the utility of machine learning model uncertainty 
estimates needed to guide active learning and challenges in designing and 
bounding the chemical search space. We will conclude with an examination of an 
early test of one round of the active learning loop applied to the design of a 
selective kinase inhibitor.

[NMusers] Webinar: QSP model of Schizophrenia

[Rebecca Baillie]

Development of a QSP Platform to Quantify Benefits of DAAO Inhibition in 
Schizophrenia

Sergio Iadevaia, PhD
Scientific Director QSP, Pharmacometrics and Data Analysis at Takeda
Wednesday June 17, 2020, 12:00 to 1:00 pm EDT
Register for free at https://www.rosaandco.com/webinars

Abstract:
Hypofunctioning of the N-methyl-d-aspartate receptor (NMDAR) and reduction of 
the NMDAR primary coagonist, d-serine, have been associated with the 
pathophysiology of schizophrenia. Inhibition of d-amino acid oxidase (DAAO) 
results in increased d-serine and may lead to improvement in negative symptoms 
of schizophrenia. TAK-831, a highly selective and potent inhibitor of DAAO, 
increased d-serine levels in the cerebellum of mice and demonstrated a positive 
effect on cognition and social interaction in rodent cognition and behavioral 
models.

In collaboration with scientists from Rosa, Takeda developed a mechanistic 
platform that includes plasma, cerebrospinal fluid and brain compartments, the 
primary site of DAAO inhibition and a cerebellar tripartite synapse for 
modulation of NMDAR signaling, to enable quantitative assessment of the 
clinical benefits of TAK-831.

This webinar will discuss how the platform was developed, the result that it 
yielded, and the impact it had on clinical decision making.

[NMusers] Webinar on Modeling and Policy Change

[Rebecca Baillie]

Of "clever" models and "dumb" spreadsheets": what is more effective to drive 
policy change?
Paolo Denti, PhD, Associate Professor at the University of Cape Town
When: Wednesday May 20, 2020, 12:00 to 1:00 pm EDT
Register for free at https://www.rosaandco.com/webinars
Abstract:
What often prevents modelling results from contributing to policy change is not 
lack of good science, but ineffective communication to the target audience of 
clinicians and decision-makers.
Dosing of anti-infectives in children is a glaring example of this. While the 
theory of maturation and allometric scaling are widely assumed as the gold 
standard within the pharmacokinetic modelling community, a number of 
international guidelines for dosing in children is still based on weight-bands 
targeting the same mg/kg dose as in adults. This happens for drugs in neglected 
diseases, when no directly observed data is available in children, but also for 
common diseases such as HIV or tuberculosis. This results in millions of 
children potentially receiving sub-optimal doses.
How can we get across the message of our models and use it to improve policy? 
Sometimes a simple and easy-to-use solution like an Excel spreadsheet can do 
the trick better than sleek-looking Visual Predictive Checks and impressively 
low parameter precision or shrinkage values.

[NMusers] Webinar: gQSPSim: a SimBiology-based GUI app for standardized QSP model development and application

[Rebecca Baillie]

gQSPSim: a SimBiology-based GUI app for standardized QSP model development and 
application

Iraj Hosseini, PhD and Justin Feigelman, PhD
 Scientists at Genentech, Inc

Wednesday, April 15, 2020, 12:00 to 1:00 pm EDT

Register for free at 
https://register.gotowebinar.com/register/5872881235744396299?source=website

Abstract:
Quantitative Systems Pharmacology (QSP) models are often implemented using a 
wide variety of technical workflows and methodologies. To facilitate 
reproducibility, transparency, portability, and reuse for QSP models, we have 
developed gQSPSim, a GUI-based MATLAB(r) application that performs key steps in 
QSP model development and analyses.

The capabilities of gQSPSim include 1) model calibration using global and local 
optimization methods, 2) development of virtual subjects to explore variability 
and uncertainty in the represented biology, and 3) simulations of virtual 
populations for different interventions. gQSPSim works with SimBiology(r)-built 
models, utilizing components such as species, doses, variants, and rules. All 
functionalities are equipped with an interactive visualization interface and 
the ability to generate presentation-ready figures. In addition, standardized 
gQSPSim sessions can be shared or saved for future extension and reuse.

In this work, we demonstrate gQSPSim's capabilities with a standard 
target-mediated drug disposition model and a published model of anti-PCSK9 
treatment of hypercholesterolemia.

[NMusers] 2020 BAPKPD Poster Social

[Rebecca Baillie]

Please join us at the annual Bay Area PKPD 2020 Poster Social on Wednesday, 
March 25th from 1:00 pm to 5:00 pm at Merck Research Laboratories in South San 
Francisco.
We welcome you to submit your recent posters presented at other meetings for a 
"second chance" to be displayed for the benefit of the scientific community of 
the Bay Area to showcase the use of PK/PD modeling and simulation in drug 
research, safety assessment, clinical pharmacology, clinical trial design, 
regulatory dossiers, etc.
Browse posters, network with fellow scientists and listen to our podium 
presentation by:
Jin Yan Jin, Ph.D., Senior Director and Principal Scientist of Clinical 
Pharmacology at Genentech giving a presentation titled, "Model-Informed Drug 
Development in Today's Pharmaceutical Industry: Genentech's Experience,"
Kathy Giacomini, Ph.D., Professor and Co-Chair, Bioengineering and Therapeutic 
Sciences Professor at the University of California, San Francisco and her talk 
titled, "Genetic Polymorphisms in Transporters and Their Effects on PK/PD."
For more information and to sign up, please go to http://bapkpd.org/upevent.html

[NMusers] Upcoming webinar: Jeffrey Sachs from Merck on MIDD

[Rebecca Baillie]

MIDD: Vaccine R Gets a Shot in the Arm from Pharmacometrics
Jeffrey R. Sachs, PhD
Senior Principal Scientist at Merck & Co.
Wednesday Jan 22, 2020, 12:00 to 1:00 pm EST
Register for free at https://www.rosaandco.com/webinars
Abstract:
The objective is to (1) inform the audience about pharmacometrics (PMX) 
opportunities in vaccine discovery and development (D), and (2) to motivate, 
by examples, PMX practitioners to impact vaccine D
Prophylactic vaccines are safe and effective and have made an immense 
contribution to human and animal health [1]. Pharmacometrics (PMX) has only 
recently been introduced to vaccine discovery and development, and is now 
becoming fully integrated into, and impactful on decision-making. This has 
resulted in better scientific understanding, increased POS, substantial 
savings, and other benefits that have been seen in the other therapeutic areas 
that have adopted PMX. The impact of this work has included go/no-go decisions, 
design of efficient pre-clinical and clinical trials, integration of 
preclinical and clinical data, quantitative prediction for go/no-go and 
dose-level decisions, and integration of data across multiple trials for more 
informed decision-making. The methods used include QSP modeling, trial 
simulation, Bayesian inference, and model-based meta-analyses ("comparator 
modeling").
The presentation will start with a background on vaccine discovery and 
development including a brief overview of: the risk/benefit considerations in 
vaccines, the choices and uses of biomarkers to mitigate risk, vaccine 
terminology, the immune system, and vaccine platforms (DNA, protein, VLP, 
etc.). This will be followed by examples across the spectrum of applications 
from discovery through development and across the many kinds of decisions 
impacted and methods used. These will include applications of M that

  *   supported both Go and No-Go decisions
  *   increased power in trial design while saving considerable cost by 
optimizing sampling of subjects' disease state.
  *   providing a novel phase 3 endpoint substantially increasing power of a 
proposed trial design

[NMusers] Upcoming Modeling Webinar by Dr. Britta Goebel for World Diabetes Day

[Rebecca Baillie]

Quantitative Systems Pharmacology Modeling Support
in Development of Novel Diabetes Treatments

Dr. Britta Goebel

Head of Translational Disease Modeling (Diabetes-CV & I)
at Sanofi, Frankfurt

Thursday Nov 14, 2019, 12:00 to 1:00 pm EST
(World Diabetes Day)

Register for free at https://www.rosaandco.com/webinars

Abstract:
Mechanistic Quantitative Systems Pharmacology (QSP) models inform decision 
making along the value chain from drug discovery to development. To support the 
development of novel diabetes therapies, we apply QSP models ranging from 
mechanistic model platforms via mechanistic PK/PD models to clinical trial 
simulators.

The following use cases will be presented:

  *   We use a diabetes model platform covering all relevant parts of human 
physiology (e.g., glucose-insulin homeostasis, lipid metabolism) and 
pharmacology integrating data from various studies to provide mechanistic 
understanding and to assess the potential benefit and risk of new mechanisms of 
action.
  *   We apply focused mechanistic models of the glucose-insulin system to 
model clinical data at the individual patient level (time course data of 
glucose, insulin, c-peptide after meal challenge). Thereby, we provide 
mechanistic insights into effects of a novel dual agonist (so-called oral 
minimal model method). In particular, we quantify drug effects in terms of 
insulin action, secretion, and meal glucose rate of appearance, estimated by 
using meal test data of a Phase 1 clinical study.
  *   We apply a Diabetes Simulator (trained with individual patient data) to 
inform clinical study design for novel insulins by running clinical trial 
simulations in virtual populations.

[NMusers] Upcoming webinar: Hybrid Genetic Algorithm Approaches to Model Selection

[Rebecca Baillie]

Hybrid Genetic Algorithm Approaches to Model Selection

Speaker: Robert Bies, Pharm.D. Ph.D
 Associate Professor of Pharmaceutical Sciences,
Member Computational and Data Enabled Sciences Program
University at Buffalo, NY

Thursday, Oct 24, 2019, 12:00 to 1:00 pm EDT

Register for free at https://www.rosaandco.com/webinars

Abstract:
A newly implemented version of an established Genetic Algorithm based approach 
using NONMEM with some modifications into an R-shiny package that provides the 
same functionality with additional flexibility is presented.

Some refinements have been made in the selection process for evaluating models 
compared with the original evolutionary algorithm. Several population analyses 
using the updated algorithm are compared with results obtained using classical 
stepwise approaches in xenograft tumor growth profiles and population 
pharmacokinetic analyses

[NMusers] Webinar: Attack of the Clones: Understanding the kinetics of resistance to cancer treatment

[Rebecca Baillie]

Attack of the Clones: Understanding the kinetics of resistance to cancer 
treatment
James Yates, MMath, PhD, Principal Scientist, AstraZeneca, Cambridge UK
Wednesday Sep 18, 2019, 12:00 to 1:00 pm EDT
Register for free at 
https://register.gotowebinar.com/register/4098877494283132684

Abstract:
Despite survival gains that have been made with targeted anti-cancer medicines, 
patients ultimately relapse due to drug resistant disease. It is widely 
understood that this is due to the presence of drug resistant cells present in 
the tumour - a concept acknowledged since the 1970s. Acquired genetic lability 
is required for cancer cells to express a phenotype that can escape both 
normal, homeostatically controlled, tissue turnover as well as evade immune 
surveillance. Therefore, it is no surprise that some cancer cells will gain 
further advantage via drug resistance. The onset of this resistance will 
dictate the time to progression and ultimately death. It would therefore be 
beneficial to anticipate the evolution of resistance in treated tumours to 
inform the optimal treatment regimen, optimal sequence of treatments, 
combination strategies and to prioritise mutations to target with new medicines.
In this talk current knowledge of resistance kinetics in the clinic based upon 
observations and model-based analyses will be reviewed. The question of whether 
drug resistance is innate or acquired on treatment will be discussed as well as 
evidence for both processes. Using models of resistance kinetics, the 
relationship between resistant disease, PFS and OS can be demonstrated. These 
models can also be used to understand the optimal regimen to control resistant 
disease. However, an ongoing challenge is how to interrogate clinical data that 
represent the "patient journey" through multiple lines of therapy. This is 
important so that the influence treatment history has on the duration of 
response to subsequent treatment options can be understood.
Moving back to animal models of cancer, a review of modelling of xenografted 
tumour experiments reveals that similar resistance kinetics are observed. This 
suggests that modelling assumptions of the relative fitness of drug resistant 
vs sensitive cells can be tested along with modelling the impact of spatially 
constrained solid tumour growth. An example of using in vitro data for 
different NSCLC EGFR driven mutants to predict clonal selection in vivo will be 
used to demonstrate these concepts. Thus, these nonclinical in vitro and in 
vivo systems, coupled with mathematical modelling, could prove to be useful 
tools for investigating clonal evolution.

[NMusers] Open positions for Scientists

[Rebecca Baillie]

Position announcement
Associate Life Scientist and Senior Life Scientist, PhysioPD
Rosa & Co. LLC is seeking Associate and Senior Life Scientists to join our 
PhysioPD(tm) consulting practice.
Rosa is a drug-disease modeling and simulation company created to provide 
innovative modeling solutions to its biopharmaceutical clients and meaningful 
equity ownership to its employees.  See 
www.rosaandco.com.
Life Scientists work with Engineers in our PhysioPD practice to develop 
physiologically-based, dynamic mathematical models and conduct simulations of 
disease, drug action, and (pre)clinical studies.
Life Scientists are responsible for:
*   Data mining and interpretation to support the construction of 
mathematical models.
*   Identifying and implementing realistic laboratory and clinical tests 
for validating models, and conducting simulations of disease, drug action, and 
preclinical and clinical studies;
*   Defining key issues, presenting results, and developing associated 
regulatory reports
*   Leading overall projects, assisting with client relationship 
management, and participating in Rosa's business management, depending on the 
candidate's experience and professional goals.
The ideal Life Scientist candidate will have:
*   A post-graduate degree in clinical pharmacology, medicine, physiology, 
biophysics, biochemistry, or a related field, preferably with a focus on 
quantitative analysis;
*   For Senior Life Scientists, a minimum of five (5) years of relevant 
industry experience; for example, in pharmacology, physiology, biophysics, 
biochemistry, or physical chemistry;
*   For Senior Life Scientists, an understanding, and preferably some 
experience, with industry-standard dynamic modeling software such as MatLab 
SimBiology or R is ideal;
*   Demonstrated ability to address complex drug development problems with 
an understanding of signaling pathways, homeostasis, physiological mechanisms, 
and explanatory factors;
*   Excellent written and verbal communication skills, and ability to work 
independently and as part of a collaborative Rosa-client project team.
Full and part-time employment and project-based contracting opportunities are 
available throughout the United States.  Interested individuals should email 
their resume or CV and an introductory letter to 
h...@rosaandco.com.
Rosa is an equal opportunity employer. Upon hiring, proof of eligibility to 
work in the United States will be required.

[NMusers] Seeking Biological Systems Modelers

[Rebecca Baillie]

Position Announcement
Biological Systems Modeler
Rosa & Co. LLC is seeking a Modeling Engineer to join our PhysioPD(tm) 
consulting practice.
Rosa is a drug-disease modeling and simulation company created to provide 
innovative modeling solutions to its pharmaceutical clients and meaningful 
equity ownership to its employees. See 
www.rosaandco.com. PhysioPD is a type of mechanistic 
quantitative systems pharmacology (QSP) modeling.
Modeling Engineers work with Life Scientists in our PhysioPD practice to 
develop QSP models and conduct simulations of disease, drug action, and 
(pre)clinical studies.
Biological Systems Modelers are responsible for:
*  Developing physiologically-based dynamic mathematical models 
and conducting simulations of disease, drug action, and (pre)clinical studies;
*  Working in conjunction with Rosa Scientists to cultivate 
data in support of model construction and interpretation;
*  Defining key issues, presenting results, and developing 
associated presentations and reports;
*  Assisting with client relationship management, and 
participating in Rosa's business development, depending on the candidate's 
experience and professional goals.
The ideal Biological Systems Modeler candidate will have:
*A PhD in bioengineering, pharmacology/pharmacometrics, systems 
biology, chemical engineering, applied mathematics, physics, or a related 
quantitative field with experience in nonlinear dynamic systems and control 
theory;
*A minimum of two (2) years of relevant pharmaceutical industry 
experience in, for example, quantitative systems pharmacology (QSP), 
mathematical modeling and simulation, or pharmacometrics;
*Proficiency with industry-standard dynamic modeling software such as 
MATLAB/SimBiology or R;
*Excellent written and verbal communication skills, and ability to work 
both independently and collaboratively as part of a Rosa-client joint project 
team;
*An understanding of the pharmaceutical R process and experience in 
applying modeling to support decision-making in that context is highly 
desirable.
Full and part-time employment and project-based contracting opportunities are 
available throughout the United States.  Interested individuals should email 
their resume or CV and an introductory letter to 
h...@rosaandco.com.
Rosa is an equal opportunity employer. Upon hiring, proof of eligibility to 
work in the United States will be required.

[NMusers] Webinar with Dr. Trujillo, Merck on QSP Model for Proinsulin to Insulin Conversion Therapy

[Rebecca Baillie]

Leveraging a Diabetes QSP Model to Drive Decisions in Target Identification and 
Validation for Proinsulin to Insulin Conversion Therapy

Maria Trujillo PhD
Principal Scientist, Merck and Co Inc, Kenilworth, NJ

July 18, 2019 12:00-1:00 PM EDT
Registration (Free) at 
https://register.gotowebinar.com/register/4089794427217565195?source=website

Abstract: Proinsulin is a precursor to insulin that is co-secreted into the 
blood by the beta cell as a result of incomplete processing. Circulating 
proinsulin levels increase with increasing insulin resistance in type 2 
diabetes mellitus (T2DM). Unlike insulin, proinsulin has limited activity on 
the insulin receptor. To assess whether the development of peptides engineered 
to convert proinsulin to insulin in the blood would provide therapeutic value 
in T2DM, we leveraged a diabetes quantitative systems pharmacology (QSP) model 
(a physiologically based computational model of glucose homeostasis in humans); 
internal clinical datasets, and external data from the literature.

In silico hypothesis testing included 1) the addition and qualification of 
proinsulin biology into our diabetes QSP model, 2) the creation of virtual 
patients (VP) to determine whether proinsulin conversion therapy may provide 
value to a subpopulation of patients with T2DM based on phenotypic traits, 
either as a monotherapy or in addition to standards of care (sulfonylureas and 
metformin), and 3) the simulation of a phase 3 clinical trial with relevant 
endpoints (including HbA1c and glucose, insulin, and proinsulin) and additional 
mechanistic readouts (changes in circulating hormones and metabolites during 
meals and glucose tolerance tests) to interrogate and interpret results.

As monotherapy, proinsulin conversion to insulin led to a ~0.2% reduction in 
HbA1C in diabetic VPs with lesser effects (~0.1%) when added to a standard of 
care. Virtual patients with higher proinsulin: insulin ratios at baseline 
showed the greatest reductions. However, to achieve a clinically meaningful 
HbA1C reduction of ≥ 0.5%, most VPs needed ratios above the reported 
physiological range. The minimal influence of proinsulin conversion could be 
explained by the proinsulin secretion and degradation rates relative to 
respective rates for insulin; these system dynamics were a key learning from 
the QSP modeling effort.

The lack of projected impact on HbA1C through conversion of proinsulin to 
insulin was not intuitive prior to the in silico hypothesis testing using QSP 
approaches. The simulation results were examined and challenged with rigor both 
quantitatively and qualitatively and led to a recommendation not to pursue 
proinsulin conversion as a potential T2DM therapy. The QSP modeling approach 
was chosen to capture not only the dynamic interplay between proinsulin and 
insulin kinetics but their impact on a complex multi-organ system that 
maintains glucose homeostasis in the body. By thoroughly evaluating the 
putative therapeutic in diabetic VPs in a Phase 3 setting, we were able to 
generate sufficient scientific rationale for the termination decision. This 
effort demonstrates how in silico hypothesis testing through QSP modeling may 
aid in target identification and validation efforts in the discovery space, 
conserving R resources for targets with greater probability of clinical 
success.

[NMusers] Webinar with Dr. Bansal, GSK on QSP Models

[Rebecca Baillie]

Webinar: Modular development and application of platform QSP models to support 
a broad R portfolio: Examples from immuno-oncology and respiratory 
therapeutic areas

Loveleena Bansal, PhD
Scientific Leader, GSK Associate Fellow, at GSK, Collegeville PA

Jun 20, 2019 12:00-1:00 PM EDT
Registration (Free) at 
https://register.gotowebinar.com/register/6137023505483805197?source=website

Abstract: QSP modeling provides an integrated systems approach to model the 
mechanism of action of drugs as well as obtain a deeper understanding of the 
pathogenesis of diseases. It has thus emerged as an important tool to advance 
the discovery and development of therapeutic drugs in the pharmaceutical 
industry. However, one of the major challenges facing QSP modelers is rapid 
development of these models under strict timelines to allow impactful 
contributions to programs and scaling up to other targets/drugs within the same 
disease area as well as other disease areas of interest. Thus, a strategy for 
widely applying QSP models for several disease areas in GSK has been developed 
by leveraging modular development to allow extensive re-use of developed models 
and automation tools for accelerating model development and analysis.

In this talk, developments on application strategy of QSP modeling and its 
impact on programs will be discussed. Model development workflow will be 
illustrated for a QSP platform for evaluating immune-oncology (IO) therapeutics 
which covers description of several immune cells and templates for 
coreceptor-ligand interactions on the surface of cells that can be applied to 
number of different coreceptors to evaluate IO combination therapies. Secondly, 
a QSP modeling platform has been developed to support the diverse COPD 
portfolio in GSK. The model is supporting translation of in-vitro drug effects 
to patients to enable efficacious dose prediction, selection of biomarkers that 
can be used as early indicators of efficacy, as well as clinical trial design 
by estimating the length of study required to observe clinical benefits.

[NMusers] Upcoming webinar: Expanding from Basic Towards Systems Pharmacodynamic Models for Methylprednisolone

[Rebecca Baillie]

Expanding from Basic Towards Systems Pharmacodynamic Models for 
Methylprednisolone

Vivaswath S. Ayyar, Ph.D., Research Assistant Professor of Pharmaceutical 
Sciences, University at Buffalo, NY and PK/PD Scientist, Janssen 
BioTherapeutics, Spring House, PA

Thursday May 23, 2019, 12:00 to 1:00 pm EDT

Register at 
https://register.gotowebinar.com/register/2512261322183894796?source=website

Abstract: Evolving upon foundational principles of classical pharmacology 
mostly applied to static systems, a diversity of basic 
pharmacokinetic/pharmacodynamic (PK/PD) models have emerged. Placing emphasis 
on parsimony, the basic "mechanism-based" models incorporate and relate plasma 
pharmacokinetics, receptor binding, and/or relevant homeostatic mechanisms 
controlling drug response.
Continued refinement of PK/PD models based upon a progressively deeper 
mechanistic appreciation of physiologically-based PK, pharmacology of 
drug-target interactions, and systems physiology from the molecular (genomic, 
proteomic, metabolomic) to cellular to whole body scales have laid the 
foundation for building mechanistic quantitative systems pharmacology (QSP) 
models. Previous research based on various animal, clinical, and theoretical 
studies with corticosteroids have provided ideas to broadly advance the fields 
of pharmacokinetics and pharmacodynamics.
Our recent work on modeling diverse aspects of corticosteroid systems 
pharmacology reflect the integration of basic pharmacodynamic models along with 
the assimilation of fundamental insights gained from many focused studies of 
corticosteroids. These models highlight the application of combined systems 
(experimental and modeling) approaches to decipher "horizontal" and "vertical" 
pharmacokinetic/ pharmacodynamic/ pharmacogenomic (PK/PD/PG) relationships of 
the synthetic corticosteroid, methylprednisolone, in relation to 1) circadian 
gene expression and inter-tissue responses, 2) biological signaling networks, 
and 3) sex differences, using systems pharmacology approaches supported with 
data from microarray and proteomics analysis, systemic physiological 
measurements, and/or more focused quantitation of mechanistic biomarker(s). The 
modeling strategies employed, major findings, and lessons learned from these 
studies are described.

[NMusers] Pharmacometrics Webinar with Stephan Schmidt, March 14th

[Rebecca Baillie]

Pharmacometrics, PBPK, QSP - What's Next?

Stephan Schmidt, B.Pharm, Ph.D., F.C.P.
Center for Pharmacometrics & Systems Pharmacology, University of Florida

 Thursday, March 14, 2019, 12:00 to 1:00 pm EDT 

Register at https://www.rosaandco.com/webinars

Abstract: 
Modeling & simulation (M) tools have long been used in engineering and 
aerospace industries to develop products that would be prohibitively expensive 
to optimize through iterative improvement of prototypes. Modern drug 
development is now adapting and integrating analogous tools based on 
information from all phases of the development process since it is neither 
cost-effective nor time-efficient to tackle all open questions experimentally. 
As a result, an increasing number of decisions are now based on M at various 
levels of physiological and temporal complexity. However, prospective 
identification of clinically relevant sources of variability remain a 
challenge. To overcome this challenge the integrated use of multidisciplinary 
research approaches is needed.

The objective of this webinar is to provide selected case examples that 
highlight the added value of integrated use of real-world outcomes-, 
pharmacometric, and physiologically based pharmacokinetic approaches for 
identifying clinically-relevant sources of variability and translating them 
into actionable items.

[NMusers] Webinar: Dynamic Models for Personalized QSP: How models can help us explore big data

[Rebecca Baillie]

Dynamic Models for Personalized QSP: How models can help us explore big data

Professor Ioannis (Yannis) P. Androulakis ,
Biomedical Engineering, Rutgers University

Tuesday, Feb 12, 2019, 12:00 to 1:00 pm EST

Register at https://www.rosaandco.com/webinars

Abstract:
In this presentation Professor Androulakis will discuss the broader role 
modeling has played, historically, in enabling rationalization of (small) data 
and we will explore its evolving role towards advancing personalized medicine 
in the era of "big" data.

[NMusers] QSP/oncology webinar

[Rebecca Baillie]

Enhancing Phase I Clinical Decision Making Through QSP Modeling:
A Case Study of Mosunetuzumab in Relapsed/Refractory Non-Hodgkin's Lymphoma

Chi-Chung Li, Ph.D., Senior Scientist,
and Iraq Hosseini, Ph.D., Scientist
at
Genentech

Wednesday, Jan 23, 2019, 12:00 to 1:00 pm EST

Register at https://www.rosaandco.com/webinars

Abstract:
T cell-engaging bispecific antibodies (T-BsAb) are becoming important molecular 
entities in the development of immune-oncology agents. The promises in clinical 
efficacy with the potent mechanism of action comes with the consideration to 
mitigate potential acute toxicity concerns, driven by the systemic cytokine 
release following T cell activation.

To aid in the clinical development and maximize the potential therapeutic 
benefits of mosunetuzumab, an anti-CD20/CD3 T-BsAb, a QSP model was built based 
on preclinical and literature data and used to simulate and compare the 
systemic cytokine profiles following various dosing regimens of mosunetuzumab. 
Results indicate that a step-fractionated dosing regimen may mitigate the peak 
cytokine release levels and thereby allowing further dose escalation of 
mosunetuzumab in Phase I development.

The QSP modeling efforts enhanced the design rationale of the Phase 1 clinical 
studies and was used to enable a successful regulatory interaction with the FDA 
on protocol amendments. The presentation will touch on factors that are 
important for the successful clinical impact of modeling by focusing on the 
appropriate clinical questions and context.

[NMusers] Webinar by Eric Sobie: QSP, Machine Learning, Cardiotoxicity

[Rebecca Baillie]

Combining mechanistic modeling with machine learning to predict cardiotoxicity 
and streamline drug development

Eric Sobie, PhD
Professor, Pharmacological Sciences
Icahn School of Medicine at Mount Sinai, New York, NY

Wednesday, December 12, 2018, 12:00 to 1:00 pm EST
Register at 
https://www.rosaandco.com/webinars/2018/combining-mechanistic-modeling-machine-learning

Abstract:
Quantitative Systems Pharmacology (QSP) uses simulations with mechanistic 
mathematical models to improve drug development. Dr. Sobie will discuss recent 
and ongoing studies to exploit the power of QSP to improve understanding and 
prediction of cardiotoxicity. Specifically, Dr. Sobie will describe how 
simulations with mechanistic QSP models can be combined with machine learning 
algorithms to address unresolved questions.

This integration of statistical and dynamical modeling provides a powerful 
approach for prioritizing experimental studies, overcoming limitations of 
experimental models, and identifying patient subgroups most at risk of 
developing cardiotoxicity.

[NMusers] Upcoming webinar on how to use gPKPDSim

[Rebecca Baillie]

In Rosa's next monthly webinar on Oct 24th at 1:pm EDT, Iraj Hosseini, the 
scientist at Genentech who was one of the main drivers behind gPKPDSim, a 
SimBiology-based GUI for PKPD modeling in drug development, will demonstrate 
his app and show others how they can use it in their research.
Register for the free webinar at: https://lnkd.in/gQqiUGC
Abstract: Modeling and simulation is increasingly used in drug development to 
characterize pharmacokinetic-pharmacodynamic (PKPD) relationships and support 
various efforts such as target feasibility assessment, molecule selection, 
human PK projection, and preclinical and clinical dose and schedule 
determination.
While model development typically requires mathematical modeling expertise, 
model exploration and simulations could in many cases be performed by 
scientists in various disciplines to support the design, analysis and 
interpretation of experimental studies. To this end, we have developed a 
versatile graphical user interface (GUI) application to enable easy use of any 
model constructed in SimBiology(r) to execute various common PKPD analyses.
The MATLAB(r)-based GUI application, called gPKPDSim, has a single screen 
interface and provides functionalities including simulation, data fitting 
(parameter estimation), population simulation (exploring the impact of 
parameter variability on the outputs of interest), and non-compartmental PK 
analysis. Further, gPKPDSim is a user-friendly tool with capabilities including 
interactive visualization, exporting of results and generation of 
presentation-ready figures. gPKPDSim was designed primarily for use in 
preclinical and translational drug development, although broader applications 
exist. gPKPDSim is a MATLAB(r)-based open-source application and is publicly 
available to download from MATLAB(r) Central(tm). We illustrate the use and 
features of gPKPDSim using multiple PKPD models to demonstrate the wide 
applications of this tool in pharmaceutical sciences.

[NMusers] Webinar: System Level Modeling of the Human Gut Microbiome

[Rebecca Baillie]

System Level Modeling of the Human Gut Microbiome

Prasad Dhurjati, PhD
 Professor of Chemical & Bio-molecular Engineering, Joint appointment as 
Professor Biological Sciences and Professor of Mathematical Sciences, 
University of Delaware
 
Thursday, September 20, 2018, 12:00 pm to 1:00 pm EDT 

Register for free at https://www.rosaandco.com/webinars

Abstract: 
The human gut is host to a diverse and complex ecosystem of over a thousand 
microorganisms that are collectively known as the " gut microbiome". The gut 
microbiome has already been correlated with over a hundred diseases and has 
created a revolution in medical science. These microbes play a major role in 
providing nutrition for our body and maintaining a strong immune system. 

 Modeling of the spatiotemporal dynamics of the microbes is useful to 
investigate how the gut microbiome may influence "disease progression". The 
models could also help in designing strategies to dynamically manipulate the 
system trajectory away from a "disease" state to a "healthy" state. The 
importance of microbial dynamics will be illustrated in the context of our 
research on Autism Spectrum Disorders (ASD). System level modeling approaches 
using connectivity maps, rules, and mathematical equations have provided 
insights into the dynamic changes in the gut microbiome in ASD. Such 
system-level approaches can potentially be used for personalized and predictive 
diagnoses and for preventive nutritional recommendations.

[NMusers] Webinar AI and drug development

[Rebecca Baillie]

Advancing Drug Development and Outcomes Research with Artificial Intelligence, 
Machine Learning, and Predictive Models
 
Richard Gliklich, MD
Founder and CEO, OM1
 
Wed, July 18, 2018, 1:00 pm to 2:00 pm EDT 

Register for free at https://www.rosaandco.com/webinars

Abstract: The convergence of AI, machine learning, predictive modeling and big 
data enable the ability to measure, predict and personalize care in a way 
previously not possible. 

Join Dr. Richard Gliklich, CEO of OM1, as he explores the applications of AI 
tools and models to accelerate research, measure and benchmark outcomes, and 
demonstrate value.

[NMusers] Webinar: Ten Simple Rules of Credible Practice of Modeling and Simulation in Healthcare:

[Rebecca Baillie]

Webinar: Ten Simple Rules of Credible Practice of Modeling and Simulation in 
Healthcare: Application to Bone Remodeling and Heart Valve Modeling

Lealem Mulugeta and Andrew Drach
Executive Committee Members of the Committee for the Credible Practice of 
Modeling & Simulation in Healthcare

Thursday, May 17, 2018, 1:00 pm to 2:00 pm EST 

Register at 
https://www.rosaandco.com/webinars/2018/ten-simple-rules-of-credible-practice-of-modeling-and-simulation-in-healthcare

Abstract: 
Computational modeling and simulation (M) has the potential to play a 
critical role in precision medicine and personalized healthcare. However, there 
are few formalized processes and procedures to support the credible use of M 
in healthcare and biomedical research, limiting their adoption. To address this 
gap, the Committee on Credible Practice of Modeling & Simulation in Healthcare 
(CPMS) was established under the Interagency Modeling and Analysis Group (IMAG) 
and the Multiscale Modeling (MSM) Consortium, which includes representatives 
from government agencies such as the National Institutes of Health, NASA, and 
the U.S. Food and Drug Administration. One of CPMS' primary goals is to develop 
"The Ten Simple Rules of Credible Practice of M in Healthcare." These rules 
were derived via synthesis of the Committee's expertise in this domain and by 
surveying the global stakeholder community to ensure a balanced representation 
of interests and perspectives concerning credible practice of M in 
healthcare.  
This presentation will introduce these rules, present two case studies 
(modeling heart valves and bone remodeling) to demonstrate their value, and 
discuss strategies for incorporating these rules into workflows.

[NMusers] Webinar: Ensuring your QSP model is useful

[Rebecca Baillie]

How to Ensure Your QSP Model is Useful - Illustrative Examples and Lessons 
Learned

Christina Friedrich, PhD and Mike Reed, PhD
Chief Engineer and Chief Scientist,  Rosa & Co.

Wednesday, April 25, 2018, 12:00 pm to 1:00 pm EST 
Register at https://www.rosaandco.com/webinars

Abstract: Quantitative Systems Pharmacology (QSP) modeling considers drug 
mechanism of action in the context of biological disease mechanisms to improve 
understanding of human biology and pharmacology. QSP models differ from PK/PD 
models in their purpose and data requirements, and there can be significant 
variability among QSP models in scope and level of detail, research questions 
addressed, and available data. 
If all models are wrong, but some are useful, how can you make sure that your 
QSP model is as useful as possible? In this Webinar, we address some common 
missteps and inefficiencies in mechanistic QSP modeling that can reduce the 
usefulness of a model. We will draw on illustrative examples from personal 
experience and the public domain to illustrate relevant aspects of modeling 
approach and model quality and summarize lessons learned for ensuring that QSP 
models are as useful as possible.

[NMusers] Webinar by Richard Gray, FDA

[Rebecca Baillie]

The Potential Impact of Physiological Computer Models in Medicine: Regulation 
and Considerations for Ensuring Patient Safety

Richard A. Gray, PhD, Senior Research Biomedical Engineer
 Food and Drug Administration (FDA)
 
Wednesday, Mar 14, 2018, 12:00 pm to 1:00 pm EST 

Register at 
https://www.rosaandco.com/webinars/2018/the-potential-impact-of-physiological-computer-models-in-medicine-regulation-and-considerations-for-ensuring-patient-safety

 Abstract: 
Computational modeling has the potential to accelerate the ongoing revolution 
in healthcare underway attributable to personalized medicine and information 
technology.

Some potentially clinically useful computer models are based on the underlying 
mechanisms of human physiology which have unique benefits, but also present 
unique challenges. The benefits are numerous and span the entire medical 
product life-cycle from design to clinical outcomes as well as regulatory 
evaluation. The challenges are also numerous and include establishing trust, 
obtaining useful data, incorporating patient variability (if necessary) as well 
as ensuring models are validated and their use doesn't endanger patients. 

This talk will begin with a brief overview of ongoing modeling efforts at the 
FDA, and then focus on a discussion of the factors and methodologies regarding 
the appropriate evaluation metrics for physiological computer models to be used 
clinically. Examples of verification, validation, and uncertainty 
quantification (VVUQ) for complex cardiac electrophysiological models will be 
presented as well as discussing methods for assessing credibility for specific 
contexts of use.

[NMusers] Webinar: Automated scale reduction of nonlinear QSP models with an example of a bone biology system

[Rebecca Baillie]

Webinar: Automated scale reduction of nonlinear QSP models with an example of a 
bone biology system
 
Chihiro Hasegawa, PhD
 PK/PD Scientist, Ono Pharma, Japan, 
Otago Pharmacometrics Group, University of Otago, New Zealand
 
Wednesday Jan 24, 2018, 4:00 pm to 5:00 pm EST
Notice: different time than normal!
Register at https://attendee.gotowebinar.com/register/6393325151373548035

Abstract: 
QSP models are increasingly used in drug development to provide a deeper 
understanding of the mechanism of action of drugs and their likely effects on 
the system as well as to identify appropriate disease targets in preclinical 
settings. Irrespective of the purpose of development, such models are generally 
not suitable for estimation purposes due to the large number of states and 
parameters to be handled, even if all unidentifiable parameters were fixed.

Based on identifying a specific input-output relationship, however, the system 
may be reduced to fewer states and parameters that may then be suitable for 
estimation purposes. Proper lumping has been used for order reduction of 
complicated linear models. This technique is however not straightforward to 
apply for nonlinear differential equations that are not uncommon in QSP models.

In this presentation, I will be discussing the simplification of a nonlinear 
systems bone biology model by inductively linearizing the system followed by 
automated lumping. The reduced model will then be utilized to extrapolate 
long-term bone mineral density responses.

[NMusers] Webinar: Quantitative Modeling in Support of the Development of a Lupus Drug

[Rebecca Baillie]

Quantitative Modeling in Support of the Development of a Systemic Lupus 
Erythematosus Drug

Konstantinos (Kostas) Biliouris, PhD
Principal Pharmacometrics Scientist, Novartis, Cambridge, MA, USA

Wednesday, Dec 13, 2017, 12:00 pm to 1:00 pm EST 

Register at https://register.gotowebinar.com/register/1722787768122350082

Abstract: Systemic lupus erythematosus (SLE) is a rare, chronic auto-immune 
disease. Type I interferons, that are primarily produced in plasmacytoid 
dentritic cells, play a major role in the pathogenesis of SLE as well as its 
cutaneous form CLE. 

BIIB059 is a humanized Fc effector function-competent immunoglobulin G1 
monoclonal antibody (mAb) under development for the treatment of SLE and CLE. 
It specifically binds BDCA2, a receptor uniquely expressed on the surface of 
human and non-primate pDCs. BDCA2 receptor engagement by BIIB059 has been shown 
to mediate inhibition of IFNα/β expression, which is expected not only to treat 
the active disease (e.g. lupus skin lesions) but also to inhibit disease 
progression in SLE. 

In this presentation, I will be discussing the development of a population 
PK/PD model for BIIB059. This model will be utilized in selecting the doses for 
upcoming Phase 2 studies.

[NMusers] Webinar: QSP modelling - At the heart of the action

[Rebecca Baillie]

QSP modelling - At the heart of the action

Speaker: Nelleke Snelder, Senior Consultant, LAP Consultants, Leiden, The 
Netherlands 
Thursday, Nov 16, 2017, 12:00 pm to 1:00 pm EDT 

Register at https://register.gotowebinar.com/register/3899274333265939713

Abstract: 
PKPD modelling has developed from an empirical and descriptive approach into a 
scientific discipline based on the (patho-) physiological mechanisms behind 
PKPD relationships. As a result, PKPD models range from purely empirical 
models, i.e. descriptive models to mechanism-based and Quantitative Systems 
Pharmacology (QSP) models with an increasing level of complexity and increasing 
level of predictive power. The preferred PK/PD modelling approach (i.e. 
fit-for-purpose modelling) highly depends on the questions that need to be 
answered in addition to what level of detail is required. Empirical and 
mechanism-based models have already demonstrated their value in 
drug-development and examples of the application of QSP models are emerging. 
Application of QSP models, however, may be challenging as they are more complex 
and require a larger amount of informative data. Moreover, combining different 
types of information is essential for QSP approaches. 
The questions for the modeller at hand are: (1) what knowledge is required to 
answer the question, (2) how to select and integrate informative data, (3) how 
to balance between complexity and quantifiability and (4) when am I happy to 
stop? These questions, as well as the potential to recycle the developed 
systems model for other purposes, will be addressed during this webinar using 
the development of a systems pharmacology model for the cardiovascular effects 
of S1P agonists as an example. 

[NMusers] Upcoming modeling webinar

[Rebecca Baillie]

Development of a multiscale skin barrier model for de novo, in silico prediction
Dr. Ryan Tasseff, Scientist, Procter and Gamble
September 13, 2017 12:00-1:00 PM EDT

Register for free: https://register.gotowebinar.com/register/722139264316130

We developed a multiscale, many-cell skin barrier model. Our strategy was the 
integration of four distinct models that have been previously validated and 
described in the literature. Modeling cells as discrete elements in a 
continuous environment, the foundation is a three-dimensional, agent-based 
model of barrier formation and epidermal homeostasis. A continuum 
representation is used for transport of molecular species in the extracellular 
space and water transport, which modulates swelling of cellular agents and 
impacts TransEpidermal Water Loss (TEWL). Finally, we apply a system of 
ordinary differential equations in each basal cells to capture intracellular 
biomolecular processes related to cell cycle control.

We employed the high-performance computing platform Biocellion. Unlike other 
platforms, Biocellion provides scalability across CPU threads or cluster nodes 
with virtually no overhead. It allows modeling at the level of detail and 
flexibility necessary to maintain the integrity of the underlying source 
models. Because we model individual cells, reactions and transport at micron 
length scales, and because we simulate whole tissue scales of mm and days, this 
model is a true three-dimensional, multiscale representation of a dynamic skin 
barrier.

To demonstrate utility, we investigated the potential for de novo, in silico 
prediction of barrier response to external stimuli. For this initial 
case-study, we chose a strong chemical stimuli, a CDK1/CDC2 inhibitor. All 
parameters were found in existing sources and no training was required. The 
results showed the feedback from inhibitor penetration to reduced proliferation 
to barrier breakdown that leads to increased penetration. In addition, the 
simulation also predicts increases in TEWL, connecting the computational model 
to clinically relevant measures of human barrier function.

[NMusers] Upcoming QSP webinar with Valeriu Damian, GSK

[Rebecca Baillie]

QSP - What’s in it for me? Case study examples

July 19, 2017
12:00-1:00 PM EDT 
Register at http://www.rosaandco.com/webinars/2017/damian

Valeriu Damian, Director
Systems Modeling and Translational Biology 
GlaxoSmithKline, King of Prussia, PA

Drug discovery efforts, often based on qualitative link between target and 
disease have resulted in a high rate of late stage clinical failures due to 
lack of efficacy. Quantitative Systems Pharmacology (QSP) promises to provide 
the missing quantitative link between the target modulation and clinical 
outcomes and reduce late stage attrition. Over the last few years QSP has grown 
as a discipline gaining traction in industry, in academia and in regulatory 
agencies however there is still a long way to go until all drug discovery and 
development projects would use QSP models at all stages of development.

This talk will look at the QSP modeling from the point of view of project 
specialists in drug discovery and development and try to address the question 
in the title: What's in it for me? How can QSP help with my project? Why should 
I invest in a QSP model? How much can I trust the model? What data do I need to 
provide? Can I get some insights without having to build and qualify the full 
QSP model?

I would demonstrate with several case studies the QSP modeling benefits and 
challenges looking both at the decisions that can be influenced by QSP modeling 
but also at the cost of building and qualifying the model.

[NMusers] Approaches to Reproducibility in Systems and Physiological Modeling

[Rebecca Baillie]

Upcoming webinar: Approaches to Reproducibility in Systems and Physiological 
Modeling
 
By Herbert Sauro, PhD
Associate Professor, Department of Bioengineering, University of Washington, 
Seattle, WA
  
June 14, 2017, 12:00 pm to 1:00 pm EDT 

Register at http://www.rosaandco.com/webinars/2017/sauro
 
Abstract: 
Reproducibility is the cornerstone of the scientific method; that is the 
ability to successfully and independently recreate an experiment.

Over the last 17 years a number of successful standards have emerged in systems 
biology modeling that are poised to allow modelers to create and publish 
reproducible simulation experiments. However, issues still remain. Though 
technically solved on a number of levels, the most significant impediment to 
achieving reproducible science is sociological. The reward system in science 
(at least in biology) puts more emphasis on publication metrics than on the 
actual results themselves.  
 
Reproducibility therefore takes a backstage, at least in systems biology 
modeling. This means that the bulk (>95%) of published models are not 
reproducible. Since our reward system is unlikely to change in the near future, 
efforts have to be directed instead at the journals. 

In this talk I will discuss some of the efforts we are trying to put in place, 
to encourage journals to put a higher emphasis on reproducibility, that 
includes both technical and cultural changes.   
  

[NMusers] Upcoming PK-PD Webinar

[Rebecca Baillie]

A PK-PD Modeling and Simulation Based Strategy for Clinical Translation of 
Antibody-Drug Conjugates: A case study with T-DM1

Aman Singh, PhD 
PhD Candidate 
Department of Pharmaceutical Sciences, University at Buffalo, Buffalo, NY
 April 19, 2017, 12:00 pm to 1:00 pm EDT

Register at http://www.rosaandco.com/webinars/2017/singh

Abstract: Successful clinical translation of Antibody-Drug Conjugates (ADCs) 
can be challenging due to complex pharmacokinetics and differences between 
preclinical and clinical tumors. In this webinar, we will present application 
of a PK-PD based strategy for successful bench to bedside translation of ADCs 
using T-DM1 as an example. A mechanistic cellular disposition model was 
developed incorporating intracellular ADC degradation and passive diffusion of 
unconjugated drug across tumor cells. Specific biomeasures and chemomeasures 
reported for T-DM1 in the literature were incorporated in the model of ADC to 
characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When 
the cellular model was integrated with an in vivo tumor disposition model, the 
model was able to a priori predict tumor DM1 concentrations in xenograft mice. 
Later, our integrated preclinical PK-PD model was used to characterize tumor 
growth inhibition (TGI) data in multiple HER2+ mouse models with varying level 
of HER2 expression. Clinical pharmacokinetics of T-DM1 was predicted by 
allometric scaling of monkey PK parameters. Finally, the predicted human PK, 
preclinically estimated efficacy parameters, and clinically observed volume and 
growth parameters for breast cancer were combined to develop a translated 
clinical PK-PD model capable of performing clinical trial simulations. Our 
model simulated PFS rates for HER2 1+ and 3+ populations were comparable to the 
rates observed in 3 different clinical trials. The model predicted only a 
modest improvement in ORR with an increase in clinically-approved dose of 
T-DM1. However, the model suggested that a fractionated dosing regimen (e.g. 
front loading) may provide an improvement in the efficacy. In general, the 
PK-PD M based strategy presented here is capable of a priori predicting the 
clinical efficacy and this strategy has now been validated for all clinically 
approved ADCs.

[NMusers] Upcoming QSP webinar

[Rebecca Baillie]

Considerations for Adapting Published Models for PhysioPD-Style Research
Michael Weis, PhD
Senior Engineer, PhysioPD™
Rosa & Co LLC,  San Carlos, CA
 
March 22, 2017, 12:00 pm to 1:00 pm EDT  
Registration: http://www.rosaandco.com/webinars/2017/weis

Abstract: The utilization of published models is an attractive strategy for 
quantitative systems pharmacology research. The process of adapting existing 
published models for new uses can present significant technical and scientific 
challenges and should be undertaken with appropriate expectations. In this 
webinar, we will discuss considerations for choosing and adapting existing 
models for new research purposes. 

Register at http://www.rosaandco.com/webinars/2017/weis

[NMusers] Webinar: Applying QSAR and PBPK Modeling to Bridge Discovery and Assessment of Clinical Potential

[Rebecca Baillie]

Webinar: Applying QSAR and PBPK Modeling to Bridge Discovery and Assessment of 
Clinical Potential

Michael Bolger, Ph.D. and Ted Grasela, Ph.D. 
Oct 19, 2016,  12:00 to 1:00 EDT 

Abstract: 
Mechanistic absorption and physiologically-based pharmacokinetic models play an 
ever increasing role in assessing the clinical potential of compounds in 
discovery, guiding the selection of lead compounds, and optimizing their 
chances for commercial success. There are many important questions about how to 
best develop and apply these models.  What are the best sources of input 
parameters: bottom up (in silico and in vitro) or top down (in vivo)? How 
reliable are the simulation results? Should these models play a role in dose 
estimation for First-in-Human (FIH) clinical studies? Can these be used for 
regulatory submissions and potential biowaivers?

Register at http://www.rosaandco.com/webinar.html

[NMusers] Webinar: Mathematical Optimization of Combination Therapy Regimens

[Rebecca Baillie]

Mathematical Optimization of Combination Therapy Regimens
by Helen Moore, Ph.D., Associate Director 
Quantitative Clinical Pharmacology 
Bristol-Myers Squibb; Princeton, NJ 
September 14, 2016
12:00 to 1:00 pm EDT

Register at http://rosaandco.com/webinarMoore.html

Abstract: Combination therapy is increasingly important, especially when 
resistance to drugs is a concern. However, finding the best possible doses to 
use can be challenging. If three drugs are to be combined, and there are 4 dose 
levels of each to be tested, this gives 43 dose combinations to test. Instead 
of running 64 studies, we can use mathematical modeling and simulation to gain 
insight into which dose levels should be combined to achieve optimal outcomes.
Essential components of optimizing outcomes include developing mathematical 
models of in-host disease dynamics, and quantifying the desired outcomes. 
Disease dynamics may be represented with semi-mechanistic models that include 
several cell types. Desired outcomes might include, for example, tumor size 
that is small at the end of treatment, but also not too large throughout the 
treatment period. Additionally, we don't want to use too much of any one drug, 
due to possible toxicity. Quantifying and giving relative weighting to these 
factors provide an objective that can be mathematically optimized.
I will discuss the optimal control framework and show examples in which control 
theory was applied to optimize combination therapy regimens. These include 
comparisons to more-traditional regimens, and optimization in the presence of 
constraints such as fixed allowable dose levels typical for patient therapies 
used in the clinic. 

[NMusers] Upcoming QSP Webinar

[Rebecca Baillie]

AbbVie Dermatology QSP Platform: Build Strategy and Applications in Psoriasis 
Drug Discovery and Clinical Development

by Oliver Ghobrial, Sr. Scientist III
Systems Pharmacology/DMPK/PKPD, AbbVie, Inc.

April 13, 2016 12:00 - 1:00 pm EDT

Abstract: In immune disease, complex interactions between predisposing 
genetics, the inflammatory network, and the tissue parenchyma are thought to 
play a role in disease pathogenesis. QSP provides a framework to integrate 
these correlations and uncover the precise combination and sequence of 
mechanisms responsible for disease. A psoriasis (Ps) QSP platform was developed 
to describe the homeostatic interactions between the skin dynamics, and the 
immune system in health and disease. This QSP platform enables a quantitative 
predictive understanding for the role of predisposing genetics and cause of 
disease (vs. downstream consequence of disease), value of novel therapies, 
responder/non-responder subpopulations to them, and the signature biomarker 
patterns to identify these subpopulations. The virtual patient, virtual cohort, 
and virtual population, development strategies will be presented and utility of 
this approach to explore competing hypotheses of Ps pathophysiology will be 
demonstrated.

Register at http://rosaandco.com/webinarGhobrial.html

[NMusers] Director level job opening

[Rebecca Baillie]

Principal, PhysioPD™
Rosa & Co. LLC is the world-wide commercial leader in quantitative systems 
pharmacology.  Rosa supports clients with their critical decisions – from 
preclinical through clinical development – with the creation and use of 
mathematical PhysioPD Research Platforms. These Platforms are customized to 
allow clients to simulate normal and disease physiology, drug action, patient 
variability, and trial outcomes and efficiently answer the full spectrum of 
questions related to targets, mechanisms, and outcomes. Principals form Rosa’s 
management team and lead Rosa’s client service engagements.

Principals are responsible for:
•   Leading PhysioPD service engagements and assisting with strategic 
client relationships
•   Supporting sales via capability presentations, scientific insight, and 
proposal preparation
•   Contributing to Rosa’s PhysioPD capabilities and intellectual property
•   Conducting specific marketing efforts such as publications or 
conference presentations
•   Mentoring staff in functional (science or engineering) and client 
service roles
•   Participating in Rosa’s business management.

The ideal Principal candidate will have:
•   A post-graduate degree in a relevant life science field (e.g., 
pharmacology, medicine, physiology) or technical field (e.g., chemical 
engineering, biophysics, biochemistry)
•   Significant professional experience in a related discipline (e.g., 
mechanistic modeling, physiology, pharmacology, pharmacometrics, biophysics, 
biochemistry, physical chemistry) and in management of complex projects, either 
in a consulting or client organization
•   Demonstrated ability to address complex biological or drug development 
challenges with an understanding of signaling pathways, homeostasis, 
physiological mechanisms, etc.
•   Excellent written and verbal communication skills, detail orientation, 
and demonstrated ability to work both independently and as part of a 
cross-functional project team
•   Preferably a basic understanding of dynamic modeling software such as 
JDesigner/ PathwayDesigner or SimBiology/MATLAB
•   Ability to travel (up to approximately 20% time, depending on location).

Please reply to h...@rosaandco.com

[NMusers] Next Impact Webinar by Satyaprakash Nayak, Pfizer

[Rebecca Baillie]

Development of a Quantitative Systems Pharmacology Model of the Blood 
Coagulation Network & its Application to Clinical Programs
by Satyaprakash Nayak, Pharmacometrician, Systems Biologist at Pfizer
September 16, 2015, 1:00 to 2:00 pm EDT
Abstract: A number of therapeutics have been developed or are under development 
aiming to modulate the coagulation network to treat various diseases such as 
Hemophilia. We developed a quantitative systems pharmacology model to better 
understand the effect of modulating various components on blood coagulation. 
The model of the coagulation network was composed of mass balance reactions and 
was optimized to describe ex vivo biomarkers including thrombin generation 
assay (TGA) parameters, activated Partial Thromboplastin Time (aPTT) and 
Prothrombin Time (PT) and in vivo biomarkers under non-bleeding conditions, 
including prothrombin fragment 1+2 (PF1+2), D-dimer and thrombin anti-thrombin 
III complex (TAT) Protein synthesis and degradation were turned on to describe 
the in vivo model, but were turned off when describing ex vivo biomarker 
changes. Platelet activation-dependent reactions also had different reaction 
rates for the ex vivo vs. in vivo biomarkers to reflect differences in assay 
conditions. Both in house data with various concentrations of recombinant 
factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor 
VIII-deficient plasma1,2 and literature data were used for estimating model 
parameters. Sensitivity analysis applied to the model revealed that biomarkers 
show different sensitivity to changes in coagulation factors’ concentrations 
and the type of plasma used (normal or factor VIII-deficient).

The model was validated through a comparison between clinical data from the 
recently concluded Pfizer study in Hemophilia subjects2. We observed good 
agreement between clinical observations and model predicted results, providing 
us with further confidence to apply the model in dose selection strategies for 
subsequent phases. Finally, we applied the model to predict how variability in 
concentrations of the proteins in coagulation network may impact the response 
to FVIIa treatment in Hemophilia subjects.
Register at http://rosaandco.com/webinarNayak.html

[NMusers] Next Impact Webinar by Peter Bonate, Astellas

[Rebecca Baillie]

Modeler. Tailor. Listener. Buy
Peter L. Bonate, PhD, Senior Director, Astellas
Global Head - Pharmacokinetics, Modeling, and Simulation
Global Clinical Pharmacology  Exploratory Development
July 15, 2015
1:00 - 2:00 pm EDT
Presenting modeling and simulation results to an audience can be a challenge.  
Treating a presentation as a one-size-fits all exercise is a recipe for epic 
failure.  Successful presentations require tailoring your presentation to your 
audience, whether that audience is one person or a roomful.  This presentation 
will discuss how to tailor your MS presentations to get your audience to buy 
into your results and how in today’s global corporate climate how cultural 
differences can sabotage your success.
Register for free at http://rosaandco.com/webinarBonate.html

[NMusers] Next Rosa Impact Webinar Announcement

[Rebecca Baillie]

Quantitative Cardiovascular Systems Pharmacology: exploiting mathematical 
models to generate novel insight

by Eric A. Sobie, Associate Professor
Department of Pharmacology and Systems Therapeutics
Icahn School of Medicine, Mount Sinai, NY 
June 17, 2015, 1:00 to 2:00 pm EDT

Abstract: I will discuss recent efforts in my lab to gain insight into both 
therapeutic and detrimental effects of drugs in the heart. We obtain these new 
insights through Systems Pharmacology, an emerging discipline that combines 
simulations with mechanistic mathematical models and statistical analyses of 
large data sets. I will discuss how we have used these approaches to understand 
differences between individuals in the response to drugs that cause 
arrhythmias, to develop novel methods to distinguish between safe drugs and 
harmful drugs, and how we experimentally test mathematical modeling predictions 
to understand variability between samples. 

Register for this free webinar at 
www.rosaandco.com/webinar.htmlhttp://www.rosaandco.com/webinar.html. After 
registering you will receive a confirmation email containing information about 
joining the webinar. More information about the webinar series, an archive of 
past webinars, and a list of future webinar speakers may be found at 
www.rosaandco.com/webinar.htmlhttp://www.rosaandco.com/webinar.html

Rebecca Baillie

Rebecca Baillie
Chief of Staff
Rosa  Co, LLC
rbail...@rosaandco.com

[NMusers] Next Impact Webinar on Analysis of Big Data with Dr. Ghosh at The Systems Biology Institute

[Rebecca Baillie]

Garuda: Fly to the future of biology



 by Samik Ghosh, Ph.D.

Senior Scientist

The Systems Biology Institute

Tokyo, Japan



 March 11, 2015

12:00 to 1:00 pm EST



Abstract:

 With the ever-increasing diversity of omics-scale experimental data, a key 
challenge is the ability to discover the right tools for a specific analysis 
and navigate through their specific formats. Garuda is an open, 
community-driven, platform that provides a framework to discover, connect  
navigate through different applications in bio-medical research.



  This presentation is a part of the monthly world-wide webinar series Impact 
of Modeling  Simulation in Drug Development hosted by Rosa  Co. LLC.



The purpose of the Impact series is to foster the use of MS activities in 
all phases of drug development by illustrating the advantages and enhancing the 
applicability of MS in product discovery, development, and marketing programs. 
The series is intended for drug development project team members from discovery 
to phase IV clinical trials. This includes pharmacologists, ADME scientists, 
PK/PD modelers, clinical pharmacologists, clinical development team members, 
regulatory affairs specialists, and other interested professionals.





Register for this free webinar at 
www.rosaandco.com/webinar.htmlhttp://www.rosaandco.com/webinar.html.   After 
registering you will receive a confirmation email containing information about 
joining the webinar. More information about the webinar series, an archive of 
past webinars, and a list of future webinar speakers may be found at 
www.rosaandco.com/webinar.htmlhttp://www.rosaandco.com/webinar.html.