[NMusers] Sr. Statistical Programmer\Statistical Programmer I\II - PK/PD - position can be adjusted for knowledge and expirience

[Pavel Belo]

Sr. Statistical Programmer\Statistical Programmer I\II




Job Location: Tarrytown, NY
Auto req ID: 1208BR
Job Posting Title: Sr. Statistical Programmer/Statistical Programmer 
I/II

Person Type: Regular
Posting Category: Research & Discovery
Location: Tarrytown
Job Summary and Essential Functions:

Known for its scientific and operational excellence, Regeneron is a 
leading science-based biopharmaceutical company that discovers, invents, 
develops, manufactures, and commercializes medicines for the treatment 
of serious medical conditions. Regeneron markets medicines for eye 
diseases, colorectal cancer, and a rare inflammatory condition and has 
product candidates in development in other areas of high unmet medical 
need, including hypercholesterolemia, oncology, rheumatoid arthritis, 
allergic asthma, and atopic dermatitis.


Responsibilities:

This individual will be responsible for the collection of data from the 
LIMS and clinical data set, aligning/merging data sets, apply binding 
codes, and prepare merged data sets for pharmacokinetic analysis.
Generate tables accounting for the disposition of patients and samples, 
descriptive statistics of pharmacokinetic variables, and results.
Generate figures to include but not limited to: concentration over time, 
pharmacokinetic parameters over dose, concentration effect relationship, 
and PKJPD analyses.
As appropriate, lend support to the Preclinical Pharmacokinetics 
department.
Growth opportunity for this individual is in the area of 
Pharmacokinetics; both non-compartmental and compartmental, as weD as in 
the use of NONMEM, pharmacokinetic simulation and population 
pharmacokinetics.


Basic understanding of biostatistics
Experienced in data
Able to take directives and work independently
Excellent interpersonal skills
Manage and prioritize multiple programs with competing and aggressive 
time lines.

Experience and Required Skills:

Requirements:
Proficient in SAS programming (certificated in both basic and Advanced 
SAS programing). Basic to advanced understanding of "R" Knowledge of 
Sigma Plot and/or other graphing applications (e.g., Prism) is desirable 
Proficient in Excel and Excel macro generation. Basic understanding of 
pharmacokinetic principals and some exposure to WinNonLin and/or NONMEM 
is a plus


Statistics, computer data management, natural or applied sciences 
(including mathematics) Must have certificates in both Basic and 
Advanced SAS


This is an opportunity to join our select team that is already leading 
the way in the Pharmaceutical/Biotech industry. Apply today and learn 
more about Regeneron’s unwavering commitment to combining good science & 
good business.


To all recruitment agencies: Regeneron is using the agency management 
company, Candex - www.candex.com  . Please, no 
phone calls or emails to any employee of Regeneron about this opening. 
All resumes submitted by search firms/employment agencies to any 
employee at Regeneron via-email, the internet or in any form and/or 
method without a valid written search firm agreement in place for this 
position will be deemed the sole property of Regeneron. No fee will be 
paid in the event the candidate is hired by Regeneron as a result of the 
referral or through other means.


Regeneron is an equal opportunity employer.



 

RE: [NMusers] backward integration from t-a to t

[Bauer, Robert]

Pavel:
I am glad someone informed you of the ALAG option for handling your problem.  
My colleague w...@buffalo.edu and his associates have 
published on the general aspects of time delay differential equations, of which 
yours is a particular example.  Although Jacob Ribbing has already discussed as 
to whether or not using AUC for driving efficacy is appropriate from a 
mechanistic stand-point, and Leonid Gibiansky has offered another way of 
looking at the problem, it is nonetheless worthwhile to present to the nmusers 
audience an example of how to use the ALAG option for your particular case, 
from which they may generalize for use of other time delay problems.

In the following simple absorption model example developed by me and Alison 
Boeckmann for illustration purposes, compartments 1, 2, and 3 are the "real 
time" depot, central, auc,
and  compartments  4,5,6 are the "delayed time" depot, central, auc. So, the 
base model (non-time delay) system (compartments 1,2,3) is replicated  
(compartments 4,5,6) for the time delay portion.  In addition, the data set 
duplicates the dose information of compartment 1 into compartment 4, and 
setting ALAG4 to a non-zero value in the control stream file provides a lag 
time to any doses inputted into compartment 4 (so this would take care of 
multiple dose problems as well).  This allows for assessment and availability 
of AUC(t) and AUCT(t-time-delay) at any time t.  The comments explain the 
meaning of each compartment.

$PROB TEST AUC DELAY
$INPUT ID TIME AMT CMT DV
$DATA DELAYDATA IGNORE=@
$SUBR ADVAN6 TOL=5
$MODEL
COMP=(DEPOT) COMP=(CENTRAL) COMP=(AUC)
COMP=(D_DEPOT) COMP=(D_CENTR) COMP=(D_AUC)
COMP=(AUCDIFF)
$PK
TDY=THETA(1)*EXP(ETA(1))
ALAG4=TDY

KA=THETA(2)*EXP(ETA(2))
KE=THETA(3)*EXP(ETA(3))
$DES
DADT(1)=-KA*A(1)
DADT(2)= KA*A(1)-KE*A(2) ; C(T)
DADT(3)= A(2)  ; AUC(T)
DADT(4)=-KA*A(4)
DADT(5)= KA*A(4)-KE*A(5) ; C(T-TDY)
DADT(6)= A(5)  ; AUC(T-TDY)
DADT(7)= A(2)-A(5) ; AUC(T-TDY)

$ERROR
A1=A(1)
A2=A(2)
A3=A(3)
A4=A(4)
A5=A(5)
A6=A(6)
A7=A(7)
DAUC=A(3)-A(6) ; AUC(T)-AUC(T-TDY)
Y=F+EPS(1)
$THETA 3
$THETA 1 2
$OMEGA 1 1 1
$SIGMA 1
$TABLE ID TIME A1 A2 A3 A4 A5 A6 A7 DAUC NOAPPEND NOPRINT FILE=aucdelay.tbl 
FORMAT=sF8.3

And the example data set:
ID TIME AMT   CMTDV
1  01001  .
1  01004  .
1  1  .2  .
1  2  .2  .
1  3  .2  .
1  4  .2  .
1  5  .2  .
1  6  .2  .
1  7  .2  .
1  8  .2  .
1  9  .2  .
1  10 .2  .
1  11 .2  .
1  12 .2  .
1  13 .2  .
1  14 .2  .
1  15 .2  .


Robert J. Bauer, Ph.D.
Vice President, Pharmacometrics, R&D
ICON Development Solutions
7740 Milestone Parkway
Suite 150
Hanover, MD 21076
Tel: (215) 616-6428
Mob: (925) 286-0769
Email: robert.ba...@iconplc.com
Web: www.iconplc.com

From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On 
Behalf Of Pavel Belo
Sent: Tuesday, January 14, 2014 1:45 PM
To: Bauer, Robert
Cc: nmusers@globomaxnm.com
Subject: [NMusers] backward integration from t-a to t

Dear Robert,

Efficacy is frequently considered a function of AUC.  (AUC is just an integral. 
It is obvious how to calculate AUC any software which can solve ODE.)  A 
disadvantage of this model of efficacy is that the effect is irreversable 
because AUC of concentration can only increase; it cannot decrease.  In many 
cases, a more meaningful model is a model where AUC is calculated form time t 
-a to t (kind of "moving average"), where t is time in the system of 
differential equations (variable T in NONMEM).   There are 2 obvious ways to 
calculate AUC(t-a, t).  The first is to do backward integration, which looks 
like a hard and resource consuming way for NONMEM.  The second one is to keep 
in memory AUC for all time points used during the integration and calculate 
AUC(t-a,t) as AUC(t) - AUC(t-a), there AUC(t-a) can be interpolated using two 
closest time points below and above t-a.

Is there a way to access AUC for the past time points (
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RE: [NMusers] backward integration from t-a to t

[Pavel Belo]

Jello Jacob,


 


Someone genious just helped me.  Tlag can be used.  How did I miss such 
simple solution? 



 


I was talking about multiple doses.  There are cases AUC is better 
predictor than concentration (for example, long duration of treatment is 
needed; very slow but good drug effect), but when it comes to multiople 
doses, it does not work well because it is necessary to predict drug 
withdrawal.   If "moving average"-like approach is used, the drug effect 
disappears slowly, which can be the case.



 


Of course this approach has to tested for some unexpected results and 
adjusted if possible. 



 


Thanks,


Pavel  


 


 
On Wed, Jan 15, 2014 at 07:49 AM, Ribbing, Jakob wrote:

 


 





Hi Pavel,

 

I agree with you it is not uncommon to have AUC drive efficacy or safety 
endpoints.


However, you seem to have the impression this is commonly done using 
cumulative AUC and I can assure you that is rarely the case.


I have only seen that for safety endpoints where it has been justified 
(treatment is limited to a few cycles due to accumulation of side effect 
which for practical purposes can be regarded as irreversible).


Even for cases where treatment/disease is completely curative it is not 
a standard approach to use cumulative AUC to drive efficacy (e.g. 
antibiotics, where infection may be eradicated, but the 
bacterial-killing effect wears off after the drug has been eliminated; 
so even if disease does not come back the actual drug effect has worn 
off).


 

At steady state multiple dosing, AUC over a dosing interval (or Cav,ss) 
can sometimes be used to drive steady-state efficacy or safety.


However, it seems in your case you have fluctuations in drug response 
even at steady state?


Otherwise, this AUC can be expressed as an analytical solution or added 
as an input variable in your dataset, in case you are concerned about 
run times.


But with that approach you would not see a fluctuation in drug response 
at steady state, so in your case maybe better to use concentrations to 
drive efficacy?


 

For a “moving average” it would sometimes be possible to calculate AUC 
analytically.


However, a moving average AUC would rarely be a mechanistic description 
of effect delay. Leonid provide one possible solution (like an effect 
compartment).


However, there are many alternatives and it is not possible to say which 
is the best in your specific case(s), without more information, e.g.


· Are you thinking about single dose, multiple dosing, and in 
the latter case is it sufficient to describe your endpoint at stead 
state?


· And is the effect appearing with great delay over many 
days/weeks or it rather fluctuates with fluctuating concentrations? 
(e.g. at multiple dosing for a low dose, do you have fluctuations over a 
dosing interval in your efficacy endpoint that are due fluctuations in 
PK, i.e. aside from any circadian variation?)


· Does a higher dose reach its efficacy-steady state faster than 
a lower dose (time to efficacy-steady state; not the level of response 
which should be different)?


· What is the mechanisms for effect delay (i.e. the delay in on 
and offset of effect that is not due to accumulation of PK at start of 
treatment)


 

Are you aware of the standard models for effect delay that one would 
commonly consider and why did you dismiss these?


 

Best regards

 

Jakob

 



From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] 
On Behalf Of Pavel Belo

Sent: 14 January 2014 18:45
To: Bauer, Robert
Cc: nmusers@globomaxnm.com
Subject: [NMusers] backward integration from t-a to t


 





Dear Robert,






 




Efficacy is frequently considered a function of AUC.  (AUC is just an 
integral. It is obvious how to calculate AUC any software which can 
solve ODE.)  A disadvantage of this model of efficacy is that the effect 
is irreversable because AUC of concentration can only increase; it 
cannot decrease.  In many cases, a more meaningful model is a model 
where AUC is calculated form time t -a to t (kind of "moving average"), 
where t is time in the system of differential equations (variable T in 
NONMEM).   There are 2 obvious ways to calculate AUC(t-a, t).  The first 
is to do backward integration, which looks like a hard and resource 
consuming way for NONMEM.  The second one is to keep in memory AUC for 
all time points used during the integration and calculate AUC(t-a,t) as 
AUC(t) - AUC(t-a), there AUC(t-a) can be interpolated using two closest 
time points below and above t-a. 





 




Is there a way to access AUC for the past time points (integration routine? It seems like an easy thing to do.    





 




Kind regards,




Pavel  


 

[NMusers] backward integration from t-a to t

[Pavel Belo]

Please never mind. Someone suggested tose tlag, which is a simple 
and efficient enough way to do it.



 


Regards,


Pavel 


 


 


 


Dear Robert,




 


Efficacy is frequently considered a function of AUC.  (AUC is just an 
integral. It is obvious how to calculate AUC any software which can 
solve ODE.)  A disadvantage of this model of efficacy is that the effect 
is irreversable because AUC of concentration can only increase; it 
cannot decrease.  In many cases, a more meaningful model is a model 
where AUC is calculated form time t -a to t (kind of "moving average"), 
where t is time in the system of differential equations (variable T in 
NONMEM).   There are 2 obvious ways to calculate AUC(t-a, t).  The first 
is to do backward integration, which looks like a hard and resource 
consuming way for NONMEM.  The second one is to keep in memory AUC for 
all time points used during the integration and calculate AUC(t-a,t) as 
AUC(t) - AUC(t-a), there AUC(t-a) can be interpolated using two closest 
time points below and above t-a. 



 


Is there a way to access AUC for the past time points (integration routine? It seems like an easy thing to do.    



 


Kind regards,


Pavel  

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RE: [NMusers] backward integration from t-a to t

[Ribbing, Jakob]

Hi Pavel,

I agree with you it is not uncommon to have AUC drive efficacy or safety 
endpoints.
However, you seem to have the impression this is commonly done using cumulative 
AUC and I can assure you that is rarely the case.
I have only seen that for safety endpoints where it has been justified 
(treatment is limited to a few cycles due to accumulation of side effect which 
for practical purposes can be regarded as irreversible).
Even for cases where treatment/disease is completely curative it is not a 
standard approach to use cumulative AUC to drive efficacy (e.g. antibiotics, 
where infection may be eradicated, but the bacterial-killing effect wears off 
after the drug has been eliminated; so even if disease does not come back the 
actual drug effect has worn off).

At steady state multiple dosing, AUC over a dosing interval (or Cav,ss) can 
sometimes be used to drive steady-state efficacy or safety.
However, it seems in your case you have fluctuations in drug response even at 
steady state?
Otherwise, this AUC can be expressed as an analytical solution or added as an 
input variable in your dataset, in case you are concerned about run times.
But with that approach you would not see a fluctuation in drug response at 
steady state, so in your case maybe better to use concentrations to drive 
efficacy?

For a “moving average” it would sometimes be possible to calculate AUC 
analytically.
However, a moving average AUC would rarely be a mechanistic description of 
effect delay. Leonid provide one possible solution (like an effect compartment).
However, there are many alternatives and it is not possible to say which is the 
best in your specific case(s), without more information, e.g.

· Are you thinking about single dose, multiple dosing, and in the 
latter case is it sufficient to describe your endpoint at stead state?

· And is the effect appearing with great delay over many days/weeks or 
it rather fluctuates with fluctuating concentrations? (e.g. at multiple dosing 
for a low dose, do you have fluctuations over a dosing interval in your 
efficacy endpoint that are due fluctuations in PK, i.e. aside from any 
circadian variation?)

· Does a higher dose reach its efficacy-steady state faster than a 
lower dose (time to efficacy-steady state; not the level of response which 
should be different)?

· What is the mechanisms for effect delay (i.e. the delay in on and 
offset of effect that is not due to accumulation of PK at start of treatment)

Are you aware of the standard models for effect delay that one would commonly 
consider and why did you dismiss these?

Best regards

Jakob

From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On 
Behalf Of Pavel Belo
Sent: 14 January 2014 18:45
To: Bauer, Robert
Cc: nmusers@globomaxnm.com
Subject: [NMusers] backward integration from t-a to t

Dear Robert,

Efficacy is frequently considered a function of AUC.  (AUC is just an integral. 
It is obvious how to calculate AUC any software which can solve ODE.)  A 
disadvantage of this model of efficacy is that the effect is irreversable 
because AUC of concentration can only increase; it cannot decrease.  In many 
cases, a more meaningful model is a model where AUC is calculated form time t 
-a to t (kind of "moving average"), where t is time in the system of 
differential equations (variable T in NONMEM).   There are 2 obvious ways to 
calculate AUC(t-a, t).  The first is to do backward integration, which looks 
like a hard and resource consuming way for NONMEM.  The second one is to keep 
in memory AUC for all time points used during the integration and calculate 
AUC(t-a,t) as AUC(t) - AUC(t-a), there AUC(t-a) can be interpolated using two 
closest time points below and above t-a.

Is there a way to access AUC for the past time points (