From: Jonathan Moss <*jjmoss*>

Date: Fri, 13 May 2016 10:36:40 +0100

Dear all,

I would like to share with you and get people's opinions on a recent issue I

had.

I have a data set of 46 patients, orally dosed, with very dense sampling

during absorption (0.25h, 0.5h, 0.75h, 1h, 1.5h, 2h, 3h, 4h, 6h, 8h, 12h,

24h, 36h), Cmax at around 4 hours.

During modelling, I found that the residual error was not evenly

distributed. Plotting CWRES against time after dose, the result looked like

an "hourglass" shape. I.e. A wide spread during absorption, narrower near

Cmax time, then wider at later time points.

My thinking was as follows: Residual error contains both the assay / model

spec. error, and the error in recorded observation time. When the gradient

of the PK curve is large, any error in recorded observation time equals a

large error in the recorded concentration, whereas if the gradient is small

then the recorded concentration error will be small.

I "split" the residual error into its assay/model spec and time-error parts

in the $ERROR block:

$ERROR

GRAD = KA*A(2) - K20*A(3)

IF (GRAD.LT.0) GRAD = -1*GRAD

C_1 = A(3)/V ; Concentration in the

central compartment

IPRED = C_1

SD = SQRT(EPROP*C_1**2) ; Standard deviation of

predicted concentration

Y=IPRED+SD*(1+val*GRAD)*EPS(1)

Note: Sigma is fixed to one and EPROP is estimated as a theta. Here, GRAD is

the right hand side of the differential equation for A(3), in order to

recover the gradient. Val is estimated by NONMEM.

This approach vastly improved the model fit (OFV drop of around 350!). All

GOF plots, VPCs, NPCs, NPDEs, individual fits looked good. This got me

thinking, and I tried this approach on some of my other popPK models. I

found for the simpler models, the result was nearly always a significant

improvement in the model fit. For the more complicated models, NONMEM had

trouble finishing the runs.

I struggled to find any approach like this in the literature, which leads me

to believe that there is something wrong, as it is a relatively simple

concept. Please, what are peoples thoughts on this?

Thanks,

Jon

Jon Moss, PhD

Modeller

BAST Inc Limited

Loughborough Innovation Centre

Charnwood Wing

Holywell Park

Ashby Road

Loughborough, LE11 3AQ, UK

Tel: +44 (0)1509 222908

Received on Fri May 13 2016 - 05:36:40 EDT

Date: Fri, 13 May 2016 10:36:40 +0100

Dear all,

I would like to share with you and get people's opinions on a recent issue I

had.

I have a data set of 46 patients, orally dosed, with very dense sampling

during absorption (0.25h, 0.5h, 0.75h, 1h, 1.5h, 2h, 3h, 4h, 6h, 8h, 12h,

24h, 36h), Cmax at around 4 hours.

During modelling, I found that the residual error was not evenly

distributed. Plotting CWRES against time after dose, the result looked like

an "hourglass" shape. I.e. A wide spread during absorption, narrower near

Cmax time, then wider at later time points.

My thinking was as follows: Residual error contains both the assay / model

spec. error, and the error in recorded observation time. When the gradient

of the PK curve is large, any error in recorded observation time equals a

large error in the recorded concentration, whereas if the gradient is small

then the recorded concentration error will be small.

I "split" the residual error into its assay/model spec and time-error parts

in the $ERROR block:

$ERROR

GRAD = KA*A(2) - K20*A(3)

IF (GRAD.LT.0) GRAD = -1*GRAD

C_1 = A(3)/V ; Concentration in the

central compartment

IPRED = C_1

SD = SQRT(EPROP*C_1**2) ; Standard deviation of

predicted concentration

Y=IPRED+SD*(1+val*GRAD)*EPS(1)

Note: Sigma is fixed to one and EPROP is estimated as a theta. Here, GRAD is

the right hand side of the differential equation for A(3), in order to

recover the gradient. Val is estimated by NONMEM.

This approach vastly improved the model fit (OFV drop of around 350!). All

GOF plots, VPCs, NPCs, NPDEs, individual fits looked good. This got me

thinking, and I tried this approach on some of my other popPK models. I

found for the simpler models, the result was nearly always a significant

improvement in the model fit. For the more complicated models, NONMEM had

trouble finishing the runs.

I struggled to find any approach like this in the literature, which leads me

to believe that there is something wrong, as it is a relatively simple

concept. Please, what are peoples thoughts on this?

Thanks,

Jon

Jon Moss, PhD

Modeller

BAST Inc Limited

Loughborough Innovation Centre

Charnwood Wing

Holywell Park

Ashby Road

Loughborough, LE11 3AQ, UK

Tel: +44 (0)1509 222908

Received on Fri May 13 2016 - 05:36:40 EDT