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Short description:

Evacetrapib pharmacokinetics was described by using a two-compartment model with first-order absorption

Format:	PharmML 0.8.x (0.8.1)
Related Publication:	The pharmacokinetics and pharmacokinetic/pharmacodynamic relationships of evacetrapib administered as monotherapy or in combination with statins. Friedrich S, Kastelein JJ, James D, Waterhouse T, Nissen SE, Nicholls SJ, Krueger KA CPT: pharmacometrics & systems pharmacology, 1/2014, Volume 3, pages: e94 Affiliation: Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, USA. Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands. Cleveland Clinic Coordinating Center for Clinical Research, Cleveland Clinic, Cleveland Abstract: Evacetrapib is a novel cholesteryl ester transfer protein (CETP) inhibitor currently being evaluated in a late-stage cardiovascular outcome trial. Using population-based models, we analyzed evacetrapib concentration data along with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) data from a 12-week study in dyslipidemic patients treated with evacetrapib alone or in combination with atorvastatin, simvastatin, or rosuvastatin. Evacetrapib pharmacokinetics were characterized using a two-compartment model with first-order absorption. Evacetrapib exposure increased in a less than dose-proportional manner, similar to other CETP inhibitors. No patient factors had a clinically relevant impact on evacetrapib pharmacokinetics. The relationships between evacetrapib exposure and HDL-C and LDL-C were characterized using Emax models. The theoretical maximal mean HDL-C increase and LDL-C decrease relative to baseline were 177 and 44.1%, respectively. HDL-C change from baseline was found to be negatively correlated with baseline HDL-C. A pharmacologically independent LDL-C reduction was found when evacetrapib was coadministered with statins.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e94; doi:10.1038/psp.2013.70; published online 22 January 2014.
Contributors:	Paolo Magni

Context of model development:	Disease Progression model;
Model compliance with original publication:	Yes;
Model implementation requiring submitter’s additional knowledge:	No;
Modelling context description:	Evacetrapib is a novel cholesteryl ester transfer protein (CETP) inhibitor currently being evaluated in a late-stage cardiovascular outcome trial. Using population-based models, we analyzed evacetrapib concentration data along with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) data from a 12-week study in dyslipidemic patients treated with evacetrapib alone or in combination with atorvastatin, simvastatin, or rosuvastatin. Evacetrapib pharmacokinetics were characterized using a two-compartment model with first-order absorption. Evacetrapib exposure increased in a less than dose-proportional manner, similar to other CETP inhibitors. No patient factors had a clinically relevant impact on evacetrapib pharmacokinetics. The relationships between evacetrapib exposure and HDL-C and LDL-C were characterized using Emax models. The theoretical maximal mean HDL-C increase and LDL-C decrease relative to baseline were 177 and 44.1%, respectively. HDL-C change from baseline was found to be negatively correlated with baseline HDL-C. A pharmacologically independent LDL-C reduction was found when evacetrapib was coadministered with statins.;
Modelling task in scope:	estimation;
Nature of research:	Early clinical development (Phases I and II);
Therapeutic/disease area:	Endocrinology;

Validation Status:	Annotations are correct.
Certification Comment:	This model is not certified.

Mandatory file
- Executable_Friedrich_2014_PK.xml

Additional Files

Model owner: Paolo Magni
Submitted: Dec 12, 2015 10:02:46 AM
Last Modified: Oct 13, 2016 6:20:59 PM

Revisions

Version: 7
- Submitted on: Oct 13, 2016 6:20:59 PM
- Submitted by: Paolo Magni
- With comment: Edited model metadata online.
Version: 5
- Submitted on: Jul 16, 2016 5:14:38 PM
- Submitted by: Paolo Magni
- With comment: Updated model annotations.
Version: 2
- Submitted on: Dec 12, 2015 10:02:46 AM
- Submitted by: Paolo Magni
- With comment: Edited model metadata online.

Name

Generated from MDL. MOG ID: friedrich_pk_mog

Independent Variables

Function Definitions

proportionalError : real (proportional : real, f : real) = proportional \cdot f

Covariate Model: $cm$

Continuous Covariates

DDI

CGCL

TALD

Parameter Model: $pm$

Random Variables

{ETA_CL}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.PPV_CL)

{ETA_V2}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.PPV_V2)

{ETA_Q}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.PPV_Q)

{EPS_Y}_{vm_err.DV} ~ Normal2 (mean = 0, var = pm.SIGMA_RES_Y)

Population Parameters

KA

POP_CL

POP_V2

POP_Q

V3

BETA_DOSE_CL

BETA_CGCL_CL

RUV_PROP

PPV_CL

PPV_V2

PPV_Q

SIGMA_RES_Y

COV_CL_V2

GRPCL = pm.POP_CL \cdot (1 + pm.BETA_DOSE_CL \cdot cm.DDI) \cdot (1 + pm.BETA_CGCL_CL \cdot (cm.CGCL - 91.28))

Individual Parameters

\ln (CL) = \ln (pm.GRPCL) + pm.ETA_CL

\ln (V2) = \ln (pm.POP_V2) + pm.ETA_V2

\ln (Q) = \ln (pm.POP_Q) + pm.ETA_Q

K23 = \frac{pm.Q}{pm.V2}

K32 = \frac{pm.Q}{pm.V3}

S2 = \frac{pm.V2}{1000}

S3 = \frac{pm.V3}{1000}

Random Variable Correlation

cov (ETA_CL, ETA_V2) = pm.COV_CL_V2

Structural Model: $sm$

Variables

\begin{matrix} \frac{ⅆ}{ⅆ T} A1 = - (sm.A1 \cdot pm.KA) \\ A1 (T = 0) = 0 \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} A2 = pm.KA \cdot sm.A1 - pm.K23 \cdot sm.A2 + pm.K32 \cdot sm.A3 - \frac{pm.CL}{pm.V2} \cdot sm.A2 \\ A2 (T = 0) = 0 \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} A3 = pm.K23 \cdot sm.A2 - pm.K32 \cdot sm.A3 \\ A3 (T = 0) = 0 \end{matrix}

C2 = \frac{sm.A2}{pm.S2}

C3 = \frac{sm.A3}{pm.S3}

Observation Model: $om1$

Continuous Observation

Y = sm.C2 + proportionalError (proportional = pm.RUV_PROP, f = sm.C2) + pm.EPS_Y

External Dataset

OID	$nm_ds$
Tool Format	NONMEM

File Specification

Format	$csv$
Delimiter	comma
File Location	Simulated_data_PK_reduced.csv

Column Definitions

Column ID	Position	Column Type	Value Type
$ID$	$1$	$id$	$int$
$TIME$	$2$	$idv$	$real$
$DV$	$3$	$dv$	$real$
$MDV$	$4$	$mdv$	$int$
$CMT$	$5$	$cmt$	$int$
$AMT$	$6$	$dose$	$real$
$DDI$	$7$	$covariate$	$real$
$CGCL$	$8$	$covariate$	$real$
$TALD$	$9$	$covariate$	$real$

Column Mappings

Column Ref	Modelling Mapping
$ID$	$vm_mdl.ID$
$TIME$	$T$
$DV$	$om1.Y$
$AMT$	${\begin{cases} sm.A1 & if & AMT > 0 \end{cases}$
$DDI$	$cm.DDI$
$CGCL$	$cm.CGCL$
$TALD$	$cm.TALD$

Estimation Step

OID	$estimStep_1$
Dataset Reference	$nm_ds$

Parameters To Estimate

Parameter	Initial Value	Fixed?	Limits
pm.KA	$0.3$	true	$()$
pm.POP_CL	$20$	false	$(0, 50)$
pm.POP_V2	$250$	false	$(0, 1000)$
pm.POP_Q	$10$	false	$(0, 100)$
pm.V3	$1000$	false	$(0, 10000)$
pm.BETA_DOSE_CL	$0.001$	false	$(0, 0.005)$
pm.BETA_CGCL_CL	$0.001$	false	$(0, 0.005)$
pm.RUV_PROP	$1$	true	$()$
pm.PPV_CL	$0.5$	false	$()$
pm.PPV_V2	$0.5$	false	$()$
pm.PPV_Q	$1$	false	$()$
pm.SIGMA_RES_Y	$0.5$	false	$()$
pm.COV_CL_V2	$0.3$	false	$()$

Operations

Operation: $1$

Op Type

generic

Operation Properties

Name	Value
algo	$foce$

Step Dependencies

Step OID	Preceding Steps
$estimStep_1$

DDMODEL00000118: Friedrich_2014_PK

Revisions

Name

Independent Variables

Function Definitions

Covariate Model: cm

Continuous Covariates

Parameter Model: pm

Random Variables

Population Parameters

Individual Parameters

Random Variable Correlation

Structural Model: sm

Variables

Observation Model: om1

Continuous Observation

External Dataset

File Specification

Column Definitions

Column Mappings

Estimation Step

Parameters To Estimate

Operations

Operation: 1

Operation Properties

Step Dependencies

Covariate Model: $cm$

Parameter Model: $pm$

Structural Model: $sm$

Observation Model: $om1$

Operation: $1$