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Short description:

Population PKPD model of thrombocytopenia characterizing the effect of trastuzumab emtansine (T-DM1) on platelet counts in patients with HER2-positive metastatic breast cancer

Format:	PharmML 0.8.x (0.8.1)
Related Publication:	A population pharmacokinetic/pharmacodynamic model of thrombocytopenia characterizing the effect of trastuzumab emtansine (T-DM1) on platelet counts in patients with HER2-positive metastatic breast cancer. Bender BC, Schaedeli-Stark F, Koch R, Joshi A, Chu YW, Rugo H, Krop IE, Girish S, Friberg LE, Gupta M Cancer chemotherapy and pharmacology, 10/2012, Volume 70, Issue 4, pages: 591-601 Affiliation: Genentech, Inc., South San Francisco, CA, USA. brendan.bender@farmbio.uu.se Abstract: PURPOSE: Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate in the development for the treatment of human epidermal growth factor receptor 2-positive cancers. Thrombocytopenia (TCP) is the dose-limiting toxicity of T-DM1. A semimechanistic population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to characterize the effect of T-DM1 on patient platelet counts. METHODS: A PK/PD model with transit compartments that mimic platelet development and circulation was fit to concentration-platelet-time course data from two T-DM1 single-agent studies (TDM3569g; N = 52 and TDM4258g; N = 112). NONMEM(®) 7 software was used for model development. Data from a separate phase II study (TDM4374g; N = 110) were used for model evaluation. Patient baseline characteristics were evaluated as covariates of model PD parameters. RESULTS: The model described the platelet data well and predicted the incidence of grade ?3 TCP. The model predicted that with T-DM1 3.6 mg/kg given every 3 weeks (q3w), the lowest platelet nadir would occur after the first dose. Also predicted was a patient subgroup (46 %) having variable degrees of downward drifting platelet-time profiles, which were predicted to stabilize by the eighth treatment cycle to platelet counts above grade 3 TCP. Baseline characteristics were not significant covariates of PD parameters in the model. CONCLUSIONS: This semimechanistic PK/PD model accurately captures the cycle 1 platelet nadir, the downward drift noted in some patient platelet-time profiles, and the ~8 % incidence of grade ?3 TCP with T-DM1 3.6 mg/kg q3w. This model supports T-DM1 3.6 mg/kg q3w as a well-tolerated dose with minimal dose delays or reductions for TCP.
Contributors:	Paolo Magni

Context of model development:	Mechanistic Understanding; Clinical end-point;
Discrepancy between implemented model and original publication:	A mixture model implementation in NONMEM was used to estimate the probability of a lower (POP1) or higher (POP2) value of Kdeplete, resulting in two patient subgroups with an apparent stable platelet time profile (POP1) or with an apparent decline (POP2); since mixture is not supported at the time of submission, the individual parameter Kdeplete has two different typical values depending on the categorical covariate POP. The OMEGA "SAME" option in NONMEM was used for IIV on Kdeplete; since the OMEGA SAME option is not currently supported a unique ETA was used for the individual KDEP.;
Model compliance with original publication:	No;
Model implementation requiring submitter’s additional knowledge:	No;
Modelling context description:	Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate in the development for the treatment of human epidermal growth factor receptor 2-positive cancers. Thrombocytopenia (TCP) is the dose-limiting toxicity of T-DM1. A semimechanistic population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to characterize the effect of T-DM1 on patient platelet counts. METHODS: A PK/PD model with transit compartments that mimic platelet development and circulation was fit to concentration-platelet-time course data from two T-DM1 single-agent studies (TDM3569g; N = 52 and TDM4258g; N = 112). NONMEM 7 software was used for model development. Data from a separate phase II study (TDM4374g; N = 110) were used for model evaluation. Patient baseline characteristics were evaluated as covariates of model PD parameters. RESULTS: The model described the platelet data well and predicted the incidence of grade ?3 TCP. The model predicted that with T-DM1 3.6 mg/kg given every 3 weeks (q3w), the lowest platelet nadir would occur after the first dose. Also predicted was a patient subgroup (46 %) having variable degrees of downward drifting platelet-time profiles, which were predicted to stabilize by the eighth treatment cycle to platelet counts above grade 3 TCP. Baseline characteristics were not significant covariates of PD parameters in the model. CONCLUSIONS: This semimechanistic PK/PD model accurately captures the cycle 1 platelet nadir, the downward drift noted in some patient platelet-time profiles, and the ~8 % incidence of grade ?3 TCP with T-DM1 3.6 mg/kg q3w. This model supports T-DM1 3.6 mg/kg q3w as a well-tolerated dose with minimal dose delays or reductions for TCP.;
Modelling task in scope:	estimation;
Nature of research:	Early clinical development (Phases I and II);
Therapeutic/disease area:	Haematology; Oncology;

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

Mandatory file
- Executable_Bender_2012_thrombocytopenia_TDM1.xml

Additional Files

Model owner: Paolo Magni
Submitted: Dec 12, 2015 1:19:57 PM
Last Modified: Oct 13, 2016 6:06:21 PM

Revisions

Version: 7
- Submitted on: Oct 13, 2016 6:06:21 PM
- Submitted by: Paolo Magni
- With comment: Edited model metadata online.
Version: 5
- Submitted on: Jul 16, 2016 5:46:32 PM
- Submitted by: Paolo Magni
- With comment: Updated model annotations.
Version: 2
- Submitted on: Dec 12, 2015 1:19:57 PM
- Submitted by: Paolo Magni
- With comment: Edited model metadata online.

Name

Generated from MDL. MOG ID: bender2012_mog

Independent Variables

Function Definitions

additiveError : real (additive : real) = additive

Covariate Model: $cm$

Continuous Covariates

ICL

IV1

IV2

IQ

CAVG

VISI

POP

DAY

TRT

Parameter Model: $pm$

Random Variables

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

{ETA_SLOPE1}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.PPV_SLOPE1)

{ETA_SLOPE2}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.PPV_SLOPE2)

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

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

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

{ETA_BASE1}_{vm_mdl.ID} ~ Normal2 (mean = 0, var = pm.PPV_BASE1)

{EPS_1}_{vm_err.DV} ~ Normal2 (mean = 0, var = 1)

Population Parameters

POP_KDEP1

POP_KDEP2

POP_SLOPE1

POP_SLOPE2

POP_MTT

POP_GAM

POP_BASE

POP_BASE1

RUV_ADD

PPV_KDEP

PPV_SLOPE1

PPV_SLOPE2

PPV_MTT

PPV_GAM

PPV_BASE

PPV_BASE1

K10 = \frac{cm.ICL}{cm.IV1}

K12 = \frac{cm.IQ}{cm.IV1}

K21 = \frac{cm.IQ}{cm.IV2}

KDEP = {\begin{cases} \frac{pm.POP_KDEP1}{1000000} \cdot ⅇ^{pm.ETA_KDEP} & if & cm.POP = 1 \\ \frac{pm.POP_KDEP2}{1000000} \cdot ⅇ^{pm.ETA_KDEP} & otherwise \end{cases}

SLOPE = {\begin{cases} \frac{pm.POP_SLOPE1}{1000} \cdot ⅇ^{pm.ETA_SLOPE1} & if & cm.VISI = 1 \\ \frac{pm.POP_SLOPE2}{1000} \cdot ⅇ^{pm.ETA_SLOPE2} & otherwise \end{cases}

Individual Parameters

MTT = pm.POP_MTT \cdot ⅇ^{pm.ETA_MTT}

GAM = pm.POP_GAM \cdot ⅇ^{pm.ETA_GAM}

BASE = pm.POP_BASE \cdot ⅇ^{pm.ETA_BASE}

BASE1 = pm.POP_BASE1 \cdot ⅇ^{pm.ETA_BASE1}

KTR = \frac{4}{pm.MTT}

Structural Model: $sm$

Variables

\begin{matrix} \frac{ⅆ}{ⅆ T} A1 = pm.K21 \cdot sm.A2 - pm.K12 \cdot sm.A1 - pm.K10 \cdot sm.A1 \\ A1 (T = 0) = 0 \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} A2 = pm.K12 \cdot sm.A1 - pm.K21 \cdot sm.A2 \\ A2 (T = 0) = 0 \end{matrix}

CP = \frac{sm.A1}{cm.IV1}

KDEPEFF = pm.KDEP \cdot cm.CAVG

BT = (pm.BASE - pm.BASE1) \cdot ⅇ^{(- sm.KDEPEFF \cdot T)} + pm.BASE1

EFF = pm.SLOPE \cdot sm.CP

\begin{matrix} \frac{ⅆ}{ⅆ T} PP = - pm.KTR \cdot sm.PP + pm.KTR \cdot sm.PP \cdot (1 - sm.EFF) \cdot {\frac{sm.BT}{sm.PLT}}^{pm.GAM} \\ PP (T = 0) = pm.BASE \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} T1 = - pm.KTR \cdot sm.T1 + pm.KTR \cdot sm.PP \\ T1 (T = 0) = pm.BASE \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} T2 = - pm.KTR \cdot sm.T2 + pm.KTR \cdot sm.T1 \\ T2 (T = 0) = pm.BASE \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} T3 = - pm.KTR \cdot sm.T3 + pm.KTR \cdot sm.T2 \\ T3 (T = 0) = pm.BASE \end{matrix}

\begin{matrix} \frac{ⅆ}{ⅆ T} PLT = - pm.KTR \cdot sm.PLT + pm.KTR \cdot sm.T3 \\ PLT (T = 0) = pm.BASE \end{matrix}

logPLT = {\begin{cases} \ln (sm.PLT) & if & sm.PLT > 0 \\ \ln (0.025) & otherwise \end{cases}

Observation Model: $om1$

Continuous Observation

Y = sm.logPLT + additiveError (additive = pm.RUV_ADD) + pm.EPS_1

External Dataset

OID	$nm_ds$
Tool Format	NONMEM

File Specification

Format	$csv$
Delimiter	comma
File Location	Simulated_data10_POP.csv

Column Definitions

Column ID	Position	Column Type	Value Type
$ID$	$1$	$id$	$int$
$TRT$	$2$	$covariate$	$real$
$NAMT$	$3$	$undefined$	$real$
$DSFQ$	$4$	$undefined$	$real$
$AMT$	$5$	$dose$	$real$
$DUR$	$6$	$undefined$	$real$
$RATE$	$7$	$rate$	$real$
$TIME$	$8$	$idv$	$real$
$DAY$	$9$	$covariate$	$real$
$ODV$	$10$	$undefined$	$real$
$DV$	$11$	$dv$	$real$
$EV$	$12$	$undefined$	$real$
$CMT$	$13$	$cmt$	$int$
$MDV$	$14$	$mdv$	$int$
$ICL$	$15$	$covariate$	$real$
$IV1$	$16$	$covariate$	$real$
$IQ$	$17$	$covariate$	$real$
$IV2$	$18$	$covariate$	$real$
$CAVG$	$19$	$covariate$	$real$
$VISI$	$20$	$covariate$	$real$
$POP$	$21$	$covariate$	$real$

Column Mappings

Column Ref	Modelling Mapping
$ID$	$vm_mdl.ID$
$TRT$	$cm.TRT$
$AMT$	${\begin{cases} sm.A1 & if & AMT > 0 \end{cases}$
$TIME$	$T$
$DAY$	$cm.DAY$
$DV$	$om1.Y$
$ICL$	$cm.ICL$
$IV1$	$cm.IV1$
$IQ$	$cm.IQ$
$IV2$	$cm.IV2$
$CAVG$	$cm.CAVG$
$VISI$	$cm.VISI$
$POP$	$cm.POP$

Estimation Step

OID	$estimStep_1$
Dataset Reference	$nm_ds$

Parameters To Estimate

Parameter	Initial Value	Fixed?	Limits
pm.POP_KDEP1	$50$	false	$(0)$
pm.POP_KDEP2	$4$	false	$(0)$
pm.POP_SLOPE1	$3$	false	$(0)$
pm.POP_SLOPE2	$2$	false	$(0)$
pm.POP_MTT	$36$	false	$(0)$
pm.POP_GAM	$0.13$	false	$(0)$
pm.POP_BASE	$255$	false	$(10)$
pm.POP_BASE1	$110$	false	$(5)$
pm.RUV_ADD	$0.18$	false	$(0)$
pm.PPV_KDEP	$0.75$	false	$(0)$
pm.PPV_SLOPE1	$0.13$	false	$(0)$
pm.PPV_SLOPE2	$0.31$	false	$(0)$
pm.PPV_MTT	$0.06$	false	$(0)$
pm.PPV_GAM	$0$	true	$()$
pm.PPV_BASE	$0.1$	false	$(0)$
pm.PPV_BASE1	$0.14$	false	$(0)$

Operations

Operation: $1$

Op Type

generic

Operation Properties

Name	Value
algo	$foce$

Step Dependencies

Step OID	Preceding Steps
$estimStep_1$

DDMODEL00000119: Bender_2012_thrombocytopenia_TDM1