DDMoRe Model Repository

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Model Definition
Estimation Steps

Short description:

Population pharmacokinetic model of tamoxifen and active metabolite endoxifen in breast cancer patients considering the impact of CYP2D6 and CYP3A4/5 phenotypes on PK. The model implemented a hypothetical liver compartment to account for the fraction of tamoxifen directly metabolised to endoxifen.

Format:	PharmML (0.6.1)
Related Publication:	Population pharmacokinetic modelling to assess the impact of CYP2D6 and CYP3A metabolic phenotypes on the pharmacokinetics of tamoxifen and endoxifen. ter Heine R, Binkhorst L, de Graan AJ, de Bruijn P, Beijnen JH, Mathijssen RH, Huitema AD British journal of clinical pharmacology, 9/2014, Volume 78, Issue 3, pages: 572-586 Affiliation: Department of Clinical Pharmacy, Meander Medical Center, Amersfoort, The Netherlands. Abstract: AIMS: Tamoxifen is considered a pro-drug of its active metabolite endoxifen. The major metabolic enzymes involved in endoxifen formation are CYP2D6 and CYP3A. There is considerable evidence that variability in activity of these enzymes influences endoxifen exposure and thereby may influence the clinical outcome of tamoxifen treatment. We aimed to quantify the impact of metabolic phenotype on the pharmacokinetics of tamoxifen and endoxifen. METHODS: We assessed the CYP2D6 and CYP3A metabolic phenotypes in 40 breast cancer patients on tamoxifen treatment with a single dose of dextromethorphan as a dual phenotypic probe for CYP2D6 and CYP3A. The pharmacokinetics of dextromethorphan, tamoxifen and their relevant metabolites were analyzed using non-linear mixed effects modelling. RESULTS: Population pharmacokinetic models were developed for dextromethorphan, tamoxifen and their metabolites. In the final model for tamoxifen, the dextromethorphan derived metabolic phenotypes for CYP2D6 as well as CYP3A significantly (P < 0.0001) explained 54% of the observed variability in endoxifen formation (inter-individual variability reduced from 55% to 25%). CONCLUSIONS: We have shown that not only CYP2D6, but also CYP3A enzyme activity influences the tamoxifen to endoxifen conversion in breast cancer patients. Our developed model may be used to assess separately the impact of CYP2D6 and CYP3A mediated drug-drug interactions with tamoxifen without the necessity of administering this anti-oestrogenic drug and to support Bayesian guided therapeutic drug monitoring of tamoxifen in routine clinical practice.
Contributors:	Lena Klopp-Schulze

Context of model development:	Variability sources in PK and PD (CYP, Renal, Biomarkers);
Discrepancy between implemented model and original publication:	a) EVID and L2 item (as in dataset and used in original model) not us for the estimation task utilising DDMoRe framework; b) Residual error for parent (tamoxifen) and metabolite (endoxifen) estimated without considering correlations and as standard deviations (thus as structural parameters in NONMEM as THETAs); c) lag time handled differently by ddmore products than by NONMEM. These differences led to minor differences in estimates (esp. tlag).;
Long technical model description:	Joint parent-metabolite (tamoxifen and endoxifen) PK model with one compartment each and an additonal hypothetical liver compartment to account for the fraction of tamoxifen directly metabolised to endoxifen. CYP2D6 and CYP3A4/5 phenotypes (dextromethorphan model-based individual CL values) have been implemented as power functions and centred around the population median on the formation of endoxifen (CL23).;
Model compliance with original publication:	No;
Model implementation requiring submitter’s additional knowledge:	No;
Modelling context description:	To better understand the highly variable pharmacokinetics of tamoxifen and its major metabolite endoxifen in breast cancer patients considering CYP2D6 and CYP3A4/5 phenotypes.;
Modelling task in scope:	estimation;
Nature of research:	Clinical research & Therapeutic use;
Therapeutic/disease area:	Oncology;

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

Mandatory file
- Executable_tamoxifen.xml

Additional Files

Model owner: Lena Klopp-Schulze
Submitted: Aug 30, 2016 12:49:08 PM
Last Modified: Aug 30, 2016 12:49:08 PM

Revisions

Version: 8
- Submitted on: Aug 30, 2016 12:49:08 PM
- Submitted by: Lena Klopp-Schulze
- With comment: File naming convention implemented.

Independent variable T

Function Definitions

proportionalError (proportional, f) = (proportional \times f)

Structural Model sm

Variable definitions

SC2 = \frac{(V2 \times Mtam)}{10^{6}}

SC3 = \frac{(V3 \times Mendx)}{10^{6}}

C2 = \frac{CMT_TAM}{V2}

RATEIN = {\begin{matrix} (GUT \times K12) & if & (T \geq ALAG1) \\ 0 & otherwise \end{matrix}

C_HEP = \frac{(RATEIN + (Q1 \times C2))}{(Q1 + CL23)}

\frac{dGUT}{dT} = - RATEIN

\frac{dCMT_TAM}{dT} = (((Q1 \times C_HEP) - (Q1 \times C2)) - (CL20 \times C2))

\frac{dCMT_ENDX}{dT} = ((CL23 \times C_HEP) - (K30 \times CMT_ENDX))

CTAM = \frac{CMT_TAM}{SC2}

CENDX = \frac{CMT_ENDX}{SC3}

Initial conditions

GUT = 0

CMT_TAM = 0

CMT_ENDX = 0

Variability Model

Level	Type
DV	residualError
ID	parameterVariability

Covariate Model

Continuous covariate CYP2D6

logPHENO2D6 = log (\frac{CYP2D6}{1560})

Continuous covariate CYP3A4

logPHENO3A4 = log (\frac{CYP3A4}{44.7})

Parameter Model

Parameters

POP_CL20

;

POP_V2

;

POP_K12

;

POP_CL23

;

POP_Q1

;

POP_ALAG1

;

BETA_CL23_PHENO2D6

;

BETA_CL23_PHENO3A4

;

RUV_TAM

;

RUV_ENDX

;

PPV_CL20

;

PPV_V2

;

PPV_CL23

;

Cannot display simple parameters.

ETA_CL20 \sim N (0.0, PPV_CL20)

— ID

ETA_V2 \sim N (0.0, PPV_V2)

— ID

ETA_CL23 \sim N (0.0, PPV_CL23)

— ID

EPS_TAM \sim N (0.0, 1.0)

— DV

EPS_ENDX \sim N (0.0, 1.0)

— DV

log (CL20) = (log (POP_CL20) + ETA_CL20)

log (V2) = (log (POP_V2) + ETA_V2)

K12 = POP_K12

log (CL23) = (log (POP_CL23) + ((log (\frac{CYP2D6}{1560}) \times BETA_CL23_PHENO2D6) + ((log (\frac{CYP3A4}{44.7}) \times BETA_CL23_PHENO3A4) + ETA_CL23)))

Q1 = POP_Q1

ALAG1 = POP_ALAG1

Covariance matrix for level ID and random effects: ETA_CL20, ETA_V2

(\begin{matrix} 1 & 0.01 \\ 0.01 & 1 \end{matrix})

Observation Model

Observation TAM_OBS
Continuous / Residual Data

Parameters

TAM_OBS = (CTAM + (proportionalError (RUV_TAM, CTAM) \times EPS_TAM))

Observation ENDX_OBS
Continuous / Residual Data

Parameters

ENDX_OBS = (CENDX + (proportionalError (RUV_ENDX, CENDX) \times EPS_ENDX))

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Initial estimates for non-fixed parameters

$POP_CL20 = 10$
$POP_V2 = 800$
$POP_K12 = 2$
$POP_CL23 = 0.3$
$POP_Q1 = 10$
$POP_ALAG1 = 0.5$
$BETA_CL23_PHENO2D6 = 0.2$
$BETA_CL23_PHENO3A4 = 0.2$
$RUV_TAM = 0.1$
$RUV_ENDX = 0.1$
$PPV_CL20 = 0.1$
$PPV_V2 = 0.1$
$PPV_CL23 = 0.1$

Estimation operations

1) Estimate the population parameters

Algorithm FOCEI

Step Dependencies

estimStep_1

DDMODEL00000212: TerHeine_2014_PK_Tamoxifen_ActiveMetabolite_CYP2D6

Revisions

Function Definitions

Structural Model sm

Variability Model

Covariate Model

Parameter Model

Observation Model

Observation TAM_OBSContinuous / Residual Data

Observation ENDX_OBSContinuous / Residual Data

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Estimation operations

Step Dependencies

Observation TAM_OBS
Continuous / Residual Data

Observation ENDX_OBS
Continuous / Residual Data