DDMODEL00000186: Friberg_2002_Oncology_Paclitaxel_Myelosuppression

  public model
Short description:
Model to describe the time-course of leukocytes (total white blood cell counts) following administration of paclitaxel with linear concentration-effect relationship.
PharmML (0.6.1)
  • Model of chemotherapy-induced myelosuppression with parameter consistency across drugs.
  • Friberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO
  • Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 12/2002, Volume 20, Issue 24, pages: 4713-4721
  • Division of Pharmacokinetics and Drug Therapy, Uppsala University, Uppsala, Sweden. lena.friberg@farmbio.uu.se
  • PURPOSE: To develop a semimechanistic pharmacokinetic-pharmacodynamic model describing chemotherapy-induced myelosuppression through drug-specific parameters and system-related parameters, which are common to all drugs. PATIENTS AND METHODS: Patient leukocyte and neutrophil data after administration of docetaxel, paclitaxel, and etoposide were used to develop the model, which was also applied to myelosuppression data from 2'-deoxy-2'-methylidenecytidine (DMDC), irinotecan (CPT-11), and vinflunine administrations. The model consisted of a proliferating compartment that was sensitive to drugs, three transit compartments that represented maturation, and a compartment of circulating blood cells. Three system-related parameters were estimated: baseline, mean transit time, and a feedback parameter. Drug concentration-time profiles affected the proliferation of sensitive cells by either an inhibitory linear model or an inhibitory E(max) model. To evaluate the model, system-related parameters were fixed to the same values for all drugs, which were based on the results from the estimations, and only drug-specific parameters were estimated. All modeling was performed using NONMEM software. RESULTS: For all investigated drugs, the model successfully described myelosuppression. Consecutive courses and different schedules of administration were also well characterized. Similar system-related parameter estimates were obtained for the different drugs and also for leukocytes compared with neutrophils. In addition, when system-related parameters were fixed, the model well characterized chemotherapy-induced myelosuppression for the different drugs. CONCLUSION: This model predicted myelosuppression after administration of one of several different chemotherapeutic drugs. In addition, with fixed system-related parameters to proposed values, and only drug-related parameters estimated, myelosuppression can be predicted. We propose that this model can be a useful tool in the development of anticancer drugs and therapies.
Zinnia Parra-Guillen
Context of model development: Mechanistic Understanding;
Discrepancy between implemented model and original publication: The model is the same, some accomodations on the dataset were needed;
Long technical model description: The model consists of one compartment representing drug-sensitive cells in the bone marrow, three compartments representing the maturation of the white blood cells and one compartment for the circulating cells, where the measurements had been done. A feedback mechanism triggered by the number of circulting cells was implemented over the proliferation rate constant. The drug effect was described by Emax-model.;
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: chemotherapy-induced myelosuppression through drug-specific parameters and system-related parameters, common to all drugs;
Modelling task in scope: estimation;
Nature of research: Clinical research & Therapeutic use;
Therapeutic/disease area: Oncology;
Annotations are correct.
This model is not certified.
  • Model owner: Zinnia Parra-Guillen
  • Submitted: Jul 15, 2016 12:14:56 PM
  • Last Modified: Aug 24, 2016 9:03:17 AM
Revisions
  • Version: 11 public model Download this version
    • Submitted on: Aug 24, 2016 9:03:17 AM
    • Submitted by: Zinnia Parra-Guillen
    • With comment: Edited model metadata online.
  • Version: 7 public model Download this version
    • Submitted on: Jul 15, 2016 12:14:56 PM
    • Submitted by: Zinnia Parra-Guillen
    • With comment: Edited model metadata online.

Independent variable T

Function Definitions

proportionalError(proportional,f)=(proportional ×f)

Structural Model sm

Variable definitions

Q=204
NN=3
KTR=(NN+1)MTT
dAcdT=(((-QV1I ×Ac)-(CLIV1I ×Ac))+(QV2I ×Ap))
dApdT=((QV1I ×Ac)-(QV2I ×Ap))
CONC=AcV1I
EDRUG=(1-(SLOPU ×CONC))
FEED=CIRC0CIRCGAMMA
dCIRCdT=((KTR ×TRANSIT3)-(KTR ×CIRC))
dPROLdT=((((KTR ×PROL) ×EDRUG) ×FEED)-(KTR ×PROL))
dTRANSIT1dT=((KTR ×PROL)-(KTR ×TRANSIT1))
dTRANSIT2dT=((KTR ×TRANSIT1)-(KTR ×TRANSIT2))
dTRANSIT3dT=((KTR ×TRANSIT2)-(KTR ×TRANSIT3))

Initial conditions

Ac=0
Ap=0
CIRC=CIRC0
PROL=CIRC0
TRANSIT1=CIRC0
TRANSIT2=CIRC0
TRANSIT3=CIRC0

Variability Model

Level Type

DV

 residualError

ID

 parameterVariability

Covariate Model

Continuous covariate CLI

Continuous covariate V1I

Continuous covariate V2I

Parameter Model

Parameters
POP_CIRC0;  POP_MTT;  POP_GAMMA;  POP_SLOPU;  PROP_ERROR;  OMEGA_CIRC0;  OMEGA_MTT;  OMEGA_SLOPU;  SIGMA_ERROR; 
eta_CIRC0∼N(0.0,OMEGA_CIRC0) — ID
eta_MTT∼N(0.0,OMEGA_MTT) — ID
eta_SLOPU∼N(0.0,OMEGA_SLOPU) — ID
eps_ERROR∼N(0.0,SIGMA_ERROR) — DV
log(CIRC0)=(log(POP_CIRC0)+eta_CIRC0)
log(MTT)=(log(POP_MTT)+eta_MTT)
log(SLOPU)=(log(POP_SLOPU)+eta_SLOPU)
GAMMA=POP_GAMMA

Observation Model

Observation Y
Continuous / Residual Data

Parameters
Y=(CIRC+(proportionalError(PROP_ERROR,CIRC) ×eps_ERROR))

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Fixed parameters

 SIGMA_ERROR=1

Initial estimates for non-fixed parameters

  • POP_CIRC0=7.21
  • POP_MTT=124
  • POP_GAMMA=0.239
  • POP_SLOPU=28.9
  • PROP_ERROR=0.286
  • OMEGA_CIRC0=0.107
  • OMEGA_MTT=0.0296
  • OMEGA_SLOPU=0.176
Estimation operations
1) Estimate the population parameters
    Algorithm FOCEI

    Step Dependencies

    • estimStep_1
     
    Help