DDMODEL00000195: Sibrotuzumab_PK_Carcinoma

  public model
Short description:
Population pharmacokinetic model of sibrotuzumab, a humanized monoclonal antibody directed against fibroblast activation protein. The model was built from 1844 serum concentrations after multiple i.v. infusions in 60 advanced or metastatic carcinoma patients in three Phase I and II clinical studies.
PharmML (0.6.1)
  • Population pharmacokinetics of sibrotuzumab, a novel therapeutic monoclonal antibody, in cancer patients.
  • Kloft C, Graefe EU, Tanswell P, Scott AM, Hofheinz R, Amelsberg A, Karlsson MO
  • Investigational new drugs, 1/2004, Volume 22, Issue 1, pages: 39-52
  • Freie Universitaet Berlin, Germany. ckloft@zedat.fu-berlin.de
  • Population pharmacokinetics of sibrotuzumab, a humanized monoclonal antibody directed against fibroblast activation protein, were determined after multiple intravenous infusions of dosages ranging from 5 mg/m(2) to an absolute dose of 100 mg, in patients with advanced or metastatic carcinoma. In total, 1844 serum concentrations from 60 patients in three Phase I and II clinical studies were analyzed. The structural model incorporated two disposition compartments and two parallel elimination pathways from the central compartment, one linear and one nonlinear. Finally estimated pharmacokinetic parameters (%RSE) were: linear clearance CLL 22.1 ml/h (9.6), central distribution volume V1 4.13l (3.7), peripheral volume V2 3.19l (8.8), inter-compartmental clearance Q 37.6 ml/h (9.6); for the nonlinear clearance Vmax was 0.0338 mg/h (25) and Km 0.219 microg/ml (57). At serum concentrations between approximately 20 ng/ml and 7 microg/ml, the effect of the nonlinear clearance on pharmacokinetics was marked. Only at >7 microg/ml did CLL dominate overall clearance. Interindividual variability was 57% for CLL, 20% for V1 and V2, and 29% for Vmax and was larger than the inter-occasional variability of 13%. Of the many investigated patient covariates, only body weight was found to contribute to the population model. It significantly affected CLL, V1, V2 and Vmax resulting in marked differences in the model-predicted concentration-time profiles after multiple dosing in patients with low and high body weights. In conclusion, a robust population pharmacokinetic model was developed and evaluated for sibrotuzumab, which identified a possible need to consider body weight when designing dosage regimen for future clinical cancer trials.
Niklas Hartung
Context of model development: Variability sources in PK and PD (CYP, Renal, Biomarkers);
Discrepancy between implemented model and original publication: Contary to the original publication, inter-occasion variability on bioavailability was not implemented.;
Long technical model description: Two-compartment PK model with combined linear and saturable elimination. Inter-individual variability on linear and nonlinear clearance and volumes of distribution. Inter-occasion variability on bioavailability after repeated IV dosing (also accounts for uncertainty in actual dose level). Body weight is used as a covariate on linear and nonlinear clearance and volumes of distribution.;
Model compliance with original publication: No;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: To understand PK and variability in cancer patients of a new monoclonal antibody;
Modelling task in scope: simulation;
Nature of research: Early clinical development (Phases I and II);
Therapeutic/disease area: Oncology;
Annotations are correct.
This model is not certified.
  • Model owner: Niklas Hartung
  • Submitted: Jul 15, 2016 10:24:49 AM
  • Last Modified: Jul 15, 2016 10:24:49 AM
  • Version: 5 public model Download this version
    • Submitted on: Jul 15, 2016 10:24:49 AM
    • Submitted by: Niklas Hartung
    • With comment: Updated model annotations.

Independent variable T

Function Definitions

combinedError1(additive,proportional,f)=(additive+(proportional ×f))

Structural Model sm

Variable definitions

dACdT=(((Q ×(CP-CC))-(CLL ×CC))-(Vmax ×CC)(Km+CC))
dAPdT=(Q ×(CC-CP))

Initial conditions


Variability Model

Level Type





Covariate Model

Continuous covariate WT

Parameter Model

POP_CLL POP_V1 POP_V2 POP_Q POP_Vmax POP_Km BETA_CLL_WT BETA_V1_WT BETA_V2_WT BETA_Vmax_WT RUV_PROP RUV_ADD PPV_CLL PPV_V1 PPV_V2 PPV_Vmax CLL_WT=(POP_CLL ×(1+(BETA_CLL_WT ×(WT-75)))) V1_WT=(POP_V1 ×(1+(BETA_V1_WT ×(WT-75)))) V2_WT=(POP_V2 ×(1+(BETA_V2_WT ×(WT-75)))) Vmax_WT=(POP_Vmax ×(1+(BETA_Vmax_WT ×(WT-75))))
ETA_V1N(0.0,PPV_V1) — ID
ETA_V2N(0.0,PPV_V2) — ID
ETA_VmaxN(0.0,PPV_Vmax) — ID
EPS_YN(0.0,1.0) — DV
V1=(V1_WT ×exp(ETA_V1))
V2=(V2_WT ×exp(ETA_V2))
Vmax=(Vmax_WT ×exp(ETA_Vmax))

Observation Model

Observation Y
Continuous / Residual Data

Y=(CC+(combinedError1(RUV_ADD,RUV_PROP,CC) ×EPS_Y))

Estimation Steps

Estimation Step estimStep_1

Estimation parameters

Fixed parameters


Initial estimates for non-fixed parameters

  • POP_CLL=0.0221
  • POP_V1=4.13
  • POP_V2=3.19
  • POP_Q=0.0376
  • POP_Vmax=0.0338
  • POP_Km=8
  • BETA_CLL_WT=0.0182
  • BETA_V1_WT=0.0125
  • BETA_V2_WT=0.0105
  • BETA_Vmax_WT=0.00934
  • RUV_PROP=0.0491
  • RUV_ADD=0.093
  • PPV_CLL=0.57
  • PPV_V1=0.2
  • PPV_V2=0.2
Estimation operations
1) Estimate the population parameters
    Algorithm FOCEI

    Step Dependencies

    • estimStep_1