Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response

Br J Clin Pharmacol. 2015 Jan;79(1):56-71. doi: 10.1111/bcp.12258.


In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic-pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed.

Keywords: PKPD; biomarkers; oncology; population modelling; time-to-event; tumour.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Antineoplastic Agents / adverse effects
  • Antineoplastic Agents / pharmacokinetics*
  • Antineoplastic Agents / therapeutic use*
  • Biomarkers, Pharmacological
  • Biomarkers, Tumor
  • Computer Simulation*
  • Humans
  • Models, Biological*
  • Neoplasms / drug therapy*
  • Time Factors


  • Antineoplastic Agents
  • Biomarkers, Pharmacological
  • Biomarkers, Tumor