Beschreibung:
<jats:p>In oncology trials, overall survival (<jats:styled-content style="fixed-case">OS</jats:styled-content>) 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 (<jats:styled-content style="fixed-case">PKPD</jats:styled-content>) modelling and parametric time‐to‐event (<jats:styled-content style="fixed-case">TTE</jats:styled-content>) models are becoming more frequently applied. Population <jats:styled-content style="fixed-case">PKPD</jats:styled-content> and <jats:styled-content style="fixed-case">TTE</jats:styled-content> 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 <jats:styled-content style="fixed-case">PKPD</jats:styled-content> 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 <jats:styled-content style="fixed-case">PD</jats:styled-content> response and predict <jats:styled-content style="fixed-case">OS</jats:styled-content> for oncology drugs and their indications are also discussed.</jats:p>