Speaker
Description
Mechanistic pharmacokinetic/pharmacodynamic (PK/PD) modeling provides a quantitative framework for characterizing how drug dose and exposure relate to efficacy and toxicity over time \cite{bender2015pkpd_oncology, mould2015exposure_response}. In oncology, such models are increasingly used to inform dose selection, treatment evaluation and the interpretation of therapeutic response. However, the clinical meaning of model-derived quantities is often left implicit. In practice, estimated dose–exposure–response relationships may correspond to different causal questions depending on how post-initiation events such as dose reductions, treatment discontinuation, rescue medication, switching, and death are handled. The ICH E9(R1) estimand framework offers a principled structure for linking pharmacometric and tumor-forecasting models to clinically interpretable treatment effects \cite{ich2019e9r1}. In addition, epidemiologic concepts such as confounding, selection bias, immortal time bias, and effect modification illustrate how post-baseline summaries can yield misleading inferences when the underlying causal question is not explicitly defined \cite{hernan2020whatif}. The aim is not to replace mechanistic modeling with epidemiologic methods, but to show how causal thinking can sharpen model interpretation, improve alignment with regulatory and clinical decision questions and strengthen the relevance of model-based evidence in oncology.
Bibliography
@article{bender2015pkpd_oncology,
author = {Bender, Brendan C. and Schindler, Emilie and Friberg, Lena E.},
title = {Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response},
journal = {British Journal of Clinical Pharmacology},
year = {2015},
volume = {79},
number = {1},
pages = {56--71},
doi = {10.1111/bcp.12258}
}
@misc{ich2019e9r1,
author = {{International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH)}},
title = {{ICH E9(R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials}},
year = {2019},
note = {Step 4, endorsed by the ICH Assembly on 20 November 2019; final adoption by CHMP on 30 January 2020},
institution = {European Medicines Agency},
number = {EMA/CHMP/ICH/436221/2017}
}
@book{hernan2020whatif,
author = {Hern{\'a}n, Miguel A. and Robins, James M.},
title = {Causal Inference: What If},
year = {2020},
address = {Boca Raton},
publisher = {Chapman and Hall/CRC}
}
@article{mould2015exposure_response,
author = {Mould, D. R. and Walz, A.-C. and Lave, T. and Gibbs, J. P. and Frame, B.},
title = {Developing Exposure/Response Models for Anticancer Drug Treatment: Special Considerations},
journal = {CPT: Pharmacometrics \& Systems Pharmacology},
year = {2015},
volume = {4},
number = {1},
pages = {e00016},
doi = {10.1002/psp4.16}
}
