Cancer drug makers adopt AI models to predict patient outcomes faster
Pharmaceutical companies are increasingly using computational models that simultaneously track tumor size, drug side effects, and patient survival to accelerate oncology drug development. The approach allows drugmakers to test dosing strategies and identify patient subgroups more efficiently, potentially cutting development timelines and reducing failed trials.
Originaltitel: Integrated modeling of biomarkers, survival and safety in clinical oncology drug development
<p>Model-based approaches, including population pharmacokinetic-pharmacodynamic modeling, have become an essential component in the clinical phases of oncology drug development. Over the past two decades, models have evolved to describe the temporal dynamics of biomarkers and tumor size, treatment-related adverse events, and their links to survival. Integrated models, defined here as models that incorporate at least two pharmacodynamic/ outcome variables, are applied to answer drug development questions through simulations, e.g., to support the exploration of alternative dosing strategies and study designs in subgroups of patients or other tumor indications. It is expected that these pharmacometric approaches will be expanded as regulatory authorities place further emphasis on early and individualized dosage optimization and inclusive patient-focused development strategies. This review provides an overview of integrated models in the literature, examples of the considerations that need to be made when applying these advanced pharmacometric approaches, and an outlook on the expected further expansion of model-informed drug development of anticancer drugs.Keywords: Anticancer drugs; Dose individualization; Dose optimization; Joint models; Model-informed drug development; Pharmacodynamic; Pharmacokinetic; Pharmacometrics; Tumor growth inhibition model.</p>