Building Hybrid Pharmacometric‐Machine Learning Models in Oncology Drug Development: Current State and Recommendations

Classic and hybrid pharmacometric-machine learning models (hPMxML) are gaining momentum for applications in clinical drug development and precision medicine, especially within the oncology therapeutic area. However, standardized workflows are needed to ensure transparency, rigor, and effective communication for broader adoption. In this tutorial, we review pharmacometric (PMx) and machine learning (ML) reporting standards and evaluate them against hPMxML works in oncology contexts as a motivational example to identify current deficiencies and propose mitigation strategies for future efforts. The revealed gaps include insufficient benchmarking, absence of error propagation, feature stability assessments, and ablation studies, limited focus on external validation and final parametrization, and discrepancies between the performance metrics chosen and the original clinical questions. To address these, we propose a checklist for hPMxML model development and reporting, consisting of steps for estimand definition, data curation, covariate selection, hyperparameter tuning, convergence assessment, model explainability, diagnostics, uncertainty quantification, validation and verification with sensitivity analyses. This standardized approach is expected to enhance the reliability and reproducibility of hPMxML outputs, enabling their confident application in oncology clinical drug development, while fostering trust among all stakeholders.