This review article addresses the critical challenge of improving cancer treatment outcomes, with 19.3 million new diagnoses and 10 million deaths globally in 2020. It explores the integration of pharmacometrics and personalized medicine in oncology to enhance patient care. Pharmacometrics focuses on optimizing dosing regimens, while personalized medicine uses genetic and biomarker data to tailor treatments to individual patients. Together, these approaches promise improved efficacy, reduced side effects, and better patient stratification. The review examines how pharmacometrics modeling, utilizing tools such as Population Pharmacokinetics (PopPK), systems pharmacology, and machine learning, combined with the personalized medicine emphasis on genetic, environmental, and lifestyle factors, can revolutionize cancer therapy. The use of multi-omics data, including genomic, transcriptomic, and clinical profiles, and techniques such as liquid biopsies enhances our understanding of tumor biology and patient-specific responses. Computational models and mechanistic approaches enable clinicians to design personalized treatment plans in real-time. Additionally, emerging technologies such as digital twins, adaptive clinical trial designs, and the integration of Electronic Health Records (EHRs) with predictive models are transforming oncology practice. Regulatory agencies, such as the FDA, are increasingly endorsing Model-Informed Drug Development (MIDD), highlighting the clinical relevance of these strategies. Despite challenges such as data standardization, computational complexity, and regulatory variability, continued interdisciplinary collaboration and advances in high-throughput technologies are crucial for translating these approaches into practical applications. This integrated strategy represents a promising path toward precision oncology, where therapies are not only effective but also safer and more patient-centric.
Dharavath et al. (Sat,) studied this question.