Operationalizing Precision Medicine in Drug Development: Predictive Biomarkers, Companion Diagnostics, and Regulatory Pathways

Non-Oncology Biomarkers in Precision Medicine: A Narrative Review.

Engineering innovations for precision medicine: sensors, AI biomarkers, and predictive interventions.

This paper summarizes recent advances in biosensor technologies, AI-derived biomarkers, and predictive frameworks used to help identify patients more accurately, monitor their progress continuously, and receive optimized therapies.

Towards Predictive, Preventive, and Participatory Care: The Promise of Personalized Medicine

Personalized medicine is a transformative healthcare approach that designs prevention, diagnosis, and treatment to each individual’s unique genetic, molecular, and environmental profile. By integrating multi-omics technologies—genomics, transcriptomics, proteomics, and metabolomics—with advanced computational analytics, it enables early disease prediction, precise classification, and patient-specific therapy design. Advances in next-generation sequencing, artificial intelligence, and additive manufacturing, including 3D printing, have driven pharmacogenomic drug optimization, biomarker-guided targeted therapies, and on-demand production of customized dosage forms. Clinical applications span oncology, infectious, cardiovascular, neurological, and rare genetic diseases, improving therapeutic precision, reducing adverse effects, and enabling proactive disease management. Key benefits include more accurate drug-response prediction, fewer treatment failures, enhanced patient safety, and long-term economic savings through reduced healthcare utilization. However, widespread adoption faces challenges such as high cost, limited access in low-resource settings, and protection of sensitive genomic data. Ethical concerns—privacy, informed consent, and equitable benefit sharing—require robust regulation and public engagement. Future progress depends on expanding genomic diversity, building curated multi-omics repositories, and leveraging AI for biomarker discovery and predictive modeling. Emerging innovations in continuous manufacturing and flexible 3D printing will support individualized small-batch drug production, while adaptive policy frameworks must ensure safety, affordability, and accessibility. With coordinated scientific, technological, and ethical efforts, personalized medicine promises predictive, preventive, precise, and participatory healthcare worldwide.

An integrated methodological roadmap for real-world biomarker studies: advancing oncology precision medicine through robust methodologies and machine learning integration.

The selection of biomarker-specific patient populations is essential in targeted cancer therapies to enhance precision and efficacy. To ensure a successful launch, it is vital to promote awareness and adoption of biomarker testing at diagnosis, tailor implementation strategies to accommodate local variations, and ensure testing is accessible and reimbursed. An integrated evidence generation plan should address critical questions, including the prevalence of biomarker expression and agreement between local and central labs, across different platforms, antibodies and pathologists. Furthermore, understanding prognostic effects and associations with other biomarkers is of considerable interest. Real-world studies (RWS) play a pivotal role in addressing these questions but are inherently challenged by confounding factors, biases (e.g. immortal time bias), missing data, agreement assessment complexities, and low biomarker expression prevalence. This manuscript provides an integrated methodological roadmap for designing and analyzing RWS. We address key challenges by integrating robust statistical methodologies with advanced machine learning (ML) methods. Core methods discussed include the use of time-dependent Cox models to mitigate immortal time bias, inverse probability of biomarker weighting to adjust for confounding, and ML-based tree ensemble approaches to model complex relationships between covariates and outcomes and handle missing data. By systematically applying this integrated roadmap, we demonstrate how to enhance the validity and robustness of RW biomarker research. This approach overcomes common analytical pitfalls, enabling more reliable evidence generation for clinical decision-making. Ultimately, this roadmap helps drive precision oncology forward by ensuring that biomarker-driven therapeutic strategies are based on sound, high-quality RW evidence.

From Small Data to Big Decisions: How Clinical Pharmacology Shapes Rare Disease Development

Rare‑disease drug development is constrained by small and heterogeneous patient populations, limited natural‑history data, and the impracticality of large, randomized trials. Despite increasing regulatory acceptance of totality‐of‐evidence and mechanism‐based development pathways, generating reliable, decision‐ready evidence under these constraints remains challenging. This review describes how clinical pharmacology contributes within an evidence‑integration and decision‑support framework through quantitative, model‑informed approaches to address this gap. By integrating nonclinical data, pharmacokinetics, pharmacodynamics, biomarkers, natural‑history information, and clinical efficacy and safety outcomes, and through close collaboration with clinical, statistical, and translational experts, clinical pharmacology supports interpretation of treatment effects and quantitative characterization of uncertainty when conventional evidence is limited. In practice, these approaches inform key development decisions, including dose selection, innovative trial designs, extrapolation and bridging across populations, use of external controls, and evaluation of biomarkers and surrogate endpoints. Importantly, such practices help align regulatory expectations with patient needs, particularly in pediatric and ultra‑rare settings, by enabling appropriate dosing, reduced trial and patient burden, and quantitative assessment of benefit/risk. Examples from rare‑disease programs illustrate how integrated quantitative evidence has supported regulatory decisions, including label expansion and accelerated approval when data may be sparse, heterogeneous, or evolving. Looking ahead, emerging technologies such as artificial intelligence, digital biomarkers, and individualized approaches are expected to further advance rare‑disease drug development. With this evolving landscape, clinical pharmacology is expected to continue playing an important role in evaluating mechanistic plausibility, ensuring analytic rigor, and translating small datasets into meaningful evidence to inform development and regulatory decisions in rare diseases.

Innovative Clinical Pharmacology, Modeling, and Simulation Strategies for Accelerating Rare Disease Drug Development

Clinical drug development for rare diseases continues to face significant challenges due to disease heterogeneity, fewer available patients, and incomplete understanding of pathogenesis, resulting in trials with limited clinical data, thus constraining traditional development pathways. Clinical pharmacology, modeling, and simulation‐based approaches can help address these challenges by informing decision‐making, mitigating uncertainty, and guiding optimal dose and regimen selection for the appropriate patient population. These approaches help streamline trial designs by reducing the scope and number of clinical trial evaluations, using exposure–response analyses to optimize dosing, the use of mechanistic‐physiologically based pharmacokinetics (M‐PBPK)‐based approaches for biopharmaceutical and formulation optimization, evaluations of drug–drug interactions, and organ impairment. These strategies increase development efficiency across all stages of drug development, thereby improving the probability of success. This review highlights case studies that applied innovative clinical and quantitative pharmacology approaches across early and late stages of drug development and regulatory decision‐making in rare diseases. The specific examples illustrate the application of pharmacokinetics/pharmacodynamics (PK/PD) and model‐informed drug development (MIDD) strategies to support dose and regimen selection, enabling efficient use of direct or adaptive trial designs, facilitating bridging across populations and indications, biopharmaceutics‐based transitions, and generating integrated PK/PD evidence to support labeling. Examples include drug repurposing, characterizing PK/PD in early phase to inform late‐phase development, population PK analysis to guide trial dosing and label recommendations, using phenotype‐targeted study design to address disease heterogeneity, expanding dosing regimen across indications using MIDD, quantitatively evaluating immunogenicity to support mitigation strategies, biomarker bridging, and applying M‐PBPK to predict clinical PK in organ impairment populations.