Phenotypic response surface-guided pharmacotherapy for personalized combination treatment in complex diseases

Optimizing multidrug regimens for complex diseases remains a major challenge in precision medicine because responses are shaped by interpatient heterogeneity, nonlinear drug interactions, and incomplete mechanistic knowledge. Phenotypic Response Surfaces (PRS) have emerged as a promising computational and translational framework for guiding pharmacotherapy by linking phenotypic information with the rational optimization of combination treatments.

This review synthesizes literature identified through PubMed, Web of Science, and related biomedical databases, emphasizing PRS platforms, artificial intelligence-enabled phenotypic optimization, and personalized combination pharmacotherapy. Evidence from oncology, infectious diseases, transplantation medicine, and autoimmune disorders is examined to show how PRS-guided approaches can identify effective drug combinations and dose configurations, particularly where experimental, mechanistic, or clinical data are limited. The review also evaluates translational developments, clinical investigations, and emerging advances that may accelerate clinical implementation of PRS-guided treatment design.

PRS-guided pharmacotherapy represents a transformative strategy for personalized combination treatment in complex diseases by integrating phenotypic response data with computational optimization to improve therapeutic precision. However, broader clinical adoption will require rigorous external validation, integration of multimodal patient data, improved interpretability of AI-assisted models, and supportive regulatory frameworks. With these advances, PRS-based approaches may become an important component of next-generation precision pharmacotherapy.