Optimization of multimodal analgesic drug combinations under the concept of precision anesthesia.

Accumulating clinical and experimental evidence has highlighted multimodal analgesia as a pivotal strategy for improving perioperative outcomes, particularly within the emerging framework of precision anesthesia. In this paradigm, inadequately controlled postoperative pain and opioid-centric regimens are recognized not only as drivers of acute morbidity, but also as key contributors to persistent postsurgical pain, chronic opioid use, and impaired functional recovery. Multimodal analgesic combinations, integrating opioids with nonsteroidal anti-inflammatory drugs, acetaminophen, gabapentinoids, NMDA receptor antagonists, α2-agonists, and systemic or regional local anesthetics, target distinct levels of the nociceptive pathway to achieve synergistic analgesia while limiting dose-dependent toxicity. However, the design of such regimens is profoundly influenced by patient-specific biology, psychological and contextual factors, procedure-related nociceptive patterns, temporal dynamics across the perioperative course, and institutional constraints. This review summarizes the pharmacological basis and synergistic mechanisms underlying multimodal analgesia, delineates key variables that shape the optimization of drug combinations, and traces the evolution from empiric, protocol-driven practice to model-informed and data-driven design. We further discuss current challenges - including heterogeneity, biomarker gaps, and implementation barriers - and outline future directions toward learning health system-based, decision-support-enabled strategies that can deliver individualized, context-aware multimodal analgesic regimens under the concept of precision anesthesia.