Designing Dosage Forms across Competing Objectives: A Decision-Theoretic Framework for Artificial Intelligence–Guided Formulation Development Linking Excipient Compatibility, Drug Release, Manufacturability, and Patient Use Download PDF

Artificial intelligence is increasingly used to predict formulation performance, screen excipient combinations, estimate drug-release behavior, characterize manufacturing processes, and prioritize candidate dosage forms. Yet formulation development rarely presents a single objective for which the highest predicted value identifies the most defensible product. A formulation with favorable release behavior may create incompatibility risk, processing difficulty, scale-up vulnerability, unacceptable quality variation, or burdensome administration for the intended patient population. This article develops a non-empirical decision-theoretic account of AI-guided formulation development in which compatibility, release, manufacturability, quality, and patient use are represented as interacting but analytically distinct decision dimensions. The framework distinguishes predictive evidence from decision consequences, compensable trade-offs from feasibility violations, and model confidence from the evidentiary sufficiency required to advance a candidate. It also treats patient-use requirements as upstream formulation objectives rather than downstream implementation considerations. The central proposition is that AI becomes most useful when it helps identify trade-offs, update uncertain evidence states, and prioritize informative experiments rather than when it collapses heterogeneous formulation objectives into a single optimization score. A candidate should therefore advance only when its advantages remain defensible after objective-specific uncertainty, manufacturing feasibility, compatibility evidence, release relevance, and patient-use consequences are considered together. Under conditions of domain shift, incomplete validation, non-compensable constraints, or decision-relevant uncertainty, the appropriate response may instead be to revise the formulation, request additional evidence, or stop development.  This formulation-centered decision logic provides a basis for using AI without allowing predictive performance to substitute for pharmaceutical judgment.