Physics-Informed Learning for Dissolution and Precipitation of Poorly Soluble Phytochemicals Across Formulation Conditions, Gastrointestinal Environments, and Dynamic Exposure States

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Poorly soluble phytochemicals frequently encounter a moving physicochemical target after oral administration, as dissolution, supersaturation, precipitation, colloidal association, formulation disintegration, gastrointestinal transit, and changes in luminal composition occur on overlapping time scales. Predictive approaches often simplify these processes into equilibrium properties or isolated empirical relationships. This article develops a proposed physics-informed pharmaceutical-science framework for learning dissolution and precipitation behavior across formulation conditions and dynamic gastrointestinal environments. The framework treats experimentally accessible concentration trajectories and formulation attributes as observations of latent physical states governed, where defensible, by mass balance, dissolution, transport, and precipitation relationships. Physics-informed neural or hybrid architectures are proposed to constrain learning while retaining data-driven components for processes that are incompletely specified. Particular attention is given to distinguishing molecularly dissolved drug from apparent dissolved or colloid-associated material, learning uncertain kinetic parameters from sparse experiments, and representing pH, bile-related composition, hydrodynamics, digestion, and transit as changing boundary conditions rather than fixed labels. The intended output is not a universally predictive model but an evidence-bounded strategy for linking mechanistic structure, incomplete experimental data, and exposure-relevant state estimation. Poorly soluble phytochemicals provide a demanding application space because formulation engineering may substantially alter their apparent dissolution and subsequent exposure. The proposed framework therefore requires compound-specific parameterization, uncertainty analysis, orthogonal experimental validation, comparison with mechanistic and data-only baselines, and prospective testing outside the calibration domain before claims of generalizability or translational utility are warranted.