From Molecule to Patient: Learning Formulation Behavior across Material, Dosage-Form, Physiological, and Exposure Scales Download PDF

Pharmaceutical formulation behavior is commonly evaluated through isolated measures such as molecular solubility, physical stability, dissolution, release, permeability, or systemic exposure. These measures are scientifically useful, but none independently represents the sequence of transformations through which a formulated drug becomes an absorbed and systemically distributed entity. The unresolved problem is therefore not simply how to predict individual formulation attributes, but how to learn the conditional relationships connecting molecular properties, excipient interactions, material organization, dosage-form transformation, physiological transport, absorption, and exposure. This article proposes a Molecule-to-Patient Formulation Learning Model as an original multiscale pharmaceutics construct. The model represents formulation performance as a series of connected, time-dependent states rather than as a direct mapping from composition to a single endpoint. Its architecture separates molecular, material, dosage-form, physiological, absorption, and exposure layers while linking them through explicitly defined state, flux, boundary-condition, and uncertainty interfaces. Mechanistic constraints and pharmaceutical knowledge may be incorporated without assuming that physical admissibility establishes causal correctness or prospective validity. The central contribution is an evidence-organizing architecture that distinguishes prediction from mechanism, concentration from mass flux, model confidence from calibrated uncertainty, and benchmark performance from pharmaceutical usefulness. The proposed construct may support more interpretable formulation hypotheses, cross-scale experimental planning, and identification of the scale at which a formulation-development assumption fails. However, it is not an empirically validated predictive system, universal formulation ontology, clinical decision tool, regulatory framework, or autonomous optimization platform. Its value depends on route-specific implementation, temporally aligned evidence, transparent provenance, applicability assessment, and prospective experimental evaluation.