%0 Journal Article
%T Self-Correcting Pharmaceutical Formulations That Sense Biological Conditions, Predict Performance Drift, and Adapt Drug Release across Changing Patient States
%A George Wilson
%A Chloe Bennett
%A Ethan Wright
%A Jack Turner
%J International Journal of Pharmaceutical Research and Allied Sciences
%@ 2277-3657
%D 2026
%V 15
%N 1
%R 10.51847/4UdUjIDkNq
%P 44-53
%X Pharmaceutical dosage forms are conventionally designed around predetermined release profiles, even though the physiological conditions governing dissolution, transport, absorption, clearance, tissue access, and therapeutic need can change during use. Stimuli-responsive materials partly address this mismatch, but a material reaction to pH, glucose, enzymes, reactive oxygen species, or another cue does not by itself establish reliable patient-state interpretation or safe adaptation. This article proposes a non-empirical closed-loop formulation theory for products that sense biological conditions, estimate patient and formulation states, predict performance drift, and modify drug release within explicit constraints. The proposed Sensing–Prediction–Adaptation with Constraint and Fail-Safe architecture separates signal acquisition, signal-quality assessment, latent-state estimation, drift prediction, bounded release control, material or device actuation, response verification, and fallback behaviour. It defines self-correction as verified, uncertainty-aware adjustment of release relative to an intended release–exposure relationship, rather than simple stimulus-triggered cargo liberation. Stability is treated dynamically through cycling, hysteresis, fatigue, recovery, and reservoir integrity; reversibility requires attenuation when the triggering state resolves; and fail-safe behaviour requires a bounded release condition when sensing, prediction, or actuation becomes unreliable. Validation must use changing and confounded physiological conditions, connect material response to exposure and therapeutic consequence, and test fault states rather than only nominal performance. Manufacturing translation requires traceable material attributes, process controls, data lineage, comparability, usability, and product-specific clinical evidence. The principal contribution is an original conceptual architecture and testable evaluation logic. It remains hypothesis-generating, indication- and dosage-form-specific, and unsuitable for clinical or regulatory decision use until empirically validated. 
%U https://ijpras.com/article/self-correcting-pharmaceutical-formulations-that-sense-biological-conditions-predict-performance-dr-g88rrfyjlr5spcx