In Silico and Computational Approaches for the Analytical, Bioanalytical, and Forced-Degradation Characterization of SGLT2 Inhibitors and Their Fixed-Dose Combinations

Sodium-glucose co-transporter-2 (SGLT2) inhibitors (“gliflozins”), foundational cardiorenal therapeutics usually co-formulated with metformin and dipeptidyl-peptidase-4 inhibitors as fixed-dose combinations (FDCs), have an overwhelmingly experimental, computationally shallow analytical literature. This critical review examines in silico approaches to analytical and bioanalytical method development and to forced-degradation and stability profiling, then compares software platforms, separating gliflozin-specific evidence from prospective capability. From structure alone, computation reproduces the class’s defining analytical problem: a several-log-unit polarity gap between neutral, moderately lipophilic gliflozins and hydrophilic cationic metformin. Property and ADME prediction is reliable only for rank-ordering, because inter-algorithm log P estimates diverge. Computer-assisted retention modelling with design of experiments, the most mature contribution, supports a defensible ICH Q14 method operable design region (MODR); quantitative structure–retention relationships, computationally designed molecularly imprinted polymers, and in silico degradation and impurity prediction remain largely prospective. ICH M7(R2) recommends two complementary (Q)SAR methodologies for potentially mutagenic impurities. The clearest opportunities are coupling degradation-pathway prediction to LC–HRMS to identify degradants prospectively, a first step toward closing the forced-degradation mass-balance gap, and externally validated retention models spanning the polarity gap. Every prediction (log P, retention time, degradant, docking score) is a hypothesis for experimental confirmation, never a substitute for validation.