Applying Computational Predictions of Biorelevant Solubility Ratio Upon Self-Emulsifying Lipid-Based Formulations Dispersion to Predict Dose Number

Computational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators success. This study investigated if silico predictions solubility gain i.e. ratios (SR), after dispersion a SEDDS biorelevant media could be predicted from properties. Apparent upon two FaSSIF was measured for 30 structurally diverse poorly water soluble drugs. Increased observed all cases, with higher SRs cationic and neutral versus anionic drugs at pH 6.5. Molecular descriptors solid-state properties were used as inputs during partial least squares (PLS) modelling resulting predictive models SRMC (r2 = 0.81) SRLC 0.77). Multiple linear regression (MLR) facilitated generation simplified SR equations high predictivity (SRMC r2 0.74; 0.69), requiring only three properties; partition coefficient 6.5 (logD6.5), melting point (Tm) aromatic bonds fraction total (F-AromB). Through using the to inform developability classification system (DCS) classes that have already been licensed lipid-based merits 2/3