An empirical predictive model for determining the aqueous solubility of BCS class IV drugs in amorphous solid dispersions.

Context: Determining solubility of drugs is laborious and time-consuming process that may not yield meaningful results. Amorphous solid dispersion (ASD) is a widely used solubility enhancement technique. Predictive models could streamline this process and accelerate the development of oral drugs with improved aqueous solubilities.

Objective: This study aimed to develop a predictive model to estimate the solubility of a compound from the ASDs in polymer matrices.

Methods: ASDs of model drugs (acetazolamide, chlorothiazide, furosemide, hydrochlorothiazide, sulfamethoxazole) with model polymers (PVP, PVPVA, HPMC E5, Soluplus) and a surfactant (TPGS) were prepared using hotmelt process. The prepared ASDs were characterized using DSC, FTIR, and XRD. The aqueous solubility of the model drugs was determined using shake-flask method. Multiple linear regression was used to develop a predictive model to determine aqueous solubility using the molecular descriptors of the drug and polymer as predictor variables. The model was validated using Leave-One-Out Cross-Validation.

Results: The ASDs' drug components were identified as amorphous via DSC and XRD Studies. There were no significant chemical interactions between the model drugs and the polymers based on FTIR studies. The ASDs showed a significant (p < 0.05) improvement in solubility, ranging from a 3-fold to 118-fold, compared with the pure drug. The developed empirical model predicted the solubility of the model drugs from the ASDs containing model polymer matrices with an accuracy greater than 80%.

Conclusion: The developed empirical model demonstrated robustness and predicted the aqueous solubility of model drugs from the ASDs of model polymer matrices with an accuracy greater than 80%.