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

Abstract

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%.

Graphical Abstract

Keywords:

• Solubility predictive model
• amorphous solid dispersions
• BCS class IV drugs
• PVP
• PVPVA
• hpmc e5
• soluplus

Acknowledgements

We extend our sincere appreciation to Dr. Satya Goda and Dr. Yifan Lu from Formurex, Inc. for their consistent and invaluable technical guidance that significantly enhanced this project.

A special note of gratitude goes to Dr. Sevak at the University of the Pacific, whose expertise in statistical analysis contributed to the use of proper statistical testing in developing and validating the model in this study.

Data availability statement

The data sets collected and analyzed are available from the corresponding author are available on reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.