Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design

Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API–carrier mixture and the principal API–carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error. To that end, molecular modeling has been applied to simulate ASD properties and mechanisms. Quantum mechanical methods elucidate the strength of API–carrier non-bonding interactions, while molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms. Statistical learning models have been recently applied to the prediction of a variety of drug formulation properties and show immense potential for continued application in the understanding and prediction of ASD solubility. Continued theoretical progress and computational applications will accelerate lead compound development before clinical trials. This article reviews in silico research for the rational formulation design of low-solubility drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned.

Download the full publication here: Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design

or continue reading here: Walden, D.M.; Bundey, Y.; Jagarapu, A.; Antontsev, V.; Chakravarty, K.; Varshney, J. Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design. Molecules 2021, 26, 182.

Keywords: solubility; bioavailability; drug development; amorphous solid dispersions; molecular modeling; molecular dynamics; machine learning

Summary of commercially available ASD formulations

Product	API	Carrier	Dosage Form
Afeditab	Nifedipine	Poloxamer/PVP	Tablet
Afinitor	Everolimus	HPMC	Tablet
Certican	Everolimus	HPMC	Tablet
Cesamet	Nabilone	PVP	Tablet
Crestor	Rosuvastatin	HPMC	Tablet
Cymbalta	Duloxetine	HPMC-AS	Capsule
Fenoglide	Fenofibrate	PEG	Tablet
Florfenicol	Florfenicol	Cellulose acetate phthalate	Powder
Gris-PEG	Griseofulvin	PEG	Tablet
Incivek	Teleprevir	HPMC-AS	Tablet
Incivo	Etravirine	HPMC	Tablet
Intelence	Etravirin	HPMC	Tablet
Isoptin	Verapamil	HPC/HPMC	Tablet
Kaletra	Lopinavir	PVP	Capsule
Kalydeco	Ivacaftor	HPMC-AS	Tablet
Nimotop	Nimodipine	PEG	Capsule
Nivadil	Nivaldipine	HPMC	Tablet
Novir	Ritonavir	PVP	Tablet
Onmel	Itraconazole	HPMC	Tablet
Prograf	Tacrolimus	HPMC	Capsule
Rezulin	Troglitazone	HPMC	Tablet
Shuilinjia	Silibinin	Lecithin	Capsule
Sporanox	Itraconazole	HPMC	Capsule
Stivarga	Regorafenib	HPMC	Tablet
Votubia	Everolimus	HPMC	Tablet
Zelboraf	Vemurafenib	Hypromellose acetate succinate	Tablet
Zortess	Everolimus	HPMC	Tablet

PVP: polyvinylpyrrolidone; HPMC: hydroxypropyl methylcellulose; HMPC-AS: hydroxypropyl methylcellulose acetate succinate; PEG: polyethylene glycol.

Data from Table 1: Walden, D.M.; Bundey, Y.; Jagarapu, A.; Antontsev, V.; Chakravarty, K.; Varshney, J. Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design. Molecules 2021, 26, 182.