Next Article in Journal
Adapting the Accelerated Solvent Extraction Method for Resin and Rubber Determination in Guayule Using the BÜCHI Speed Extractor
Next Article in Special Issue
Solubility Determination of c-Met Inhibitor in Solvent Mixtures and Mathematical Modeling to Develop Nanosuspension Formulation
Previous Article in Journal
7β-(3-Ethyl-cis-crotonoyloxy)-1α-(2-methylbutyryloxy)-3,14-dehydro-Z Notonipetranone Attenuates Neuropathic Pain by Suppressing Oxidative Stress, Inflammatory and Pro-Apoptotic Protein Expressions
Previous Article in Special Issue
The Influence of Temperature and Viscosity of Polyethylene Glycol on the Rate of Microwave-Induced In Situ Amorphization of Celecoxib
Open AccessReview

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

VeriSIM Life Inc., 1 Sansome St, Suite 3500, San Francisco, CA 94104, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Guy Van den Mooter and Korbinian Löbmann
Molecules 2021, 26(1), 182; https://doi.org/10.3390/molecules26010182
Received: 10 December 2020 / Revised: 28 December 2020 / Accepted: 29 December 2020 / Published: 1 January 2021
(This article belongs to the Collection Poorly Soluble Drugs)
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. View Full-Text
Keywords: solubility; bioavailability; drug development; amorphous solid dispersions; molecular modeling; molecular dynamics; machine learning solubility; bioavailability; drug development; amorphous solid dispersions; molecular modeling; molecular dynamics; machine learning
Show Figures

Figure 1

MDPI and ACS Style

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. https://doi.org/10.3390/molecules26010182

AMA Style

Walden DM, 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(1):182. https://doi.org/10.3390/molecules26010182

Chicago/Turabian Style

Walden, Daniel M.; Bundey, Yogesh; Jagarapu, Aditya; Antontsev, Victor; Chakravarty, Kaushik; Varshney, Jyotika. 2021. "Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design" Molecules 26, no. 1: 182. https://doi.org/10.3390/molecules26010182

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop