Molecular Simulation and Statistical Learning Methods Toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design
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molecules Review Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design Daniel M. Walden, Yogesh Bundey, Aditya Jagarapu, Victor Antontsev, Kaushik Chakravarty and Jyotika Varshney * VeriSIM Life Inc., 1 Sansome St, Suite 3500, San Francisco, CA 94104, USA; [email protected] (D.M.W.); [email protected] (Y.B.); [email protected] (A.J.); [email protected] (V.A.); [email protected] (K.C.) * Correspondence: [email protected] Abstract: 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 inter- actions 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 Citation: Walden, D.M.; Bundey, Y.; stability, solubility, and dissolution mechanisms. Statistical learning models have been recently Jagarapu, A.; Antontsev, V.; applied to the prediction of a variety of drug formulation properties and show immense potential for Chakravarty, K.; Varshney, J. continued application in the understanding and prediction of ASD solubility. Continued theoretical Molecular Simulation and Statistical progress and computational applications will accelerate lead compound development before clinical Learning Methods toward Predicting trials. This article reviews in silico research for the rational formulation design of low-solubility Drug–Polymer Amorphous Solid drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are Dispersion Miscibility, Stability, and discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned. Formulation Design. Molecules 2021, 26, 182. https://doi.org/10.3390/ Keywords: solubility; bioavailability; drug development; amorphous solid dispersions; molecular molecules26010182 modeling; molecular dynamics; machine learning Academic Editors: Guy Van den Mooter and Korbinian Löbmann Received: 10 December 2020 1. Introduction Accepted: 29 December 2020 Published: 1 January 2021 Drug solubility plays a vital role in the drug discovery and development pipeline, with around 40 to 70% of drugs displaying poor aqueous solubility due to highly stable solid Publisher’s Note: MDPI stays neu- lattice arrangements and/or pronounced hydrophobicity [1–3]. Decreases in bioavailability tral with regard to jurisdictional clai- due to poor solubility are prevalent for oral dosages in which therapeutic compounds must ms in published maps and institutio- be in solution to reach systemic circulation and achieve clinically relevant concentrations nal affiliations. at the site of action. Considerable attention during discovery and lead optimization is therefore given to compound structural characteristics and the resulting physicochemical properties that affect solubility [4,5], with notable parameters, among many others, being a drug’s molecular weight, octanol:water partition coefficient (logP)[6], and possible acid- Copyright: © 2021 by the authors. Li- K censee MDPI, Basel, Switzerland. base ionization equilibria (p a)[7]. There is complex interplay and tradeoff between the This article is an open access article optimal chemical scaffold and functional groups for physiological response at the target distributed under the terms and con- and the ensuing effect on solubility and oral bioavailability [8]. Overcoming low drug ditions of the Creative Commons At- solubility while maintaining critical molecular components for therapeutic activity often tribution (CC BY) license (https:// necessitates precise formulation strategies during the development stage [9–11]. creativecommons.org/licenses/by/ A number of approaches exist for addressing the poor solubility of BCS (Biopharma- 4.0/). ceutics Classification System) class II (low solubility, high permeability) and IV (low solubil- Molecules 2021, 26, 182. https://doi.org/10.3390/molecules26010182 https://www.mdpi.com/journal/molecules Molecules 2021, 26, x FOR PEER REVIEW 2 of 24 Molecules 2021, 26, 182 2 of 23 A number of approaches exist for addressing the poor solubility of BCS (Biopharma- ceutics Classification System) class II (low solubility, high permeability) and IV (low sol- ubility, low permeability) active pharmaceutical ingredients (APIs) [1], including particle size reductionity, low permeability) (micronization) active [12,13], pharmaceutical salt formation ingredients and co-crystallization (APIs) [1], including [14,15], particle lipid- size reduction (micronization) [12,13], salt formation and co-crystallization [14,15], lipid-based based delivery [16,17], and cyclodextrin complexation [18]. Among the variety of strate- delivery [16,17], and cyclodextrin complexation [18]. Among the variety of strategies, amor- gies, amorphous solid dispersions (ASDs) have seen consistent use as formulation and phous solid dispersions (ASDs) have seen consistent use as formulation and drug delivery drug delivery systems, especially for BCS class II APIs [19–22]. In this system, lipophilic systems, especially for BCS class II APIs [19–22]. In this system, lipophilic API is dispersed API is dispersed with a carrier compound, often a water-soluble amphiphilic polymer ex- with a carrier compound, often a water-soluble amphiphilic polymer excipient, forming a cipient, forming a multi-component matrix (Figure 1a). Compared to the more thermody- multi-component matrix (Figure1A). Compared to the more thermodynamically stable namically stable API crystalline form, once dispersed within the carrier matrix, the API API crystalline form, once dispersed within the carrier matrix, the API occupies an elevated occupies an elevated energetic state, and solubility and dissolution rate enhancement of energetic state, and solubility and dissolution rate enhancement of the mixed ASD phase the mixed ASD phase relative to crystalline API results from increased solvent-exposed relative to crystalline API results from increased solvent-exposed surface area [23]. While surface area [23]. While this instability is beneficial due to the increased dissolution rate, this instability is beneficial due to the increased dissolution rate, relative ASD instability in- relative ASD instability incurs a practical challenge of crystallization out of the amorphous curs a practical challenge of crystallization out of the amorphous state during shelf life [24]. stateAn during ideal shelf ASD life must [24]. strike An ideal a delicate ASD must balance strike between a delicate sufficient balance API between saturation sufficient within the API saturationcarrier and within recrystallization the carrier [25and]. Predictingrecrystallization the stability [25]. Predicting and miscibility the stability of a given and API miscibilitywithin of a carriera given and API the within resulting a carrier solubility and the and resulting dissolution solubility rate of theand mixture dissolution (Figure rate1B) is of theof mixture paramount (Figure importance 1b) is of toparamount guide pharmaceutical importance to formulation guide pharmaceutical and production formula- decisions. tion and production decisions. FigureFigure 1. (a) 1.Forming(A) Forming an ASD an (amorphous ASD (amorphous solid disper solidsion) dispersion) from mixing from poorly mixing water-soluble poorly water-soluble APIs APIs(active (active pharmaceutical pharmaceutical ingredients) ingredients) with polymeric with polymeric carriers. carriers. (b) General (B) General ASD properties ASD properties and and phenomenaphenomena covered covered in this in review. this review. (c) Molecular (C) Molecular modeling modeling and mach andine machinelearning are learning in silico are in silico methods for predicting ASD properties and phenomena. Miscibility estimated by calculation of methods for predicting ASD properties and phenomena. Miscibility estimated by calculation of solubility (δ) and Flory–Huggins interaction (χFH) parameters. solubility (d) and Flory–Huggins interaction (χFH) parameters. Several experimental methods have been developed and applied towards understand- ing and characterizing ASD solid-state nanostructure and properties [26,27]. Key solid-state endeavors include probing for regions of API crystallinity and the presence of impurities, Molecules 2021, 26, 182 3 of 23 both of which can serve as further crystal nucleation sites [28]. Measurement of the glass transition temperature (Tg) routinely guides API–carrier miscibility, homogeneity, and physical stability evaluations [29]. Experimental deduction of intermolecular interactions occurring between API and carrier yields fundamental indicators of miscibility and overall ASD stability [30]. While experimental methods have yielded considerable understanding of the nanostructure and solid-state properties of ASD formulations, the metastable ASD system can also provide a significant challenge for experimental characterization. Appro- priately matching