pharmaceuticals Review Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers Shaymaa A. Abd-algaleel 1, Hend M. Abdel-Bar 2 , Abdelkader A. Metwally 3,4 and Rania M. Hathout 3,* 1 Department of Pharmaceutics, Egyptian Drug Authority, Cairo 12618, Egypt; [email protected] 2 Department of Pharmaceutics, Faculty of Pharmacy, University of Sadat City, Sadat 32897, Egypt; [email protected] 3 Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo 11566, Egypt; [email protected] 4 Health Sciences Center, Department of Pharmaceutics, Faculty of Pharmacy, Kuwait University, Safat, Kuwait 13110, Kuwait * Correspondence: [email protected] Abstract: This review describes different trials to model and predict drug payload in lipid and polymeric nanocarriers. It traces the evolution of the field from the earliest attempts when numerous solubility and Flory-Huggins models were applied, to the emergence of molecular dynamic simula- tions and docking studies, until the exciting practically successful era of artificial intelligence and machine learning. Going through matching and poorly matching studies with the wet lab-dry lab results, many key aspects were reviewed and addressed in the form of sequential examples that Citation: Abd-algaleel, S.A.; highlighted both cases. Abdel-Bar, H.M.; Metwally, A.A.; Hathout, R.M. Evolution of the Computational Pharmaceutics Keywords: lipid; polymer; simulations; docking; machine learning; in-silico Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers. Pharmaceuticals 2021, 14, 645. https:// 1. Introduction doi.org/10.3390/ph14070645 Nowadays, many efforts are exerted in the pharmaceutical field regarding the devel- opment of different types of drug delivery systems (DDS). This aims to keep the therapeutic Academic Editor: Dimitris Tsiourvas efficacy of the active pharmaceutical ingredient (API), and successfully deliver the proper dose to the physiological target site. Among different types of existing DDS, nanocarriers Received: 31 May 2021 have gained great interest due to their ultra-small size that controls the pharmacokinetics Accepted: 29 June 2021 and pharmacodynamics of the drug [1,2]. Nanocarriers made specifically of lipids and Published: 5 July 2021 polymers are extensively investigated in the literature, and the number of papers deal- ing with them is dramatically increasing over time [3–6]. A core feature for a successful Publisher’s Note: MDPI stays neutral lipid/polymeric formulation is the capacity of the carrier to retain its cargo, i.e., the drug with regard to jurisdictional claims in payload [7–9]. Selecting the optimum carrier for this purpose is an issue of time, cost published maps and institutional affil- and effort if the design is solely built on the basis of wet-lab methods. In silico pharma- iations. ceutical formulation design now constitutes a key part of contemporary drug delivery research. It is gaining popularity because it offers the experimentalists a highly pixilated picture of their target based on molecular details of both the drug and the carrier. With this knowledge, designing nanocarriers with optimized properties is much faster with Copyright: © 2021 by the authors. minimal laboratory effort and cost concerns. The scope of the current review is to illustrate Licensee MDPI, Basel, Switzerland. the different reported techniques to model and predict he drug payload in the lipid and This article is an open access article the polymeric nanocarriers. Evaluation of the consistency of these models with respect to distributed under the terms and their experimental validation results is another perspective. Given the diversity of APIs conditions of the Creative Commons Attribution (CC BY) license (https:// and the heterogeneity of lipids and polymeris that are used in nanocarriers preparation, creativecommons.org/licenses/by/ many modelling techniques are described in literature to explain the complex relationship 4.0/). of drug-carrier systems. Some groups emphasized the use of the Flory-Huggins theory Pharmaceuticals 2021, 14, 645. https://doi.org/10.3390/ph14070645 https://www.mdpi.com/journal/pharmaceuticals Pharmaceuticals 2021, 14, x FOR PEER REVIEW 2 of 25 Pharmaceuticals 2021, 14, 645 of drug-carrier systems. Some groups emphasized the use of the Flory-Huggins2 of 29 theory and the different solubility models for drug loading prediction. Similarly, increased un- derstanding of the drug-carrier interaction process began reaching its full potential upon reviewsand the discussing different solubility molecular models dynamics for drug simulations loading prediction. and docking Similarly, applications increased in un- this area. Furthermore,derstanding of recent the drug-carrier related research interaction reports process that began deal reachingwith larger its full number potential of uponparameters tendreviews to apply discussing novel molecular concepts dynamics of prediction simulations such andas the docking smart applications artificial intelligence-based in this area. Furthermore, recent related research reports that deal with larger number of parameters models. Thereby, the aim of this review is to cover all these techniques from naïve primi- tend to apply novel concepts of prediction such as the smart artificial intelligence-based tivemodels. approaches Thereby, to the the aim most of this innovative review is to on coveres. allThe these investigated techniques fromstudies naïve include primitive the poly- mericapproaches and the to lipid the most nanocarriers innovative along ones. Thewith investigated hybrids of studies them. includeEach section the polymeric of the review addressesand the lipid one nanocarriersmodelling area along from with the hybrids perspect of them.ive of Eachits implications section of the for review DL prediction, ad- todresses the scientific one modelling and technical area from challenges the perspective associated of its implications with its development. for DL prediction, Important to the liter- aturescientific examples and technical demonstrating challenges these associated aspects with are its highlighted. development. 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ContributionContribution ofof different different analytical analytical and and in silicoin silico tools tools to the to drug the loadingdrug loading optimization optimization in in differentdifferent classes classes of nanocarriers.nanocarriers. 2. Solubility Parameters and Flory-Huggins Theory 2. SolubilityThe essence Parameters of DL optimization and Flory-Huggins is to predict molecules’ Theory preferences in excipients depend- ing onThe their essence physicochemical of DL optimization properties [is10 to,11 predict]. These aremolecules’ empirical preferences mathematical in equations excipients de- pendingparameterized on their to wrapphysicochemi the physicalcal details properties and some [10,11]. structural These integration are empirical of the involved mathematical equationsmolecules parameterized [11]. The most frequentlyto wrap the noticed physical one is details the Flory-Huggins and some structural theory (FH). integration It is a of thethermodynamic involved molecules model first [11]. introduced The most in frequently the world ofnoticed polymers one to is explain the Flory-Huggins their behavior theory in solutions [12], and this is well documented in polymers literature [13–15]. Recently, this (FH). It is a thermodynamic model first introduced in the world of polymers to explain technique was used to extract valuable information regarding the plausible loading of many theirdrugs behavior in polymeric in solutions [16–19] and [12], even and lipid this [20 is]—carriers well documented via predicting in theirpolymers mutual literature solubil- [13– 15].ity/miscibility. Recently, this It is technique well known was that used one to of extract the most valuable important information factors that determineregarding the the plau- sibleloading loading capacity of many of a carrier drugs is thein solubilitypolymeric of the[16–19] drug and in this even carrier lipid [10 ,[20]—carriers21,22]. Thus, this via pre- dictingmodel their depends mutual on estimating solubility/miscibility. specific solubility It is parameters well known (SP), that usually one of Hildebrand the most andimportant factorsHansen that SP, todetermine calculate thethe final loading FH interaction capacity parameterof a carrier (X His) asthe follows: solubility of the drug in this carrier [10,21,22].