Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

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 simulations and docking...

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Main Authors:	Shaymaa A. Abd-algaleel, Hend M. Abdel-Bar, Abdelkader A. Metwally, Rania M. Hathout
Format:	Article
Language:	English
Published:	MDPI AG 2021-07-01
Series:	Pharmaceuticals
Subjects:	lipid polymer simulations docking machine learning in-silico
Online Access:	https://www.mdpi.com/1424-8247/14/7/645

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spelling	doaj-d488e157da8249c184712f82dd0e7d882021-07-23T13:59:59ZengMDPI AGPharmaceuticals1424-82472021-07-011464564510.3390/ph14070645Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric NanocarriersShaymaa A. Abd-algaleel0Hend M. Abdel-Bar1Abdelkader A. Metwally2Rania M. Hathout3Department of Pharmaceutics, Egyptian Drug Authority, Cairo 12618, EgyptDepartment of Pharmaceutics, Faculty of Pharmacy, University of Sadat City, Sadat 32897, EgyptDepartment of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo 11566, EgyptDepartment of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo 11566, EgyptThis 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 simulations 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 highlighted both cases.https://www.mdpi.com/1424-8247/14/7/645lipidpolymersimulationsdockingmachine learningin-silico
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Shaymaa A. Abd-algaleel Hend M. Abdel-Bar Abdelkader A. Metwally Rania M. Hathout
spellingShingle	Shaymaa A. Abd-algaleel Hend M. Abdel-Bar Abdelkader A. Metwally Rania M. Hathout Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers Pharmaceuticals lipid polymer simulations docking machine learning in-silico
author_facet	Shaymaa A. Abd-algaleel Hend M. Abdel-Bar Abdelkader A. Metwally Rania M. Hathout
author_sort	Shaymaa A. Abd-algaleel
title	Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers
title_short	Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers
title_full	Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers
title_fullStr	Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers
title_full_unstemmed	Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers
title_sort	evolution of the computational pharmaceutics approaches in the modeling and prediction of drug payload in lipid and polymeric nanocarriers
publisher	MDPI AG
series	Pharmaceuticals
issn	1424-8247
publishDate	2021-07-01
description	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 simulations 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 highlighted both cases.
topic	lipid polymer simulations docking machine learning in-silico
url	https://www.mdpi.com/1424-8247/14/7/645
work_keys_str_mv	AT shaymaaaabdalgaleel evolutionofthecomputationalpharmaceuticsapproachesinthemodelingandpredictionofdrugpayloadinlipidandpolymericnanocarriers AT hendmabdelbar evolutionofthecomputationalpharmaceuticsapproachesinthemodelingandpredictionofdrugpayloadinlipidandpolymericnanocarriers AT abdelkaderametwally evolutionofthecomputationalpharmaceuticsapproachesinthemodelingandpredictionofdrugpayloadinlipidandpolymericnanocarriers AT raniamhathout evolutionofthecomputationalpharmaceuticsapproachesinthemodelingandpredictionofdrugpayloadinlipidandpolymericnanocarriers
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Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

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