Cargando…

A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery

In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug del...

Descripción completa

Detalles Bibliográficos
Autores principales: Noorain, Labiba, Nguyen, Vu, Kim, Hae-Won, Nguyen, Linh T. B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9966002/
https://www.ncbi.nlm.nih.gov/pubmed/36839817
http://dx.doi.org/10.3390/pharmaceutics15020495
_version_ 1784896907603083264
author Noorain, Labiba
Nguyen, Vu
Kim, Hae-Won
Nguyen, Linh T. B.
author_facet Noorain, Labiba
Nguyen, Vu
Kim, Hae-Won
Nguyen, Linh T. B.
author_sort Noorain, Labiba
collection PubMed
description In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug delivery. A new paradigm is required to ease the translation of lab discoveries to clinical practice. Due to their previous success in antiviral activity, it is vital to accelerate the discovery of novel drugs to treat and manage viruses. Machine learning is a subfield of artificial intelligence and consists of computer algorithms which are improved through experience. It can generate predictions from data inputs via an algorithm which includes a method built from inputs and outputs. Combining nanotherapeutics and well-established machine-learning algorithms can simplify antiviral-drug development systems by automating the analysis. Other relationships in bio-pharmaceutical networks would eventually aid in reaching a complex goal very easily. From previous laboratory experiments, data can be extracted and input into machine learning algorithms to generate predictions. In this study, poly (lactic-co-glycolic acid) (PLGA) nanoparticles were investigated in antiviral drug delivery. Data was extracted from research articles on nanoparticle size, polydispersity index, drug loading capacity and encapsulation efficiency. The Gaussian Process, a form of machine learning algorithm, could be applied to this data to generate graphs with predictions of the datasets. The Gaussian Process is a probabilistic machine learning model which defines a prior over function. The mean and variance of the data can be calculated via matrix multiplications, leading to the formation of prediction graphs—the graphs generated in this study which could be used for the discovery of novel antiviral drugs. The drug load and encapsulation efficiency of a nanoparticle with a specific size can be predicted using these graphs. This could eliminate the trial-and-error discovery method and save laboratory time and ease efficiency.
format Online
Article
Text
id pubmed-9966002
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99660022023-02-26 A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery Noorain, Labiba Nguyen, Vu Kim, Hae-Won Nguyen, Linh T. B. Pharmaceutics Article In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug delivery. A new paradigm is required to ease the translation of lab discoveries to clinical practice. Due to their previous success in antiviral activity, it is vital to accelerate the discovery of novel drugs to treat and manage viruses. Machine learning is a subfield of artificial intelligence and consists of computer algorithms which are improved through experience. It can generate predictions from data inputs via an algorithm which includes a method built from inputs and outputs. Combining nanotherapeutics and well-established machine-learning algorithms can simplify antiviral-drug development systems by automating the analysis. Other relationships in bio-pharmaceutical networks would eventually aid in reaching a complex goal very easily. From previous laboratory experiments, data can be extracted and input into machine learning algorithms to generate predictions. In this study, poly (lactic-co-glycolic acid) (PLGA) nanoparticles were investigated in antiviral drug delivery. Data was extracted from research articles on nanoparticle size, polydispersity index, drug loading capacity and encapsulation efficiency. The Gaussian Process, a form of machine learning algorithm, could be applied to this data to generate graphs with predictions of the datasets. The Gaussian Process is a probabilistic machine learning model which defines a prior over function. The mean and variance of the data can be calculated via matrix multiplications, leading to the formation of prediction graphs—the graphs generated in this study which could be used for the discovery of novel antiviral drugs. The drug load and encapsulation efficiency of a nanoparticle with a specific size can be predicted using these graphs. This could eliminate the trial-and-error discovery method and save laboratory time and ease efficiency. MDPI 2023-02-02 /pmc/articles/PMC9966002/ /pubmed/36839817 http://dx.doi.org/10.3390/pharmaceutics15020495 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Noorain, Labiba
Nguyen, Vu
Kim, Hae-Won
Nguyen, Linh T. B.
A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery
title A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery
title_full A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery
title_fullStr A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery
title_full_unstemmed A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery
title_short A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery
title_sort machine learning approach for plga nanoparticles in antiviral drug delivery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9966002/
https://www.ncbi.nlm.nih.gov/pubmed/36839817
http://dx.doi.org/10.3390/pharmaceutics15020495
work_keys_str_mv AT noorainlabiba amachinelearningapproachforplgananoparticlesinantiviraldrugdelivery
AT nguyenvu amachinelearningapproachforplgananoparticlesinantiviraldrugdelivery
AT kimhaewon amachinelearningapproachforplgananoparticlesinantiviraldrugdelivery
AT nguyenlinhtb amachinelearningapproachforplgananoparticlesinantiviraldrugdelivery
AT noorainlabiba machinelearningapproachforplgananoparticlesinantiviraldrugdelivery
AT nguyenvu machinelearningapproachforplgananoparticlesinantiviraldrugdelivery
AT kimhaewon machinelearningapproachforplgananoparticlesinantiviraldrugdelivery
AT nguyenlinhtb machinelearningapproachforplgananoparticlesinantiviraldrugdelivery