Noorain, Labiba;
Nguyen, Vu;
Kim, Hae-Won;
Nguyen, Linh TB;
(2023)
A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery.
Pharmaceutics
, 15
(2)
p. 495.
10.3390/pharmaceutics15020495.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/pharmaceutics15020495 |
| Publisher version: | https://doi.org/10.3390/pharmaceutics15020495 |
| Language: | English |
| Additional information: | © 2023 by the authors. 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/). |
| Keywords: | nanoparticles; machine learning; antiviral; PLGA |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Eastman Dental Institute UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Eastman Dental Institute > Biomaterials and Tissue Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10164449 |
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