Preprint Communication Version 1 Preserved in Portico This version is not peer-reviewed

Modeling Drugs-PLGA Nanoparticles Interactions Using Gaussian Processes: Pharmaceutics Informatics Approach

Version 1 : Received: 16 April 2021 / Approved: 20 April 2021 / Online: 20 April 2021 (09:47:24 CEST)

How to cite: Hathout, R.M.; Mahmoud, O.A.; Ali, D.S.; Mamdouh, M.; Metwally, A.A. Modeling Drugs-PLGA Nanoparticles Interactions Using Gaussian Processes: Pharmaceutics Informatics Approach. Preprints 2021, 2021040524 (doi: 10.20944/preprints202104.0524.v1). Hathout, R.M.; Mahmoud, O.A.; Ali, D.S.; Mamdouh, M.; Metwally, A.A. Modeling Drugs-PLGA Nanoparticles Interactions Using Gaussian Processes: Pharmaceutics Informatics Approach. Preprints 2021, 2021040524 (doi: 10.20944/preprints202104.0524.v1).

Abstract

The objective of this study was to correlate the binding of drugs on a very popular nanoparticulate polymeric matrix; PLGA nanoparticles with their main constitutional, electronic and physico-chemical descriptors. Gaussian Processes (GPs) was the artificial intelligence machine learning method that was utilized to fulfil this task. The method could successfully model the results where optimum values of the investigated descriptors of the loaded drugs were deduced. A percentage bias of 12.68 % ± 2.1 was obtained in predicting the binding energies of a group of test drugs. As a conclusion, GPs could successfully model the drugs-PLGA interactions associated with a good predicting power. The GPs-predicted binding energies (ΔG) can easily be projected to the drugs loading as was previously proven. Adopting the “Pharmaceutics Informatics” approach can save the pharmaceutical industry and the drug delivery scientists a lot of exerted resources, efforts and time.

Subject Areas

machine learning; Gaussian; nanoparticles; descriptors; docking; simulations; PLGA

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