Cargando…
Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent
Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO(2) was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility....
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640585/ https://www.ncbi.nlm.nih.gov/pubmed/36344531 http://dx.doi.org/10.1038/s41598-022-21233-0 |
_version_ | 1784825887587303424 |
---|---|
author | Alshahrani, Saad M. Almutairy, Bjad K. Alfadhel, Munerah M. Belal, Amany Abourehab, Mohammed A. S. Saqr, Ahmed Al. Alshetaili, Abdullah S. Venkatesan, Kumar Alsubaiyel, Amal M. Pishnamazi, Mahboubeh |
author_facet | Alshahrani, Saad M. Almutairy, Bjad K. Alfadhel, Munerah M. Belal, Amany Abourehab, Mohammed A. S. Saqr, Ahmed Al. Alshetaili, Abdullah S. Venkatesan, Kumar Alsubaiyel, Amal M. Pishnamazi, Mahboubeh |
author_sort | Alshahrani, Saad M. |
collection | PubMed |
description | Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO(2) was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308–338 K, and pressure 120–400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R(2)-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E−04, 7.40E−05, and 9.73E−06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E−01, DTR shows an error rate equal to 1.63E−01, and GPR as the best mode illustrates 5.06E−02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E−03). |
format | Online Article Text |
id | pubmed-9640585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96405852022-11-15 Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent Alshahrani, Saad M. Almutairy, Bjad K. Alfadhel, Munerah M. Belal, Amany Abourehab, Mohammed A. S. Saqr, Ahmed Al. Alshetaili, Abdullah S. Venkatesan, Kumar Alsubaiyel, Amal M. Pishnamazi, Mahboubeh Sci Rep Article Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO(2) was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308–338 K, and pressure 120–400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R(2)-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E−04, 7.40E−05, and 9.73E−06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E−01, DTR shows an error rate equal to 1.63E−01, and GPR as the best mode illustrates 5.06E−02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E−03). Nature Publishing Group UK 2022-11-07 /pmc/articles/PMC9640585/ /pubmed/36344531 http://dx.doi.org/10.1038/s41598-022-21233-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Alshahrani, Saad M. Almutairy, Bjad K. Alfadhel, Munerah M. Belal, Amany Abourehab, Mohammed A. S. Saqr, Ahmed Al. Alshetaili, Abdullah S. Venkatesan, Kumar Alsubaiyel, Amal M. Pishnamazi, Mahboubeh Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent |
title | Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent |
title_full | Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent |
title_fullStr | Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent |
title_full_unstemmed | Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent |
title_short | Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent |
title_sort | computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640585/ https://www.ncbi.nlm.nih.gov/pubmed/36344531 http://dx.doi.org/10.1038/s41598-022-21233-0 |
work_keys_str_mv | AT alshahranisaadm computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent AT almutairybjadk computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent AT alfadhelmunerahm computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent AT belalamany computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent AT abourehabmohammedas computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent AT saqrahmedal computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent AT alshetailiabdullahs computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent AT venkatesankumar computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent AT alsubaiyelamalm computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent AT pishnamazimahboubeh computationalsimulationandtargetpredictionstudiesofsolubilityoptimizationofdecitabinethroughsupercriticalsolvent |