Modeling and estimation of physiochemical properties of cancer drugs using entropy measures
Abstract Hyaluronic acid-paclitaxel conjugate is a nanoparticle-based drug delivery system that combines hyaluronic acid with paclitaxel, enhancing its solubility, stability, and targeting specificity. This conjugate shows promise in treating breast, lung, and ovarian cancers with reduced side effec...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87755-5 |
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| author | Qasem M. Tawhari Muhammad Naeem Abdul Rauf Muhammad Kamran Siddiqui Oladele Oyelakin |
| author_facet | Qasem M. Tawhari Muhammad Naeem Abdul Rauf Muhammad Kamran Siddiqui Oladele Oyelakin |
| author_sort | Qasem M. Tawhari |
| collection | DOAJ |
| description | Abstract Hyaluronic acid-paclitaxel conjugate is a nanoparticle-based drug delivery system that combines hyaluronic acid with paclitaxel, enhancing its solubility, stability, and targeting specificity. This conjugate shows promise in treating breast, lung, and ovarian cancers with reduced side effects. Entropy measures are used to predict physical and chemical properties of drugs. In this paper, we compute entropy measures for the hyaluronic acid-paclitaxel conjugate using the edge/connectivity partition approach. We establish a quantitative structure-property relationship using reverse entropy measures to predict physical properties of cancer drugs. Multiple linear, Ridge, Lasso, ElasticNet, and Support Vector regression models are employed using Python software. Our results show that reverse entropy measures exhibit high predictive capability for physical properties, based on the highest coefficient of determination and lowest mean squared error. We conclude that physical properties, including boiling point, enthalpy of vaporization, flash point, molar refractivity, molar volume, polarization, molecular weight, monoisotopic mass, topological polar surface area, and complexity, can be predicted using reverse entropy measures. We propose models for each relationship, including only the most significant models for estimating uncalculated physical properties. |
| format | Article |
| id | doaj-art-8370e29dd6b14d059173110019ab48da |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8370e29dd6b14d059173110019ab48da2025-02-09T12:28:19ZengNature PortfolioScientific Reports2045-23222025-02-0115111810.1038/s41598-025-87755-5Modeling and estimation of physiochemical properties of cancer drugs using entropy measuresQasem M. Tawhari0Muhammad Naeem1Abdul Rauf2Muhammad Kamran Siddiqui3Oladele Oyelakin4Department of Mathematics, College of Science, Jazan UniversityDepartment of Mathematics, National University of Sciences and Technology (NUST)Department of Mathematics, Air University Multan CampusDepartment of Mathematics, Comsats University IslamabadChemistry Unit, School of Arts & Sciences, University of The GambiaAbstract Hyaluronic acid-paclitaxel conjugate is a nanoparticle-based drug delivery system that combines hyaluronic acid with paclitaxel, enhancing its solubility, stability, and targeting specificity. This conjugate shows promise in treating breast, lung, and ovarian cancers with reduced side effects. Entropy measures are used to predict physical and chemical properties of drugs. In this paper, we compute entropy measures for the hyaluronic acid-paclitaxel conjugate using the edge/connectivity partition approach. We establish a quantitative structure-property relationship using reverse entropy measures to predict physical properties of cancer drugs. Multiple linear, Ridge, Lasso, ElasticNet, and Support Vector regression models are employed using Python software. Our results show that reverse entropy measures exhibit high predictive capability for physical properties, based on the highest coefficient of determination and lowest mean squared error. We conclude that physical properties, including boiling point, enthalpy of vaporization, flash point, molar refractivity, molar volume, polarization, molecular weight, monoisotopic mass, topological polar surface area, and complexity, can be predicted using reverse entropy measures. We propose models for each relationship, including only the most significant models for estimating uncalculated physical properties.https://doi.org/10.1038/s41598-025-87755-5Physiochemical characteristicsQSPR AnalysisdrugsTopological indicesReverse entropy measures |
| spellingShingle | Qasem M. Tawhari Muhammad Naeem Abdul Rauf Muhammad Kamran Siddiqui Oladele Oyelakin Modeling and estimation of physiochemical properties of cancer drugs using entropy measures Scientific Reports Physiochemical characteristics QSPR Analysis drugs Topological indices Reverse entropy measures |
| title | Modeling and estimation of physiochemical properties of cancer drugs using entropy measures |
| title_full | Modeling and estimation of physiochemical properties of cancer drugs using entropy measures |
| title_fullStr | Modeling and estimation of physiochemical properties of cancer drugs using entropy measures |
| title_full_unstemmed | Modeling and estimation of physiochemical properties of cancer drugs using entropy measures |
| title_short | Modeling and estimation of physiochemical properties of cancer drugs using entropy measures |
| title_sort | modeling and estimation of physiochemical properties of cancer drugs using entropy measures |
| topic | Physiochemical characteristics QSPR Analysis drugs Topological indices Reverse entropy measures |
| url | https://doi.org/10.1038/s41598-025-87755-5 |
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