Мақола

Raloxifene solubility in supercritical CO2 and correlation of drug solubility via hybrid machine learning and gradient based optimization

Hadil Faris AlotaibiDepartment of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint AbdulRahman University, Riyadh, 11671, Saudi Arabia. [email protected]Chou‐Yi HsuThunderbird School of Global Management, Arizona State University Tempe Campus, Phoenix, AZ, 85004, USAFadhil Faez SeadDepartment of Dentistry, College of Dentistry, The Islamic University, Najaf, IraqAnupam YadavDepartment of Computer engineering and Application, GLA University, Mathura, 281406, IndiaS. Renuka JyothiDepartment of Biotechnology and Genetics, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, IndiaSwati MishraDepartment of Pharmacology, IMS and SUM Hospital, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751003, Odisha, IndiaBilakshan PurohitCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, IndiaAnorgul AshirovaDepartment of General Professional Sciences, Mamun University, Khiva, UzbekistanIslom KhudayberganovDepartment of Chemistry, Urgench State University, Urgench, Khorezm Region, UzbekistanAshish Singh ChauhanDivision of research and innovation, Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, Uttarakhand, India

Scientific Reportsjournal2025en

ABI

Аннотация

One of the problems with new medications is their poor water solubility that is possible to be addressed by using supercritical method. This study aims to predict the solubility of raloxifene and the density of supercritical CO2 using temperature and pressure as inputs to analyze the supercritical processing for production of drug nanoparticles. Three regression models, Extra Trees (ET), Random Forest (RF), and Gradient Boosting (GB) were proposed and optimized using Gradient-based optimization to predict density and solubility of drug. In predicting the density of supercritical CO₂, GB attained an R² value of 0.986, reflecting an excellent agreement between its estimates and the true measurements. The model exhibited an RMSE of 23.20, indicating high accuracy, with a maximum error of 33.06. Regarding the solubility of raloxifene, the ET model yielded the highest R-squared score of 0.949, indicating a good fit to the data. The model exhibited an RMSE of 0.41, with a maximum error of 0.90. Comparatively, the RF and GB models obtained slightly lower precision, for the solubility of raloxifene. The RF model exhibited an RMSE of 0.55, while the GB model had an RMSE of 0.72. The optimized models were found to be reliable in predicting solubility and density within the supercritical processing field.

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Мавзулар

Phase Equilibria and Thermodynamics Analytical Chemistry and Chromatography

Идентификаторлар

DOI: 10.1038/s41598-025-17642-6

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