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Machine learning estimation and optimization for evaluation of pharmaceutical solubility in supercritical carbon dioxide for improvement of drug efficacy

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, Mathura, 281406, IndiaRenuka Jyothi SDepartment 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, UzbekistanMurodjon YaxshimuratovDepartment of Chemistry, Urgench State University, Urgench, UzbekistanAshish Singh ChauhanDivision of research and innovation, Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, Uttarakhand, India

Scientific Reportsjournal2025en

ABI

Annotatsiya

This study focuses on predicting the solubility of paracetamol and density of solvent using temperature (T) and pressure (P) as inputs. The process for production of the drug is supercritical technique in which the focus was on theoretical investigations of drug solubility and solvent density as well. Machine learning models with a two-input, two-output structure were developed and validated using experimental data on paracetamol solubility as well as density. Ensemble models with decision trees as base models, including Extra Trees (ETR), Random Forest (RFR), Gradient Boosting (GBR), and Quantile Gradient Boosting (QGB) were adjusted to predict the two outputs. The results are useful to evaluate the feasibility of process in improving the efficacy of the drug, i.e., its enhanced bioavailability. The hyper-parameters of ensemble models as well as parameters of decision tree tuned using WOA algorithm separately for both outputs. The Quantile Gradient Boosting model showed the best performance for mole fraction (drug solubility), while the R2 score of 0.985 was determined. For density of solvent, the Extra Trees model performed the best with an R2 equal to 0.997.

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Mavzular

Computational Drug Discovery Methods Phase Equilibria and Thermodynamics Analytical Chemistry and Chromatography

Identifikatorlar

DOI: 10.1038/s41598-025-19873-z

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