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

Links and Files

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

Authors

Abstract

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.

Journal

Scientific Reports volume 15, Article number: 35881 (2025)

DOI

https://doi.org/10.1038/s41598-025-19873-z

Type of publication

Peer-reviewed journal

Affiliations

Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint AbdulRahman University, Riyadh, Saudi Arabia
Thunderbird School of Global Management, Arizona State University Tempe Campus, Phoenix, USA
Department of Dentistry, College of Dentistry, The Islamic University, Najaf, Iraq
Department of Medical Analysis, Medical Laboratory Technique College, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
Department of Computer Engineering and Application, GLA University Mathura, Mathura, India
Department of Biotechnology and Genetics, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India
Department of Pharmacology, IMS and SUM Hospital, Siksha ’O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
Department of General Professional Sciences, Mamun University, Khiva, Uzbekistan
Department of Chemistry, Urgench State University, Urgench, Uzbekistan
Division of Research and Innovation, Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, Uttarakhand, India

Article Classification

Research article

Classification Areas

Process Modeling

Created 24 days ago