Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models

Abouzied, Amr S.; Alshahrani, Saad M.; Hani, Umme; Obaidullah, Ahmad J.; Al Awadh, Ahmed Abdullah; Lahiq, Ahmed A.; Al-fanhrawi, Halah Jawad

doi:10.1016/j.csite.2023.103101

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…Extensive experimental screening, although the most reliable, is limited due to the time, effort, and costs needed. Hence, machine learning offers a real alternative for exploring the solvent space, provided that a reliable model has been developed [ 39 , 40 , 41 , 42 ]. As was established in previous studies [ 42 , 43 ], combining quantum chemical methods, such as COSMO-RS (Conductor-like Screening Model for Real Solvents) with machine learning methods, is a quite promising approach, providing good-quality predictions.…”

Section: Introductionmentioning

confidence: 99%

Finding the Right Solvent: A Novel Screening Protocol for Identifying Environmentally Friendly and Cost-Effective Options for Benzenesulfonamide

2023

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This study investigated the solubility of benzenesulfonamide (BSA) as a model compound using experimental and computational methods. New experimental solubility data were collected in the solvents DMSO, DMF, 4FM, and their binary mixtures with water. The predictive model was constructed based on the best-performing regression models trained on available experimental data, and their hyperparameters were optimized using a newly developed Python code. To evaluate the models, a novel scoring function was formulated, considering not only the accuracy but also the bias–variance tradeoff through a learning curve analysis. An ensemble approach was adopted by selecting the top-performing regression models for test and validation subsets. The obtained model accurately back-calculated the experimental data and was used to predict the solubility of BSA in 2067 potential solvents. The analysis of the entire solvent space focused on the identification of solvents with high solubility, a low environmental impact, and affordability, leading to a refined list of potential candidates that meet all three requirements. The proposed procedure has general applicability and can significantly improve the quality and speed of experimental solvent screening.

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