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. 2024 Sep 4;19(9):e0309242.
doi: 10.1371/journal.pone.0309242. eCollection 2024.

Mathematical modeling and numerical simulation of supercritical processing of drug nanoparticles optimization for green processing: AI analysis

Khalid Aljohani  1
Affiliations

Mathematical modeling and numerical simulation of supercritical processing of drug nanoparticles optimization for green processing: AI analysis

Khalid Aljohani. PLoS One. .

Retraction in

Abstract

In recent decades, unfavorable solubility of novel therapeutic agents is considered as an important challenge in pharmaceutical industry. Supercritical carbon dioxide (SCCO2) is known as a green, cost-effective, high-performance, and promising solvent to develop the low solubility of drugs with the aim of enhancing their therapeutic effects. The prominent objective of this study is to improve and modify disparate predictive models through artificial intelligence (AI) to estimate the optimized value of the Oxaprozin solubility in SCCO2 system. In this paper, three different models were selected to develop models on a solubility dataset. Pressure (bar) and temperature (K) are the two inputs for each vector, and each vector has one output (solubility). Selected models include NU-SVM, Linear-SVM, and Decision Tree (DT). Models were optimized through hyper-parameters and assessed applying standard metrics. Considering R-squared metric, NU-SVM, Linear-SVM, and DT have scores of 0.994, 0.854, and 0.950, respectively. Also, they have RMSE error rates of 3.0982E-05, 1.5024E-04, and 1.1680E-04, respectively. Based on the evaluations made, NU-SVM was considered as the most precise method, and optimal values can be summarized as (T = 336.05 K, P = 400.0 bar, solubility = 0.00127) employing this model. Fig 4.

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

Fig 1
Fig 1. Pearson plot of solubility data.
Fig 2
Fig 2. Schematic of a DT with 4 internal and 5 terminal nodes.
Fig 3
Fig 3. Predicted versus expected values for Oxaprozin solubility in the supercritical carbon dioxide (SCCO2) system using the Nu-SVM model, indicating significant agreement between predicted and expected values.
Fig 4
Fig 4. Predicted versus expected values for Oxaprozin solubility in the SCCO2 system using the Linear-SVM model, highlighting a moderate level of agreement between predicted and expected values.
Fig 5
Fig 5. Predicted versus expected values for Oxaprozin solubility in the SCCO2 system using the decision tree (DT) model, showing a reasonable agreement between predicted and expected values.
Fig 6
Fig 6
a. Input-Output projection (NU-SVM). b. Predicted Solubility based on Temperature. c. Predicted Solubility based on Pressure.
See this image and copyright information in PMC

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