Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models

Abstract

Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120–400 bar, 308–338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E−06, 1.95E−06, and 3.45E−06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E−06, 2.34E−06, and 5.26E−06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e−05).

Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine

“…As illustrated in Figure 5i, the three-dimensional model depicts the solubility data of tamoxifen in SCCO 2 with pressure and temperature as axes. Employing AI methodologies, including Decision Trees (DT), Adaptive Boosting Decision Trees (ADA-DT), and Nu-Support Vector Regression (Nu-SVR), enables the prediction of tamoxifen solubility in SCCO 2 [21]. The fundamental concepts and characteristics of these three models are outlined below: DT is a fundamental and commonly used data analysis model capable of handling regression or classification tasks.…”

“…Each node in the tree represents a feature, each branch signifies a feature value, and the leaf nodes indicate the final prediction result. [21,65,68,69] Extra Trees In the context of decision tree-based ensemble algorithms, a random feature is chosen during node splitting. [63,66] Table 6.…”

“…SVR is capable of handling non-linear relationships and exhibits a certain degree of robustness against outliers, distinguishing it from conventional regression approaches. [21,67] In recent years, significant progress has been made in the fields of AI-SCF technology and nanomedicine, owing to the substantial advancements in computer hardware that have greatly enhanced computational capabilities. In the realm of SCF technology, AI models are not only applied to predict drug solubility but also assist in optimizing experimental design and parameters [72].…”