Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent

Abstract

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308–338 K, and pressure 120–400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E−04, 7.40E−05, and 9.73E−06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E−01, DTR shows an error rate equal to 1.63E−01, and GPR as the best mode illustrates 5.06E−02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E−03).

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

“…Consequently, in numerous studies, researchers have resorted to utilizing classical ML algorithms to address tasks related to SCF-based pharmaceutical formulation preparation. Remarkably, these approaches have yielded commendable predictive results [65][66][67][68]. Conversely, employing models with a high number of parameters may lead to overfitting of the data, resulting in less-than-ideal predictive outcomes.…”

“…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.…”