Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation

Refining the rheological characteristics of high drug loading ointment via SDS and machine learning

This paper presents an optimized preparation process for external ointment using the Definitive Screening Design (DSD) method. The ointment is a Traditional Chinese Medicine (TCM) formula developed by Professor WYH, a renowned TCM practitioner in Jiangsu Province, China, known for its proven clinical efficacy. In this study, a stepwise regression model was employed to analyze the relationship between key process factors (such as mixing speed and time) and rheological parameters. Machine learning techniques, including Monte Carlo simulation, decision tree analysis, and Gaussian process, were used for parameter optimization. Through rigorous experimentation and verification, we have successfully identified the optimal preparation process for WYH ointment. The optimized parameters included drug ratio of 24.5%, mixing time of 8 min, mixing speed of 1175 rpm, petroleum dosage of 79 g, liquid paraffin dosage of 6.7 g. The final ointment formulation was prepared using method B. This research not only contributes to the optimization of the WYH ointment preparation process but also provides valuable insights and practical guidance for designing the preparation processes of other TCM ointments. This advanced DSD method enhances the screening approach for identifying the best preparation process, thereby improving the scientific rigor and quality of TCM ointment preparation processes.

Development of Hollow Gold Nanoparticles for Photothermal Therapy and Their Cytotoxic Effect on a Glioma Cell Line When Combined with Copper Diethyldithiocarbamate

Gold-based nanoparticles hold promise as functional nanomedicines, including in combination with a photothermal effect for cancer therapy in conjunction with chemotherapy. Here, we synthesized hollow gold nanoparticles (HGNPs) exhibiting efficient light absorption in the near-IR (NIR) region. Several synthesis conditions were explored and provided monodisperse HGNPs approximately 95-135 nm in diameter with a light absorbance range of approximately 600-720 nm. The HGNPs were hollow and the surface had protruding structures when prepared using high concentrations of HAuCl4. The simultaneous nucleation of a sacrificial AgCl template and Au nanoparticles may affect the resulting HGNPs. Diethyldithiocarbamate (DDTC) is metabolized from disulfiram and is a repurposed drug currently attracting attention. The chelation of DDTC with copper ion (DDTC-Cu) has been investigated for treating glioma, and here we confirmed the cytotoxic effect of DDTC-Cu towards rat C6 glioma cells in vitro. HGNPs alone were biocompatible and showed little cytotoxicity, whereas a mixture of DDTC-Cu and HGNPs was cytotoxic in a dose dependent manner. The temperature of HGNPs was increased by NIR-laser irradiation. The photothermal effect on HGNPs under NIR-laser irradiation resulted in cytotoxicity towards C6 cells and was dependent on the irradiation time. Photothermal therapy by HGNPs combined and DDTC-Cu was highly effective, suggesting that this combination approach hold promise as a future glioma therapy.

Extreme Gradient Boosting Combined with Conformal Predictors for Informative Solubility Estimation

We used the extreme gradient boosting (XGB) algorithm to predict the experimental solubility of chemical compounds in water and organic solvents and to select significant molecular descriptors. The accuracy of prediction of our forward stepwise top-importance XGB (FSTI-XGB) on curated solubility data sets in terms of RMSE was found to be 0.59-0.76 Log(S) for two water data sets, while for organic solvent data sets it was 0.69-0.79 Log(S) for the Methanol data set, 0.65-0.79 for the Ethanol data set, and 0.62-0.70 Log(S) for the Acetone data set. That was the first step. In the second step, we used uncurated and curated AquaSolDB data sets for applicability domain (AD) tests of Drugbank, PubChem, and COCONUT databases and determined that more than 95% of studied ca. 500,000 compounds were within the AD. In the third step, we applied conformal prediction to obtain narrow prediction intervals and we successfully validated them using test sets' true solubility values. With prediction intervals obtained in the last fourth step, we were able to estimate individual error margins and the accuracy class of the solubility prediction for molecules within the AD of three public databases. All that was possible without the knowledge of experimental database solubilities. We find these four steps novel because usually, solubility-related works only study the first step or the first two steps.