Machine learning modeling to identify affinity improved biobetter anticancer drug trastuzumab and the insight of molecular recognition of trastuzumab towards its antigen HER2

Machine learning-based prediction of vancomycin concentration after abdominal administration in patients with peritoneal dialysis-related peritonitis

INTRODUCTION

Peritonitis is a serious complication of peritoneal dialysis (PD), in which insufficient control of antibacterial drug concentrations poses a significant risk for poor outcomes. Predicting antibacterial drug concentrations is crucial in clinical practice. The limitations imposed by compartment models have presented a considerable challenge.

METHODS

In this study, we employed machine learning as model-free methods to circumvent the constraints of compartment models. We collected data from 68 observations from 38 patients with peritoneal dialysis-related peritonitis who were treated with vancomycin from the EHR system. This data included information about drug administration, demographic details, and experimental indicators as predictors. We constructed models using Genetic Adaptive Supporting Vector Regression (GA-SVR), KNN-regression, GBM, XGBoost, and a stacking ensemble model. Additionally, we used RMSE loss and partial-dependence profiles to elucidate the effects of these predictors.

RESULTS

GA-SVAR outperformed other large-scale models. In 10-fold cross-validation, the RMSE ratio and R-squared values for direct concentration prediction were 23.5% and 0.633, respectively. The ROC AUC for predicting concentrations below 15 and exceeding 20 μg/mL were 0.890 and 0.948, respectively. Notably, the most influential predictors included times of drug administration and weight. These predictors were also influenced by residual kidney function.

CONCLUSION

To assist in controlling vancomycin concentrations for patients with PD-related peritonitis in clinical practice, we developed GA-SVR and a corresponding explainer model. Our study improves the controlling of vancomycin in clinical settings by enhancing our understanding of vancomycin concentration in patients with PD-related peritonitis.

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

Plant-Derived Antiviral Compounds as Potential Entry Inhibitors against Spike Protein of SARS-CoV-2 Wild-Type and Delta Variant: An Integrative in SilicoApproach

The wild-type SARS-CoV-2 has continuously evolved into several variants with increased transmissibility and virulence. The Delta variant which was initially identified in India created a devastating impact throughout the country during the second wave. While the efficacy of the existing vaccines against the latest SARS-CoV-2 variants remains unclear, extensive research is being carried out to develop potential antiviral drugs through approaches like in silico screening and drug-repurposing. This study aimed to conduct the docking-based virtual screening of 50 potential phytochemical compounds against a Spike glycoprotein of the wild-type and the Delta SARS-CoV-2 variant. Subsequently, molecular docking was performed for the five best compounds, such as Lupeol, Betulin, Hypericin, Corilagin, and Geraniin, along with synthetic controls. From the results obtained, it was evident that Lupeol exhibited a remarkable binding affinity towards the wild-type Spike protein (-8.54 kcal/mol), while Betulin showed significant binding interactions with the mutated Spike protein (-8.83 kcal/mol), respectively. The binding energy values of the selected plant compounds were slightly higher than that of the controls. Key hydrogen bonding and hydrophobic interactions of the resulting complexes were visualized, which explained their greater binding affinity against the target proteins-the Delta S protein of SARS-CoV-2, in particular. The lower RMSD, the RMSF values of the complexes and the ligands, Rg, H-bonds, and the binding free energies of the complexes together revealed the stability of the complexes and significant binding affinities of the ligands towards the target proteins. Our study suggests that Lupeol and Betulin could be considered as potential ligands for SARS-CoV-2 spike antagonists. Further experimental validations might provide new insights for the possible antiviral therapeutic interventions of the identified lead compounds and their analogs against COVID-19 infection.