Hadagali Ashoka

Nanomaterials (NMs) are gaining attention for their high surface area, tuneable reactivity, and potential in targeted therapeutic applications such as antibacterial, antifungal, and antioxidant effects. Their relevance is particularly noted in countering elevated reactive oxygen species (ROS), which drive oxidative stress (OS), DNA damage, inflammation, and diseases like cancer and neurodegeneration. In the commonly known neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease, ROS accumulation leads to mitochondrial dysfunction, endoplasmic reticulum dysfunction neuronal death, and neuroinflammation. However, NM cytotoxicity remains a major concern. This study proposes a green, computationally guided synthesis of non-toxic antioxidant nanoparticles using Litsea glutinosa bark powder. A machine learning-based Nano Quantitative Structure-Activity Relationship (nano-QSAR) model was developed to identify structural features and screen for low-cytotoxicity nanoparticles using existing literature data. Gradient Boosting Regression achieved the highest predictive accuracy (R² = 0.85), guiding the selection of carbon-based nanoparticles due to their low cost and high biocompatibility. Carbon nitride nanoparticles from Litsea glutinosa (CNNP-LG) were synthesized using a hydrothermal method, with synthesis parameters- temperature, Litsea glutinosa powder, urea, and duration optimized via Box-Behnken Design. CNNP-LG was characterized using XRD, FTIR, and SEM-EDS techniques. Antioxidant activity assays showed an IC₅₀ of 15.62 µg/ml and 95% DPPH scavenging activity at 125 µg/ml, comparable to Ascorbic Acid (94.5%). Cytotoxicity analysis using the MTT assay confirmed high SH-SY5Y cell viability (87.8%) even at 500 µg/ml. To further understand the cytotoxic potential, the experimental results from CNNP-LG were incorporated into an existing carbon nitride nanoparticle dataset and used to validate a machine learning-guided nano-QSAR model developed for predicting cell viability. This integrated approach effectively reduces experimental workload, enhances nanoparticle safety profiling, and offers a promising pathway for developing safer nanomedicines with neurotherapeutic potential.

Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic neuronal loss in Alzheimer's disease. We developed a QSAR model to facilitate the identification of effective butyrylcholinesterase inhibitors. The model employs multi-feature selection and feature learning, improving the in silico screening efficiency and accelerating drug discovery efforts. This study aims to integrate Human Intestinal Absorption (HIA) values of butyrylcholinesterase (BChE) target inhibitors and their 50% inhibitory concentration (IC₅₀) with machine learning tools. The model was developed using chemical descriptors in combination with supervised machine learning classification algorithms. Random Forest Classifier algorithm proved to be the ultimate best fit for classification model metrics including log loss probability (0.04225), accuracy score (98.88%) and Matthew's correlation coefficient (0.98). Furthermore, a subset of the active dataset was used to study the regression based on HIA values using multi-feature selection and feature learning. The models were validated using precision, recall and F1 score for regression modelling. After integrating HIA data with existing machine learning algorithms, we observed a significant reduction of 89.63% in the number of inhibitors. The findings provide valuable pharmacological insights that can help in future design of drug development schemes different from conventional methods.

Analytical method development and validation for determination of favipiravir (FVPR) in pure and tablet dosage forms by liquid chromatography with tandem mass spectrometry/mass spectrometry (LC-MS/MS) technique. A simple LC-MS/MS method was developed for determination of a new antiviral drug, FVPR in pharmaceutical formulations. The stationary phase employed was a Shim pack GISS, C₁₈ (100 mm × 2.1 mm, 1.9 μm) column and mobile phase used in pump A was 10.0 mM ammonium acetate and in pump B methanol was used. The gradient program was used with fixed mobile phase flow rate at 0.4 mL min^-1. Total run time was 5.0 min. The proposed method was validated according to International Conference on Harmonization (ICH) guidelines. The established method found better outcomes. The linearity graph was found in the range of 50-200 μg/mL and the correlation coefficient value (R²) obtained was found to be 1.0. The limit of detection (LOD) and limit of quantification (LOQ) were 4.044 μg/mL and 12.253 μg/mL, respectively. Tremendous recovery outcomes were observed and found to be 101%, 99.0%, and 99.5% for FVPR at 150% upper, 100% middle, and 50% lower concentrations, respectively. All outcomes obtained comply with ICH guidelines. The developed method was simple, unique, accurate, robust, precise, and reproducible for determination of FVPR in tablet formulation. The method is novel and could be adopted in formulation industry.