Environmental contamination by pharmaceuticals has become a matter of concern as they are released in sewage systems at trace levels, thus impacting biological systems. Increasing concerns about the low-level occurrence of pharmaceuticals in the environment demands sensitive and selective monitoring. Owing to their high sensitivity and specificity carbon dots (CDs) have emerged as suitable fluorescent sensors. This review discusses the current scenario of the status of pharmaceuticals in the environment, limitations associated with traditional techniques employed for their detection, and benefits offered by CDs like easy surface modification and tunable optical properties for sensing applications. Several representative means by which CDs interact with other molecules such as inner filter effect (IFE), dynamic quenching (DQ), static quenching (SQ), Förster resonance energy transfer (FRET), among others, are also discussed along with co-referencing fluorophores to design sensors. Based on developments described herein, CDs-based sensors can be expected to sense pharmaceuticals ranging from nanogram to picogram, target real-time industrial and spiked sample analysis, etc., which provides direction for future research.

The aqueous and ethanolic extracts of C. grandis (Family: Leguminosae) were evaluated for antidiabetic activity by a glucose tolerance test, in normal rats and alloxan-induced diabetic rats. The aqueous and ethanolic extracts showed that they significantly lowered the blood glucose levels to normal in the glucose tolerance test. In alloxan-induced diabetic rats the maximum reduction in blood glucose was observed after three hours, at a dose level of 150 mg/kg of body weight. The percentage of protection given by the aqueous and ethanolic extracts was 32.72 and 46.42%, respectively. In the long-term treatment of alloxan-induced diabetic rats, the degree of protection was determined by measuring the blood glucose, cholesterol, and triglycerides on the tenth day. Both the extracts showed a significant antidiabetic activity comparable to that of glibenclamide. These results showed that the Cassia grandis possessed significant antidiabetic activity.

Background: The development of Multi-Target-Directed Ligands (MTDLs) has emerged as a significant strategy for addressing complex, overlapping pathologies such as cancer and Alzheimer’s disease (AD). This study aims to provide a robust computational framework for the design of dual-target inhibitors. Methods: This study presents an integrated and rigorous computational pipeline combining Quantitative Structure–Activity Relationship (QSAR) modeling, Molecular Docking, and Molecular Dynamics (MD) simulations with Dynamic Cross-Correlation Matrix (DCCM) analysis. Using a dataset of 57 known tubulin inhibitors, two high-performing QSAR models were developed to guide the rational design of 16 novel trimethoxyphenyl-based analogues. Results: Following ADMET and drug-likeness filtering, Lead Candidates 15 and 16 were identified. Quantitative activity predictions confirmed their enhanced potency thresholds, which were subsequently validated through static docking against β-tubulin (PDB: 4O2B) and Acetylcholinesterase (PDB: 1EVE). In total, 100 ns MD simulations and MM-GBSA calculations demonstrated superior binding stability and energetically favorable profiles for both targets, while DCCM analysis confirmed the functional synchrony of the protein–ligand complexes. Conclusions: The results provide a validated structural hypothesis for dual-target inhibition. The identified leads, 15 and 16, demonstrate strong predictive potential and are prioritized for chemical synthesis and in vitro biological evaluation.

A validated and highly sensitive method for quantifying N-nitroso-dabigatran etexilate in dabigatran etexilate mesylate using LC-MS/MS.

Alzheimer’s disease (AD) represents an escalating global neuropharmacological crisis, with prevalence in high-growth demographic regions such as India projected to exceed 14 million by 2040. This study addresses the urgent need for high-potency, dual-site acetylcholinesterase (AChE) inhibitors through an integrated computational pipeline. We address the failure of mono-target paradigms by designing scaffolds capable of simultaneously anchoring the Catalytic Active Site (CAS) and the Peripheral Anionic Site (PAS). A robust GA-MLR QSAR model was developed from 115 quinoline analogs using 11,135 descriptors. Lead candidates were prioritized via cavity directed molecular docking (7XN1) and 100 ns molecular dynamics (MD) simulations. The five-descriptor model (R² = 0.7569, $Q_{LOO}^2$ = 0.7244) was validated by an external set of 8 experimental compounds ($R_{ext}^2$ = 0.8620). Lead Compound 19 emerged as a superior candidate (ΔG = −11.1 kcal/mol), exhibiting a stable MD trajectory (PL-RMSD ≈ 2.4 Å) and preserving essential Gly121-His447 catalytic anti-correlations. This study provides a statistically validated scaffold and computational mechanistic foundation for future in vitro experimental validation, advancing the high throughput screening of neuroprotective agents on a global scale.