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Isolation of new phytometabolites from Alpinia galanga willd rhizomes

Sharma PK, Fuloria S, Ali M, Singh A, Kushwaha SP, Sharma VK, et al.

Pak J Pharm Sci, 2021 Jul;34(4):1397-1401.
PMID: 34799313

The current research was aimed to isolate newer phyto-metabolites from rhizomes of Alpinia galanga plant. Study involved preparation of Alpinia galanga rhizome methanolic extract, followed by normal phase column chromatography assisted isolation of new phytometabolites (using different combinations of chloroform and methanol), and characterization (by UV, FTIR, 13C-NMR, 1H-NMR, COSY, DEPT and Mass spectrometry). The isolation and characterization experiment offered two phytometabolites: an ester (Ag-1) and tetrahydronapthalene type lactone (Ag-2). Present study concludes and reports the two phytometabolites, benzyl myristate (Ag-1) and 3-Methyl-6α, 8β-diol-7-carboxylic acid tetralin-11, 9β-olide (Ag-2) for the first time in Alpinia galanga rhizome. The study recommends that these phytometabolites Ag-1 and Ag-2 can be utilized as effective analytical biomarkers for identification, purity and quality control of this plant in future.
Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Sharma V, Singh A, Chauhan S, Sharma PK, Chaudhary S, Sharma A, et al.

Curr Drug Deliv, 2024;21(6):870-886.
PMID: 37670704 DOI: 10.2174/1567201821666230905090621

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.
Surface properties of Indonesian-made narrow dynamic compression plates

Dewo P, Sharma PK, van der Tas HF, van der Houwen EB, Timmer M, Magetsari R, et al.

Med J Malaysia, 2008 Jul;63 Suppl A:21-2.
PMID: 19024964

The enormous need of orthopaedic (surgical) implants such as osteosynthesis plates is difficult to be fulfilled in developing countries commonly rely on imported ones. One of the alternatives is utilization of local resources, but only after they have been proven safe to use, to overcome this problem. Surface properties are some of the determining factors of safety for those implants. We have succeeded in developing prototype of osteosynthesis plate and the results indicate that Indonesian-made plates need improvement with regards to the surface quality of physical characterization.
The Impacts and Changes Related to the Cancer Drug Resistance Mechanism

Varshney P, Sharma V, Yadav D, Kumar Y, Singh A, Kagithala NR, et al.

Curr Drug Metab, 2023;24(12):787-802.
PMID: 38141188 DOI: 10.2174/0113892002266408231207150547

BACKGROUND: Cancer drug resistance remains a difficult barrier to effective treatment, necessitating a thorough understanding of its multi-layered mechanism.
OBJECTIVE: This study aims to comprehensively explore the diverse mechanisms of cancer drug resistance, assess the evolution of resistance detection methods, and identify strategies for overcoming this challenge. The evolution of resistance detection methods and identification strategies for overcoming the challenge.
METHODS: A comprehensive literature review was conducted to analyze intrinsic and acquired drug resistance mechanisms, including altered drug efflux, reduced uptake, inactivation, target mutations, signaling pathway changes, apoptotic defects, and cellular plasticity. The evolution of mutation detection techniques, encompassing clinical predictions, experimental approaches, and computational methods, was investigated. Strategies to enhance drug efficacy, modify pharmacokinetics, optimizoptimizee binding modes, and explore alternate protein folding states were examined.
RESULTS: The study comprehensively overviews the intricate mechanisms contributing to cancer drug resistance. It outlines the progression of mutation detection methods and underscores the importance of interdisciplinary approaches. Strategies to overcome drug resistance challenges, such as modulating ATP-binding cassette transporters and developing multidrug resistance inhibitors, are discussed. The study underscores the critical need for continued research to enhance cancer treatment efficacy.
CONCLUSION: This study provides valuable insights into the complexity of cancer drug resistance mechanisms, highlights evolving detection methods, and offers potential strategies to enhance treatment outcomes.