Metastasis is the main cause of colon cancer deaths worldwide. However, due to the disease's complexity, understanding its spread and finding natural treatments remain major challenges. The study aimed to identify therapeutic compounds in pearl millet and test them on metastatic colon cancer cell lines. The study used LC-MS and FTIR spectroscopy to identify small compounds and their functional groups. Further, theoretical chemistry, computational techniques, and ADMET analysis were applied for virtual screening and evaluating the pharmacokinetics of potential bioactive candidates of pearl millet. The 55 compounds of pearl millet were identified through the LC-MS, whereas 37 compounds were reported in PubChem database. Out of these, 15 compounds were predicted as potential orally active leads of pearl millet. Adhesion G protein-coupled receptor F5 was found as a potential target of these lead candidates. Further, molecular docking exhibited Tosifen (-7.9 kcal/mol), Sufentanil (-6.4 kcal/mol), Pemirolast (-6.3 kcal/mol), and Levosimendan (-6.3 kcal/mol) have similar binding affinity to the antineoplastics against the targeted protein. The "root mean square fluctuations (RMSFs)" analysis and deformability graph validated the docked complexes. Additionally, a comparative cell lines study against colon adenocarcinoma highlights Pemirolast as a promising candidate, demonstrating better activity compared to standard antineoplastics. This computational study predicted potential anticancer compounds in pearl millet and recommended further investigation through in vitro and in vivo studies.

01 Sep 2022·Chemical Biology & Drug Design

Pyrazolopyridine: An efficient pharmacophore in recent drug design and development

Review

Author: Atukuri, Dorababu

AbstractAmong the various heterocyclic molecules employed for drug design and discovery, pyrazolopyridine is one of the promising pharmacophores. Pyrazolopyridine is a result of fusion of pyrazole and pyridine rings. The potent pharmacology of pyrazolopyridine may be the synergistic effect of pyrazole and pyridine moieties in a single framework. It has been used in drug design of a wide range of diseases such as anticancer, antimicrobial, anti‐inflammatory, and neuroprotection. Cancer has become a common disease among elderly people now a days that might be because of genetic inheritance to some extent, carcinogens, pollution, and some infectious diseases. Whatever may be the reason, cancer is one of the major causes of deaths worldwide. In addition, over‐usage and improper usage of antibiotics have led to drug resistance of microbes. Further, inflammation is a cause of various diseases such as arthritis, and other diseases. Thus, proinflammatory kinases are considered as primary target for inhibition of inflammation. In view of this, a work that compiles potent pharmacology of recently reported pyrazolopyridine analogs has been planned. The review is aimed to discuss pharmacology in brief along with structure–activity relationship (SAR). The review would emphasize importance of pyrazolopyridines in future drug design and discovery and may help in design of potent pharmacological agents.

01 Jan 2011·Molecular Systems BiologyQ1 · BIOLOGY

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

Q1 · BIOLOGY

ArticleOA

Author: Yabuuchi, Hiroaki ; Takematsu, Hiromu ; Hara, Takafumi ; Niijima, Satoshi ; Tsujimoto, Gozoh ; Hirokawa, Takatsugu ; Ogawa, Teppei ; Okuno, Yasushi ; Ida, Tomomi ; Minowa, Yohsuke

The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound-protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.

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