Sayoni Maiti

Bio: Sayoni Maiti is an academic researcher from VIT University. The author has contributed to research in topics: Self-healing hydrogels & Anaplastic lymphoma kinase. The author has an hindex of 3, co-authored 4 publications receiving 24 citations.

Implementation of a Pipeline Using Disease-Disease Associations for Computational Drug Repurposing.

Abstract: Drug repurposing is a powerful technique which has been recently employed in both industry and academia to discover and validate previously approved drugs for new indications. It provides the quickest possible transition from bench to bedside. In essence, computational strategies are appealing because they putatively nominate the most encouraging candidate for a given indication. A wide range of computational methods exist for repositioning. In this chapter we present the guidelines for performing integrated drug repurposing strategy by combining disease-disease association and molecular simulation analysis.

Implementation of pharmacophore-based 3D QSAR model and scaffold analysis in order to excavate pristine ALK inhibitors

Abstract: The utilisation of anaplastic lymphoma kinase (ALK) inhibitors has unveiled a magnificent clinical activity in ALK-positive non-small cell lung cancer (NSCLC), as well as against the sanctuary site of CNS in selected patients. However, the unsatisfactory survival rates along with unaccomplished overall cure for NSCLC (specifically in metastatic diseases), create an importunity for superior and perpetuating research for the establishment of novel ALK inhibitors in order to ameliorate the consequences of NSCLC. Intriguingly, a few plant-based drugs have paved their way to phase II clinical trial, inspired by which, the present study essayed to unearth novel ALK inhibitors from the NPACT database which comprises 1574 plant-derived compounds that exhibit anti-cancerous activity, using 3D QSAR model (AAADD.1882). Furthermore, multiple docking algorithms (PL-PatchSurfer2 and Glide) were employed to eliminate the false positive prediction. In essence, the strength of the association between the IC50 values and docking score was measured by Pearson’s correlation coefficient (r). Altogether, our anatomisation yielded three hits, namely; obovaten (NPACT00821), pinoresinol (NPCT00008) and (3S)-3′,7-dihydroxy-2′,4′,5′,8-tetramethoxyisoflavan (NPACT00018) with higher docking scores, predicted anti-cancer and pharmaceutically appurtenant properties with greater CNS involvement. Ultimately, molecular dynamic (MD) simulation highlights the real time evidence for stability of these hit compounds. It is noteworthy to mention that all the hits constitute of particular scaffolds which play a major role in the downregulation of some ALK-positive lung cancer pathways. We speculate that the outcomes of this research are of substantial prominence in the rational designing of novel and efficacious ALK inhibitors.

Drug targets for COVID-19 therapeutics: Ongoing global efforts

Abstract: The current global pandemic COVID-19 caused by the SARS-CoV-2 virus has already inflicted insurmountable damage both to the human lives and global economy There is an immediate need for identification of effective drugs to contain the disastrous virus outbreak Global efforts are already underway at a war footing to identify the best drug combination to address the disease In this review, an attempt has been made to understand the SARS-CoV-2 life cycle, and based on this information potential druggable targets against SARS-CoV-2 are summarized Also, the strategies for ongoing and future drug discovery against the SARS-CoV-2 virus are outlined Given the urgency to find a definitive cure, ongoing drug repurposing efforts being carried out by various organizations are also described The unprecedented crisis requires extraordinary efforts from the scientific community to effectively address the issue and prevent further loss of human lives and health

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Abstract: Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

An up-to-date overview of computational polypharmacology in modern drug discovery.

Abstract: In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variet...