Literature DB >> 35579166

Structure-Activity Relationship Insight of Naturally Occurring Bioactive Molecules and Their Derivatives Against Non-Small Cell Lung Cancer: A Comprehensive Review.

Subham Das1, Shubham Roy2, Seikh Batin Rahaman3, Saleem Akbar4, Bahar Ahmed4, Debojyoti Halder1, Anu Kunnath Ramachandran1, Alex Joseph1.   

Abstract

BACKGROUND: Non-small cell lung cancer (NSCLC) is a deadly disease that affects millions globally and its treatment includes surgery, chemotherapy, and radiotherapy. Chemotherapy and radiotherapy have many disadvantages, which include potential harmful side effects. Due to the widespread use of drugs in lung cancer, drug treatment becomes challenging due to multidrug resistance and adverse reactions. According to the recent findings, natural products (NPs) and their derivatives are being used to inhibit and suppress cancer cells.
OBJECTIVE: Our objective is to highlight the importance of phytochemicals for treating NSCLC by focusing on the structural features essential for the desired activity with fewer side effects compared to synthetic molecules.
METHODS: This review incorporated data from the most recent literature, including in vitro, in vivo, nanoformulation-based recent advancements, and clinical trials, as well as the structure-activity relationship (SAR), described for a variety of possible natural bioactive molecules in the treatment of NSCLC.
RESULTS: The analysis of data from recent in vitro, in vivo studies and ongoing clinical trials are highlighted. The SAR studies of potential NPs signify the presence of several common structural features that can be used to guide future drug design and development.
CONCLUSION: The role of NPs in the battle against NSCLC can be effective, as evidenced by their structural diversity and affinity toward various molecular targets. The main purpose of the review is to gather information about NPs used in the treatment of NSCLC. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

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Keywords:  Lung cancer; SAR; chemotherapy; machine learning; medicinal chemistry; natural products; treatments

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Year:  2022        PMID: 35579166     DOI: 10.2174/0929867329666220509112423

Source DB:  PubMed          Journal:  Curr Med Chem        ISSN: 0929-8673            Impact factor:   4.740


  1 in total

1.  Molecular docking and dynamics based approach for the identification of kinase inhibitors targeting PI3Kα against non-small cell lung cancer: a computational study.

Authors:  Debojyoti Halder; Subham Das; Aiswarya R; Jeyaprakash R S
Journal:  RSC Adv       Date:  2022-08-03       Impact factor: 4.036

  1 in total

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