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Review Article

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

[ Vol. 29 , Issue. 39 ]

Author(s):

Subham Das, Shubham Roy, Seikh Batin Rahaman, Saleem Akbar, Bahar Ahmed*, Debojyoti Halder, Anu Kunnath Ramachandran and Alex Joseph*   Pages 6030 - 6062 ( 33 )

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.

Keywords:

Lung cancer, natural products, treatments, chemotherapy, SAR, machine learning, medicinal chemistry.

Affiliation:



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