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 the treatment of NSCLC by focusing on the structural features essential for the desired activity with fewer side effects compared to synthetic molecules. Method: 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 their affinity towards a variety of molecular targets. The main purpose of the review is to gather information about NPs used in the treatment of NSCLC.

New Pentacyclic Triterpenes from the Roots of Hemidesmus indicus. -Phytochemical studies on the roots of hemidesmus indicus results in the isolation of new pentacyclic triterpenes, such as (I)-(IV). -(ROY, S. K.; ALI, M.; SHARMA, M. P.; RAMACHANDRAM, R.; Pharmazie 56 (2001) 3, 244-246; Fac. Pharm., Hamdard Univ., Hamdard Nagar, New Delhi 110 062, India; EN)

Objective: This study was aimed to analyze the inhibitory effect of the flavonoid class of phytochemicals present in ginger (Zingiber Officinale), garlic (Allium sativum), and curry leaf (Murrayakoenigii) against some receptors of type-2 diabetes such as human aldose reductase receptor, mitogen synthase kinase receptor, as well as dipeptidyl peptidase receptor by implementing several in silico analysis techniques. Methods: The 3D structures of the flavonoid class of phytochemicals of all the three plants were retrieved from the PubChem database in 3D SDF format and were converted to PDB format using PyMol software. These phytochemicals were subjected to in silico tools such as SwissADME,  Pre-ADMET, and iMODS web server. The PDB-IDs of the targeted receptors human aldose reductase, dipeptidyl peptidase-IV, and mitogen synthase kinase were retrieved from Protein Data Bank in PDB format. All these receptors were then prepared for docking procedure using Autodock Tools. Now, both the prepared proteins and ligands were subjected to docking analysis using Pyrex (AutodockVina). Results: Naringenin and kaempferol showed excellent docking results with the aldose reductase receptor. On the other hand, rutin showed the best docking score with dipeptidyl peptidase receptor-IV, whereas, epigallocatechin showed the best docking results with mitogen synthase kinase receptor. The ADME analysis showed that resveratrol had the best gastrointestinal absorption as well as high blood-brain barrier permeability. Conclusion: Overall, the molecular docking results when analyzed showed a good binding affinity with the targeted receptors of diabetes. The ADME analysis and molecular docking results of the phytochemicals concluded that these compounds can be used as a potential cure for treating diabetes.

DPP-IV rapidly degrades glucagon-like peptide-1 and glucose-dependent insulinotropic peptides. Delaying the breakdown of endogenous incretin hormones with DPP-IV inhibitors may help correct the physiologic deficit. The purpose of this work is to identify new compounds that inhibit the DPP-IV enzyme. The anticipated compounds were potent anti-diabetic candidates in this investigation. Two 2d QSAR models were created using 179 different substances from diverse sources. QSAR models were created using two methods. The first technique included docking score as an additional descriptor, while the second did not. Docking-based QSAR considered 74 compounds out of 179. Another approach used 40 molecules from 179 compounds. Each method had a precise strategy. Descriptors were computed using DRAGON for both training and test sets. Using DRAGON data, SYSTAT generated regression curves. The docking-based QSAR model produced R2=0.7098 (training set) and R2=0.9987 (test set), whereas the other technique produced R2=0.7644 (training set) and R2=0.9857 (test set).

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