Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Das, Agneesh Pratim; Agarwal, Subhash Mohan

doi:10.1007/s11030-022-10590-7

Cited by 17 publications

(12 citation statements)

References 168 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…QSAR analysis is an efficient method for finding effective and less toxic phytochemical drug candidates. 4 Prior knowledge of the biological system, various factors concerning physiological processes and pathological conditions, molecular dynamics and their properties, and modulating factors of drugs highly contribute to the development of effective QSAR models. 14 Many QSAR studies have contributed to the determination of the biological properties of various phytochemicals.…”

Section: Example Of Qsar With Specific Phytochemicalsmentioning

confidence: 99%

“…New domains of QSAR applications include refinement in process chemistry and prediction and optimization of synthetic routes. Besides these advances, QSAR modeling has relevance in drug discovery and identifying pharmacological activities of phytochemicals 4–6 …”

Section: Introductionmentioning

confidence: 99%

“…3 Along with prediction of biological activities, QSAR models help in identifying the parameters responsible for biological responses essential for lead compound identification and optimization. 4 As such, QSAR analysis has a significant contribution in rational drug design in the present context. The field of QSAR has experienced significant advancements in recent years, with the development of new methods and practices.…”

mentioning

confidence: 99%

“…Besides these advances, QSAR modeling has relevance in drug discovery and identifying pharmacological activities of phytochemicals. [4][5][6] There are different statistical packages and specific software programs for QSAR analysis, but with different degrees of reliability. 7 These packages may belong to either commercial or open source products.…”

mentioning

confidence: 99%

See 3 more Smart Citations

R software for QSAR analysis in phytopharmacological studies

Ningthoujam¹,

Nath

Kityania

et al. 2023

Phytochemical Analysis

View full text Add to dashboard Cite

IntroductionIn recent decades, quantitative structure–activity relationship (QSAR) analysis has become an important method for drug design and natural product research. With the availability of bioinformatic and cheminformatic tools, a vast number of descriptors have been generated, making it challenging to select potential independent variables that are accurately related to the dependent response variable.ObjectiveThe objective of this study is to demonstrate various descriptor selection procedures, such as the Boruta approach, all subsets regression, the ANOVA approach, the AIC method, stepwise regression, and genetic algorithm, that can be used in QSAR studies. Additionally, we performed regression diagnostics using R software to test parameters such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity.ResultsThe workflow designed in this study highlights the different descriptor selection procedures and regression diagnostics that can be used in QSAR studies. The results showed that the Boruta approach and genetic algorithm performed better than other methods in selecting potential independent variables. The regression diagnostics parameters tested using R software, such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity, helped in identifying and diagnosing model errors, ensuring the reliability of the QSAR model.ConclusionQSAR analysis is vital in drug design and natural product research. To develop a reliable QSAR model, it is essential to choose suitable descriptors and perform regression diagnostics. This study offers an accessible, customizable approach for researchers to select appropriate descriptors and diagnose errors in QSAR studies.

show abstract

Section: Example Of Qsar With Specific Phytochemicalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

R software for QSAR analysis in phytopharmacological studies

Ningthoujam¹,

Nath

Kityania

et al. 2023

Phytochemical Analysis

View full text Add to dashboard Cite

show abstract

“…Thus, there is a need for the identification of new and potent antilung cancer compounds . In several nations globally, medicinal plant-based remedies are popularly used. , As per the latest and most comprehensive natural product drug discovery review published by Newman and Cragg in 2020, around 33.5% of the small-molecule anticancer drugs developed during 1981–2019 were either direct natural products (NPs) or their derivatives . This establishes that NPs continue to be a primary source for the development of new therapeutic anticancer drugs.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning, Molecular Docking, and Dynamics-Based Computational Identification of Potential Inhibitors against Lung Cancer

Das,

Mathur,

Agarwal

2024

ACS Omega

Self Cite

View full text Add to dashboard Cite

Lung cancer is the most prevalent cause of cancer deaths worldwide. However, its treatment faces a significant hurdle due to the development of resistance. Phytomolecules are an important source of new chemical entities due to their rich chemical diversity. Therefore, a machine learning (ML) model was developed to computationally identify potential inhibitors using a curated data set of 649 phytomolecules with inhibitory activity against lung cancer cell lines. Four distinct ML approaches, including k-nearest neighbor, random forest, support vector machine, and extreme gradient boosting, were used in conjugation with MACCS and Morgan2 fingerprints to generate the models. It was observed that the random forest model developed by using the MACCS fingerprint shows the best performance. To further explore the chemical space and feature importance, k-means clustering, t-SNE analysis, and mean decrease in impurity had been calculated. Simultaneously, ∼400 000 natural products (NPs) retrieved from the COCONUT database were filtered for pharmacokinetic properties and taken for a multistep screening using docking against epidermal growth factor receptor (EGFR) mutant, a therapeutic drug target of lung cancer. Thereafter, the best-performing random forest model was used to predict the antilung cancer potential of the NPs having binding affinity better than the cocrystal ligand. This allowed the identification of 205 potential inhibitors, wherein the molecules with an indolocarbazole scaffold were enriched in top-scoring molecules. The top three indolocarbazole molecules with the lowest binding energy were further evaluated through 100 ns molecular dynamics (MD) simulations, which suggested that these molecules are strong binders. Also, structural similarity analysis against known drugs revealed that these NPs are similar to staurosporine, which demonstrates potent and selective activity against EGFR mutants. Thereby, the consensus analysis employing ML, molecular docking, and dynamics revealed that the molecules having an indolocarbazole scaffold are the most promising NPs that can act as potential inhibitors against lung cancer.

show abstract

Design and Synthesis of Propargyloxy Functionalized Pyrazole‐Based Aurones: Exploring Their Potent Anticancer Properties Against AGS and MCF‐7 Cell Lines

Lathwal,

Kumar,

Sharma

et al. 2024

Chemistry & Biodiversity

View full text Add to dashboard Cite

FDA‐approved numerous commercial and natural drugs used in cancer treatment feature either pyrazole or alkyne moieties. Based on this, we designed and synthesized twenty novel propargyloxy‐substituted pyrazole‐based aurones (10a‐j and 11a‐j) and evaluated for their anticancer potential against cancerous MCF‐7 and AGS cell lines, as well as normal cell line HEK‐293, through MTT assay. Among these tested compounds, five (10d‐f, 11e, and 11f) displayed potent cytotoxic properties for AGS cancer cell line with IC50 values ranging from 19.7 µM ‐ 28.5 µM, better than the reference drugs Leucovorin (IC50 = 30.8 µM) and Oxaliplatin (IC50 = 29.8 µM). Furthermore, compounds 10b, 10c 11a, 11c, and 11d demonstrated a significant cytotoxic potential against the MCF‐7 cancer cell line with a single‐digit micromolar IC50 potency (4.8 µM – 8.5 µM) compared to the standard drug Paclitaxel (IC50 = 19.7 µM). The cytotoxic studies of above selected potent active hybrid compounds, against HEK‐293 (human embryonic kidney 293), normal cell line, further highlight the potential use of 10c molecule (IC50 = 4.8 μM against MCF‐7 cells) as an anti‐cancer agent for breast cancer with a selectivity index of 2.597. The cytotoxic results were further supported by the molecular docking studies.

show abstract

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Cited by 17 publications

References 168 publications

R software for QSAR analysis in phytopharmacological studies

R software for QSAR analysis in phytopharmacological studies

Machine Learning, Molecular Docking, and Dynamics-Based Computational Identification of Potential Inhibitors against Lung Cancer

Design and Synthesis of Propargyloxy Functionalized Pyrazole‐Based Aurones: Exploring Their Potent Anticancer Properties Against AGS and MCF‐7 Cell Lines

Contact Info

Product

Resources

About