Shikha Joon 1 , Rajeev K Singla 1 , Bairong Shen 1 , Mohammad Amjad Kamal 2 Show Affiliations »
Abstract
BACKGROUND: 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin-4(1H)-ones are promising antitumor agents with enormous data on their profound cytotoxic effects on the human cancer cell lines. OBJECTIVES: We sought to perform a Quantitative structure cytotoxicity relationship (QSCR) analysis of a series of previously reported fluoroquinolone analogues using computerassisted multiple regression analysis and investigate the cytotoxicity-inducing structural parameters among these congeners. METHODS: The dataset was segregated into training and test sets of 6-Fluoro-3-(4H-1,2,4- triazol-3-yl)quinolin-4(1H)-ones by using a random selection method embedded in Vlife MDS 4.6 software and subjected to QSCR analysis. Next, cross-validation of the generated QSCR models was performed along with the external test set prediction. Finally, the data was analyzed and contour plots were developed to deduce the cytotoxicity-inducing structural parameters among these congeners using Minitab® software. RESULTS: The validated QSCR model exhibited a statistically significant predictive value of 92.27 percent. Our QSCR model revealed a direct proportionality between hydrogen counts and cytotoxicity, and exclusion of sulphur and nitrogen with lesser crowding of cyclopropyl rings in future potential 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin-4(1H)-one analogues. Based on the QSCR model predictions and contour plot analysis, the de novo REPUBLIC1986 molecule provided the best hit with predicted IC50 (μM) of 0.45 against CHO cell line and is amenable to salt formation crucial for anti-ovarian cancer activity. CONCLUSION: These findings suggest the relevancy of the developed QSCR model in designing novel, potent, and safer anti-cancer drugs with 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin- 4(1H)-ones as seed compounds. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
BACKGROUND: 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin-4(1H)-ones are promising antitumor agents with enormous data on their profound cytotoxic effects on the human cancer cell lines. OBJECTIVES: We sought to perform a Quantitative structure cytotoxicity relationship (QSCR) analysis of a series of previously reported fluoroquinolone analogues using computerassisted multiple regression analysis and investigate the cytotoxicity-inducing structural parameters among these congeners. METHODS: The dataset was segregated into training and test sets of 6-Fluoro-3-(4H-1,2,4- triazol-3-yl)quinolin-4(1H)-ones by using a random selection method embedded in Vlife MDS 4.6 software and subjected to QSCR analysis. Next, cross-validation of the generated QSCR models was performed along with the external test set prediction. Finally, the data was analyzed and contour plots were developed to deduce the cytotoxicity-inducing structural parameters among these congeners using Minitab® software. RESULTS: The validated QSCR model exhibited a statistically significant predictive value of 92.27 percent. Our QSCR model revealed a direct proportionality between hydrogen counts and cytotoxicity, and exclusion of sulphur and nitrogen with lesser crowding of cyclopropyl rings in future potential 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin-4(1H)-one analogues. Based on the QSCR model predictions and contour plot analysis, the de novo REPUBLIC1986 molecule provided the best hit with predicted IC50 (μM) of 0.45 against CHO cell line and is amenable to salt formation crucial for anti-ovarian cancer activity. CONCLUSION: These findings suggest the relevancy of the developed QSCR model in designing novel, potent, and safer anti-cancer drugs with 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin- 4(1H)-ones as seed compounds. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
Entities: Chemical
Keywords:
2; 4-triazol-3-yl)quinolin-4(1H)-ones; 6-Fluoro-3-(4H-1; Chinese hamster ovary cell line; Computer-assisted multiple regression analysis; Contour plots.; Ovarian cancer; Quantitative Structure Cytotoxicity Relationship (QSCR)
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Year: 2022
PMID： 34165404 DOI： 10.2174/0929867328666210623150552
Source DB: PubMed Journal: Curr Med Chem ISSN： 0929-8673 Impact factor: 4.530