Scaffold and cell line based approaches for QSAR studies on anticancer agents

Abstract

Based on a linear heuristic method, a quantitative structure-activity relationship was developed for the prediction of available in vitro anticancer activity. Each type of compound was represented by several calculated structural descriptors. Most of the computational studies were carried out targeting an insufficient number of cell lines. Predictive models were built for 482 compounds with experimental data against 30 different cancer cell lines. Strong statistical analysis showed a high correlation, cross validation coefficient values and provided a range of QSAR equations. Quantum chemical descriptors were found in 42 out of 46 models, electrostatic in 16, topological in 12, geometrical in 7, thermodynamic in 5 and constitutional in 7. It is interesting to note that in most cases, three descriptor-based models were relevant. Pancreatic cancer cell lines showed the best statistical values (average R 2 ¼ 0.87) followed by leukaemia (average R 2 ¼ 0.86).

QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2

“…To filter out the possible failures in the drug development stage, several approaches are being used like the in silico, in vitro, etc. An example of an in silico approach is the modeling of a quantitative structure-activity relationships (QSARs) which can be used to screen chemical libraries, understand drugs action and design novel compounds [8,9]. Combinatorial approaches are an influential technique adopted in the selection to reduce the time of drug development with various mechanisms of action and it has been used to treat cancer [10,11].…”

Discovery of monocarbonyl curcumin hybrids as a novel class of human DNA ligase I inhibitors: in silico design, synthesis and biology

“…33-7.27 (m, 8H), 7.21-7.19 (m, 3H), 6.97-6.95 (m, 2H), 5.17 (s, 1H), 5.03 (d, J ¼ 1.3 Hz, 4H), 3.73 (q, J 1 ¼ 6.8 Hz, J 2 ¼ 11.8 Hz, 2H), 2.70 (t, J ¼ 6.8 Hz, 2H), 2.43 (s, 6H); 13 C (100 MHz, DMSO-d 6 ): d 187. 1, 182.2, 139.9, 139.8, 136.4, 132.1, 131.3, 129.9, 129.0, 128.7, 126.4, 48.9, 47.5, 34.9, 21.5 H, 6.48; N, 6.00; found, C, 77.49; H, 6.28; N, 6.36. (3E,5E)-3,5-Bis(4-methoxybenzylidene)-4-oxo-Nphenethylpiperidine-1-carbothioamide (24) The title compound was synthesized from (3E,5E)-3,5-bis(4-uorobenzylidene)piperidin-4-one (12) and phenethyl isothiocyanate in 85% yield as yellow solid. mp: 162-164 C; IR (KBr) n (cm À1 ): 3412, 1636, 1384, 1215, 1069; 1 H NMR (400 MHz, CDCl 3 ): d 7.…”

“…11 Natural products and natural product inspired libraries are the major resources for drug discovery. 12 Classical drug discovery process is a lengthy and expensive process, with low chances of success. 13 To maximize chances of success and limit failures to initial stages, in silico approaches are available now.…”

Design of potential anti-melanoma agents against SK-MEL-5 cell line using QSAR modeling and molecular docking methods

“…To filter out the potential failures in the drug development stage, several methods are being used such as in silico, in vitro, etc. An example of an in silico approach is the modeling of a quantitative structure activity relationships (QSARs) that can be used to screen chemical libraries, understand drugs action and design novel compounds [14,15]. Combinatorial approaches are an influential technique used in the selection to speed up drug discovery and with various mechanisms of action and it has being used to cure cancer [16,17].…”