QSAR Modeling on Benzo[c]phenanthridine Analogues as Topoisomerase I Inhibitors and Anti-cancer Agents

Abstract

Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, hologram-QSAR, 2D-QSAR and 3D-QSAR models were developed for BCPs on topoisomerase I inbibitory activity and cytotoxicity against seven tumor cell lines including RPMI8402, CPT-K5, P388, CPT45, KB3-1, KBV-1and KBH5.0. The hologram, 2D, and 3D-QSAR models were obtained with the square of correlation coefficient R 2 = 0.58 − 0.77, the square of the crossvalidation coefficient q 2 = 0.41 − 0.60 as well as the external set's square of predictive correlation coefficient r 2 = 0.51 − 0.80. Moreover, the assessment method based on reliability test with confidence level of 95% was used to validate the predictive power of QSAR models and to prevent over-fitting phenomenon of classical QSAR models. Our QSAR model could be applied to design new analogues of BCPs with higher antitumor and topoisomerase I inhibitory activity.

QSAR Model of Indeno[1,2-b]indole Derivatives and Identification of N-isopentyl-2-methyl-4,9-dioxo-4,9-Dihydronaphtho[2,3-b]furan-3-carboxamide as a Potent CK2 Inhibitor

“…A QSAR study aims to find a correlation between physicochemical properties of a list of compounds and their biological activities. A reliable QSAR model should be able to predict the activities of compounds not included in the training set [27]. Appropriate estimation of such a model is performed by using a statistical model and by determining an acceptable correlation coefficient (r 2 ), as well as a correlation coefficient of the cross validation (q 2 ) [28].…”

Structure–activity relationships study of mTOR kinase inhibition using QSAR and structure-based drug design approaches

“…Many studies uses only the r 2 to do so with the risk of QSAR over-fitting or over-estimation leading if their values are high;30,38 however, Thai et al and Liao et al show that the QSAR model with high r 2 value does not necessarily correlate with a good predictive model,39,40 and for classic QSAR studies, 95% confidence interval is commonly used for models validation. In our study, the 2D-QSAR model was determined as having a good ability to predict accurately based on an interval of confidence of 95% (blue dots for training set and red dots for prediction set in Figure 6).…”

In silico identification of novel IL-1β inhibitors to target protein–protein interfaces

“…A properly made QSAR model should have the ability to predict the activities of those compounds present other than training set (Thai et al, 2012). Assessment of model is done by noting q 2 value in order to avoid the over-estimation and over-fitting (Cherkassky andMa, 2009, Gramatica, 2007).…”