A Combined 2D‐ and 3D‐QSAR Study on Analogues of ARC‐111 with Antitumor Activity

Abstract

Two-Dimensional (2D) and Three-Dimensional (3D) Quantitative Structure -Activity Relationships (QSARs) of a series of analogues of ARC-111 with antitumor activity expressed as pIC 50 , which is defined as the negative value of the logarithm of necessary molar concentration of these compounds to cause 50% growth inhibition against tumor cell lines P388, have been studied by using a combined method of the DFT, MM2, and statistics for 2D, as well as the Comparative Molecular Field Analysis (CoMFA) for 3D. The established 2D-QSAR model shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient R 2 A of 0.869 and the square of the crossvalidation coefficient q 2 of 0.834 as well as the square of predictive correlation coefficient R 2 pred of 0.867 for the test set. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R 2 (0.993) and q 2 (0.674) when CoMFA fields are used for the whole studied compounds. The results from 2D-and 3D-QSAR analyses conformably show that the steric interaction plays a crucial role in determining the cytotoxicity of the compounds and that selecting a moderate-size substituent R as well as increasing the negative charge of C 28 at the same time are advantageous to improving the antitumor activity. Such results might offer some useful theoretical references for understanding the action mechanism and directing the molecular design of this kind of compound with antitumor activity.

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).…”

Comparative QSAR modeling of antitumor activity of ARC-111 analogues using stepwise MLR, PLS, and ANN techniques

“…However, in our stepwise MLR and PLS regression analyses, ClogP was not an important descriptor for antitumor activities against RPMI 8402 tumor cells to analogues of ARC-111. Stepwise regression and PLS study showed that the antitumor activity against RPMI was controlled mainly by electronic factors instead of hydrophobicity, which is different from the situation for cytotoxicity against P388 tumor cells (Liao et al, 2008). …”

The QSAR Modeling of Cytotoxicity on Anthraquinones

“…QSAR studies on cytotoxicity have given good results on small and homogeneous datasets. For instance Liao et al [2,3] or Chan et al [4] obtained models with good statistical quality (R 2 close to 0.9) for datasets ranging from 10 to 25 compounds. The main parameters used to model cytotoxicity have been for example water-octanol partition coefficient, which describes a compounds membrane permeability and molecular orbital energies that characterize reactivity [2,3,4].…”