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Sissi, C., Moro, S., Richter, S., Gatto, B., Menta, E., Spinelli, S., Krapcho, A.P., Zunino, F. and Palumbo, M. (2000) DNA-Interactive Anticancer Aza-Anthrapyrazoles: Biophysical and Biochemical Studies Relevant to the Mechanism of Action. Molecular Pharmacology, 59, 96.
https://doi.org/10.1124/mol.59.1.96

has been cited by the following article:

• TITLE: Mechanistic Study of Anticancer Activity of Some Known Aminopyrimidoisoquinolinequinones via QSAR Classification Methodology

  AUTHORS: Mohamed Osman Elfaki, Mukhtaar Qaaed S. Sultan, Inas Osman Khojali Mohammed

  KEYWORDS: Aminopyrimidoisoquinolinequinones, QSAR, Classification Analysis, Mechanistic Profile

  JOURNAL NAME: Computational Chemistry, Vol.8 No.1, November 22, 2019

  ABSTRACT: The impact of lipophilicity as represented by the logarithm of octanol/water partition coefficient (logP), the combined steric/polarizability effect as represented by molar refractivity (MR) and bulk as represented by molar volume (MV) on the biological activity of 29 known aminopyrimidoisoquinolinequinones APIQ were analyzed using quantitative structure activity relationships methodology (QSAR). The activity data chosen was the inhibitory concentration (IC50) against human gastric adenocarcinoma (AGS) cell line. On running regression analysis, the physicochemical parameters and IC50 show very weak correlations as evident by the low values of Pearson Correlation R2 (0.1 to 0.2). Since the individual compounds show appreciable activity (ranging from 20 to 0.5 μM), classification was resorted to in order to expose mechanistic nesting subgroups. This was done by clustering data points around various trend lines extracted from the scattered plot relating parameters to activity using R2 as an index. The correlation of IC50 versus MV was chosen a base of classification owing to higher statistical metrics it yield. This gave five regression lines, each of which is believed to represent a separate mechanistic profile. Additional descriptors were used to consolidate the clustering approach and to give depth to the assumed mechanistic profiles of each cluster.