Activity and toxicity modelling of some NCI selected compounds against leukemia P388ADR cell line using genetic algorithm-multiple linear regressions

Abstract Cancer-causing nature is one of the toxicological endpoints bringing about the most elevated concern. Likewise, the standard bioassays in rodents used to survey the cancer-mitigating capability of chemicals and medications are expensive and require the sacrifice of animals. Thus, we have endeavored the development of a worldwide QSAR model utilizing an information set of 85 compounds, including drugs for their anti-leukemia potential. Considering expansive number of information focuses with different structural elements utilized for model development (ntraining = 68) and model validation (ntest = 17), the model developed in this study has an encouraging statistical quality (leave-one-out Q2 = 0.833, R2pred = 0.716) for pLC50 and (leave-one-out Q2 = 0.744, R2pred = 0.614) for pGI50. Our developed model suggests that the absence of methanal fragments, low dipole moment and presence of some 2D autocorrelated molecular descriptors reduces the carcinogenicity. Branching, size and shape are found to be crucial factors for drug-mitigating carcinogenicity.

[1]  Manuela Pavan,et al.  DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS , 2006 .

[2]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[3]  Zhuoyong Zhang,et al.  A novel two-step QSAR modeling work flow to predict selectivity and activity of HDAC inhibitors. , 2013, Bioorganic & medicinal chemistry letters.

[4]  K. Nikolić,et al.  QSAR studies and design of new analogs of vitamin E with enhanced antiproliferative activity on MCF-7 breast cancer cells , 2016 .

[5]  J. R. Schmidt,et al.  Molecular bonding-based descriptors for surface adsorption and reactivity , 2015 .

[6]  Gideon Adamu Shallangwa,et al.  Quantum modelling of the Structure-Activity and toxicity relationship studies of some potent compounds on SR leukemia cell line , 2016 .

[7]  Davidr . Evans,et al.  History of the Harvard ChemDraw project. , 2014, Angewandte Chemie.

[8]  R. Ahlrichs,et al.  Treatment of electronic excitations within the adiabatic approximation of time dependent density functional theory , 1996 .

[9]  V. V. Kleandrova,et al.  Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. , 2012, Bioorganic & medicinal chemistry.

[10]  S. Vilar,et al.  A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. , 2009, Journal of theoretical biology.

[11]  Julio Caballero,et al.  3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds. , 2010, Journal of molecular graphics & modelling.

[12]  Kunal Roy,et al.  Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data , 2013, J. Comput. Chem..

[13]  Ron Wehrens,et al.  Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences , 2011 .

[14]  M. Estrada,et al.  Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors , 2015 .

[15]  Zhenjiang Li,et al.  Personal Experience with Four Kinds of Chemical Structure Drawing Software: Review on ChemDraw, ChemWindow, ISIS/Draw, and ChemSketch , 2004, J. Chem. Inf. Model..

[16]  Tian Zhang,et al.  Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources. , 2015, Journal of hazardous materials.

[17]  A. Alanazi,et al.  Design, synthesis and biological evaluation of some novel substituted quinazolines as antitumor agents. , 2014, European journal of medicinal chemistry.

[18]  Alexander Golbraikh,et al.  Rational selection of training and test sets for the development of validated QSAR models , 2003, J. Comput. Aided Mol. Des..

[19]  Gideon Adamu Shallangwa,et al.  Insilco study on the toxicity of anti-cancer compounds tested against MOLT-4 and p388 cell lines using GA-MLR technique , 2016 .

[20]  E. Aubert,et al.  Synthesis, characterization, crystal structure and DFT study of two new polymorphs of a Schiff base (E)-2-((2,6-dichlorobenzylidene)amino)benzonitrile , 2016 .

[21]  R. Boggia,et al.  Genetic algorithms as a strategy for feature selection , 1992 .

[22]  Danny Rischin,et al.  A randomised crossover trial of chemotherapy in the home: patient preferences and cost anaiysis , 2000 .

[23]  Maykel Pérez González,et al.  Quantitative structure-activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds. , 2005, Bioorganic & medicinal chemistry.

[24]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .

[25]  CHUN WEI YAP,et al.  PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints , 2011, J. Comput. Chem..

[26]  M. Yashiro,et al.  Advantages of adjuvant chemotherapy for patients with triple-negative breast cancer at Stage II: usefulness of prognostic markers E-cadherin and Ki67 , 2011, Breast Cancer Research.

[27]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[28]  Alexander Tropsha,et al.  Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.

[29]  M. Ganjali,et al.  Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine. , 2009, Journal of hazardous materials.

[30]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[31]  Eslam Pourbasheer,et al.  Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. , 2009, European journal of medicinal chemistry.

[32]  Carlos Fernandez-Lozano,et al.  Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models , 2015, Journal of theoretical biology.

[33]  V. V. Kleandrova,et al.  Chemoinformatics in anti-cancer chemotherapy: multi-target QSAR model for the in silico discovery of anti-breast cancer agents. , 2012, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[34]  Frederick P. Roth,et al.  Chemical substructures that enrich for biological activity , 2008, Bioinform..

[35]  Supratik Kar,et al.  On a simple approach for determining applicability domain of QSAR models , 2015 .

[36]  E. Giertsen,et al.  An in vitro Oral Biofilm Model for Comparing the Efficacy of Antimicrobial Mouthrinses , 2002, Caries Research.

[37]  M. R. Akl,et al.  Optimization, pharmacophore modeling and 3D-QSAR studies of sipholanes as breast cancer migration and proliferation inhibitors. , 2014, European journal of medicinal chemistry.