Estimation of half-wave potential of anabolic androgenic steroids by means of QSER approach

Abstract

The quantitative structure-property relationship (QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential (E_1/2) using quantum and physico-chemical molecular descriptors. The descriptors were calculated by semi-empirical calculations. Models were established using partial least square (PLS) regression and back-propagation artificial neural network (BP-ANN). The QSPR results indicate that the descriptors of these derivatives have significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set.

Keywords

anabolic androgenic steroids / half-wave reduction potential / model validation / quantitative structure-electrochemistry relationship

References

[1] SchwarzJ M, MccarthyM M. Steroid-induced sexual differentiation of the developing brain: Multiple pathways, one goal [J].. J Neurochem, 2008, 105(5): 1561-1572

[2] Alvarez-GinarteY M, Marrero-PonceY, RuizgarcíaJ A, Garcia-De La VegaJ M, Noheda-MarinP, Crespo-OteroR, Torrens-ZaragozaF, GarcíadomenechR. Applying pattern recognition methods plus quantum and physico-chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids [J].. J Comput Chem, 2008, 29: 317-333

[3] LynchG S, SchertzerJ D, RyallJ G. Therapeutic approaches for muscle wasting disorders [J].. Pharmacol Ther, 2007, 113: 461-487

[4] MazzarinoM C, BraganòM, DonatiF, de la TorreX, BotrèF. Effects of propyphenazone and other non-steroidal anti-inflammatory agents on the synthetic and endogenous androgenic anabolic steroids urinary excretion and/or instrumental detection [J].. Anal Chim Acta, 2010, 657: 60-68

[5] BossolaM, PacelliF, TortorelliA, DogliettoG B. Cancer cachexia: It’s time for more clinical trials [J].. Ann Surg Oncol, 2006, 14: 276-285

[6] O'haganD, RzepaH S. Some influences of fluorine in bioorganic chemistry [J].. Chem Commun, 1997, 7: 645-652

[7] ShamsipurM, SiroueinejadA, HemmateenejadB, AbbaspourbA, SharghiH, AlizadehK, ArshadiS. Cyclic voltammetric, computational, and quantitative structureelectrochemistry relationship studies of the reduction of several 9,10-anthraquinone derivatives [J].. J Electroanal Chem, 2007, 600: 345-358

[8] KrivenkoA G, KotkinA S, KurmazV A. Thermodynamic and kinetic characteristics of intermediates of electrode reactions: Determination by direct and combined electrochemical methods. Russ [J]. J Electrochem, 2005, 41: 122-136

[9] HemmateenejadB, YazdaniM. QSPR models for half-wave reduction potential of steroids: A comparative study between feature selection and feature extraction from subsets of or entire set of descriptors [J].. Anal Chim Acta, 2009, 634: 27-35

[10] HemmateenejadB, ShamsipurM. Quantitative structureelectrochemistry relationship study of some organic compounds using PC-ANN and PCR [J].. Internet Electron J Mol Des, 2004, 3: 316-334

[11] NesmerakK, NemecI, StichaM, WaisserK, PalatK. Quantitative structure-property relationships of new benzoxazines and their electrooxidation as a model of metabolic degradation [J].. Electrochim Acta, 2005, 50: 1431-1437

[12] Garkani-NejadZ, Rashidi-NodehH. Comparison of conventional artificial neural network and wavelet neural network in modeling the half-wave potential of aldehydes and ketones [J].. Electrochim Acta, 2010, 55: 2597-2605

[13] ChengZ J, ZhangY T, FuW Z. QSAR study of carboxylic acid derivatives as HIV-1 Integrase inhibitors [J].. Eur J Med Chem, 2010, 45: 3970-3980

[14] DaiY-m, LiuH, LiX, ZhuZ-p, ZhangY-f, CaoZ, ZhuL-x, ZhouYue. An novel group contribution-based method for estimation of flash points of ester compounds [J].. Chemom Intell Lab Syst, 2014, 136: 138-146

[15] DaiY-m, ZhuZ-p, CaoZ, ZhangY-f, ZengJ-l, LiXun. Prediction of boiling points of organic compounds by QSPR tools [J].. J Mol Graph Model, 2013, 44: 113-119

[16] DaiY-m, HuangK-l, LiX, CaoZ, ZhuZ-p, YangD-wu. Simulation of ¹³C NMR chemical shifts of carbinol carbon atoms using quantitative structure-spectrum relationships [J].. Journal of Central South University of Technology, 2011, 18: 323-330

[17] DeanJ ALang’s Handbook of chemistry [M], 2005137-164

[18] ZumanZSubstituent effects in organic polarography [M], 1967128-130

[19] MauriA, ConsonniV, PavanM, TodeschiniR. Dragon software: An easy approach to molecular descriptor calculations [J].. Match, 2006, 56(2): 237-248

[20] KatritzkyA R, Stoyanova-SlavovaI B, TammK, TamnT, KarelsonM. Application of the QSPR Approach to the boiling points of Azeotropes [J].. The Journal of Physical Chemistry A, 2011, 115(15): 3475-3479

[21] StewartJ J P. Optimization of parameters for semiempirical methods—II. Applications [J]. J Comput Chem, 1989, 10: 221-264

[22] LukovitsI, ShabanA, KálmánE. Thiosemicarbazides and thiosemicarbazones: Non-linear quantitative structure-efficiency model of corrosion inhibition [J].. Electrochim Acta, 2005, 50: 4128-4133

[23] MarreroP Y. Linear indices of the “molecular pseudograph’s atom adjacency matrix”: Definition, significance-interpretation and application to QSAR analysis of flavone derivatives as HIV-1 integrase inhibitors [J].. J Chem Inf Comput Sci, 2004, 44: 2010-2026

[24] Garkani-NejadZ, Poshteh-ShiraniM. Application of multivariate image analysis in QSPR study of ¹³C chemical shifts of naphthalene derivatives: A comparative study [J].. Talanta, 2010, 83: 225-232

[25] ZhouC Y, NieC M, LiS, LiZ H. A novel semi-empirical topological descriptor Nt and application to study on QSPR/QSAR [J].. J Comput Chem, 2007, 28: 2413-2423

[26] GuhaR, SerraJ R, JursP C. Generation of QSAR sets with a self-organizing map [J].. J Mol Graph Model, 2004, 23: 1-14

[27] WoldS, RuheA, WoldH, DunnW. The collinearity problem in linear regression, the partial least squares approach to generalized inverse SIAM [J].. J Sci Stat Comp, 1984, 5: 735-743

[28] WoldS. PLS for multivariate linear modeling [J]. Chemometric Methods in Molecular Design, 1995, 2: 195

[29] AjmaniS, AgrawalA, KulkarniS A. A comprehensive structure-activity analysis of protein kinase B-alpha (Akt1) inhibitors [J].. J Mol Graph Model, 2010, 28: 683-694

[30] GoodarziM, DuchowiczP R, WuC H, FernandezF M, CastroE A. New hybrid genetic based support vector regression as QSAR approach for analyzing flavonoids-GABA(A) complexes [J].. J Chem Inf Model, 2009, 49: 1475-1485

[31] GoodarziM, FreitasM P, WuC H, DuchowiczP R. pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression [J].. Chemom Intell Lab Sys, 2010, 101: 102-109

[32] RoyP P, RoyK. On some aspects of variable selection for partial least squares regression models [J].. QSAR Comb Sci, 2008, 27: 302-313

[33] ZhuM J, GeF, ZhuR L, WangX Y, ZhengX Y. A DFT-based QSAR study of the toxicity of quaternary ammonium compounds on Chlorella vulgaris [J].. Chemosphere, 2010, 80: 46-52

[34] ZhouC Y, ChuX, NieC M. Predicting thermodynamic properties with a novel semi-empirical topological descriptor and path numbers [J].. J Phys Chem B, 2007, 111: 10174-11079

[35] CaoC-zhongThe substituent effect in organic chemistry [M], 200320-24

[36] GolmohammadiH, SafdariM. Quantitative structure–property relationship prediction of gas-to-chlorofm partition coefficient using artificial neural network [J].. Microchim J, 2010, 95: 140-151

[37] LiuG S, YuJ G. QSAR analysis of soil sorption coefficients for polar organic chemicals: Substituted anilines and phenols [J].. Water Res, 2005, 39: 2048-2055

[38] StojkovicG, NovicM, KuzmanovskiI. Counterpropagation artificial neural networks as a tool for prediction of pKBH+ for series of amides [J].. Chemom Intell Lab Syst, 2010, 102: 123-129

[39] WoldS, SjöströmM, ErikssonL. PLS-regression: A basic tool of chemometrics [J].. Chemometrics and Intelligent Laboratory Systems, 2001, 58(2): 109-130

[40] GolbraikhA, TropshaA. QSAR modeling using chirality descriptors derived from molecular topology [J].. J Comput Aided Mol Des, 2002, 16: 357-369

[41] GolbraikhA, TropshaA. Beware of q²! [J].. J Mol Graphic Model, 2002, 20: 269-276