MLR-Based QSAR Models for Predicting Inhibitory Activity of Reverse Transcriptase by HEPT Derivatives using GETAWAY Descriptors

Authors

  • Alex A. Tardaguila Department of Physical Science, Pamantasan ng Lungsod ng Maynila, Intramuros, Manila
  • Jennifer C. Sy Department of Physical Science, Pamantasan ng Lungsod ng Maynila, Intramuros, Manila
  • Marielyn R. Omañada Department of Physical Science, Pamantasan ng Lungsod ng Maynila, Intramuros, Manila
  • Eric R. Punzalan Department of Chemistry, De La Salle University, Malate, Manila

DOI:

https://doi.org/10.26534/kimika.v24i2.2-17

Keywords:

QSAR, HIV, reverse transcriptase, HEPT, MLR

Abstract

In this study, quantitative structure-activity relationship (QSAR) models for non-nucleoside reverse transcriptase inhibitors based on 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine (HEPT) derivatives were generated. The structures of the compounds and their activities were obtained from the literature. The data set were divided into two sets: training set (N=91) and validating set (N=10). All 3-D structures of these inhibitors were optimized by semi-empirical method, AM1 prior to calculations of 3-D molecular descriptors, GETAWAY. Multiple linear regression (MLR) using stepwise method was applied to determined significant descriptors. Out of 197 GETAWAY descriptors, 4-14 molecular descriptors have significant relationships with the activities (expressed as log (1/EC50)) of HEPT. The MLR method generated 14 models. The predictive power of these models were evaluated internally by applying the following statistical parameters for the training set and test set: root-mean-square error for prediction (RMSE), correlation coefficient (R), squared correlation coefficient (R2), adjusted squared correlation coefficient (R2adj), difference between R2 and R2adj (R2 – R2adj), squared cross-validation correlation coefficient (Q2). External validation was performed by employing Golbraikh and Tropsha criteria. Moreover, residual analysis was performed. Internal validation of Model XX (N = 91) revealed that it has the highest predictive power (RMSE = 0.4288, R = 0.9393, R2 = 0.882, R2adj = 0.8620, R2 - R2adj = 0.0203, Q2 = 0.8317). However, external validation (using the validating set, N=10) showed that Model XII has the highest predictive power (R2 = 0.961, R20 = 0.9565, k = 0.8648, k’ = 0.9800, [R2 - R20] = 0.0066, [R2 - R20] /R2 = 0.0069, R2pred = 0.9481) based on Golbraikh and Tropsha criteria. Residual analysis confirmed that both models are valid.

References

Baba M, Tanaka H, De Clercq E, Pauwels R, Balzarini J, Schols D et al. Highly specific inhibition of human immunodeficiency virus type 1 by a novel 6-substituted acyclouridine derivative. Biochem. Biophys. Res. Commun. 1989; 165(3): 1375 - 81.

Basu A, Basu S, and Modak MJ. Structure-activity analyses of HIV-1 reverse transcriptase. Biochem. Biophys. Res. Commun. 1992; 183(3): 1131 - 38.

Bazoui H, Zahouily M, Boulajaaj S, Sebti S, and Zakarya D. QSAR for anti-HIV activity of HEPT derivatives.SAR QSAR Environ Res. October 2002; 13(6): 567 - 577.

Castro EA, Toropov AA, Toropova AP, and Mukhamedzhanove DV. QSAR Modeling of Anti-HIV-1 Activity of HEPT Derivatives. Optimization of Correlation Weights of Morgan Extended Connectivity in Graph of Atomic Orbitals. J. Argent. Chem. Soc. 2005; 93(4 - 6): 109 - 121.

Chan YH. Biostatistics 201: Linear Regression Analysis. Singapore Med. J. 2004; 45(2): 55-61.

Consonni V, Todeschini R and Pavan M. Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors, 1. Theory of the Novel 3D Molecular Descriptors. J. Chem. Inf. Comp. Sci.2002;: 682-692.

Consonni V, Todeschini R, Pavan M, and Gramatica P. Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors, 2. Application of the Novel 3D Molecular Descriptors to QSAR/QSPR Studies. Chem. Inf. Comp. Sci. 2002; 42: 693 - 705.

De Clercq E. The role of nonnucleoside reverse transcriptase inhibitors (NNRTIs) in the therapy of HIV-1 infection. Antiviral Res. 1998; 38: 153–179.

De Clercq E. New developments in anti-HIV chemotherapy. Pure Appl. Chem. 2001; 73(1): 55–66.

Duda-Seiman C, Duda-Seimana D, Putz MV and Ciubotariub D. QSAR Modelling of Anti-HIV Activity with HEPT Derivatives. Digest Journal of Nanomaterials and Biostructures. June 2007; 2(2): 207-219.

Golbraikh A and Tropsha A. Beware of q2!. J Mol Graph Model. January 2002; 20(4): 269-276.

Golbraikh A and Tropsha A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J Comput Aided Mol Des. May 2002; 16(5 -6): 357-369.

Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH and Tropsha A. Rational selection of training and test sets for the development of validated QSAR models . J Comput Aided Mol Des. February 2003; 17(2 - 4): 241-253.

Hannongbua S, Lawtrakul L and Limtrakul J. Structure-activity correlation study of HIV-1 inhibitors: Electronic and molecular parameters. J Comput Aided Mol Des. April 1996; 10(2):145-152.

Hannongbua S, Nivesanond K, Lawtrakul L, Pungpo P, and Wolschann P. 3D-quantitative structure-activity relationships of HEPT derivatives as HIV-1 reverse transcriptase inhibitors, based on Ab initio calculations. J Chem Inf Comput Sci. May - Jun 2001; 41(3): 848 - 55.

Hawkins DM, Basak SC and Mills D. Assessing model fit by cross-validation. J. Chem. Inf. Comput. Sci. 2003; 43(2): 579-86.

Hopkins A, Ren J, Esnouf R, Willcox B, Jones E, Ross C et al. Complexes of HIV-1 reverse transcriptase with inhibitors of the HEPT series reveal conformational changes relevant to the design of potent non-nucleoside inhibitors. J Med Chem. April 1996; 12(39): 1589 - 600.

Ivan D, Crisan L, Funar-Timofei S, and Mracec M. A quantitative structure – activity relationships study for the anti - HIV - 1activities of 1-[(2-hydroxyethoxy)methyl]-6- -(phenylthio)thymine derivatives using the multiple linear regression and partial least squares methodologies. J Serb Chem. Soc. 2013; 78,(4): 495 - 506.

Kohlstaedt L, Wang J, Friedman J, Rice P and Steitz T. Crystal structure at 3.5 A resolution of HIV-1 reverse transcriptase complexed with an inhibitor. Science. 1992; 256: 1783-1790

Kola I and Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov. 2. 2004; 3: 711–716.

Kubinyi H, van de Waterbeemd H, Testa B and Folkers G, editors. In Computer-Assisted Lead Finding and Optimization. Basel, Weinheim: VHChA and VCH; 1997.

Kubinyi H, Hamprecht FA and Mietzner T. Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. J Med Chem. 1998; 41: 2553-2564.

Kutner MH, Nachtsheim CJ and Neter J. Applied Linear Regression Models. 4th. McGraw-Hill Irwin; 2004.

Le Grice S F J and A M S S P G, editors. New York: Cold Spring Harbor laboratory press; 1993.

Miyasaka T, Tanaka H, Walker RT, Balzarini J, De Clercq E. A novel lead for specific anti-HIV-1 agents: 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine. J Med Chem. 1989;32.

Schuur JH, Selzer P and Gasteiger J. The coding of the three-dimensional structure of molecules by molecular transforms and its application to structure-spectra correlations and studies of biological activity. J Chem Inf Comput Sci. 1996; 36: 334–344.

Tiwari BK, Thakur A, Thakur M, Pandey ND, Narvi SS and Thakur S. Modeling of cytotoxicity on some non - nucleoside reverse transcriptase inhibitors of HIV-1: role of physicochemical parameters. Arkivoc. 2006: 213 - 225.

Todeschini R and Consonni V. Molecular Descriptors for Chemoinformatics (2 volumes). Weinheim (Germany): WILEY-VCH; 2009.

Verma J, KhedkarVM and Coutinho EC. 3D-QSAR in drug design--a review. Current Topics in Medicinal Chemistry. 2010; 10: 95-115.

Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, and Agrawal RK. Validation of QSAR Models - Strategies and Importance. Int J of Drug Disc. July - September 2011;2(3).

Vracˇko, M and Gasteiger J. A QSAR study on a set of 105 flavonoid derivatives using descriptors derived from 3D structures. Internet Electron J Mol Des. 2002; 1: 527–544.

Zahouily M, Rakik J, Lazar M, Bahlaoui MA and Rayadh A and Komiha N. Exploring QSAR of non-nucleoside reverse transcriptase inhibitors by artificial neural networks: HEPT derivatives . ARKIVOC. 2007; 14: 245-256.

Zarei K, and Morteza A. QSAR Study of Anti-HIV Activities Against HIV-1 and Some of Their Mutant Strains for a Group of HEPT Derivatives. Jnl Chinese Chemical Soc. 2009; 56: 206-213.

Downloads

Published

2013-07-01

How to Cite

Tardaguila, A. A., Sy, J. C., Omañada, M. R., & Punzalan, E. R. (2013). MLR-Based QSAR Models for Predicting Inhibitory Activity of Reverse Transcriptase by HEPT Derivatives using GETAWAY Descriptors. KIMIKA, 24(2), 2–17. https://doi.org/10.26534/kimika.v24i2.2-17

Issue

Vol. 24 No. 2 (2013)

Section

Research Articles

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).