Abstract
With the growth of combinatorial chemistry methods in drug discovery, a huge number of compounds are synthesized and screened in parallel for in vitro pharmacological activity, which surprisingly increased the demands of efficient mathematical models to predict desired biological activity. In the present study, an in silico approach using moving average analysis has been applied to a dataset comprising 73 analogues of indanylacetic acid for development of mathematical models for the prediction of each PPAR subtype as well as pan agonistic activity. The value of majority of molecular descriptors (n = 50) for each analogue in the dataset was computed by means of E-Dragon software (version 1.0). Three molecular descriptors, i.e. eccentric adjacency topochemical index-3, information content index-neighbourhood symmetry of 5-order and 2-path kier alpha-modified shape index, yielded the best PPAR subtype specific and sum/pan models by means of moving average analysis. The overall accuracy of the prediction for all individual mathematical models proposed with regard to PPAR α, γ and δ agonistic activity was found to be ≥87, ≥93 and ≥83 %, respectively. Surprisingly, high predictability of the order of ≥86 % was found in the case of sum/pan models. The statistical significance of models/indices was assessed through intercorrelation analysis, sensitivity, specificity and Matthew’s correlation coefficient. High predictability authenticates proposed models for prediction of each PPAR subtype (α/γ/δ) specific as well as pan agonistic activity.
Similar content being viewed by others
References
Bajaj S (2002) Study on topochemical descriptors for the prediction of physiochemical and biological properties of molecules. Phd thesis, Guru Gobind Singh Indraprastha University, Delhi, India
Bajaj S, Sambi SS, Madan AK (2004) Predicting anti-HIV activity of phenethylthiazolethiourea (PETT) analogs: computational approach using Wiener’s topochemical index. J Mol Struct (Theochem) 684:197–203
Bajaj S, Sambi SS, Madan AK (2005) Prediction of anti-inflammatory activity of N-arylanthranilic acids: computational approach using refined Zagreb indices. Croat Chem Acta 78:165–174
Bajaj S, Sambi SS, Gupta S, Madan AK (2006) Model for prediction of anti-HIV activity of 2-pyridinone derivatives using novel topological descriptor. QSAR Comb Sci 25:813–823
Balakumar P, Rose M, Ganti SS, Krishan P, Singh M (2007) PPAR dual agonists: are they opening Pandora’s Box? Pharmacol Res 56:91–98
Baldi P, Bruank S, Chauvin Y, Andersen CAF, Nielsen H (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16:412–424
Basak SC, Mills D, Hawkins DM (2008) Predicting allergic content dermatitis: a hierarchical structure–activity relationship (SAR) approach to chemical classification using topological and quantum chemical descriptors. J Comput-Aided Mol Des 22:339–343
Basak SC, Roy, AB and Ghosh, JJ (1980) Study of the structure-function relationship of pharmacological and toxicological agents using information theory. In: Avula XJR, Bellman R, Luke YL, Rigler, AK (Eds) Proceedings of the Second International Conference on Mathematical Modelling, University of Missouri-Rolla, USA
Bonchev D (1983) Information theoretic indices for characterization of chemical structures. Wiley–Interscience, New York
Brown RD, Martin YC (1997) The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. J Chem Inf Comput Sci 37:1–9
Carugo O (2007) Detailed estimation of bioinformatics prediction reliability through the fragmented prediction performance plots. BMC Bioinf 8:380
Casanola-Martin GM, Marrero-Ponce Y, Tareq M, Khan H, Ather A, Khan KM, Torrens F, Rotondo R (2007) Dragon method for finding novel tyrosinase inhibitors: biosilico identification and experimental in vitro assays. Eur J Med Chem 42:1370–1381
Chemical abstracts service http://www.cas.org. Accessed 10 Oct 2012
Chinetti-Gbaguidi G, Fruchart J-C, Staels B (2005) Role of the PPAR family of nuclear receptors in the regulation of metabolic and cardiovascular homeostasis: new approaches to therapy. Curr Opin Pharmacol 5:177–183
Dong P, Zhang Y, Ge G, Ai C, Liu Y, Yang L, Liu C (2008) Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors. Acta Pharmacol Sin 29:385–396
Dureja H, Madan AK (2005) Topochemical models for prediction of cyclin-dependent kinase 2 inhibitory activity of indole-2-ones. J Mol Model 11:525–531
Dureja H, Gupta S, Madan AK (2008a) Predicting anti-HIV-1 activity of 6-arylbenzonitriles: computational approach using superaugmented eccentric connectivity topochemical indices. J Mol Graph Model 26:1020–1029
Dureja H, Gupta S, Madan AK (2008b) Topological models for prediction of pharmacokinetic parameters of cephalosporins using random forest, decision tree and moving average analysis. Sci Pharm 76:377–394
Dutt R, Madan AK (2009) Improved superaugmented eccentric connectivity indices for QSAR/QSPR Part I: development and evaluation. Med Chem Res 19:431–447
Estrada E, Molina E (2001) Novel local (fragment–based) topological molecular descriptors for QSPR/QSAR and molecular design. J Mol Graph Model 20:54–64
Etgen GJ, Oldham BA, Johnson WT, Broderick CL, Montrose CR, Brozinick JT, Misener EA, Bean JS, Bensch WR, Brooks DA, Shuker AJ, Rito CJ, McCarthy JR, Ardecky RJ, Tyhonas JS, Dana SL, Bilakovics JM, Paterniti JR Jr, Ogilvie KM, Liu S, Kauffman RF (2002) A tailored therapy for the metabolic syndrome: the dual peroxisome proliferator–activated receptor-α/γ agonist LY465608 ameliorates insulin resistance and diabetic hyperglycemia while improving cardiovascular risk factors in preclinical models. Diabetes 51:1083–1087
FDA (2004) Challenge and opportunity on the critical path to new medical products. Food and Drug Administration, U.S Department of Health and Human Services
Goel A, Madan AK (1995) Structure-activity study on anti-inflammatory pyrazole carboxylic acid hydrazide analogs using molecular connectivity indices. J Chem Inf Comput Sci 35:510–514
Gross B, Staels B (2007) PPAR agonists: multimodal drugs for the treatment of type-2 diabetes. Best Pract Res Clin Endocrinol Metab 21:687–710
Gupta S (2002) Application and development of graph invariants for drug design. Phd thesis Punjabi University, Patiala, India
Gupta S, Singh M, Madan AK (2000) Connective eccentric index: a novel topological descriptors for predicting biological activity. J Mol Graph Model 18:18–25
Gupta S, Singh M, Madan AK (2001) Predicting Anti- HIV activity: computational approach using novel topological indices. J Comput-Aided Mol Des 15:671–678
Gupta S, Singh M, Madan AK (2003) Novel topochemical descriptors for predicting anti-HIV activity. Ind J Chem 42A:1414–1425
Gutman I, Trinajstic N (1972) Graph theory and molecular orbitals: total π- electron energy of alternant hydrocarbon. Chem Phys Lett 17:535–538
Han L, Wang Y, Bryant SH (2008) Developing and validating predictive decision tree models from mining chemical structural fingerprints and high throughoutput data in PubChem. BMC Bioinf 9:401
Hollas B (2003) An analysis of the autocorrelation descriptor for molecules. J Math Chem 33:91–101
Javiya VA, Patel JA (2006) The role of peroxisome proliferator-activated receptors in human disease. Ind J Pharmacol 38:243–253
Kapetanovic IM (2008) Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem Biol Interact 171:165–176
Kasuga J, Yamasaki D, Ogura K, Shimizu M, Sato M, Makishima M, Doi T, Hashimotoa Y, Miyachi H (2008) SAR-oriented discovery of peroxisome proliferator-activated receptor pan agonist with a 4-adamantylphenyl group as a hydrophobic tail. Bioorg Med Chem Lett 18:1110–1115
Kier LB (1986) Shape indexes of orders one and three from molecular graphs. Quant Struct–Act Relat 5:1–7
Lamanna C, Bellini M, Padova A, Westerberg G, Maccari L (2008) Straightforward recursive partitioning model for discarding insoluble compounds in the drug discovery process. J Med Chem 51:2891–2897
Leach AR, Bryce RA, Robinson AJ (2000) Synergy between combinatorial chemistry and de novo design. J Mol Graph Model 18:358–367
Lengauer T, Lemmen C, Rarey M, Zimmermann M (2004) Novel technologies for virtual screening. Drug Discov Today 9:27–34
Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phase lysozyme. Biochim Biophys Acta 405:442–451
McGee P (2005) Modelling success with in silico tools. Drug Discov Today 8:23–28
Mohajeri A, Dinpajooh MH (2008) Structure–toxicity relationship for aliphatic compounds using quantum topological descriptors. J Mol Struct (Theochem) 855:1–3
Nadine S, Christine J, Claudia A, Michael CH (2008) Gradual in silico filtering for drug like substances. J Chem Inf Model 48:613–628
Nikolić S, Kovačević G, Miličević A, Trinajstić N (2003) The Zagreb indices 30 years after. Croat Chem Acta 76:113–124
Peltason L, Bajorath J (2007) SAR index: quantifying the nature of structure-activity relationships. J Med Chem 50:5571–5578
Prabhakar YS, Gupta MK (2008) Chemical structure indices in In silico design. Sci Pharm 76:101–132
Randic M (1975) On characterization of molecular branching. J Am Chem Soc 97:6609–6615
Randic M, Mihalic Z (1994) Graphical bond orders: novel structural descriptors. J Chem Inf Comput Sci 34:403–409
Reddy AS, Pati SP, Kumar PP, Pradeep HN, Sastry GN (2007) Virtual screening in drug discovery—A computational perspective. Current Protein Pept Sci 8:329–351
Rudolph J, Chen L, Majumdar D, Bullock WH, Burns M, Claus T, Cruz FED, Daly M, Ehrgott FJ, Johnson JS, Livingston JN, Schoenleber RW, Shapiro J, Yang L, Tsutsumi M, Ma X (2007) Indanylacetic acid derivatives carrying 4-thiazolyl-phenoxy tail groups, a new class of potent PPAR α/γ/δ Pan agonists: synthesis, structure-activity relationship, and in vivo efficacy. J Med Chem 50:984–1000
Selassie CD (2003) History of quantitative structure–activity relationships. In: Abraham DJ (ed) Burger’s medicinal chemistry and drug discovery, vol 1, 6th edn. Wiley, New York, pp 1–43
Shearer BG, Billin AN (2007) The next generation of PPAR drugs: do we have the tools to find them? Biochim Biophys Acta 1771:1082–1093
Smolenskii EA, Vlasova GV, Platunov DY, Ryzhov A (2006) Ad hoc optimal topological indices for QSPR. Russ Chem Bull, Int Ed 55:1508–1515
Sundriyal S, Bharatam PV (2009a) Important pharmacophoric features of pan PPAR agonists: common chemical feature analysis and virtual screening. Eur J Med Chem 44:3488–3495
Sundriyal S, Bharatam PV (2009b) ‘Sum of activities’ as dependent parameter: a new CoMFA-based approach for the design of pan PPAR agonists. Eur J Med Chem 44:42–53
Tetko IV, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P, Palyulin VA, Radchenko EV, Zefirov NS, Makarenko AS, Tanchuk VY, Prokopenko VV (2005) Virtual computational chemistry laboratory—design and description. J Comput-Aided Mol Des 19:453–463
Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics. Wiley–VCH, Weinheim
Trinajstić N (1983) Chemical graph theory. CRC Press, Boca Raton, Florida
Trinajstić N, Nikolic S, Basak SC, Lukovits I (2001) Distance indices and their hyper counterparts: intercorrelation and use in the structure-property modelling. SAR QSAR Environ Res 12:31–54
Turner JV, Maddalena DJ, Cutler DJ (2004) Pharmacokinetic parameter prediction from drug structure using artificial neural networks. Int J Pharm 270:209–219
Walters WP, Stahl MT, Murcko MA (1998) Virtual screening-an overview. Drug Discov Today 3:160–178
Wiener H (1947) Structural determination of the paraffin boiling points. J Am Chem Soc 69:2636–2638
Willson TM, Brown PJ, Sternbach DD, Henke BR (2000) The PPARs: from orphan receptors to drug discovery. J Med Chem 43:527–550
Zettl H, Steri R, Lämmerhofer M, Schubert-Zsilavecz M (2009) Discovery of a novel class of 2-mercaptohexanoic acid derivatives as highly active PPARα agonists. Bioorg Med Chem Lett 19:4421–4426
Zhao YH, Le J, Abraham MH, Hersey A, Eddershaw PJ, Luscombe CN, Boutina D, Beck G, Sherborne B, Copper I, Platts JA (2001) Evaluation of human intestinal absorption data and subsequent derivation of quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J Pharm Sci 90:749–784
Zoete V, Grosdidier A, Michielin O (2007) Peroxisome proliferator-activated receptor structures: ligand specificity, molecular switch and interactions with regulators. Biochim Biophys Acta 1771:915–925
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dutt, R., Madan, A.K. Models for the prediction of PPARs agonistic activity of indanylacetic acids. Med Chem Res 22, 3213–3228 (2013). https://doi.org/10.1007/s00044-012-0315-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00044-012-0315-4