Skip to main content
Log in

Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations

  • Review Article
  • Published:
Archives of Toxicology Aims and scope Submit manuscript

Abstract

Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies implemented to determine toxicophores may be broadly classified into biological, bioanalytical and computational approaches. The biological approach involves analysis of bioactivated metabolites, whereas the computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique and a few toxicophore identification/estimation tools. Being one of the major steps in drug discovery process, toxicophore identification has proven to be an essential screening step in drug design and development. The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting and determining toxicophores with an emphasis on their scope and limitations. Such information may prove vital in the appropriate selection of methodology and can be used as screening technology by researchers to discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be eliminated or removed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Alex JM, Singh S, Kumar R (2014) 1-Acetyl-3, 5-diaryl-4, 5-dihydro (1H) pyrazoles: exhibiting anticancer activity through intracellular ROS scavenging and the mitochondria-dependent death pathway. Arch Pharm 347:717–727

    Article  CAS  Google Scholar 

  • ATSDR U (1997) Agency for toxic substances and disease registry, Case Studies in environmental medicine. http://www.atsdr.cdc.gov/HEC/CSEM/csem.html

  • Babu RA, Borkar RM, Raju G, Raju B, Srinivas R (2014) Liquid chromatography electrospray ionization tandem mass spectrometry study of nilutamide and its stress degradation products: in silico toxicity prediction of degradation products. Biomed Chromatogr 28:788–793

    Article  Google Scholar 

  • Barnum D, Greene J, Smellie A, Sprague P (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36:563–571

    Article  CAS  PubMed  Google Scholar 

  • Benigni R, Bossa C, Alivernini S, Colafranceschi M (2012) Assessment and validation of US EPA’s OncoLogic® Expert system and analysis of its modulating factors for structural alerts. J Environ Sci Health 30:152–173

    Article  CAS  Google Scholar 

  • Bessems JG, Vermeulen NP (2001) Paracetamol (acetaminophen)-induced toxicity: molecular and biochemical mechanisms, analogues and protective approaches. CRC Crit Rev Toxicol 31:55–138

    Article  CAS  Google Scholar 

  • Bhavani S, Nagargadde A, Thawani A, Sridhar V, Chandra N (2006) Substructure-based support vector machine classifiers for prediction of adverse effects in diverse classes of drugs. J Chem Inf Model 46:2478–2486

    Article  CAS  PubMed  Google Scholar 

  • Boverhof DR, Chamberlain MP, Elcombe CR, Gonzalez FJ, Heflich RH, Hernandez LG, Jacobs Jacobson-Kram D, Luijten M, Maggi A (2011) Transgenic animal models in toxicology: historical perspectives and future outlook. Toxicol Sci 121:207–233

    Article  CAS  PubMed  Google Scholar 

  • Burden FR, Winkler DA (2000) A quantitative structure-activity relationships model for the acute toxicity of substituted benzenes to Tetrahymena pyriformis using Bayesian-regularized neural networks. Chem Res Toxicol 13:436–440

    Article  CAS  PubMed  Google Scholar 

  • Chauhan M, Kumar R (2013) Medicinal attributes of pyrazolo [3, 4-d] pyrimidines: a review. Bioorg Med Chem 21:5657–5668

    Article  CAS  PubMed  Google Scholar 

  • Chauhan M, Kumar R (2014) A comprehensive review on bioactive fused heterocycles as purine-utilizing enzymes inhibitors. Med Chem Res 24:2259–2282

    Article  Google Scholar 

  • Chen Y, Ung C (2001) Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand–protein inverse docking approach. J Mol Graph Model 20:199–218

    Article  CAS  PubMed  Google Scholar 

  • Chen Y, Zhi D (2001) Ligand–protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Protein Struct Funct Bioinform 43:217–226

    Article  CAS  Google Scholar 

  • Cronin MT, Walker JD, Jaworska JS, Comber MH, Watts CD, Worth AP (2003) Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances. Environ Health Perspect 111:376

    Article  Google Scholar 

  • da Silva VB, Kawano DF, Carvalho I, Conceicao EC, Freitas O, de Paula Silva CHT (2009) Psoralen and bergapten: in silico metabolism and toxicophoric analysis of drugs used to treat vitiligo. Int J Pharm Pharm Sci 12:378–387

    Article  Google Scholar 

  • Dashwood RH (1992) Protection by chlorophyllin against the covalent binding of 2-amino-3-methylimidazo [4, 5-f] quinoline (IQ) to rat liver DNA. Carcinogenesis 13:113–118

    Article  CAS  PubMed  Google Scholar 

  • Dearden JC, Barratt MD, Benigni R, Bristol DW, Combes RD, Cronin MT, Judson PN, Payne MP, Richard AM, Tichy M (1997) The development and validation of expert systems for predicting toxicity. In: Workshop (ECVAM Workshop 24), pp 2

  • Ding Xinxin, Kaminsky LS (2003) Human extrahepatic cytochromes P450: function in xenobiotic metabolism and tissue-selective chemical toxicity in the respiratory and gastrointestinal tracts. Annu Rev Pharmacol Toxicol 43:149–173

    Article  CAS  PubMed  Google Scholar 

  • Drwal MN (2014) ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res 42:W53–W58

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dudek AZ, Arodz T, Galvez J (2006) Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb Chem High Throughput Screen 9:213–228

    Article  CAS  PubMed  Google Scholar 

  • Ehrlich P (1909) Über den jetzigen Stand der Chemotherapie. Ber Dtsch Chem Ges 42:17–47

    Article  CAS  Google Scholar 

  • Eriksson L, Johansson E, Lundstedt T (2005) Regression-and projection-based approaches in predictive toxicology. In: Helma C (ed) predictive toxicology, 1st edn. Taylor & Francis, New York, pp 177–221

    Chapter  Google Scholar 

  • Erve JC, Gauby S, Maynard JW Jr, Svensson MA, Tonn G, Quinn KP (2013) Bioactivation of sitaxentan in liver microsomes, hepatocytes, and expressed human P450s with characterization of the glutathione conjugate by liquid chromatography tandem mass spectrometry. Chem Res Toxicol 26:926–936

    Article  CAS  PubMed  Google Scholar 

  • Gamache PH, Meyer DF, Granger MC, Acworth IN (2004) Metabolomic applications of electrochemistry/mass spectrometry. J Am Soc Mass Spectrom 151:717–1726

    Google Scholar 

  • Garg D, Gandhi T, Gopi Mohan C (2008) Exploring QSTR and toxicophore of hERG K+ channel blockers using GFA and HypoGen techniques. J Mol Graph Model 26:966–976

    Article  CAS  PubMed  Google Scholar 

  • Garg M, Chauhan M, Singh PK, Alex JM, Kumar R (2015) Pyrazoloquinazolines: synthetic strategies and bioactivities. Eur J Med Chem 97:444–461

    Article  CAS  PubMed  Google Scholar 

  • Goldsworthy TL, Reico L, Brown K, Donehower LA, Mirsalis JC, Tennant RW (1994) Transgenic animals in toxicology. Toxicol Sci 22:8–19

    Article  CAS  Google Scholar 

  • Gopi Mohan C, Gandhi T, Garg D, Shinde R (2007) Computer-assisted methods in chemical toxicity prediction. Mini Rev Med Chem 7:499–507

    Article  PubMed  Google Scholar 

  • Graham EE, Walsh RJ, Hirst CM, Maggs JL, Martin S, Wild MJ, Wilson ID, Harding JR, Kenna J, Peter RM (2008) Identification of the thiophene ring of methapyrilene as a novel bioactivation-dependent hepatic toxicophore. J Pharmacol Exp Ther 326:657–671

    Article  CAS  PubMed  Google Scholar 

  • Greene N (2002) Computer systems for the prediction of toxicity: an update. Adv Drug Deliv Rev 54:417–431

    Article  CAS  PubMed  Google Scholar 

  • Hansch C, Fujita T (1964) ρ-σ-π Analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616–1626

    Article  CAS  Google Scholar 

  • Helma C (2006) Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity. Mol Divers 10:147–158

    Article  CAS  PubMed  Google Scholar 

  • Helma C, Kazius J (2006) Artificial intelligence and data mining for toxicity prediction. Curr Comput Aided Drug Des 2:123–133

    Article  CAS  Google Scholar 

  • Hsu C, Lin C (2002) A comparison of methods for multiclass support vector machines, neural networks. IEEE Trans 13:15–425

    Google Scholar 

  • Jeong H (1999) Inhibition of cytochrome P450 2E1 expression by oleanolic acid: hepatoprotective effects against carbon tetrachloride-induced hepatic injury. Toxicol Lett 105:215–222

    Article  CAS  PubMed  Google Scholar 

  • Ji ZL, Wang Y, Yu L, Han LY, Zheng CJ, Chen YZ (2006) In silico search of putative adverse drug reaction related proteins as a potential tool for facilitating drug adverse effect prediction. Toxicol Lett 164:104–112

    Article  CAS  PubMed  Google Scholar 

  • Judson PN (1994) Rule induction for systems predicting biological activity. J Chem Inf Comput Sci 34:148–153

    Article  CAS  Google Scholar 

  • Kalgutkar A, Dalvie D, Obach R, Smith D (2012) Pathways of reactive metabolite formation with toxicophores/‐structural alerts. React Drug Metab, 93–129

  • Kar S, Roy K (2013) Predictive chemometric modeling and 3D-toxicophore mapping of diverse organic chemicals causing bioluminescent repression of the bacterium genus Pseudomonas. Ind Eng Chem Res 52:17648–17657

    Article  CAS  Google Scholar 

  • Kaur G, Cholia RP, Mantha AK, Kumar R (2014) DNA repair and redox activities and inhibitors of apurinic/apyrimidinic endonuclease 1/redox effector factor 1 (APE1/Ref-1): a comparative analysis and their scope and limitations toward anticancer drug development: Miniperspective. J Med Chem 57:10241–10256

    Article  CAS  PubMed  Google Scholar 

  • Kazius J, McGuire R, Bursi R (2005) Derivation and validation of toxicophores for mutagenicity prediction. J Med Chem 48:312–320

    Article  CAS  PubMed  Google Scholar 

  • King RD, Muggleton SH, Srinivasan A, Sternberg M (1996) Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proc Natl Acad Sci 93:438–442

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • King RD, Srinivasan A, Dehaspe L (2001) Warmr: a data mining tool for chemical data. J Comput-Aided Mol Des 15:173–181

    Article  CAS  PubMed  Google Scholar 

  • Klopman G (1992) MULTICASE 1 A hierarchical computer automated structure evaluation program. Quant Struct-Act Relatsh 11:176–184

    Article  CAS  Google Scholar 

  • Klopman G, Saiakhov R, Rosenkranz HS, Hermens JL (1999) Multiple Computer-Automated structure evaluation program study of aquatic toxicity 1: Guppy. Environ Toxicol Chem 18:2497–2505

    Article  CAS  Google Scholar 

  • Kortagere S, Ekins S, Welsh WJ (2008) Halogenated ligands and their interactions with amino acids: implications for structure–activity and structure–toxicity relationships. J Mol Graph Model 27:170–177

    Article  CAS  PubMed  Google Scholar 

  • Kuschewski J, Hui S, Zak SH (1993) Application of feedforward neural networks to dynamical system identification and control, control systems technology. IEEE Trans 1:37–49

    Google Scholar 

  • Lee A (2006) Adverse drug reactions, 2nd edn. Pharmaceutical Press, United Kingdom

    Google Scholar 

  • Lewis D, Ioannides C, Parke DV (1995) A retrospective evaluation of COMPACT predictions of the outcome of NTP rodent carcinogenicity testing. Environ Health Perspect 103:178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lewis D, Ioannides C, Parke D (1996) COMPACT and molecular structure in toxicity assessment: a prospective evaluation of 30 chemicals currently being tested for rodent carcinogenicity by the NCI/NTP. Environ Health Perspect 104:1011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liao Q, Yao J, Yuan S (2007) Prediction of mutagenic toxicity by combination of recursive partitioning and support vector machines. Mol Divers 11:59–72

    Article  CAS  PubMed  Google Scholar 

  • Lu D, Giles K, Li Z, Rao S, Dolghih E, Gever JR, Geva M, Elepano ML, Oehler A, Bryant C (2013) Biaryl amides and hydrazones as therapeutics for prion disease in transgenic mice. J Pharmacol Exp Ther 347:325–338

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Merlot C, Domine D, Cleva C, Church DJ (2003) Chemical substructures in drug discovery. Drug Discov Today 8:594–602

    Article  CAS  PubMed  Google Scholar 

  • Munns AJ, De Voss JJ, Hooper WD, Dickinson RG, Gillam EM (1997) Bioactivation of phenytoin by human cytochrome P450: characterization of the mechanism and targets of covalent adduct formation. Chem Res Toxicol 10:1049–1058

    Article  CAS  PubMed  Google Scholar 

  • Nakayama S, Atsumi R, Takakusa H, Kobayashi Y, Kurihara A, Nagai Y, Nakai D, Okazaki O (2009) A zone classification system for risk assessment of idiosyncratic drug toxicity using daily dose and covalent binding. Drug Metab Dispos 37:1970–1977

    Article  CAS  PubMed  Google Scholar 

  • Niazi A, Jameh-Bozorghi S, Nori-Shargh D (2008) Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines. J Hazard Mater 151:603–609

    Article  CAS  PubMed  Google Scholar 

  • Noorlander C, Zeilmaker M, van Eijkeren J, Bourgeois F, Beffers R, Brandon E, Bessems J (2008) Data collection on kinetic parameters of substances. Arch Toxicol 87:767–769

    Google Scholar 

  • Parke D, Ioannides C, Lewis D (1990) Safety evaluation of drugs and chemicals by the use of computer optimised molecular parametric analysis of chemical toxicity (COMPACT). Alternatives to laboratory animals: ATLA, New York

    Google Scholar 

  • Piparo EL, Maunz A, Helma C, Vorgrimmler D, Schilter B (2014) Automated and reproducible read-across like models for predicting carcinogenic potency. Regul Toxicol Pharm 70:370–378

    Article  Google Scholar 

  • Rana A, Alex JM, Chauhan M, Joshi G, Kumar R (2015) A review on pharmacophoric designs of antiproliferative agents. Med Chem Res 24:903–920

    Article  CAS  Google Scholar 

  • Ray O, Broda K, Russo A (2004) A hybrid abductive inductive proof procedure. Logic J IGPL 12:371–397

    Article  Google Scholar 

  • Richard AM, Gold LS, Nicklaus MC (2006) Chemical structure indexing of toxicity data on the internet: moving toward a flat world. Curr Opin Drug Discov Dev 9:314

    CAS  Google Scholar 

  • Rufer CE, Glatt H, Kulling SE (2006) Structural elucidation of hydroxylated metabolites of the isoflavan equol by gas chromatography-mass spectrometry and high-performance liquid chromatography-mass spectrometry. Drug Metab Dispos 34:51–60

    Article  PubMed  Google Scholar 

  • Sanderson D, Earnshaw C (1991) Computer prediction of possible toxic action from chemical structure; The DEREK system. Hum Exp Toxicol 10:261–273

    Article  CAS  PubMed  Google Scholar 

  • Schymanski EL, Jeon J, Gulde R, Fenner K, Ruff M, Singer HP, Hollender J (2014) Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ Sci Technol 48:2097–2098

    Article  CAS  PubMed  Google Scholar 

  • Sharma M, Sharma P, Mondal S, Garg V (2011) Toxicophore and pharmacophore dependent toxicity: perspective review. Pharmacol Online 1:219–235

    Google Scholar 

  • Sherwin CM, Christiansen SB, Duncan IJ, Erhard HW, Lay DC Jr, Mench JA, O’Connor CE, Petherick JC (2003) Guidelines for the ethical use of animals in applied ethology studies. Appl Anim Behav Sci 81:291–305

    Article  Google Scholar 

  • Smellie A, Teig S, Towbin P (1995) Poling: promoting conformational variation. J Comput Sci 16:171–187

    CAS  Google Scholar 

  • Smithing MP, Darvas F (1992) HazardExpert: an expert system for predicting chemical toxicity. In: ACS symposium series American chemical society

  • Snyder RD, Pearl GS, Mandakas G, Choy W, Goodsaid F, Rosenblum I (2004) Assessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical molecules. Environ Mol Mutagen 43:143–158

    Article  CAS  PubMed  Google Scholar 

  • Stepan AF, Walker DP, Bauman J, Price DA, Baillie TA, Kalgutkar AS, Aleo MD (2011) Structural alert/reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States. Chem Res Toxicol 24:1345–1410

    Article  CAS  PubMed  Google Scholar 

  • Williams DP, Park B (2003) Idiosyncratic toxicity: the role of toxicophores and bioactivation. Drug Discov Today 8:1044–1050

    Article  CAS  PubMed  Google Scholar 

  • Williams DP, Antoine DJ, Butler PJ, Jones R, Randle L, Payne A, Howard M, Gardner I, Blagg J, Park BK (2007) The metabolism and toxicity of furosemide in the Wistar rat and CD-1 mouse: a chemical and biochemical definition of the toxicophore. J Pharmacol Exp Ther 322:1208–1220

    Article  CAS  PubMed  Google Scholar 

  • Xu L, Ball J, Dixon S, Jurs P (1994) Quantitative structure-activity relationships for toxicity of phenols using regression analysis and computational neural network. Environ Toxicol Chem 13:841–851

    Article  CAS  Google Scholar 

  • Yang S (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15:444–450

    Article  CAS  PubMed  Google Scholar 

  • Zhou X, Su F, Lu H, Senechal-Willis P, Tian Y, Johnson R, Meldrum D (2012) An FRET-based ratiometric chemosensor for in vitro cellular fluorescence analyses of pH. Biomaterials 33:171–180

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

R.K. and M.C. thank Department of Science and Technology, New Delhi, for the financial assistance (F. No. SR/FT/CS-71/2011). Support from Research Seed Money (RSM) from CUPB is also acknowledged. R.K. also thanks University Grant Commission-Major Grant (F. No. 42-676/2013(SR).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Raj Kumar.

Ethics declarations

Conflict of interest

Authors do not have any conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, P.K., Negi, A., Gupta, P.K. et al. Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations. Arch Toxicol 90, 1785–1802 (2016). https://doi.org/10.1007/s00204-015-1587-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00204-015-1587-5

Keywords

Navigation