1. Varun Khanna and Shoba Ranganathan Macquarie University, Sydney, Australia vkhanna@cbms.mq.edu.au Physiochemical property space distribution among human metabolites, drugs and toxins

2. 2 Outline of the presentation  Introduction  Chemoinformatics and current drug discovery approach  Drug-likeness and related measures  Molecular bioactivity space  Results  Conclusion

3. 3 Chemoinformatics  Chemistry + Informatics = Chemoinformatics  Brown 1998; Willett 2007  Involves many sub-disciplines today, such as: Similarity and diversity analysis CASD-Computer Aided Synthesis Design CASE-Computer Aided Structure Elucidation QSAR-Quantitative Structure Activity Relationship

4. 4 Current drug discovery process: 10-15 years & US $1 billion Disease Target identification Lead identification Preclinical testing Human clinical trials Approved by regulatory authorities Market

5. 5 Toxicity: major cause of drug failures  Schuster D, Laggner C, Langer T: Why drugs fail - a study on side effects in new chemical entities. Curr Pharm Des 2005, 11(27):3545-3559.  Gut J, Bagatto D: Theragenomic knowledge management for individualised safety of drugs, chemicals, pollutants and dietary ingredients. Expert Opin Drug Metab Toxicol 2005, 1(3):537-554.  However, there is no comparison of toxins to drugs or any other drug-like set of molecules.  Data resources available:  Distributed Structure-Searchable Toxicity (DSSTox) Carcinogenic Potency Database ( potency.berkeley.edu )

6. 6 Outline of the presentation  Introduction  Chemoinformatics and current drug discovery approach  Drug-likeness and related measures  Molecular bioactivity space  Results  Conclusion

7. 7 A very brief history of “drug-likeness”  Lipinski’s Rule of Five (Ro5) dominated drug design and discovery since 1997  A molecule is “non-drug-like” if it has >5 five hydrogen bond donors, >10 hydrogen bond acceptors, molecular mass >500 and lipophilicity (measured as AlogP) >5.  Recently, metabolite-likeness is important for designing targeted drugs, that act on specific metabolic pathways (Dobson et al., 2009)  Data resources available are:  Human Metabolite Database ( www.hmdb.ca )  DrugBank ( www.drugbank.ca )

8. 8 Molecular bioactivity space NP DT RO UX GI SN S METABOLITES

9. 9 Large scale physiochemical property comparison In this paper, we present  Comprehensive analysis of  Drugs  Metabolites  Toxins  Comparison of  Ro5  1D  3D  Clustered (or representative) vs. unclustered (or raw) datasets (for the first time)

10. 10 Clustered and unclustered (raw) datasets DatasetMetabolitesDrugsToxins UnclusteredM: 6582D: 4829T: 1448 ClusteredCM: 4568CD: 3248CT: 995

11. 11 Properties of drug-like molecules  Lipinski properties (Ro5)  1D properties  Number of atoms  Number of nitrogen and oxygen atoms  Number of rings  Number of rotatable bonds  3D properties  Molecular volume  Molecular surface area  Molecular polar surface area  Molecular solvent accessible surface area Analysis Software  SciTegic Pilot ( accelrys.com/products/scitegic )  Clustering: Using “ Cluster Clara” algorithm and employing ECFP_4 fingerprints as molecular descriptors.

12. 12 Outline of the presentation  Introduction  Chemoinformatics and current drug discovery approach  Drug-likeness and related measures  Molecular bioactivity space  Results  Conclusion

13. 13 “Rule of five” analysis Datasets Lipinski Properties Molecular weight <500 Da H-bond Donor <=5 H-bond Acceptor <=10 Log P <5 HMDB (Metabolites) 34%84% 35% DDB (Drugs) 84%86%87%92% CPDB (Toxic molecules) 94%98%97%92%

14. 14 Lipinski properties a. Molecular weightb. Alog P c. Lipinski hydrogen bond donord. Lipinski hydrogen bond acceptor Percentage of molecules

15. 15 1D property comparison a. Number of atoms b. Number of carbon atoms c. Number of nitrogen atomsd. Number of oxygen atoms Percentage of molecules

16. 16 3D property comparison a. Molecular surface areab. Molecular volume c. Molecular polar surface aread. Molecular solvent accessible volume Percentage of molecules

17. Clustered vs. raw datasets - I a. Number of oxygen atoms ~ 10 % drop b. Number of rings ~ 9 % drop Percentage

18. Clustered vs. raw datasets -II Percentage b. Molecular polar surface area a. Molecular solubility Percentage ~ 10 % drop ~ 15 % rise

19. 19 Functional group analysis Functional Group Metabolite dataset Drugs dataset Toxin dataset Aromatic atom17.4%70.6%62.3% Benzene10.3%56.0%53.0% HBA Ester56.3%13.8%15.4% Primary amine28.0%14.4%12.0% Secondary amine11.4%64.0%41.2% Tertiary amine44.6%80.0%60.0% Quaternary Amine15.3%02.1%00.5% Primary amide01.5%04.5%03.9% Secondary amide11.4%31.0%14.5% Tertiary amide02.8%16.8%09.2% Alkyl halide~0.5% 03.2% Azo00.0%~0.5%03.4% Nitroso~0.5%00.6%08.4%

20. 20 Outline of the presentation  Introduction  Chemoinformatics and current drug discovery approach  Drug-likeness and related measures  Molecular bioactivity space  Results  Conclusion

21. 21 Conclusions  70% of the metabolites are outside Lipinski universe whereas 90% of the toxins abide by Lipinski’s rule.  Ro5 does not explicitly take toxicity into account and therefore present day drugs are more akin to toxins.  Empirical rules like the “Ro5” can be refined to increase the coverage of drugs or drug-like molecules that are clearly not close to toxic compounds.  Clustered and unclustered datasets are very similar, except in the case of the number of oxygen atoms, the molecular polar surface area and the number of rings.

22. 22 Related work  Customary medicinal plant database  Gaikwad J, Khanna V, Vemulpad S, Jamie J, Kohen J, Ranganathan S: CMKb: a web-based prototype for integrating Australian Aboriginal customary medicinal plant knowledge. BMC Bioinformatics 2008, 9 Suppl 12:S25.  Invited Chemoinformatics book chapter  Khanna V, Ranganathan S: In Silico Methods for the Analysis of Metabolites and Drug Molecules, in Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications, eds. M. Elloumi and A.Y. Zomaya, Wiley, 2009, accepted.

23. 23 Acknowledgement  VK is grateful to:  Macquarie University for the award of a Research Excellence Scholarship (MQRES)  PhD Supervisor and Co-supervisors @ MQ  Colleagues and friends @ MQ  InCoB2009 Program and Organizing Committee members

24. 24 Thank you and Questions

25. 25 Supplementary data

26. 26 Similar Structures  Similar Properties  Chemicals structures and properties appear to be linked  Similar structure leads to similar properties  e.g. caffeine (coffee, tea), theophylline (tea) and theobromine (chocolate) CaffeineTheophyllineTheobromine

27. 27 Morphine HeroinNaloxone Molecular diversity Codeine  Morphine, codiene and heroin are opioid receptor agonists: Naloxone is an antagonist  Naloxone is thus an example for molecular diversity

28. 28 Data explosion in chemistry