Physiochemical property space distribution among human metabolites, drugs and toxins

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

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

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

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

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. Molecular bioactivity space

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. Clustered and unclustered (raw) datasets

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

13. “Rule of five” analysis

14. a. Molecular weight b. Alog P Percentage of molecules Percentage of molecules c. Lipinski hydrogen bond donor d. Lipinski hydrogen bond acceptor Percentage of molecules Percentage of molecules Lipinski properties

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

16. a. Molecular surface area b. Molecular volume c. Molecular polar surface area d. Molecular solvent accessible volume 3D property comparison Percentage of molecules Percentage of molecules Percentage of molecules Percentage of molecules

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

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

19. Functional group analysis

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

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. 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. 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. Thank you and Questions

25. Supplementary data