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Physiochemical property space distribution among human metabolites, drugs and toxins. Varun Khanna and Shoba Ranganathan. Macquarie University, Sydney, Australia. vkhanna@cbms.mq.edu.au. Outline of the presentation. Introduction Chemoinformatics and current drug discovery approach

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physiochemical property space distribution among human metabolites drugs and toxins

Physiochemical property space distribution among human metabolites, drugs and toxins

Varun Khannaand Shoba Ranganathan

Macquarie University, Sydney, Australia

vkhanna@cbms.mq.edu.au

outline of the presentation
Outline of the presentation
  • Introduction
    • Chemoinformatics and current drug discovery approach
  • Drug-likeness and related measures
    • Molecular bioactivity space
  • Results
  • Conclusion
slide3

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
slide4

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

slide5

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)
outline of the presentation1
Outline of the presentation
  • Introduction
    • Chemoinformatics and current drug discovery approach
  • Drug-likeness and related measures
    • Molecular bioactivity space
  • Results
  • Conclusion
a very brief history of drug likeness
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)
slide9

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)
properties of drug like molecules
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.
outline of the presentation2
Outline of the presentation
  • Introduction
    • Chemoinformatics and current drug discovery approach
  • Drug-likeness and related measures
    • Molecular bioactivity space
  • Results
  • Conclusion
slide14

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

slide15

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

slide16

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

slide17

~ 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

slide18

a. Molecular solubility

~ 10 % drop

Percentage

Percentage

~ 15 % rise

Percentage

Clustered vs. raw datasets -II

b. Molecular polar surface area

Percentage

Percentage

Percentage

outline of the presentation3
Outline of the presentation
  • Introduction
    • Chemoinformatics and current drug discovery approach
  • Drug-likeness and related measures
    • Molecular bioactivity space
  • Results
  • Conclusion
slide21

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.
slide22

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.
slide23

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