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Computational Toxicology and Virtual Development in Drug Design. Dale E. Johnson, Pharm.D., Ph.D. Chief Scientific Officer ddplatform LLC. The “Problem” in pharmaceutical R&D. The “Solution” for R&D . ~ $700 MM and over 10 years to develop novel drug

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computational toxicology and virtual development in drug design

Computational Toxicology and Virtual Development in Drug Design

Dale E. Johnson, Pharm.D., Ph.D. Chief Scientific Officer

ddplatform LLC

the solution for r d

The “Problem” in pharmaceutical R&D

The “Solution” for R&D
  • ~ $700 MM and over 10 years to develop novel drug
  • Approximately 75% of overall R&D cost attributed to failures
  • Identify/eliminate problematic drugs early
  • Design desirable properties into drugs
drug discovery the hunting process where is toxicology today
Drug Discovery: the hunting process where is toxicology today?

From: Rosamond and Allsop, Science 287, 1973 (2000)

early toxicology at the lead optimization step still a high failure rate high cost to r d
Early toxicology at the Lead Optimization Step: still a high failure rate – high cost to R&D

ADME, PK, TOX Lead optimization

Secondary in vitro screening

In vivo and

mechanistic

screens

Lead

selection

Primary & secondary efficacy screening

Chemical Libraries

Chemical Libraries

Development

Candidate

65%

Drop Out

IND enabling studies

Phase I, II

the toxicology solution
The toxicology solution
  • Incorporate predictive toxicology concept throughout discovery & development
  • Design reduced toxicity into chemical libraries
  • Create expert systems to accelerate and increase success rate
    • Expert systems must be multi-disciplinary for real impact
major needs in predictive toxicology recent industry surveys
Major needs in Predictive Toxicology: Recent industry surveys
  • Predictive software with updated databases
  • Improved data mining capabilities
  • Enhanced in vitro mechanistic screens
  • Ready access to human hepatocytes and other cells
  • Relevant application of new technologies ie. toxicogenomics
major needs in predictive toxicology recent industry surveys7
Major needs in Predictive Toxicology: Recent industry surveys
  • Predictive software with updated databases
  • Improved data mining capabilities
  • Enhanced in vitro mechanistic screens
  • Ready access to human hepatocytes and other cells
  • Relevant application of new technologies ie. toxicogenomics
missing elements in the toolbox
Missing elements in the toolbox
  • Quality data from controlled sources
  • Newly created database(s) using “pharmaceutical” chemical space
  • Multi-disciplinary chem-tox Information / decision tools
    • Data mining via “med chem building blocks”
  • Flexibilityto incorporate all data from internal and external sources
  • Web-based, platform independent
leadscope tm technology
LeadScopeTM Technology
  • Structural analysis based on familiar structural features
  • Powerful graphical representations and dynamic querying
  • Refine structure alerts to reflect new assay results
  • Statistically test structural hypotheses
rtecs database liver toxicity
RTECS database & liver toxicity
  • ~7000 compounds with liver toxicity codes
  • Expert conversion to grades (risk)
    • Ordinal ranks using severity of findings, dose, regimen, species
  • Create 1o liver tox – chemical space
  • Data mining with ToxScopeTM: correlations between chemical structure and liver toxicity
slide11

Feature Hierarchy

Graphic Panel

Filter Panel

Information Windows

slide12

Portion of the Heterocycles hierarchy showing 3 levels of the pyridine subhierarchy

Selected subset of compounds containing a pyridine substructure with an acyclic alkenyl group in the 2-position

Subset contains 2 compounds

slide13

Each structure feature in the hierarchy is defined as a substructure search query

Structural definition

atom and bond restrictions

uncovering bias in chemical space within data sets
Uncovering bias in chemical space within data sets
  • Detect + and – coverage within a desired chemical space
  • Understand decision errors that can be introduced with biased space
structural alerts
Structural alerts
  • Can rapidly find structural alerts
  • Can view new libraries in relation to structural alerts
  • Can evaluate impact of alert on optimization scheme
toxscope tm components
ToxScopeTM Components
  • LeadScopeTM Enterprise Technology
  • Several public or commercial databases
  • New databases using “pharmaceutical" chemical space
    • New specific organ toxicity database
      • Structural alerts
    • Continual updates on target organs
conclusion

Conclusion

“… an in silico revolution is emerging that will alter the conduct of early drug development in the future.”

“Preclinical safety must transition from an experimental-based process into a knowledge-based, predictive process, where experimentation is used primarily to confirm existing knowledge”

acknowledgements grushenka wolfgang co author
Acknowledgements Grushenka Wolfgang, Co-author

Julie Roberts Kevin Cross

Bill Snyder Michael Crump

Chris Freeman Jeff Miller

Don Swartz Michael Murray

Ilya Utkin Mark Balbes

Wayne Johnson Zhicheng Li

Allen Richon Yan Wang

Paul Blower Limin Yu

Glenn Myatt Sighle Brackman

Emily Johnson Lisa Balbes