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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
• ~ $700 MM and over 10 years to develop
novel drug
• Approximately 75% of overall R&D cost
attributed to failures
The “Solution” for R&D
• Identify/eliminate problematic drugs early
• Design desirable properties into drugs
Drug Discovery: the hunting process
where is toxicology today?
Target
Selection
Lead
Identification
Lead
Optimization
• Identification of
potential targets
• Screen development • Lead explosion/
optimization
• Target verification
• High-throughput
screening
• Potency in disease
• Target selection
• Secondary assays/
• Pharmacokinetics
mechanism of action
• Hits to leads
From: Rosamond and Allsop, Science 287, 1973 (2000)
• Early toxicology
Early toxicology at the Lead Optimization Step: still a
high failure rate – high cost to R&D
ADME, PK, TOX
Lead optimization
Chemical
Libraries
Primary &
secondary
efficacy
screening
Secondary
in vitro
screening
In vivo and
mechanistic
screens
Lead
selection
Development
Candidate
65%
Drop Out
IND enabling studies
Phase I, II
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
• 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 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
• 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”
• Flexibility to incorporate all data from
internal and external sources
• Web-based, platform independent
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
• ~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
Feature Hierarchy
Information Windows
Graphic Panel
Filter Panel
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
Each structure feature in the hierarchy is defined
as a substructure search query
Structural definition
atom and bond restrictions
Compounds containing a pyridine, 2-(alkenyl, acyc)
substructure
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
• Can rapidly find structural alerts
• Can view new libraries in relation to
structural alerts
• Can evaluate impact of alert on
optimization scheme
RTECS grade 5 only
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
“… 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
Julie Roberts
Bill Snyder
Chris Freeman
Don Swartz
Ilya Utkin
Wayne Johnson
Allen Richon
Paul Blower
Glenn Myatt
Emily Johnson
Kevin Cross
Michael Crump
Jeff Miller
Michael Murray
Mark Balbes
Zhicheng Li
Yan Wang
Limin Yu
Sighle Brackman
Lisa Balbes