1. Hot Topics in Chemoinformatics in the Pharmaceutical Industry
David J. Wild, Ph.D.
Scientific Computing Consultant, and
Adjunct Professor of Pharmaceutical Engineering at the University of Michigan

2. About me B.Sc Computer Science Ph.D. Chemoinformatics (Willett Lab)
Worked for 5 years in Scientific Computing leadership at Pfizer, responsible for the development of computational tools for scientists
Now run a consulting firm based in Ann Arbor, Mich., and am also an Adjunct Professor at the University of Michigan.
doing some research
Wild Ideas Consulting
University of Michigan
www-personal.engin.umich.edu/~wildd

3. What we’ll cover today
Overview of early-stage drug discovery and the big industry concerns
Using information and technology together to improve the chances of finding a new drug
Example – High Throughput Screening
Some other examples of “hot” areas
Genomics & Proteomics Information Handling
Virtual Screening
Combinatorial Chemistry
Design of scientific software

4. Characteristics of the pharmaceutical industry
Very segmented market – largest company (Pfizer) only has an 11% market share
High risk, long term – takes years to develop a drug, and most drugs fail to get to market
Highly regulated (by FDA)
High profit margins for drugs which do make it
Investors traditionally expect high return on investment
Four main phases: discovery, development, clinical trials and marketing

5. R&D spending up, new drugs down
Taken from

6. Drug Discovery & Development
Identify disease
Find a drug effective
against disease protein
(2-5 years)
Isolate protein
involved in
disease (2-5 years)
Preclinical testing
(1-3 years)
Human clinical trials
(2-10 years)
File IND
Formulation &
Scale-up
File NDA
FDA approval
(2-3 years)

7. Impact of new technology on drug discovery
The last few years have seen a number of “revolutionary” new technologies:
Gene chips, genomics and HGP
Bioinformatics & Molecular biology
More protein structures
High-throughput screening & assays
Virtual screening and library design
Docking
Combinatorial chemistry
In-vitro ADME testing
Other computational methods
How do we make it all work for us?

8. GENOMICS, PROTEOMICS & BIOPHARM.
Potentially producing many more targets
and “personalized” targets
HIGH THROUGHPUT SCREENING
Identify disease
Screening up to 100,000 compounds a
day for activity against a target protein
VIRTUAL SCREENING
Using a computer to
predict activity
Isolate protein
COMBINATORIAL CHEMISTRY
Rapidly producing vast numbers
of compounds
Find drug
MOLECULAR MODELING
Computer graphics & models help improve activity
Preclinical testing
IN VITRO & IN SILICO ADME MODELS
Tissue and computer models begin to replace animal testing

9. There is little “hard data” on using the new technologies
In a sense, the drug design process is becoming a big experiment
Do we continue as before, and carefully introduce new technologies as we deem appropriate, or do we radically change the way things are done?
Lots of pressure for the new technologies to yield results quickly
How do we measure the results?

10. Some questions being asked
Is our increasing spending on R&D and new technologies really going to pay off? Or was it a red herring?
Is the paucity of drugs in the pipeline because we’re not doing things right, or are we just hitting limits on the number of major diseases with potential treatments still to be found? (“all the low-hanging fruit has gone”)
Should we be looking in new areas (e.g. “life enhancment” rather than “life saving” or “quality of life”)

11. What’s being done
Trying to get the right Attrition (=drugs dropping out of the pipeline). Aim to increase early-stage attrition and reduce late-stage attrition
Risk analysis – look ideally for low-risk, high-payoff drugs
Using metrics to monitor successes and failures

12. Analyzing risk
High risk
Low payoff
High payoff
Low risk

13. Using metrics to monitor improvement
Split the discovery process into discrete units, with key decisions at the end of each unit.
Come up with measurable properties that can be used to gauge success
Look for good and bad decisions and why they were made
Stage
Decision Point
Target exploration
Go with this target?
HTS
Was the screen successful?
HTS Analysis
Follow up these 5-10 series
Series Followup
Produce 2-3 lead compounds
ADME study
Are compounds safe?

14. Summary
The pharmaceutical industry is a high-risk industry with very long development times and short product lifespans
There has been a lot of investment in new technologies for early stage drug discovery, but so far these are not resulting in more drug candidates (or profits)
Companies are looking at ways to address this problem including managing attrition, risk analysis and metrics.

15. How Chemoinformatics can help out
Producing and manage information for metrics
In-silico analysis to reduce risk, e.g.
Virtual screening
Library design,
Docking
Cost/benefit analyses
Making information available at the right time and the right place
Needs to be integrated into processes

16. An example: High-Throughput Screening
Screening perhaps millions of compounds in a corporate collection to see if any show activity against a certain disease protein

17. High-Throughput Screening
Drug companies now have millions of samples of chemical compounds
High-throughput screening can test 100,000 compounds a day for activity against a protein target
Maybe tens of thousands of these compounds will show some activity for the protein
The chemist needs to intelligently select the classes of compounds that show the most promise for being drugs to follow-up

18. Informatics Implications
Need to be able to store chemical structure and biological data for millions of datapoints
Computational representation of 2D structure
Need to be able to organize thousands of active compounds into meaningful groups
Use cluster analysis or machine learning methods to group similar structures together and relate to activity
Need to learn as much information as possible from the data (data mining)
Apply statistical methods to the structures and related information

19. HTS Tools – Tripos SAR Navigator
SAR Navigator is © Tripos, inc.,

20. BioReason ClassPharmer
Clusters actives into groups representing series
Attempts to find a scaffold using MCS algorithm
Recovers inactives back into series
Presents series as rows in a “spreadsheet” view
Gives other statistics on series, such as activity distribution

21. BioReason Classpharmer

22. BioReason Classpharmer

23. Strategy for “HTS Triage”
Run HTS
Decided which compounds are “active” and which are “inactive”
Cluster the actives to put them into series
Visualize clusters of actives (showing 2D structures) and pick series of interest
Identify “scaffold” for each series
Use similarity or substructure search on inactives to find inactives related to these series
Use SAR techniques to discover differences between actives and inactives in a series

24. Information generated at different points in the Drug Design process
Gene chip experiments
Protein structures
Project selection decisions
Assay protocols
HTS results
Series selection decisions
SAR studies
Combinatorial Expts.
Pharmacophores
ADME studies
Lead cmpd decisions
Toxicology studies
Scaleup reactions
File IND
File NDA
Clinical Trials data
Doctor/patient studies
Marketing, surveys, etc

25. Information generated at different sites

26. Distributed goals model

27. Shared goals model

28. Information storage breakdowns
Large amounts of information generated:
Some is not kept at all
Some is kept but loses its meaning
Often data is kept, but not semantics or decisions
e.g. keep “the HVX2 assay result for this compound was 3.2”, but not what the assay protocol was, whether the compound was considered ‘active’, nor whether it was followed up on.
“Bigger picture” or derivative information is usually not stored
E.g. “all the compounds with a tri-methyl group seemed to have much lower activity for this project”

29. Information access breakdowns
Some information is only available in one physical location
Some information is only available within one part of the discovery process
Often information is not “contextualized” for use outside a particular domain
When someone is clear about a piece of information they need; that piece of information exists, but they don’t know how to access it.
E.g. What system to use, what Oracle table it’s in, or even the knowledge of whether that piece of information does exist!

30. “Missed opportunities”
Not a specific breakdown, but if the right piece of information had been available at the right time, better decisions could have been made
E.g.
A group of compounds is being followed up as potential drugs, but a rival company just applied for a patent on the compounds
A large amount of money is being spent developing an HTS assay for a target, but marketing research shows any drug is unlikely to be a success
A group of compounds is selected from an HTS as good candidates for follow up, but 20 years ago they were followed up for a similar project and had severe solubility problems

31. Information use breakdowns
The meaning of data is incorrectly interpreted
A single piece of information is used, whilst using a wider range of information would lead to different conclusions
Lessons learned from one project are incorrectly applied to another
“Fuzzy” information is taken as concrete information

32. What do we do? No large company has really solved the problem
But ongoing attempts include:
Defining information produced and needed at each stage of the discovery process
Improving processes to be more consistent, especially across different sites
Improving information flow between departments and sites
Harmonizing terminology across disciplines and sites
Defining needed “management information” as well as raw data
Looking for “quick win” opportunities
This will presumably impact what is stored in databases and what software is used
Oracle Chemistry Cartridges help

33. Some Other Examples Genomics & Proteomics Information Handling
Virtual Screening
Combinatorial Chemistry
Design of scientific software

34. Genomics & Proteomics Information Handling
Understanding the link between diseases, genetic makeup and expression of proteins

35. Genomics
Genomics is fast-forwarding our understanding of how DNA, genes, proteins and protein function are related, in both normal and disease conditions
Human genome project has mapped the genes in human DNA
Hope is that this understanding will provide many more potential protein targets
Allows potential “personalization” of therapies
ATACGGAT
TATGCCTA
functions

36. e.g. obese, cancer, caucasian compounds administered
Gene Chips
“Gene chips” allow us to look for changes in protein expression for different people with a variety of conditions, and to see if the presence of drugs changes that expression
Makes possible the design of drugs to target different phenotypes
people / conditions
e.g. obese, cancer, caucasian
compounds administered
expression profile
(screen for 35,000 genes)

37. “Chemogenomics” from Vertex
Video:

38. Virtual Screening
Build a computational model of activity for a particular target
Use model to score compounds from “virtual” or real libraries
Use scores to decide which to make, or pass through a real screen

39. Computational Models of Activity
Machine Learning Methods
E.g. Neural nets, Bayesian nets, SVMs, Kahonen nets
Train with compounds of known activity
Predict activity of “unknown” compounds
Scoring methods
Profile compounds based on properties related to target
Fast Docking
Rapidly “dock” 3D representations of molecules into 3D representations of proteins, and score according to how well they bind

40. Present molecules to model
We may want to virtual screen
All of a company’s in-house compounds, to see which to screen first
A compound collection that could be purchased
A potential combinatorial chemistry library, to see if it is worth making, and if so which to make
Model will come out with with either prediction of how well each molecule will bind, or a score for each molecule

41. Combinatorial Chemistry
By combining molecular “building blocks”, we can create very large numbers of different molecules very quickly.
Usually involves a “scaffold” molecule, and sets of compounds which can be reacted with the scaffold to place different structures on “attachment points”.

42. Example Combinatorial Library
Scaffold
“R”-groups
Examples
R1 = OH
OCH3
NH2 Cl
COOH
R2 = phenyl OH Br F CN
R3 = CF3
NO2
phenoxy OH NH R1 NH CN O OH OH C OH NH NH R2 OH
CF3 R3 O
CH3 O OH C
For this small library, the number
of possible compounds is
5 x 6 x 5 = 150 NH OH O

43. Combinatorial Chemistry Issues
Which R-groups to choose
Which libraries to make
“Fill out” existing compound collection?
Targeted to a particular protein?
As many compounds as possible?
Computational profiling of libraries can help
“Virtual libraries” can be assessed on computer

44. Design of Scientific Software
Problems with scientific software tend to occur because of deficiencies in one of three areas:
Software Relevance
Software Usability
Software Management

45. Software Relevance
To be able to make software relevant requires the software designer to understand:
the science, i.e. the domain
the scientific computing techniques that are used in the domain
the possibilites and limitations of software development.
Even with this, it’s hard to match the things we can do with the things that people want or need to do
Techniques like personas and contextual inquiry simply help us understand the people who use the software, their goals, and tasks they want to do

46. Software relevance: Bridge between computation & science
clustering
sim. searching
activity models
scaffold detection
docking
logp calculation
goals:
e.g. produce compounds that have high biological activity
tools
tasks:
“doing a cluster analysis”
“identifying activity-related fragments”
tasks:
work out a chemical synthesis
choose good reagents
try and document some reactions ?
chemoinformatics
science

47. Software Usability
Tend to focus on the method and the science, but not how easy it is for people to get their job done using the software
Programmers tend to make software intuitive for them, but not necessarily the people it is designed for
A usability lab and other techniques can make a HUGE difference to the satisfaction of users and programmers alike!

48. Software Management Disparate set of tools & platforms
Disparate programming styles, languages
A variety of people tend to be writing software
Trained software developers
Enthusiastic scientists
Scientific computing specialists
Focus on the science tends to mean software management is neglected
Everyone hates traditional software management “rules”
But there are ways of making everything work better and having more fun doing it!
Have a recommended basic setup that should help a lot

49. Foundation reading “The Inmates Are Running the Asylum” by Alan Cooper
“Contextual Design” by Hugh Beyer and Karen Holtzblatt
“Usability Engineering” by Jakob Nielsen
“The Visual Display of Quantitative Information” by Edward Tufte
“Don’t Make Me Think!” by Steve Krug
See also,

50. Summary
R&D in the pharmaceutical industry is undergoing a lot of technological changes, and there is pressure to make the investment pay off
There is a big need to sensibly use the large amounts of chemical and biological-related information produced in the process
Thoughtful use of chemoinformatics methods and software is becoming crucial to the success of drug discovery