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DRAFT
Genomics and Bioinformatics for Drug Discovery and Development
Mary Chitty
  mchitty@healthtech.com 617-630-1316
not being updated

Scope: These sections focus on the use of genomics and proteomics (and other -omics) in combination with bioinformatics, cheminformatics for drug discovery and development, current and potential uses in pre- clinical, and clinical trials, and the quickly growing, still evolving role of  molecular medicine  Biopharmaceutical manufacturing is not a major focus, but the efforts involved in ramping up to higher throughput and scaling in going from the R&D lab into clinical use are not inconsequential.

Biotechnology began as a means for producing biopharmaceuticals.  It is still in the process of becoming an integral part of drug discovery and development. Use of genomics and proteomics is still primarily at the earlier stages of drug discovery pipelines.

Key information resources
Biology
Genomics Information resources

Proteomics  information resources 
Bioinformatics  information resources

Chemistry
Combinatorial chemistry & chemical genomics  information resources
Cheminformatics  information resources

Business information resources
Drug discovery and development Information resources
     Pharmacogenomics  information resources 
     Toxicogenomics  information resources
       Clinical trials

Molecular Medicine  Information resources
     Redefining diseases
     Redefining diagnostics

Bioethics information resources
Bio Terminology information resources

Summary What do (and donít) we know now?
Genomics
We still donít know what over half of human genes do (functional genomics).
Simpler organisms, used experimentally, can help determine human functions (comparative genomics).
Genomics is still at the earliest stages of the drug discovery and development process.

Bioinformatics
Isn't just about sequencing anymore. 
Pharmaceutical R&D is pushing the computational envelope trying to analyze and meaningfully interpret very large quantities of extremely noisy biological data.

Proteomics
But proteins do most of the work.
Humans may only have 30K genes. (Wheat and barley have more.)
Estimated number of human proteins: 
With alternative splicing, post-translational modifications and differential gene and protein expression, in different cells and tissues and during different points in cell cycles AND varying in health and disease there is still a lot to understand.

The most commonly expressed proteins are probably the least informative. Better ways of measuring and studying rare proteins and transcripts are needed.

Chemistry
There isnít enough matter in the universe to make all the possible combinations of combinatorial chemistry
Chemical genomics uses chemical probes to aid both drug target identification and target validation.

Cheminformatics
Keeping track of and meaningfully interpreting all the variables gets even harder with three dimensional protein structures, and four dimensions plus (temporal- spatial aspects) and highly dimensional (protein-protein interactions) data.
New algorithms are required. More and better data is needed. 

Drug discovery
We have an unprecedented glut of novel targets. Technologies for validating targets havenít caught up yet.

Drug development
Is increasingly information- driven.
(How many drugs now have known mechanisms of action? 30% up from zero since when?)
Linear handoffs between biology and chemistry need to become more parallel.
Compounds that will fail eventually need to fail sooner (reducing attrition) to lower costs of later phase trials. 

Target validation (not just target suspects and target identification) must reflect specified diseases, showing "reasonable evidence" that target modulation reverses (or ameliorates) disease phenotypes, not just suggest that a gene/protein "causes" a disease.
Weather prediction is a useful metaphor.

Technologies
Metabolic profiling (high throughput assessment of tissue or cellular metabolites), can create effective molecular phenotypes.
Integrating metabolic profiling with genomic and proteomic data for a systems biology approach is promising.
RNAi (RNA interference) is being used for more effective target screening and to reduce costs.
Using functional validation earlier in the pipeline process is attractive.  Current functional validation strategies (such as antisense) will be used at earlier stages if cost and throughput limitations are overcome.
Microarrays  have promising clinical research applications, particularly in oncology. (So do even more nascent protein arrays).
Automating, reproducibility, ramping up throughput and cost-effectiveness are all serious challenges in moving technologies from the R&D lab into clinical testing, and eventual FDA approval.

Pharmacogenomics/Personalized medicine holds great potential Ė and peril, threatening further fragmentation of an already fragmented industry.
But could also speed clinical trials, create more targeted, less toxic drugs, and reduce costs incurred now by overtreating patients not needing, or unlikely to benefit from specific drugs.

Toxicogenomics
Appears much closer to adding value than pharmacogenomics, reducing time required for safety evaluations and perhaps lessening the likelihood of late stage (expensive) tox surprises.

Molecular medicine is already here (600+ clinical tests in GeneClinics, 300+ research), but not for all patients or all diseases.

Business
It's nice to get paid on a regular basis. 
Current increases in R&D costs and decreases in new NCEs and NMEs is not sustainable in the long run.

The risk-adjusted cost per new drug developed under precedented targets came to $700 million, according to Philip Ma, (of McKinsey, in August 2002 Nature Drug Discovery Reviews). By comparison, the risk-adjusted cost per novel target new drug came to $1 billion. Over time, as experience is gained with targets in new protein family classes, the difficulty of developing newer compounds will begin to diminish, and the value of working on novel targets will become more favorable. Just as there are reasons to expect performance to improve in target validation, improved returns on investments in genomics for other applications should as well.

Genomics is now in an awkward adolescence. Disappointments have been great because the tremendous potentials created unrealistic expectations. Even experts failed to recognize, or chose not to focus on, the challenges involved in implementing strategies with such profound implications. Maturation and adjustments are an inevitable part of introducing new technologies into the drug discovery and development process.

What Information professionals can do
Recognize tough choices.
Focus, without putting all eggs in one basket. 
Look for allies and potential partners (in house and outside) and ways to leverage existing strengths. 
Look for ways of integrating information and communicating between "information silos". 
Learn the language and terminology of different specialties. 
The more you know, the more you can admit all you donít know.
There is a lot nobody knows yet. 
Help people articulate what would/might help Ė at a time when this gets harder to express or figure out. 
Working harder isnít always possible. Working smarter Ė and with others Ė probably is.
Expect "equity bets". Plan for ongoing, profoundly dynamic change.
Think globally.  Act locally.

What it means
"A month (or two) in the lab can save you an hour in the [virtual or physical] library." More true than ever. 

Key Information Resources
Genomics and Bioinformatics (and beyond)

Journals and news
Nature, particularly the front matter.
Nature Biotechnology
Nature Reviews Drug Discovery
Science, particularly the front matter.

Business Week, Economist, Financial Times, Forbes, 

 Wall Street Journal others?  Which are the most indispensable?  Business journals

Google News http://news.google.com/  Search for specific topics.
Genome News Network

Databases Database directories

Websites
NIH Roadmap
http://nihroadmap.nih.gov/

What else?  Please send me your nominations mchitty@healthtech.com

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