Technological Innovation and Complex Technological Innovation and Complex Biological Systems: An OverviewBiological Systems: An Overview
Anthony J. Sinskey, Sc.D.
Massachusetts Institute of TechnologyMassachusetts Institute of TechnologyProgram on the Pharmaceutical Industry Program on the Pharmaceutical Industry
(POPI)(POPI)
Technology Influences Drug DevelopmentTechnology Influences Drug Development
Agenda:•Current Status•Network Biology•Symposium Overview
13 December 2001
Things have been ChangingThings have been Changing
Drug Discovery Paradigms Shift with Advancing Technological Capabilities
• “Random” drug discovery: screening compounds using whole or partial animal screens
• Mechanism-Driven drug discovery: screening against a specific known or suspected mechanism
• Fundamental Science discovery
13 December 2001
Lag from Initial Discovery to Product PersistsLag from Initial Discovery to Product PersistsDate of Key Enabling Discovery
Date of Market Introduction
Lag TimePharmaceutical
fluconazole (Diflucan®)gemfibrozil (Lopid®)ketoconazole (Nizoral®)nifedipine (Procardia®)tamoxifen (Nolvadex®)captopril (Capoten®)cimetidine (Tagamet®)finasteride (Proscar®)fluoxetine (Prozac®)lovastatin (Mevacor®)omeprazole (Prilosec®)ondansetron (Zofran®)sumatriptan (Imitrex®)cisplatin (Platinol®)erythropoietin
(Epogen®, Procrit®)
197819621965196919711965194819741957195919781957195719651950
199019811981198119921981197719921987198719891991199219781989
121916122116291830281134351339
Mec
hani
sm-D
riven
Ran
dom
Basi
c Sc
ienc
e
Source: Iain Cockburn, & Rebecca Henderson
13 December 2001
Why the Persistent Lag?Why the Persistent Lag?
• Single new technology advance necessary but not always sufficient to lead to an innovative pharmaceutical product
• Convergence of technologies needed
13 December 2001
Challenges to Pharmaceutical DevelopmentChallenges to Pharmaceutical Development
• “The easy drugs have been done”• Acute diseases or chronic diseases with simpler symptom
profiles
• Simple endpoints (blood pressure, serum cholesterol level) are being exploited
• New drugs will require new technologies and new approaches for disease and patient stratification and staging• Examples include: cancer, diabetes, infectious diseases,
sepsis, MS, autoimmune disorders and diseases
13 December 2001
Opportunities for Pharmaceutical DevelopmentOpportunities for Pharmaceutical Development
• Unprecedented number of new chemical entities to investigate• Products of biotechnology revolution
• New technologies for investigating complex biological systems
• New technologies for measuring drug effects
• New technologies for predicting outcomes
• Integrating New Technologies Effectively will be KEY
The Importance of Network BiologyThe Importance of Network Biology
13 December 2001
Investigating Complex Systems Increases Investigating Complex Systems Increases Knowledge ReturnKnowledge Return
IncreasingKnowledge
Return
gene sequencespharmacophore information
expression profilestructure-activity relationship
protein interaction mapsmolecular interaction networks
cell pathway networks
cell-cell interactiontissue organization
organ networks
organism pathways
drug-dise
ase &
econom
ic modelin
g
Increasing Complexity
13 December 2001
Refining the Understanding of PathogenesisRefining the Understanding of Pathogenesis
symptoms (body)symptoms (body)
pathology (organ/tissue)pathology (organ/tissue)
biochemistry (cell)biochemistry (cell)
mechanism (molecules)mechanism (molecules)
13 December 2001
Human Health Depends on WellHuman Health Depends on Well--Functioning Functioning Complex AssembliesComplex Assemblies
13 December 2001
“If we hope to understand biology, instead of looking at one little protein at a time, which is not how biology works, we will need to understand the integration of thousands of proteins in a dynamically changing environment” – Craig Venter (1999), CEO Celera Genomics as quoted in Butler (Nature (1999)402:C67-C70.)
13 December 2001
Understanding Spatial RelationshipsUnderstanding Spatial Relationships
Controlling Cell Cycle in Fission YeastControlling Cell Cycle in Fission Yeast
Figure from Tyson, et al. Nature Reviews Molecular Cell Biology (2001)2:908.
13 December 2001
Understanding Temporal RelationshipsUnderstanding Temporal Relationships
Simulated Time Course of Cell Cycle in Fission YeastSimulated Time Course of Cell Cycle in Fission Yeast
Figure from Tyson, et al. Nature Reviews Molecular Cell Biology (2001)2:908.
Technological Advances with Implications Technological Advances with Implications for Drug Discovery and Developmentfor Drug Discovery and Development
13 December 2001
Current Scientific & Technological AdvancesCurrent Scientific & Technological Advances
• High-Throughput and Paralleltechniques
• Miniaturization to facilitate high-throughput and parallel experiments
• Prediction / Modeling
• More testing in silico
• Information Technology
• Information management
• Bioinformatics
Data Generation
Knowledge Generation
13 December 2001
Integrating Chemical & Biological Microsystems Integrating Chemical & Biological Microsystems –– Klavs Jensen, PhD, MITKlavs Jensen, PhD, MIT
• Drug discovery • Clinical diagnostics
• Advantages:• small volumes of
expensive reagents,• parallel operation,• high throughput screening
J.D. Harrison (Univ. Alberta)
www.gyrosmicro.com
www.aclara.com
www.nanogen.com
www.caliper.com
13 December 2001
Information Needs to be Transformed into KnowledgeInformation Needs to be Transformed into Knowledge
• Identifying drug targets alone (i.e. genes involved in diseases) will not yield many new drugs
• Sequence implies Structure implies Function
image from Searls (2000) Drug Discovery Today(5)4:135
13 December 2001
OmicsOmics
• Genomics, Proteomics, Metabolomics, Phenomics, Epigenomics, Ligandomics, etc.
• Definition: Study of entities in aggregate, e.g. the entire complement of RNA, DNA or other molecule in a cell, tissue or organism
• Databases of molecular data generated
• Valuable tools for analyzing cells
Science (1998) 282:627.Nature Biotech. (2000) 18:127.
13 December 2001
MicroarraysMicroarrays
• Microarrays allow parallel, combinatorial analysis on small amounts of reagents
13 December 2001
Small Molecule MicroarraysSmall Molecule Microarrays
13 December 2001
Predicting Outcomes Predicting Outcomes The Learn/Confirm ApproachThe Learn/Confirm Approach
Present Future
•experiments
VERIFYPREDICTIONS
COLLECTDATA
PREDICTOUTCOMES
•experiments
RELATEDATA
MODELOUTCOMES
13 December 2001
Potential for Pharmaceutical Innovation from Potential for Pharmaceutical Innovation from Current Scientific AdvancesCurrent Scientific Advances
Improved Medicines to Address:• Unmet Medical Needs
• Treatments for known diseases that currently lack treatments
• Treatments for diseases not yet recognized• Drug Efficacy
• More reliable patient response to therapies• Drug Safety
• Fewer side effects
13 December 2001
Challenges for Pharmaceutical Innovation Challenges for Pharmaceutical Innovation from Current Advancesfrom Current Advances
• Effective acquisition and integration of technological advances
• Conversion of data from genomics, proteomics and other high-throughput data-gathering technologies into medically relevant knowledge – i.e. understanding of complex systems that underlie cell physiology
• Successful application of that knowledge toward improved productivity in drug development
Overview of Symposium:Overview of Symposium:Technological Advances Influencing the Technological Advances Influencing the
Future of Drug Discovery and DevelopmentFuture of Drug Discovery and Development
13 December 2001
Complex Biology Complex Biology –– the Future of Target Selectionthe Future of Target Selection
• Integrated chemical and biological microsystems to speed throughput and reduce sample use – Klavs F. Jensen, PhD, MIT.
• Combining combinatorial biochemistry techniques and bioinformatics to predict signaling pathways system-wide – Michael Yaffe, PhD MD, MIT.
• Multiplexing protein analysis for rapid discovery of system-wide protein interactions and specificityscreening – Gavin MacBeath, PhD, Harvard.
• Image informatics – quantitating biological images for screening complex cellular processes by imaging – Peter K. Sorger, PhD, MIT.
13 December 2001
Technological Advances for Addressing Technological Advances for Addressing Complex, Therapeutically Challenging DiseasesComplex, Therapeutically Challenging Diseases
• Models for improving drug development efficiency that integrate available information for use in predicting outcomes and gauging risk – Terrance F. Blaschke, MD PhD, Pharsight Corp.
• Cell-based methods for characterizing targets in molecular oncology – John D. Haley, PhD, OSI Pharmaceuticals.
• New technologies affecting care of diabetes ranging from genomics and proteomics for understanding etiology to new drug delivery methods and point of service diagnostics – Alan C. Moses, MD, Joslin Diabetes Center.
• New measurement technologies could facilitate trials by advancing clinical investigation practices – Robert H. Rubin, MD, MIT.
13 December 2001
Innovations in Outcomes Research for Innovations in Outcomes Research for Managing Drug DevelopmentManaging Drug Development
• Applications of information technology to retrospective health care data analysis – Stan N. Finkelstein, MD, MIT POPI.
• Characteristics of retrospective health care databases with important implications for statistical analyses – William H. Crown, PhD, The MEDSTAT Group.
• Randomized clinical trials and tensions over drug costs – Suzanne Wait, PhD, Bristol-Myers Squibb.
• Regulatory view of non-randomized controlled data for evaluating pharmaceuticals – Robert T. O’Neill, PhD, CDER FDA.
Promoting Pharmaceutical Innovation Promoting Pharmaceutical Innovation through Technological Advancesthrough Technological Advances
13 December 2001
Major Initiative in Network Biology at MITMajor Initiative in Network Biology at MIT
13 December 2001
Promise of Pharmaceutical InnovationPromise of Pharmaceutical Innovation
Improved Medicines to Address Persistent Health Care Problems:• Unmet Medical Needs• Drug Efficacy• Drug Safety
13 December 2001
Driving Pharmaceutical InnovationDriving Pharmaceutical Innovation
• Advances in basic biomedical science and technology
• Growing amounts of biomedical information: network biology
• Improved technology for predicting outcomes
• Effective integration of technologies
