Technological Innovation and Complex Biological Systems: An Overview. Anthony J. Sinskey, Sc.D.

Transcription

1 Technological Innovation and Complex Biological Systems: An Overview Anthony J. Sinskey, Sc.D. Massachusetts Institute of Technology Program on the Pharmaceutical Industry (POPI)

2 Technology Influences Drug Development Agenda: Current Status Network Biology Symposium Overview

3 Things 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

4 Lag from Initial Discovery to Product Persists Pharmaceutical Date of Key Enabling Discovery Date of Market Introduction Lag Time fluconazole (Diflucan ) Mechanism-Driven Random Basic Science 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 ) Source: Iain Cockburn, & Rebecca Henderson

5 Why the Persistent Lag? Single new technology advance necessary but not always sufficient to lead to an innovative pharmaceutical product Convergence of technologies needed

6 Challenges 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

7 Opportunities 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

8 The Importance of Network Biology

9 Investigating Complex Systems Increases Knowledge Return Increasing Knowledge Return drug-disease & economic modeling protein interaction maps molecular interaction networks expression profile structure-activity relationship gene sequences pharmacophore information cell-cell interaction tissue organization cell pathway networks organism pathways organ networks Increasing Complexity

10 Refining the Understanding of Pathogenesis symptoms (body) pathology (organ/tissue) biochemistry (cell) mechanism (molecules)

11 Human Health Depends on Well-Functioning Complex Assemblies

12 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 Understanding Spatial Relationships Controlling Cell Cycle in Fission Yeast Figure from Tyson, et al. Nature Reviews Molecular Cell Biology (2001)2:908.

14 Understanding Temporal Relationships Simulated Time Course of Cell Cycle in Fission Yeast Figure from Tyson, et al. Nature Reviews Molecular Cell Biology (2001)2:908.

15 Technological Advances with Implications for Drug Discovery and Development

16 Current Scientific & Technological Advances Data Generation High-Throughput and Parallel techniques Miniaturization to facilitate highthroughput and parallel experiments Prediction / Modeling Knowledge Generation More testing in silico Information Technology Information management Bioinformatics

17 Integrating Chemical & Biological Microsystems Klavs Jensen, PhD, MIT Drug discovery Clinical diagnostics Advantages: small volumes of expensive reagents, parallel operation, high throughput screening J.D. Harrison (Univ. Alberta)

18 Information 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

19 Omics 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.

20 Microarrays Microarrays allow parallel, combinatorial analysis on small amounts of reagents

21 Small Molecule Microarrays

22 Predicting Outcomes The Learn/Confirm Approach Present Future COLLECT DATA experiments PREDICT OUTCOMES RELATE DATA MODEL OUTCOMES VERIFY PREDICTIONS experiments

23 Potential for Pharmaceutical Innovation from Current 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

24 Challenges for Pharmaceutical Innovation from 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

25 Overview of Symposium: Technological Advances Influencing the Future of Drug Discovery and Development

26 Complex Biology the 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 specificity screening Gavin MacBeath, PhD, Harvard. Image informatics quantitating biological images for screening complex cellular processes by imaging Peter K. Sorger, PhD, MIT.

27 Technological Advances for Addressing Complex, 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.

28 Innovations in Outcomes Research for Managing 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.

29 Promoting Pharmaceutical Innovation through Technological Advances

30 Major Initiative in Network Biology at MIT

31 Promise of Pharmaceutical Innovation Improved Medicines to Address Persistent Health Care Problems: Unmet Medical Needs Drug Efficacy Drug Safety

32 Driving 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