FDA’s Critical Path to New Medical Product Development Opportunities from the Center for Devices and Radiological Health Larry Kessler, Sc.D., Director Office of Science and Engineering Laboratories, CDRH

Outline Devices are different and varied Our vision and the critical path Device “opportunities” Directions for the future Evaluative tools: The techniques & methodologies needed to evaluate the safety, efficacy & quality of medical products as they move down the path

Examples of some of these medical devices… Test Strips Infusion pump Patient Examination Table Heart Valve Contact Lens Hip Implant Pacemaker Biopsy Device Blood Pressure Cuff Stethoscope

CDRH’s Vision Ensuring the Health of the Public Throughout the Total Product Life Cycle -- It's Everybody's Business Underlying Assumptions: Covering products from concept to obsolescence Connected global public health community Stakeholders are our partners Science fuels the regulatory engine Meeting all statutory responsibilities Meeting our own standards for quality

Areas that FDA’s Critical Path Can Impact

Imaging and the ROC curve Digital mammography Initial studies ignored reader variability True clinical trials too big Multiple-reader multiple-case ROC analysis developed by FDA (and others) Studies became accessible Products on the market!

Imaging of Peripheral Vasculature Drug eluting stents proven for coronary Will standard imaging of lower extremities provide adequate data? Requires independent study to establish data and methods for later trials

Ablative Therapies Ablative therapies for atrial fibrillation AF – commonly diagnosed Problem in demonstrating true patient benefit Requires large patient series Requires agreement on endpoints and data analysis from co-sponsored study

Cataractous Crystalline Lens Intraocular lens (IOLs) Successful but has limits Single focal length unlike human lens Critical path issues: assessment of – Optical quality Material properties Biocompatibility

Genomic and proteomic devices Microarray expression discovery phase For multiple expression profile an algorithm is needed for interpretation Current algorithms are “learning” FDA products require reliability and consistency No standards yet for these algorithms

Future Directions Identify key problems in devices Prioritize scientific efforts Pursue collaborations Within FDA With NIH (e.g., NIBIB, NCI) With industry