Ray Scherzer FDA ACPS October 26, 2005 Breaking with Tradition: The Manufacturing Challenges Ahead!

2 Setting the stage The Traditional Pharma Business Model … changes underway Our current technology The challenge ahead! A vision of the future What can you do??

3 The 70s, 80s and 90s … the traditional model Double Digit Growth + + Profits, $$$$$ Challenging Regulators

4 Industry pressures today Pharma Industry Regulatory Scrutiny Attrition Payer resistance Politics Pricing Generics& Re-importation

5 Fundamental change in regulatory framework Past Avoid change Quality “tested in” Regulatory fear Silo organisations Empirical science Future Opportunities Innovation Good science Collaboration Efficiency Quality by design Real time release Regulatory hurdles PAT CGMPs Critical path

6 Regulatory changes Major shift by FDA Dedicated Pharma inspectorate Approvals and inspections focused on scientific and engineering principles Hiring physicists, chem engineers, C&I engineers, statisticians... Plus! Empirical methods are last resort

7 Significant impacts Higher scrutiny of existing products Higher expectations for new products “If you can’t explain how your manufacturing processes work in the first 25 pages of your submission … the approval process will become much more difficult!” Moheb Nasr, FDA, Director Office of New Drug Chemistry …

8 The changing pharma business model Discover Develop Test Launch Market 80s: 7 -8 yrs 90s: yrs 00s: 10+? yrs $500m $800m $1,700m Launch costs Launch time Avg. ROI, % > 10

9 Today’s business is much different than yesterdays! The Industry will and is changing!!

10 Current Technology

11 DESCRIPTIVE KNOWLEDGE CORRELATIVE KNOWLEDGE CAUSAL KNOWLEDGE MECHANISTIC KNOWLEDGE 1st Principles Control and release against process signature Establish process outline Prediction of performance (in-vivo) Correlate process inputs and outputs Relate critical process variables to quality attributes of finished product Extent of knowledge Manufacturing process knowledge Industry

12 V Blenders: lab and pilot plant scale

13 Commercial scale

14 “We’ve got a few problems going from lab scale to full scale commercial”

15 The Challenge Ahead!!

16 DESCRIPTIVE KNOWLEDGE CORRELATIVE KNOWLEDGE CAUSAL KNOWLEDGE MECHANISTIC KNOWLEDGE 1st Principles Control and release against process signature Establish process outline Prediction of performance (in-vivo) Correlate process inputs and outputs Relate critical process variables to quality attributes of finished product Extent of knowledge Manufacturing process knowledge Industry target

17 Significant gaps exist: Manufacturing and scale up sciences Unit operation technology and control Academic training & skilled resources Industrial organization and structure Correlation to in vivo performance

18 First steps: Unit operation science Reaction Crystallization Drying Separation Particle engineering Formulation Dispensing Blending Granulation Compression Coating Filling Aseptic operations Packaging Material Handling, Analytical Industry basic unit operations

19 Unit operations goals Well understood platform technologies Develop the science of all unit operations Fully instrumented Closed loop control … fully automated Material interactions (formulation & devices) Predictable scale effects Design/use the right equipment Predict performance without extensive experimentation Math modeling to speed design GOAL: Final testing to confirm operations

20 Once UOs understood and platform technologies developed, then Integrated Process Designs

21 Integrated process design: Objectives Aligns with FDA’s Quality by Design Concept Link “Platform Technologies” in an integrated process design 1 ST step of API to primary pack and device performance Identify CCPs that affect up and down stream ops Control systems will manage variability within the process Link CCPs to traditional release testing; i.e. dissolution, assay, CU, ACI Produce in spec product by monitoring and controlling critical parameters … rather than end point testing Obtain real time release

Integrated process design

23 Engineering models would: Process design tool of preference Rapid evaluation of excipients, DS, formulations, equipment, environmental, devices, etc. Narrow alternatives in silica Reduce scale up trial and error … focus testing on high probability results … time & money!! After confirmation, use model to demonstrate full process understanding … regulatory expectation Would be the basis for continuous improvement studies

24 The future manufacturing vision! Fundamental understanding of the science Develop mfg scale processes … before registration Small scale, contained, dedicated, automated, continuous processes Late stage customization On line measurement and control Real time release Product plants … not component plants Leverage relationships … internal, academia, industry, regulatory agencies to develop the science

25 Gaps in skills and facilities exist Manufacturing sciences Powder technology Chemical / Process engineers Rheologists (non Newtonian fluids) Physicists Spectromisists Chemometricians Process development pilot plants (not CT PPs) “Soft skills” & Business skills

26 Some current activities Industry/Company Culture changes underway Empirical to fundamental sciences Industry pressures key driver External work lays foundation (FDA, ASTM, CAMP, ISPE, IFPATma) Develop the next level of manufacturing science PAT and cGMPs for 21st century Pharma professional of the future … engineering + Universities need to develop and teach the science Capitalize on today’s situation to forge an even stronger future

27 Your role in this: Support/create means for fundamental research in the Pharma manufacturing sciences Encourage students into science & engineering careers Encourage universities to create the programs Be consistent and science based in your activities Give this priority and attention

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29 Questions