Strategy for Design Space/stability Considerations

1. Strategy for Design Space/stability Considerations Generate process materials Build Design Space Physical Evaluations Chemical Evaluations Develop correlation Correlate to shelflife

2. Incorporating Stability in Design Space Manuf. Design Space Model End of Expiry • Key Research Objectives • What Design Space Outputs Link to Shelf-life • How can the Design Space/Stability Model be used to strengthen or simplify manufacturing design

3. Key Linkage Manuf. Design Space Model Post- Manuf. Stability Model End of Expiry • Post-manufacturing stability model that accounts for storage effects in a predictive way

4. Incorporating Stability in Design Space Manuf. Design Space Model Post- Manuf. Degradation Model Lt End of Expiry L0 F0 • Key Research Objectives • Characterize process altered API • Identify methods to measure L0 and F0 • Develop predictive degradation model • Define effect of processing variation on predictive model • Validate predictive model with long term studies

5. Underlying premise Physical Forms Chemically-active API Degraded API Tendency to transform Formulation Manufacturing Attributes

6. STEPWISE 1 Manuf. Design Space Model Manufacturing Variables Stability-relevant Outputs What are “Stability-relevant” outputs? Data base to develop design space models

7. STEPWISE 2 Time to Expiry (Shelf-life) Storage Variables Post- Manuf. Stability Model Design space Outputs Form of post-manufacturing stability model to account for storage variables (RH, temperature, excipients) Parameterization of model: short-term deg studies Demonstrate of model predictability: long-term deg. studies

8. Effects of manufacturing stress formulation Intact API Altered API Degraded API API MANUFACTURING STRESS CONDITIONS • SSNMR • Initial rate • in-process lactam

9. Development of degradation model Time to Expiry (Shelf-life) Storage Variables Post- Manuf. Stability Model Design space Outputs Form of post-manufacturing stability model to account for storage variables (RH, temperature, excipients) Parameterization of model: short-term deg studies Demonstrate of model predictability: long-term deg. studies

10. Preliminary Post-Manufacturing Degradation Model Disordered GABA LACTAM GABA GABA (G): crystalline (Form II) gabapentin Disorderd-GABA (D): gabapentin with some loss of criticalcrystallinity Lactam (L):Chemically –altered and non-crystalline

11. Linking Stability in Design Space Manuf. Design Space Model Post- Manuf. Degradation Model Lt End of Expiry L0 D0 • Key Research Findings • Methods characterize process altered API: MSM • Solid state degradation model form accounts for temperature, humidity, excipients • Preliminary correlation between MSM and shelf-life • SSNMR methods to verify manufacturing effects

12. The Pharmaceutical Stability Predicament Manufacturing stress Shipping stress Product use stress Storage stress catastrophic gradual Probability of failure (multimodal Performance Drug release kinetics Potency Safety Utility Acceptability critical failure stable Accumulative stress and time lee-kirsch@uiowa.edu (4/14/10)

13. Current and Future Paradigm • Deterministic • stable or not • Measurability-based • “significant change” based on detection • Impact arbitrary • historical rather than situational-based • Prediction based on post-assembly stress • storage environment and time • Stochastic • based on probability • Performance-based • “significant change” based on performance • Therapeutic impact • evaluation of the effects dose regimen, patient population, in vivo performance on stability limits • Prediction includes design, assembly and post-assembly stress lee-kirsch@uiowa.edu (4/14/10)

14. Research Opportunities Design of models to link design space-stability to clinical performance in relevant patient populations based on intended therapeutic use regimens Future state Methodologies for incorporating design space models into stability prediction models Tools to assemble scientifically-rational stability design space models Fundamental physical and biophysical studies of exemplary drug instability processes in complex systems Current state lee-kirsch@uiowa.edu (4/14/10)

15. Overarching objective: integrating stability in QbD 1.Physical and Chemical Markers 2. Design Space Model L0&F0 3.Post-Manufacturing Degradation Model Lt 4. Therapeutic Utility/Safety Model lee-kirsch@uiowa.edu (4/14/10)

16. NIPTE Project Team for Gabapentin Case Study • Research • H. Arastapour , ChE, IIT • Fluidization & multiphase systems • R.Bogner, PhSci, UCONN • Drug release, solid dosage forms • A.Cuitino, ME, Rutgers • Material mechanics, Multiscale • modeling • J. Drennen, PhSci, Duquesne • PAT and Risk Management • S. Hoag, PhSci, Umaryland • compression modeling • M. Khan, PhSci, FDA • Pharmaceutical Technology • L. Kirsch, PhSci, Iowa • Drug stability & quality • J. Litster, ChE & IPPH, Purdue • Granulation & Powder Technology • E. Munson, PhSci, Kansas • Characterization of solid pharmaceuticals • F. Muzzio, ChE, Rutgers • Powder mixing & flow behavior • G.Reklaitis, ChE, Purdue • Process systems engineering • R. Suryanarayanan, PhSci, UMinn • Material science of pharmaceuticals • NIPTE Administration • P. Basu, Exec Director, NIPTE • QbD & Pharmaceutical economics • V. Gurvich, Assoc Director, NIPTE • Medicinal chemistry & organic technology lee-kirsch@uiowa.edu (4/14/10)

17. Essential research questions for addressing instability mechanisms What are the relevant structural probes for identifying and quantifying reactive forms? What is the relationship between physical and chemical transitions? Are there underlying rules that can be used to predict instability based on inherent chemical and physical properties of drug substances and excipients in complex milieu (e.g. solid state formulations) or for complex drugs (e.g. biopolymers)? lee-kirsch@uiowa.edu (4/14/10)

18. 2. Integrating stability probes into design space models: Traditional approach using response surface (e.g. milling) Surface Area Stability lee-kirsch@uiowa.edu (4/14/10)

19. Design Space: acceptable surface area and stability lee-kirsch@uiowa.edu (4/14/10)

20. Essential research questions for advancing design space • What are sophisticated modeling approaches that move away from the flashlight in the cave syndrome? • Methods that incorporate prior knowledge (e.g. Bayesian approaches) • Methods that make realistic parameter distribution estimations • Modeling methods that incorporate our understanding of unit operations physics and material properties • Dr. Drennen’s review of recent approaches lee-kirsch@uiowa.edu (4/14/10)

21. 3. Linking shelf-life and manufacturing models Intact API Altered API Degraded API Degraded API STORAGE STRESS CONDITIONS Formulation Shelf-life lee-kirsch@uiowa.edu (4/14/10)

22. Key research questions: linking DS to stability prediction models • What are effective methods for incorporating the output of design space models (stability-relevant material characteristics) into shelf-life prediction models ? • Application of Bayesian approaches to estimate parameter distributions rather than single-point estimation • Development of biomolecule and small molecule stability models based on isoconversional concepts • Determination of key manufacturing –induced physical changes that form the basis for subsequent physical and chemical instability under environmental stress • Assessment of excipient roles in shelf-life prediction models : Do they catalyze/stabilize chemical or physical transformations lee-kirsch@uiowa.edu (4/14/10)

23. What is a meaningful stability specification? • Is 90 or 95 % potency relevant for the therapeutic use of all drugs irrespective of therapeutic use and index, population variability, pharmacokinetics or pharmacodynamics? • Is 1% or 2% level of a specific related substance meaningful irrespective of the drug-like properties, pharmacokinetics, dosage regimen, or toxicokinetics of that related substance? • Does it make sense from a QbD-standpoint to fix the impurity profile of a drug product based on toxicology studies on pre-clinical drug product batches? • How can we meaningfully address the potential safety and efficacy issues that relate to drug product stability as determined by product design, manufacturing and storage? lee-kirsch@uiowa.edu (4/14/10)

24. Simplified model Dosage Regimen Ranges Probability of Mild Adverse Effects Average Steady-state Concentration Response Model Variation Degradation product profile Clearance Variation lee-kirsch@uiowa.edu (4/14/10)

25. 1.00 0.75 0.50 0.25 0.00 0 .01 .02 Monte-Carlo simulation and logistical regression Meaningful Degradation Product Specification Probability of MAE Maximum acceptable risk fraction of degradation product lee-kirsch@uiowa.edu (4/14/10)

26. Summary of Suggested Stability Research Investments • Molecular basis of instability pathways for complex molecules or for simple molecules in complex formulation milieus • Development of quantitative frameworks for relating the effects of product design variation and manufacturing stress on stability-relevant material characteristics • Methodologies for incorporating the output of design space models shelf-life prediction models • Design and development of population-based clinical product performance models to link design space-stability models to clinical performance in relevant patient populations based on intended therapeutic use regimens lee-kirsch@uiowa.edu (4/14/10)