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

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 L0F0L0F0 Post- Manuf. Degradation Model L t End of Expiry Key Research Objectives Characterize process altered API Identify methods to measure L 0 and F 0 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 Formulation Manufacturing Attributes Tendency to transform

6. STEPWISE 1 Manuf. Design Space Model Manufacturing Variables Stability-relevant Outputs 1.What are “Stability-relevant” outputs? 2.Data base to develop design space models

7. STEPWISE 2 Post- Manuf. Stability Model Time to Expiry (Shelf- life) Storage Variables Design space Outputs 3.Form of post-manufacturing stability model to account for storage variables (RH, temperature, excipients) 4.Parameterization of model: short-term deg studies 5.Demonstrate of model predictability: long-term deg. studies

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

9. Development of degradation model Post- Manuf. Stability Model Time to Expiry (Shelf- life) Storage Variables Design space Outputs 3.Form of post-manufacturing stability model to account for storage variables (RH, temperature, excipients) 4.Parameterization of model: short-term deg studies 5.Demonstrate of model predictability: long-term deg. studies

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

11. Linking Stability in Design Space Manuf. Design Space Model L0D0L0D0 Post- Manuf. Degradation Model L t End of Expiry 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 lee-kirsch@uiowa.edu (4/14/10) Performance Drug release kinetics Potency Safety Utility Acceptability Manufacturing stress Storage stress Shipping stress Product use stress Probability of failure (multimodal Accumulative stress and time gradual catastrophic stable critical failure

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

15. Overarching objective: integrating stability in QbD 2. Design Space Model L 0 &F 0 3.Post-Manufacturing Degradation Model LtLt lee-kirsch@uiowa.edu (4/14/10) 1.Physical and Chemical Markers 4. Therapeutic Utility/Safety Model

16. NIPTE Project Team for Gabapentin Case Study lee-kirsch@uiowa.edu (4/14/10) 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

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) lee-kirsch@uiowa.edu (4/14/10) Surface AreaStability

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 STORAGE STRESS CONDITIONS Intact API Degraded API Altered API Degraded API 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? lee-kirsch@uiowa.edu (4/14/10) 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?

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

25. Monte-Carlo simulation and logistical regression lee-kirsch@uiowa.edu (4/14/10) 0.00 0.25 0.50 0.75 1.00 0.01.02 Probability of MAE fraction of degradation product Maximum acceptable risk Meaningful Degradation Product Specification

26. Summary of Suggested Stability Research Investments 1.Molecular basis of instability pathways for complex molecules or for simple molecules in complex formulation milieus 2.Development of quantitative frameworks for relating the effects of product design variation and manufacturing stress on stability-relevant material characteristics 3.Methodologies for incorporating the output of design space models shelf-life prediction models 4.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)