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Multiple Storage Conditions: Accumulating Uncertainty

Multiple Storage Conditions: Accumulating Uncertainty

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Multiple Storage Conditions: Accumulating Uncertainty

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  1. Multiple Storage Conditions: Accumulating Uncertainty Brad Evans Pfizer MBSW, May 23, 2012

  2. Question • How to model Stability across: • DS storage, multiple temperatures/times • DP storage, multiple temperatures/times • We run studies at each temperature, in parallel (to save the time) • However, final commercial product goes through each temperature in serial fashion (and may not hit max time)

  3. Outline • Biologics,but concepts apply to small molecules • Proteins • Vaccines • Stability of Biologics • DS and DP Stability data (ICH, other) • Modeling, assumptions, data in, output • Usage scenarios • Questions, future work

  4. Stability of Biologics • Storage: • In process holds: ambient, refrigerated • Bulk Drug Substance • In-process • Frozen • Cryo-Vessels • Glass vials (lyophilized powder typically, some PFS) • Temperatures: • 25C, 40C – accelerated conditions • -40C “typical” DS frozen storage. • DP (liquid or lye powder) typically 2-8C (5C here) • -70C and -20C also used at times

  5. Storage cost$ • -40°C walk in that can hold up to 40 x 300L cryovessels: ~ $1.33 million • CU5000 + Freeze/Thaw 100 : $700,000+ • CU5000 + cryomixer: almost $500,000  • Modular freezer that can hold 9 x 300L cryovessels is over $500,000 • Cost: Controls, engineering, scale, freeze profiles

  6. Control Unit and Cryovessel Freeze / Thaw unit

  7. Exposure / Stability data • Data collected at different steps • In Process Holds (DS/API) • Storage/Packaging/Labeling • Formal ICH Stability lots • Excipient Robustness Studies • Shipping studies • Long Term Storage (DP)

  8. Patient Exposure vs Data • At patient usage, the product has been exposed to multiple temperatures for varying amounts of time • Modeling should reflect this • Could go to conditional modeling if data were available (out of scope) • Stability testing in parallel

  9. Total Degradation • What is the net impact? Net uncertainty? • Which attributes are the most limiting? • Which storage temperature / time combination is the most limiting? • What times periods could/should be changed? • Tradeoff of storage cost and degradation • Statistics can play a strategic role here

  10. Design • Patients: • samples exposed to maximum storage at each intended temperature Analytical • Remaining samples  “next” storage for maximum time  Analytical (ie serial study) • Would only need data from ‘last’ condition • Internal Studies: • Ideal study would take too long • Data collected in “parallel” • Need data from all conditions

  11. Stability Data • ICH Batches: 0,1,3,6,9,12,18,24 (typical) • Ad hoc studies • Excipient Robustness studies • In process studies • Shipping studies • Assays of interest • Size Exclusion • Monomer / Purity • etc

  12. Degradation Rates from Stability Studies of different lengths, at different Temperatures Three rates for Drug Substance Two rates for Drug Product 0 9 18 27 36

  13. Cumulative Degradation over Maximum Storage Time Spec Limit = problem? (what about allowance for uncertainty?) DP 2 Each study estimates the Degradation rate for that condition. Net degradation within a condition = Rate * Time DP 1 DS3 To predict the mean we can use the slopes and intercepts from each study Stability studies may be of different lengths than the storage conditions DS 2 DS 1 12 24 36 48

  14. Combining across Temperatures • Follow ICH guidance? • at every storage • use worst case • which batches / lots /studies to combine • Reasonable model at each storage? • Not an ICH stability analysis • Slope may not be stable initially • Common Slope / Intercept at each storage? • Follows the center of the process • Gives an idea if storage times might work

  15. Modeling • Assumptions • Slopes within storage condition independent of starting values • Linear fit regardless of length of storage • “Daisy Chain” the predictions together • data at end of “this” storage lines up with data at the start of “next” storage • This is what **would** happen if the study were conducted serially • rate of change estimated within each temperature condition

  16. Flow • Data in: • Stability Exposure Data (actual, multi temperature) • Production Exposure Data – many scenarios possible (what if scenarios?) • Multiple Assay(s)? • Output (per assay) • Predictions within each storage • Predictions ACROSS multiple storage

  17. Scenarios • What is limiting assay? • What storage “burns” the spec window? • Should a storage time be shortened? • Could a storage time be lengthened? • Would a different assay be limiting under a different scenario? • What is the cost to go to colder storage relative to degradation slope: • 5C (slope, cost) vs -20C (slope, cost) • Limiting assay may change

  18. Stacking across storage • Time scale is easy • New Time = sum (all times so far) • May not be the same for actual vs. production • Response is a little trickier: • Initial storage: need Intercept (Tzero) and slope • Subsequent: need only the slope, ie rate • New Response = Tzero + Sum(duration x slope) • Depends on actual or production • Actual – the analytical going into the model • Production – the storage times you want to consider

  19. Statistical Details • Prediction at “exposure” is sum of predictions: • Each piece has an associated variance • df error add across the regression models • RMSE could be a pooled RMSE • more efficient estimation • assumes variance does not depend on the mean • model selection would be more complicated • rationale: “same” assay, “same” compound

  20. Statistical Details Similar calculations for each group (SAS loop through groups) var = rmse1**2 * ( 1/n1 + (x - xbar1)**2/ss_x1); Varp = rmse1**2 * (1 + 1/n1 + (x - xbar1)**2/ss_x1); Pred = int1 + slope1 * x; se = sqrt(var); sep = sqrt(varp); t = tinv(1-&alphalevel/2,df1); accumulate across storage, eventually leading to: upperCLM = pred + t * se; lowerCLM = pred - t * se; upperCLP = pred + t * sep; lowerCLP = pred - t * sep; Variances add but t-mult drop with increasing degrees of freedom, so width grows at less than sqrt(# groups)

  21. Comment • I do NOT see this as a spline or change point regression • To do this, we would need to conduct the serial study • All samples exposed to ONE and only one condition • Initial sample: Tzero or baseline, ie intercept • First storage: Tzero + degradation in that condition • Second: Tzero + degradation in that condition • Etc • Total degradation to the product: • Tzero + Degradation in Storage 1 + Degradation Storage 2 +

  22. Caveats • Worst batch may change over time • All Stability data used even if production is less • RMSE from full stability design • Prediction variance based on full design, ie all time points • Model fit should be checked • Nonlinearity may occur • One batch / one result may be very influential

  23. Input

  24. Output

  25. Output for CS/CI

  26. Common Slope Common Intercept

  27. Separate Slopes, Separate Intercepts (worst case)

  28. Recap / Questions • Modeling reflects exposure seen in consumer product • Uses any data available • Can consider different scenarios • ICH, CS/CI, alpha levels • Varying time within each temperature • Internal decision making tool