strategy for design space/stability considerations generate process materials chemical evaluations...
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Strategy for Design Space/stability Considerations
Generate process materials
ChemicalEvaluations
PhysicalEvaluations
Develop correlation
Correlate to shelflife
Build Design Space
Incorporating Stability in Design SpaceManuf.Design SpaceModel
Endof
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
Key Linkage
Manuf.Design SpaceModel
Post-Manuf.
StabilityModel
Endof
Expiry
• Post-manufacturing stability model that accounts for storage effects in a predictive way
Incorporating Stability in Design Space
Manuf.Design SpaceModel
L0
F0
Post-Manuf.
Degradation
Model
LtEndof
Expiry
• 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
Underlying premise
Physical Forms
Chemically-active API
Degraded API
FormulationManufacturing Attributes
Tendency to
transform
STEPWISE 1
Manuf.Design SpaceModel
Man
ufac
turing
Variab
les
Stab
ility
-rel
evan
t
Out
puts
1. What are “Stability-relevant” outputs?
2. Data base to develop design space models
STEPWISE 2
Post-Manuf.
StabilityModel
Time to
Expiry(Shelf-
life)
Stor
age
Variab
les
Des
ign
spac
e
Out
puts
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
Effects of manufacturing stress
API
MANUFACTURINGSTRESS CONDITIONS
IntactAPI
DegradedAPI
AlteredAPI
formulation
• SSNMR• Initial rate• in-process lactam
Development of degradation model
Post-Manuf.
StabilityModel
Time to
Expiry(Shelf-
life)
Stor
age
Variab
les
Des
ign
spac
e
Out
puts
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
Preliminary Post-Manufacturing Degradation Model
GABA (G): crystalline (Form II) gabapentinDisorderd-GABA (D): gabapentin with some loss of critical crystallinityLactam (L): Chemically –altered and non-crystalline
GABA
DisorderedGABA
LACTAM
Linking Stability in Design Space
Manuf.Design SpaceModel
L0
D0
Post-Manuf.
Degradation
Model
LtEndof
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
The Pharmaceutical Stability Predicament
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PerformanceDrug release kinetics
PotencySafetyUtility
Acceptability
Manufacturing stress
Storage stress
Shipping stress
Product use stress
Pro
bab
ilit
y o
f fa
ilu
re(m
ult
imo
dal
Accumulative stress and time
gradual
catastrophic
stable
critical failure
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
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Research Opportunities
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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 incorporatingdesign 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
Overarching objective: integrating stability in QbD
2. Design Space Model
L0&F0
3.Post-Manufacturing Degradation Model
Lt
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1.Physical and Chemical Markers
4. Therapeutic Utility/Safety Model
NIPTE Project Team for Gabapentin Case Study
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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, DuquesnePAT and Risk Management
• S. Hoag, PhSci, Umarylandcompression modeling
• M. Khan, PhSci, FDAPharmaceutical Technology
• L. Kirsch, PhSci, IowaDrug stability & quality
• J. Litster, ChE & IPPH, PurdueGranulation & Powder Technology
• E. Munson, PhSci, KansasCharacterization of solid pharmaceuticals
• F. Muzzio, ChE, RutgersPowder mixing & flow behavior
• G.Reklaitis, ChE, PurdueProcess systems engineering
• R. Suryanarayanan, PhSci, UMinnMaterial science of pharmaceuticals
NIPTE Administration• P. Basu, Exec Director, NIPTE
QbD & Pharmaceutical economics• V. Gurvich, Assoc Director, NIPTE
Medicinal chemistry & organic technology
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)?
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2. Integrating stability probes into design space models: Traditional approach using response surface (e.g. milling)
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Batch size
2 4 6 8 10
Mill
ing
Sp
eed
4
5
6
7
8
5 10 15 20 25 30
Batch Size
2 4 6 8 10M
illin
g S
pe
ed
4
5
6
7
8
0.6 0.8 1.0 1.2 1.4 1.6 1.8
Predicted Degradation (% mole)
Surface Area Stability
220
2111211222110, PPPPPPStabilitySA
Design Space: acceptable surface area and stability
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Batch Size
2 4 6 8 10
Mill
ing
sp
eed
4
5
6
7
8
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
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3. Linking shelf-life and manufacturing models
STORAGESTRESS CONDITIONS
IntactAPI
DegradedAPI
AlteredAPI
DegradedAPI
FormulationShelf-life
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API stressed-process offraction total
ingmanufactur of endat API altered-process undegraded of (%)fraction
ingmanufactur of endat API degraded chemically of (%)fraction
00
0
0
FLF
F
L
total
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
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What is a meaningful stability specification?
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• 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?
Simplified model
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Degradationproduct profile
Dosage RegimenRanges
ClearanceVariation
AverageSteady-state
Concentration
ResponseModel
Variation
Probabilityof Mild
AdverseEffects
Monte-Carlo simulation and logistical regression
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0.00
0.25
0.50
0.75
1.00
0 .01 .02
Pro
bab
ilit
y o
f M
AE
fraction of degradation product
Maximum acceptable risk
Meaningful Degradation Product Specification
Summary of Suggested Stability Research Investments1. 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
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