Predicting the Long-Term Stability of Solid-State Pharmaceuticals
London, March 2015 Garry Scrivens, Ph.D.
Pfizer Global R&D, Sandwich, UK
ASAP (Accelerated Stability Assessment Program): Theory, Limitations and Applications
Garry Scrivens, Pfizer 2
Environmental Factors that Influence the rate of chemical degradation in the solid state
1. Temperature
2. Humidity
3. Light – Accepted rapid ICH accelerated conditions
exist – Packaging used for most solid drug
products protect from light
4. Oxygen level
(etc.?)
Not in scope of presentation
Garry Scrivens, Pfizer
Temperature and Humidity… n Schumacher (1972) and Grimm (1986, 1998) proposed four long-term stability storage
conditions – Zone 1: “Temperate” 21°C/45%RH – Zone 2: “Subtropical and Mediterranean” 25°C/60%RH – Zone 3: “Hot and Dry” 30°C/35%RH – Zone 4: “Hot and Humid” 30°C/70%RH n Temperature, Arrhenius equation (ca. 1889):
k = Ae-Ea/(RT)
Ln k = Ln A - Ea/(RT) Until recently only thought to apply ‘in general terms’ to solid-state pharmaceutical systems
Collision frequency “pre-exponential factor”
Activation energy
Gas constant
Temperature (in K)
Rate constant (e.g. %deg per day)
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Arrhenius Plots
The rate of a chemical reaction at a particular temperature can be interpolated / extrapolated from the rates at other temperatures
Ln k = Ln A – (Ea/R).(1/T)
Ln k
(1/T)
x
x x
x x
Intercept of line = Ln A Slope of line = -Ea/R
Accelerated (high temperature) results
e.g. T = 25ºC
Predicted rate of degradation at
25ºC
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Accurate application of Arrhenius to the solid state
Ken Waterman et.al.1 cites two main reasons that led to the historical misconception that Arrhenius does not apply accurately to solid-state pharmaceuticals:
a) API is in multiple different micro-environments in solid state (This can lead degradation curves that cannot be defined as simply 0th, 1st or 2nd order curves - which can lead to errors in defining a reliable rate constant for chemical degradation)
b) Effect of relative humidity is not factored into the Arrhenius equation2
1. Waterman, K.C.; Carella, A.J.; Gumkowski, M.J.; Lukulay, P.; MacDonald, B.C.; Roy, M.C.; Shamblin, S.L. Improved protocol and data analysis for accelerated shelf-life estimation of solid dosage forms. Pharmaceutical Research 24 (2007) 780-790.
2. Actually back in 1977, a paper by Genton and Kesselring covered some of the ground J.Pharm Sci 66: 676–680 (1977)
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API Micro-Environments in Solid-State
n Solution: – Molecules are in same environment – Reactivity shows homogeneous kinetics
n Solid State: – Molecules in different microenvironments:
• crystal lattice • surface • amorphous • solid-solution
– Multiple k’s – Heterogeneous kinetics – formation of product is a superposition of multiple
rates
– Shape of degradation curve in solid state may not be well described by simple 0th, 1st or 2nd order kinetics
[Pt] = Σikit (different k for each API state)
Garry Scrivens, Pfizer
Degradation Curves
7
Zero Order First Order Second Order
All curves appear linear over the first few % of degradation… …if all API molecules are in same environment
00.20.40.60.81
1.21.41.61.82
0 500 1000 1500 2000
% Degradation
Time (Days)
00.20.40.60.81
1.21.41.61.82
0 500 1000 1500 2000
% Degradation
Time (Days)
00.20.40.60.81
1.21.41.61.82
0 500 1000 1500 2000
% Degradation
Time (Days)
Garry Scrivens, Pfizer
Real-world Solid-State Degradation Curves
n ~50% (in our experience) appear to be essentially linear
n ~50% exhibit a degree of curvature
8
00.20.40.60.81
1.21.41.61.82
0 500 1000 1500 2000
% Degradation
Time (Days)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 500 1000 1500 2000
% Degradation
Time (Days)
Overall degradation curve observed
Degradation curve from a small proportion of API in reactive environment
Degradation curve from a large proportion of API in a more stable environment
n Other causes of curvature are discussed later….
This curve is not accurately described by a simple 0th ,1st or 2nd order rate equation
Garry Scrivens, Pfizer
Dealing with real-world solid-state degradation curves
n Objective: to calculate ‘k’ (rate constant for the degradation) over a range of temperatures so that an Arrhenius plot can be produced
n Problem…in order to calculate k, we need to apply a model to account for the curvature of degradation
n Plan A: acquire %deg results at multiple timepoints so that a empirical model can be applied to the data (à k) – Labour intensive – Prone to errors associated with fitting models to data
(>1 parameter is required to model the data)
n Plan B: use a ‘Time to failure’ or ‘Isoconversion’ approach 9
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Degradation Kinetics at Different Conditions
For a given system, the shape of the curve (i.e. degradation kinetics) is usually very similar across different stability conditions, just the timescale is different
(cases where this assumption is invalid are discussed later)
3.1x faster
3.1x faster
Etc.
11.7x faster
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Traditional (Constant Time) Approach vs. Isoconversion Approach
Time
%Deg Prod
30ºC
60ºC
70ºC
Spec. Limit
kT = Slope of line
30 days
Constant Time
t1 t2
Isoconversion
kT = Slope of line
With an isoconversion approach, the shape of the degradation curve is unimportant
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Isoconversion Principle - Summary
n Select timepoints for each Temp / %RH condition to give approximately the level of degradation that you’re interested in (typically the specification limit) – Shape of the degradation curve (order of reaction) is unimportant – Degradation far removed from specification level may lead to an inaccurate
shelf life prediction – Proportion of reaction from different API environments assumed to be
consistent across different conditions
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Caution: Isoconversion Approaches
Time
%Deg Prod
Spec. Limit
Actual Degradation
Predicted Degradation
Accurate prediction at (e.g.) spec level
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-8
-7
-6
-5
-4
-3
-2
-1
2.9 3 3.1 3.2 3.3 3.4
1/T (X1000)
ln k
10% RH
75% RH
Applying Arrhenius to the Solid State: 2. Effect of Relative Humidity
30°C 20°C 60°C 40°C 70°C
Degradation of Aspirin Tablets:
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Humidity Sensitivity
n Observation: solid-state degradation rates increase exponentially with %RH
n At constant temp, ln k = B(%RH)
Moisture dependence(Constant Temperature)
-9-8.5
-8-7.5
-7-6.5
-60 20 40 60 80 100
%RH
ln k
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Humidity Corrected Arrhenius Equation
Ln k = Ln A - Ea/(RT) + B(%RH)
Measured (calculated from %degradation results)
Selected by user, at least 3 combinations required
3 parameters need to be determined (using multilinear regression) for each
degradation reaction
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Accelerated Stability Protocol Design
n Isoconversion: aim to degrade sample to the specification level for all conditions – Initial trials: use average (typical) Ln A, Ea and B values – Subsequent trials on same drug product / API can use Ln A,
Ea and B values from previous studies to provide better isoconversion (an iterative process)
n A minimum of 3 different temperature - %RH combinations are required (3 unknown parameters, Ln A, Ea and B to be determined) – More than 3 conditions are required in order to provide
greater confidence in prediction and to provide some measure of goodness of fit to ASAP model (an ‘over-determined’ system)
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Standard (Default) ASAP Protocol*
Protocol T (°C) %RH Days
API Stability
70 5 14 70 75 14 80 5 14 80 40 14
Drug Product Stability
50 75 14 60 40 14 70 5 14 70 75 1 80 40 2
Conditions and durations chosen for their practicality and to provide about 0.5% degradation based on typical Ln A, Ea and B values
*Other temperature / humidity / duration combinations can be used to meet the needs of the particular application
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Protocol Design: Practicalities
n Humidity-controlled ovens
n Saturated Salt Solutions, e.g.: – 30%RH: MgCl2
– 50%RH: NaBr – Etc.
n Amebis
19
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ASAP Drug Product Design Space (DOE)
0
1020
3040
50
6070
80
45 55 65 75 85
Temperature (C)
%R
H
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ln k
1/T
%RH
x
x
x x
x o
o o o
ln k = ln A - Ea/R(1/T) + B(%RH)
40/75 30/75
30/65 25/60
70/75
80/40
70/ 5
50/75 60/40
Visualizing the ASAP Experiment
Ln A
B
Ea/R
Garry Scrivens, Pfizer
22
Interpretation of Ea and B Values: Quantifying the effect of temperature and %RH
Ea Term: a measure of the temperature dependence of the degradation
Ea = 50 KJ.mol-1, degradation rate 1.9x between 30ºC and 40ºC Ea = 100 KJ.mol-1, degradation rate 3.6x between 30ºC and 40ºC Ea = 150 KJ.mol-1, degradation rate 6.7x between 30ºC and 40ºC
B Term: a measure of the moisture dependence of the degradation
B = 0.07, degradation rate doubles for every 10% RH increase B = 0.035, degradation rate doubles for every 20% RH increase
Garry Scrivens, Pfizer
Using Ea and B to Quantify the effects of Temperature and %RH…Examples
23
Ea = 220 KJ/mol
Ea = 140 KJ/mol
Ea = 60 KJ/mol
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
25 30 35 40
%D
eg p
er m
onth
Temperature (ۥC)
B= 0.08 B= 0.04
B= 0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
10 15 20 25 30 35 40 45 50 55 60 65 70 75
%D
eg p
er m
onth
%RH
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Typical Ea and B values (n=60)
Mean Ea ~124 KJ.mol-1
Mean B ~0.04
0
0.02
0.04
0.06
0.08
0.1
0.12
50 100 150 200
B
Activation Energy / KJ.mol-1
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Checking the Goodness of Fit of data to the ASAP Model
n Comparison of prediction against actual long-term stability is of course the ‘definitive-test’ of the ASAP approach – Many examples of excellent predictions on historical batches
(retrospective analysis)
n How can we assure ourselves that the ASAP approach will work on new products in development (without waiting 2 years to find out)? – Internal validation of model: ability to predict ‘itself’ – e.g. use 4 of the
ASAP conditions to predict the 5th; evaluating how well data fit the model % Degradation: Observed vs Predicted
-0.5
0
0.5
1
1.5
0 0.5 1 1.5
Observed % Deg
Pred
icte
d %
Deg
70C / 5% RH 70C / 75% RH
50C / 75% RH 60C / 40% RH
80C / 40% RH
Best Fit
Ideal
Garry Scrivens, Pfizer Garry Scrivens, Pfizer
Checking the Goodness of Fit of data to the ASAP Model
– Examination of residuals:
– Examination of Arrhenius Plots:
– R2prediction
20%RH
20%RH
40%RH
40%RH
60%RH
60%RH0%RH
0%RH
80%RH
80%RH
-9
-7
-5
-3
-1
1
3
50.0028 0.0029 0.0029 0.003 0.003 0.0031 0.0031 0.0032
1/T
ln k
75% RH
40% RH
5% RH
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Estimation of Shelf-Life for Packaged Products
n Ln A, Ea and B terms can be used to predict the rate of degradation: just need to know temperature and humidity
n But the humidity inside the packaging needs to be known for accurate packaged product stability predictions – Humidity inside packaging changes over time, e.g.:
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
Days
%RH
2 tablets
15 tablets
15 tablets + desiccant
empty bottle
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0
1
2
3
4
5
0 10 20 30 40 50 60 70
Days
%Degradant 2 tablets
15 tablets 15 tablets + desiccant
Predicted (lines) vs. Measured Degradation
open bottle
Drug Product ‘A’ in 60-cc HDPE Bottles (40°C/75%RH)
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The humidity inside the packaging can be accurately predicted
n In order to do this you need to know: 1. The ‘MVTR’ (moisture vapour transmission rate) of the packaging, and 2. The Moisture vapour sorption isotherm for your product (can be obtained by
combining the isotherms for the individual excipients of the product) and the desiccant (if using)
3. The ingoing water content / water activity of the tablets (& desiccants)
See Next Presentation
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Product Degradant Real Time ASAP Prediction
Comments
Product A- MR Tablets 9 months, 25°C/60%RH
A1 4.1% 4.2% + 0.84 Hydrolysis A2 1.5% 1.2% + 0.24 Esterification
Product B- 5mg Tablets 12 months, 30°C/75%RH B1 0.02% 0.07% + 0.02 Oxidative degradation
Product B- 1mg Tablets 12 months, 30°C/75%RH B1 0.05% 0.29% + 0.1 Oxidative degradation
Product C- 100mg IR Tablets 3 months, 25°C/60%RH C1 5.3 ppm 6.2 ppm + 1 Low- level oxidative
degradant
Product A- Oral Solutions (5 Formulations) 7 months, 5°C
A1
1. 0.56% 2. 0.35% 3. 0.47% 4. 0.32% 5. 0.53%
1. 0.60% ± 0.03 2. 0.36% ± 0.01 3. 0.61% ± 0.03 4. 0.30% ± 0.02 5. 0.69% ± 0.03
Hydrolysis
Product D- Oral Solution 2 years, 30°C D1 0.31% 0.4% ± 0.08 Lactam formation
ASAP: How Well Does it Work?
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Product Degradant Real Time ASAP Prediction
Comments
Product E- Patch 6 months, 40°C
E1 E2 E3
0.15% 1.72% 0.89%
0.12% ± 0.08 1.19% ± 0.24 0.88% ± 0.17
Prod D- formamide Acetyl- Prod D
Hyrdoxy- Prod D
Product F- Tablets 2 years, 25°C/60%RH
F1
0.70 0.98% + 0.08
Hydrolysis
API Qualifications, Root Cause
Investigative studies & Formulation Screenings
2 years , 30°C/75%RH 1.51 1.93%+ 0.16
6 months, 40°C/75 %RH 4.80 3.85% + 0.24
Product G - POS 6 months, 40°C/75%RH G1
(106.7 mg/g) 3% potency
loss
(103.4 mg/g ) 6% potency loss Hydrolysis Tech Transfer & API
Qualification
Product H- Tablets 4 years, 25°C/60%RH
H1 H2
0.22% 0.06%
0.22% + 0.06 0.07 %+ 0.02
Lactam formation
Ester (Lactone) formation
Oxidation
Lactam formation
Oxidation
Proposed Package Changes,
Shelf Life Extension & Replacement of Annual Stability Commitments
Product I- 100mg Tablets 2 years, 25°C/60%RH I1 0.01% 0.01% + 0.01
Product J- Capsules 2 years, 25°C/60%RH J1 0.08 0.08%+ 0.0
Product K- Capsules 3 years, 25°C/60%RH K1 0.03 0.03%+ 0.10
ASAP: How Well Does it Work?
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Product L (45 mg tablets) stored at 30°C/75%RH
ASAP: How Well Does it Work?
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 1 2 3
%D
egra
datio
n
Years
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 1 2 3 Years
Foil / foil blisters HDPE Bottles (30 count) + desiccant
Garry Scrivens, Pfizer 33
Example Applications of ASAP (1)
n Accurate prediction of shelf-life of API and drug products. ASAP can be used to set interim use-periods (e.g. for IMPDs and INDs). – Unpackaged Study (no delay in starting the study), 14 day protocol
n During Development: Helps to quantify stability risks and accelerates development: – Quantifies the effects of temperature and humidity on stability performance (e.g.
“10% increase in RH or 10ºC increase in temperature increases rate by x-fold”) – Allows the stability impact of any changes throughout development to be rapidly
assessed. • Formulations/processes • Synthetic routes • Assessing batch-batch equivalence of API and drug products
– Reduces or eliminates the need to wait for long-term stability readouts at key timepoints during development
n Packaging Selection. This is a major benefit of the ASAP approach: the stability performance in any pack-type can be predicted and compared ‘at the touch of a button’ (all that is needed is the MVTR of the pack-type). The need to conduct expensive, lengthy packaging selection studies is reduced or eliminated.
n Prediction of Stability in any Climatic Zone. The effect of changing storage conditions from (e.g.) 25ºC / 60% RH to (e.g.) 30ºC / 75% RH can be assessed and the risks quantified.
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Example Applications of ASAP (2)
n At Registration – Use ASAP as supportive data or as an alternative to traditional stability
to minimize stability commitments – Use diagnostic tools to demonstrate applicability of the ASAP model
applied for each drug product
n QBD for Stability: – ASAP / Packaging Tool is in-line with the QBD principle of
understanding and modelling the effects of parameters that may affect stability performance (e.g. temperature and humidity).
– ASAP can also be used as a tool for rapidly quantifying the stability effects of changes to the product or process (e.g. ref. Kougoulos et. Al., AAPS PharmSci Tech, 2011) QBD for Stability.
n Post-Approval – Use ASAP as part of post-approval change protocol – Annual stability commitments: costs / overheads can be reduced
Garry Scrivens, Pfizer
1g
2g
3g
4g
5g
85.0%
87.5%
90.0%
92.5%
95.0%
97.5%
100.0%
12 14 16 18 20 22 24 26 28 30 32 34 36Minim
um Probability of Reaching Shelf Life
Shelf Life
30°C60cc bottle, 30 count
65%RH75%RH25°C/60%RH, 1g desiccant
Case Study 1: Global Registration, Climatic Zone 4 and Package Selection
Garry Scrivens, Pfizer
– Tighter control of water activity at point of packaging reduces batch-batch variability
Tighter Control on Water Activity
Case Study 2: Using ASAP to understand and quantify the effect of different extents of drying of a wet-granulated product
Garry Scrivens, Pfizer 37
Case Study 3: Simulation of In-Use Stability
Humidity inside unopened bottles
Humidity inside bottles during in-‐use period
Initiated at 45months
Initiated at 57 months
a a a a ab b b b bc
cc
c
cd
d
dd
d
a. Desiccant removed on Day 1 of in-‐use stability; patient regimen (1 tablet removed daily)
b. Desiccant removed on Day 1 of in-‐use stability; analytical testing regimenc. Desiccant kept inside bottle during in-‐use stability; patient regimen
(1 tablet removed daily)d. Desiccant kept inside bottle during in-‐use stability; analytical testing regimen
0
10
20
30
40
50
60
70
80
0 1 2 3 4 5 6
Internal RH (%
)
Years
ab
cd
Initiated at 33 months
Initiated at 21 months
Initiated at 9 months
Initiated at 0 months
0
10
20
30
40
50
60
70
80
0 1 2 3
%RH
in P
acka
ging
Time (years)
0
0.05
0.1
0.15
0.2
0.25
0.3
0 1 2 3
% D
eg
Time (yrs)
0
10
20
30
40
50
60
70
80
0 1 2 3
%RH
in P
acka
ging
Time (years)
0
0.05
0.1
0.15
0.2
0.25
0.3
0 1 2 3
% D
eg
Time (yrs)
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Case Study 4: Temperature Excursions
0
10
20
30
40
50
60
70
13/04/11 17/04/11 21/04/11 25/04/11
Date
Tem
pera
ture
(ºC
)
Temperature Excursion
00.0020.0040.0060.0080.01
0.0120.0140.0160.018
13/04/11 17/04/11 21/04/11 25/04/11
Date
Deg
rada
tion
Rat
e, k
(% p
er h
our)
Degradation Rate (Product D, % per hour)
Degradation Rate (Product E, % per hour)
00.10.20.30.40.50.60.70.80.9
13/04/11 17/04/11 21/04/11 25/04/11
Date
Cum
ulat
ive
Deg
rada
tion
(%).
Cumulative Degradation (Product D, %)
Cumulative Degradation (Product E, %)
Temperature Logger data
A, Ea
Degradation Rate
Cumulative
% Degradation (as measured by e.g. HPLC)
Thank you For Listening
Questions / Discussion
Garry Scrivens, Ph.D. Pfizer Global R&D, Sandwich, UK
http://www.linkedin.com/pub/garry-scrivens/17/8a0/4a1
Garry Scrivens, Pfizer 40
Garry Scrivens, Pfizer 41
Packaged-Product Stability
H2O H2O
H2O
Moisture transfer depends on MVTR, ΔRH and temperature
MVTR = P. ΔRH
H2O
Moisture inside packaging equilibrates between headspace (RH), tablets, desiccant (vapor sorption isotherms)
Garry Scrivens, Pfizer 42
Moisture Vapour Sorption Isotherms
– The water content varies with water activity, Aw (≡ %relative humidity) according to the ‘water vapour sorption isotherm’ for the material, e.g.:
Aw
PVP
– GAB parameters are used to describe water vapour sorption isotherm curves
Garry Scrivens, Pfizer 43
Moisture Vapour Sorption Isotherms
0
100
200
300
400
500
600
700
800
900
1000
0 20 40 60%RH
Wat
er (m
g) Total
Desiccant
Drug Product
Head Space
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Potential Pitfalls
1. Non-isoconversion
2. Form / Phase changes caused by temperature and/or humidity (e.g. melts, glass transitions, anhydrate / hydrate formation, deliquescence etc.)
3. Secondary Degradation (consecutive reactions)
4. Competitive Processes
5. Significant contribution to overall degradation from multiple API environments that have significantly different Ea and B parameters
6. Significant contribution to overall degradation from multiple degradation pathways that have significantly different Ea and B parameters
7. Combinations of the above
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Potential Causes of Poor Fit / Prediction
Form / Phase changes caused by temperature and/or humidity (e.g. melts, glass transitions, anhydrate / hydrate formation, deliquescence etc.)
ln k
1/T
%RHln k
1/T
%RH
B
Ea/R
x
x80/40 x
x
x ooo
o
40/7530/75
30/6525/60
70/75
50/7560/40
70/5
Garry Scrivens, Pfizer Garry Scrivens, Pfizer 46
Potential Causes of Poor Fit / Prediction
ASAP %RH condition(s) exceed Critical Relative Humidity, CRH (e.g. of one of the excipients), which leads to deliquescence.
Excipient CRH @ 20ºC CRH @ 40ºC PEG 3350 94 85 Dextrose 100 88 Fructose 72 64 Sorbitol 80 69 Sucrose 86 83 Xylitol 91 73
Tartaric Acid 85 78 Potassium Chloride 84 82
Sodium Chloride 75 75 Sodium Citrate 61 78
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Potential Sources of Inaccuracy: Degradation Kinetics at Different Conditions
Low Temperature
High Temperature
00.20.40.60.81
1.21.41.6
0 10 20 30 40 50 60
Time
%Degradant
0 100 200 300 400 500 600
For a given system, the shape of the curve (i.e. degradation kinetics) is usually very similar across different stability conditions, just the timescale is different …
…sometimes this is not the case
Garry Scrivens, Pfizer 48
Potential Sources of Inaccuracy: Complex systems
Significant contribution to overall degradation from multiple API environments that have significantly different Ea and B parameters
APIForm A
APIForm B Deg Prod
kA f(Ea,A and BA)
kB f(Ea,B and BB)
-9
-8
-7
-6
-5
-4
-3
-2
-1
00.0028 0.003 0.0032 0.0034
Ln k
1/T
Contribution from Form A
Contribution from Form B
Overall Observed Arrhenius Plot
xx
x
Prediction error
This problem can manifest as a curved Arrhenius plot. This is rare because degradation usually derives predominantly from one form of API
Garry Scrivens, Pfizer 49
Potential sources of Inaccuracy: Complex Systems
Significant contribution to overall degradation from multiple degradation pathways that have significantly different Ea and B parameters
API Deg Prod kA f(Ea,A and BA)
kB f(Ea,B and BB)
-9
-8
-7
-6
-5
-4
-3
-2
-1
00.0028 0.003 0.0032 0.0034
Ln k
1/T
Contribution from Path A
Contribution from Path B
Overall Observed Arrhenius Plot
xx
x
Prediction error
This problem can manifest as a curved Arrhenius plot. This is rare because degradation usually derives predominantly from a single pathway
Garry Scrivens, Pfizer 50
O
OH
O
R
OH
OHR
O
O
O
R
O
OH
OR
Hydrolysis Oxidation
Oxidative Dimerisation
OH
RO
O
OR
k = f(T,RH)
k = f(T,RH)
k = f(T,RH)
“Deg C”
“Deg B” “Deg G”
“Deg D”
Complex Systems: Competitive and Consecutive Processes Kinetic Simulations
Garry Scrivens, Pfizer
Complex Systems: Competitive and Consecutive Processes Kinetic Simulations
Form A (unstable form) à Form B (stable form) (nucleation-type kinetics)
Form A à 10-H (degradation product) (1° kinetics) Form A à 10-K (degradation product) (1° kinetics) 10-K à Other Degs (1° kinetics)
Form B ßà Dihydrate (nucleation-type kinetics)
Etc. 30°C/65%RH, 30-count 60cc HDPE bottle No Desiccant: 25°C/60%RH, 30-count 60cc HDPE bottle With Desiccant:
51
F'/G
10-‐H10-‐Kother
DihydrateGH0
0.5
1
1.5
2
2.5
3
0 200 400 600 800
% Degradation
Time (Days)
F'/G
Form B
10-‐H10-‐KotherDihydrateGH0
20
40
60
80
100
120
0 200 400 600 800
% Degradation
Time (Days)
10-‐H10-‐K
otherDihydrateGH0
0.5
1
1.5
2
2.5
3
0 200 400 600 800
% Degradation
Time (Days)
F'/G
Form B10-‐H10-‐KotherDihydrateGH0
20
40
60
80
100
120
0 200 400 600 800
% Degradation
Time (Days)
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