High Throughput and Predictive Stability Approaches for

Report
High Throughput and Predictive Stability
Approaches for Parallel Drug Product
Development
Pharmaceutical Development and Manufacturing
Sciences, PDMS, Janssen pharmaceutica NV
Likun Wang, Sabine Thielges, Maarten van der Wielen, Stefan Taylor
Disclaimer
The opinions expressed in this presentation are those of the presenter only and do
not necessarily reflect the positions or opinions of Janssen Research &
Development, LLC. (“Janssen”) or any other individuals or affiliates of Janssen. The
presenter makes no warranties with respect to the accuracy or completeness of the
data or materials presented. All information is provided for informational purposes
only and does not constitute advice or endorsement of any products or processes.
2
Janssen Pharmaceutica NV
• A Global Pharmaceutical
Company
• A pharmaceutical company of
Johnson & Johnson
• HQ in Beerse, Belgium
Janssen, Beerse, Belgium
• Multiple R&D sites in Europe,
US, China and India
Janssen, Geel, Belgium
3
Outline
Background & Challenges in Pharmaceutical R&D
Overview of LEA platform in Janssen Pharmaceutical Research &
Development
Case Studies:
•
Excipient Compatibility
•
Accelerated Stability Assessment Program (ASAP)
4
Our challenge in pharmaceutical R&D
• More complex products (the easy ones are gone)
• Constantly increasing regulatory and patient expectations
• Cost of drug development is rising exponentially, and timelines are expanding
• We need more shots on goal due to high attrition
• Need more killer experiments
The solution???
5
Drug Product Development
• Can be developed only if
• Bioavailability
• Processability
• Stability
are achieved simultaneously
• Parallel concept development
is the major approach to
accelerate drug product
development process
Bioavailability
Drug
Product
Processability
Stability
6
Parallel concept development – a design space perspective
• Need systematic
experimentation,
e.g.DoE
Narrowed design space
Entire design space
• Parallel concept
development
Good stability
subspace
• Need higher throughput
• Down-scaling and
automation is the key
Good
processability
subspace
Good bioavailability
subspace
7
Amount of information
Challenges with down-scaled, automatic experiments
DoE
Analysis
(different
software)
Reporting
(Excel ?)
Material handling
(different softwares)
Reporting
Central
information
storage
Material
handling
DoE (Minitab, Design
expert, etc.)
Analysis
Smaller scale and more automation
Progress of experiment
8
LEA: centralized information handling platform
DoE
Library Studio
Database
RAS
Automation Studio
CM3 Hamilton UPLC …
9
Integrating Hardware and software:
SM Development labs example
10
Product Design and Developability Workflows
• Support to Drug substance and drug product
development
• 16 active screening workflows implemented
and used as part of our platform-based
development approach
API workflows
DP workflows
1. Polymorph screen
1. Thermodynamic solubility screen
2. Salt screen
2. Excipient compatibility
3. Re-crystallization screen
3. Solid Dispersion
4. Morphology screen
4. Aqueous solution formulation
5. Forced degradation
5. IV formulation screen
6. Accelerated Stability Assessment
Program (DS)
6. Accelerated Stability Assessment
Program (DP)
7. Miniaturized powder flowability
6. Precipitation Inhibition
7. Nano-milling & physical stability
8. Co-solvent & lipid formulation screen
9. Powder blend segregation
11
PART I. Excipient Compatibility – The Dynamics
of Drug Product Stability
12
Excipient Compatibility
• Study chemical compatibility behavior between API and excipients
• Closely related to drug safety and efficacy
• Normally carried out in early development phase
• Sometimes included in the preformulation package
• Solid state form selection need to be done before excipient compatibility
• Final morphology, particle size are preferred
• Final synthesis route is best in place
13
Different Approaches Towards Excipient Compatibility
1:1 mixtures
• Easy to set-up
• May overestimate (Not the actual ratio)
• May underestimate (Synthetic effect)
Full Blend then N-1 method (remove one excipients per time)
• Gives more information
• 2-step method
• More time consuming
DoE approach – Mixture Design
•
Able to predict the dynamics of mixture
•
Much more samples need to be prepared
14
Challenges to conquer before getting the benefits of mixture DoE
Component 1
Run
A:MCC
%
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
14.00
15.00
16.00
17.00
18.00
19.00
20.00
21.00
22.00
23.00
24.00
Component 2
Component 3
Component 4
Component 5
Component 6
B:Mannitol
C:Lactose
D:Aerosil 200 Pharma
E:Croscarmellose sodium
F:SLS
%
%
%
%
%
12.89
12.89
12.89
4.43
4.43
0.00
0.00
32.00
10.00
0.00
12.89
12.89
12.89
4.43
4.43
45.00
0.00
0.00
0.00
0.00
12.89
12.89
12.89
4.43
4.43
38.00
0.00
0.00
10.00
0.00
0.00
38.00
0.00
0.00
10.00
0.00
32.00
0.00
0.00
10.00
0.00
0.00
45.00
0.00
3.00
43.00
0.00
0.00
0.00
10.00
12.89
12.89
12.89
4.43
4.43
12.89
12.89
12.89
4.43
4.43
12.89
12.89
12.89
4.43
4.43
45.00
0.00
0.00
2.00
0.00
0.00
0.00
27.00
10.00
10.00
0.00
45.00
0.00
0.00
0.00
0.00
0.00
43.00
0.00
10.00
0.00
43.00
0.00
10.00
0.00
0.00
0.00
45.00
0.00
0.00
28.00
0.00
0.00
10.00
10.00
12.89
12.89
12.89
4.43
4.43
0.00
43.00
0.00
10.00
0.00
0.00
0.00
27.00
10.00
10.00
0.00
32.00
0.00
0.00
10.00
Component 7
G:Magnesium stearate
%
3.00
2.48
6.00
5.00
3.00
2.48
6.00
2.00
3.00
2.48
0.00
5.00
0.00
5.00
6.00
5.00
0.00
5.00
0.00
0.00
3.00
2.48
3.00
2.48
3.00
2.48
6.00
0.00
6.00
0.00
6.00
2.00
0.00
0.00
0.00
0.00
3.00
5.00
0.00
5.00
3.00
2.48
0.00
0.00
6.00
0.00
6.00
5.00
• Powder dispensing
• Mixing powder homogeneously in small scale
15
Powder dispensing
LEA
Database
RAS
• Time Stamp
• Actual Dispenses
Automation Studio
• Chemical Maps
• Dispensing Tags
• Processing Tags
• Chemical Maps
• Dispensing Tags
• Processing Tags
SV hopper
• Time Stamp
• Actual Dispenses
Right Arm Z2
Vial Plate gripper
16
Powder dispensing
RAS
LEA
Database
RAS
Automation Studio
17
Mixing in Small Scale
• Magnetic stirrer bar/disk
Particle size/morphology may affected
Longer mixing time
0.3
RSD of blend homogenity
• Vortex Mixing
Particle size/morphology not affected
Mixing efficiency depends on load
Gentle mixing
0.25
0.2
30mg load
0.15
100mg load
0.1
0.05
0
Blend Load (mg)
• Turbula Mixer
Particle size/morphology may affected
Good mixing efficiency
Gentle mixing
RSD of blend homogenitiy
0.6
0.5
0.4
1000 rpm
0.3
1400 rpm
2000 rpm
0.2
0.1
0
Vortex mixing speed (rpm)
18
Case Study I: Compound X formulation challenge
• Standard capsule formulation
• Poor flowability (formulator suggested to add more silicon dioxide)
• High Dose (around 50% API load)
No silicon dioxide
Medium silicon dioxide
High silicon dioxide
19
Case Study I: Compound X formulation challenge
• Silicon dioxide could cause degradation
• Interactions between silicon dioxide with fillers were revealed
• Optical formulation ranges can be suggested from stability
perspective
• The amount of silicon dioxide need to be carefully controlled
• Mixture DoE and small-scale experiments can be used for excipient
compatibility studies
20
PART II. Accelerated Stability Assessment
Program– The Kinetics of Drug Product Stability
21
The concept of Accelerated Stability Assessment Program (ASAP)
• Relative Humidity corrected
Arrhenius equation
Ea
ln k  ln A 
 B( RH )
RT
• Monte-Carlo simulation
• Packaging
% Degradant
• Isoconversion
70°C
50°C
25°C
Time
KC Waterman, AAPS PharmSciTech Vol 12 No.3, September 2011
22
Case Study II: Bench Mark the Stability Behavior of Compound Y concepts
• Compound Y is under BCS Class II (Low solubility, high permeability)
• Need amorphous solid dispersion to boost bioavailabiilty
• 28 amorphous solid dispersion concepts were investigated
• Need to predict/compare shelf life for each concepts
•
12 samples need to be prepared for each concept according to ASAP
•
336 samples in total prepared by CM3
23
Case Study II: Bench Mark the Stability Behavior of Compound Y concepts
The samples preparation is finished within 2 days on CM3
…
Concepts
Y/PVPVA 64
Y/HPMC-AS
Y/HPMC E5
Y/Eudragit
L100-55
…
Predicted ShelfLife (year)
Based on worst
degradant
<1
2.4
2.3
2.6
…
• Automation enabled timely stability study for parallel drug product development
• Shelf-life can be predicted via ASAP approach
24
Conclusion & Challenges
• With DoE and ASAP, down-scale and automation has added-on value for
stability studies
• Parallel drug product development could benefit from down-scale and
automation
• CM3 is not GMP certified yet
• Combine dynamics and kinetics studies
• Data handling challenge (HPLC peak identification)
25
Thank for your attention!
Questions ?
26
Accelerated Aging—ASAPprimeTM Approach
Bimodal Degradation
0.5% specification limit
%Degradant
0.5
60°C
0.4
70°C
50°C
0.3
0.2
0.2% specification limit
0.1
ASAP isoconversion: % degradant fixed at
specification limit, time adjusted
0
0
7
14
21
28
35
42
Time (days)
49
56
63
70
27
Accelerated Aging—ASAPprimeTM Approach
Bimodal Degradation
0.5% specification limit
%Degradant
0.5
60°C
50°C
70°C
0.4
0.3
0.2
0.1
0
0
7
14
21
28
35
42
Time (days)
49
56
63
70
28

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