Mitigating Product
Risk in Development
Identify Opportunities
to Maximize Efficiency
Ilgaz Akseli, PhD, MBA
Boehringer Ingelheim, USA
FDA/PQRI Conference
Bethesda, MD, October 06, 2015
Introduction
2
INNOVATIVE
TECHNOLOGY
CASE
STUDIES
BUSINESS IMPACT
Agenda
Macro Trends and Key Challenges• Status Today the drug tablet is the most popular drug delivery
device Production: 100+B tablets/year � Megatrend: Personalized Medicine, Individual Dosing, Targeted
� Drug Delivery Global Spending: > $920B in 2015 (prescription drugs)
� Global R&D Expenditure: $142B in 2014 and expected to be $160B in 2020
• Key Challenges and Opportunities � Expiring Patents/IP
� How will the tablet adopt to the Personalized Medicine/Individual Dosing paradigm?
� Trial-and-error development versus right-all-the-time
� Predictable/dial-in/modifiable performance of the tablet for Personalized Medicine
� Emergence of biopharmaceuticals and delivery techniques
• Pushes/Pulls � Market Pull: Novel and cost-effective drugs, needs for delivery methods
� Regulatory Push: Process predictability, quality assurance (FDA’s PAT/QbD initiatives)
� Technology Push: Wireless, IT and nano/micro-manufacturing4
Pharmaceutical Industry Success Rates
Percent Calculated to
Achieve 1 Approval
Success Rate
for Each Phase
NME Success Rates by Phase an Overall 2007-2011 Industry Portrait, Pure
• Only 12% of molecules that enter Phase II will reach the market: analysis by the Pharmaceutical
Benchmarking Forum (PBF)
• Healthcare costs are becoming unaffordable
• R&D spend and productivity is disproportional5
Hewitt, Campbell & Cacciotti., Oliver Wyman Report, Beyond the shadow of a drought, 2011.
6
State-of-the-Art Home Telecare System
Non-science Based Systems• API = Potassium salt ethanol solvate
• Formulation: Liquid filled capsule
• Stressed Stability = Previously unknown form
• New Polymorph = Form of neutral API
• Cause = Neutralization of salt by surfactants in
formulation
• Action = New tablet formulation needed1
71Cote, A., et al., American Pharmaceutical Review, Nov/Dec, 2010, p46-51
8
Launch
DiscoveryAPI Solid Form Control• Advanced analytics
• Solid state characterization
API Engineering• Candidate Enabling
• Particle engineering
• Understand desired API powder property
Advanced Computational Modeling•Mathematical modeling and simulation
• Bridging between DS and DP development
• Product & process understanding and robustness
• Scalable formulations development with lower risk
Advanced Predictive Technologies • Prediction and optimization of manufacturing
• Supplier and/or site change (variability analysis)
• Project complexity and efficiency predictive tools
•Mitigate risk & identify opportunities to maximize efficiency 9
DIGITAL
MODELS
INTEGRATED
DESIGN
EXP.
ANALYSIS
PREDICTIVE
TECH
Transformational Structure
Multi-functional
“QbD” based on
integrated scientific
understanding that
enables the design
of better & faster
products to Patients
What is Big Data?
10
Experimental and Computer generated data (e.g.
web data, credit card, social network, stores, etc)
� VolumeMany factors contribute to the increase in
data volume
� Velocity
Data is streaming in at unprecedented speed
and must be dealt with in a timely manner
� VarietyData today comes in all types of formats
� VariabilityIn addition to the increasing velocities and
varieties of data, data flows can be highly
inconsistent with periodic peaks
� Complexity
Today's data comes from multiple sources
Data Volume� Unconstrained growth
� Current systems do not scale elastically
Why is Big Data Hard
(and Getting Harder)?
Source: IDC Source: Teradata
0
5
10
15
20
25
Traditional Structured Data Unstructured data
72.7 %
27.3 %
INTEGRATED
DESIGN
EXPERIMENTAL
ANALYSIS
DIGITAL
MODELS
PREDICTIVE
TECHNOLOGIES
12
Big Compute build in Big DataInfrastructure
Interactive
Local intelligence
Elastic
Platform
Real time
Efficient
Low power
Scientific
High performance
Parallel Languages
Scientific computing
Software
Indexing
Search
Crawling
Big Compute + Big Data Stack
High Level
Composable Data
Weak Signatures,
Noisy Data,
Dynamics
Novel Analytics for:
Text, Cyber, Bio
Interactive
Super-
computing
High Performance Computing:
SmartCode
Distributed
Database/
Distributed File
System
Distributed Database:
triple virtual store
• Combining Big Compute and Big Data enables entirely new domains
A
C
E
B
Array
Algebra
D
Akseli et al. University of Cambridge, 2007
What’s Driving Big Compute build in Big data?
14
- Ad-hoc querying and reporting
- Data fitting techniques
- Small to mid-size datasets
- Bulk properties
- Late decisions � missing opportunities
- Optimizations and predictive modeling
- Complex statistical analysis
- All types of data, and many sources
- Intrinsic properties
- Proactive decisionsPredictive Modeling
and Simulation
Business
Intelligence
Akseli et al., AAPS, San Diego, 2014
Integration - Validation & Test
De
ve
lop
me
nt
Syste
m C
on
fide
nce
System Development, Verification & Sustainment
Each simulation is used to support different phases of Integration, Test,
and Verification & Validation to increase overall system confidenceAkseli et al., US Dept of Energy, Theory and Application Workshop, Washington DC, 2004
16
The Virtual Process and The Virtual Tablet
• Before we “mix and formulate powders”
we want to build a Virtual Tablet!
• To build a virtual tablet we need a
virtual Process!
We’re building a virtual PROCESS so we can design better PRODUCTS
The Virtual Patient
Akseli et al. American Institute of Chemical Engineers, 21, 44-51,2007 Dalby et al., International Journal of Pharmaceutics 283, 1-9
pMDISMI
Dynamical Time-Scale from 1-D to 4-D
• Time
• Scale
• Space
• Ambient conditions
System-Level Design in 4-D
Processing of Powders from 2-D to 4-D
• ~15,000,000 - 25,000,000 irregular particles
• infinite contact points between particles until
powder starts to deform elasto-plastically
Prediction of the powder movement in 3-D
• Combining pharmaceutical, materials, and
engineering sciences
• Solving ~20,000,000 advanced mathematical
equations (PDEs and ODEs,etc) in < 2 hrs
Actual Virtual
Value to Business
ResourceResource Active Pharmaceutical Ingredient
Active Pharmaceutical Ingredient
Rework/re-do’sRework/re-do’s Time DelaysTime Delays
QUALITY
19
• Successfully and proactively implemented predictive process modeling, product design
and materials characterization
• Savings of $$MM (including APIs, resources, equipment, plant utilization, etc.)
Stage 1
Flag risk
Stage 2
Understand Risk
Stage 3
Mitigate Risk
20
Predictive formulation
profiling / API sparing
Mechanistic & Scientific
understanding
Provide solution to guide
product development
Collaboration in a Fast Changing World
21
Impact of Speed to Patient
Seamless Prediction
22
• High sales volume product
• Tablets fail QC dissolution test/OOS
• Conventional approach/tests cannot identify the root cause
• Production (6 products including 3 strength for each mono and combo)
was stopped by the end of 2012 with $10 million API in stock
• Big Compute build in Big Data is used to improve dissolution
performance without reformulation and filing changes
Business Impact• Mono & Combo were successfully re-launched in 2014&2015
• Able to utilize purchased API
• Recoup the lost revenue and market share, significant financial gain
Product A – Innovation to Re-Launch
23
Perfect Storm or Perfect Wave• BCS II compound (low solubility, high
permeability)
• Bioavailability and Manufacturability
• Excipient/coating supplier change
(variability)
• Requires more sophisticated
understanding & complex dosage
form development
• Robust formulation & process
understanding with ~90g API (BCS II)
• Mono and Combo products were re-
launched in 2014 & 2015
Predicting DS / DP Interface
As crystallized: >200 um Wet-milled : 30-50 um Jet milled: <10 um
Particle Size Bulk Density
(g/mL)x10 (µm) Predicted x50 (µm) Predicted x90 (µm) Predicted
Unmilled 2.6 2.57 20.3 22.2 142.7 144.9 0.28
Wet milled 2.4 2.38 13.3 14.38 34.9 38.3 0.41
Jet milled 0.7 0.72 1.5 1.61 3.1 3.64 0.18
24
Actual Virtual Actual Virtual Actual Virtual
• Enables scalable formulation development with lower risk
• Same crystal polymorph but with different morphology
Akseli et al. AAPS, 2014
Particle Aggregation Modeling
25
Tablet Coating Modeling
26
Edge of Failure & Root Cause Analysis
Erosion is observed @ t=3 minutes and beyond
The contours are showing the rate of erosion for the particular formulation and tablet shape
Rate of erosion (RoE) - Blue > Green. ε = f {mass loss, Process conditions}
Manufacturing Challenge
Understanding Risk
28
3.99 mm
1.47 mm
Possible sticking regions
ρcomputational = 1133 g/cm^3
ρPress = 1127 g/cm^3
29
Advanced Mathematical Modeling
)(.0 2+∆∂
∂+∆
∂
∂+∆
∂
∂+∆
∂
∂+∆
∂
∂+∆
∂
∂+∆
∂
∂+∆Ε
Ε∂
∂=∆ T
Tooling
fS
Speed
fF
Force
fA
Adhesion
fG
G
ffffST
jjjjjjjji ν
νρ
ρ
Akseli et al. Journal of Pharmaceutical Sciences, 103, 1652-1663
30
4.29 mm
1.32 mm
Predictive Formulation Design
Mitigating Risk
31
OOS product
OOS product
Acceptable
Acceptable
Actual Virtual Design
Predictive Process and Product Design
Sample Coating Erosion Powder Sticking Dissolution Failure Stability Issue
Problematic Product Yes Yes Yes Yes
Tailored Product No No No No
32
• Advanced modeling and simulations are essential
tools throughout the entire life-cycle of product
development
• Implementation of modeling increases process
understanding, shorten development timelines and
reduce development costs
• Integration of multidisciplinary approaches in
pharmaceutical R&D enhance scientific product and
process understanding & robustness and the
likelihood of successful Tech Transfer and Scale-up
from early stage to launch
Final Remarks
33
Measure a thousand times,
Cut once.
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