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.