The Power of Data to Accelerate Development of Robust ... · 2096-WellPlate_AN_HR.pdf. 5. ......

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The Power of Data to Accelerate Development of Robust & Scalable

Manufacturing Processes

Susan Abu-Absi

Director, Manufacturing Sciences & Technology

Bristol-Myers Squibb

Devens, MA USA

May 11, 2016

CMC Strategy Forum Europe 2016

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Speed to Patient

On Platform

Readiness forCommercial

Launch Process

Phase 1 LifecyclePhase 2 Phase 3

Adaptive Trial Design

* *

* = traditional starting point

Accelerate early-phase compounds into late-stage trials

HARMONIZATION & PLATFORMS

DATA AGGREGATION

& ANALYSIS

Greater utilization of prior knowledge

Enable ongoing knowledge generation


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• Ongoing assurance that process remains in control

• Statistics-based monitoring and intervention when shifts and trends are identified

Process Development Ranging Studies Full-Scale Manufacturing Process Validation

• Acceptance criteria developed from small-scale data

• Fewer batches required

• Prior knowledge• Platform Technologies

− Cell Lines− Media− Resins− Methods

• Risk-Based Approach• Design of Experiments (DOE)

− Increased process knowledge− Understanding of parameter

interactions• Leverage scale-down models• Few full-scale runs with commercial

process before performance qualification lots

Process Development

Process Performance Qualification

Traditional

Enhanced

Time

Time

Process Characterization

Full-Scale Experience

Continued Process Verification

Evolution of Product Development


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• High throughput, rapid data generation

• Thorough exploration of design space and edge of failure

• Troubleshooting and continuous improvement

The Power of Scale-Down Models


TAP Biosystems ambr™http://www.tapbiosystems.com

TPP TubeSpin® Bioreactorhttp://www.tpp.ch

PALL Life Sciences Application Note USTR 2795

http://www.pall.com/pdfs/Biopharmaceuticals/10-4610_DoE%2096-WellPlate_AN_HR.pdf

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Importance of Well-Designed Studies


• Scale-down models must be predictive

• Value of large dataset dependent on design of the study and robustness of analysis

Kidney International, Vol. 62 (2002), pp. 1524–1538 [Cartoon originally copyrighted by the authors; G. Renee Guzlas, artist]

ScopeScreen

Optimize

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Data Accumulation


Product and Process DevelopmentProcess

Character-ization

PPQ BLA and PAI

Post-Approval Changes

FIH “Go”

Commercialization “Go”

BLA “Go”

Pre-Clinical Phase I Phase II Phase III Registration Lifecycle Management

Tox Lots

FIH Lots

Clinical Lots

Lab-Scale Data

PPQ Lots

Launch Lots

Commercial Lots

time

data

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IT Solutions for Data Management


Information links

Business Process Management

Structured AuthoringTools

Unified User Interface

Online Requesting

Experiment Planning

Resource Management

and Scheduling

Experiment Tracking Dashboards

Anal

ytic

s To

olki

tInformation Consumers

Stat Analyst

EmpowerSharePoint EmpowerDocument Repositories

Information ProvidersStat Analyst

Search Reporting Publish

ELN

ELN

Plas

mid

s

Inve

ntor

y

PD L

IMS

Dev

LIM

S

VelQ

uest

NuG

enes

is

Empo

wer

Nau

tilus

OSI

PI

ELN

SpotFire

JMP

SAS

PLA

UMETRICS

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Case Study: Data Aggregation


• Production process designed to control glycoform profile of an Fc fusion protein

• Second chromatography column main point of control to ensure CQA

• New, more sensitive orthogonalmethods developed

• Gap in understanding of process control for new methods

• Rapid mobilization of DoE studies in scale-down model to fill knowledge gap

Production Bioreactor

Chromatography#1

Chromatography #2

Chromatography #3

Drug Substance

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Case Study: Data Aggregation (cont’d)


• Small-scale data combined with manufacturing data to bolster empirical models

• Control strategy adjusted to ensure product quality

12 g/Lresinloading

Predicted CQA #1(full-scale)

Bior

eact

or C

QA

Chrom #2 Wash Conductivity

Predicted CQA #1 (complete dataset)



Predicted CQA #3 (lab-scale)

22 g/Lresinloading


Bior

eact

or C

QA

Chrom #2 Wash Conductivity




Predicted CQA #3 (lab-scale)

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Data Aggregation: Key Messages


• Data generation in scale-down models – Enables rapid assessment of process and method

changes

– Substitution for full-scale exploration

– Enables mapping of multi-dimensional control space to understand impact on CQAs

• Data aggregation during product development– Facilitates alignment of process capability and

product specifications

Less validation is required at commercial scale for licensure

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Continued Process Verification


• Ongoing verification that the process remains in a state of control during commercial manufacturing

• Trending of data and reaction to shifts or trends

Outside control limit

6 consecutive descending points

Out-of-specification

9 consecutive points above average

USL

LSL

LCL

UCL

Avg.

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IT Solutions for Data Management


Robustness VISION

IT TOOLS

Automate

and Enable

From Reactive

To Proactive

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CPV: Cycle of Continuous Improvement


CQATiter

Multivariate Analysis

Improved Process Understanding & Enhanced Control

Reduced In-Process Variability

YoY Improvements

Monitor Batch Data in Real Time

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Case Study: CPV


• Monitoring of high molecular weight (HMW) at final control point in purification process

• Ongoing issue that “came and went” several times over years of commercial manufacturing

• Mitigated by pulling process levers that also reduce yield

• CPV enabled identification of root cause– Multi-variate analysis identified hypotheses,

– Laboratory studies confirmed link between process control and HMW clearance

– Changes implemented in manufacturing

CQATiter

Multivariate Analysis

Improved Process Understanding & Enhanced Control

YoY Improvements

Monitor Batch Data in Real Time

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Case Study: CPV (cont’d)


Decrease in column load Increased peak cutoff

① Improved column pack quality

② Increased resin residence time

≤ 8.5 ≤ 8.0 g/L resin

Resin/Pack

Commercial production over two years

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Case Study: CPV (cont’d)


• Greater understanding of how process inputs impact critical quality attributes and/or yields often results in movement within the acceptable operating range to improve robustness

Proven Acceptable Space

Normal Operating Range

Acceptable Operating Range

Setp

oin

t

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Multi-Site CPV


• Holistic end-to-end CPV approach

• Monitor performance at all sites of manufacture

• Leverage best practices across sites

• Identify common issues and share learnings

11%11%

78%

Site #1

15%4%

81%

Site #2

8%

92%

Site #3

Site #1 Site #2 Site #3

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Data Analysis Maturity


• Univariate control charts may not be efficient or adequate

• Multivariate models reduce the dimensionality of the problem to a few variables that explain the most variation

• Enables fault detection in real-time

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Fault Detection


1. Input data from PI or Discoverant

2. Create offline Batch Evolution models (BEM) on Simca

4. Test BEM model by using “good”

and “bad” batches5. Input final control limits to Simca-

online

6. Monitor the new batches in real time

by Simca-online

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Case Study: Data-Driven Intervention


• The batch score control charts detect the outliers and identify the parameters that are contributing to them

• In the model for online parameters, the control chart of t1 detected an outlier which was caused by higher sparge rate

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CPV: Key Messages


• CPV enables knowledge generation to continue post approval

With a robust CPV program can we increase speed to patient even more? What value is derived from traditional PPQ?

• Continued process optimization based upon manufacturing-scale data analysis coupled with small-scale confirmation ensures robustness

Regulatory flexibility is key to enabling robustness improvements

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• Brendan Hughes• Lori Harmon• Francis Sidnam• Syama Adhibhatta• Jeffrey Savard• Itzcoatl Pla

Acknowledgements


• Joseph Shiminsky• Cheng Zhang• Cheng Wenren• Kedar Dave• Marc d’Anjou

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