Risk and Knowledge Management

IFPAC January 2015Kevin Nepveux – Pfizer Global Supply

Pfizer OperationsAn aggregation of 25+ companies!

(1985)

(2000)AgouronGene/NetworksHickson Int’lPlc

Institute FarmacoBiologico Pagni

Plaistow

+ + + +

American Home

Products

A.H Robbins

+

(1989)

American Cyanamid

+

(1987–88)

+

(1992)

+(1994)

Animal divisions of:

Kabi Pharmacia

Pharmacia AB

Upjohn Company

Sugen (1999)

(1990)(1995)

Kelco Chemicals (1994)

+

(1993)+ +

+

+Kabi Vitrum

(1990) (1997) (1998) (1999)+

(2009)

(2003)

– SmithKline Beecham Animal Health (1995)– Restivo Italiana (1994 – through Roerig) – Koshin Medical KK (1992)– CSL Ltd Animal Health Unit (2004)

– Vicuron Pharmaceuticals Ltd (2005)– Idun Pharma (2005) — Apoptech

(1994)– Synbiotics (2010)– King Pharmaceuticals (2011)2

Risk and Knowledge Management

• Risk– Throughout product lifecycle but differs by phase– Unique risks by technology – common to all products

• Knowledge Management– Product-specific (across lifecycle)– Technology (across products)– General (across network)

Risk and Knowledge Management

• Risk – Product Lifecycle– Development – lack of experience, don’t know enough

(traditionally knowledge = experience)– KM approaches

• Risk Assessment based on institutional experience by unit operation

• Design experimental plan based on RA – iterative process• Modeling based on first principal equations

– Dynamic unit operations– Predictive stability models (ASAP)

• Capture knowledge in on-line, query-driven data system

Design Space

ConceptsQbD Concepts Right First Time Workflow

Commercial Manufacturing Process

(API or DP)

Risk AssessmentPrioritized Plans - Experimental PAT Risk Mitigation

Experimentation Method Dev

Documentation

Process Capability Monitoring

Continuous Improvement

(Process Changes)

e.g. Cpk

Launch

Process Understanding

Process Control Continuous Improvement

Control Strategy

Change Control Strategy

Regulatory Filing/Approval

Benefits“Platform Risk Assessment”

• How: Built by numerous SMEs• What: Pre-scored risk assessment that

captures institutional knowledge for specific technology platform

• When: Initial risk assessment on new project• Why: Save hours of pre-work, quicker

consensus, leverages prior knowledge

Example:

Platform Risk Assessment for a Low Dose, Dry Granulated, IR, Coated Tablet

Platform Risk Assessments –“Cause & Effect”• Blending• Lubrication• Granulation• Extragran Lub• Compress• Coating

Platform Risk Assessment for a Low Dose, Dry Granulated, IR, Coated Tablet

Blending

MODELING: Filtration Scale-Up

Risk: – Risk of filtration challenges and quality issues at scale-up

of “Next Gen” API isolation step – Highly compressible API - potential for long plant filtration

times if suboptimal filtration recipe used– Quality concerns also due to high inorganic content in cake

& potential difficultly in washing if cake compressed

Risk mitigation : – Developed from first principles a software model in

DynoChem to predict lab, pilot plant & plant centrifuge performance

– Software model was developed using lab data – later validated at pilot scale.

– Model used to gain process understanding of the filtration & to optimise the filtration recipe for commercial plant manufacturing

– Centrifuge model case study published in DynoChem and available to all subscribers

Result: Successful manufacturing scale up for key filing batch

campaign Filtration times were on target & the API passed quality

Lab Filter

Actual vs Predicted Filtration Rates

Pilot Plant Centrifuge

DynoChem Model

PLANT

DynoChem Model

MODELING: Capsule filling process

Early Design Recommended Full Scale Design

Velo

city

Novel predictive modelling used to develop robust commercial process and equipment

•Reduced over mixing•Improve dissolution performance•Now made 5 batches at full commercial scale•Traditional development paradigm

•More development batches•Slower•Expensive – API/PGS facility costs

0102030405060708090

100110

0 5 10 15 20 25 30 35 40 45 50 55 60 65

% D

issol

ved

Throughput (kg)

0102030405060708090

100110

0 10 20 30 40 50 60 70 80 90 100 110

% D

issol

ved

Throughput (kg)

Dissolution performance at different stage of the productions run

Pfizer Confidential │ 10

Small-scale process models are applied to solve critical processing issues at large-scale

0.085 1620

24

0.125

2.0 °C

8.0 °C

5.0 °C

[oxidising agent]

Time (h)

T(C)

Influence of Time and Temp under fixed Mol. Eq.

Model <1 g active10s mL scale

Large-scale >100 g activeMultiple L scale

Fix process variability using input from modelling studies

Oxidation of a polysaccharide for conjugate vaccine production

Accelerated Stability Assessment Program (ASAP): Science and Risk Based Approach to Stability Studies

• ASAP is a statistical tool used to model and predict the long term stability behavior of drug products based on short-term accelerated stability studies

• Leverages a humidity corrected Arrhenius equation to predict shelf life from stability data generated within a two to four week time period

In k = lnA - Ea/(RT) + B(%RH)

• Samples are placed in an open environment under six or more stressed conditions (e.g. 50ºC/75%RH, 60ºC/40%RH, 70ºC/10%RH, 70ºC/75%RH and 80ºC/40%RH) at pre-determined time points to force degradation

• Stability data generated at each condition is entered into a computer model that solves the humidity corrected Arrhenius equation for the terms LnA, Ea and B

• The established ASAP model is utilized in extrapolating shelf life estimates at any desired storage conditions (e.g. 25ºC/60%RH, 40ºC//75%RH)

Areas of Application• Formulation screenings• API synthetic route optimization (e.g. solvents, particle size, form)• Process changes (e.g. dry vs wet granulation, scale differences, direct compression vs granulation)• Raw material changes (e.g. different vendors, grades)• Packaging configurations (e.g. PvC to Foil-foil blisters, bottles w/w-o dessicant)

Knowledge Management ToolPharma InvestigatorTM Home Screen

PFDs, Risk Assessment Studies

©Copyright 2014 Light Pharma Inc. All rights reserved.

Organize Process & Parameters

QRM

Investigations Control Strategy Summaries

KM

KM13

Risk and Knowledge Management

• Risk – Product Lifecycle– Growth phase – scale-up & transfer, optimization; new

sites, new/different equipment, new raw material suppliers, experts move on to other products

– KM approaches• Continue RA approach for all significant changes• On-Line/At-Line PAT applications to look for “differences” as well as key

parameters• Use metrics (process capability) to identify highest variability• Ensure all product knowledge is accessed (Process Understanding Plan)• Ensure all data from similar products/unit operations available• Populate models with data – tune parameters (APC)

PAT - API Example

Risk: New API process being scaled up at launch facility. Development lab runs showed potential to form two polymorphs for step 2 product. One polymorph does not filter well. Risk discussed during co-development risk assessment process and associated cross functional PAT discussions. Risk of isolation challenges and quality issues at scale up.

Risk mitigation : Supersaturation control enabled by process analytics was used to develop an optimised crystallisation cooling profile to reduce the risk of forming the wrong polymorph. Innovative approach to control particle size distribution based on process understanding and knowledge.

Result:Modified cooling profile implemented at plant scale during successful validation campaign.

The plot above shows the solubility curve calculated using MIR and FBRM data.

Staying between the two curves delivers product with the correct particle size

distribution and polymorph composition

PAT Bio DS Example

Risk: Variability in molecular weight observed at lab scale during development of new vaccine. Risk to clinical supply.

Risk mitigation : Process and Analytical experts from development and manufacturing connected via internal cross functional ‘knowledge sharing’ forum (IPA). Focus on rapidly building process understanding using process analytics and identifying control strategy to address the risk.

Result:Innovative process analytical technology solutions used to understand how process parameters impacted molecular weight variability. Real time Mid IR integrated to control dissolved CO2 levels. Clinical material manufactured with consistent molecular weight. Target Product Quality Profile achieved.

• Connecting people and knowledge

• Process driven innovation

• Risk Reduction

PAT Aseptic DP Example

Risk: pH conversion by NaOH addition from an insoluble API to a soluble salt has to be stopped when the solution is clear. Process risk - Excess NaOH precipitates a non-recoverable white paste.

Risk mitigation : Process knowledge and process monitoring key. Use of both turbidity meter and stoichiometric chemical addition tracking provide clear target to the zero end point, thus mitigating risk.

Result:Process Understanding + real time process knowledge allows risk mitigation

Turbidity monitoring

Conversion Control

Metrics To Identify RiskProcess Capability

10%

90%

19%

81%

~150 studies with ~1800 QAs31%

69%

Acquired/In-licensed Non-QbD: 9/8/11

Legacy Products

New Products

In-Licensed

Pie charts represent total attributes evaluatedGreen = Cpk>1, Red = Cpk<1

Judging Capability – Using Process Robustness Contours!

Attribute Content Uniformity

(AV)

Dissolution (4 hour)

Impurity x Potency Total Impurities

Yield

Specs <15% 35 – 55% <0.2% 95 – 105% <1% >90%3.24 52.56 0.09 100.95 0.12 93.397.64 53.96 0.07 96.89 0.16 93.84

DATA 9.57 51.63 0.12 99.71 0.21 94.912.73 47.86 0.11 99.85 0.18 94.498.79 54.60 0.06 98.88 0.19 93.103.98 49.89 0.05 97.85 0.23 94.28

• Example: Dissolution: All values are within specification acceptance criteria• Don’t just look at the tabulated data; plot it [mean (x-axis) and SD (y-axis)]• Plot summarizes the data set; where does the “X” lie and should it be repositioned

19

Judging Capability – Using Process Robustness Contours

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Co-Dev Ownership PGS Ownership

POC Phase 2b Phase 3 ICH CTD Submission Approval Commercialize

Initial PUP Experimental PUP Final (Co-Dev) PUP Commercial PUP

RA #1 RA #2 RA #3Issue Final PUP/F2F

meeting

Update Final PUP &

Handover to PGM

Milestones

PUP Commercialization

Description:A PUP is the organized methodology to capture critical product knowledge throughout the lifecycle (Development through to product discontinuance) of a product

• Summary of Product & Process Understanding

• Index of technical reports (accessible thru hyperlinks)

• Capture output from Risk Assessments

Purpose:The PUP is a strategic roadmap to support PAIs, product investigations, transfer activity, continuous improvement initiatives and summarizes the key knowledge gained.

How maintained:The PUP is supported in a cross functional manner throughout its development and commercial manufacturing life with the technical disciplines in each functional area taking leadership roles.Key stakeholders for the PUP process include sponsors, site leaders and program facilitators

Currently a Word document, heavily linked to the authoritative sources

Process Understanding Plan (PUP)

Product Transfer Risk Assessment Methodology

Build Process in Pharma Investigator

Cause & EffectRisk assessment

Failure mode and effect analysis

API-DP risk assessment

Feedback Loop / Lessons Learned

Building the model to explain the process and the chemistry to another colleague

Capture historical & current process challenges (process, equipment, quality, analytical etc.,Capture subject matter expert tacit knowledge about the processIdentifies gaps in process understanding and challenges assumptions

How will the process fit into the receiving sites equipment & are there sufficient controls in place

What is the DP sites experience with changes to the physical / chemical properties of the API –what is the potential impact on the drug product formulation

Identifies where there is a need; to establish a baseline for transfer – where additional experimentation is needed such as process or analysis, mxing modelling etc.,

Did the process behave as predicted or did the transfer team identify the most obvious sources of risk to control

Multivariate Soft Sensor Model tablet bed temperature during coating

Model Calibration: 3-component PLS model & Predictive Performance

Variable Importance Plot

Y Tablet Bed Temperature

X

Inlet TemperatureExhaust TemperatureSolution Flow Inlet Air FlowInlet Fan VSD Speed (Hz)Exhaust Fan VSD Speed (Hz)to Room PressureDrum Load PressureBypass Valve Open FeedbackPlenum Seal PressureDrum Motor VSD Speed (Hz)Atomising Air FlowPattern Air FlowInlet Dew Point (Humidity)Exhaust Dew Point

Process Inputs & Output

4 inputs were used to create a robust Dynamic ARX-PLS Softsensor – used as back up to IR sensor (blinds sometimes)

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Obs ID (Primary)

YPredPS[3](Tablet Bed Temperature PV)YVarPS(Tablet Bed Temperature PV)

SIMCA-P+ 12.0.1 - 2012-03-13 14:52:39 (UTC-5)

Model Prediction (Black) vs Existing IR Sensor

Coater Opened

Fault diagnosis as batch evolves

1) Batch Score Control Chart 2) Score Contribution Plot

3) Variable Batch Plot (Inlet Fan Speed)Batch Score Control Chart

Bad BatchInlet Fan Speed was lower than average till time point 800

Why t2 went out of limits?

Soft Sensor PSD (D50)

• Captures major process dynamics impacting PSD

• Based on Population balance models

• Takes into account wet granulation step parameters as well as drying

Particle Size Prediction Model

0

100

200

D-v

alu

es (

mic

rons)

The softsensor is used to predict and control particle attrition in fluid bed dryer

Particle Size Prediction Model

0

5

10

15

20

25 Typical LOD (%) Vs Time for APC & Nominal Controlled Batches

ModelRequired End Point

LOD & PSD

Advanced Process Control: MBPC

Fluid Bed dryer airflow & temp

setpoints

Moisture(LOD)Model Based Optimization

NIR /sensorsSoftsensors

APC

Non APC

Batch Drying Time (Target Endpoint LOD 2%)

LOD

(%)

The application of the softsensors

and model predictive APC

demonstrates very good control of final LOD with

30% reduction in batch time

Risk and Knowledge Management

• Risk – Product Lifecycle– Mature Phase – transfer (partners/CMOs), reduction in

tech support & response time, changes in specs/standards/methods

– KM approaches• Continue RA approach for all significant changes• Focus on major events (transfers) – deep dive RA (understand control

strategy vs accept)• Maintain PUP – requires discipline & easy on-line system• Ensure all data from similar product

Risk and Knowledge Management

• Risk – Technology– SM API – physical properties– Solid Oral – physical & chemical stability, content

uniformity, dissolution– Biologicals DS – lot-lot comparability (degree of

conjugation, glycosilation), transfer/scale-up, analytical characterization (chemical & biological assays), contamination

– Aseptic – sterility, particulates, temperature (cold chain)

Traditional Small Molecule Pharmaceutical Biologic

Small Molecules vs. BiologicsBiologic Production Process Is More Complex

Bioreactor Centrifugation

Affinity Chromatograph

y

Tangential Flow

Filtration

Ion Exchange(Cation)

Ion Exchange

(Anion)

Viral Retention

Filter

Tangential Flow

Filtration

Lyophilize Formulation

Distribution Supply Chain

Conventional Small Molecule Pharmaceuticals• Less complex chemical process• Multiple ways to manufacture a product • Straightforward controls and monitoring• Less risk of biologic growth or contamination

Biologic Products• Complex, multistep manufacturing process involving

biologic organisms• Product depends upon proprietary process• Elaborate controls and monitoring• Favorable environment for growth of biologic

contaminants (bioburden)

Ingredients Excipient addition

Mixing/blending

CompressionFormulation

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Traditional Small Molecule Pharmaceutical Biologic

Small Molecules vs Bios- Complexity and Structure

1. Kresse GB. Biosimilars—Science, status, and strategic perspective. Eur J Pharm Biopharm. 2009;72(3):479-486. 2. In: Cooper GM, Hausman RE. The Cell: A Molecular Approach.3rd Ed. 2004:366-369.

• Well-defined stable structure held together by strong chemical bonds

Spatial structures (secondary & tertiary) based on relatively weak bonds and post-translational modifications to form the 3D conformation

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Small Molecule Drugs

Biologic Drugs

Analysis of Small Molecules and Biologics

1. Fox A. J Biopharm Stat. 2010;20:3-9. 2. Locatelli F, et al. Nephrol Dial Transpl. 2006;21(suppl 5):v13-v16.

• Methodologies exist for precise analysis of composition and purity

• Exact structure of each ingredient can be determined

• Functional assays exist

• Requires multiple techniques; each with potential limitations

• No analytic tools to determine precise structure

• Functional assays exist but do not fully predict in vivo activity

• Difficult to fully predict potential immunogenicity

Analysis

Analysis

IdenticalVariation

31

Risk and Knowledge Management

• Knowledge Management Approaches– Gather information/expertise by product AND by technology– Platform technology models can be applied across products– Standardize on equipment, nomenclature, KM recording

approaches– Many tools, approaches used – no single parameter or

approach can completely characterize Risk• NONE OF THIS WORKS without engagement of

colleagues.– Visible Commitment from Leadership– Examples Demonstrating Value– Tools (user-friendly)– Training, Support, Patience ….

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PGS Leveraging People, Process, & Technology for a holistic approach to KM and business results

Critical process and business knowledge, capturing tacit

knowledge of subject matter experts

Leveraging subject matter experts, global colleagues

help solve problems, surface replication opportunities

Information findability, increase information & knowledge reuse,

culture of information/ knowledge curation

Connecting systems to extract and transform data to enable new knowledge and insights,

KM Strategy Element Focus Driving

-Regulatory Compliance (efficiency & performance)- Decision making/problem solving

-Cost efficiencies -Product Supply Reliability-Colleague engagement-Colleague capabilities

-Cost efficiencies -Product Supply Reliability-Colleague engagement

-Regulatory Compliance-Decision making/problem solving- Agility

Acknowledgments

• Vince McCurdy• Paige Kane• Brian Fitzpatrick• Mojgan Moshgbar• Llurellyn Malcolm• Kim Vukovinsky• Steve Hammond

• Joe Brennan• Mark Hand• Seamus O’Neill• Enda Moran• Tom Lantz• Edel Hughes• GK Raju (LightPharma)

Contact Details:Kevin NepveuxVP Global Technology Services, Pfizer Global Manufacturing kevin.nepveux@pfizer.com

For the PGS KM Program: PGSKM@Pfizer.com