quality control strategies in continuous manufacturingccpmj.org/downloads/3_prof.muzzio(en).pdf ·...

Quality Control Strategies in Continuous Manufacturing

Fernando J. Muzzio, Distinguished ProfessorDepartment of Chemical and Biochemical Engineering,

Rutgers University, NJ, USAPresented at CCPMJDecember 12, 2018

1

Advanced Manufacturing

• Advanced manufacturing is:– Predictively designed– Automated– Optimized– Scalable– Transferable/portable

– ACHIEVABLE

Contents• Historical Background: C-SOPS and CM• What does it take to implement CM?• Collaborations with FDA• The Janssen Partnership• The GSK Partnership• The Powder Process Engineering Toolbox • Modes of Interaction• Conclusions

Rutgers/C-SOPS’ engineering approach and industrial engagement model has created an engagement sandbox whereindustry, academia, and regulators come together on continuous pharmaceutical manufacturing.

Working with industry, C-SOPS has been involvedin the regulatory approval of some of the firstcontinuously manufactured solid dose products andis helping to shape the future of solid dose processdevelopment.

Chronology of CM at Rutgers• 1998-2002 – F. Muzzio proposes CM to companies, CAMP, FDA• 2003 – Rutgers forms CM consortium (Pfizer, Merck, GEA, Apotex)• 2006 – C-SOPS funded – CM consortium becomes Test Bed 1• 2008 – Proof of concept achieved on CM with PAT• 2009 – NSF CM Commercialization Funds received ($1.8 million)• Feb 2011 – JnJ funding for Rutgers INSPIRE2 work approved ($1.9M)• Dec 2014 – Janssen/Rutgers partnership funded (3 more products) ($3.25M)• Feb 2015 – Modeling of Consigma 25 approved ($2 M)• Jan 2016 - C-SOPS receives $4M award from FDA to develop regulatory guidance elements on materials, PAT,

control • March 2016 - Presidential Report listed C-SOPS as top example of successful government invention in critical

emerging technology area of advance pharmaceutical manufacturing• April 2016 – FDA approval of Prezista® CM• June 2016 – USP and C-SOPS launch partnership on CM standards• July 2016 – CSOPS submits proposed draft guidance to FDA• December 2016 – 21st Century Cures Act – authorization for $25 M for CM • May 2017 – OSD Continuous Manufacturing in the Current Regulatory Landscape. Malta• December 2017 – CSOPS articulates Advanced Manufacturing Toolbox• March 2018 – Rutgers & GSK launch advanced manufacturing partnership• June/July 2018: $5.8M awarded by FDA for Industry 4.0, Continuous Biomanufacturing

Interactions with FDA• FDA member of C-SOPS since 2007• Funded Continuous Process Modeling, 2013 ($500K)• Funded Material Properties and Process Control, 2015 ($4M)

– Week long training for 20 FDA employees in 2016, 27 in 2018

• Funded Industry 4.0, 2018 ($4M)• Funded continuous MAB manufacturing modeling, 2018 ($1.8M)• Three additional proposals in prep. for Nov. 2018: Knowledge management, Continuous HME for

opioids, Environmentally controlled material property testing facility• Major center proposal – target $50 million

– White paper in 2017– Authorization as part of 21st century cures– Visit to OPQ in Jan 2018– Visit by Commissioner Gottlieb and Congressman Pallone (115th congress H.R. 5568)– Preproposal submitted July 2018– Visit to OPQ in Aug 2018– Full proposal planned for Dec 2018

M

M

PID

M

M

PID

M

M

PID

API feeder

Excipientfeeder

Lubricantfeeder

Co‐mill

Blender

Feed frame & Tablet Press

Continuous Manufacturing: integration of equipment, sensors, and controls

PAT

ProcessControl

How we do itIntegrated Process Model

Experimental & Design Parameters

Material Properties

Unit Operations

e.g., Flow, Bulk Density, Angle of Repose

y f (x,a,t,m,n)dydt

g(x,a,t,m,n)

e.g., BlendersGMP Implementation

Non‐GMP Implementation

Feedback control

Feed forward control

Predictive Modeling

Rational process design – 12 steps

1. Rough conceptual design2. Material property characterization3. Specification of individual unit operations4. Develop unit operation models5. Develop integrated model of open loop system6. Examine open loop performance7. Develop PAT methods8. Implement open loop kit with PAT and IPCs (OLIF)9. Design sensing and control architecture10. Develop integrated model of closed loop system (CLIF)11. Characterize closed loop performance (Validation)12. Optimize process performance

Advanced Manufacturing Toolbox

Sensors/data analytics

Material Properties

Process Modeling

Process Control

Process Integration

Pharmaceu

ticals

Catalysts

Food

produ

cts

Faculty

, Stud

ents,

Post docs

Spon

sor

technical

person

nel

Adv. Man. Toolbox

RTQA / Sensor toolbox

Mat. Prop. Library

Modeling library

Control toolboxes, QbC

Integration toolbox

Cosm

etics

Batteries

ToolsImplementation methods

Quality Assurance

Demonstrated PlatformsManufacturing Science Process Understanding

KnowledgeProcess Science

Scientific leadership

Trained scientists

Step 1: Develop a realistic plan

- Which product(s)?- Which platforms?- Flexible or dedicated?- How much sensing and control?- How much modeling?

Outcome: Select a first system, implement it, gain experience, bring new core capabilities into company

Implementing Continuous Manufacturing

Step 2: Characterize material properties

- Which materials?- Which measurements?- How will the information be used?

- Models- Algorithms

- How will the information be maintained?

Outcome: create a systematic program to characterize materials that becomes a knowledge reservoir


Tablet Press

Lubricant

Feeders

M

API

Mill

Blender

13

M

Failure Mode

M

M

M

M

EX

M

EX

M

System Response

Flow Rate Set Point divergence

Q3: Are agglomerates present?

Q4: Can blend homogeneity be achieve?

Q7: Can we get tablets at target dissolution at reasonable high flow rate?

Flow rate variability

Related Material Properties

Q2: Can each ingredient be fed with variability below certain threshold?

Q1: Can each ingredient be fed at the required flow rate? Density, Permeability

Cohesion, Compressibility, Stickiness

Fail to comply hardness

Q6: Can we get tablets at target hardness at reasonable high flow rate?

Q5: Are blend flow properties good enough to support Weight Uniformity?

Agglomerates Electrostatics, Surface Energy, Adhesion, PSD

PSD, Adhesion, Cohesion, Electrostatics, Surface Energy

Chocking, Jamming, Discontinuous flow

Blend Cohesion, Density, Compressibility

Content uniformity/ Blend RSD

Blend Lubrication, Bonding, Plasticity, Elasticity.

Fail to comply dissolution

Blend Lubrication, Bonding, Plasticity, Elasticity, PSD,

crystallinity.

Screw Coating and “sticky” powders

Coating of the screws reduces the space available material that can be fed and reduces the capacity of the feeder.

Environmentally Controlled Powder Testing System

• Layout is a set of glove boxes with controlled humidity, temperature, pressure, N2 blanket

• Samples are conditioned in first box, then travel as needed to perform required tests

• Performance in process equipment is tested for conditioned samples

Conditioning: target moisture content, shear,

PSD

Density, picnometry, flow

propertiesp

Electrostatics, dipolar m

oment

charge acquisitionim

pedance,

Segregation, W

ettability

Surface energyVPE

Process Equip.

CompactabilitySolid state rheology

Air, Moisture, T, N2, Pressure

Sample

Sample

Sample Storage

Step 3 and 4: Unit Operations Characterization and Modeling- Identify all unit operations and transitions- Obtain performance databases for relevant materials- Develop/adapt dynamic models for all relevant unit operations

- A lot of models already available- Models incorporate IPCs- Expertise available through partners

Outcome: create a model library that is a reservoir of process knowledge

16


Investigate Process Variables for Models

Model variable selection is based on the understanding of critical unit operation process parameters, variables and responses

Phenomenological model inputs are design parameters and operating variables. Unit responses are the model outputs

Selecting Process Variables for Models

Unit Operation Model Development

MATERIAL INPUTS

PROCESS OUTPUTS

Granular Material BlendsWith many properties

Critical Process ParametersVariables affecting outputs

Semi-Empirical ModelsRelationship - process inputs and outputs

PROCESS MODELS

Understanding the effect of process inputs and material properties to process outputs is a critical step for moving towards in silico modeling and predictability

PROCESS INPUTS

Product PropertiesVary based on inputs

Empirical ModelsCorrelate material to model parameters

MATERIAL MODELS

M

M

We repeat these experiments many times, but we ought to collect this data and make it valuable

Modeling in Pharmaceutical Manufacturing

Steps 5 and 6: Create and Validate Open Loop Integrated Flowsheet Model (OLIF model)- Create integrated dynamic model of entire line

- Tools already available from PSE and Aspen- Expertise available at partners

- Validate OLIF by comparison to experiments- Identify critical material attributes, critical process parameters, critical product

quality attributes- Determine feasible space

Outcomes: Understand interactions between unit operations, define critical variables to be monitored and controlled, establish operational space

19


2.9

3

3.1

3.2

100 150 200

API flow rate

Dynamic simulation

Time [s]

[kg/h]

Temporal variations due to refill

M

M

PID

M

M

PID

M

M

PID

API feeder

Excipientfeeder

Lubricantfeeder

Co‐mill

Blender


Integrated flowsheet modeling

Time [s]

Wt%

of A

PI

Dynamic simulation

Variations dampened in the blender

M

M

PID

M

M

PID

M

M

PID

API feeder

Excipientfeeder

Lubricantfeeder

Co‐mill

Blender


Flowsheet modeling

Steps 7 and 8: Integrate line, specify and implement sensors- Integrate physical line- Select and validate measurement systems

- Specify chemometric models for spectroscopic measurements- Validate in the line

- Identify process information data streams- Implement data analytics- Incorporate sensors and IPCs into OLIF model

Outcome: Ability to monitor material properties, process parameters, product quality in real time

22


23


Sensor Critical Quality Attribute (CQA)

Ultrasound (US) and Laser Triangulation (LT)

Tablet thickness, hardness, Tensile strength - tablets

NIR spectroscopy Composition, Content uniformity – powders, tablets

Raman spectroscopy Composition, Contentuniformity, crystalline vs amorphous – powders, tablets

Raman Imaging (3D) Material distribution (eg. agglomeration), particle size –powders, tablets

Eyecon Particle Size Distribution (PSD), particle shape

Thermal Imaging Compaction, defects

X-Ray Structure, crystallinity, density distribution, mass flow

Terahertz (THz) Spectroscopy & Imaging

Tablet (internal layers)thickness, defects, material distribution

Feedback control

Feedforward control

General Dissolution Prediction Methodology

Define target condi ons

0

50

100

150

0 20 40 60 80100120

Drugrelease%

Time/min

y = 0.9833x R² = 0.86308

0

50

100

150

200

250

300

0 50 100 150 200 250 300

α p

red

icte

d

α reference

Reference vs predicted

0 20 40 60 80

100

0 20 40 60 80 100 120 % Drug Dissolved

Time (min)

reference predic on

f2=79.13

Iden fy dissolu on mechanism

1. APAP – 1st demonstration formulation (IR)2. Commercial Product (IR)3. PEO – 2nd demonstration formulation (SR)

Three Case Studies:

26

Case Study 1 Predicting individual tablet dissolution profile

Reference: actual dissolution profilesPredicted: NIR PCs

40

50

60

70

80

90

100

110

0 10 20 30 40 50 60 70

API %

rele

ased

Dissolution time (min)

Predicted vs Reference

Run 12- Pred Run 12-Ref

Case Study 2: Commercial IR product

Case Study 3: 24 h release from PEO matrix

• Results demonstrated that individual tablet dissolution can be predicted with high accuracy

• This can be extended to more complex formulations

Integrated product, process, analytical development

Steps 9, 10, 11: Supervisory Process Control - Use OLIF to define and specify supervisory control architecture

- Control loop structure- Controller type- Controller parameter

- Implement selected control architecture to create Closed Loop Integrated Flowsheet (CLIF) model.

- Implement and evaluate accuracy and effectiveness of implemented control architecture in physical line

Outcome: Real time quality control

30


31


31

Tablet Press

Blender

Feeders

mill

API

M

M

M

M

M

M

M

M

Content/DensityBlend Uniformity

NIR, Raman

LT

Thickness

Density

US

Hardness

NIR

Dissolution (check)


Force

Weight

Compression Gap

Cross‐check

Content (check)

Feedback control


Advanced hybrid MPC-PID control system

32Control variables: API composition; Powder level; Tablet weight; Tablet hardness

3

6

4

5

2

1

RSD

Turret speed

Steps 12: Runtime Optimization - Define outcomes to be optimized (productivity, quality, cost)- Implement optimization methods in CLIF - Search for optimum- Confirm optimization in physical line

Outcome: optimized system

33


Enabling a different future

34

Sensors/data analytics

Material Properties

Process Modeling

Process Control

Process Integration

Pharmaceu

ticals

Catalysts

Food

produ

cts

Cosm

etics

Batteries

Material PropertyDatabase

+ Process Models

Predictive Material

Performance

Meaningful Materials

Specifications

Sensors + Process Control

Predictive Process Design

Optimum Process

PerformanceOptimum Product

Quality

MaximumProfit

Process Integration Methods

• Contact Information:• [email protected], [email protected], • 732-735-8618, • https://www.linkedin.com/in/fjmuzzio/

Questions?

quality control strategies in continuous manufacturingccpmj.org/downloads/3_prof.muzzio(en).pdf ·...

Documents