Development and Applications of PAT in API Unit Operations

1
Development and Applications of PAT in API Unit
Operations
San Kiang September 11, 2006 Bristol-Myers
Squibb New Brunswick, NJ
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Overview

• Industry Challenges
• New API Development Paradigm
• Enabling Technology Tools
• Case Studies
• Summary

3
Pharmaceutical Industry Commercial Challenges

• Current cost of bringing new drug to market
  gt800 million
• Success rate 1 in 10
• Time to Market 10 to 15 years
• Highly competitive
• Highly regulated

4
Regulatory Challenges ICH Q8 Guidance

• Demonstrate a higher degree of understanding of
  manufacturing processes and process control for
  more flexible regulatory approaches
• Risked based regulatory decisions (reviews
  inspections)
• Process improvements within the design space
  without further regulatory review
• Real time quality control to reduce end-product
  release testing

Courtesy FDA/CDER
5
Regulatory ChallengesNew Pharmaceutical Quality
Assessment System (PQAS)
Objective To Ensure that Necessary Quality
Attributes are Built in (QbD) and the Drug
Product can be Manufactured Consistently with
High Quality for its Intended Use (i.e., Safety
and Efficacy)

• Submissions rich in scientific knowledge
  demonstrating understanding of product and
  process
• Specifications set based on product requirements
  for safety, efficacy, and stability
• Process designed and controlled to robustly and
  reproducibly deliver quality product

Moheb Nasr, FDA
6

• Industry Challenges
• New API Development Paradigm
• Quality by Design
• Process Work Flow
• Strategic Benefits in API Unit Operations
• Enabling Technology Tools
• Case Studies
• Summary

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Work Smarter, Not HarderStrategy for a New
Paradigm
Meet the Challenges by executing fundamental
changes to the development and manufacturing
approach
Integrated scientific, manufacturing,
commercial objectives Development of the best
route with full intrinsic process
knowledge Apply new technologies for effective
utilization of process knowledge
Integration
Science
Manufacturing
Bottom Line How to Achieve these ?
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Fundamental Approach in Development Paradigm
Shift
Quality by Design
Traditional Approach
Real Time Control for Continuous Improvement
Causal Links Predict Performance
Scale Up Prediction
Modeling for Mechanistic Understanding
Decision Based On Univariate Approach
PAT
DOE (Multivariate Systems Approach)
Data Derived From Trial Error Experimentation
Identification of PCCP
PCCP Process Critical Control Parameter
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Quality by Design (QbD)21st Century cGMP
Manufacturing

• The product is designed to meet intended use
• The process is designed to consistently meet
  product critical quality attributes
• The impact of starting materials and process
  parameters on product quality is understood
• Critical sources of process variability (raw
  materials, process) are identified and controlled
• The process is continually monitored and updated
  to allow for consistent quality over time

Moheb Nasr, FDA
10
Road Map to QbD
Designing for the API Manufacturing
Quality By Design
Chemistry Selection

Process Modeling
Process Devel.
Process Control
Process Scale Up

Manufacturing Science
Science of Design
Evaluate
Design
Predict
Verify
Control
Deliverables
Best Production Chemistry
Intrinsic Process Knowledge
Optimized Unit Operations
Demonstrated Production Process
Real Time Continuous Improvements
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API Process Development Workflow in 21st Century
Molecular model of physical properties, kinetics,
selectivity, solvent effects, and transport
data Disciplined route selection
Form selection
Form screening
Molecular and unit operation models guide
experimental design. Determining design space
early on
Route feasibility
Route selection
Unit operation model on reaction, distillation,
crystallization, and drying Disciplined process
selection
Process dev. studies
Process selection
Develop calibrate Model
Plant data to verify model
Integrated reactor studies
Model qualification 1
PAT Thermo-dynamics data
Glass plant
Pilot plant
Model qualification 2
Tech transfer Manufac. imp.
Predict scale up with qualified model Model
design space QbD
API
Equipment Train capability model, Equipment
capability, (Unit Operation Model)
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• Industry Challenges
• New API Development Paradigm
• Quality by Design
• Process Work Flow
• Strategic Benefits in API Unit Operations
• Enabling Technology Tools
• Case Studies
• Summary

13
API Manufacturing - Typical Unit Operations
PAT
Raw Material
Reaction
Purification
Crystallization
Isolation Drying
API
Right API Properties Enables Formulation
of Quality Drug Product

• Drug Product
• Drug-excipient compatibility
• Stability
• Dosage form design
• Bioavailability
• Manufacturability

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Impact of API Powder Properties on Pharmaceutical
Product
Inhomogeneous Distribution of API with Excipient
Wide PSD
Poor Dissolution Performance

RAMAN m
SEM
size
Homogeneous Distribution of API with Excipient
Narrow PSD
Good Dissolution Performance

RAMAN m
size
SEM
Excipient
API
Narrower particle size distributions (PSD)
minimize segregation problems during
mixing, rendering a more homogeneous distribution
of components in the final product
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Impact of On-line Process Analyzers - Reaction
PAT
Raw Material
Reaction
Purification
Crystallization
Isolation Drying
API

• Safety
• Minimize handling
• Tight hazardous process control

• Quality/Productivity
• Mechanistic kinetic knowledge
• Parametric boundary
• Access to extreme conditions
• Real time monitoring control
• Continuous quality assurance

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Impact of On-line Process Analyzers - Downstream
Process
PAT
Raw Material
Reaction
Purification
Crystallization
Isolation Drying
API

• Quality
• Process knowledge
• Parametric boundary
• Real time monitoring control
• Continuous quality assurance

• Productivity
• Seamless scale up
• Minimize OOS product
• Rapid troubleshoot
• Optimal process efficiency

• Safety
• Minimize handling
• exposure

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• Industry Challenges
• New API Development Paradigm
• Enabling Technology Tools
• PAT
• Predictive Modeling
• Risk Analysis
• Integrated Technology Solutions
• Case Studies
• Summary

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PAT Tools Driving Enabling Quality by Design
Process Knowledge
Critical Parameters Identified
Explained Variability Managed by Process Product
Quality Attributes Reliability Predicted Continuou
s Quality Assurance
Multivariate Data Acquisition Analysis
On-Line Sensors for Monitoring Control
Process Modeling
Risk Analysis
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Applications of On-Line Process Analyzers during
Crystallization
FBRM Probe

• FBRM to monitor control Particle Size
  Distribution
• Isolation/Drying Efficiency
• Powder properties
• Formulation performance

Frequency (number of particles)
size (microns)
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PAT Tools Enabling Quality by Design
Process Knowledge
Critical Parameters Identified
Explained Variability Managed by Process Product
Quality Attributes Reliability Predicted Continuou
s Quality Assurance
Multivariate Data Acquisition Analysis
On-Line Sensors for Monitoring Control
Process Modeling
Risk Analysis
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Science-Risk Based Approach - Learn Predict for
Optimized Process
Predictive Modeling Risk Analysis
Experimental Studies
Theoretical Analysis

• Direct Experiment
• Correlate Experimental Results
• Predict Scale Up Performance
• Determine Parametric Sensitivities
• Risk Management

• Identify Critical Process Parameters
• Design Scale Down System
• Collect Process Data

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Applications of Modeling in BMS
Molecular Modeling
System Modeling
Unit Operation Modeling

• Molecular Structure
• Relative Reactivity Selectivity
• Reaction Pathway Mechanism
• Thermochemistry (Heat of Reaction)
• Relative pKa Value of Intermediates
• Physical Properties
• Homogeneous Heterogeneous Catalyst Selection
• Polymorph Selection

• Equilibrium Kinetic Knowledge
• Prediction of Process Results
• Prediction of Scale Up Performance

• Chemical Route Selection
• Process Synthesis
• Process Options Selection
• Cost Analysis for Metrics
• Optimal Scheduling for PRD Pilot Facilities
• Optimization of Equipment Use for Multi-batch
  Campaigns
• Plant Fit Determination Selection of
  Manufacturing Site

Mixing Reaction Crystallization Extraction Chromat
ography Distillation
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• Industry Challenges
• New API Development Paradigm
• Enabling Technology Tools
• PAT
• Predictive Modeling
• Risk Analysis
• Integrated Technology Solutions
• Case Studies
• Summary

24
PAT Tools Enabling Quality by Design
Process Knowledge
Critical Parameters Identified
Explained Variability Managed by Process Product
Quality Attributes Reliability Predicted Continuou
s Quality Assurance
Multivariate Data Acquisition Analysis
On-Line Sensors for Monitoring Control
Process Modeling
Risk Analysis
25
Risk Management ConceptICH Q9 Quality Risk
Management
Risk Assessment
Risk Identification
Risk Analysis
Risk Evaluation
unacceptable
Risk Control
Risk Reduction
Risk Acceptance
Risk Communication
Risk Review
acceptable
Risk Communication
Risk Acceptance
Output of the Risk Management Process
Review Events
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Risk Assessment Tools
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FMEA Analysis Applied to Pharmaceutical Process
Critical parameter identification analysis
FMEA execution based on Critical Parameters
Evaluation of risk reduction methods
All RPN gt RPNacceptable
Risk Prioritization Number (RPN) ranking
All RPN lt RPNacceptable
Communication of FMEA results
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Severity, Probability and Detection evaluated for
each of the subsystems in System 1 (Final step to
prepare API)
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Implementation of risk reduction steps and FMEA
for RPNs above acceptable RPN
Final RPNs
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• Industry Challenges
• New API Development Paradigm
• Enabling Technology Tools
• PAT
• Predictive Modeling
• Risk Analysis
• Integrated Technology Solutions
• Case Studies
• Summary

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Technology Solution - Road Map to Design a
Better Process
Particle Engineering
PAT

• Collect Fundamental Data
• Physicochemical properties
• Kinetics Equilibrium
• Mixing/Diffusion Effect
• Mass Transfer Effect
• Heat Transfer Effect

Linked
Statistical Design
Automation

Intrinsic Process Knowledge

• Formulation
• Critical formulation parameters
• Optimal formulation
• Physicochemical properties
• Delivery technologies

• Process
• Critical process parameters
• Mechanistic Kinetics
• Physicochemical properties
• Manufacturability

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Material Science - API Influence on Drug Product
Integration of On-line Process Analyzers for
Consistent Physicochemical Properties

Crystal SizeCrystal Shape Polymorph
Control Cycle Time Process Design Equipment
Selection
Cycle TimeEquipment Selection
Flowability Compactibility Bioavailability
Formulation Dosage Manufacturability
API
Particle Size Distribution
Supersaturation Kinetics
Drying
Filtration
Crystallization
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API Properties Controlled by Different Unit
Operations

• Crystallization
• Form
• Particle Size Distribution
• Morphology
• Filtration/Drying
• Attrition/Agglomeration
• Form
• Milling
• Particle Size Distribution
• (de-glomeration, size reduction)

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Particle Engineering - API Influence on Drug
Product
Agglomerated Particles
Attrited Particles
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Particle Engineering API Influence on Tablet
Performance
Original procedure Large primary particles
150-300 um
Poor toughness chipped tablets
100 ?m
Modified procedure (High S) Agglomerates, good
flowability
Optimized procedure 10-20um primary particles
excellent toughness
500 ?m
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• Industry Challenges
• New API Development Paradigm
• Enabling Technology Tools
• Case Studies
• Reaction
• Hydrogenation
• Crystallization
• Summary

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Case Study Nucleophilic Addition
Stoichiometry
SM1 1.1 SM2 3.2 C 2.5 D ? Prod 0.1 F
3.3 G 1.1 H 1.5 I
Impurities (non-stoichiometric)
?
Dimer SM2Dimer IMP1
Major reactions
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Inline Monitoring

• Real-time concentrations of species,
• including some reaction intermediates
• Large quantity of precise kinetic data
• Calibrated by HPLC of stable species

Raman
FTIR
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Byproduct Formation
PAT and model show good agreement
Coupling Agent Degradation (Slow)
k5 0.0033 L3/2/mol3/2s
SM2 C ? J H ? Imp1 G J SM2 ?
SM2Dimer H
C
k5
SM2
J
r a Sm22C1/2 (high base conc.) r a Sm2
C (low base conc.)
k6 4.8 x 10-5 L/mols
Series Coupling to Form Dimer (Slower)
Product
k6
Prod SM2 ? Dimer H
SM2
Dimer
r k6ProdSM2
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Complete Model Synthesis

• The main reaction steps have much higher rate
  constants than the byproduct steps
• The deprotonation steps have rate constants
  consistent with the acidity of the proton removed
  (i.e. k1 gt k4 gt k2)

• The Rate Limiting Step is either the second
  depro-tonation of SM2 - (Step 2) or the coupling
  reaction (Step 3)

SM2
Prod
Dimer
Full model prediction is in agreement w. exp
G
SM1
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Case Study Summary

• Kinetics experiments used online FTIR
  spectroscopy for mechanistic investigation and
  quantitative data collection
• BatchCAD kinetic model has been developed to
  provide a more complete understanding of the
  overall reaction and byproduct pathways
• Six-step kinetic mechanism
• formation of desired products through
  deprotonation and coupling
• degradation of SM2
• coupling with Product to produce Dimer
• Model provides a way to simulate operating ranges
  and inputs for FMEA for risk assessment of common
  operating issues
• fast base addition
• under and over-charging of reactants

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• Industry Challenges
• New API Development Paradigm
• Enabling Technology Tools
• Case Studies
• Reaction
• Hydrogenation
• Crystallization
• Summary

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Case Study Carbobenzyloxy Deprotection Reaction

• Spec. for Reaction end point 0.6 RAP of RNHCBz
• Removal of CO2

THF
FTIR
Hydrogenation
THF 10 Catalyst Loading

• End Point Determination - rate of impurity
  formation increases 25 after the desired
  reaction is complete
• Hydrogen uptake measurement is not reliable to
  monitor the kinetics of the reaction since CO2 is
  evolved as a by-product
• CO2 removal upon reaction completion to ensure
  product stability

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Kinetic Understanding of CBz Deprotection - CO2
Effects
CO2 poisons the catalyst. Reaction rate depends
on changes of the reactor headspace due to CO2
amount variations in liquid
Reaction changing from first to zero order as
headspace is changed Reaction is zero order in
substrate.
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Chemometric Model for Reaction End point and CO2
Removal
CO2 in the liquid as a function of headspace in
the absence of catalyst at 25 oC.

• Determining peak for SM

(1746cm-1 1696.62cm-1)

• Considerations
• Kinetics of the reaction
• VLE of CO2

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Validation of Model

• Model validated on scale up data
• RSD value is 1.43
• Good agreement between experimental data and
  quant model predictions

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• Industry Challenges
• New API Development Paradigm
• Enabling Technology Tools
• Case Studies
• Reaction
• Hydrogenation
• Crystallization
• Summary

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Case Study Improving Bioavailability of BMS-XXX
Issue Aqueous solubility of only 0.04 mg/mL at
pH 7
In process solvents
Polymorphs of BMS-XXX
Solvates or Hydrates Dihydrate H2-2 AN-3 DS-3 0.5P
G-4
Neat Form N-1
Desired Form

• Batch Crystallization to Produce N-1
• Crystal Rod shape with 50 ?m diameter 200 ?m
  long
• Polymorph Transformation Need 24 hours
• Size Reduction 2 passes in pin-mill for
  D9550 ?m

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Case Study Improving Bioavailability of BMS-XXX
N-1 crystals from batch crystallization, unmilled

• Objective
• D95lt 50 ?m to improve bioavailability
• Eliminate multiple milling
• Rugged process for scale up
• Faster polymorph transformation

In process solvents
D95180 ?m
Polymorph Transformation in the process solvent
of PG/H2O
Anhydrous form N-1 (short rods)
Dihydrate H2-2(needles)
D9535 ?m
Transformation from H2-2 to N-1 produce small
crystals
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Particle Engineering Solution -Continuous
Polymorph Transformation
Homogenizer
Small N-1 Crystal
FA
T
H2-2 slurry, RT
T 55-65 ?C ? V/FA 20-25 mins
V
T

• Batch
• D95 200 ?m
• 24 hours
• Multiple Pass Milling

• Continuous
• D95 30 to 40 ?m
• 20 to 25 min
• No Milling

51
Model Prediction Validated by Scale Up Data
N1
H2-2
Time( arb)
H2-2
N1
Time
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Summary Seamless Path From Quality Product to
Compliance
Generate Manage Process Knowledge
Quality Drug Product Process

• Strategy
• PAT to generate fundamental data for mechanistic
  kinetics understanding
• Particle Engineering for designer particles
• Predictive modeling risk analysis
• Real-time control by PAT for continuous process
  improvement
• Integrated API Drug Product development efforts

• Benefits
• Eliminate potential formulation problems
• Streamline pharmaceutical process operation
• Better product quality control
• Eliminate technical uncertainties for seamless
  scale transition
• Facilitate Regulatory Filing

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Acknowledgement

• Mauricio Futran
• Yeung Chan
• Jale Muslehiddinoglu
• John Shabaker
• Steve Wang
• Olav Lyngberg
• Soojin Kim
• Chiajen Lai
• Bing-Shiou Yang