IN SILICO MODELING APPROACHES TOWARDS …ESTABLISHING ROBUST DESIGN AND CONTROL ... * Undey et al.,...
Transcript of IN SILICO MODELING APPROACHES TOWARDS …ESTABLISHING ROBUST DESIGN AND CONTROL ... * Undey et al.,...
PROCESS DEVELOPMENTCENK ÜNDEY, PhD
IN SILICO MODELING APPROACHES TOWARDS ESTABLISHING ROBUST DESIGN AND CONTROL STRATEGIES: BIO/PHARMA INDUSTRY PERSPECTIVE
4th PQRI/FDA Conference on Advancing Product Quality, Rockville, MD, Apr.9-11, 2019
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• Process and product development paradigm– Prior knowledge, empirical, first principles, ML approaches
• Case Studies:– First principles modeling based UFDF for buffer composition
predictions– Drug product T/P filling process modeling – Process monitoring and predictive control examples– Incorporating PAT and models towards control strategy
OUTLINE
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• Process and product development paradigm– Prior knowledge, empirical, first principles, ML approaches
• Case Studies:– First principles modeling based UFDF for buffer composition predictions– Drug product T/P filling process modeling– Process monitoring and predictive control examples– Incorporating PAT and models towards control strategy
OUTLINE
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IN SILICO MODELING HAS SIGNIFICANT POTENTIAL TO INCREASE SPEED AND EFFICIENCIES OF PROCESS AND PRODUCT DEVELOPMENT
Pre-PT FIH Development
Clinical Support
Commercial Process
DevelopmentCommercial
AdvancementCommercial
Support
First principles predictive modeling(reduced wet exp.)
Cell Line SelectionCell Line Development
Increased data flow, digitalization and in silico model use across the product lifecycle
Process and Product PerformanceManagement
Predictive modeling for QTPP
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solv
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Cul
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Bio
reac
torL
evel
Agi
tatio
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sel P
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Tem
pera
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tribu
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Variable
N-3_N-2_N-1_NDModXPS Contribution M5:N
(0010066284:0.0638889 - Average:0.0638889), Weight=RX[4]
SIMCA-Batch On-Line Client 3.3.0.2 - 2010-09-28 14:59:20 (UTC-5)
Robust design (6σ)Patient centric
Feedback loop
Design Qualification Continued Verification
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RISK ASSESSMENT, DESIGN SPACE AND CONTROL STRATEGY ARE KEY IN SETTING UP PERFORMANCE-BASED CONTROL
* CMC Biotech WG, 2009, A-Mab: A Case Study in Bioprocess Development. V2.1
*
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PRIOR KNOWLEDGE ASSESSMENT AND AGGREGATING HISTORICAL DATA FOR PLATFORM PROCESSES OFFER UNIQUE ADVANTAGES TOWARDS PROCESS DESIGN AND PROCESS PERFORMANCE QUALIFICATION (PPQ)
Courtesy of Dr. Roger Hart
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• Much of the current modeling has its roots in engineering, biology, chemistry, physics and fluid mechanics (mass transfer, kinetics, etc.)
• In some cases, current scientific understanding and/or analytical resolution insufficient to utilize mechanistic modeling
ADVANCES IN COMPUTATIONAL POWER AND SCIENTIFIC UNDERSTANDING AREENABLING MORE MODELING OF BIOPHARMACEUTICALS PD & MFG
Q11 - Design and conduct studies (e.g., mechanistic and/or kinetic evaluations, multivariate design of experiments, simulations, modeling) to identify and confirm the links and relationships of material attributes and process parameters to
drug substance CQAs
• Convergence of disciplines: in silico modeling to enable Smart PDcomputational platforms
• in silico experiments for Process Characterization• Predictive modeling for RM selection
and variation control• in silico tooling and device design
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WE ARE ADVANCING THE USE OF FIRST PRINCIPLES MODELING IN PROCESS, PRODUCT DEVELOPMENT AND IN MANUFACTURING
TODO: Chrom
Fluid VelocityAppliedForce
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THE MODELING PROCESS (IDEALIZED)
4. SolutionAnalysis
3. NumericalApproximation
2. ProblemFormulation
1. DomainDefinition
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Calibration and validation of first-principles model required a small number of targeted experiments
• a fraction of conventional DOE-based process characterization approaches
More operating parameters and wider ranges explored via fast and inexpensive “in-silico” experiments
• investigating parameters difficult to study experimentally (e.g., bed compaction)
• replacing costly or time-demanding experiments(e.g., column size)
Reducing costly and time consuming experimentation
MODEL-BASED PROCESS DEVELOPMENT
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Automated Cytopathic Effect (CPE) Detection via Deep Learning
WE HAVE BEEN ADVANCING DEEP LEARNING APPLICATIONS TO CREATE SIGNIFICANT EFFICIENCIES TOWARDS ACCELERATING PD
Deep CNN
ANN
https://www.tutorialspoint.com/python_deep_learning/python_deep_learning_deep_neural_networks.htm
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• Process and product development paradigm– Prior knowledge, empirical, first principles, ML approaches
• Case Studies:– First principles modeling based UFDF for buffer composition predictions– Drug product T/P filling process modeling– Process monitoring and predictive control examples– Incorporating PAT and models towards control strategy
OUTLINE
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GOAL: ACCURATELY PREDICT pH AND EXCIPIENT CONCENTRATIONS IN PROTEIN DRUG SUBSTANCE
Challenge: High concentrations of charged mAb during UF/DF commonly result in offsets in pH/excipients between UF/DF pool and formulation buffer• Molecular basis for this offset is the selective retention or rejection of ions due to:
– Charge interactions between ions and the protein: ions attracted or repelled– Volume exclusion: solutes excluded from volume occupied by high concentration of mAb
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FIRST PRINCIPLES UNDERSTANDING OF UF/DF AND BUFFER EXCIPIENTS WAS USED VIA IN SILICO MODELING TO REDUCE WET EXPERIMENTS DURING PC
Model Verification
Model Prediction
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IN SILICO DOE: MECHANISTIC MODEL OF EXCIPIENT EXCHANGE DURING PROTEIN ULTRAFILTRATION & DIAFILTRATION (MUD)
• (Right→) In-silico DoE performed in MUD to explore the formulation buffer design space for targeted experimentation
• (Below↓) Worst-case buffer conditions were tested experimentally and results agreed with MUD predictions
Buffer Conditions
Result Source
Acetate (mM)
Phe(mM)
Sorbitol (%)
Osmolality (mOsm) pH
Buffer Formulation #1
| Exp. – Pred. |2.0 2.1 0 3 0.02
Buffer Formulation #2 0.3 0.4 0.31 7 0.02
Process robustness:Model accurately predicts UF/DF pool pH
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• Process and product development paradigm– Prior knowledge, empirical, first principles, ML approaches
• Case Studies:– First principles modeling based UFDF for buffer composition predictions– Drug product T/P filling process modeling– Process monitoring and predictive control examples– Incorporating PAT and models towards control strategy
OUTLINE
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TIPCOM RECOMMENDATIONSFOLLOWED, SUBSTITUTINGLOGISTICALLY PREFERRED TUBING
EXTENDED DOSING TIME TOACCOMMODATE TUBING(TDOSE > 0.6 S)
CPK: 5.0-9.0
BENCHTOP FILLER
TIPCOM USE IN DP FILLING PROCESS DEVELOPMENT
• TARGET FILL VOLUME• VISCOSITY• DENSITY• VIAL GEOMETRY
SKU CHARACTERISTICS
• ORIFICE GEOMETRY• NEEDLE DIMENSIONS• TUBING DIMENSIONS• PINCH VALVE GEOMETRY
HARDWARE CHARACTERISTICS
Formalisms
INPUT ODEPDE(FEA,CFD)
FILLCHARACTERISTICS
MOTORDISPLACEMENT
DOSINGCONTROL
PRODUCTTEMPERATURE,
PRESSURE
FLOW INFILLING SET
FLOW INCOMPRESSED
TUBE
RECIPE
ALGEBRAIC
TIPCOM
DESIGN SPACE IN-SILICO PROCESS CHARACTERIZATION DESIGN CANDIDATES
LSL USLTarget
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2.1 2.15 2.2 2.25
OFFLINE CONFIRMATION
<1 HOUR TO COMPLETE IN-SILICOPROCESS CHARACTERIZATION
32 FILLING SETS, 6720 RECIPE SETTINGS EXPLORED• 6 USABLE FILLING SETS IDENTIFIED• 2 FILLING SETS W/ RECIPES RECOMMENDED
TIPCOM-RECOMMENDED RECIPES
TIPCOM FILLING PROCESS CHARACTERIZATION PRIOR TO B6 BENCHTOPCHARACTERIZATION ENABLED:
33% TIME SAVINGS IN WET EXPERIMENTS
ELIMINATION OF EXPERIMENTS AT COMMERCIAL LINE
ASSURANCE OF OPTIMAL RECIPE SELECTION
HIGHLY CAPABLE FILLING PROCESS (CPK: 5.0-9.0)
AN EVOLUTION IN DEPLOYMENT
• IMPROVED USER INTERFACE
• CUSTOMIZED EQUIPMENT-BASED APPS
• SITE- AND LINE-SPECIFIC WORKFLOWS
• AUTO-GENERATED PDF REPORTS
• CROSS-PLATFORM
• NO INSTALLATION REQUIRED
• VALIDATED (TECH REPORT)
TIPCOM 3.0
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• Process and product development paradigm– Prior knowledge, empirical, first principles, ML approaches
• Case Studies:– First principles modeling based UFDF for buffer composition predictions– Drug product T/P filling process modeling– Process monitoring and predictive control examples– Incorporating PAT and models towards control strategy
OUTLINE
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BIOPHARMACEUTICAL PROCESSES, COMPRISED OF CONSECUTIVE UNITS, GENERATE ABUNDANT DATA: PERFORMANCE-BASED CONTROL IS CRITICAL
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• Per process characterization, culture duration and seeding density are positively correlated to PQA
• A reliable real-time prediction model for PQAs enables improved control
PERFORMANCE PREDICTION MODEL CAN BE USED TO ADJUST CULTURE DURATION FOR OVER SEEDED BATCHES
. . .
Seed
ing
Den
sity
Batches
Cul
ture
Dur
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Batches
Batches
PQA
Over-seeded batch
Batch kept at target
duration
High PQA
Batch kept at lower
duration
Seed
ing
Den
sity
Culture Duration
Acceptable PQA
Production Bioreactor BDS
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MULTIVARIATE PREDICTIVE MODEL DEVELOPED AND DEPLOYED FOR PERFORMANCE-BASED CONTROL FOR SEED BIOREACTORS
Open-loop control
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KPI’S AND PQA’S ARE PREDICTED TO CONTROL BIOREACTOR CULTURE HARVEST*
* Undey et al., 2015, Predictive Monitoring and Control Approaches in Biopharmaceutical Manufacturing, European Pharmaceutical Review, 20(4), pp. 63-69 .
Open-loop control
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• Hierarchical modeling helps identifying variability within and across unit operations between process and product performance variables in real time
• Batch fingerprints are generated to compare batch behavior to historical batches
Unit Op 1 . . .
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PQAs
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Batch Fingerprint-UOP1
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Unit Op 2 Unit Op NBDS
Phas
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Phas
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ENHANCED CONTINUED PROCESS VERIFICATION (eCPV) CAN ENABLE HOLISTIC PERFORMANCE-BASED MONITORING AND CONTROL
Batch Fingerprint-UOP NBatch Fingerprint-UOP2
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eCPV HELPS IDENTIFYING VARIABILITY WITHIN AND ACROSS UNIT OPERATIONS BETWEEN PROCESS AND PRODUCT PERFORMANCE VARIABLES
Unit Op 1 Unit Op 2
Phas
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. . . BDSPQA 1
PQA 2
PQA N
M54 M29
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Phase N
Model suggested an increase in PQA2 in BDS pool was correlated to a Phase N in Unit Operation 2
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Phas
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MACHINE LEARNING-BASED PREDICTIVE METHODS ARE PROVEN PROMISING: COMBINING DATA FROM DIFFERENT SCALES IMPROVED THE CQA PREDICTIONS
MFG Scale Only Combined
Training MFG Site A (8) MFG Site A (8) + Bench-scale (29)
Testing MFG Site B (6) MFG Site B (6)
Convolutional Neural Networks
Input Data(Predictors)
CQ
A
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• Process and product development paradigm– Prior knowledge, empirical, first principles, ML approaches
• Case Studies:– First principles modeling based UFDF for buffer composition predictions– Drug product T/P filling process modeling– Process monitoring and predictive control examples– Incorporating PAT and models towards control strategy
OUTLINE
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CONTINUOUS MANUFACTURING BIOREACTOR OPERATION: CONTROL AND LOT STRATEGY
Key considerations for the Cell Culture process
• Supporting high cell densities for extended durations
• Cell separation at high cell densities
• Perfusion rates, media formulation, liquid handling
• Lot strategy
• Detect and segregate NC material
00 Time
Biom
ass
Initiate material collection
Cell Expansion
Lot #1
Steady-State Production
Lot #2 Lot #3 Lot #N
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TARGETED HIGH MANNOSE CONTROL IS ACHIEVED USING MPC METHODSlide courtesy of Jack Huang
Automation – Closing the loop for PQA Control
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OVERVIEW OF GLUCOSE FEEDBACK CONTROL STRATEGY
Glucose Control Logic:
1. The control equation is based on mass balance of the system
2. The control equation is checked at a set frequency to determine if glucose addition is required
Actual vs. Predicted Glucose
Residual Plot
MVA Model
Raman Spectra
Raman AnalyzerBioreactor
Real time Glucose
ValueDeltaV
Automation System
Logic:
If Glucose value is less than Glucose
target
Turn pump
on
Real-Time Glucose Feedback Control
Glucose MVA Model
Development
• Other typical cell culture measurements (e.g., lactate, glutamine, VCD, viability, pCO2, osmolality, etc.) can also be correlated to Raman spectra
Glucose Pump
(Source: Watson-Marlow)
Automation – Closing the loop with Process Analytical Chemistry Tools
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MODEL PREDICTIVE CONTROL FRAMEWORK ENABLES PERFORMANCE-BASED CONTROL
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EXPERIMENTS WERE USED TO DETERMINE THE APPROPRIATE LEVERS TO CONTROL PQA; FEED A AND FEED B ACT IN OPPOSITE DIRECTIONS AS IDEAL LEVERS FOR MPC
Automation – Closing the loop for PQA Control
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WE WERE ABLE TO MEET PQA TARGET WITHIN +/- 2.5% OF ITS DESIRED VALUE USING MPC IN PRODUCTION BIOREACTOR
• Two control points were available in this run• MPC used to add Feed A and/or Feed B at control points
Control Points
Automation – Closing the loop for PQA Control
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First-principlesmodels
PAT Method 1: MALS for Controlling %HMW species
PAT Method 2: First principles model for controlling %HMW species
* Rathore et al., 2010, Large Scale Demonstration of a Process Analytical Technology Application in Bioprocessing, Biotechnology Progress, 26(2), 448.
ROBUSTNESS AND CONTROL OF OPERATING SPACE CAN BE IMPROVED USING PAT AND FIRST PRINCIPLES MODELS
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Every patient, every time
CONCLUSIONS
In silico first principles modeling:
• is an instrumental tool for rapid process design and knowledge-based manufacturing
• enables an efficient use of experimental efforts• enables a richer characterization of the robust design space• can enable next-generation process monitoring and control
applications
Augmented with PAT, sensors and in silico models offer advanced process performance management capabilities
Artificial Intelligence and Machine Learning-based in silico models are emerging, offering unique opportunities in bio/pharma
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• Tony Wang• Seyma Bayrak• Sinem Oruklu• Pablo Rolandi• Xiaoxiang Zhu• Fabrice Schlegel• Richard Wu• Jack Huang
ACKNOWLEDGEMENTS
• Will Johnson• Chris Garvin• Myra Coufal• Gerd Kleemann• Deirdre Piedmonte• Stephen Brych• Justin Ladwig
• Roger Hart• Karin Westerberg