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ISPE D/A/CH Workshop: Data Science Assisted Biopharmaceutical Tech Transfer and Process Characterization

27. Mai - 28. Mai


Biopharmaceutical Processes
Process Validation
Process Characterization Studies (Stage 1) Validation and Technology Transfer


Problem Statement

Regulatory expectations for statistically underpinned Process Validation (PV) has found its way into current guidelines such as the FDA’s life cycle approach to process validation from 2011 leading to demonstrating Established Conditions (ECs) in ICH Q12.
However, specific critical components to successful and accelerated biopharmaceutical tech transfer along the life cycle and processes validation (Stage 1-3) remain unresolved in industrial practice. This is due to the necessity of using scale down models, cost intensive set up of experiments and the complexity due to the interactivity of a multitude of unit operations. Most of the tasks, such as risk assessment, scale down model qualification, experimental criticality assessment, setting of control strategies, PPQ number estimation, and Continued Process Verification (CPV) require statistically based rationales to meet regulatory expectations in terms of risk-aware decision making.
Additionally, next generation bioproducts, such as biosimilars and Advanced Therapy Medicinal Products (ATMPs) accelerate the demand of clear workflows towards an efficient process validation. The commonly accepted hypothesis is that sound data science approaches will be a success factor in this endeavour.

To focus the work, the 2020 Data Science Assisted Biopharmaceutical Process Validation Workshop will focus on the first part of the above tasks: Process Characterization Studies (Stage 1) Validation and Technology Transfer. It will focus on the contemporary and novel approaches to meeting Stage 1 Validation and Technology Transfer expectations and ensuring consistent product quality. Methods and best practices embedded in workflows based on data science will be presented interactively, using case studies and hands-on workshops that enable reduced experimental effort towards a robust process. Thereby reduced out-of-specification rates, accelerated time to market and lower manufacturing trouble-shooting costs can be achieved.

Learning Objectives

• Understand regulatory perspectives on challenges and concerns in bioprocess validation
• Gain insight into new tools and workflows to target critical components towards a successful
    bioprocess stage 1 validation and technology transfer
• Gain hands on experience in solving challenges for bioprocess validation, such as scale down model
    qualification, experimental design & evaluation, as well as setting up a control strategy
• Understand challenges in process validation life cycle
• Explore challenges and emerging solutions in process validation for current and next generation
    bioproducts
• Participate in demonstrated application through review of multiple case studies

Who Should Attend?

• Process Engineers and Development scientists
• Manufacturing Science & Technology scientists
• Process Validation team members
• Statisticians supporting validation
• Operations managers, Laboratory managers

Course Setup

• Three half days starting 1st day noon and ending the afternoon on the 2nd day.
• Networking evening event
• Mixture of international reputed lecturers from industry
• Lectures and integrated exercises (bring your laptop)

Topics for the Biopharmaceutical Process Validation Workshop include:

Process Validation:

• Current regulatory perspectives on process validation as background
• Stage 1 Process Validation for biopharmaceutical products:
    • Risk Assessment and statistically underpinned FMEAs
    • Impurity clearance analysis
    • Scale down model qualification
    • Experimental criticality assessment
    • Process control strategy development
    • Integrated process modelling for successful process characterization life cycle
    • Tech Transfer

Tech Transfer:

• Show how using Data Science can help to simplify the tech transfer from
         Development to Commercial
    • Designing robustness: Strategies for platform based, scale and site independent
         process understanding
    • Focusing on critical unknowns: Data science assisted risk assessment and data science
         for proving comparability
    • Anticipating holistic criticality: Using digital twins/integrated data analytics for anticipating
         critical process parameters along the full process chain
    • Compensating the differences: Process Control strategies
• Biosimilar and ATMP break-out sessions
    • Regulatory perspective on submissions
    • Process development, validation, and process characterization
    • Process validation challenges
    • Case studies for process development & validation
• Benchmarking in panel discussion sessions

Agenda Day 1 / Day 2:

Halfday 1

Process Control Strategy (PCS)
• Current regulatory perspectives on process validation as background
• Stage 1 Process Validation for biopharmaceutical products:
           • Risk Assessment and statistically underpinned FMEAs
           • Impurity clearance analysis
           • Scale down model qualification
           • Experimental criticality assessment

Halfday 2

Process Control Strategy (PCS)
• Process control strategy development
• Integrated process modelling for successful process characterization life cycle
• Tech Transfer

Biosimilar Advanced Therapy Medicinal Products (ATMPs) Breakout
• Regulatory perspective on submissions
• Process development, validation, and process characterization
• Process validation challenges
• Case studies for process development & validation

Halfday 3

Tech Transfer from Development to Commercial
(Show how using Data Science can help to simplify the tech transfer from Development to Commercial)
• Designing robustness: Strategies for platform based, scale and site independent process
           understanding
• Focusing on critical unknowns: Data science assisted risk assessment and data science for proving
            comparability
• Anticipating holistic criticality: Using digital twins/integrated data analytics for anticipating
            critical process parameters along the full process chain
• Compensating the differences: Process Control strategies

Benchmarking
in panel discussion sessions

When?

27 – 28 May 2020

Where?

Hochschule für Life Sciences FHNW (Nordwestschweiz)
Hofackerstraße 30
CH-4132 Muttenz
Schweiz
Map

Hotel Recommendation

Hotel Baslertor
St. Jakob-Strasse 1
CH-4132 Muttenz
Telefon +41 (61) 465 5555

Email hotel-baslertor@balehotels.ch
www.hotelbaslertor.ch
Special ISPE Price: Doubleroom CHF 155.00 / Night
Booking with Code “ISPE”
booking until 4 weeks before Workshop

Coop Tagungszentrum
Seminarstrasse 12-22
CH-4132 Muttenz
Telefon +41 61 466 11 11
Fax +41 61 466 12 14
Email tagungszentrum@coop.ch
www.cooptagungszentrum.ch
Special ISPE Price: Doubleroom CHF 190.00 / Night
Booking with Code “ISPE”
booking until 4 weeks before Workshop

Registration Fee

ISPE Members: 450 Euro
Non Members: 750 Euro

Students:
Registration Fee ISPE Members 45 Euro
Registration Fee Non Members 75 Euros
(limited offer, pls ask your ISPE Student Chapter for a Coupon Code)

To become ISPE Member: Member Form.

Workshop Booking Form (Members, Non members and Students)

Register now

Contact: ISPE D/A/CH Secretary Rolf Sopp (rolf.sopp(at)ispe-dach.org)

Details

Beginn:
27. Mai
Ende:
28. Mai
Veranstaltungskategorien:
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Veranstaltungsort

Muttenz
Muttenz,Schweiz

Veranstalter

ISPE D/A/CH
E-Mail:
info@ispe-dach.org
Website:
https://ispe-dach.org