Advanced Real-Time Release Testing (RTRT) Training Course

Advanced Real-Time Release Testing (RTRT) Training Course

Introduction

The pharmaceutical and biopharmaceutical industries are undergoing a profound transformation, moving away from traditional, time-consuming end-product testing toward integrated, data-driven manufacturing. Advanced Real-Time Release Testing (RTRT) represents the pinnacle of this evolution. RTRT is the ability to evaluate and ensure the quality of a pharmaceutical product based on process data and controls collected during manufacturing, eliminating or significantly reducing the need for final product testing. This paradigm shift, driven by Quality by Design (QbD) principles and enabled by Process Analytical Technology (PAT), promises to revolutionize product quality, operational efficiency, and supply chain agility. This course provides a deep dive into the technical, regulatory, and strategic aspects of implementing an advanced RTRT program, equipping professionals with the expertise to lead this critical transition.

Advanced Real-Time Release Testing (RTRT) Training Course is designed to empower participants with the foundational knowledge and practical skills required to design, validate, and manage robust RTRT systems. By leveraging the latest in chemometrics, data analytics, and automation, the course explores how to move beyond basic in-process control to a fully integrated release strategy. We will delve into the regulatory landscape, including key ICH guidelines, and explore real-world case studies to illustrate successful implementation and navigate common challenges. The program's core focus is on building a strong business case for RTRT, demonstrating its value in accelerating product release, reducing costs, and enhancing product quality assurance throughout the entire product lifecycle.

Course Duration

10 days

Course Objectives

1. Master the principles of Quality by Design (QbD) and its direct link to Real-Time Release Testing (RTRT).
2. Evaluate and select appropriate Process Analytical Technology (PAT) tools for real-time monitoring.
3. Design and develop robust chemometric models for predicting critical quality attributes (CQAs).
4. Validate RTRT models and control strategies in compliance with global regulatory guidelines.
5. Implement a science- and risk-based approach to RTRT program development.
6. Optimize manufacturing processes for continuous quality assurance.
7. Reduce batch release cycle times and improve operational efficiency.
8. Leverage advanced data analytics to build a strong business case for RTRT.
9. Navigate the complex regulatory landscape and prepare for successful health authority submissions.
10. Apply data integrity principles to all aspects of RTRT.
11. Identify and mitigate risks associated with continuous manufacturing and RTRT implementation.
12. Integrate RTRT with existing Quality Management Systems (QMS).
13. Explore the future of Pharma 4.0 and its impact on drug product release.

Organizational Benefits

• Significantly reduces or eliminates the need for time-consuming off-line end-product testing, leading to faster batch release and quicker market access.
• Improves process understanding and control, leading to more consistent product quality and a stronger assurance of compliance with cGMP standards.
• Reduces QC laboratory costs, minimizes inventory holding costs, and decreases waste and rejected batches by identifying and correcting issues in real-time.
• Positions the organization as a leader in adopting innovative, data-driven manufacturing strategies, attracting top talent and strengthening partnerships.
• Demonstrates a deep understanding of the manufacturing process, which can lead to a more collaborative and efficient regulatory review process.

Target Audience

• Quality Assurance (QA) and Quality Control (QC) professionals aiming to transition to a modern quality paradigm.
• Process Development and Manufacturing Engineers involved in designing and optimizing pharmaceutical processes.
• Regulatory Affairs Specialists responsible for preparing and submitting RTRT-based filings.
• Data Scientists and Statisticians working with manufacturing and analytical data in the pharmaceutical sector.
• Project Managers overseeing the implementation of new technology and quality systems.
• R&D Scientists exploring advanced analytical and control strategies.
• Senior Management seeking to understand the strategic and business benefits of RTRT.
• Automation and IT Professionals responsible for integrating real-time data systems.

Course Modules

Module 1: Foundations of RTRT

• Introduction to the RTRT paradigm and its evolution from traditional testing.
• Linking RTRT to ICH Q8(R2), Q9, Q10, and Q11 guidelines.
• Defining Critical Quality Attributes (CQAs) and Critical Process Parameters
• Exploring the components of a robust control strategy.
• Case Study: The shift from batch to continuous manufacturing for a solid oral dosage product, and how it necessitated a move to RTRT.

Module 2: Process Analytical Technology (PAT) Essentials

• Overview of PAT tools: NIR, Raman, FTIR, and other spectroscopic techniques.
• Understanding in-line, on-line, and at-line measurements.
• Sensor selection and placement strategies for optimal data collection.
• Calibration and maintenance of PAT instruments.
• Case Study: Implementing an in-line NIR sensor to monitor blend uniformity in a continuous blender, replacing traditional grab sampling and off-line testing.

Module 3: Advanced Chemometrics and Data Modeling

• Principles of multivariate data analysis (MVDA).
• Building predictive models using Partial Least Squares (PLS) and Principal Component Analysis (PCA).
• Model calibration, validation, and maintenance.
• Detecting and handling outliers and process deviations in real-time.
• Case Study: Developing a PLS model to predict tablet potency and dissolution using NIR and process data, enabling real-time release.

Module 4: Regulatory Compliance and Submissions

• Navigating global regulatory expectations for RTRT (FDA, EMA).
• Preparing a comprehensive RTRT submission package.
• Addressing common regulatory questions and challenges.
• Lifecycle management of RTRT models and systems.
• Case Study: A successful submission for an RTRT strategy for a sterile injectable product, highlighting key data packages and regulatory dialogue.

Module 5: Risk Management and Control Strategy

• Applying Quality Risk Management (QRM) to RTRT.
• Developing a robust control strategy for continuous processes.
• Establishing release specifications and acceptance criteria.
• Managing process deviations and out-of-specification (OOS) events in real-time.
• Case Study: Using a risk-based approach to justify a partial RTRT strategy for a biological drug substance, focusing on specific CQAs.

Module 6: Implementation and Validation

• Strategic planning for RTRT implementation.
• Validation of PAT tools and predictive models.
• Integrating RTRT with the Pharmaceutical Quality System (PQS).
• Training and change management for personnel.
• Case Study: The phased implementation of an RTRT system for a tablet manufacturing line, from initial proof-of-concept to full commercial operation.

Module 7: Data Integrity and System Automation

• Ensuring data integrity in a real-time environment (ALCOA principles).
• System architecture for data acquisition and analysis.
• Automation and closed-loop control systems.
• Cybersecurity considerations for connected manufacturing.
• Case Study: A bio-pharmaceutical company's journey in building a secure, integrated data infrastructure to support RTRT for monoclonal antibody production.

Module 8: The Business Case for RTRT

• Calculating the return on investment (ROI) for RTRT implementation.
• Analyzing cost savings from reduced testing and inventory.
• Quantifying the value of accelerated time-to-market.
• Building a compelling business case for senior leadership.
• Case Study: A financial analysis of a generic drug manufacturer's transition to RTRT, showcasing significant reductions in operational costs and lead times.

Module 9: Advanced Topics in RTRT

• Predicting complex attributes: Dissolution and bioavailability.
• Multi-variate statistical process control (MSPC).
• Integrating soft sensors and mechanistic models.
• Application of machine learning and AI in RTRT.
• Case Study: The use of machine learning algorithms to predict dissolution profiles of low-solubility drugs.

Module 10: RTRT in Biopharmaceuticals

• Specific challenges of RTRT in biologics.
• PAT tools for upstream and downstream bioprocessing.
• Continuous biomanufacturing and its link to RTRT.
• Regulatory considerations for biopharmaceutical RTRT.
• Case Study: Monitoring protein aggregation and impurity levels in a bioreactor using advanced spectroscopic methods for real-time release.

Module 11: Case Study: A Complete RTRT Program

• In-depth analysis of a real-world RTRT implementation from start to finish.
• Reviewing the entire project lifecycle, including challenges and lessons learned.
• Detailed examination of the data, models, and regulatory interactions.
• Q&A with the project team.
• Case Study: Pfizer’s implementation of an RTRT process for Viagra and Revatio, highlighting the technical and regulatory hurdles.

Module 12: Future Trends and Pharma 4.0

• The rise of the digital twin in pharmaceutical manufacturing.
• Blockchain for supply chain traceability and data security.
• Predictive maintenance and its role in RTRT.
• The future of a fully autonomous, lights-out pharmaceutical plant.
• Case Study: A look at a pilot program using a digital twin to simulate and optimize a continuous manufacturing line with RTRT.

Module 13: Capstone Project

• Participants apply learned concepts to a simulated RTRT project.
• Teams develop a control strategy, select PAT tools, and propose a validation plan.
• Presentation of the project and peer review.
• Expert feedback on project feasibility and design.

Module 14: Workshop: Building and Validating an RTRT Model

• Hands-on workshop using real-world datasets.
• Step-by-step guidance on data pre-processing, model building, and validation.
• Interpreting model outputs and performance metrics.
• Practical application of software tools like Unscrambler or SIMCA.

Module 15: Course Review and Certification

• Comprehensive review of all key modules.
• Final assessment to test knowledge and understanding.
• Q&A and open forum for final questions.
• Awarding of a course completion certificate.

Training Methodology

Our training methodology combines theoretical knowledge with practical application to ensure a comprehensive and engaging learning experience.

• Interactive lectures and workshops to cover core concepts and guidelines.
• Hands-on exercises and software demonstrations using industry-standard tools for data analysis and modeling.
• Group discussions and problem-solving sessions to address real-world challenges.
• In-depth case studies to illustrate successful and unsuccessful RTRT implementations.
• Q&A sessions with industry experts to provide personalized insights and guidance.

Register as a group from 3 participants for a Discount

Send us an email: [email protected] or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.