It is critical that the data generated in a pharma lab is accurate and trustworthy. This 3 day course provides the necessary knowledge and skills to ensure that attendees are able to fully integrate data integrity into laboratory quality management systems.
This course will:
• Demonstrate your commitment to data integrity in the laboratory.
• Raise awareness for all staff attending the course to understand the issues surrounding laboratory data integrity and how it affects them.
• Strengthen laboratory quality management systems to improve data integrity and remove falsification potential.
The course is made up of three days:
Day 1: Introduction to Laboratory Data Integrity; cGMP in the Pharma Lab
Day 2: Applying Data Integrity in the Laboratory; Minimising Analytical Error
Day 3: How to Improve Data Integrity in the Pharma Lab
Day 1 introduces the fundamental concepts of laboratory data integrity in the context of working within a quality management system and as such this course also acts as an introduction/refresher to laboratory cGMP.
Learning Objectives
1. Understand the principles of data integrity and how it applies to the laboratory.
2. Understand the purpose of the laboratory quality management system and its role in ensuring data integrity.
3. Interpret the relevant parts of current Good Manufacturing Practice (cGMP) regulations (primarily EU and US) and be aware of other available regulatory guidance.
4. Describe the essential components of an effective laboratory quality management system.
Day 2 deals with the effects of analytical errors on laboratory data integrity. This involves building an understanding of the nature and sources of analytical errors so that their effects can be minimised during testing, leading to high standards of data integrity and reduced numbers of OOS/OOE results due to laboratory errors.
Learning Objectives
1. Understand the significance of analytical error and how it relates to laboratory data integrity.
2. Understand the concept of inherent measurement uncertainty and the types of analytical error, namely systematic and random, together with the potential sources of error in the pharma lab.
3. Identify potential sources of human error that may contribute to a lack of data integrity and potentially the generation of out of specification (OOS) and out of expectation (OOE) results.
4. Minimise inherent analytical error and eliminate human error in analytical testing to obtain high standards of data integrity and reduced numbers of OOS/OOE results due to laboratory error.
Day 3 focusses on improving laboratory data integrity and associated quality management systems by review of laboratory processes to identify data integrity risk, and implementation of appropriate solutions, paying particular attention to deficiencies that are commonly cited by regulatory authorities.
Learning Objectives
1. Understand why data integrity has become a ‘hot’ topic for regulatory authorities and be aware of the currently available guidance.
2. Define the key terms related to data integrity.
3. Review laboratory processes and identify data integrity risks using process mapping tools and application of ALCOA+ principles.
4. Implement appropriate solutions for commonly observed data integrity problems in the pharma laboratory.
The full course takes place over 3 days but if preferred, it is possible to attend on any single day or combination of 2 days.
Open-enrolment training courses:
You can attend one of our open enrolment training courses at the following locations:
• Hilton Garden Inn Heathrow, London, UK;
• Metro Hotel Dublin Airport, Dublin, Ireland; and
• GLS Campus Berlin, Germany.
The courses are available throughout the year, refer to the MTS website for full details.
On-site training courses:
We can deliver the course at your site including any required customisation to meet your specific requirements. Contact us to discuss your training needs and for a quotation. info@mournetrainingservices.co.uk
Use of case studies and real life scenarios:
Case studies are employed extensively in all three days to allow consideration of real life scenarios. Delegates are invited to bring along any real life examples that they would like advice on during the training course. These may be discussed during group exercises, or, where intellectual property is an issue, privately with the trainer.
This course is suitable for:
Everyone associated with the data generated in the pharma laboratory, e.g., analysts, reviewers, supervisors, managers, etc.
Included in the course fees:
• Comprehensive course hand-outs;
• Certificate of Attendance;
• Access to training resources via e-MTS;
• Optional post training assessment (leading to Certificate of Training);
• Post training support;
• Lunch and refreshments (for open-enrolment courses only).
Course Outline
Day 1: Introduction to Laboratory Data Integrity; cGMP in the Pharma Lab
Principles of laboratory data integrity, to include:
• What does the pharma lab do?
• Why is data integrity so important?
• What is a quality management system?
Review of Current Good Manufacturing Practices (cGMPs) and other relevant regulatory guidance, to include:
• EU, Eudralex volume 4.
• FDA, 21 CFR.
• ICH
• Pharmacopeia
• Review of recent updates.
Components of Laboratory Quality Management Systems (QMS), to include:
• Laboratory documentation: Specifications, standard operating procedures (SOPs), analytical records, etc.
• Sampling.
• Equipment qualification, calibration and maintenance.
• Analytical methods validation, verification and transfer.
• Stability testing.
• Management of reference standards and reagents.
• Investigation of out of specification (OOS), out of trend (OOT) and out of expectation (OOE) results.
• Competence and training records.
Review of key messages.
Day 2: Applying Data Integrity in the Laboratory; Minimising Analytical Error
Analytical error and data integrity, to include:
• What is analytical error?
• How does it relate to data integrity?
Uncertainty in Analytical Measurement, to include:
• Definition of measurement uncertainty.
• Types of error - systematic and random.
• Potential sources of error in a pharma laboratory.
• Quantification of measurement uncertainty.
• Rounding and significant figures in reporting analytical results.
• Approaches to reduce uncertainty in analytical methods, e.g., replicate analysis, etc.
Identifying potential sources of human error in analytical testing, to include:
• What could go wrong?
• Common human error examples.
• Consequences of human error – may not be noticed or may cause an out of specification (OOS) or out of expectation (OOE) result.
Minimising error in analytical testing, to include:
• Best practice for basic analytical skills.
• Interpretation of analytical methods.
• Ways of working.
• Competence assessment and training records.
Review of key messages.
Day 3: How to Improve Data Integrity in the Pharma Lab
Reasons for current regulatory focus on the topic of data integrity, to include:
• Why has data integrity become a ‘hot’ topic for regulatory authorities?
• How is data integrity controlled in the pharma lab?
• Available guidance from MHRA & FDA.
Data integrity principles and definitions, to include:
• Data governance.
• ALCOA+
• Meaning of: data, raw data, metadata, audit trail, etc.
Review of laboratory processes to identify data integrity risk, to include:
• Identification of laboratory processes.
• Use of process mapping to identify risks.
• Implementing solutions to eliminate or reduce risk.
Common data integrity deficiencies cited by regulatory authorities, to include:
• Access to electronic systems
• Integration for chromatographic techniques
• Audit trails
• Use of ‘trial’ injections in HPLC
• Analytical records, paper and electronic.
Review of key messages.
This course will:
• Demonstrate your commitment to data integrity in the laboratory.
• Raise awareness for all staff attending the course to understand the issues surrounding laboratory data integrity and how it affects them.
• Strengthen laboratory quality management systems to improve data integrity and remove falsification potential.
The course is made up of three days:
Day 1: Introduction to Laboratory Data Integrity; cGMP in the Pharma Lab
Day 2: Applying Data Integrity in the Laboratory; Minimising Analytical Error
Day 3: How to Improve Data Integrity in the Pharma Lab
Day 1 introduces the fundamental concepts of laboratory data integrity in the context of working within a quality management system and as such this course also acts as an introduction/refresher to laboratory cGMP.
Learning Objectives
1. Understand the principles of data integrity and how it applies to the laboratory.
2. Understand the purpose of the laboratory quality management system and its role in ensuring data integrity.
3. Interpret the relevant parts of current Good Manufacturing Practice (cGMP) regulations (primarily EU and US) and be aware of other available regulatory guidance.
4. Describe the essential components of an effective laboratory quality management system.
Day 2 deals with the effects of analytical errors on laboratory data integrity. This involves building an understanding of the nature and sources of analytical errors so that their effects can be minimised during testing, leading to high standards of data integrity and reduced numbers of OOS/OOE results due to laboratory errors.
Learning Objectives
1. Understand the significance of analytical error and how it relates to laboratory data integrity.
2. Understand the concept of inherent measurement uncertainty and the types of analytical error, namely systematic and random, together with the potential sources of error in the pharma lab.
3. Identify potential sources of human error that may contribute to a lack of data integrity and potentially the generation of out of specification (OOS) and out of expectation (OOE) results.
4. Minimise inherent analytical error and eliminate human error in analytical testing to obtain high standards of data integrity and reduced numbers of OOS/OOE results due to laboratory error.
Day 3 focusses on improving laboratory data integrity and associated quality management systems by review of laboratory processes to identify data integrity risk, and implementation of appropriate solutions, paying particular attention to deficiencies that are commonly cited by regulatory authorities.
Learning Objectives
1. Understand why data integrity has become a ‘hot’ topic for regulatory authorities and be aware of the currently available guidance.
2. Define the key terms related to data integrity.
3. Review laboratory processes and identify data integrity risks using process mapping tools and application of ALCOA+ principles.
4. Implement appropriate solutions for commonly observed data integrity problems in the pharma laboratory.
The full course takes place over 3 days but if preferred, it is possible to attend on any single day or combination of 2 days.
Open-enrolment training courses:
You can attend one of our open enrolment training courses at the following locations:
• Hilton Garden Inn Heathrow, London, UK;
• Metro Hotel Dublin Airport, Dublin, Ireland; and
• GLS Campus Berlin, Germany.
The courses are available throughout the year, refer to the MTS website for full details.
On-site training courses:
We can deliver the course at your site including any required customisation to meet your specific requirements. Contact us to discuss your training needs and for a quotation. info@mournetrainingservices.co.uk
Use of case studies and real life scenarios:
Case studies are employed extensively in all three days to allow consideration of real life scenarios. Delegates are invited to bring along any real life examples that they would like advice on during the training course. These may be discussed during group exercises, or, where intellectual property is an issue, privately with the trainer.
This course is suitable for:
Everyone associated with the data generated in the pharma laboratory, e.g., analysts, reviewers, supervisors, managers, etc.
Included in the course fees:
• Comprehensive course hand-outs;
• Certificate of Attendance;
• Access to training resources via e-MTS;
• Optional post training assessment (leading to Certificate of Training);
• Post training support;
• Lunch and refreshments (for open-enrolment courses only).
Course Outline
Day 1: Introduction to Laboratory Data Integrity; cGMP in the Pharma Lab
Principles of laboratory data integrity, to include:
• What does the pharma lab do?
• Why is data integrity so important?
• What is a quality management system?
Review of Current Good Manufacturing Practices (cGMPs) and other relevant regulatory guidance, to include:
• EU, Eudralex volume 4.
• FDA, 21 CFR.
• ICH
• Pharmacopeia
• Review of recent updates.
Components of Laboratory Quality Management Systems (QMS), to include:
• Laboratory documentation: Specifications, standard operating procedures (SOPs), analytical records, etc.
• Sampling.
• Equipment qualification, calibration and maintenance.
• Analytical methods validation, verification and transfer.
• Stability testing.
• Management of reference standards and reagents.
• Investigation of out of specification (OOS), out of trend (OOT) and out of expectation (OOE) results.
• Competence and training records.
Review of key messages.
Day 2: Applying Data Integrity in the Laboratory; Minimising Analytical Error
Analytical error and data integrity, to include:
• What is analytical error?
• How does it relate to data integrity?
Uncertainty in Analytical Measurement, to include:
• Definition of measurement uncertainty.
• Types of error - systematic and random.
• Potential sources of error in a pharma laboratory.
• Quantification of measurement uncertainty.
• Rounding and significant figures in reporting analytical results.
• Approaches to reduce uncertainty in analytical methods, e.g., replicate analysis, etc.
Identifying potential sources of human error in analytical testing, to include:
• What could go wrong?
• Common human error examples.
• Consequences of human error – may not be noticed or may cause an out of specification (OOS) or out of expectation (OOE) result.
Minimising error in analytical testing, to include:
• Best practice for basic analytical skills.
• Interpretation of analytical methods.
• Ways of working.
• Competence assessment and training records.
Review of key messages.
Day 3: How to Improve Data Integrity in the Pharma Lab
Reasons for current regulatory focus on the topic of data integrity, to include:
• Why has data integrity become a ‘hot’ topic for regulatory authorities?
• How is data integrity controlled in the pharma lab?
• Available guidance from MHRA & FDA.
Data integrity principles and definitions, to include:
• Data governance.
• ALCOA+
• Meaning of: data, raw data, metadata, audit trail, etc.
Review of laboratory processes to identify data integrity risk, to include:
• Identification of laboratory processes.
• Use of process mapping to identify risks.
• Implementing solutions to eliminate or reduce risk.
Common data integrity deficiencies cited by regulatory authorities, to include:
• Access to electronic systems
• Integration for chromatographic techniques
• Audit trails
• Use of ‘trial’ injections in HPLC
• Analytical records, paper and electronic.
Review of key messages.
