The FDA updated its Guidance for Industry as it relates to validating methods for drugs and biologics.
These methods include analytical procedures that test for the identity, purity, potency, and stability of drug substances and drug products. Clinical laboratories also must follow stricter procedures for accreditation and validation of test methods per current ISO requirements.
Selecting the right method to validate or if a method is ready for validation starts with the research lab or method development lab and data that support the decision to proceed. Tight timelines or inadequate method development data can lead to the premature advancing of a method to validation.
Rushing into a validation can lead to failed validation runs involving repeating assay runs.
Excessive repeat rate during validation does not support the level of confidence expected for validated methods. Understanding the requirements for a method to be considered validated helps with the decision-making process to move from development to validation.
Using the Design of Experiments (DOE) can help collect a larger dataset using fewer assay runs to support the decision to move from development to validation. More data points can support statistical analysis used to set acceptance criteria for the method during validation.
Data from DOE can also help analysts understand the limits of the assay and which robustness parameters to confirm during validation. Also, having a thorough understanding of regulatory agency expectations can prevent unnecessary problems with setting the acceptance criteria for bioanalytical methods.
This 3-hour webinar will describe essential practices for bringing analytical methods from development through validation in laboratories supporting biologic products as well as qualification or validation of methods used in clinical laboratories.
The methods that are selected for each test condition must be developed with validation in mind. These methods should be rugged selective, and specific.
Validated methods are also necessary to establish the stability of large molecules. The ability to detect differences in responses between the stable drug and partially degraded drug should be significant in the validated method.
Any change that has a significant impact on the potency or identity of the drug should be detected by stability-indicating validated methods.
Data analysis, data reporting, and training of analysts are key components in the method validation process.
Specific criteria for system suitability and acceptance criteria for validation parameters have evolved with input from Sponsors and laboratories performing testing. Yet, with the availability of solid guidance documents, some laboratory personnel may still struggle with completing the validation of bioanalytical methods. The main concern is the ruggedness of the method during active long-term use for analysis of analytes in the matrix.
Adopting solid principles for the development and optimization of the method should result in an assay that is expected to meet ruggedness, precision, accuracy, selectivity, sensitivity, dilutional linearity, and specificity.
Although there is typically no set number of assay runs to complete development and optimization, collecting the most data with the minimum number of plates should be considered. However, the number of plates run should be sufficient to assess ruggedness prior to the formal validation.
The validation should be designed to accomplish the purpose in a minimum number of runs. When proper development is not performed and the decision to move into validation does not have enough supporting data, assay failure during validation can result.
Failures of runs during validation can lead to interruption of the validation with subsequent additional method development required prior to entering validation again.
This is not just time-consuming with an impact on timelines but uses reagents, that may be critical, without producing usable data.
This webinar is designed to walk attendees through steps to consider during method development that support the decision to proceed to validation.
Validation parameters will also be discussed following the recently published Bioanalytical Method Validation guidance.
These methods include analytical procedures that test for the identity, purity, potency, and stability of drug substances and drug products. Clinical laboratories also must follow stricter procedures for accreditation and validation of test methods per current ISO requirements.
Selecting the right method to validate or if a method is ready for validation starts with the research lab or method development lab and data that support the decision to proceed. Tight timelines or inadequate method development data can lead to the premature advancing of a method to validation.
Rushing into a validation can lead to failed validation runs involving repeating assay runs.
Excessive repeat rate during validation does not support the level of confidence expected for validated methods. Understanding the requirements for a method to be considered validated helps with the decision-making process to move from development to validation.
Using the Design of Experiments (DOE) can help collect a larger dataset using fewer assay runs to support the decision to move from development to validation. More data points can support statistical analysis used to set acceptance criteria for the method during validation.
Data from DOE can also help analysts understand the limits of the assay and which robustness parameters to confirm during validation. Also, having a thorough understanding of regulatory agency expectations can prevent unnecessary problems with setting the acceptance criteria for bioanalytical methods.
This 3-hour webinar will describe essential practices for bringing analytical methods from development through validation in laboratories supporting biologic products as well as qualification or validation of methods used in clinical laboratories.
The methods that are selected for each test condition must be developed with validation in mind. These methods should be rugged selective, and specific.
Validated methods are also necessary to establish the stability of large molecules. The ability to detect differences in responses between the stable drug and partially degraded drug should be significant in the validated method.
Any change that has a significant impact on the potency or identity of the drug should be detected by stability-indicating validated methods.
Data analysis, data reporting, and training of analysts are key components in the method validation process.
Why you should Attend:
Method validation guidance has become increasingly more consistent. Regulatory agency documents now offer firm guidance on expectations on assay method performance to meet the status as validated for the intended purpose.Specific criteria for system suitability and acceptance criteria for validation parameters have evolved with input from Sponsors and laboratories performing testing. Yet, with the availability of solid guidance documents, some laboratory personnel may still struggle with completing the validation of bioanalytical methods. The main concern is the ruggedness of the method during active long-term use for analysis of analytes in the matrix.
Adopting solid principles for the development and optimization of the method should result in an assay that is expected to meet ruggedness, precision, accuracy, selectivity, sensitivity, dilutional linearity, and specificity.
Although there is typically no set number of assay runs to complete development and optimization, collecting the most data with the minimum number of plates should be considered. However, the number of plates run should be sufficient to assess ruggedness prior to the formal validation.
The validation should be designed to accomplish the purpose in a minimum number of runs. When proper development is not performed and the decision to move into validation does not have enough supporting data, assay failure during validation can result.
Failures of runs during validation can lead to interruption of the validation with subsequent additional method development required prior to entering validation again.
This is not just time-consuming with an impact on timelines but uses reagents, that may be critical, without producing usable data.
This webinar is designed to walk attendees through steps to consider during method development that support the decision to proceed to validation.
Validation parameters will also be discussed following the recently published Bioanalytical Method Validation guidance.
Areas Covered in the Session:
- Understanding `validation`
- Defining what procedures are required for the drug or biologic testing
- Developing new test methods
- Selecting the reference material/standard
- Confirmation testing of the reference material/standard
- Qualifying reagents - determining critical reagents
- Defining the validation procedure - the protocol
- Writing the methods to be validated
- Using compendial methods
- Acceptance criteria and statistical methods
- Setting ranges and specifications post validation
- Training and documentation
- Life cycle management - revalidation (changes in methods)
Speaker
Gwen Wise-Blackman, Ph.D. has over 20 years of combined experience in Cell-Based Assays and Quality Systems. She has worked at DuPont Pharmaceuticals, Catalent Pharma Solutions (formerly Magellan Laboratories and Cardinal Health), and Salix Pharmaceuticals where she successfully managed multiple projects and held positions of increasing accountability for scientific and quality expertise. Currently she is the owner of Gwen Wise-Blackman Consulting, LLC, a biopharmaceutical consulting firm.Her focus has been in High-Throughput Screening, Cell-Based Assay Method Development and Validation, Ligand Binding Methods, Technology Transfer, GxP Regulations, Training, Audting, and Quality Assurance.
Dr. Wise-Blackman has a Bachelor of Science degree in biology from M.I.T and a PhD in Pharmacology from the University of Virginia. She is a member of ASQ and AAPS.
Who Should Attend
- Validation scientists in bioanalytical or clinical laboratories
- Development scientists in bioanalytical or clinical laboratories
- QA Documentation Specialists
- Regulatory Specialists
- Consultants
- Directors of Outsourcing
- Method trainers
- Statistical staff
