In the first article of this series we focus on the importance of the quality systems underlying the analytical data produced by a laboratory involved in testing of manufactured medicinal product. The ability for a laboratory to produce data that accurately reflects the quality of the product being tested is paramount. Inaccurate results can be costly and have wide reaching ramifications – it is a double edge sword. Product manufactured within specification may be destroyed unnecessarily because inaccurate data indicates the product is Out of Specification , or, product that has been manufactured Out of Specification is released to market unwittingly, as the analytical data indicates the batch is compliant. With so much at stake, it is critical that the decision makers have absolute faith in the laboratory to get it right…every time. If this statement resonates with you, consider this article a call to action, prompting that discussion with your laboratory to ensure consistent compliance with your quality expectations.
Good accuracy results from scientists who know their craft, applying painstaking attention to detail, and not accepting a mediocre outcome. We believe good accuracy is achievable even with operating constraints of money and time. As a paying customer you are entitled to understand exactly what accuracy your dollar is buying. To facilitate, we’ve provided some insight into the contributors to result quality below, and give you a vocabulary and discussion points to have with your laboratory.
- GMP agreement – review your GMP agreement with your laboratory, particularly sections dealing with investigation responsibilities should a product’s result be Out of Specification,
- Product specifications – ensure your laboratory has a copy of your product specification and refers to it when reporting your data,
- Analytical method – your laboratory should have a documented procedure for the analysis. This can be product specific, or suit a broader group of products,
- Analytical method validation – talk to your laboratory about their approach to validation of analytical methods used to test your products. Validation data is generated to demonstrate the procedure used can elicit analytical data with known integrity. Analytical method validation may be based on VICH guidelines, or follow an in-house protocol. Essential elements of a quantitative procedure based on HPLC include analyte specificity, linearity, accuracy and precision-repeatability,
- Reference material – an instrumental method (eg. HPLC) typically relies on comparison of instrument response to a calibration curve prepared using a reference standard of defined purity. Reference standards should be within their nominated expiry when used. Storage should be according to manufacturer’s instructions,
- Stock and calibration solutions – your laboratory should have procedures to prepare each stock and calibration solution, and particularly information to support the stability of the analyte for the duration of the analysis,
- Calibration curve – best practice to use the instrument response of three standards of different concentration around the target concentration, and calculate the linear regression equation of the line-of-best fit. The origin (0,0) should not be used as a point,
- Check standard – as best-practice, your laboratory should prepare second independent standard for analysis. The calculated concentration using the calibration curve should be 98-102% of the nominal concentration,
- Chromatographic resolution – for HPLC/ GC methods, higher accuracy is obtained when analytes are well separated from each other and any interfering signals,
- Duplicate analysis – talk to your laboratory to understand if the reported result is based on the analysis of one sample, or if two (or more) samples are analysed, and the result averaged. If two (typical) or more are used, your laboratory should have procedures to determine acceptability of the Relative Standard Deviation (RSD) between the two values. If the data exceed the RSD, there is valid reason for repeat analysis. Address the issues associated with payment for a second analysis,
- Analytical run quality control – your laboratory should have procedures and be able to provide evidence that the quality of the analytical run was assessed as satisfactory prior to reporting results,
- Analytical out of specification – your laboratory should have procedures to follow when results are Out of Specification. This is typically a series of checks and balances to ensure the result determined has acceptable integrity,
- Retesting – talk to your laboratory regarding retesting – a valid reason should be provided to retest a sample when the initial result is Out of Specification. Simply reanalysis in the hope of getting a result within specification is not desirable, and implies the laboratory has no confidence in the accuracy of the test. Address the issues associated with payment of a second analysis,
- Product Out of specification – examine your standard operating procedures to investigate products if and when results are reported Out of specification,
- Resampling for secondary analysis – A valid reason should be provided (and documented) for resampling.
With outputs so critical to your organisation, the emphasis should be on receiving a consistent level of quality from a laboratory. This industry is dynamic, and change is the only constant. Changes in site, ownership, structure and key personnel can impact the quality of laboratory outputs. Consider including your contract laboratory in your vendor qualification or periodic supplier audit programs, and communicating your expectations regarding quality. There are several ways of achieving this and we are happy to look at ways that work for your specific situation. One future Lab Notes articles will also look at the importance of supplier audits, so watch this space.
We are proud to say we apply the best-practices described above in our analytical service. We would be very happy to talk to you about your experiences, and see if we can meet your quality expectations and analytical service needs.
