Figure 1
Did you ever think of what the possible reasons for variation in the pharmaceutical drug product development could be? In other words, why does a process lose stability when a change is encountered?
So, what can you do about this? How do you reduce uncertainty without encumbering the production process?
That’s what we’re going to dive into. If you want the punchline, method validation. I’ll discuss the possible reasons of variation in the phase III QC lab. Additionally, through this article I’ll explain the practical steps on performing method validation and the calculations involved in each step.
Managing of the process?
Peter Drucker talks about managing things. His mantra is what can’t get measured can’t get managed.
Therefore, the fundamental task for validation is to reduce the error or better yet to estimate the error and account for it by conducting a series of controlled experiments to make information known and therefore, predictable.
Validation of the method?
Method validation is a statistical validation component used to test the truth of something in any process. It uses the statistical tools and principles to collect data on the performance of the process that can be then provide information on the quality of the process and can predict errors. It serves as an assurance that the method is correctly fit for the intended use.
There is always a requirement to validate the stability of your process. The guidelines are referred to by ICH ‘Q2(R1): validation of analytical procedures: Text and Methodology.’ The guidelines include a harmonized set of terms and definitions together with basic requirements for validation.
Analytical method validation is one type of validation that is required during drug development and manufacturing.
Standard steps used in Analytical Method Validation:
It divides data capturing into five parts – Instrument, Sample preparation, Analyst, Method (quantitation), Environment.
It evaluates data from different days, analyst.
It is crucial to have a solid validation protocol, because this is your insurance policy when uncertainty is encountered and a key to confirm safety and efficacy of a drug substance or drug product.
Areas of validation in the pharmaceutical industry:
Production processes
Cleaning procedures
Analytical methods
In-process control test procedures
Computerized systems
The purpose of validation is to depict that processes involved in the development and manufacture of drugs, can be performed in effective and reproducible manner that propels to production operations efficiency.
To ensure that quality is built in at every step, and not just tested for at the end, cGMP requires validation of analytical method. Generally, we start by writing the validation protocol for the method. It details the design of the validation study. It provides information on which characteristics will be tested during the study, how the experiments will be performed, periodic revalidation, change control, stability studies, and what results will be calculated.
In our case, validation of the method consists of an evaluation stage to see if the method is fit for purpose as used in the laboratory along with any performance parameters that may be evaluated under method development.
When performing parameter implementation, there’s four types of outcomes that could occur while running a method.
- Positive result from the sample containing the analyte (True positive).
- Negative result from the sample, which doesn’t contain the analyte (True negative).
- Positive result from the sample, which are structurally similar or closely related to the analyte (False positive).
- Negative result from the sample containing other interfering compounds that hinder the analyte signal (False positive).
Once you understand these four outcomes then we can decide what parameters need to be evaluated and how to calculate them.Firstly, you can think of the freshly developed method as “hypothesis” we don’t know if the preliminary quantitative assay results are reproducible or not, and how robust the method is to any changes. In our case, we have a draft method developed for our X product to separate and quantify (Compound X). Our method development team told us the method is yielding good results. This piqued couple questions: (1) how we ensure that the method is yielding for its intended use and reproducing accurate data every single time. (2) how quickly can we identify the root cause of any upcoming raised issues and work on remediation to generate data for product release. (3)how do we know when sample is failing, is it due to the method, the analyst, or the product. How to control the process and better yet predict the outcome which in turns translates to less lead times and high ROI. As Peter Drucker said what can’t get measured, can’t get managed. Now you guessed it right method validation!
Figure 2
In the next installement we will talk more about the the practical approach of each parameter.
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