Using Design of Experiments in Validation - This approach can produce better results for less work than traditional validation methods. - BioPharm International

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Using Design of Experiments in Validation
This approach can produce better results for less work than traditional validation methods.


BioPharm International
Volume 18, Issue 5


George R. Bandurek, Ph.D.
The statistics-based method called Design of Experiments (DoE) has a long history when applied to optimising a product or a process. This technique is well suited for running robustness trials as part of validation. A shift in emphasis from discovery to verification is all it takes. DoE techniques should improve the efficiency and the effectiveness of trials.



A thorough validation is essential if a company wants to introduce a new product or process and not spend a year in troubleshooting. DoE is a powerful tool that gives early warning of potential problems. If a process can pass a validation carried out with DoE, then the number of problems in volume manufacture will be reduced.

DoE techniques ordinarily are used to optimize a product or process, where they are an efficient way of identifying factors that improve performance or save money. The methods and statistical tools are described in many books on the topic. Their application to validation is less common but examples in electronics and mechanical engineering have been published.1,2 The pharmaceutical industry probably avoids trying DoE in validation, for two reasons:

  • Regulatory requirements have encouraged people to use tried and tested methods.
  • Some companies prefer to follow the minimum route of Installation Qualification (IQ), Operational Qualification (OQ), and Process Qualification (PQ), which will satisfy an auditor although it is not a reliable indicator of robustness in service.

FDA has proposed a new approach to validation that may further encourage the use of designed experiments.3 The advantages are simply better results for less work:
  • When compared to traditional one-at-a-time experiments, which force factors to the extremes they are likely to encounter, the number of trials is typically halved and potentially reduced to one tenth.
  • The DoE approach will identify the presence of unwelcome interactions between any two factors, something that one-at-a-time methods will always miss.

PURPOSE OF VALIDATION A validation test demonstrates that a product or process is fit for its intended purpose. A thorough test is a necessary stage-gate between development and production and it may be called validation, verification, qualification, acceptance, release or some other name, which is the common industry jargon. A receiving company, department, or individual desires a validation that is thorough and complete because this reduces the risk that the product or process is in some way deficient. A failed validation is grounds for rejection and rightly puts the burden of fixing the problem on the supplier.

If validation is incomplete and the product passes despite its hidden faults, then the vendor may have made a sale, but the purchaser will ultimately be dissatisfied and unlikely to buy again. This even applies within a company where a development department creates a process that works in a laboratory but does not provide the same yields when it is scaled up.

The first experimental stage in validation is to show that a process (or product) works as intended when the factors that can affect performance are set to nominal or target levels. Typical factors are running speed, dimensions of a component, strength of a solution, and room temperature. This is the basis of an OQ and is often used to release a design from development to production. The risk is that variation in some of these factors will have a significant effect on performance. A good validation will run all the factors that could affect performance through their ranges of expected values.

A simplistic OQ + PQ approach relies mostly on random, natural variation to allow the factors to travel to their limits. The minimal OQ test is one run at nominal conditions although sometimes a "worst case" combination is also required.4 The PQ phase is to make a minimum of three batches, with the underlying assumption that any natural variation which is ever likely to occur will reveal itself. This is an optimistic view and as an unintended consequence, many products or processes require modifications and adjustments long after they enter service.


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