Following Voluntary Guidelines Can Take Laboratory Risk Management and Quality Control to a Higher Level

A plethora of regulations and guidelines  requires US laboratories to implement measures to mitigate risk and ensure quality at every stage of the testing process. While essential to maintaining licensing and accreditation, simply doing what the laws require may not ultimately deliver excellence. As a result, many laboratories challenge themselves to go beyond compliance and meet higher, voluntary quality guidelines established not by lawmakers, but scientists and other non-governmental technical experts. 

Making the case for implementing voluntary guidelines for method development 

Voluntary guidelines can be used to develop, validate, and implement analytical methods to generate quality data that laboratory managers can use to make critical decisions. As one lab manager explained, while the traditional approach to method development yields at best an equivalent and often inferior product with less knowledge, it’s faster and requires less of an investment. So, he recommends for fellow laboratory managers to make a case to their superiors and customers for investing in voluntary guidelines. How? By pointing to the following benefits: 

Traditional method development approaches assess performance via well-controlled, rehearsed validation exercises. While scientifically sound, these exercises are performed after the fact, meaning that the knowledge they generate can’t be applied at the actual time of testing. By contrast, new guidelines are based on the concept of quality by design (QbD), which provides a systematic framework for continuous, real-time, and robust validation and verification. The result is more effective risk management, better product quality, and enhanced customer trust and confidence. 

QbD-based analytical method standards help ensure consistency of analysis from one measurement to the next. This is often crucial in the laboratory setting when, for example, identical instruments next to each other on the bench apply the same analysis but yield different results. While calibrating the results to a curve may produce results that are consistent within some standard deviation, they won’t be identical. The more you use those same side-by-side instruments to perform those same tests, the wider the inherent error of measurement will become. What laboratory managers want and need to mitigate risk, boost confidence, and gain customer trust is uniformity of results, which are robust to the details of the analysis conditions.

Another advantage of following voluntary guidelines is flexibility. These guidelines are designed for the broadest possible application and allow for implementation based on each laboratory’s unique combination of test methods, instruments, and environment. This enables regulatory compliance, including but not limited to compliance with CLIA Individualized Quality Control Plan (ICQP) requirements. 

The ICH Q14 standard

If the lab requires methods with greater robustness, which voluntary standard should be adopted? There are two QbD-based guidelines for analytical procedures: ICH Q14 and USP 1220. While the guidelines are consistent and compatible, they’re not identical: 

• USP 1220, (Chapter 1220 of the United States Pharmacopoeia National Formulary) sets a holistic framework for implementing validation activities across the entire life cycle of an analytical method
• ICH Q14, the most recent output of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH), is designed to harmonize and simplify analytical procedure development in accordance with accepted scientific standards and QbD principles.

ICH Q14, a proposed standard that hasn’t yet been finalized, builds on and enhances current ICH guidelines (principally ICH Q2 for validating analytical procedures and ICH Q8 for quality target product profile (QTPP)) and has a wide US and global following. 

Five key elements of ICH Q14 implementation 

Below is a concise briefing of the five things laboratory managers need to know about implementing ICH Q14.     

1. Analytical target profiling

The starting point for implementing ICH Q14 analytical method validation is to create a document called the analytical target profile (ATP) that defines the reason for developing the process, along with the performance characteristics or critical analyte attributes, e.g., analyte biology or types of impurities, to be measured. Nancy Ross, a US Navy-trained clinical laboratory scientist with more than 20 years of experience in hospital, reference, and academic laboratory settings, explains that the ATP also defines acceptable limits associated with the reportable result in terms of: 

• Accuracy, or how close a test result is to the analyte’s true value
• Precision, or the degree to which repeated test results on the same sample agree
• Reportable range, or the lowest and highest results that can be accurately measured, along with all the values in between
• Reference range, or the span of values for a particular test that represents the results expected in a healthy (normal) patient population.

2. Risk assessment

By linking critical quality attributes (CQAs) to critical process parameters (CPPs), the ATP enables risk assessment and determination of effective strategies for control and replication. It also provides the criteria for measuring performance and verifying that the analytical procedure is fit for its intended purpose, paving the way for risk assessment at all stages of the procedure’s life cycle.  

3. Robustness study

The essential prerequisite for implementing the ICH Q14 enhanced approach is complete understanding of the analytical process and parameters used to generate reportable results. The point of the robustness study is to generate data indicating how adjustments to individual variables impact the result and how variables interact with each other. 

Evaluating robustness isn’t a new concept. But rather than following the traditional method of determining robustness by varying individual factors one at a time (OFAT) and evaluating the impact of the change, ICH Q14 uses an advanced approach that combines multi-factor design of experiments (DOE) with advanced statistical models allowing for more efficient identification of critical variables impacting the method and variable/variable interactions. Applied properly, the result is a formula for ensuring robustness that can be applied differently for each testing technique.  

4. Development of multivariate analytical procedures

Laboratories adhering to ICH Q14 will have to develop robust multivariate analytical procedures, i.e., those that produce results utilizing more than one input variable with values obtained from a validated reference procedure or reference samples. The guidelines stress the importance of using great care in the selection of: 

• Reference samples, which should be homogenous and contain essential data that account for all potential sources of variability, such as raw material quality and/or storage conditions
• Variables, e.g., wavelength range selection for spectroscopic applications that evaluate chemical or physical properties based on a region of a spectrum 
• Data transformation methods, which should be driven by the type of data, instrument or 425 sample, intended use of the procedure, and/or prior knowledge.

Laboratory personnel that develop multivariate analytical procedures must also seek to minimize the prediction error and “provide a robust model that consistently assures the [model’s] long-term performance,” according to the guidelines text.   

5. Validation for life cycle management

Traditional models tend to treat method validation as a formality performed as the last stage of the process. By contrast, ICH Q14 and other QbD-based guidelines provide for dynamic method operable design region (MODR) validation based on ATP criteria, risk assessment, and technology selection to evaluate method performance. MODR can also be applied across the method’s entire life cycle. In addition to minimizing the need for internal investigations due to a method’s poor performance, MODR processes and the data they generate can improve regulatory submissions, method development, and method transfer while facilitating rapid and efficient adoption of new laboratory technologies.