Kimber Barnett

Analytical quality by design (AQbD) has been a topic of interest as quality by design (QbD), risk-based pharmaceutical development, and manufacturing principles have been promoted over the past decade (1). It has been the topic of several publications and presentations (2–8), and goals have mirrored those outlined for general QbD-based regulatory submissions: the development of well-designed, understood, and controlled processes for manufacturing and testing pharmaceutical substances and products. An initially proposed AQbD framework outlined a goal toward delivering robust, well-understood analytical methods for effective process control in the delivery of high-quality, well-characterized, and controlled pharmaceuticals along with regulatory flexibility to suitably manage analytical methods over a product’s lifecycle (9). Introduction of that framework afforded a platform for discussion and development of practices toward achieving these goals. Several concepts associated with quality-risk management as outlined in International Council for Harmonization (ICH) guidelines (10–12) were adopted from the QbD lexicon. Design spaces, risk assessments, and criticality assessments are three such concepts. Analytical QbD also introduced concepts such as an analytical target profile (ATP) to define method expectations, and may be analogous to the quality target product profile that contains critical quality attributes (CQAs) as a QbD-based concept to define a method as “fit for purpose”. The following is an example of an ATP for a small-molecule drug product assay:

Assay: The procedure must be able to quantify [analyte] in [presence of X,Y,Z] over a range of A% to B% of the nominal concentration with an accuracy and uncertainty so that the reportable result falls within +/-C% of the true value with at least a 90% probability determined with 95% confidence (13).


	
	Based upon commonality with QbD-based principles, some of these concepts have even been discussed in US Pharmacopeial Convention (USP) stimuli articles as an opportunity to illustrate desired analytical method practices in support of the proposed 

 Chapter <1220> Lifecycle Management of Analytical Procedures (13-15).  These USP-based stimuli articles support the use of ATPs and quality-risk based assessments for managing methods through their lifecycles to ensure that methods remain fit for purpose.

	While these concepts are easy to discuss and perhaps describe when specifications have been defined, implementation of such concepts has posed challenges over the past decade for several reasons:

		Often the ATPs incorporate statistical language and may require enhanced statistical support when establishing ATPs.

		During development, specifications may change and consequently ATPs may change to ensure that methods remain suitable for intended purpose.

		It was proposed that as long as analytical methods favorably deliver against the defined ATP method expectations, methods should remain suitable and could be updated with enhanced regulatory flexibility. Industry feedback, however, has suggested that regulatory submissions that used AQbD concepts have been accepted but without, or perhaps with very limited, reduction in regulatory reporting for method changes (9,16).

	To assess the status of AQbD implementation approximately 10 years after introduction of QbD/AQbD concepts into pharmaceutical analysis (1, 2), the IQ Consortium’s Analytical Quality by Design Working Group (AQbD WG) submitted a survey to IQ member companies. The IQ Consortium’s mission is to advance science and technology by augmenting the capability of member companies to develop transformational solutions that benefit patients, regulators, and the broader R&D community. Eighteen responses from 16 member companies were received to the 34-question survey. The responses represent the use of AQbD in 12 large (>10,000 employees) pharmaceutical or biopharmaceutical companies and four smaller companies.

	More than two-thirds of the companies (69%, or 11 of the 16) had implemented AQbD in some way (

). Not surprisingly, the large pharma companies had a higher implementation ratio (83%, or 10 of 12 companies) than the smaller pharma companies (25%, or 1 of 4 companies). Stated differently, 91%, or 10 of the 11 companies, implementing AQbD concepts were from large companies (although the large companies had significantly more survey responses). Of the varied company responses, implementation of AQbD concepts occurred for both large and small molecules as many companies indicated application for both small and large molecules with very little difference in approach.

	For those companies that have implemented AQbD concepts, several business drivers were noted. Improved method performance was the most important driver. For those five companies that had not implemented AQbD efforts into their development programs, concerns regarding increased cost and/or resource investments with no or low benefit were the primary reasons.

	Technical details and challenges with implementation

	Of the 11 companies that have implemented AQbD concepts, the respondents were almost equally split between implementation just recently started and those who have been practicing AQbD concepts for an extended time. Of the five companies not having implemented AQbD, three indicated that they are just beginning. Interestingly, and not surprisingly, most companies practice AQbD concepts during late-stage development (>80% in Phase III) and commercialization, versus earlier development stages, to obtain maximum value from their investment efforts. 

 shows a breakdown of AQbD implementation during the drug development lifecycle for the 18 respondents. This outcome is clearly consistent with the greatest value voiced from AQbD implementation: achieving robust methods, better validation packages, and much improved method knowledge during development. While not captured in 

, comments toward utilization indicate the application of subsets of AQbD tools (e.g., modeling) by some companies in earlier development stages (Phases I and II) but with less systematic rigor than in later phases for better knowledge development.

	The survey also clearly indicated that the primary focus for implementation of AQbD concepts is on drug substance and drug product methods with much lower application to the testing of other materials (

). Interestingly, and perhaps not surprisingly, respondents have applied AQbD concepts almost equally between chromatographic and non-chromatographic methods and clearly do not apply such concepts to compendial methods.

	Eight respondents stated that there was time saved in developing robust methods, while six respondents thought otherwise. Lifecycle management of methods was thought to take longer with a multivariate AQbD-based approach, therefore requiring additional resources. However, the developed methods were thought to be more robust, and hence fewer issues encountered during routine use.

	The majority of companies in the survey leveraged commonality amongst methods to gain AQbD implementation efficiencies. Two examples included the use of generic risk assessments based on method types (e.g., single risk assessment templates for reversed phase-high-performance liquid chromatography [RP-HPLC] assay methods) and the use of standardized approaches to method development, such as the use of standard chromatographic column screens. In addition, automated software systems were used to offset increased resource demand to develop methods. Thirteen out of 15 respondents developed methods using various commercially available software packages such as Fusion AE, DryLab, ChromSword, and ACD/AutoChrom. Respondents have applied statistics (92%) and experimental design (100%) to improve the efficiency AQbD implementation. This is consistent with numerous examples of models and logically designed experimental screens found in the literature in an effort to promote development of robust methods under an AQbD moniker (17–20). A simple design of experiment (DoE) approach was the most widely used experimental design tool, especially for robustness testing. Modeling and simulation tools were routinely used by some companies (64%) for AQbD application. Of note, most respondents have not leveraged any other approaches, other than the tools mentioned, to offset any potential increase in resource demand for AQbD implementation.

	Regarding technical guidance (e.g., the issuance of departmental guidelines or standard operating procedures [SOPs] for implementation of AQbD concepts), six of the 16 companies noted the existence of a departmental guideline or SOP on AQbD processes. Those six companies who had implemented AQbD guidance represent about half of the companies that have implemented AQbD concepts. The comments on this question suggested that some groups felt specific guidance on QbD implementation was not necessary.  In fact, some responses indicated that QbD aspects of analytical work were directly documented alongside or within those work packages (e.g., method development or validation) rather than as a separate entity.

	As companies consider implementation of AQbD concepts, most responders (13 of the 18 responses, or 11 of the 16 companies) have acknowledged practice of at least some AQbD concepts in their organization. As briefly mentioned above, there were several business drivers for companies to consider for implementation of AQbD concepts in their method development efforts. Of the member companies surveyed, 67% responded that the most important business driver was the expectation of improved performance of analytical methods and robustness of these methods, and 43% indicated this business driver produced the largest return on their investment of AQbD practices. Member companies also indicated other reasons that offer a business incentive: improved knowledge of analytical methodology, regulatory relief with operational flexibility, development of consistent processes for method management, and facilitating lab-to-lab transfers (see 

	However, 31% of the company responses (5 of 16 companies) had not yet implemented AQbD concepts as illustrated in 

 (though three were just beginning).  One of the appreciable concerns mentioned as a potential barrier to advancement involves additional investment cost (e.g., resource costs) with perceived limited value. Respondents were asked whether there was an additional resource requirement in their area associated with analytical QbD. The answer was predominantly yes (79% of respondents). These additional investments are often framed in terms of enhanced statistical support, enhanced infrastructure to support risk assessments and QbD programs, and lack of clear guidance for efficient implementation of new processes.

	For some companies, the value of well-designed method development studies to define and ensure robust methods for intended purpose has been accomplished without the formal implementation of many AQbD-associated concepts (e.g., definition of an analytical target profile and performing formal risk assessments). For those implementing AQbD approaches, focusing application of QbD principles to methods of high importance in the control strategy and/or to those that are particularly complex and therefore of higher risk are appropriate to manage resource investments. It is not felt that applying QbD to every analytical method in a control strategy is a sensible use of resources. The additional level of method understanding and control provided by QbD should be applied following a risk-based approach so that resources are not wasted on well-defined, general compendial methodology. Additionally, cultural barriers also can inhibit adoption of newer practices.

	One of the initially touted potential benefits of AQbD submissions included regulatory flexibility surrounding analytical controls and robustness of the control strategy based upon enhanced knowledge gained by QbD efforts. However, this regulatory driver has not materialized as initially proposed during the first several years of QbD-based submissions.  

 affords a summary of hurdles toward AQbD implementation.

	One of the benefits of adopting a QbD approach is highlighted in ICH guidance: “Areas where the demonstration of greater understanding of pharmaceutical and manufacturing sciences can create a basis for flexible regulatory approaches.  The degree of regulatory flexibility is predicated on the level of relevant scientific knowledge provided” (1). Following on from the adoption of this guidance, interested stakeholders began to think about the concept of regulatory flexibility as applied to analytical methods that have been designed, qualified, and continually verified using QbD-derived approaches. Survey responses related to implementation, filing practices, and regulatory responses are discussed below. While most survey responders concluded that the regulatory aspects were not the main benefit from adopting a QbD approach to analytical methods, other benefits included greater method understanding leading to better method controls. These benefits went some way to justifying the increased effort required. Overall, the survey concluded that benefits could be gained by using QbD tools and processes during the analytical method lifecycle and further discussions on experimental and statistical best practice culminating in the publication of examples, case studies, and ultimately industry guidance are warranted.

	There were four questions relating to regulatory implementation as part of the survey. Initially, the survey asked whether the company had filed analytical methods developed with an AQbD approach. Seven companies, representing both large and small companies, had indicated affirmatively and provided feedback on the questions regarding regulatory agency review of AQbD implementation. The experience of pharmaceutical companies in this area so far seems mixed but more negative than positive. Varied regulatory understanding and expectations as well as a lack of clarity on what level of detail should be gathered and provided was noted. Responding to whether the AQbD regulatory review was successful, three companies had successful reviews and three had either pending submissions or submissions not filed yet.

	There was a variety in the level of detail of AQbD filings. In increasing detail, responses ranged from:

		Outline of risk assessment approach and description of analytical studies

		Analytical target profiles, risk assessments, and statistical analyses.

	Companies are using various approaches, likely based on historical successes. This is inefficient because it leads to more work for the applicant to decide on the approach and to address questions during review. It also leads to more work for reviewers who must adjust to different approaches for presentation. One favorable approach has involved companies including a “change appendix” or protocol in the filing describing the criteria to allow changes outside of the validated operating range. Such protocols are advantageous in gaining prospective approval and assist in planning for additional resources.

	AQbD information was submitted to several countries and positive feedback was received with the exception of using ATP for greater operational flexibility. Regulators preferred the term Method Operable Design Region (MODR) to Analytical Design Space to avoid confusion with process and product design space. Some of the regulators commented on the lack of robustness testing. A few of the respondents indicated successful filing of AQbD methods in the United States, the European Union, Canada, and Switzerland mainly for marketing applications. Authorities responded, asking to clarify the information presented. For cases where AQbD methods were developed for drugs in early/mid stages of development, the method details were not presented in the submission documents, and therefore, no feedback was received.

	The regulatory concerns are best summarized in 

.  Five of eight respondents (62%) said they had not seen method flexibility/regulatory relief as a great value from the AQbD information they provided. The increased flexibility, which was supposed to be gained from the analytical target profile approach to method registration, has not been realized and is likely to be a continued area of negotiation. The use of method operable design regions to gain operational flexibility for an individual method is also an area that requires clarity around regulatory expectations.

	With respect to including AQbD information in filings, respondents were evenly split between those who do and do not include AQbD information. It was commented that the approach was well received in the United States, Europe, and Japan, perhaps in line with the fact that these regions adopt new regulatory practices more quickly.  However, global method changes remain challenging as part of an efficient overall method lifecycle management strategy at the moment.

	In summary, results from a 34-question survey indicate that a majority of responding companies have adopted AQbD concepts over the past decade.  About half of the companies were just starting implementation. A clear driver for adoption of AQbD principles has involved the development of more robust analytical methods, and adoption of varied tools that enhance experimental design and modeling are evidence of commitment toward that goal. Furthermore, most value is found with implementation in the late-development stages and commercialization portions of an asset for best return on investment. The results of this survey were consistent with a separate informal AQbD adoption survey in 2015 of pharmaceutical colleagues (21). In that November 2015 meeting of representatives from approximately 25 biopharmaceutical companies that spanned small-molecule, large-molecule, and vaccine-related organizations, clear interest was shown for advanced discussions of AQbD principles.  More robust analytical methods and enhanced regulatory flexibility (average score of 4.7 out of 5, with 5 being the highest rating) were identified as drivers for interest in the development of AQbD case studies to advance discussions on the topic.

	There have been certainly technical, business, and regulatory hurdles toward implementation. While reduced regulatory reporting requirements were desired by some survey respondents for streamlined analytical method changes through commitment of ATPs in a QbD-based submission, that benefit generally has not been realized. Current guidance from regulatory authorities on mechanism and requirements for changing analytical methods and divergent regulatory requirements between regulatory authorities continue to challenge efficient analytical method lifecycle management. Lengthy and cumbersome processes exist to globally change analytical methods post-licensure, and foster lack of adoption of modern methods/technologies even as analytical technologies/instrumentation become old and obsolete. Despite this obstacle, implementation of AQbD principles offers a potential operational benefit that encourages further interest and application.

	1. FDA, Pharmaceutical CGMPs for the 21st Century-A Risk Based Approach, 2004.
	2. P. Borman, et al., 

., 31 (10) (October 2007).
	3. J. Ermer and P. Nethercote, 

, 36 (10) (October 2012).
	4. S. Colgan, et al., 

, 38 (9) (September 2014).
	5. T. Graul et al., “Quality by Design for Analytical Methods,” in 

 D. Am Ende, editor (2011), 545-562.
	6. M. Pohl et al., 

(Aug 27, 2013).
	8. S. Chatterjee, “QbD Considerations for Analytical Methods-FDA Perspective,” presentation at IFPAC Annual Meeting Baltimore, January, 25, 2013.
	9. M. Schweitzer et al., 

, 34(2), 52- 59,(Feb 2010).
	10. ICH, Quality Guideline Q8(R2) 

 (ICH, August 2009).
	11. ICH, Quality Guideline Q9 

(ICH, November 2005).
	12. ICH, Quality Guideline Q10 

 (ICH, June 2008).
	13. G. Martin et al., “USP Stimuli to the Revision Process on Lifecycle Management of Analytical Procedures: Method Development, Procedure Performance Qualification, and Procedure Performance Verification,” USP-PF, 39 (5) (2013).
	14. K. Barnett, et al., “Analytical Target Profile: Structure and Application Throughout the Analytical Lifecycle,” Stimuli to the Revision Process, 

42 (5), (2016).
	15. G. Martin, et al., Proposed New USP General Chapter <1220>: The Analytical Procedure Lifecycle, USP  website, 2016 (http://www.usp.org/usp-nf/notices/stimuli-revision-process-proposed-new-usp-general-chapter-analytical-procedure-lifecycle), to also be published in 

 43(1), (2017).
	16. EMA, “EMA-FDA pilot program for parallel assessment of Quality-by-Design applications: lessons learnt and Q&A resulting from the first parallel assessment,” Aug. 20, 2013, EMA/430501/2013.
	17. C. Hubert et al., 

, 1395, 88-98 (2015).
	18. G. Reid et al., 

36 (18), 2612-2638 (2013).
	19. Miller et al., 

, 52(35), 12269-12284 (2013).
	20. J. Kochling et al.,

, 125, 130-139, (2016).
	21. T. Schofield et al., “AQbD Industry Teleconference Meeting 06-Oct-2015” and associated “AQbD Case Study Industry Telecon Meeting Minutes 10-6-2015” with informal survey responses from >25 companies interested in AQbD and representing large and small-molecule product offerings as well as vaccines. 

 This article was developed with the support of the 

International Consortium for Innovation and Quality in Pharmaceutical Development

 (IQ). IQ is a not-for-profit organization of pharmaceutical and biotechnology companies with a mission of advancing science and technology to augment the capability of member companies to develop transformational solutions that benefit patients, regulators and the broader research and development community.


	Vol. 41, No. 4
	April 2017
	Pages: 52–59

	When referring to this article, please cite it as M. Argentine et al.,"Evaluating Progress in Analytical Quality by Design," 

Articles

The authors present the results of a survey of small- and large-molecule pharmaceutical and biopharmaceutical companies on implementation of Analytical quality by design concepts.

Evaluating Progress in Analytical Quality by Design

The quality-by-design principles that enable a manufacturer to limit and control the sources of process variability are equally important to measurement systems, because the variability in any process is partly made up of the contributions of the measurement system variability used to understand the process. The authors use real-life examples from drug development projects to outline how an understanding of chromatographic measurement system variability might be achieved.

The concept of quality by design (QbD) was introduced to the pharmaceutical industry in the International Conference on Harmonization (ICH) guidance documents, ICH Q8-Q11 (1-4), as a way to develop robust manufacturing processes for pharmaceutical products and substances. The aim of these documents is to describe a framework for developing a deeper understanding of how variability in the parameters of a manufacturing process can affect the quality of the final product.

In 2010, the European Federation of Pharmaceutical Industries and Associations (EFPIA) Subteam on Analytical Methods introduced the concept of applying QbD principles to analytical methods (5) where they described two main objectives: improved method performance and increased regulatory flexibility. As yet, no pharmaceutical regulatory standards (analaogous to ICH Q8-Q11) exist that describe how to apply QbD principles to analytical procedures. This article, therefore, focuses on how QbD tools may be used to obtain improved chromatographic method performance in such a way that is aligned with holistic drug product and substance control strategies (regulatory flexibility will not be addressed here).

 A QbD approach to understanding a measurement system such as a chromatographic method involves more than the demonstration of a depth of understanding regarding the choice of chromatographic separation parameters (e.g., through multifactor experimental design/robustness studies). Instead, to comprehensively follow QbD principles, the process should start with a statement of method design intent incorporating method performance characteristics focused on the minimum quality standard of the data the method must achieve so as to be fit for purpose. The foundation of a QbD method is, therefore, a fundamental understanding of the requirements of what the method needs to measure and the reliability requirements to which the method will be judged so as to produce data in compliance with a minimum quality standard. In other industries, the understanding of data “quality” is commonly communicated through an expression of the “uncertainty” associated with a measurement “result”. This uncertainty is treated with equivalent importance to the result itself (as it gives confidence in the quality of the measurement result and facilitates understanding in situations in which, for example, pass/fail criteria with respect to specification limits are being assessed). The concepts regarding how to express the uncertainty associated with a measurement data result can be found in many publications focused on measurement uncertainty (6-13).

In the pharmaceutical industry, the approach to understanding the uncertainty associated with measurement results is being addressed under analytical QbD principles. Here, the design of method performance characteristics is the foundation for ensuring measurement data quality can be rigorously controlled. In line with this, the EFPIA subteam introduced the concept of the analytical target profile (ATP) (5), which describes the performance characteristics of the method such that data the method produces will be “fit for purpose” (e.g., for making decisions about whether a batch of drug substance or drug product meets the specification criteria for assay or purity).

Once an ATP has been defined, a systematic process follows that includes a focus on design of the method (i.e., choice of technique and drafting of suitable starting conditions) followed by a full evaluation of the method using risk assessment tools and multifactorial experimental approaches. The final step focuses on an expression of the method conditions or ranges across which the ATP may be met, together with specific instructions to ensure adequate control of the method each time it is used. This process is holistically defined in the schematic in 


. It is important to note that it is not a one-time process but instead is an iterative one that should be revisited throughout the lifecycle of a method. Risk assessments or experiments performed against the ATP should be made each time any change to the product or process is made or new knowledge is gained.


As outlined previously, the first step in QbD method development is to define the ATP. There are at least three important components to be included in this statement:

The range for which the analytical method is expected to quantify the measurand (i.e., analyte)

The total uncertainty, expressed in terms of systematic (accuracy) and random (precision) uncertainty (i.e., variability) components

A description of the analyte to be tested, including the sample or matrix in which it will be tested.

Accompanying the second consideration, the level of risk should be understood for making incorrect decisions.

The following discussion outlines ATPs for a combined assay and purity method for a real-life drug substance, referred to henceforth as examplain hydrochloride (HCl):

Assay: The procedure must be able to accurately quantify examplain HCl drug substance over a range of 90% to 110% of the nominal concentration with accuracy and precision such that measurements fall within ±2.0% of the true value with at least a 95% probability. 

Purity: The procedure must be able to accurately quantify all related impurities relative to examplain HCl in the presence of drug substance and other impurities over a range from the reporting threshold through twice the specification limit. The accuracy and precision of the procedure must be such that the measurements fall within ±15% of the true value for impurity levels ≤ 0.15% with at least 90% probability and within ±10% of the true value for impurity levels > 0.15% with at least 90% probability.          

Justification of ATP statements. Assay ATP

. The ATP describes method performance requirements that define the risk of making an incorrect decision concerning the measurand (14). Analytical methods that adhere to the criterion stated in the ATP allow decisions (e.g., to accept or reject a batch based on the reported value) to be made based on a predefined, maximum level of risk, which is particularly important when the reported value is near the specification limit. For instance, a method that conforms to the assay ATP discussed previously will produce measurements for which there exists at least 95% confidence that these measurements reside within ± 2% of the true (unknown) measurand. That is, there is no more than a 5% chance of making an incorrect decision against the stated bounds of ± 2%.

Suppose a potency result of 100% label claim for a release test of a particular lot is measured. Further, suppose the analytical method used to obtain this result has been shown to conform to the ATP discussed previously. The risk that the true, unknown potency of the lot is below the specification limit of 98% is less than 5%, because the method conforms to the statement that at least 95% of measurements will reside within ± 2% of the true value. Consequently, there exists less than a 5% chance that the true unknown value differs by more than 2% from the observed measured value (95% confidence true value within 100% ± 2% or 98-102%).

The specification criteria for the assay is 98-102% label claim. It should be noted that the initial assay specification was 97.0-103.0%, and the ATP was established based on this orginal specification. The specification was subsequently tightened to 98.0-102.0% based on global regulatory feedback, and the original ATP was found to be fit for purpose with respect to the revised specification. The ATP criteria, as with ICH method validation criteria, are established based on considerations for patient safety and product quality and are consistent with the capability of analytical methodology used to characterize APIs. 

 illustrates pictorially the ATP as a probability contour plot (i.e., parabolic region in dark grey) for the examplain HCl drug substance assay as described. Here, the total uncertainty is comprised of precision (σ, random variability) and bias (μ, systematic variability). 

 also illustrates a rectangular region corresponding to the more generally applied acceptance criteria established for analytical measurements, in which bias and precision are defined independently. In this case, the rectangle represents the following method criteria: the measurement has no more than ± 2.0% bias and no more than 1.25% variability.

. First, the probability curve (parabolic region in dark grey) is contained within the more generally applied acceptance criteria (rectangular region in green). As such, the ATP criteria are slightly more restrictive. Second, when using the probability curve approach, method precision criteria (y-axis) is dependent on bias criteria (x-axis) and vice versa. This is because the total uncertainty, which is specified in the ATP, is a combination of these two components. Intuitively, a method with no bias can accommodate more variability (or lower precision) compared to a method with some non-negligible bias while providing the same total uncertainty. As method bias increases towards the ATP limit (±2 in 

), the required method variability decreases to zero to maintain the same analytical performance in terms of total uncertainty. When using the traditional approach of independent bias and precision assessments, there is no natural trade-off between those criteria, thus implying that a method may have both high bias and high variability (indicated by the yellow diamond in the upper right-hand corner of the green rectangle in 

), which could be problematic if not properly linked to the specification range. That is, a method operating at the yellow diamond (bias approximately + 2% and precision approximately 1.25%) does not maintain a 95% assurance that measurements will be within ± 2% of the true value. If the bias can not be corrected for, then measurements have only approximately a 50% chance of being within ± 2% of the true value. Even if this method is corrected for a known 2% bias (i.e., bias can be measured as different from random uncertainty), there exist < 90% probability that measurements will reside within a ± 2% range.

The expression for defining the acceptance region is shown in 

 where µ is true mean/accuracy (a parameter); σ is true sigma/precision (a parameter); e is allowable analytical window (a fixed constant); y is individual assay value (a random variable with mean µ and standard deviation σ); T is true analytical content (fixed target); p is minimum probability for individual assay to reside within error bound e (fixed constant); ϕ is normal density function centered at µ, with standard deviation σ.

Using Equation 1, the interplay between the appropriate analytical window (e), maximum sigma (σ), and minimum probability (p) can be probed for each unique analytical method.

. In this example, the specification criteria detail the limits for all related (specified) impurities to examplain HCl with specified impurities A and B both with limits of not more than 0.15%.

 illustrates the ATP as a probability contour plot for the examplain HCl drug substance impurities as described previously (i.e., parabolic region in dark grey). The total uncertainty is comprised of precision (σ, random variability) and bias (μ, systematic variability).

 also illustrates a rectangular region corresponding to the more generally applied acceptance criteria established for analytical measurements, where bias and precision are defined independently. In this case, the rectangle represents the following method criteria: the measurement has no more than ± 15.0% bias and no more than 10% variability. Here, the maximum allowable bias and precision is consistent with what can be expected for analytical procedure performance at these levels. For example, for levels ≤ 0.15% relative to examplain, the maximum allowable precision is 9.1% RSD and the maximum allowable bias is ± 15%.

There is a trade-off between precision and accuracy, such that it is not acceptable for the method to exhibit maximum bias and maximum variability concurrently, and, as with the assay ATP, the purity ATP criteria is slightly more restrictive when compared to the ICH validation criteria that would have typically been applied. From a practical perspective, the ATP criteria can be interpreted as follows: 9 out of 10 measurements will fall within 100% ± 15% of the true value, which corresponds to a range in which at least 9 out of 10 measured values will reside within 0.13% to 0.17%. This level of measurement uncertainty ensures patient safety. It is consistent with the philosophy of ICH Q3A (Impurities in New Drug Substances) as well as the current capability of contemporary analytical methodology used to quantitate low-level impurities in pharmaceutical drug substances. In fact, contemporary method capability was taken into consideration when the limits in ICH Q3A were established.

Analogous statements can be made for ATP criteria for impurities > 0.15% where the maximum allowable precision is 6.1% RSD and maximum allowable bias is ± 10%. This means that an impurity present at 0.3% (true value) corresponds to a range in which at least 9 out of 10 measured values will reside within 0.27%- 0.33% (i.e., true value ± 0.1 x true value). This tiered approach ensures that performance of the procedure is maintained for higher level impurities while ensuring patient safety and aligning with contemporary procedure capability.

. Once appropriate ATP criteria have been established, a technique should be selected. This selection depends not only on the match between measurement technique capability and ATP, but also on other scientific, practical, and business requirements. Typical considerations to bring to the discussion table between scientists who may be involved in using the method across the development lifecycle could include the physicochemical characteristics of the molecules in question, whether the method will be run in an R&D environment or a manufacturing environment, if on-line capability might be required, what sample turn-around times (from sampling to data reporting) are going to be necessary, and a plethora of other scientific and business-focused factors.

In the case examples discussed in this article, following such a discussion between R&D and receiving laboratory analytical scientists, reversed-phase high-performance liquid chromatography (RP-HPLC) was the method of choice.

 Once a technique has been chosen, a systematic process to arrive at “starting” or “draft” method conditions should be followed. In the case of RP-HPLC, the approach is shown in 

. Here, experimental studies are combined with in-silico modeling software to maximize the value of the results by being able to predict between experimental parameters (15, 16).

At the start of the process, it is essential to define the correct key predictive sample set (KPSS). For a pharmaceutical example, the KPSS available will be highly dependent on the drug-development lifecycle stage, and the ideal KPSS for an API  purity method should include all known process related impurities and known relevant potential degradants. If structures are known, then the experimental screening strategy may be supplemented by the information to be gleaned from a Log(P) vs. pH plot (P is the octanol water distribution coefficient of all analytes of interest). The column screening strategy employed in our laboratories and for the examples discussed here encompasses four stationary phases; two organic solvents; and acidic, neutral, and basic aqueous mobile phases (15). The primary objective of the screening is to obtain the most promising starting conditions with respect to overall selectivity, peak shape, and chemical stability, as well as minimal reliance on accurate pH control.

The next step is to investigate the combined effects of temperature and gradient profile using the starting conditions from the first phase of screening experiments (i.e., stationary phase, pH, and organic modifier). This experiment aims to explore the impact of various gradient profiles together with a range of temperatures across six experiments. The data obtained are modeled using software (e.g., ACD Labs LC Simulator), which allows the scientist to interpolate or extrapolate beyond the tested range to gain maximum value from a relatively low number of experiments. In the example, the data from the six experiments are used as the input for in silico optimization experiments. The result is a resolution map and optimized chromatogram within which the optimum conditions with respect to overall peak shape, resolution, and analysis time may be predicted (

The final stage of this systematic approach to method development is to check the effect of small changes in pH on the method performance. The pH is typically varied by up to +/- 1.0 pH units across five experiments (e.g., +1.0, +0.5, 0, -0.5, -1.0 pH units). Again, the data resulting from these experiments may be evaluated using software packages, such as the LC Simulator software, which allows for a more thorough understanding regarding the most suitable pH that will yield a more robust separation.

Experience with this systematic approach to method development indicates that approximately 75% of all applications lead to a successful (i.e., fit for purpose) method. In the remaining cases, various degrees of variations from the workflows have to be explored. For example, use of alternative buffer components, ion-pairing reagents, alternative column chemistries, and even completely different separation mechanisms can be applied.

 The risk-assessment exercise involves a systematic assessment of the draft method. The risk assessment process is designed to map individual method steps (e.g., standard and sample preparation or chromatographic separation) and identify method variables with the potential to affect method performance with respect to the ATP requirements. In the case described here, this exercise involved experienced analytical chemists from the method development and receiving laboratories and included those with some experience running the method. These participants were included to ensure that knowledge from previous studies were included and to understand differences in lab practices between the development and receiving laboratories. Three distinct focus areas were examined: (1) sample and standard preparation; (2) chromatographic separation; and (3) detection and data processing. Method variables were scored based on their potential to affect method performance together with the likelihood of occurrence using a cause-and-effect Matrix. Each variable was categorized as follows:

Experimental (X). Those that may vary and require further experimentation to understand (e.g., temperature, flow rate, mobile phase composition)

Controlled (C). Variables that can be controlled or specified at unique levels (e.g., column stationary-phase type and particle size, column diameter, length, and supplier)

Noise (N). Those that cannot be controlled or are allowed to vary randomly from a specific population (e.g., column age).

Method variables with the highest scores (i.e., combined high probability and high impact on chromatographic performance relative to the ATP) were further assessed by way of multifactor experimentation. An example of the multifactor experimentation from the chromatographic separation focus area follows.

 Two separate design-of-experiments (DoE) studies were performed to identify and verify the optimum method conditions. The first DoE was conducted to explore and identify a preliminary set of chromatographic conditions for further verification. The second DoE was conducted to verify conformance of the method to the ATP criteria.

 represents the experimental region from the first wave of experimental design studies (DoE-1). This design included the following parameters which were identified during the risk assessment as having the highest potential to influence the chromatographic separation: flow rate, trifluoroacetic acid (TFA) content in mobile phase, column temperature, gradient change, and start and end times. A preliminary set of suitable operating conditions was identified by assessing the chromatographic performance measured during DoE-1 against the predefined criteria shown in 

The results of DoE-1 were used to identify the variables that may affect chromatographic performance. Statistically significant effects at the commonly-accepted 0.05 level of statistical significance were determined using Student’s critical t-values. Statistical models of these results were developed for each method attribute and used to define the variable ranges over which the method is expected to meet the predefined criteria in 

. These ranges of chromatographic variables defined a preliminary experimental design space referred to as the method operable design region (MODR).

Critical   pair resolution (impurity A: impurity B)

Relative   standard deviation (RSD) at limit of quantitation (LOQ): (impurity A at 0.05%   LOQ)

Examplain   hydrochlodride retention time

A subsequent experimental design study (DoE-2) was executed to verify that the method complied with the criteria specified in the ATP. Verification testing was performed using experimental conditions spanning the preliminary predicted MODR identified from DoE-1 results. Based on the data analyses from DoE-1, the three parameters that had the largest collective impact on both resolution and limit of quantitation (LOQ) were the gradient change time, the column temperature, and the mobile phase TFA concentration. To select the method conditions, a standard 23-1 design was chosen that spanned a predicted acceptable range of method performance. Specifically, the gradient start time was varied between 15-19 minutes, the column temperature between 25-32 C, and TFA content between 0.04-0.06%. The other parameters that had an impact on method performance were flow rate and gradient start time. For this experiment, however, those levels were fixed at values that would stress the system in terms of performance; that is, at levels nearing the predefined criteria. Flow rate was set at 1.05 mL/min and gradient start time at 0.5 minutes. Gradient end time did not affect method performance. Testing was conducted over four days, by two analysts, in two different laboratories (development and testing labs) using HPLC systems from two vendors (Agilent and Shimadzu).

The chromatographic variables used to verify the MODR are shown in 

. Verification testing was initially performed using conditions 1-4. 

 show how results using conditions 1, 3, and 4 all met ATP criteria. Results generated using condition 2, however, failed to adequately meet ATP criteria due to insufficient resolution (Rs < 1.0) for the critical pair. This is illustrated in

 where condition 2 is clearly within the red shaded region. As a result of this failure, the MODR model was refined, and a new combination of verification variables, condition 5, was added.

In lieu of preparing separate solutions, injection volumes of 9, 10, and 11 µL were used to verify conformance to the ATP over the range of 90-110% of the nominal injection concentration. The flow rate was held at 1.05 mL/min for the original verification study (conditions 1--4), as a flow rate slightly above the 1.0 mL/minute target was considered a worst case scenario based on the results from DoE-1. The flow rate was changed to 1.00 mL/min (the target condition) for condition 5. The final verified MODR incorporating verification condition 5 is illustrated in 

As a means of visualising how these results from both labs during DoE-2 comply with the ATP, 

 shows the probability contour plot (using 

) illustrating method variability (σ≈relative standard deviation or RSD) vs. calculated bias/process acceptance criteria (98-102% potency). The grey-shaded region is the graphical representation of the ATP criteria for the assay method. Each point represents 24 replicates at each of the five MODR verification parameters run in the two different labs. This graph illustrates minimal (statistically insignificant) bias relative to the proposed 98.0-102.0% assay acceptance criteria. Although slightly greater variability was observed for Lab 2 results, all results fall within the ATP for the assay method (grey region). The triangular regions surrounding each point represent the simultaneous 95% confidence interval for accuracy and precision for each of the points as described in Lindgren’s Statistical Theory (14).

Similarily, the probability contour plots in 

 illustrate method variability (σ≈ RSD) vs. calculated bias/process acceptance criteria for results generated from both laboratories for impurity A at > 0.15% and impurity B at ≤ 0.15%, respectively. Each point represents 24 replicates at each of the five DoE conditions run in the two different labs. The simultaneous 95% confidence interval for accuracy and precision is also shown on the plots. These results illustrate how impurity A and B quantitation data meets the ATP criteria for quantitation of impurities in that > 90% of data are within ± 10% of expected normalized value for values > 0.15%, and > 90% are within ±15% of the expected normalized value for values ≤ 0.15%.

Based on the statistical modelling from DoE-1 and DoE-2, the entire white region in 

 is predicted to be capable of meeting the ATP acceptance criteria. For operator simplicity, however, the operating ranges were constrained to the ranges listed in 

The quality-by-design principles used to control process variability are equally important to measurement systems because process variability includes contributions from measurement system variability. The authors use real-life examples from drug development projects to outline how an understanding of chromatographic measurement system variability might be achieved.