Dissolution Profile Similarity Assessment-Best Practices, Decision Trees and Global Harmonization.

During the write-up of the meeting summary reports from the 2019 dissolution similarity workshop held at the University of Maryland's Center of Excellence in Regulatory Science and Innovation (M-CERSI), several coauthors continued their discussions to develop a "best-practice" document defining the steps required to assess dissolution profiles in support of certain biowaivers and postapproval changes. In previous reports, current challenges related to dissolution profile studies were discussed such that the steps outlined in the two flow charts ("decision trees") presented here can be applied. These decision trees include both recommendations for the use of equivalence procedures between reference and test products as well as application of the dissolution safe space concept. Common approaches towards establishing dissolution safe spaces are described. This paper encourages the preparation of protocols clearly describing why and how testing is performed along with the expected pass/fail criteria prior to generating data on the materials to be evaluated. The target audience of this manuscript includes CMC regulatory scientists, laboratory analysts, as well as statisticians from industry and regulatory health agencies involved in the assessment of product quality via in vitro dissolution testing. Building upon previous publications, this manuscript provides a solution to the current ambiguity related to dissolution profile comparison. The principles outlined in this and previous manuscripts provide a basis for global regulatory alignment in the application of dissolution profile assessment to support manufacturing changes and biowaiver requests.

DISSOLUTION PROFILE SIMILARITY ANALYSES-STATISTICAL PRINCIPLES, METHODS AND CONSIDERATIONS.

The pharmaceutical industry and regulatory agencies rely on dissolution similarity testing to make critical product decisions as part of drug product life cycle management. Accordingly, the application of mathematical approaches to evaluate dissolution profile similarity is described in regulatory guidance with the emphasis given to the similarity factor f2 with little discussion of alternative methods. In an effort to highlight current practices to assess dissolution profile similarity and to strive toward global harmonization, a workshop entitled "In Vitro Dissolution Similarity Assessment in Support of Drug Product Quality: What, How, When" was held on May 21-22, 2019 at the University of Maryland, Baltimore. This manuscript provides in-depth discussion of the mathematical principles of the model-independent statistical methods for dissolution profile similarity analyses presented in the workshop. Deeper understanding of the testing objective and statistical properties of the available statistical methods is essential to identify methods which are appropriate for application in practice. A decision tree is provided to aid in the selection of an appropriate statistical method based on the underlying characteristics of the drug product. Finally, the design of dissolution profile studies is addressed regarding analytical and statistical recommendations to sufficiently power the study. This includes a detailed discussion on evaluation of dissolution profile data for which several batches per reference and/or test product are available.

Current Approaches for Dissolution Similarity Assessment, Requirements, and Global Expectations.

This report summarizes podium presentations and breakout sessions from the second day of the 2019 M-CERSI workshop on In Vitro Dissolution Similarity Assessment in Support of Drug Product Quality: What, How, and When? Presenters from the U.S. Food and Drug Administration (FDA), Health Canada (HC), European Medicines Agency (EMA), Brazilian Health Surveillance Agency (ANVISA), and the pharmaceutical industry shared experiences/concerns with dissolution profile similarity assessment supporting minor/moderate Chemistry, Manufacturing and Control (CMC) changes. Members from regulatory agencies explained that dissolution profile similarity testing is only part of the overall assessment of the acceptability of the proposed changes; decisions are usually made based on aggregate weight of evidence. Scientific shortcomings of f2 were highlighted but no proposal on how to replace it was made. Controlling dissolution timepoint variability and application of pairwise batch-to-batch comparisons (PBC) of dissolution profiles caused considerable debate. Several industry participants suggested increased sample sizes to raise confidence in decision-making and to avoid PBC. They proposed identification of a single mathematical method with predefined acceptance criteria and suggested that dissolution timepoint selection should follow EMA and HC guidance. A majority of meeting attendees favored applying clinically relevant dissolution specifications (CRDS) and dissolution safe space to determine the impact of minor/moderate CMC changes as opposed to dissolution profile similarity assessment via statistical methods. Day 2 of the workshop highlighted the need and opportunities for global harmonization including variability, timepoint selection, role of CRDS, and statistical methods to address the ambiguity globally operating pharmaceutical companies are currently facing.

In Vitro Dissolution Profiles Similarity Assessment in Support of Drug Product Quality: What, How, When-Workshop Summary Report.

The pharmaceutical industry and regulatory agencies rely on dissolution similarity testing to make critical product performance decisions as part of drug product life cycle management. Accordingly, the application of mathematical approaches to evaluate dissolution profile similarity is described in regulatory guidance. However, the requirements (e.g., which time points, number of time points, %CV) to apply the widely known similarity factor f2 and other alternative statistical approaches diverge noticeably across regulatory agencies. In an effort to highlight current practices to assess dissolution profile similarity and to strive towards global harmonization, a workshop entitled "in vitro dissolution similarity assessment in support of drug product quality: what, how, when" was held May 21-22, 2019, at the University of Maryland, Baltimore. This article summarizes key points from the podium presentations and breakout (BO) sessions focusing on (1) contrasting the advantages and disadvantages of several statistical methods; (2) the importance of experimental design for successful similarity evaluation; (3) the value of similarity evaluation in light of clinically relevant specifications; and (4) the need for creating a robust and scientifically appropriate path (e.g., non-prescriptive decision tree) for dissolution profile similarity assessment as a stepping stone for global harmonization.

Equivalence analyses of dissolution profiles with the Mahalanobis distance.

For some postapproval changes, the manufacturer has to demonstrate that the dissolution profile of the drug product before the change is statistically equivalent to the dissolution profile after the change. Guidelines suggest the so-called similarity factor f2 as standard approach for the equivalence analysis. f2 is a statistically questionable transformation of the Euclidean distance between both profile means and does not allow a control of the type I error rate. An alternative multivariate distance measure for quantifying the dissimilarity between both profile groups is the Mahalanobis distance. Current equivalence procedures based on the Mahalanobis distance implicate some practical problems in the dissolution context: either one chooses an exact method but the determination of a product independent equivalence margin will not be practically feasible or one chooses an approximate alternative that suffers from the bias of the Mahalanobis distance point estimate. This paper suggests the T2EQ approach for dissolution profile comparisons. T2EQ is a practically feasible equivalence procedure based on the Mahalanobis distance with an internal equivalence margin for comparing dissolution profiles. The equivalence margin is compliant with current dissolution guidelines. The operating characteristics (size, robustness, and power) are investigated via simulation: T2EQ meets the needs of both authorities and industry: not affected by the bias of the point estimate the type I error rate can be reliably controlled for various distribution assumptions and the power of T2EQ exceeds the power of methods recently discussed in the literature. These results were presented for the first time at CEN-ISBS 2017.

Comparison of Dissolution Profiles: A Statistician's Perspective.

Dissolution profile comparisons are used in the context of postapproval changes where the manufacturer has to demonstrate that the quality of the product is not affected by the change. Around this topic, basic statistical principles are in conflict with widely used interpretations of current guidelines, resulting in time-intensive discussions in pharmaceutical practice. From a statistician's perspective, the following suggestions could improve the situation regarding statistical analysis, inference, and interpretation: (1) A clear definition of the variability criterion for the similarity factor, such as that found in the EMA guideline, would be helpful. (2) Sample size recommendations should be interpreted as minimum, not as maximum, requirements. (3) In case of several batches per reference or test group, pooled comparisons should be performed instead of multiple batch-to-batch comparisons. (4) FDA Guideline recommendations concerning multivariate equivalence procedures for highly variable dissolution profiles are based on the state of statistical knowledge in 1997 and need to be updated. (5) The T2 test for equivalence is an appropriate method for comparing highly variable dissolution profiles. Application of the T2 test for equivalence enables reliable equivalence decisions and satisfies the intention of reaching scientific evidence in decision making. Software implementations of this test are available in R and SAS. The article is a summary of the poster of the same name presented at the DIA FDA Statistics Forum 2016. The poster took the third place in the poster award of the conference.

Performance of the Population Bioequivalence (PBE) Statistical Test Using an IPAC-RS Database of Delivered Dose from Metered Dose Inhalers.

This article reports performance characteristics of the population bioequivalence (PBE) statistical test recommended by the US Food and Drug Administration (FDA) for orally inhaled products. A PBE Working Group of the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) assembled and considered a database comprising delivered dose measurements from 856 individual batches across 20 metered dose inhaler products submitted by industry. A review of the industry dataset identified variability between batches and a systematic lifestage effect that was not included in the FDA-prescribed model for PBE. A simulation study was designed to understand PBE performance when factors identified in the industry database were present. Neglecting between-batch variability in the PBE model inflated errors in the equivalence conclusion: (i) The probability of incorrectly concluding equivalence (type I error) often exceeded 15% for non-zero between-batch variability, and (ii) the probability of incorrectly rejecting equivalence (type II error) for identical products approached 20% when product and between-batch variabilities were high. Neglecting a systematic through-life increase in the PBE model did not substantially impact PBE performance for the magnitude of lifestage effect considered. Extreme values were present in 80% of the industry products considered, with low-dose extremes having a larger impact on equivalence conclusions. The dataset did not support the need for log-transformation prior to analysis, as requested by FDA. Log-transformation resulted in equivalence conclusions that depended on the direction of product mean differences. These results highlight a need for further refinement of in vitro equivalence methodology.

Multivariate equivalence tests for use in pharmaceutical development.

Statistical equivalence analyses are well-established parts of many studies in the biomedical sciences. Also in pharmaceutical development and manufacturing equivalence testing methods are required in order to statistically establish similarities between machines, process components, or complete processes. This article presents a choice of multivariate equivalence testing procedures for normally distributed data as generalizations of existing univariate methods. In all derived methods, variability is interpreted as nuisance parameter. The use of the proposed methods in pharmaceutical development is demonstrated with a comparative analysis of dissolution profiles.