The International Council for Harmonisation (ICH) E6(R2) (International Council for Harmonisation (ICH). ICH harmonised guideline: integrated addendum to ICH E6(R1): guideline for good clinical practice E6(R2). 2016. https ://datab ase. ich.org/sites /defau lt/files /E6_R2_Adden dum.pdf. Accessed 5 Dec 2019) introduced Quality Tolerance Limits (QTLs) to the industry, and in doing so, modernized quality control for clinical trials. QTLs provide measured feedback on clinical trial parameters previously only used by statistical and clinical functions to track trial progress toward endpoints. Elevating these measures as part of the Quality Management System (QMS) provides greater visibility across clinical trial functions and the enterprise as well as to measures that are important indicators of the state of participant protection and reliability of trial results. In support of this new requirement, TransCelerate developed a framework to guide industry sponsors and their agents in implementing QTLs. This QTL Framework is intended to aid industry's ability to improve the quality of clinical research through the implementation of QTLs in a way that helps protect trial participants and reliability of trial results while meeting Health Authority (HA) expectations. The framework is intended to maximize efficiency and minimize confusion in the implementation of QTLs. The framework includes proposed approaches for implementation of QTLs for a clinical trial as defined in Section 5.0.4 and 5.0.7 of ICH E6(R2) (International Council for Harmonisation (ICH). ICH harmonised guideline: integrated addendum to ICH E6(R1): guideline for good clinical practice E6(R2). 2016. https ://datab ase.ich.org/ sites /defau lt/files /E6_R2_Adden dum.pdf. Accessed 5 Dec 2019) and considerations for setting thresholds.

Since the release of ICH E6(R2), multiple efforts have been made to interpret the requirements and suggest ways of implementing quality tolerance limits (QTLs) alongside existing risk-based quality management methodologies. While these efforts have contributed positively to developing a common understanding of QTLs, some uncertainty remains regarding implementable approaches. In this article, we review the approaches taken by some leading biopharmaceutical companies, offering recommendations for how to make QTLs most effective, what makes them ineffective, and several case studies to illustrate these concepts. This includes how best to choose QTL parameters and thresholds for a given study, how to differentiate QTLs from key risk indicators, and how QTLs relate to critical-to-quality factors and the statistical design of the trials.

No abstract