Historical Benchmarks for Quality Tolerance Limits Parameters in Clinical Trials

Efficient Positioning of QTL and Secondary Limit Thresholds in a Clinical Trial Risk-Based Monitoring

In the high-stakes world of clinical trials, where a company's multimillion-dollar drug development investment is at risk, the increasing complexity of these trials only compounds the challenges. Therefore, the development of a robust risk mitigation strategy, as a crucial component of comprehensive risk planning, is not just important but essential for effective drug development, particularly in the RBQM (Risk-Based Quality Management) ecosystem and its component-RBM (Risk-Based Monitoring). This emphasis on the urgency and significance of risk mitigation strategy can help the audience understand the gravity of the topic. The paper introduces a novel modeling framework for deriving an efficient risk mitigation strategy at the planning stage of a clinical trial and establishing operational rules (thresholds) under the assumption that contingency resources are limited. The problem is solved in two steps: (1) Deriving a contingency budget and its efficient allocation across risks to be mitigated and (2) Deriving operational rules to be aligned with risk assessment and contingency resources. This approach is based on combining optimization and simulation models. The optimization model aims to derive an efficient contingency budget and allocate limited mitigation resources across mitigated risks. The simulation model aims to efficiently position each risk's QTL/KRI (Quality Tolerance Limits/Key Risk Indicators at a clinical trial level) and Secondary Limit thresholds. A case study illustrates the proposed technique's practical application and effectiveness. This example demonstrates the framework's potential and instills confidence in its successful implementation, reassuring the audience of its practicality and usefulness. The paper is structured as follows. (1) Introduction; (2) Methodology; (3) Models-Risk Optimizer and Risk Simulator; (4) Case study; (5) Discussion and (6) Conclusion.

Bayesian Methods for Quality Tolerance Limit (QTL) Monitoring

In alignment with the ICH guideline for Good Clinical Practice [ICH E6(R2)], quality tolerance limit (QTL) monitoring has become a standard component of risk-based monitoring of clinical trials by sponsor companies. Parameters that are candidates for QTL monitoring are critical to participant safety and quality of trial results. Breaching the QTL of a given parameter could indicate systematic issues with the trial that could impact participant safety or compromise the reliability of trial results. Methods for QTL monitoring should detect potential QTL breaches as early as possible while limiting the rate of false alarms. Early detection allows for the implementation of remedial actions that can prevent a QTL breach at the end of the trial. We demonstrate that statistically based methods that account for the expected value and variability of the data generating process outperform simple methods based on fixed thresholds with respect to important operating characteristics. We also propose a Bayesian method for QTL monitoring and an extension that allows for the incorporation of partial information, demonstrating its potential to outperform frequentist methods originating from the statistical process control literature.

Quality Tolerance Limits: A General Guidance for Parameter Selection and Threshold Setting

The past years have sharpened the industry's understanding of a Quality by Design (QbD) approach toward clinical trials. Using QbD encourages designing quality into a trial during the planning phase. The identification of Critical to Quality (CtQs) factors and specifically Critical Data and Processes (CD&Ps) is key to such a risk-based monitoring approach. A variable that allows monitoring the evolution of risk regarding the CD&Ps is called a Quality Tolerance Limit (QTL) parameter. These parameters are linked to the scientific question(s) of a trial and may identify the issues that can jeopardize the integrity of trial endpoints. This paper focuses on defining what QTL parameters are and providing general guidance on setting thresholds for these parameters allowing for the derivation of an acceptable range of the risk.

An Overview of Current Statistical Methods for Implementing Quality Tolerance Limits

BACKGROUND

In 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced predefined quality tolerance limits (QTLs) as a quality control in clinical trials. QTLs are complementary to Quality by Design (QbD) principles (ICH-E8) and are one of the components of the risk-based clinical trial quality management system.

METHODS

Currently the framework for QTLs process is well established, extensively describing the operational aspects of Defining, Monitoring and Reporting, but a single source of commonly used methods to establish QTLs and secondary limits is lacking. This paper will primarily focus on closing this gap and include applications of statistical process control and Bayesian methods on commonly used study level quality parameters such as premature treatment discontinuation, study discontinuation and significant protocol deviations as examples.

CONCLUSIONS

Application of quality tolerance limits to parameters that correspond to critical to quality factors help identify systematic errors. Some situations pose special challenges to implementing QTLs and not all methods are optimal in every scenario. Early warning signals, in addition to QTL, are necessary to trigger actions to further minimize the possibility of an end-of-study excursion.

Quality Tolerance Limits' Place in the Quality Management System and Link to the Statistical Trial Design: Case Studies and Recommendations from Early Adopters

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.

Does Central Monitoring Lead to Higher Quality? An Analysis of Key Risk Indicator Outcomes

BACKGROUND

Central monitoring, which typically includes the use of key risk indicators (KRIs), aims at improving the quality of clinical research by pro-actively identifying and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. However, there has to-date been a relative lack of direct quantitative evidence published supporting the claim that central monitoring actually leads to improved quality.

MATERIAL AND METHODS

Nine commonly used KRIs were analyzed for evidence of quality improvement using data retrieved from a large central monitoring platform. A total of 212 studies comprising 1676 sites with KRI signals were used in the analysis, representing central monitoring activity from 23 different sponsor organizations. Two quality improvement metrics were assessed for each KRI, one based on a statistical score (p-value) and the other based on a KRI's observed value.

RESULTS

Both KRI quality metrics showed improvement in a vast majority of sites (82.9% for statistical score, 81.1% for observed KRI value). Additionally, the statistical score and the observed KRI values improved, respectively by 66.1% and 72.4% on average towards the study average for those sites showing improvement.

CONCLUSION

The results of this analysis provide clear quantitative evidence supporting the hypothesis that use of KRIs in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.