Operationalizing selection criteria for clinical trials in Alzheimer's disease: Biomarker and clinical considerations.

Alzheimer's disease (AD) staging criteria lack standardized, empirical description. Well-defined AD staging criteria are an important consideration in protocol design, influencing a more standardized inclusion/exclusion criteria and defining what constitutes meaningful differentiation among the stages. However, many trials are being designed on the basis of biomarker features and the two need to be coordinated. The Alzheimer's Association Research Roundtable (AARR) Spring 2021 meeting discussed the implementation of preclinical AD staging criteria, and provided recommendations for how they may best be incorporated into clinical trials research. Discussion also included what currently available tools for global clinical trials may best define populations in preclinical AD trials, and if are we able to differentiate preclinical from clinical stages of the disease. Well-defined AD staging criteria are key to improving early detection, diagnostics, clinical trial enrollment, and identifying statistically significant clinical changes, and researchers discussed how emerging blood biomarkers may help with more efficient screening in preclinical stages.

Data Quality Monitoring in Clinical Trials: Has It Been Worth It? An Evaluation and Prediction of the Future by All Stakeholders.

This paper summarizes the results of the CNS Summit Data Quality Monitoring Workgroup analysis of current data quality monitoring techniques used in central nervous system (CNS) clinical trials. Based on audience polls conducted at the CNS Summit 2014, the panel determined that current techniques used to monitor data and quality in clinical trials are broad, uncontrolled, and lack independent verification. The majority of those polled endorse the value of monitoring data. Case examples of current data quality methodology are presented and discussed. Perspectives of pharmaceutical companies and trial sites regarding data quality monitoring are presented. Potential future developments in CNS data quality monitoring are described. Increased utilization of biomarkers as objective outcomes and for patient selection is considered to be the most impactful development in data quality monitoring over the next 10 years. Additional future outcome measures and patient selection approaches are discussed.