Innovative Alzheimer's disease clinical trial design in the coming age of biomarkers

Abstract

Alzheimer's disease (AD) is a field with huge unmet need and only a few symptomatic treatments with limited efficacy have been made available to patients. With the testing of disease-modifying drugs in recent years, the length of AD clinical trials has tripled and the enrollment has gone up drastically. These investigational disease-modifying drugs address new targets including the amyloid beta and tau protein aggregation pathways in the brain. They have opened up a whole research field on biomarkers specific to these pathways. These biomarkers have however never been used to select a subpopulation that would enroll in clinical trials. This thesis defines a framework for assessing any AD biomarker's quality as a selection tool for enrolling a subpopulation into an AD clinical trial. Carefully selecting the patient population with appropriate biomarkers can lead to a reduction in required enrollment in a study to show statistical significance. In turn, the decreased patient enrollment helps sponsors reduce costs and allows them to test several drugs with the same budget. In order to test our framework in an applied and relevant setting, we established from www.clinicaltrials.gov that for disease-modifying drugs the primary endpoint is change in ADAS-cog points at 18 months and that the trials enrolled on average 337 patients per treatment group. These disease-modifying AD trials use the inference on means statistical model.

(cont.) The standard deviation and the treatment effect of the primary endpoint variable (the change in ADAS-cog points at 18 months) are the main leverage factors that will influence the required enrollment (or sample size) in the trial. In a first step, we defined the baseline values for those main variables from published information on past or ongoing trials. Using that information, we conducted a theoretical exercise showing how much you needed to affect these variables in order to reduce enrollment by a factor of 5x, an important reduction in enrollment that could potentially realistically be achieved. In a second step, we looked in an applied setting at how well a selection of biomarkers in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database reduces the sample size by only selecting a sub population of the patients. Even with the limited data sample available on a preliminary basis from ADNI, we found that the biomarkers ABeta 1-42, ratio of Tau/ABeta 1-42, Apoe4 carriers on both genes, and average hippocampal volume show predictive power to identify change in ADAS-cog scores. When using criteria for these biomarkers to select a subpopulation we show that you can reduce the enrolled population by up to a factor 5.0x while decreasing your trial cost by up to 73% (corresponding to a $92M reduction out of $133M, the current Phase 3 costs of an 18 months diseases-modifying drug). Under the best scenario of these cost savings the sponsor can conduct pivotal trials for three drugs instead of only one.

(cont.) In a last step, the biomarker and combination of biomarkers associated with the enrollment benefit and the cost savings were assessed with additional criteria such as effect on restricted labeling, necessity for longitudinal screening or additional enrollment difficulties. Even after that analysis, several of the biomarkers stood out as very strong candidates to select for subpopulations in future disease-modifying trials and save costs. ADNI is an industry and NIH-sponsored initiative monitoring 800 normal, Mild Cognitive Impairment (MCI) and AD patients for up to three years with regular cognitive assessments and biomarker measurements.