Drug Development in Alzheimer’s Disease Trials
Background
In Alzheimer’s Disease (AD) trials, we use clinical scales to measure how well treatments work. To improve the accuracy of trial results, we can use machine learning to create AI-generated digital twins. These digital twins give us personalized predictions of how a patient’s condition might change if they received a placebo. By including these predictions in trial designs, we can either increase the statistical power or reduce the number of participants needed in Phase 2 and 3 trials.
Method
In a double-blind Phase 2 trial called AWARE, 453 patients aged 55-85 with early AD participated. They were randomly assigned to receive either a placebo or one of three doses of tilavonemab over 96 weeks. We calculated prognostic scores to measure changes in cognitive function using two scales: the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) and the AD Assessment Scale-Cognitive Subscale 14 (ADAS-Cog 14). We assessed how much we could reduce the number of participants needed by examining the relationship between these scores and trial outcomes.
Results
At Week 96, the variability in prognostic scores was lower than the variability in trial outcomes. The correlation between the prognostic scores and the trial outcomes was moderate, indicating a good relationship. By using the digital twin methodology, we could reduce the total variability in outcomes by about 11% compared to traditional methods.
Conclusion
Using the digital twin methodology could potentially reduce the sample size needed for trials by 5-10% while still maintaining strong statistical power. This reduction could shorten recruitment times and decrease the number of patients receiving a placebo, which may lead to higher patient participation rates.
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