For patients experiencing cognitive decline due to Alzheimer’s Disease (AD), choosing the most appropriate treatment course at the right time is of great importance. A key element to these decisions is the careful consideration of the available scientific evidence, particularly from randomized clinical trials (RCTs) such as the recent lecanemab trial. Translating RCT results into patient-level decisions, however, can be challenging. This is because trial results tell us about the outcomes of groups rather than individuals. A doctor must judge how similar their patient is to the groups studied in trials. For AD, where patients vary widely in clinical presentations and rates of cognitive decline, this may be a difficult task.
As a step towards more personalized decision-making, prescribing physicians may focus on specific patient characteristics that would affect the disease course and response to treatment, like demographics (e.g., sex, age, education) or genetic factors. In fact, subgroup analyses from some RCTs suggest that at least some drugs could differ in safety or efficacy based on these factors. Nevertheless, the main limitations of these types of results are that the group sizes are often small, increasing the risk of spurious findings. Furthermore, they do not consider the overall impact of many different factors simultaneously. This is where machine learning (ML) may close the gap between data and decision-making.
ML uses patterns found in large datasets to predict health outcomes and treatment response by considering many patient characteristics at once and, further, how they may interact. This underlying model can subsequently be used to form a digital twin for a patient, or the best possible copy of their characteristics and health status. We can use this twin to ask “what if” questions. For example, “If we prescribed this patient this drug at this time, what would be their most likely outcome six months from now?” Under the hood, an ML algorithm would utilize previously collected data, such as from RCTs, to locate potential twins and use their outcomes to formulate a response. This could give us a more pinpointed prediction of patient outcomes compared to subgroup analyses. Ideally, this targeted view on patients would help facilitate better care for AD patients.
Roy S. Zawadzki
The stage is set for digital twins to play a bigger role in clinical research and practice in AD: we have the methodology, the data, and, most importantly, a large unmet clinical need for new and more effective treatments. Digital twins can be integrated in a wide variety of contexts that can potentially save clinical trial costs, quicken the time until approval, and better utilize the treatments we already have for the patients that need them the most. For these reasons, biotech companies, academic researchers, and healthcare systems alike should be investigating how digital twins can help assist their particular goals.
To learn more about real-world opportunities and considerations surrounding digital twins, please check out my latest post on my Substack
– Roy S. Zawadzki, graduate trainee with Professor Daniel Gillen and supported by the TITAN T32 training grant
