Re-engineering Precision Therapeutics Through N-of-1 Trials

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The objective of this proposal is to develop, test, and implement an innovative technology platform for conducting N-of-1 trials that transforms precision therapeutics. Right now, clinicians are engaging in clinical encounters at which they are trying to determine the best therapy for individual patients. These encounters are likely to be unsuccessful. Clinicians rely on the best available evidence (e.g., results from phase III randomized clinical trials; RCTs) for recommending therapies to a patient. Yet, conventional, between-patient RCTs only provide estimates of the effect of therapies on the average patient in those trials. Individual patients, however, often respond differently than the average patient in the phase III RCTs, and thus, heterogeneity of therapy response plagues these clinical decisions every day. The most scientifically rigorous-- and potentially transformative--method for determining optimal therapy for apatient is a single-patient (N-of-1) trial. N-of-1 trials are multiple crossover trials, usually randomized, and often masked, conducted within a single patient, with data collected objectively, continuously, and in the real-world, for a sufficient time period to determine whether the therapy, compared to a placebo or other active therapy, is optimal for a particular patient. They also yield information on off-target actions, such as side-effects, so that a more complex picture can emerge about the overall benefits and harms of a therapy for an individual patient.Clinicians and patients do not routinely engage in this type of scientific endeavor because they lack the tools. In this proposal, we will create an electronic platform that will allow clinicians and/or patients to order and conduct a single-patient trial. We will then collect RCT data to be able to estimate the benefit (if any) from using this approach. To do so, we will conduct 3 experiments using our platform for 3 different health conditions, each of which has high public health burden, high heterogeneity of therapy response, and high priority for a precision therapeutics approach as determined by previously interviewed clinicians and patients. For each, we will randomize 60 patients to receive an N-of-1 trial, or to receive usual care. We will then test a pragmatic design that simulates how the platform will be implemented in clinical practice after its release date. The application will be embedded in the clinical workflow, with clinicians having the capability of referring and tracking their patients in N-of-1 trials directly through the electronic health record. In this experiment, we will randomize 200 patients to receive the pragmatic N-of-1 trial, or to receive usual care. We will thus be able to compare our new precision therapy approach to the way therapies are typically determined for a patient. This N-of-1 trials platform will also facilitate a paradigm-shifting approach to discovery of therapeutic response phenotypes, as we will build a public facing registry of N-of-1 protocols and tools and a repository of de-identified data from N-of-1 trials. This international database of N-of-1 trial results can then be mined to identify phenotypes categorized by treatment responsiveness.