Neuropsychiatric disorders afflict more than 20% of the global population, resulting enormous personal and societal burdens, including trillions of dollars in total costs. Despite its prevalence and widespread impact, the development of drugs to treat neuropsychiatric diseases significantly lags behind other disease areas. This gap is due primarily to the fact that so few candidate drugs ever make it to the clinic; the average lag time for those that do make it is 13 years, further exacerbating the problem. The success rate for psychiatric drugs is historically low, even as financial investments in the area rise. Developing safe and effective drugs for neuropsychiatric disorders is inherently difficult, as it relies on characterizing the behavioral phenotypes of animal models. Current approaches to this are low-throughput, unreliable, expensive, and minimally informative. Most methods attempt to reduce complex behaviors that depend upon many neural circuits into one or a few quantifiable metrics, which are then used to predict how the candidates will impact the even more complex human nervous system. Syllable Life Sciences was founded on the vision of improving the way we measure and interpret changes in the behavior in the lab to improve pre-clinical drug development. To address this challenge, we have developed a behavioral analysis platform called Motion Sequencing (MoSeq). MoSeq combines machine vision and unsupervised machine learning techniques to objectively identify a set of stereotyped three-dimensional behavioral motifs (rears, turns, head-bobs, runs, pauses, etc.) that encapsulates all the spontaneous actions of mice within a particular experiment. In addition to revealing which motif (termed a ?behavioral syllable?) is expressed at each moment, MoSeq identifies the statistics that govern how syllables transition from one to anther over time (?behavioral grammar?). Using MoSeq, therefore, we can comprehensively and quantitatively profile rodent behavior. Our previous work demonstrates that MoSeq directly reflects ongoing brain activity in psychiatry- relevant brain circuits, and that it may significantly outperform more standard methods of phenotyping drug effects in mice. In this SBIR Phase I project, we propose to extend the capabilities of MoSeq and explicitly demonstrate its translational value. Specifically, in Aim 1 we will expand the purview of MoSeq to include neuropsychiatry-relevant circuits;
in Aim 2 we will build a behavioral space that describes relationships among drugs spanning the current psychopharmacopeia; and in Aim 3 we will demonstrate the clinical utility of MoSeq by using it to predict clinical trial outcomes. This project will lay essential groundwork for revolutionizing the preclinical pipeline for neuro- and psychotherapeutics.
Syllable Life Sciences is committed to addressing key preclinical bottlenecks that hinder neuropsychiatric drug development. Key to this is to develop more efficient and accurate ways to identify and measure drug impact on behavior in model systems. Motion Sequencing (MoSeq) integrates state-of-the-art 3D cameras and novel machine learning techniques to track changes in rodent behavior in response to drug, and it does so with unprecedented levels of accuracy and temporal resolution. The current proposal details plans to expand the capabilities of MoSeq and explicitly demonstrate its translational value for psychiatric drug development.
