# Motion Sequencing for Neuropsychiatric Drug Development

> **NIH NIH R43** · SYLLABLE LIFE SCIENCES, INC. · 2020 · $432,586

## Abstract

SUMMARY
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.

## Key facts

- **NIH application ID:** 9981839
- **Project number:** 5R43MH121178-02
- **Recipient organization:** SYLLABLE LIFE SCIENCES, INC.
- **Principal Investigator:** John Dunlop
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $432,586
- **Award type:** 5
- **Project period:** 2019-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9981839

## Citation

> US National Institutes of Health, RePORTER application 9981839, Motion Sequencing for Neuropsychiatric Drug Development (5R43MH121178-02). Retrieved via AI Analytics 2026-07-26 from https://api.ai-analytics.org/grant/nih/9981839. Licensed CC0.

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