SBIR/STTR Research Award
Computational prediction of RNA splicing defects and rescue by small-molecule therapeutics
Award amount
$333,843Program and phase
SBIR · Phase IAward year
2023Award details
- Awarding agency
- Department of Health and Human Services
- Branch
- National Institutes of Health
- Recipient company
- Panorama Medicine Inc
- Proposal award date
- 2023-09-06
- Contract end date
- 2024-08-31
- UEI
- VE8NXFEDB9C8
- Solicitation number
- PA22-176
- Topic code
- 400
Research abstract
PROJECT SUMMARYPanorama has developed a groundbreaking platform that combines big data and genetic sequencing to
identify the RNA sequence of splice events, a cause of up to 50% of genetic diseases. These events are believed
to be the cause of diseases such as Becker muscular dystrophy, Duchenne muscular dystrophy (DMD), spinal
muscular atrophy (SMA), and amyotrophic lateral sclerosis (ALS). The platform, PANOPLY, predicts thousands
of potential disease-causing events. Preliminary data has shown the platform to have an 83% prediction
accuracy. PANOPLY, unlike previous platforms, predicts for tissue-specific events in 53 tissues. Again, unlike
other platforms, PANOPLY provides the likelihood of that event to occur. Panorama combines PANOPLY with a
proprietary screening platform to identify small molecules that correct the disease-causing event. In combination,
Panorama can use PANOPLY to predict disease-causing events and then identify potential therapeutic solutions,
creating a unique, fast-track, drug development mechanism that leaps past discovery to identify potential causes
and treatment candidates for pre-clinical trials. We propose this SBIR project to enhance the performance of
PANOPLY by tuning the algorithm to better predict more complex disease-causing events and to demonstrate
the innovation’s potential by then identifying small-molecule compounds that rescue PANOPLY predicted events.
Specific Aim 1: Improve high-throughput predictions from PANOPLY. This aim uses laboratory data to test and
improve the prediction algorithms. To do this, we will reproduce predicted events to assess the computational
power for more complex situations. We will then tune the algorithm, which uses hundreds of variables, to perform
at a higher level for these more computationally demanding circumstances. Milestone: PANOPLY will use the
data to tune for selection criteria to achieve a prediction rate of 40-50%. Specific Aim 2: Confirm the ability of
compounds to rescue mutant variants identified by PANOPLY. The second aim brings together PANOPLY and
the results of Panorama’s target-agnostic compound screening platform to serve as a drug discovery model. To
do this we will identify small molecules that rescue the disease-causing event, and then treat the disease-causing
event with those identified small molecules. Milestone: one or more compounds will rescue a clinically significant
splice event predicted by PANOPLY.
Technology keywords
Not reported
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