Panorama 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 innovations 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 Panoramas 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.
Public Health Relevance Statement: PROJECT NARRATIVE Genetic diseases have few commercial therapeutics, and a significant cause of these diseases occurs when RNA creates deviant proteins. Panoramas innovation is a platform that predicts where in the RNA these deviations occur and couples that information with a screening platform to identify molecules that revert these deviations back to their intended function. The combination of the innovation with the screening platform bypasses the lengthy and expensive work involved with identifying cause and treatment of disease to provide candidate causes and candidate treatments for preclinical trials, significantly reducing the time and cost to achieve treatment for patient populations.
Project Terms: Algorithms; Amyotrophic Lateral Sclerosis Motor Neuron Disease; Gehrig's Disease; Lou Gehrig Disease; Amyotrophic Lateral Sclerosis; Back; Dorsum; Cell Line; CellLine; Strains Cell Lines; cultured cell line; Cells; Cell Body; Classification; Systematics; Couples; Disease; Disorder; Pharmaceutical Preparations; Drugs; Medication; Pharmaceutic Preparations; drug/agent; Duchenne muscular dystrophy; Duchene; Duchenne; Duchenne-Griesinger syndrome; Ellis-van Creveld (EvC) syndrome; Pseudohypertrophic Muscular Dystrophy; X-linked dilated cardiomyopathy; X-linked muscular dystrophy; X-linked recessive muscular dystrophy; benign X-linked recessive muscular dystrophy; childhood pseudohypertrophic muscular dystrophy; classic X-linked recessive muscular dystrophy; mild X-linked recessive muscular dystrophy; progressive muscular dystrophy of childhood; pseudohypertrophic adult muscular dystrophy; pseudohypertrophic muscular paralysis; Exons; Frustration; Gene Expression Regulation; Gene Action Regulation; Gene Regulation; Gene Regulation Process; Genes; Household; Laboratories; Spinal Muscular Atrophy; Aran-Duchenne disease; Cruveilhier disease; Nucleotides; Pathology; Plasmids; Proteins; Quality of life; QOL; Research; RNA; Non-Polyadenylated RNA; RNA Gene Products; Ribonucleic Acid; RNA Splicing; Splicing; Messenger RNA; mRNA; Specificity; Testing; Time; Tissues; Body Tissues; Transfection; Work; Drug Costs; Treatment Cost; DNA Sequence; RNA Sequences; Disease Costs; Sickness Cost; Cost of Illness; improved; Left; Site; Phase; Variation; Variant; Training; Individual; Data Bases; data base; Databases; Selection Criteria; COX deficiency; Complex IV deficiency; Cytochrome Oxidase Deficiency; Cytochrome-c Oxidase Deficiency; Therapeutic; Genetic; Reporter; Hour; Complex; Event; Pattern; preference; mutant; Performance; Protein Isoforms; Isoforms; success; deviant; deviancy; Bypass; Modeling; drug development; high throughput screening; High Throughput Assay; 3' Splice Site; Splice Acceptor Sites; Becker Muscular Dystrophy; Becker dystrophy; Becker pseudohypertrophic muscular dystrophy; Becker type progressive muscular dystrophy; Becker-Kiener muscular dystrophy; Duchenne/Becker muscular dystrophy; drug discovery; small molecule; Defect; Data; Harvest; Small Business Innovation Research Grant; SBIR; Small Business Innovation Research; Tissue-Specific Gene Expression; Differential Gene Expression; Tissue-Specific Differential Gene Expression; Tissue-Specific Splicing; Tissue Differentiation; Transcript; Process; cost; Gene variant; allele variant; allelic variant; genomic variant; genetic variant; innovate; innovative; innovation; Heritability; clinical relevance; clinically relevant; clinical significance; clinically significant; patient population; exon skipping; screenings; screening; BigData; Big Data; Hep G2; HepG2 cell line; HepG2; pre-clinical trial; preclinical trial; small molecule therapeutics; GTEx; Genotype-Tissue Expression Project; predictive algorithm; predictor algorithm; prediction algorithm; genetic condition; genetic disorder; Genetic Diseases; deep learning based neural network; deep learning neural network; deep neural net; deep neural network
