SYMPHONY PREDICTIONS

SYMPHONY PREDICTIONS

Aria’s Symphony™ platform can, using interpretable disease-specific models, predict which drug candidates are likely to be efficacious. This allows our scientists to select and advance lead candidates that have the best likelihood of success. 

We are constantly testing and improving Symphony™. One way we’ve always done that is by “rediscovering” support for successful clinical trials completed by other companies. Now we’re taking that a step further and sharing our predictions for Phase 2 candidates that have yet to report trial results.

The list below represents candidates that our Symphony™ drug discovery platform predicts will achieve efficacy in their respective Phase 2 trials. Stay tuned for additional quarterly updates.

clinical trials PREDICTIONS

DISEASE

DRUG

MECHANISM OF ACTION

CLINICAL TRIAL

END DATE

CLINICAL TRIAL LINK

PREDICTION PUBLISHED DATE

Schizophrenia

Ulotaront

Trace Amine-Associated Receptor 1 & 5-hydroxytryptamine Type 1A Against

January 6, 2023

clinicaltrials.gov

December 23, 2022

Asthma

Tozorakimab

Anti-IL33 Antibody

February 6, 2023

clinicaltrials.gov

December 15, 2022

Psoriatic Arthritis

JNJ-77242113

IL-23 Receptor Antagonist

February 24, 2023

clinicaltrials.gov

December 15, 2022

Psoriasis

JNJ-77242113

IL-23 Receptor Antagonist

February 24, 2023

clinicaltrials.gov

December 15, 2022

Nonalcoholic Steatohepatitis (NASH)

HPG-1860

FXR Agonist

March 2023

clinicaltrials.gov

December 15, 2022

Amyotrophic Lateral Sclerosis (ALS)

Pridopidine 

Sigma-1 Receptor Agonist & D2 Receptor Antagonist

March 2023

clinicaltrials.gov

December 15, 2022

Idiopathic Pulmonary Fibrosis (IPF)

BMS-986263

Serpin H1 Inhibitor

September 2022

clinicaltrials.gov

August 31, 2022

Atopic Dermatitis

CMK-389

Anti-IL18 Antibody

October 6, 2022

clinicaltrials.gov

August 31, 2022

Nonalcoholic Steatohepatitis (NASH)

Efinopegdutide

GLP-1/Glucagon Receptor Dual Agonist

October 20, 2022

clinicaltrials.gov

August 31, 2022

Type-2 Diabetes

Retatrutide

GLP-1/Glucagon Receptor/GIPR Tri-agonist

October 29, 2022

clinicaltrials.gov

October 25, 2022

Epilepsy

Darigabat

GABAA Positive Allosteric Modulator

November 2022

clinicaltrials.gov

August 31, 2022

SYMPHONY™

Aria’s drug discovery engine.

Drug discovery is complex. Our technology decodes complex biology in an unprecedented manner, combining the widest biomedical data landscapes available with proprietary, purpose-built artificial intelligence. Symphony™ is the only AI drug discovery platform that integrates and simultaneously analyzes unrelated heterogenous data in one process. We see the whole picture, increasing our chance of fully understanding the biology and detecting a signal others can’t see.

PREDICTION METHODOLOGY

Background

Aria’s proprietary drug-discovery AI-platform, Symphony™, is at the center of the company’s prediction capabilities. Using Symphony™ and its unprecedented ability to simultaneously integrate and interrogate the widest biomedical data landscapes, Aria decodes a disease’s complex biology by building an interpretable in silico model. These disease-specific models accurately predict which drug candidates will be significantly efficacious in the disease.

Validating Symphony™ predictions with “rediscovery”

One of the prime ways Aria’s researchers verify the viability of the disease-specific models built on Symphony™ is how well those models blindly “rediscover” previously investigated treatments. That is, how well the models do at retrospectively identifying drug candidates that succeeded in clinical trials even when those outcomes are hidden from Symphony™.

The way this works is that for a given disease, Aria researchers identify all treatments that have completed Phase 2 or beyond; all those drugs are then divided into two groups: training and testing. Each group is carefully chosen to ensure no overlap between drug targets, receptors, or ligands. This careful splitting is important to avoid data leakage, and thereby ensures all retrospective analyses that follow are fully blinded.

The training group of treatments is used to finalize the disease-specific models Symphony™ produces. Aria researchers can then examine the efficacy predictions Symphony™ makes for every treatment in the testing group. Because all treatments in the testing group are unlabeled to Symphony™, this is an excellent and unbiased way to measure the quality of predictions.

Aria has recently completed this process for more than 30 disease-specific models, allowing us to complete a fully blinded retrospective analysis on more than 400 completed Phase 2 trials. The result is that more than 80 percent of the treatments Symphony™ predicted to be efficacious passed their Phase 2.

The AI at work: Making prospective predictions
Retrospectively identifying clinical successes is one thing, but we understand that prospective predictions are more compelling. That’s why Aria is using those same 30+ disease models to share prospective predictions on ongoing Phase 2 treatments. This will enable us to continue to validate and further improve our prediction capabilities. The prospective predictions we provide publicly are only from the diseases we have recently modeled (30+ and counting) and for candidates that Symphony™ predicts as having a high likelihood of efficacy. In addition, similar to our own discovery efforts our researchers have examined the treatments and the trial designs for every prospective prediction. In some cases, we see rationale for efficacy, but have questions or concerns – those cases and our concerns are listed here.