Ignota Labs focuses on rescuing promising but abandoned drug candidates that failed clinical trials due to safety issues. The company utilizes its proprietary AI platform, SAFEPATH, which combines deep learning with bioinformatics and cheminformatics data to understand and mitigate drug toxicity mechanisms. This approach allows Ignota Labs to rapidly advance viable assets through development, offering new therapeutic options to patients faster than de novo discovery.
Funding
$830K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
A significant percentage of drugs fail during clinical trials due to unforeseen safety issues, leading to promising therapeutic candidates being shelved and never reaching patients in need. Identifying and mitigating potential toxicities early in the drug development process is challenging, often requiring a complex understanding of chemistry, biology, and human physiology.
Solution
Ignota Labs is focused on rescuing promising drugs that have failed due to safety concerns by leveraging its proprietary AI model, SAFEPATH©. SAFEPATH© analyzes extensive bioinformatics and cheminformatics data to pinpoint the mechanisms of drug toxicity and predict their effects on human health. This allows Ignota Labs to revive shelved drug candidates by addressing their safety liabilities while preserving their therapeutic potential. The company aims to accelerate the development of these de-risked assets, bringing them to clinical trials and ultimately to patients faster than traditional drug discovery approaches. By focusing on drugs that have already demonstrated therapeutic promise, Ignota Labs can build a robust pipeline of matured assets more efficiently.
Target Audience
Ignota Labs' primary target audience includes pharmaceutical companies, research institutions, and investors interested in de-risked drug development opportunities and novel approaches to drug safety.
Features
- SAFEPATH©: A proprietary AI model that applies deep learning to bioinformatics and cheminformatics datasets.
- Toxicity Mechanism Identification: SAFEPATH© identifies the underlying mechanisms of drug-induced toxicities.
- Toxicity Prediction: The AI model predicts the effects of drugs on the human body, balancing toxicity with therapeutic effectiveness.
- Drug Repurposing: Focuses on reviving drugs that have failed in preclinical, Phase 1, or Phase 2 trials due to safety issues.
- Target Identification: Identifies promising therapeutic targets from historically failed clinical trials and internal projects.