This drug development company uses AI and machine learning to identify and develop clinical candidates for neurodegenerative diseases. Their focus is on translating aging science research into therapies that can combat conditions like Alzheimer's and Parkinson's.
Funding
Funding not disclosed
Founders
Product
Problem
Current treatments for neurodegenerative diseases like Alzheimer's and Parkinson's often fail to address the underlying causes and have limited efficacy in slowing disease progression. Developing effective therapies is challenging due to the complexity of these diseases and the difficulty in identifying suitable drug targets.
Solution
Brenig Therapeutics is a drug development company focused on creating breakthrough medicines for neurodegenerative disorders. The company leverages an AI/ML-based discovery platform and biomarker-driven development to identify and develop clinical candidates. Their lead candidate, BT-267, is a small molecule LRRK2 inhibitor for Parkinson's disease currently in Phase 1 clinical trials. Brenig's approach aims to develop therapies that can modify the course of neurodegenerative diseases, including idiopathic cases, by targeting the root molecular causes.
Target Audience
The primary target audience includes patients suffering from neurodegenerative diseases, particularly Parkinson's disease, and their healthcare providers, as well as pharmaceutical companies seeking novel therapeutic candidates for licensing or acquisition.
Features
- AI/ML-based drug discovery platform for identifying novel drug candidates
- Focus on small molecule inhibitors with high kinome selectivity
- BT-267: A LRRK2 inhibitor with nanomolar potency against both LRRK2[WT] and the [G2019S] mutation
- Demonstrated 500-fold selectivity against other kinases and surpassing 1,000-fold selectivity against other safety panel targets
- Exhibits high and sustained brain exposure with minimal peripheral distribution
- Pristine safety profile confirmed through GLP-compliant studies and ongoing safety and PK clinical evaluations in healthy volunteers
- Advanced approach to predictive biomarkers to identify patients most likely to respond to treatment