In our mission to deliver a fast, precise, and lethal strike at a cancer’s unique drivers, we start with a clear target and product profile in mind:
Science & Platform
Finding novel ways to selectively target and strike against cancer
Driven by advances in cancer biology, medicinal chemistry and data sciences over the last two decades, the field of cancer therapy has entered a new era of more targeted, genetic-based approaches.
Pioneering the next generation of precision oncology
Through our platform, we are developing a diversified and deep pipeline of optimized drug-discovery programs at unparalleled speed and scale compared to existing benchmarks.
Through our platform, we are developing a diversified and deep pipeline of optimized drug-discovery programs at unparalleled speed and scale compared to existing benchmarks.
Our drug hunting platform enables us to draw unique insights from proprietary biological and chemistry datasets to deliver precision therapeutic solutions to patients with cancer that currently do not benefit from precision oncology approaches.
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Our Drug Hunting Platform
Science
Chemistry
Data
- Can selective target inhibition improve patients’ survival or quality of life beyond what’s possible with the current standard treatment?
- Can the target be modulated to achieve therapeutic effects without causing significant toxicity?
- Can inhibiting the target offer benefits as part of combination therapy?
We carefully select high-value targets that are present in specific patient populations and those underserved by current treatments. Then, our scientists get to work on how to achieve that profile.
We select targets where there is a clear opportunity for a precision medicine strategy to address the “therapeutic white spaces” of underserved populations. Because these targets are selected based on a mechanistic understanding of why specific patients are most likely to respond to our clinical candidates, we can generate proof-of-concept data in the earliest stages of the clinical development process.
Our product candidates aim to address three target categories in what we consider “therapeutic white spaces”:
- Best-in-class molecules targeting validated oncogene targets
- First-in-class molecules for previously undruggable targets
- First-in-class molecules for novel cancer targets
