Waypoint is developing a cell therapy platform that utilizes spatial multi-omics and pooled screening to optimize the design and development of therapies for solid tumors. This approach enhances the accuracy and speed of therapy development by measuring both cell-autonomous and non-autonomous effects, addressing the limitations of traditional in vitro and in vivo methods.
Funding
$29M 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.
HVFounders
Product
Problem
Traditional cell therapy development for solid tumors faces a trade-off between fast but inaccurate in vitro approaches and high-quality but slow and expensive in vivo methods. Existing methods often fail to capture the complex cell-cell and cell-environment interactions crucial for effective therapy design. This limitation hinders the development of optimized cell therapies that can effectively target and eliminate solid tumors.
Solution
Waypoint is developing a cell therapy platform that leverages spatial multi-omics and high-throughput pooled screening to optimize the design and development of cell therapies for solid tumors. The platform enables deep phenotyping across a multitude of cell therapy designs, using spatial biology profiling to rank and select the most optimized candidates. By measuring both cell-autonomous and non-autonomous effects, Waypoint's approach provides a more comprehensive understanding of therapy efficacy compared to traditional methods. This allows for faster and more accurate identification of promising cell therapies with improved potential for clinical success.
Target Audience
The primary target audience includes pharmaceutical and biotechnology companies developing cell therapies for solid tumors, as well as academic research institutions focused on cancer immunotherapy.
Features
- High-throughput pooled screening for rapid evaluation of multiple cell therapy designs.
- Spatial multi-omics profiling to capture cell-cell and cell-environment interactions.
- In silico protein design for optimized cell therapy components.
- Computer vision-based analysis for deep phenotyping of cell therapies.
- Ranking and selection algorithms to identify the most promising cell therapy candidates.
- Iterative refinement process to further optimize cell therapy designs.