A privately held biotech company using AI/machine learning/Big Data to develop novel LNPs and LNP formulations for particular payloads instead of using a traditional “trial and error” approach. The client’s core expertise was in informatics, and it was also building a team of chemists to synthesize and bioengineers/robots to screen formulations in a wet lab.
The company engaged Alacrita to analyze which applications/RNA payloads to focus its efforts on. This encompassed existing products or projects where a superior LNP may result in significant enhancements as well as opportunities to rescue failed/failing projects.
They asked Alacrita to:
- Identify potential LNP players that might benefit from a partnership on AI-improvements to their existing LNP technology
- Gain a better understanding of what other LNP companies are currently focused on
- Define payloads for the client to prioritize and companies to partner with.
Using publicly available information, proprietary databases and our internal knowledge of the RNA therapeutics arena, we assembled a market landscape of RNA therapeutics projects, especially the subset using LNPs for delivery, aiming to be as comprehensive as possible for commercial projects and selective for academic projects, where information availability is more sparse.
We prepared a database specifying:
- Company or academic institution name
- Country of company or academic headquarters
- Project details:
- Clinical indication(s)
- Stage of development
- LNP technology used (as far as is available in the public domain)
Our deliverable included recommendations and rationales for priority payloads and companies to approach for potential partnering.Back