ImpriMed utilizes artificial intelligence to analyze live cancer cells from dogs diagnosed with lymphoma, predicting their response to various anticancer drugs. This approach enables veterinarians and pet owners to select the most effective treatment options, potentially saving time and reducing costs associated with ineffective therapies.
Funding
$34.7M 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.
KD+1Founders
Product
Problem
Veterinarians treating dogs with lymphoma face challenges in predicting individual patient responses to various anticancer drugs, leading to potentially ineffective treatments and increased costs. Traditional methods often rely on generalized treatment protocols without considering the unique characteristics of each dog's cancer cells.
Solution
ImpriMed offers an AI-driven platform that analyzes live cancer cells from dogs diagnosed with lymphoma or leukemia to predict their response to specific anticancer drugs. By testing the effectiveness of anticancer therapy drugs directly on a dog’s live cancer cells, ImpriMed generates a personalized prediction profile. This profile combines measurements with biological information using artificial intelligence to predict which drugs are most likely to be effective for an individual dog's lymphoma or leukemia. The platform provides oncologists with drug response predictions, immune subtyping, and clonality analysis, enabling informed treatment decisions from the outset.
Target Audience
The primary customers are veterinary oncologists seeking to improve treatment outcomes for dogs with lymphoma and leukemia through personalized medicine.
Features
- AI-driven analysis of live cancer cells to predict drug response
- Personalized prediction profiles tailored to individual canine lymphoma and leukemia patients
- Comprehensive drug prediction profile combining quantitative high-throughput lab testing with AI
- Ex vivo drug sensitivity assay measuring live tumor cell response to anticancer drugs
- Machine learning algorithms trained by real-world clinical outcomes
- Identification of T- or B-cell cancer and likely drug responses
- Graphs comparing average drug responses to individual patient responses