Mosaic Therapeutics develops targeted drug combinations for cancer treatment by utilizing genome-scale CRISPR screening and advanced computational models to identify effective therapies based on the molecular characteristics of tumors. The company addresses the challenge of cancer's complexity and heterogeneity, aiming to improve patient outcomes through precision medicine in biomarker-defined settings.
Funding
$39.2M 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.
Founders
Product
Problem
Cancer's complexity and heterogeneity across organs, tumor subtypes, and molecular signatures pose a significant challenge to effective treatment. The traditional "one size fits all" approach often fails to address the unique characteristics of individual tumors, leading to suboptimal patient outcomes and the development of resistance.
Solution
Mosaic Therapeutics is developing targeted drug combinations for cancer by integrating experimental and computational biology. The company utilizes genome-scale CRISPR screening and advanced computational models to identify effective therapies based on the molecular characteristics of tumors. By studying diverse cancer models derived from patient biopsies and employing a systematic approach to data aggregation and analysis, Mosaic aims to redefine cancer therapy through molecularly-guided stratification of precision medicines, providing more effective and less toxic therapies for patients. Their approach enables the identification of novel drug combinations and stratification biomarkers, accelerating development timelines and improving the probability of clinical success.
Target Audience
Mosaic Therapeutics' primary customers are cancer patients and the healthcare providers who treat them, specifically those seeking more effective and less toxic therapies through precision medicine approaches.
Features
- Genome-scale CRISPR screening to identify novel drug targets and combination therapies
- A biobank of diverse cancer models derived from patient biopsy samples
- Combination drug screening across a large number of deeply-characterized tumor models
- Proprietary data integrated into an industry-validated computational pipeline
- Advanced statistical and machine learning models to identify drug combinations and stratification biomarkers
- Identification of novel segments based on the molecular characteristics of a patient’s tumor