insitro utilizes machine learning and high-throughput biology to create predictive in vitro human cell-derived disease models, enabling precise identification of therapeutic insights across various diseases. This approach addresses the challenges of high costs and inefficiencies in traditional drug discovery, facilitating faster and more effective development of new medicines.
Funding
$643.2M 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.
CIFounders
Product
Problem
Traditional drug discovery methods are costly and inefficient, often failing to accurately predict drug efficacy and safety in humans due to reliance on non-human models and limited understanding of disease biology. This leads to high failure rates in clinical trials and delays the availability of effective treatments for patients.
Solution
insitro leverages machine learning and high-throughput biology to build predictive, in vitro, human cell-derived disease models. These models enable the identification of therapeutic insights and interventions across a spectrum of diseases. By integrating in vitro cellular data with human clinical data, insitro's platform aims to redefine disease understanding and accelerate the development of new medicines. The platform precisely identifies therapeutic insights and interventions, improving the efficiency and effectiveness of drug discovery.
Target Audience
insitro's primary customers are pharmaceutical companies and research institutions seeking to improve the efficiency and success rate of drug discovery and development.
Features
- Machine learning-driven platform integrating in vitro cellular data with human clinical data
- Predictive in vitro human cell-derived disease models for accurate drug efficacy and safety prediction
- High-throughput biology for generating data at scale
- Identification of therapeutic insights and interventions across various diseases
- Pipeline of wholly-owned and partnered therapeutic programs in metabolism, oncology, and neuroscience