The startup develops neuromorphic chips and biomorphic robots utilizing a novel spiking neural network model to enhance the accuracy of clinical outcomes in healthcare. By integrating advanced artificial intelligence into mobile robotics, the company addresses the need for precise and efficient diagnostic tools in medical settings.
Funding
$9.7M 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.
Founders
Product
Problem
Traditional methods of determining drug clinical efficacy rely on human clinical trials, which can be time-consuming, expensive, and may not always accurately predict outcomes across diverse patient populations. This creates a need for faster, more reliable, and cost-effective ways to assess drug efficacy before widespread human trials.
Solution
Demiurge Technologies offers a virtual patient platform that leverages AI and neural networks to predict the outcomes of clinical trials. Their technology creates clinically equivalent virtual patients that functionally reconstruct major diseases, enabling the prospective prediction of phase 2 and phase 3 clinical trial results. The platform has demonstrated a high degree of accuracy in predicting clinical trial outcomes across various therapeutic areas, including immuno-oncology, Alzheimer's disease, and COVID-19. By using virtual patients, the company aims to reduce the reliance on human trials, accelerate drug development, and improve the efficiency of clinical research.
Target Audience
The primary audience includes pharmaceutical companies, research institutions, and healthcare organizations seeking to improve the efficiency and accuracy of clinical trials and drug development.
Features
- Virtual patient models built using first principles of neural networks and causal pathophysiology.
- Prediction of clinical trial outcomes for all major diseases, excluding genetic and hematological disorders.
- Specialized virtual patient models for first-in-class targets, immuno-oncology, Alzheimer's disease and COVID-19.
- Validation through prospective predictions of phase 2 and phase 3 clinical trial results.
- COVID-19 model developed with zero prior clinical data, predicting clinical efficacy of vaccines and therapeutics.