Our end-to-end discovery & development platform combines in silico screening for faster lead identification and more accurate in vitro pre-clinical testing on high-fidelity human models. Our workflow seamlessly integrates knowledge maps, machine learning and tissue engineering, so we are able to generate more accurate and predictive data that leads to better drug candidates.
The process begins with knowledge maps that provide a comprehensive understanding of disease biology and potential drug targets. Machine learning algorithms are then used to analyze large datasets and identify patterns that may be relevant to drug development. Tissue engineering is used to create human-relevant models for in vitro testing that provide more accurate results than traditional animal models. Data from each stage of the process is fed back into the machine learning models to improve accuracy and predictive power.
Our platform has already produced its first asset, a bioactive matrix that promotes wound healing. This promising result demonstrates the power of our approach and the potential for our platform to produce even more effective drug candidates. Our initial focus is on repurposing drugs for age-related diseases, a critical area for drug development given the growing aging population and the need for effective treatments for age-related conditions.