Our vision is to improve patient care using integrated approaches involving laboratory models, mathematical models and machine-learning/Al methods to reduce time to bringing new therapies to market and to optimize treatments for patients after regulatory approval. Our tools include quantitative wet laboratory models made of human-tissue, non-linear dynamical systems, and non-linear mathematical models, which we wrote from scratch and coded, for drug discovery development. Our physician dominated team uses clinical and pathophysiologic know-how, the mathematics of topology and morphism mapping and category theory, to map disease response from the preclinical models to patients, so that readouts in our preclinical models will give precise rates of responses expected in patients, not just in terms of how many patients respond to therapy, but how fast, and the optimal duration of such therapy, and the doses to minimize failure and resistance to therapy. Thus, we can forecast clinical responses in early preclinical development, and then design adaptive clinical trials de novo, including the biomarkers of therapeutic response, duration of therapy, thereby de-risking time (decades), money (millions), and clinical trial patients’ lives (unquantifiable). We also design combination therapy regimens using factorial design to find best companion drugs and doses for therapeutics. Our motto summarizes it all. Quantify. Predict. Cure.
CHAIRMAN / CEO
Level 42 AI