Matching patients with medical treatments
Mavatar offers a new way of matching patients with optimal treatments. Our diagnostic tool is designed to support clinicals in their daily work. As drug treatments grow increasingly complex, finding optimal treatment becomes more challenging.
Mavatar’s portfolio consists of different disease panels, including the most common and severe diseases.
We construct network models, or Digital Twins, from genomic data, phenotypic, and environmental factors relevant to disease mechanisms in individual patients. Patients are matched with optimal treatment by testing thousands of drugs on the patients’ Digital Twin computationally.
Diagnostic Tool for Drug Treatment
One of the most important health care problems is that a large number of patients do not respond to drug treatment. According to a report from the FDA, medication is deemed ineffective for 38-75 % of patients with common diseases.
This problem reflects the complexity of common diseases. These may involve altered interactions between thousands of genes, which differ between patients with the same diagnosis. There is a wide gap between this complexity and modern health care, in which diagnostics often relies on a limited number of unspecific diagnostic markers.
Digital twin concept
Digital twins is a concept from engineering, having been applied to complex systems such as airplane design or city planning. The key idea is to computationally model complex systems, in order to develop and test them more quickly and economically than in real life.
We apply this concept to personalize medicine, by constructing network models of all molecular, phenotypic, and environmental factors relevant to disease mechanisms in individual patients (digital twins).
Those twins are computationally treated with thousands of drugs to identify the optimal treatment for each patient.
The digital twin concept for personalized medicine
A) An individual patient with a local sign of disease (red).
B) A digital twin of the patient is constructed in unlimited copies, based on high-performance computational integration of thousands of disease-relevant variables.
C) Each twin is computationally treated with one or more of thousands of drugs. This results in digital cure of one twin (green).
D) The drug that has the best effect on the twin is selected for treatment of the patient.
