CPC G16H 20/10 (2018.01) [G16B 5/00 (2019.02)] | 30 Claims |
29. A method of screening a potential subject for enrollment in a clinical trial testing safety or efficacy, or both, of a candidate anti-inflammatory agent for atherosclerotic cardiovascular disease, the method comprising:
receiving a first input of non-invasively obtained imaging data related to a plaque from a set of test subjects;
receiving a second input of molecular expression data from the set of test subjects;
creating a training set comprising the first input and the second input;
training a neural network using the training set, the neural network configured to predict molecule levels based on the non-invasively obtained imaging data from the set of test subjects;
receiving non-invasively obtained imaging data related to a plaque from the potential subject;
generating virtual 'omics data that include predicted molecule levels of the potential subject, by applying the neural network to the non-invasively obtained imaging data from the potential subject;
providing the virtual comics data to a systems biology model of atherosclerotic cardiovascular disease to generate a subject-specific systems biology model, wherein
(i) the systems biology model represents a plurality of pathways associated with atherosclerotic cardiovascular disease,
(ii) each pathway in the plurality of pathways corresponds to one or more of an IL-1, IL1β, TNF, IL12/23, IL17, or other cytokine molecule,
(iii) the systems biology model includes a disease-associated molecule level for each molecule in the systems biology model, and
(iv) the subject-specific systems biology model includes predicted molecule levels that are updated from the disease-associated molecule level;
updating the subject-specific systems biology model with predicted molecular levels derived from information relating to an effect on inflammation by a candidate anti-inflammatory agent based on a known mechanism of action of the candidate anti-inflammatory agent; and
simulating a therapeutic response by the potential subject to the candidate anti-inflammatory agent in the updated subject-specific systems biology model to obtain a simulated therapeutic effect, wherein the simulated therapeutic effect is based on change in the predicted molecule levels in the updated subject-specific systems biology model with and without the candidate anti-inflammatory agent.
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