Non-invasive determination of likely response to anti-inflammatory therapies for cardiovascular disease

A method of identifying one or more contraindications associated with an anti-inflammatory therapy for a patient diagnosed with 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 patient;
• generating virtual comics data that include predicted molecule levels of the patient, by applying the neural network to the non-invasively obtained imaging data from the patient;
• providing the virtual comics data to a systems biology model of atherosclerotic cardiovascular disease to generate a patient-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 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 patient-specific systems biology model includes predicted molecule levels that are updated from the disease-associated molecule level;
• updating the patient-specific systems biology model with information relating to an effect on inflammation by an anti-inflammatory agent based on a known mechanism of action of the anti-inflammatory agent;
• simulating a therapeutic response by the patient to the anti-inflammatory agent in the updated patient-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 patient-specific systems biology model with and without the anti-inflammatory agent;
• and
• identifying one or more contraindications associated with the anti-inflammatory agent based on the simulated therapeutic effect.

A method of screening 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 first set of test subjects;
• receiving a second input of molecular expression data from the first 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 first set of test subjects;
• receiving non-invasively obtained imaging data related to a plaque from each of a second set of test subjects who have been diagnosed with atherosclerotic cardiovascular disease;
• for each test subject of the second set of test subjects:
• generating virtual comics data that include predicted molecule levels of a respective test subject, by applying the neural network to the non-invasively obtained imaging data from the respective test subject;
• providing the virtual comics data to a systems biology model of atherosclerotic cardiovascular disease to generate a test subject-specific systems biology model for the respective test subject, wherein
• (i) the systems biology model represents a plurality of pathways associated with atherosclerotic cardiovascular disease,
• (ii) the plurality of pathways include one or more pathways corresponding to potential targets of the candidate anti-inflammatory agent,
• (iii) the systems biology model includes a disease-associated molecule level for each molecule in the systems biology model, and
• (iv) the test subject-specific systems biology model includes predicted molecule levels that are updated from the disease-associated molecule level;
• updating the test subject-specific systems biology model with 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 respective test subject to the candidate anti-inflammatory agent in the updated test 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 test subject-specific systems biology model with and without the candidate anti-inflammatory agent.

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.