Systems and methods for full body circulation and drug concentration prediction

A method for predicting drug concentration levels as a function of time in one or more organs of a subject, comprising:

• receiving at least one subject characteristic of the subject;
• receiving at least one drug characteristic of a drug to be administered to the subject;
• executing a full body circulation engine using the at least one subject characteristic to determine a plurality of blood flow rates to the one or more organs of the subject;
• determining a first concentration of the drug in a first blood flow entering a first organ based on an initial drug dosage and the blood flow rate to the first organ;
• modifying a first drug concentration prediction model of the first organ using the at least one subject characteristic;
• determining a second concentration of the drug in the first organ using the modified first drug concentration prediction model;
• determining a third concentration of the drug in a second blood flow entering a second organ downstream of the first organ based on the second concentration and the blood flow rate to the second organ;
• modifying a second drug concentration prediction model of the second organ using the at least one subject characteristic;
• and
• determining a fourth concentration of the drug in the second organ using the modified second drug concentration prediction model.

A system for training a drug concentration prediction model, comprising:

• a training database storing a plurality of subject profiles, each subject profile including a known drug concentration and a corresponding at least one subject characteristic;
• and
• a machine learning engine including a plurality of drug concentration prediction models and at least one machine learning engine, the machine learning engine configured to:
• execute the plurality of drug concentration prediction models to generate a plurality of predicted drug concentrations;
• compare the plurality of predicted drug concentrations to a corresponding plurality of known drug concentrations to generate a comparison result;
• compare the comparison result to a comparison condition;
• responsive to the comparison result satisfying the comparison condition, output the plurality of drug concentration prediction models;
• and
• responsive to the comparison result not satisfying the comparison condition, adjust at least one parameter of the drug concentration prediction models.

A method for training a drug concentration prediction model, comprising:

• receiving a plurality of subject profiles, each subject profile including a known drug concentration and a corresponding at least one subject characteristic;
• executing, by a machine learning engine including a plurality of drug concentration prediction models and at least one machine learning engine, the plurality of drug concentration prediction models to generate a plurality of predicted drug concentrations;
• comparing the plurality of predicted drug concentrations to a corresponding plurality of known drug concentrations to generate a comparison result;
• comparing the comparison result to a comparison condition;
• responsive to the comparison result satisfying the comparison condition, outputting the plurality of drug concentration prediction models;
• and
• responsive to the comparison result not satisfying the comparison condition, adjusting at least one parameter of the drug concentration prediction models.