Generating Patient Cohorts for Simulating Clinical Trials Using Whole Body Digital Twin Technology

Abstract

A Digital Twin clinical trial simulator identifies an intervention parameter in a treatment recommendation. The treatment recommendation comprises instructions for adjusting the intervention parameter to cause a target improvement. The Digital Twin clinical trial simulator generates a cohort of patients sensitive to the intervention. The Digital Twin clinical trial simulator separates the cohort of patients into a control cohort comprising a first subset of patients and a test cohort comprising a second subset of patients. The Digital Twin clinical trial simulator determines an effect of the treatment recommendation on the cohort of patients by inputting the instructions of the treatment recommendation to a patient-specific metabolic model for each patient of the test cohort and comparing the effect predicted by the patient-specific metabolic of each patient in the test cohort to representations of metabolic states of patients in the control cohort.

What is claimed is: 
     
         1 . A method comprising:
 identifying an intervention parameter in a treatment recommendation for causing a target improvement in metabolic state, the treatment recommendation comprising instructions for adjusting the intervention parameter to cause the target improvement;   generating, from a population of patients, a cohort of patients sensitive to the intervention parameter based on correlations between changes in the metabolic state of each patient of the population and adjustments to the intervention parameter, the sensitivity of a patient representing a likelihood that adjustments to the intervention parameter will affect the metabolic state of the patient;   separating the cohort of patients into a control cohort comprising a first subset of patients and a test cohort comprising a second subset of patients, wherein the treatment recommendation is input to a patient-specific metabolic model for each patient of the test cohort to predict an effect of the treatment recommendation on the patient; and   determining an effect of the treatment recommendation on the cohort of patients, the determination comprising:
 inputting the instructions of the treatment recommendation for adjusting the intervention parameter to a patient-specific metabolic model for each patient of the test cohort to predict an effect of the treatment recommendation on the patient; and 
 comparing the effect of the treatment recommendation predicted by the patient-specific metabolic of each patient in the test cohort to representations of metabolic states of patients in the control cohort. 
   
     
     
         2 . The method of  claim 1 , wherein generating the cohort of patients further comprises:
 accessing patient data for the population of patients, the patient data comprising labels describing the sensitivity of each patient of the population of patients to the intervention parameter; and   generating the cohort of patients based on patients sensitive to the intervention parameter in the treatment recommendation based on the accessed patient data.   
     
     
         3 . The method of  claim 2 , wherein assigning the label describing the sensitivity of a patient to the intervention parameter to the patient comprises:
 determining historical changes in a metabolic state of the patient caused by previous adjustments to the intervention parameter; and   assigning the patient to either a first subset of patients sensitive to the intervention parameter or a second subset of patients insensitive to the intervention parameter based the historical changes.   
     
     
         4 . The method of  claim 3 , further comprising:
 comparing the historical changes in the metabolic state to a threshold change; and   assigning the patient to either the first subset of patients or the second subset of patients based on the comparison.   
     
     
         5 . The method of  claim 1 , wherein generating the cohort of patients further comprises:
 identifying, from the population of patients, a subset of patients whose metabolic state is below a threshold metabolic state; and   generating the cohort of patients from the identified subset of the population of patients.   
     
     
         6 . The method of  claim 1 , wherein generating the cohort of patients further comprises:
 determining a long-term effect of adjustments to the intervention parameter on each patient of the population of patients based on historical changes in the metabolic state of the patient; and   generating the cohort of patients based on the long-term effect of adjustments to the intervention parameter determined for each patient of the population of patients.   
     
     
         7 . The method of  claim 1 , wherein the treatment recommendation comprises instructions or adjusting a plurality of intervention parameters and generating the cohort of patients comprises:
 for each patient of the population of patients,
 determining a sensitivity of each patient to each intervention parameter of the plurality; and 
 determining an overall sensitivity of the patient to the treatment recommendation based on the sensitivity of the patient to each intervention parameter of the plurality; and 
   generating the cohort of patients based on the overall sensitivity of each patient of the population of patients.   
     
     
         8 . The method of  claim 1 , wherein generating the cohort of patients comprises:
 categorizing the population of patients into categories of patients with a shared metabolic state;   for each category of patients,
 predicting an effect of the treatment recommendation on each patient of the category by inputting the treatment recommendation to a patient-specific metabolic model of the patient; and 
 determining an overall sensitivity of the category of patients to the treatment recommendation based on the predicted effect of the treatment recommendation on each patient of the category; and 
   determining a category of patients most sensitive to the treatment recommendation based on a comparison of the overall sensitivity of each category of patients; and   generating the cohort of patients based on the category of patients most sensitive to the treatment recommendation.   
     
     
         9 . The method of  claim 1 , further comprising:
 determining that the effect of the treatment recommendation on the cohort of patients satisfies a threshold improvement in a metabolic state of each patient of the cohort of patients; and   generating instructions for performing a physical experiment to validate the treatment recommendation.   
     
     
         10 . The method of  claim 1 , wherein determining the effect of the treatment recommendation on the cohort of patients further comprises:
 encoding a feature vector representation of the treatment recommendation; and   inputting the feature vector representation to the patient-specific metabolic model of each patient of the test cohort.   
     
     
         11 . A non-transitory computer readable medium storing instructions encoded thereon that, when executed by a processor, cause the one or more processors to:
 identify an intervention parameter in a treatment recommendation for causing a target improvement in metabolic state, the treatment recommendation comprising instructions for adjusting the intervention parameter to cause the target improvement;   generate, from a population of patients, a cohort of patients sensitive to the intervention parameter based on correlations between changes in the metabolic state of each patient of the population and adjustments to the intervention parameter, the sensitivity of a patient representing a likelihood that adjustments to the intervention parameter will affect the metabolic state of the patient;   separate the cohort of patients into a control cohort comprising a first subset of patients and a test cohort comprising a second subset of patients, wherein the treatment recommendation is input to a patient-specific metabolic model for each patient of the test cohort to predict an effect of the treatment recommendation on the patient; and   determine an effect of the treatment recommendation on the cohort of patients, the instructions for determining the effect of the treatment recommendation further comprise instructions that cause the processor to:
 input the instructions of the treatment recommendation for adjusting the intervention parameter to a patient-specific metabolic model for each patient of the test cohort to predict an effect of the treatment recommendation on the patient; and 
 compare the effect of the treatment recommendation predicted by the patient-specific metabolic of each patient in the test cohort to representations of metabolic states of patients in the control cohort. 
   
     
     
         12 . The non-transitory computer readable medium of  claim 11 , wherein instructions for generating the cohort of patients further comprise instructions that cause the processor to:
 access patient data for the population of patients, the patient data comprising labels describing the sensitivity of each patient of the population of patients to the intervention parameter; and   generate the cohort of patients based on patients sensitive to the intervention parameter in the treatment recommendation based on the accessed patient data.   
     
     
         13 . The non-transitory computer readable medium of  claim 12 , wherein assigning the label describing the sensitivity of a patient to the intervention parameter to the patient further comprise instructions that cause the processor to:
 determine historical changes in a metabolic state of the patient caused by previous adjustments to the intervention parameter; and   assign the patient to either a first subset of patients sensitive to the intervention parameter or a second subset of patients insensitive to the intervention parameter based the historical changes.   
     
     
         14 . The non-transitory computer readable medium of  claim 11 , wherein instructions for generating the cohort of patients further comprise instructions that cause the processor to:
 identify, from the population of patients, a subset of patients whose metabolic state is below a threshold metabolic state; and   generate the cohort of patients from the identified subset of the population of patients.   
     
     
         15 . The non-transitory computer readable medium of  claim 11 , wherein instructions for generating the cohort of patients further comprise instructions that cause the processor to:
 determine a long-term effect of adjustments to the intervention parameter on each patient of the population of patients based on historical changes in the metabolic state of the patient; and   generate the cohort of patients based on the long-term effect of adjustments to the intervention parameter determined for each patient of the population of patients.   
     
     
         16 . The non-transitory computer readable medium of  claim 11 , wherein the treatment recommendation comprises instructions or adjusting a plurality of intervention parameters and instructions for generating the cohort of patients further comprise instructions that cause the processor to:
 for each patient of the population of patients,
 determine a sensitivity of each patient to each intervention parameter of the plurality; and 
 determine an overall sensitivity of the patient to the treatment recommendation based on the sensitivity of the patient to each intervention parameter of the plurality; and 
   generate the cohort of patients based on the overall sensitivity of each patient of the population of patients.   
     
     
         17 . The non-transitory computer readable medium of  claim 11 , wherein instructions for generating the cohort of patients further comprise instructions that cause the processor to:
 categorize the population of patients into categories of patients with a shared metabolic state;   for each category of patients,
 predict an effect of the treatment recommendation on each patient of the category by inputting the treatment recommendation to a patient-specific metabolic model of the patient; and 
 determine an overall sensitivity of the category of patients to the treatment recommendation based on the predicted effect of the treatment recommendation on each patient of the category; and 
   determine a category of patients most sensitive to the treatment recommendation based on a comparison of the overall sensitivity of each category of patients; and   generate the cohort of patients based on the category of patients most sensitive to the treatment recommendation.   
     
     
         18 . The non-transitory computer readable medium of  claim 11 , wherein instructions for generating the cohort of patients further comprise instructions that cause the processor to:
 determine that the effect of the treatment recommendation on the cohort of patients satisfies a threshold improvement in a metabolic state of each patient of the cohort of patients; and   generate instructions for performing a physical experiment to validate the treatment recommendation.   
     
     
         19 . The non-transitory computer readable medium of  claim 11 , wherein instructions for determining the effect of the treatment recommendation on the cohort of patients further comprise instructions that cause the processor to:
 encode a feature vector representation of the treatment recommendation; and   input the feature vector representation to the patient-specific metabolic model of each patient of the test cohort.   
     
     
         20 . A system comprising:
 one or more processors; and   a non-transitory computer readable medium storing instructions encoded thereon that, when executed by the one or more processors, cause the one or more processors to:
 identify an intervention parameter in a treatment recommendation for causing a target improvement in metabolic state, the treatment recommendation comprising instructions for adjusting the intervention parameter to cause the target improvement; 
 generate, from a population of patients, a cohort of patients sensitive to the intervention parameter based on correlations between changes in the metabolic state of each patient of the population and adjustments to the intervention parameter, the sensitivity of a patient representing a likelihood that adjustments to the intervention parameter will affect the metabolic state of the patient; 
 separate the cohort of patients into a control cohort comprising a first subset of patients and a test cohort comprising a second subset of patients, wherein the treatment recommendation is input to a patient-specific metabolic model for each patient of the test cohort to predict an effect of the treatment recommendation on the patient; and 
 determine an effect of the treatment recommendation on the cohort of patients, the instructions for determining the effect of the treatment recommendation further comprise instructions that cause the processor to:
 input the instructions of the treatment recommendation for adjusting the intervention parameter to a patient-specific metabolic model for each patient of the test cohort to predict an effect of the treatment recommendation on the patient; and 
 compare the effect of the treatment recommendation predicted by the patient-specific metabolic of each patient in the test cohort to representations of metabolic states of patients in the control cohort.