Researchers Submit Patent Application, “Generating Patient Cohorts for Simulating Clinical Trials Using Whole Body Digital Twin Technology”, for Approval (USPTO 20230112187): Patent Application

2023 MAY 03 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventors Banerjee, Abhik (Milpitas, CA, US); Mohammed, Jahangir (Mountain View, CA, US); Poon, Terrence Chun Yin (Foster City, CA, US); Wilson, James (Sunnyvale, CA, US), filed on October 11, 2022, was made available online on April 13, 2023.

No assignee for this patent application has been made.

News editors obtained the following quote from the background information supplied by the inventors: “

“Field of Art

“The disclosure relates generally to a patient health management platform, and more specifically, to a patient health management platform for simulating clinical trials for candidate metabolic treatment recommendations using digital representations of metabolic states for population of patients.

“Description of the Related Art

“Conventional medicine relies on clinical trials to validate medical treatments. Because such clinical trials are often expensive, time-intensive, and labor-intensive, the number of clinical trials that can be run during a given time period is limited. This is particularly challenging for lifestyle interventions such as improvements in nutrition, physical activity, sleep, and meditative breathing, because each intervention is highly complex with an extremely large number of possible treatments (e.g., a large number of possible nutrition plans based on a combination of many different foods, quantities, timings, etc.). Further complicating the problem, attempts to personalize these nutrition plans are inhibited by an insufficient amount of data or by an insufficient number of similar patients to perform a trial that would yield a significant result. Due to these issues, the results of clinical trials are often averaged across the trial population instead of being tailored towards individual patients. Further, the effectiveness of the trial may be affected by variations in physiological and medical conditions across the tested populations.

“Additionally, given the amount of time that lifestyle interventions take to affect the metabolism of a patient, clinical trials may take years and an excess of financial funding. As a result, traditional approaches to validating medical treatments often result in effective lifestyle treatments being disregarded because of the high resource cost for completing a clinical trial.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “A Digital Twin clinical trial simulator simulates various aspects of clinical trials using digital models of individuals that each capture the biology of the individual’s body (e.g., a whole body digital twin or WBDT). The models are generated using an array of inputs, such as biomarkers from sensors (e.g., wearable sensors), parameters taken from laboratory or other testing (e.g., blood tests), symptoms and other information reported by a user, medications reported to be consumed by the user, etc., and that outputs. Using these digital models that act as representatives or twins of individuals, the Digital Twin clinical trial simulator effectively has a population of patients for whom it can perform various clinical trial simulations with a substantial savings in time, expense, and labor relative to what is typically required with conventional clinical trials where live tests are performed on actual patients. The Digital Twin clinical trial simulator can test a large number of scenarios without the negative consequences to patients that clinical trials sometimes entail. In addition, it allows for analysis across more controlled populations and has access to a larger population of patients and substantially more data than is possible in a conventional clinical trial, ultimately providing a more accurate and tailored result.

“In one embodiment, the Digital Twin clinical trial stimulator generates a pool of candidate treatments for effecting a target improvement in health (e.g., metabolic health). Each candidate treatment provides instructions for adjusting a distinct combination of one or more intervention parameters. Intervention parameters refer to the various aspects of patient data known to affect metabolic health, for example micronutrients, macronutrients, biota nutrients, lifestyle data, physical activity routines, and sleep habits.

“For each candidate treatment, the Digital Twin clinical trial simulator identifies a cohort of sensitive patients based on a likelihood that the candidate treatment will affect the patient’s metabolic health. Described differently, patients in the identified cohort are determined to have the strongest correlation between the intervention parameter(s) adjusted by the candidate treatment and their own metabolic health.

“The Digital Twin clinical trial simulator inputs a feature vector representation of each candidate treatment recommendation to patient-specific metabolic models of each patient in the cohort to generate a prediction of whether the candidate treatment will affect the metabolic health of the patient to achieve the target improvement. Accordingly, the Digital Twin clinical trial simulator predicts the efficacy of each candidate treatment. The Digital Twin clinical trial simulator may additionally identify new intervention parameters or new features of metabolic health by extracting novel correlations between the metabolic profiles of patients in the identified cohort and intervention parameters identified in effective or ineffective candidate treatments. The Digital Twin clinical trial simulator may additionally identify features of metabolic health where the Digital Twin clinical trial simulator lacks sufficient data for patient-specific metabolic models to generate accurate predictions. For such features, the Digital Twin clinical trial simulator may supplement the data with synthetically generated data and validate the candidate treatment using the supplemented data.

“Based on the predicted effectiveness of each candidate treatment, the Digital Twin clinical trial simulator identifies a shortlist of the most effective candidate treatments for further evaluation by physical experiments. The shortlist of candidate treatments may additionally be generated based on the accuracy of the predictions and the confidence intervals of the predictions. The Digital Twin clinical trial simulator may additionally define instructions/procedures and additional insight for performing the physical experiments.

“In one embodiment, the clinical trial simulator identifies an intervention parameter in a treatment recommendation for causing a target improvement in metabolic state. The treatment recommendation comprises instructions for adjusting the intervention parameter to cause the target improvement. From a population of patients, the clinical trial simulator generates 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 represents a likelihood that adjustments to the intervention parameter will affect the metabolic state of the patient. The 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 clinical trial simulator determines an effect of the treatment recommendation on the cohort of patients by 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. The clinical trial simulator further compares 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.

“The figures depict various embodiments of the presented invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein.”

The claims supplied by the inventors are:

“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.”

There are additional claims. Please visit full patent to read further.

For additional information on this patent application, see: Banerjee, Abhik; Mohammed, Jahangir; Poon, Terrence Chun Yin; Wilson, James. Generating Patient Cohorts for Simulating Clinical Trials Using Whole Body Digital Twin Technology. U.S. Patent Application Number 20230112187, filed October 11, 2022 and posted April 13, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230112187)&db=US-PGPUB&type=ids

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