“Simulating Clinical Trials Using Whole Body Digital Twin Technology” in Patent Application Approval Process (USPTO 20230111605): Patent Application

2023 MAY 02 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- 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, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application has not been assigned to a company or institution.

The following quote was obtained by the news editors 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.”

In addition to the background information obtained for this patent application, NewsRx journalists 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 generates a plurality of candidate treatment recommendations for causing a target improvement in metabolic state. Each candidate treatment recommendation comprises one or more intervention parameters and instructions for adjusting the one or more intervention parameters to cause the target improvement. For each of the candidate treatment recommendations, the Digital Twin clinical trial simulator identifies a cohort of patients from a population of patients. Each patient of the cohort is sensitive to adjustments to the one or more intervention parameters. For each patient of the cohort of patients, the Digital Twin clinical trial simulator predicts effects of the candidate treatment recommendation on a metabolic state of the patient by inputting the candidate treatment recommendation to a digital twin of the patient. The Digital Twin clinical trial simulator identifies a shortlist of one or more effective treatments based on the effects predicted by the plurality of patient-specific metabolic models of the digital twin for the pin. The Digital Twin clinical trial simulator displays the shortlist of candidate treatment recommendations to a patient or medical provider via an application on a computing device.

“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: generating a plurality of candidate treatment recommendations for causing a target improvement in metabolic state, wherein each of the candidate treatment recommendations comprises an intervention parameter and instructions for adjusting the intervention parameter to cause the target improvement; for each of the candidate treatment recommendations, generating, from a population of patients, a cohort of patients sensitive to 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; for each patient of the cohort of patients, predicting effects of the candidate treatment recommendation on a metabolic state of the patient by inputting the candidate treatment recommendation to a digital twin of the patient, the digital twin comprising a plurality of patient-specific metabolic models trained to predict effects of candidate treatment recommendations on metabolic states based on a training dataset of previously adjusted intervention parameters and effects each previously adjusted intervention parameter on the metabolic state; identifying, from the plurality of candidate treatment recommendations, a shortlist comprising one or more effective treatments identified based on their effects predicted by the plurality of patient-specific metabolic models of the digital twin; and displaying the shortlist of candidate treatment recommendations to a patient or medical provider via an application on a computing device.

“2. The method of claim 1, wherein each candidate treatment recommendation of the population of candidate treatments represents a distinct combination of intervention parameters of the population of intervention parameters

“3. The method of claim 1, further comprising: responsive to receiving a domain for the plurality of candidate treatment recommendations, identifying patient data recorded for the population of patients within the domain, wherein the domain represents types of patient data to be measured for evaluating each candidate treatment; and generating the plurality of candidate treatment recommendations based on patient data identified within the domain.

“4. The method of claim 1, further comprising: defining a population of intervention parameters, wherein each intervention parameter of the population represents a feature of the candidate treatment to be input to the patient-specific metabolic model; and generating the plurality of candidate treatment recommendations based on the population of intervention parameters, wherein each of the plurality of candidate treatment recommendation represents a distinct combination of intervention parameters of the population of intervention parameters.

“5. The method of claim 1, wherein generating the cohort of patients 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 candidate treatment recommendation based on the accessed patient data.

“6. The method of claim 5, 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.

“7. The method of claim 1, wherein generating the cohort of patients 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 subset of patients.

“8. The method of claim 1, wherein generating the cohort of patients 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.

“9. The method of claim 1, wherein the candidate 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 candidate 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.

“10. 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 candidate treatment recommendation on each patient of the category by inputting the candidate treatment recommendation to a patient-specific metabolic model of the patient; and determining an overall sensitivity of the category of patients to the candidate treatment recommendation based on the predicted effect of the candidate treatment recommendation on each patient of the cohort; and determining a category of patients most sensitive to the candidate treatment recommendation based on a comparison of the overall sensititvity of each category of patients; and generating the cohort of patients based on the category of patients most sensitive to the candidate treatment recommendation.

“11. The method of claim 1, further comprising: separating the cohort of patients into a control cohort and a test cohort, wherein the candidate treatment recommendation is input to the patient-specific metabolic model for each patient of the test cohort; and determining the overall effect of the candidate treatment recommendation on the cohort based on a comparison of the effect of the candidate treatment recommendation predicted by the patient-specific metabolic model of each patient in the test cohort to representations of metabolic states of patients in the control cohort.

“12. The method of claim 1, wherein each patient-specific metabolic model of the plurality models an aspect of a metabolic state of the patient and predicting the effect of the candidate treatment recommendation on the metabolic state of the patient further comprises: identifying a primary metabolic model from the plurality of patient-specific metabolic models based on the one or more intervention parameters of the candidate treatment recommendation, wherein the one or more intervention parameters directly affect an output of the primary metabolic model; identifying one or more secondary metabolic models from the plurality of patient-specific metabolic models, wherein the output of the primary metabolic model directly affects an output of each secondary model; inputting the candidate treatment recommendation to the primary metabolic model to predict an effect of the candidate treatment recommendation on the aspect of the metabolic state corresponding to the primary model; and inputting the candidate treatment recommendation and the effect predicted by the primary model to each secondary model to predict effects of the candidate treatment recommendation on aspects of the metabolic state of the patient corresponding to the secondary model.

“13. The method of claim 12, wherein the primary metabolic model is identified based on: an effect of the one or more intervention parameters on outputs of each patient-specific metabolic model of the digital twin; or features of metabolic health measured to validate the effect of the one or more intervention parameters.

“14. The method of claim 12, wherein predicting the effect of the candidate treatment recommendation on the metabolic state of the patient further comprises: predicting an effect of the candidate treatment recommendation on the metabolic state of each patient of the cohort of patients; determining a change in metabolic state of each patient of the cohort of patients based on a comparison of the predicted effect and a current metabolic state of the patient; and determining an aggregate effect of the candidate treatment recommendation on the cohort of patients based on an average change between the predicted effect and current metabolic state of patients in the cohort of patients.

“15. The method of claim 1, wherein predicting the effect of the candidate treatment recommendation on the metabolic state of the patient further comprises: encoding a feature vector representation of the candidate treatment recommendation; and inputting the feature vector representation into each of the patient-specific metabolic models of the digital twin for each patient of the cohort .

“16. The method of claim 1, further comprising: determining a strength of correlation between an intervention parameter adjusted for the candidate treatment recommendation and a feature of patient data; responsive to determining an amount of data collected for the feature to be below a threshold amount, generating synthetic data for the feature; and labeling the synthetic data with a first label and the collected data with a second label.”

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

URL and more information on this patent application, see: Banerjee, Abhik; Mohammed, Jahangir; Poon, Terrence Chun Yin; Wilson, James. Simulating Clinical Trials Using Whole Body Digital Twin Technology. U.S. Patent Application Number 20230111605, filed October 11, 2022 and posted April 13, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230111605)&db=US-PGPUB&type=ids

(Our reports deliver fact-based news of research and discoveries from around the world.)