WO2024050133A1 - Digital twin for diagnostic and therapeutic use - Google Patents

Abstract

Embodiments include methods of determining a patient health state using a digital twin. The health state of a subject can include factors such as diagnosis of disease, disease prevention, treatment planning, managing disease/ailments, promoting longevity, etc. In aspects, the methods include genomic, transcriptomic, metabolomic, epigenetic and microbiome datasets combined with up-to-date disease knowledge and real-time data scraping to evaluate the patient's state of health and disease conditions.

Description

DIGITAL TWIN FOR DIAGNOSTIC AND THERAPEUTIC USE

RELATED APPLICATIONS

[001 ] This application claims priority to U.S. provisional patent application number 64/403,285 filed on September 1 , 2022. The contents of the aforementioned application are incorporated herein by reference.

FIELD OF THE INVENTION

[002] The invention relates generally to patient modeling and, more particularly, to improved systems and methods to generate a patient digital twin for healthcare use.

BACKGROUND

[003] Health care spending in the U.S. increased to $4.1 trillion or $12,530 per capita in 2020. Accounting for 19.7 percent of the U.S. GDP and continuing to grow. Despite these high expenditures, the quality of American healthcare needs improvement. The U.S. consistently ranks below most developed countries when comparing factors such as access to care, care process, administrative efficiency, health care equity and health care outcomes.

[004] The practice of medicine today relies heavily on analytical and diagnostic tools that are used to characterize a condition or disease. Often, the use of analytical and diagnostic tools involves determination of bioindicator levels to point to a symptom or disease. Moreover, the bioindicator data can suggest a general treatment plan because the advanced arts of pharmaceutical sciences and medicinal chemistry have identified drugs for many different conditions and diseases.

[005] Drawbacks of the current practice of healthcare include inefficient use of the clinician's time in administering and monitoring a treatment plan. Other drawbacks include inefficient use of the clinician's time in gathering and analyzing bioindicator data to provide a diagnosis or prognosis. One way to improve healthcare is to provide for wellness. To deliver wellness to subjects, information can be provided to the subjects so that they can take control of their personal healthcare needs. For example, wellness information can include details concerning preventative medicine, use of pharmaceuticals, diet and nutrition, exercise, or self-abusive behavior.

[006] One drawback of wellness programs is that they are not personalized to take into account certain biomarkers of an individual subject. A further drawback is a general lack of monitoring a subject for changes in health or changes in various biomarker levels. Without these aspects, wellness programs can fail to allow a subject to take control of their personal healthcare needs.

[007] A person's overall health and wellness is the result of a number of different factors. Genetic profiles, medical history, fitness activity and nutrition all affect a person's overall health and wellness. The interrelationships between these factors are not fully understood and are the subject of continuing research. However, even for factors which are individually known to be the cause of disease or promote health and wellness, there is no mechanism for a person or even a healthcare provider to attempt to understand these factors, how they relate to each other and how they may be utilized in optimizing a person's overall health and wellness.

[008] Approaches to improve healthcare have included personalized health care. Conventional approaches to personalize treatment have typically separated people into different groups — with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease. The terms personalized medicine, precision medicine, stratified medicine and medicine are used interchangeably to describe this concept; though some authors and organizations use these expressions separately to indicate particular nuances.

[009] A digital twin is a real-time virtual representation of a real-world physical system or process (a physical twin) that serves as the indistinguishable digital counterpart of it for practical purposes, such as system simulation, integration, testing, monitoring and maintenance. The concept of digital twin in the healthcare industry was originally proposed and first used in product and equipment prognostics. A digital twin allows for a more data-driven approach to healthcare. The availability of technologies makes it possible to build personalized models for patients, continuously adjustable based on tracked health and lifestyle parameters. This can ultimately lead to a virtual patient, with a detailed description of the healthy state of an individual patient derived not only from previous records. Furthermore, the digital twin enables an individual's records to be compared against the population to easily find patterns with great detail. The biggest benefit of the digital twin on the healthcare industry is the fact that healthcare can be tailored to anticipate the responses of individual patients. Digital twins can offer better resolutions for defining the health of an individual patient but can also change the expected profile of a healthy patient. Rather than define “healthy” as the absence of disease indications, a patient can be compared to the rest of the population to more accurately define healthy.

[0010] Digital twins can bridge the physical and digital worlds by allowing us to understand past and present processes and predict the future. Computational medicine enables prediction and early diagnosis of disease states by applying in silico modeling and simulations which can help prevent disease, diagnose, plan treatments, and manage disease states.

[0011] While there is a clear benefit to the use of digital twin technologies, conventional approaches have had shortcomings. At present, the interaction of the human body's 37 trillion cells is viewed as near impossible to resolve and follow the biochemical processes. An improved digital twin would alleviate this shortcoming by encompassing data and analysis from the myriad of the body’s biochemical processes. Aspects of the present invention fulfill this need and provide further related advantages as described in the following summary.

SUMMARY OF THE INVENTION

[0012] Aspects of the present disclosure teach certain benefits in construction and use which give rise to the exemplary advantages described below.

[0013] The present disclosure solves the problems described above by providing machine learning algorithms which have categorized over 20 Billion protein: protein and protein:small molecule interactions and mapped these interactions into the biochemical pathways associated with various disease states.

[0014] Embodiments also include methods of using a digital twin for personalized health care. In one aspect, healthcare data (“data sets”) is sourced from electronic medical records, pharmacy databases, laboratory databases, insurer databases, clinic/hospital databases and/or physician database.

[0015] Embodiments include a digital twin within an in silico platform to mimic physiological processes. The platform combines mathematical equations derived from machine learning and neural networks with equation libraries used to mimic human multiomic interactions. The platform can be used as a representation of a new synthetic individual study subject or mimic an existing individual study subject.

[0016] Accordingly, embodiments include methods of determining a patient health state using a digital twin. The health state of a subject can include factors such as diagnosis of disease, disease prevention, treatment planning, managing disease/ailments, promoting longevity, etc.

[0017] Embodiments include computational methods for use in healthcare as a “digital twin.” In aspects, the methods include genomic, transcriptom ic, metabolomic, epigenetic and microbiome datasets combined with up-to-date disease knowledge and real-time data scraping which provides a current understanding of the human health and disease conditions.

[0018] Embodiments also include a system for implementing personalized health and wellness programs. The system can include: a) a storage device to store health data from a subject; b) memory configured to store instructions and historical data; c) a processor coupled with the storage device and memory, wherein the instructions are configured to cause the processor to apply training and simulations across human datasets created across multiple diseases and apply training and simulations at every node and edges of the nodes to capture transactions of the disease and health states in a comprehensive model. The processor can then generate new synthetic study patients to be used in predictive modeling of outcomes. The outcomes can be represented by synthetic omic predictions such as gene count, biological algorithm, Expression levels, relative prevalence or other standard omic file.

[0019] Embodiments include methods of determining a patient health state using a digital twin. Based on the subject’s health state, a treatment plan can be surmised to manage disease, prevent disease/ailments, promote longevity, etc.

[0020] The methods can be synthesized to represent a cohort of new human subjects. The methods can be used for predictive quantification of omics in post perturbed states such as, for example, clinical trials, prognosis of infected individuals (viral, bacterial or other), changes in an individual’s physiological state with exogenous influence. The exogenous influence can be, for example, a new gene mutation, aging, endogenous change states, spontaneous/randomized change of states (e.g., exposure to a chemical agent and environmental stressors).

[0021] Embodiments include methods of determining a patient health state. The method can employ mathematical equations and machine learning integrated into a platform for the use of predicting human states via change in disease or through drug influence.

[0022] The methods can be used for simulated clinical trial outcomes using multiple individual semi-autonomous simulated patients and simulated drugs or procedures.

[0023] In aspects, the systems and methods can utilize additional biomedical information and environmental factors.

[0024] The methods can be used to predict chemical signals from exogenous sources and the effects on entire systems biology. In aspects, the methods can use wave form degradation function and interpolation to simulate propagation of change in relative prevalence of omics in the body. In aspects, predicted change in relative prevalence of omics can be used to predict phenotypic outcomes of an in silico clinical trial such as side effects, sensations, states of mind, symptoms, diseases or toxicity. [0025] In aspects, the methods can predict disease states using a drug droid or simulated human omic model based on relative prevalence of omics such as proteins and mRNA or other combination of quantitative level of testable omic such as miRNA, siRNA, Acetylation, Proteomics, or lipids.

[0026] The methods described herein can include a step of treating a patient for an ailment/disease. In aspects, the methods include a step of developing a treatment plan for disease prevention/treatment. In aspects, the methods can include a step promoting fitness and/or longevity.

[0027] The system can integrate machine learning models and neural networks linked together with mathematical models (e.g., Markov models). The system can function through gated decisions driven by both mathematical models and a probability matrix which employs many Python libraries of both mathematics and functional processes. These libraries capture a very large number of mathematical equations and functions which are then selected by the platform and integrated into the processes controlled by the machine learning algorithms.

[0028] In aspects, the system is unsupervised and objectively neutral. In aspects, the system can be used to target a specific disease state and/or a known biological process.

[0029] In aspects, the system can function as a human-like, in-silico platform that mimics physiological processes. In aspects, the system can combines mathematical equations derived from machine learning and neural networks with equation libraries used to mimic human multiomic interactions.

[0030] In aspects, the system can represent a new synthetic individual study subject or mimic an existing individual study subject. The system can be synthesized to represent a cohort of new human subjects.

[0031] The system can enables predictive quantification of omics in post perturbed states including: (a) a clinical trial, (b) an infected individual (e.g., viral, bacterial or other), (c) the change in an individual’s physiological state with exogenous influence, (d) a new gene mutation, (e) aging, (f) endogenous change states, (g) spontaneous or randomized changing states, (h) exposure to a chemical agent (i) exposure to an environmental stressor.

[0032] In aspects, the system can use of mathematical equations and Machine learning integrated into a platform for the use of predicting human states of change in disease or through drug influence. In aspects, the system can be used to simulated clinical trials outcomes using multiple individual semi-autonomous simulated patients and simulated drugs or procedures. In aspects, the system can use predicted chemical signal from exogenous sources and its effects on the entire systems biology. In aspects, the system can use wave form degradation function and interpolation to simulate propagation of change in relative prevalence of omics in the body.

[0033] In aspects, predicted change in relative prevalence of omics is used to predict phenotypic outcomes of an in silico clinical trial such as side effects, sensations, states of mind, symptoms, diseases or toxicity.

[0034] In aspects, prediction of disease states using a drug droid or simulated human omic model is based on relative prevalence of omics such as proteins and mRNA or other combination of quantitative level of testable omic such as miRNA, siRNA, Acetylation, Proteomics, or lipids.

[0035] In aspects, a classification algorithm determines which equations fit which data type. Lagrange interpolation and/or transfer learning can be used to predict the change in equation constants.

[0036] Other features and advantages of aspects of the present invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principle aspects of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS [0037] The accompanying drawings illustrate aspects of the present invention. In such drawings:

[0038] FIG. 1 is a diagram of a deep learning model for a digital twin according to embodiments.

[0039] FIG. 2 is a flow chart that depicts the steps of using a digital twin for diagnostic and therapeutic use according to embodiments.

Definitions

[0040] Reference in this specification to "one embodiment/aspect" or "an embodiment/aspect" means that a particular feature, structure, or characteristic described in connection with the embodiment/aspect is included in at least one embodiment/aspect of the disclosure. The use of the phrase "in one embodiment/aspect" or "in another embodiment/aspect" in various places in the specification are not necessarily all referring to the same embodiment/aspect, nor are separate or alternative embodiments/aspects mutually exclusive of other embodiments/aspects. Moreover, various features are described which may be exhibited by some embodiments/aspects and not by others. Similarly, various requirements are described which may be requirements for some embodiments/aspects but not other embodiments/aspects. Embodiment and aspect can in certain instances be used interchangeably.

[0041] The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Certain terms that are used to describe the disclosure are discussed below, or elsewhere in the specification, to provide additional guidance to the practitioner regarding the description of the disclosure. It will be appreciated that the same thing can be said in more than one way.

[0042] Consequently, alternative language and synonyms may be used for any one or more of the terms discussed herein. Nor is any special significance to be placed upon whether or not a term is elaborated or discussed herein. Synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended to further limit the scope and meaning of the disclosure or of any exemplified term. Likewise, the disclosure is not limited to various embodiments given in this specification.

[0043] Without intent to further limit the scope of the disclosure, examples of instruments, apparatus, methods and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions, will control.

[0044] The term “personalized health care” or “precision medicine” refers to an emerging practice of medicine that uses an individual's genetic profile to guide decisions made in regard to the prevention, diagnosis and treatment of disease. The approach can also take into account other characteristics of an individual (e.g., biomarker levels, protein and molecular interactions, etc.). For the patient/consumer, personalized health care means being recognized as a unique individual based on their health, genetic makeup, chemistry, history and circumstances, and receiving treatment tailored to one’s needs. This leads to better health outcomes in preventing, diagnosing, treating and managing health and disease.

[0045] The term “algorithm” refers to a procedure for solving a mathematical problem in a finite number of steps that frequently involves repetition of an operation.

[0046] The term “artificial intelligence” or “Al” refers to intelligence exhibited by machines, rather than humans. The term, as applied herein, refers to when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving." [0047] The term “neural network” refers to a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.

[0048] “Artificial neural networks” or “ANNs” are distributed computing systems that include a number of neurons interconnected through connection points called synapses. Each synapse encodes the strength of the connection between the output of one neuron and the input of another. The output of each neuron is determined by the aggregate input received from other neurons that are connected to it. Thus, the output of a given neuron is based on the outputs of connected neurons from preceding layers and the strength of the connections as determined by the synaptic weights. An ANN is trained to solve a specific problem (e.g., pattern recognition) by adjusting the weights of the synapses such that a particular class of inputs produce a desired output.

[0049] Various algorithms can be used for this learning process. Certain algorithms may be suitable for specific tasks such as image recognition, speech recognition, or language processing. Training algorithms lead to a pattern of synaptic weights that, during the learning process, converges toward an optimal solution of the given problem.

[0050] Artificial neural networks include, for example, a feedforward neural network, a radial basis function network, a self-organizing map, learning vector quantization, a recurrent neural network, a Hopfield network, a Boltzmann machine, an echo state network, long short term memory, a bi-directional recurrent neural network, a hierarchical recurrent neural network, a stochastic neural network, a modular neural network, an associative neural network, a deep neural network, a deep belief network, a convolutional neural networks, a convolutional deep belief network, a large memory storage and retrieval neural network, a deep Boltzmann machine, a deep stacking network, a tensor deep stacking network, a spike and slab restricted Boltzmann machine, a compound hierarchical-deep model, a deep coding network, a multilayer kernel machine, or a deep Q-network. [0051] A “Convolutional neural network” or “CNN” refers to a class of deep, feedforward artificial neural networks, most commonly applied to analyzing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of convolutional layers, pooling layers, fully connected layers and normalization layers. Convolutional layers apply a convolution operation to the input, passing the result to the next layer. Local or global pooling layers combine the outputs of neuron clusters at one layer into a single neuron in the next layer. Fully connected layers connect every neuron in one layer to every neuron in another layer. CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns the filters that in traditional algorithms were hand-engineered. This independence from prior knowledge and human effort in feature design is a major advantage.

[0052] The term “big data” refers to large data sets that can be analyzed computationally to reveal patterns, trends, and associations, especially relating to human physiological responses, human behavior and interactions. This can include, for example, a databases of electronic health records from a hospital or clinic.

[0053] The term “classifier” refers to the mathematical function, implemented by a classification algorithm that maps input data to a category. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier.

[0054] The term “Controller Area Network,” “CAN” or “CAN bus” refers to a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host computer.

[0055] The term “data fusion system” refers to a system that can align/integrate data sets and combine them to produce a meaningful result or conclusion.

[0056] The term “computer learning” or “machine learning” refers to an application of artificial intelligence (Al) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

[0057] The term “transfer learning” or “deep transfer learning” is a type of machine learning that can leverage existing, generalizable knowledge from other related tasks to enable learning of a separate task with a small set of data. The data sets available to train models for in silico drug discovery efforts are often small. Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence-assisted drug discovery. One solution to this problem is to develop algorithms that can cope with relatively heterogeneous and scarce data. Deep transfer learning is the most commonly used type of transfer learning in the field of drug discovery.

[0058] The term “Markov model” refers to a stochastic model used to model pseudo- randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modeling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property.

[0059] The term “historical data” refers to data (e.g. physiological measurements and actions of individuals) recorded and/or stored in a database that can be accessed for analysis and/or comparison. The data can be raw (e.g. sensor data) or processed (e.g. fused data). Historical data can include (a) data compiled from groups/populations of individuals and (b) data compiled from an individual person. For example, data can be recorded from healthy people and people with known ailments. An analysis of the data can indicate variations in physiological measurements that can be correlated with ailments. Likewise, data from an individual person can be recorded and stored. This can allow the system to identify patterns, variations and/or aberrations in activity for that particular person. [0060] The term “signal decay” or “exponential decay” refers to the process of reducing an amount by a consistent percentage rate over a period of time. It can be expressed by the formula y = a (1 - b)^x wherein y is the final amount, a is the original amount, b is the decay factor, and x is the amount of time that has passed.

[0061] The term “Euler's constant,” “Euler's number” or “e” refers to a mathematical constant approximately equal to 2.71828 which can be characterized in many ways. It is the base of the natural logarithms. It is the limit of “ⁿ“ as n approaches infinity, an expression that arises in the study of compound interest.

[0062] The term “formulation” as used herein refers to the antibodies disclosed herein and excipients combined together which can be administered and has the ability to bind to the corresponding receptors and initiate a signal transduction pathway resulting in the desired activity. The formulation can optionally comprise other agents.

[0063] The term "administration" refers to the introduction of an amount of a predetermined substance into a patient by a certain suitable method. The composition disclosed herein may be administered via any of the common routes, as long as it is able to reach a desired tissue, for example, but is not limited to, intraperitoneal, intravenous, intramuscular, subcutaneous, intradermal, oral, topical, intranasal, intrapulmonary, or intrarectal administration. However, since peptides are digested upon oral administration, active ingredients of a composition for oral administration should be coated or formulated for protection against degradation in the stomach.

[0064] The term “medicament,” “active agent” or “active ingredient” refers to a substance, compound, or molecule, which is biologically active or otherwise, induces a biological or physiological effect on a subject to which it is administered to. In other words, “active agent” or “active ingredient” refers to a component or components of a composition to which the whole or part of the effect of the composition is attributed. An active agent can be a primary active agent, or in other words, the component(s) of a composition to which the whole or part of the effect of the composition is attributed. An active agent can be a secondary agent, or in other words, the component(s) of a composition to which an additional part and/or other effect of the composition is attributed.

[0065] The term “pharmaceutical composition” is intended to include the combination of an active agent with a carrier, inert or active, in a sterile composition suitable for diagnostic or therapeutic use in vitro, in vivo or ex vivo. In one aspect, the pharmaceutical composition is substantially free of endotoxins or is non-toxic to recipients at the dosage or concentration employed.

[0066] In an embodiment, a “subject” of diagnosis or treatment is, without limitation, a prokaryotic or a eukaryotic cell, a tissue culture, a tissue or an animal, e.g. a mammal, including a human. Nonhuman animals subject to diagnosis or treatment include, for example, without limitation, a simian, a murine, a canine, a leporid, such as rabbits, livestock, sport animals, and pets.

[0067] The terms “treating,” “treatment” and the like are used herein, without limitation, to mean obtaining a desired pharmacologic and/or physiologic effect. The effect may be prophylactic in terms of completely or partially preventing a disorder or sign or symptom thereof, and/or may be therapeutic in terms of amelioration of the symptoms of the disease or infection, or a partial or complete cure for a disorder and/or adverse effect attributable to the disorder.

[0068] The term “prognosis” refers to the likely outcome or course of a disease and/or the chance of recovery or recurrence. This is in contrast to a “diagnosis” which refers to identifying an ailment or disease, usually from examining a subject.

[0069] The term “health evaluation” or “health assessment” refers to a plan of care that identifies the specific needs of a person and how those needs will be addressed by the healthcare system or healthcare provider. Conventionally, a health assessment follows an evaluation of a subject's health status by performing a physical exam after taking a health history.

[0070] The term "biomarker" refers generally to a DNA, RNA, protein, carbohydrate, or glycolipid-based molecular marker, the expression or presence of which in a sample can be detected by standard methods (or methods disclosed herein) and is predictive or prognostic of the effective responsiveness or sensitivity of a mammalian subject with an ailment. Biomarkers may be present in a test sample but absent in a control sample, absent in a test sample but present in a control sample, or the amount of biomarker can differ between a test sample and a control sample. For example, protein biomarkers can be present in such a sample, but not in a control sample, or certain biomarkers are seropositive in the sample, but seronegative in a control sample. Also, expression of such a biomarker may be determined to be higher than that observed from a control sample. The terms "marker" and "biomarker" are used herein interchangeably.

[0071] The amount of the biomarker can be measured in a test sample and compared to the “normal control level,” utilizing techniques such as reference limits, discrimination limits, or risk-defining thresholds to illustrate cutoff points and abnormal values for an ailment. The normal control level means the level of one or more biomarkers or combined biomarker indices typically found in a subject not suffering from the ailment. Such normal control level and cutoff points can vary based on whether a biomarker is used alone or in a formula combining with other biomarkers into an index. Alternatively, the normal control level can be a database of biomarker patterns from previously tested subjects who did not experience the ailment over a clinically relevant time.

[0072] Tests to measure biomarkers and biomarker panels can be implemented on a variety of diagnostic test systems. Diagnostic test systems are apparatuses that typically include means for obtaining test results from biological samples. Examples of such means include modules that automate the testing (e.g., biochemical, immunological, nucleic acid detection assays). Some diagnostic test systems are designed to handle multiple biological samples and can be programmed to run the same or different tests on each sample. Diagnostic test systems typically include means for collecting, storing and/or tracking test results for each sample, usually in a data structure or database. Examples include well-known physical and electronic data storage devices (e.g., hard drives, flash memory, magnetic tape, paper printouts). It is also typical for diagnostic test systems to include means for reporting test results. Examples of reporting means include visible display, a link to a data structure or database, or a printer. The reporting means can be a data link to send test results to an external device, such as a data structure, data base, visual display, or printer.

[0073] The term "detecting" or "determining" with respect to a biomarker value includes the use of both the instrument required to observe and record a signal corresponding to a biomarker value and the material/s required to generate that signal. In various embodiments, the biomarker value is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0074] The term “physiological event” refers to a response or reaction of the body to a stimulus. Most are automatic/instinctive physiological responses. The healthy state of the body depends upon the integrity of various organ systems. The organ systems in the body function in a particular manner constantly. The mechanisms, by which the organ systems of the body function, can be referred to as “physiological mechanisms.” Physiological mechanisms explain any health-related events or outcomes.

Physiological mechanisms can be altered voluntarily. For example, exercise causes alteration in the cardiac physiology of resting state.

[0075] The term “omics” refers to utilizing multiple molecular disciplines that involve the characterization of global sets of biological molecules such as DNAs, RNAs, proteins, and metabolites. For example, genomics investigates thousands of DNA sequences, transcriptom ics investigates all or many gene transcripts, proteomics investigates large numbers of proteins, and metabolomics investigates large sets of metabolites. Omic data can include genomic data, transcriptom ics, proteomics, epigenomics and metabolomics.

[0076] The term “quantitative proteomics” refers generally to an analytical technique for determining the amount of proteins in a sample. Quantitative proteomics has distinct applications in the medical field, especially in the fields of drug and biomarker discovery. LC-MS/MS techniques have started to over take more traditional methods like the western blot and EUSA due to the cumbersome nature of labeling different and separating proteins using these methods and the more global analysis of protein quantification. Mass spectrometry methods are more sensitive to difference in protein structure like post-translational modification and thus can quantify differing modifications to proteins. Quantitative proteomics can circumvent these issues, only needing sequence information to be performed. It can be applied on a global proteome level, or on specifically isolating binding partners in pull-down or affinity purification experiments.

[0077] As used herein, "additional biomedical information" refers to one or more evaluations of an individual, other than using any of the biomarkers described herein, that are associated with health and/or susceptibility to addiction. Accordingly, "additional biomedical information" includes any of the following: physical descriptors of an individual, the height, weight and/or BMI of an individual, the gender of an individual, the ethnicity of an individual, family history, smoking history, occupational history, etc. Additional biomedical information can be obtained from an individual using routine techniques known in the art, such as from the individual themselves by use of a routine patient questionnaire or health history questionnaire, etc., or from a medical practitioner, etc. Testing of biomarker levels in combination with an evaluation of any additional biomedical information may, for example, improve sensitivity, specificity, and/or AUC for predicting vulnerability to addiction (or other related uses) as compared to biomarker testing alone or evaluating any particular item of additional biomedical information alone.

[0078] The term “comorbid disorders” refers to additional diseases or conditions that a subject has at the same time as a primary health concern. Conditions described as comorbidities are often chronic or long-term conditions.

[0079] The term “environmental factor” refers to exposures to substances (e.g., pesticides, industrial waste, etc.) where a subject lives or works. Common environmental factors include (a) chemicals (e.g., mold, pesticides, etc.), (b) air pollution, (c) climate change and natural disasters, (d) diseases caused by microbes, (e) lack of access to health care, (f) infrastructure issues, (g) poor water quality and (h) global environmental issues.

[0080] As applicable, the terms "about" or "generally", as used herein in the specification and appended claims, and unless otherwise indicated, means a margin of +/- 20%. Also, as applicable, the term "substantially" as used herein in the specification and appended claims, unless otherwise indicated, means a margin of +/- 10%. It is to be appreciated that not all uses of the above terms are quantifiable such that the referenced ranges can be applied.

[0081] Many known and useful compounds and the like can be found in Remington’s Pharmaceutical Sciences (13^th Ed), Mack Publishing Company, Easton, PA — a standard reference for various types of administration. As used herein, the term “formulation(s)” means a combination of at least one active ingredient with one or more other ingredient, also commonly referred to as excipients, which may be independently active or inactive. The term “formulation” may or may not refer to a pharmaceutically acceptable composition for administration to humans or animals and may include compositions that are useful intermediates for storage or research purposes.

[0082] As the patients and subjects of the invention method are, in addition to humans, veterinary subjects, formulations suitable for these subjects are also appropriate. Such subjects include livestock and pets as well as sports animals such as horses, greyhounds, and the like.

DETAILED DESCRIPTION

[0083] Embodiments of the invention relate to the fields of wellness healthcare, personalized medicine and medical diagnosis. Particular embodiments include systems, devices and methods for creating and delivering a wellness healthcare prognosis and/or treatment plan, or a medical diagnosis and/or treatment plan, for personalized healthcare of a subject. In aspects, the systems, devices and methods are used to create a digital twin by combining patient personal data and differentiating conditions for providing prognosis, diagnosis, treatment or wellness of a subject. [0084] The systems, devices and methods can use a combination of patient-specific health care information and medical bioindicator data to determine disease diagnosis and prognosis for a patient, and to provide wellness healthcare. Additional embodiments include patient-specific treatment and/or wellness plans that are derived by processing and transforming health care information and medical bioindicator data. Because the treatment and/or wellness plans can be tailored for patient-specific conduct, lifestyle and biochemistry over a period of time, this invention can provide patient care management for personalized healthcare or wellness care.

[0085] FIG. 1 is a diagram of a deep learning model for a digital twin. In specific embodiments, a digital twin is developed based on physiological events and characteristics that are measured in a subject. Data can include graph convolution, fingerprints and molecular descriptors. The physiological events and characteristics can include, for example, genomics, RANA, proteinomics, protein: protein and protein:small molecule interactions and mapping these interactions into the biochemical pathways associated.

[0086] Applicants have developed machine learning algorithms to categorize myriad (i.e., over 20 billion) interactions from a subject (e.g., protein: protein and protein:small molecule interactions) and mapped these interactions into the biochemical pathways associated with various disease states. This silico medicine platform can represent a humanoid digital twin possessing massive information gleaned from multiple-omics including genomic, transcriptomic, metabolomic, epigenetic and microbiome datasets combined with up-to-date disease knowledge and real-time data scraping which provides a current understanding of the human health and disease conditions.

[0087] The humanoid digital twin technology loads either a single individual, a cohort of individuals or every human dataset across every disease and applies training and simulations at every node and edges of the nodes capturing all transactions of the disease and health states in a comprehensive model.

[0088] The system can integrate machine learning models and neural networks linked together with mathematical models such as Markov models. The system can function through gated decisions driven by both mathematical models and a probability matrix which employs many Python libraries of both mathematics and functional processes. These libraries capture a very large number of mathematical equations and functions which are then selected by the platform and integrated into the processes controlled by the machine learning algorithms. This platform can be unsupervised and objectively neutral or it can be related to disease states and known biological processes. The system can compute the best fit in a selection process or it can write its own algorithm to describe data and outcomes. In order to connect the causal variables associated with disease states the system may either use healthy controls and disease patients or simply disease-only mapped onto a trained biological “healthy” model in order to uncover non-obvious factors of the disease.

[0089] The system can write an equation between the disease and healthy individual using transfer learning, or other learning types with a wave dampening function to account for negative feedback loops and signal propagation. This results in triage and identification of causal biomarkers through constrained analyses which accurately represent the disease state and path to treatment.

[0090] FIG. 2 is a flow chart that depicts the steps of using a digital twin for diagnostic and therapeutic use. The system and methods can use individual data from a subject 105. The data can include, for example, a subject’s chief complaint, levels of one or more biomarkers, biochemistry, answers to assessments/queries from providers, results of examination, body posture/movements (e.g., irregularities in gait), risk factors and other observations from providers. The system and methods can also utilized healthcare data (“data sets”) which can be sourced from, for example, electronic medical records, pharmacy databases, laboratory databases, insurer databases, clinic/hospital databases and/or physician database.

[0091] The individual data can undergo post-processing 110. In aspects, a fusion step will follow with introduction of data sets 145 (e.g., historical data). The system can use Al to detect and/or predict a condition 120. In aspects, the system will calculate a confidence level 103. With a satisfactory confidence level, the system can yield a diagnosis, suggested means of prevention, treatment plan and/or suggested method of managing the disease/ailment 140.

[0092] In aspects, the system uses the data and subsequent analysis to determine a subject’s health state. A treatment plan can be surmised to manage disease, prevent disease/ailments, promote longevity, improve quality of life, reduce signs/symptoms of an ailment, etc.

Device and Processor for Diagnosis, Prognosis and Patient Care

[0093] A system for healthcare delivery of this disclosure can include a diagnosisprognosis device that uses a processor to determine differences between a patient's condition versus normal and disease states.

[0094] As used herein, a “normal” or “healthy” state can be represented through bioindicator levels and normal state health information including normal subject habitual, corporeal and personal data and health indicator communications. A disease state can be represented through bioindicator levels and disease state health information including disease subject habitual, corporeal and personal data and health indicator communications. In some embodiments, the bioindicators are genomic, proteomic, or clinical. Examples of clinical bioindicator data include blood tests.

[0095] The number of bioindicators used in a device or processor of this invention for diagnosis, prognosis or patient care can vary from 1 to 2000, or from 1 to 1000, or from 1 to 500, or from 1 to 100, or from 1 to 50, or from 1 to 30, or from 1 to 20, or from 1 to 10. The number of bioindicators used in a device or processor of this invention for diagnosis, prognosis or patient care can be 1 , or 2, or 3, or 4, or 5, or 6, or 7, or 8, or 9, or 10, or 15, or 20, or 30, or 40, or 50, or 100, or 200, or 500, or 1000, or 2000.

[0096] Data and communications relating to patient personal indicators can be received or obtained electronically through an internet portal and entered and stored in a patient personalization module.

Methods [0097] Methods for treating, preventing or ameliorating a disease, disorder, a condition, or a symptom thereof or a condition related thereto are provided herein. Preferred, but non-limiting embodiments are directed to methods for treating, preventing, inhibiting or ameliorating a disease, disorder, a condition, or a symptom described below.

[0098] In embodiments, the disease or ailment is one or more of atherosclerosis, osteoarthritis, osteoporosis, hypertension, arthritis, cataracts, cancer, Alzheimer’s disease, chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis. Other ailments (including age-related conditions) associated with age or senescence include hair graying, sarcopenia, adiposity, neurogenesis, fibrosis and glaucoma. Still other ailments include cardiovascular disease (e.g., atherosclerosis, angina, arrhythmia, cardiomyopathy, congestive heart failure, coronary artery disease, carotid artery disease, endocarditis, coronary thrombosis, myocardial infarction, hypertension, aortic aneurysm, cardiac diastolic dysfunction, hypercholesterolemia, hyperlipidemia, mitral valve prolapsed, peripheral vascular disease, cardiac stress resistance, cardiac fibrosis, brain aneurysm, and stroke). An ailment can also be an inflammatory or autoimmune disease or disorder (e.g., osteoarthritis, osteoporosis, oral mucositis, inflammatory bowel disease or kyphosis). An ailment can also be a neurodegenerative disease (e.g., Alzheimer's disease, Parkinson's disease, Huntington's disease, dementia, mild cognitive impairment or motor neuron dysfunction). An ailment can be a metabolic disease (e.g., diabetes, diabetic ulcer, metabolic syndrome or obesity). An ailment can also be a pulmonary disease (e.g., pulmonary fibrosis, chronic obstructive pulmonary disease, asthma, cystic fibrosis, emphysema, bronchiectasis or age-related loss of pulmonary function). An ailment can also be an eye disease or disorder (e.g., macular degeneration, glaucoma, cataracts, presbyopia or vision loss). An ailment can be renal disease, renal failure, frailty, hearing loss, muscle fatigue, skin conditions, skin wound healing, liver fibrosis, pancreatic fibrosis, oral submucosa fibrosis or sarcopenia. An ailment can also be a dermatological disease or disorder (e.g., eczema, psoriasis, hyperpigmentation, nevi, rashes, atopic dermatitis, urticaria, diseases or disorders related to photosensitivity or photoaging). [0099] Pharmacokinetic parameters such as bioavailability, absorption rate constant, apparent volume of distribution, unbound fraction, total clearance, fraction excreted unchanged, first-pass metabolism, elimination rate constant, half-life, and mean residence time can be determined by methods well known in the art.

[00100] If desired, other therapeutic agents can be employed in conjunction with those provided in the above-described compositions. The amount of active ingredients that may be combined with the carrier materials to produce a single dosage form will vary depending upon the host treated, the nature of the disease, disorder, or condition, and the nature of the active ingredients.

[00101 ] It is understood that a specific dose level for any particular patient will vary depending upon a variety of factors, including the activity of the specific active agent; the age, body weight, general health, sex and diet of the patient; the time of administration; the rate of excretion; possible drug combinations; the severity of the particular condition being treated; the area to be treated and the form of administration. One of ordinary skill in the art would appreciate the variability of such factors and would be able to establish specific dose levels using no more than routine experimentation.

[00102] Pharmacokinetic parameters such as bioavailability, absorption rate constant, apparent volume of distribution, unbound fraction, total clearance, fraction excreted unchanged, first-pass metabolism, elimination rate constant, half-life, and mean residence time can be determined by methods well known in the art.

[00103] Aspects of the present specification disclose that the symptoms associated with a disease or disorder described herein are reduced by at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% and the severity associated with a disease or disorder described herein is reduced by at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95%. Aspects of the present specification disclose the symptoms associated with disease or disorder are reduced by about 10% to about 100%, about 20% to about 100%, about 30% to about 100%, about 40% to about

100%, about 50% to about 100%, about 60% to about 100%, about 70% to about

100%, about 80% to about 100%, about 10% to about 90%, about 20% to about 90%, about 30% to about 90%, about 40% to about 90%, about 50% to about 90%, about 60% to about 90%, about 70% to about 90%, about 10% to about 80%, about 20% to about 80%, about 30% to about 80%, about 40% to about 80%, about 50% to about 80%, or about 60% to about 80%, about 10% to about 70%, about 20% to about 70%, about 30% to about 70%, about 40% to about 70%, or about 50% to about 70%.

[00104] Aspects of the present specification disclose that the symptoms associated with a disease or disorder described herein are reduced by at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% and the severity associated with a disease or disorder described herein is reduced by at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95%. Aspects of the present specification disclose the symptoms associated with disease or disorder are reduced by about 10% to about 100%, about 20% to about 100%, about 30% to about 100%, about 40% to about

100%, about 50% to about 100%, about 60% to about 100%, about 70% to about

100%, about 80% to about 100%, about 10% to about 90%, about 20% to about 90%, about 30% to about 90%, about 40% to about 90%, about 50% to about 90%, about 60% to about 90%, about 70% to about 90%, about 10% to about 80%, about 20% to about 80%, about 30% to about 80%, about 40% to about 80%, about 50% to about 80%, or about 60% to about 80%, about 10% to about 70%, about 20% to about 70%, about 30% to about 70%, about 40% to about 70%, or about 50% to about 70%.

[00105] The system is typically comprised of a central server that is connected by a data network to a user's (e g., a healthcare provider’s) computer. The central server can be comprised of one or more computers connected to one or more mass storage devices. The precise architecture of the central server does not limit the claimed invention. Further, the user's (i.e. healthcare practitioner’s) computer can be a laptop or desktop type of personal computer. It can also be a cell phone, smart phone or other handheld device, including a tablet. The precise form factor of the user's computer does not limit the claimed invention. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand held, laptop or mobile computer or communications devices such as cell phones and PDA's, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The precise form factor of the user's computer does not limit the claimed invention. In one embodiment, the user's computer is omitted, and instead a separate computing functionality provided that works with the central server. In this case, a user would log into the server from another computer and access the system through a user environment.

[00106] The user environment can be housed in the central server or operatively connected to it. Further, the user can receive from and transmit data to the central server by means of the Internet, whereby the user accesses an account using an Internet web-browser and browser displays an interactive web page operatively connected to the central server. The central server transmits and receives data in response to data and commands transmitted from the browser in response to the customer's actuation of the browser user interface. Some steps of the invention may be performed on the user's computer and interim results transmitted to a server. These interim results may be processed at the server and final results passed back to the user.

[00107] The methods described herein can be executed on a computer system, generally comprised of a central processing unit (CPU) that is operatively connected to a memory device, data input and output circuitry (I/O) and computer data network communication circuitry. Computer code executed by the CPU can take data received by the data communication circuitry and store it in the memory device. In addition, the CPU can take data from the I/O circuitry and store it in the memory device. Further, the CPU can take data from a memory device and output it through the I/O circuitry or the data communication circuitry. The data stored in memory may be further recalled from the memory device, further processed or modified by the CPU in the manner described herein and restored in the same memory device or a different memory device operatively connected to the CPU including by means of the data network circuitry. The memory device can be any kind of data storage circuit or magnetic storage or optical device, including a hard disk, optical disk or solid state memory. The I/O devices can include a display screen, loudspeakers, microphone and a movable mouse that indicate to the computer the relative location of a cursor position on the display and one or more buttons that can be actuated to indicate a command.

[00108] The computer can display on the display screen operatively connected to the I/O circuitry the appearance of a user interface. Various shapes, text and other graphical forms are displayed on the screen as a result of the computer generating data that causes the pixels comprising the display screen customer’s actuation of the browser user interface. Some steps of the invention can be performed on the user's computer and interim results transmitted to a server. These interim results can be processed at the server and final results passed back to the user.

[00109] The invention may also be entirely executed on one or more servers. A server may be a computer comprised of a central processing unit with a mass storage device and a network connection. In addition a server can include multiple of such computers connected together with a data network or other data transfer connection, or, multiple computers on a network with network accessed storage, in a manner that provides such functionality as a group. Practitioners of ordinary skill will recognize that functions that are accomplished on one server may be partitioned and accomplished on multiple servers that are operatively connected by a computer network by means of appropriate inter process communication. In addition, the access of the website can be by means of an Internet browser accessing a secure or public page or by means of a client program running on a local computer that is connected over a computer network to the server. A data message and data upload or download can be delivered over the Internet using typical protocols, including TCP/IP, HTTP, TCP, UDP, SMTP, RPC, FTP or other kinds of data communication protocols that permit processes running on two remote computers to exchange information by means of digital network communication. As a result a data message can be a data packet transmitted from or received by a computer containing a destination network address, a destination process or application identifier, and data values that can be parsed at the destination computer located at the destination network address by the destination application in order that the relevant data values are extracted and used by the destination application. The precise architecture of the central server does not limit the claimed invention. In addition, the data network may operate with several levels, such that the user's computer is connected through a fire wall to one server, which routes communications to another server that executes the disclosed methods.

[00110] Computer program logic implementing all or part of the functionality previously described herein may be embodied in various forms, including, but in no way limited to, a source code form, a computer executable form, and various intermediate forms (e.g., forms generated by an assembler, compiler, linker, or locator.) Source code may include a series of computer program instructions implemented in any of various programming languages (e.g., an object code, an assembly language, or a high-level language such as C, C-HF, C#, Action Script, PHP, ECMAScript, JavaScript, JAVA, or 5 HTML) for use with various operating systems or operating environments. The source code may define and use various data structures and communication messages. The source code may be in a computer executable form (e.g., via an interpreter), or the source code may be converted (e.g., via a translator, assembler, or compiler) into a computer executable form.

[00111 ] The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer program and data may be fixed in any form (e.g., source code form, computer executable form, or an intermediate form) either permanently or transitorily in a tangible storage medium, such as a semiconductor memory device (e.g., a RAM, ROM, PROM, EEPROM, or flash-programmable RAM), a magnetic memory device (e.g., a diskette or fixed hard disk), an optical memory device (e.g., a CD-ROM or DVD), a PC card (e.g., PCMCIA card), or other memory device. The computer program and data may be fixed in any form in a signal that is transmittable to a computer using any of various communication technologies, including, but in no way limited to, analog technologies, digital technologies, optical technologies, wireless technologies, networking technologies, and internetworking technologies. The computer program and data may be distributed in any form as a removable storage medium with accompanying printed or electronic documentation (e.g., shrink-wrapped software or a magnetic tape), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the communication system (e.g., the Internet or World Wide Web.) It is appreciated that any of the software components of the present invention may, if desired, be implemented in ROM (read-only memory) form. The software components may, generally, be implemented in hardware, if desired, using conventional techniques.

[00112] The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. Practitioners of ordinary skill will recognize that the invention may be executed on one or more computer processors that are linked using a data network, including, for example, the Internet. In another embodiment, different steps of the process can be executed by one or more computers and storage devices geographically separated but connected by a data network in a manner so that they operate together to execute the process steps. In one embodiment, a user's computer can run an application that causes the user's computer to transmit a stream of one or more data packets across a data network to a second computer, referred to here as a server. The server, in turn, may be connected to one or more mass data storage devices where the database is stored. The server can execute a program that receives the transmitted packet and interpret the transmitted data packets in order to extract database query information. The server can then execute the remaining steps of the invention by means of accessing the mass storage devices to derive the desired result of the query. Alternatively, the server can transmit the query information to another computer that is connected to the mass storage devices, and that computer can execute the invention to derive the desired result. The result can then be transmitted back to the user's computer by means of another stream of one or more data packets appropriately addressed to the user's computer. In one embodiment, the relational database may be housed in one or more operatively connected servers operatively connected to computer memory, for example, disk drives. In yet another embodiment, the initialization of the relational database may be prepared on the set of servers and the interaction with the user's computer occur at a different place in the overall process.

[00113] It should be noted that the flow diagrams are used herein to demonstrate various aspects of the invention, and should not be construed to limit the invention to any particular logic flow or logic implementation. The described logic may be partitioned into different logic blocks (e.g., programs, modules, functions, or subroutines) without changing the overall results or otherwise departing from the true scope of the invention. Oftentimes, logic elements may be added, modified, omitted, performed in a different order, or implemented using different logic constructs (e.g., logic gates, looping primitives, conditional logic, and other logic constructs) without changing the overall results or otherwise departing from the true scope of the invention.

EXAMPLES

[00114] The following non-limiting examples are provided for illustrative purposes only in order to facilitate a more complete understanding of representative embodiments now contemplated. These examples are intended to be a mere subset of all possible contexts in which the components of the formulation may be combined. Thus, these examples should not be construed to limit any of the embodiments described in the present specification, including those pertaining to the type and amounts of components of the formulation and/or methods and uses thereof.

Example 1 Signal Decay Mathematics

[00115] In this example, signal decay mathematics are used in combination with transfer learning to calibrate a biological activity within the body. One such equation may be a derivative of the following or any other signal propagation and or decay equation combined with a biological signal transduction learning process. In this example, exponential signal decay is used and the decay factor is calculated by signal transduction transfer learning from datasets derived through mRNA response to drug administration in a whole transcription read. f(x) = abx f(x) = a (1 - r)^x

P = Po e-k t where, a (or) Po = Initial amount b = decay factor r = Rate of decay (for exponential decay) x (or) t = time interval (years, days, months, etc.), k = constant of proportionality e - Euler's constant

Computation and transfer learning of b, decay factor, r, rate of decay and k, constant of proportionality.

[00116] One base methodology that can be used includes primary datasets. A primary dataset such as a "pre-treatment" model paired with a treatment model across like biological units and translated using vectored networks mapped onto biochemical pathways. This base is to be used in surrogate-based optimization, and a transfer learning framework to share the gained knowledge. Surrogates (metamodels) are regression tools to map the input space to the output space y using low fidelity models. The response of the surrogate model can be expressed as:

F(x)=yt+s

[00117] Here, ytey is the real output, F(x) is the objective function approximation at XG and s is the error between the real and the predicted outputs. F is obtained by an iterative training procedure where a training dataset of input-output pairs are fed to the regressor. As a result of the training, coefficients of the predefined metamodel (in this case the weight and biases described as signal propagation and decay for multilayer neural networks) are obtained.

[00118] Depending on the similarity between the input-output spaces such as pathway and biological unit subtype, the knowledge can be transferred from one domain (source) to another (target). The target can be represented as b, r, or k depending on the primary data source.

Example 2

Vaish Approach

[00119] Deep learning using neural networks tries to ‘learn’ the complex relationship between a set of prediction attributes and their corresponding class values. The prediction attributes here may be any quantifiable data (e.g., omic data, fold changes, etc.) and the class values may be any data pertaining to states (e.g., cell state).

[00120] In nature, cells are responsible for information processing and regulating cell states. This learning model tries to mimic the biological processes occurring in humans.

[00121 ] The learning model in one of the forms may possess a fully-connected, generic neural network. It is represented as a directed graph of nodes and edges. Each node is a protein/gene and each edge is a mechanistic interpretation of reactions on/along pathways. Every edge is assigned a random, probabilistic weight before the training begins. Then, during training, the weights of the edges are iteratively updated based on the observed training data. This improves the accuracy of the deep learning model in being able to predict class values for nodes. With time and sufficient training data, this model can learn highly complex relationships between prediction attributes and class values. This model is then enhanced using domain knowledge (domain knowledge refers to any observed relationships and reactions in biological systems, annotated in public databases). The flow of information in this neural network is designed to imitate cells where signals are transduced from receptors through signaling proteins to transcription factors, which in turn induce changes in the gene expression. [00122] The input to this model is the change in the gene expression at each node.

This model is capable of capturing phenotypic cell states and the ability of each node to interact with their environments.

Example 3

Time Series Analysis

[00123] Time series forecasting characterizes the measurement of signal propagation over time. Recurrent neural networks are used to model time series problems. A long short term memory network (LSTM) is a type of RNN that has feedback connected. This makes it useful to represent signal transduction pathways and their propagation. An LSTM network has a cell, input gate, output gate, and forget gate. The forget gates and feed-forward mechanisms allow the network to retain information, forget extraneous inputs, and update the forecasting procedure to model and forecast complex time series problems. This nature of operation of an LSTM makes it useful in finding disease states, progression of condition over time, as well as response to drugs.

Example 4

Digital Twin for Managing Diabetes (T1)

[00124] Applicants recognize that there are unique molecular pathways involved in disease progression. In this example, a seemingly healthy individual (25-year-old male) seeks an evaluation of his health from a health care provider. The provider uses a digital twin to replicate the physiology of the subject. Using signal decay (based on genomics, RNA and proteinomics), the digital twin uses a trained physiological outcome to evaluate the subject’s current state of health and predict future changes/ailments.

[00125] In this example, several physiological events are identified that are indicative of Type 1 diabetes. The subject is advised of his predisposition and treated to help prevent the condition (e.g., with immunomodulating agents).

Example 5

Digital Twin for Managing Diabetes (T2)

[00126] In this example, a seemingly healthy individual seeks an evaluation of his health. The subject (45-year-old male) presents a high BMI and above average weight. Although not clinically obese, the healthcare provider recognizes obesity as a potential comorbidity. As above, a digital twin is used to replicate physiology of the subject.

[00127] In this example, physiological events indicate that the subject will likely become insulin resistant within three to five years. Further analysis recognizes physiological events causing atherosclerosis. These ailments are predicted despite normal levels of glucose, A1 c and lipids.

Example 6

Digital Twin for Predicting Dementia

[00128] Not a specific disease, dementia is a group of conditions characterized by impairment of at least two brain functions, such as memory loss and judgment. Symptoms include forgetfulness, limited social skills, and thinking abilities so impaired that it interferes with daily functioning.

[00129] In this example, a 60-year-old female seeks an evaluation of her health. Several physiological events are recognized that are associated with dementia. The healthcare provider identifies the physiological events as potential targets for therapeutic intervention. The subject is advised of the potential and included in clinical studies aimed at preventing/ameliorating dementia.

[00130] The systems and methods described herein can be used in the treatment of an ailment, the prediction of developing an ailment and/or the development of a health plan to improve a subject’s outcome/health. The ailment can be, for example, Abdominal aortic aneurysm, Acne, Acute cholecystitis, Acute lymphoblastic leukaemia, Acute lymphoblastic leukaemia: Children, Acute lymphoblastic leukaemia (teenagers and young adults), Acute myeloid leukaemia, Acute myeloid leukaemia (children), Acute myeloid leukaemia: Teenagers and young adults, Acute pancreatitis, Addison's disease, Alcohol-related liver disease, Allergic rhinitis, Allergies, Alzheimer's disease, Anal cancer, Anaphylaxis, Angioedema, Ankylosing spondylitis, Anorexia nervosa, Anxiety, Anxiety disorders in children, Appendicitis, Arthritis, Asbestosis, Asthma, Atopic eczema, Attention deficit hyperactivity disorder (ADHD), Autistic spectrum disorder (ASD), Bacterial vaginosis, Benign prostate enlargement, Bile duct cancer (cholangiocarcinoma), Binge eating, Bipolar disorder, Bladder cancer, Blood poisoning (sepsis), Bone cancer, Bone cancer: Teenagers and young adults, Bowel cancer, Bowel incontinence, Bowel polyps, Brain stem death, Brain tumours, Breast cancer, Bronchiectasis, Bronchitis, Bulimia, Bunion, Cancer, Carcinoid syndrome and carcinoid tumours, Catarrh, Cellulitis, Cervical cancer, Chest infection, Chest pain, Chickenpox, Chilblains, Chlamydia, Chronic fatigue syndrome, Chronic kidney disease, Chronic lymphocytic leukaemia, Chronic myeloid leukaemia, Chronic obstructive pulmonary disease, Chronic pancreatitis, Cirrhosis, Clostridium difficile, Coeliac disease, Cold sore, Coma, Common cold, Common heart conditions, Congenital heart disease, Conjunctivitis, Constipation, Coronavirus (COVID-19), Cough, Crohn's disease, Croup, Cystic fibrosis, Cystitis, Deaf blindness, Deep vein thrombosis, Dehydration, Dementia, Dementia with Lewy bodies, Dental abscess, Depression, Dermatitis herpetiformis, Diabetes, Diarrhoea, Discoid eczema, Diverticular disease and diverticulitis, Dizziness (Lightheadedness), Down's syndrome, Dry mouth, Dysphagia (swallowing problems), Dystonia, Earache, Earwax build-up, Ebola virus disease, Ectopic pregnancy, Endometriosis, Epilepsy, Erectile dysfunction (impotence), Escherichia coli (E. coli) 0157, Ewing sarcoma, Ewing sarcoma, Eye cancer, Febrile seizures, Fever in adults, Fever in children, Fibroids, Fibromyalgia, Flatulence, Flu, Foetal alcohol syndrome, Food poisoning, Fungal infection, Fibromyalgia, Gallbladder cancer, Gallstones, Ganglion cyst, Gastroenteritis, Gastro-oesophageal reflux disease (GORD), Genital herpes, Genital warts, Germ cell tumours, Glandular fever, Glioblastoma, Gonorrhoea, Gout, Gum disease, Haemorrhoids (piles), Hand, foot and mouth disease, Hay fever, Head and neck cancer, Head lice and nits, Headaches, Hearing loss, Heart failure, Hepatitis A, Hepatitis B, Hepatitis C, Hiatus hernia, High cholesterol, HIV, Hodgkin lymphoma, Hodgkin lymphoma, Hodgkin lymphoma: Teenagers and young adults, Huntington's disease, Hyperglycaemia (high blood sugar), Hyperhidrosis, Hypoglycaemia (low blood sugar), Idiopathic pulmonary fibrosis, Impetigo, Indigestion, Inherited heart conditions, Insomnia, Iron deficiency anaemia, Irritable bowel syndrome (IBS), Irritable hip, Itching, Itchy bottom, Kaposi's sarcoma, Kidney cancer, Kidney infection, Kidney stones, Labyrinthitis, Lactose intolerance, Laryngeal (larynx) cancer, Laryngitis, Leg cramps, Lichen planus, Liver cancer, Liver disease, Liver tumours, Loss of libido, Lung cancer, Lupus, Lyme disease, Lymphoedema, Lymphogranuloma venereum (LGV), Malaria, Malignant brain tumour (cancerous), Malnutrition, Measles, Meningitis, Menopause, Mesothelioma, Middle ear infection (otitis media), Migraine, Miscarriage, Motor neurone disease (MND), Mouth cancer, Mouth ulcer, Multiple myeloma, Multiple sclerosis (MS), Mumps, Meniere's disease, Nasal and sinus cancer, Nasopharyngeal cancer, Neuroblastoma: Children, Neuroendocrine tumours, Nonalcoholic fatty liver disease (NAFLD), Non-Hodgkin lymphoma, Non-Hodgkin lymphoma, Norovirus, Nosebleed, Obesity, Obsessive compulsive disorder (OCD), Obstructive sleep apnoea, Oesophageal cancer, Oncogene mutations, Oral thrush in adults, Osteoarthritis, Osteoporosis, Osteosarcoma, Otitis externa, Ovarian cancer, Ovarian cancer: Teenagers and young adults, Ovarian cyst, Overactive thyroid, Paget's disease of the nipple, Pancreatic cancer, Panic disorder, Parkinson's disease, Pelvic inflammatory disease, Pelvic organ prolapse, Penile cancer, Peripheral neuropathy, Personality disorder, Pleurisy, Pneumonia, Polymyalgia rheumatica, Post-traumatic stress disorder (PTSD), Postnatal depression, Pregnancy and baby, Pressure ulcers, Prostate cancer, Psoriasis, Psoriatic arthritis, Psychosis, Pubic lice, Rare tumours, Raynaud's phenomenon, Reactive arthritis, Restless legs syndrome, Retinoblastoma, Rhabdomyosarcoma, Rheumatoid arthritis, Ringworm and other fungal infections, Rosacea, Scabies, Scarlet fever, Schizophrenia, Scoliosis, Septic shock, Shingles, Shortness of breath, Sickle cell disease, Sinusitis, Sjogren's syndrome, Skin cancer (melanoma), Skin cancer (non-melanoma), Slapped cheek syndrome, Soft tissue sarcomas, Soft tissue sarcomas: Teenagers and young adults, Sore throat, Spleen problems and spleen removal, Stillbirth, Stomach ache and abdominal pain, Stomach cancer, Stomach ulcer, Stress, anxiety and low mood, Stroke, Sudden infant death syndrome (SIDS), Suicide, Sunburn, Swollen glands, Syphilis, Testicular cancer, Testicular cancer, Testicular lumps and swellings, Thirst, Threadworms, Thrush, Thyroid cancer, Thyroid cancer: Teenagers and young adults, Tinnitus, Tonsillitis, Tooth decay, Toothache, Transient ischaemic attack (TIA), Trigeminal neuralgia, Tuberculosis (TB), Tumor, Type 1 diabetes, Type 2 diabetes, Trichomonas infection, Ulcerative colitis, Underactive thyroid, Urinary incontinence, Urinary tract infection (UTI), Urinary tract infection (UTI), Urticaria (hives), Vaginal cancer, Varicose eczema, Venous leg ulcer, Vertigo, Vitamin B12 or folate deficiency anaemia, Vomiting, Vulval cancer, Warts and verrucas, Whooping cough, Wilms’ tumor, Womb (uterus) cancer, Yellow fever

[00131 ] Certain embodiments of the present invention are described herein, including the best mode known to the inventors for carrying out the invention. Of course, variations on these described embodiments will become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventor expects skilled artisans to employ such variations as appropriate, and the inventors intend for the present invention to be practiced otherwise than specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described embodiments in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.

[00132] Groupings of alternative embodiments, elements, or steps of the present invention are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other group members disclosed herein. It is anticipated that one or more members of a group may be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

[00133] Unless otherwise indicated, all numbers expressing a characteristic, item, quantity, parameter, property, term, and so forth used in the present specification and claims are to be understood as being modified in all instances by the term “about.” As used herein, the term “about” means that the characteristic, item, quantity, parameter, property, or term so qualified encompasses a range of plus or minus ten percent above and below the value of the stated characteristic, item, quantity, parameter, property, or term. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and attached claims are approximations that may vary. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical indication should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and values setting forth the broad scope of the invention are approximations, the numerical ranges and values set forth in the specific examples are reported as precisely as possible. Any numerical range or value, however, inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Recitation of numerical ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate numerical value falling within the range. Unless otherwise indicated herein, each individual value of a numerical range is incorporated into the present specification as if it were individually recited herein.

[00134] Specific embodiments disclosed herein may be further limited in the claims using “consisting of” or “consisting essentially of” language. When used in the claims, whether as filed or added per amendment, the transition term “consisting of” excludes any element, step, or ingredient not specified in the claims. The transition term “consisting essentially of” limits the scope of a claim to the specified materials or steps and those that do not materially affect the basic and novel characteristic(s).

Embodiments of the present invention so claimed are inherently or expressly described and enabled herein.

[00135] Groupings of alternative embodiments, elements, or steps of the present invention are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other group members disclosed herein. It is anticipated that one or more members of a group may be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims. [00136] All patents, patent publications, and other publications referenced and identified in the present specification are individually and expressly incorporated herein by reference in their entirety for the purpose of describing and disclosing, for example, the compositions and methodologies described in such publications that might be used in connection with the present invention. These publications are provided solely for their disclosure prior to the filing date of the present application. Nothing in this regard should be construed as an admission that the inventors are not entitled to antedate such disclosure by virtue of prior invention or for any other reason. All statements as to the date or representation as to the contents of these documents is based on the information available to the applicants and does not constitute any admission as to the correctness of the dates or contents of these documents.

[00137] In closing, it is to be understood that although aspects of the present specification are highlighted by referring to specific embodiments, one skilled in the art will readily appreciate that these disclosed embodiments are only illustrative of the principles of the subject matter disclosed herein. Therefore, it should be understood that the disclosed subject matter is in no way limited to a particular methodology, protocol, and/or reagent, etc., described herein. As such, various modifications or changes to or alternative configurations of the disclosed subject matter can be made in accordance with the teachings herein without departing from the spirit of the present specification. Lastly, the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention, which is defined solely by the claims. Accordingly, the present invention is not limited to that precisely as shown and described.

Claims

CLAIMS What is claimed is:

1 . A computer-implemented method for evaluating health or predicting an ailment in a subject, the method comprising: a) providing historical data; b) identifying one or more markers and physiological events from the historical data; c) associating the one or more markers and physiological events with a disease state; d) collecting health data from a subject; e) detecting the one or more markers and physiological events in the subject; f) predicting a health state of the subject based on altered levels of the markers and physiological events.

2. The computer-implemented method of claim 1 , wherein the markers and physiological events comprise one or more of proteins and mRNA, miRNA, siRNA, acetylation, proteomics, genomics, RANA, proteinomics, protein: protein interactions, and protein:molecule interactions.

3. The computer-implemented method of claim 1 , wherein the step of predicting a health state of the subject uses deep learning and/or a neural network.

4. The computer-implemented method of claim 1 , wherein the step of predicting a health state of the subject uses signal decay mathematics with transfer learning.

5. The computer-implemented method of claim 1 , wherein the step of predicting a health state comprises identifying an ailment or disease.

6. The computer-implemented method of claim 1 , further comprising a step of developing a treatment plan for one or more of disease prevention, treating a disease/ailment, promoting fitness and/or promoting longevity.

7. A computer-implemented method for evaluating health or predicting an ailment in a subject, the method comprising: a) providing historical data on protein: protein and protein:small molecule interactions; b) identifying one or more disease states based on the protein: protein and protein:small molecule interactions from the historical data; c) collecting health data from a subject; and d) predicting a health state of the subject, wherein the health data from the subject includes protein:protein and protein:small molecule interactions from the subject.

8. The computer-implemented method of claim 7, wherein the step of predicting a health state of the subject uses deep learning and/or a neural network.

9. The computer-implemented method of claim 7, wherein the step of predicting a health state of the subject uses signal decay mathematics with transfer learning.

10. The computer-implemented method of claim 7, wherein the step of predicting a health state comprises identifying an ailment or disease.

11 . The computer-implemented method of claim 7, further comprising a step of developing a treatment plan for one or more of disease prevention, treating a disease/ailment, promoting fitness and/or promoting longevity.

12. A system for evaluating the health condition of a subject, the system comprising: a) a means to detect one or more biomarkers, and b) a means to detect one or more physiological events, wherein levels of the one or more biomarkers are recorded to establish baseline biomarker levels for the subject; wherein the one or more physiological events are identified and recorded to establish activity levels for the subject; wherein levels of the one or more biomarkers are compared with historical data to detect altered biomarker levels; wherein the one or more physiological events are compared with historical data to detect altered activity levels; and wherein the system determines a health condition based on the altered levels or biomarkers and/or the altered activity levels.

13. The system of claim 12, wherein the biomarkers and comprise one or more of proteins, DNA, mRNA, miRNA and siRNA.

14. The system of claim 12, wherein the physiological events comprise one or more of acetylation, proteomics, genomics, RANA, proteinomics, protein: protein interactions, and protein:molecule interactions.

15. The system of claim 12, wherein the system comprises a processor for deep learning and/or a neural network.

16. The system of claim 12, wherein health condition comprises an ailment or disease.