Predicting the effectiveness and improving the outcomes of anti-aging treatments based on a biological response to active ingredients

Abstract

Predicting the efficacy and improving the outcomes of anti-aging treatments based on a biological response to active ingredients. In some embodiments, the invention relates to techniques for improving the outcomes of anti-aging treatments. A computer system measures at least one clinical sign of aging for a subject. The computer system receives a plurality of types of biological data for the subject. For each type of biological data, the computer system uses at least one classifier associated with the type of biological data to determine whether the subject is in at least one responder category / exhibits a biological response to a given active ingredient to improve at least one clinical sign of aging.The computer system predicts treatment outcomes for at least one clinical sign of aging for the subject across multiple treatments based on at least one responder category. The computer system determines a skincare regimen including at least one product containing an active ingredient indicated by the predicted treatment outcomes. These techniques can maximize treatment efficacy by placing the subject in a higher-level responder group for an active ingredient. Figure for abstract: 1.

Description

Title of the invention: PREDICTION OF EFFICACY AND IMPROVEMENT OF TREATMENT RESULTS OF AGING BASED ON A BIOLOGICAL RESPONSE TO ACTIVE INGREDIENTS

[0001] The present invention relates to a computer-implemented method of improving aging treatment results, the method comprising:

[0002] the measurement, by a computer system, of at least one clinical sign of aging for a subject;

[0003] receiving, by the computer system, a plurality of types of biological data for the subject;

[0004] for each type of biological data, the use, by the computer system, of at least one classifier associated with the type of biological data to determine whether the subject is in at least one responder category;

[0005] predicting, by the computer system, treatment outcomes for the at least one clinical sign of aging for the subject for a plurality of treatments based on at least one responder category;

[0006] generating, by the computer system, a visualization of a predicted treatment outcome of at least one treatment of the plurality of treatments based on other subjects having similar responder data; and

[0007] determining, by the computer system, a skin care regimen based on the predicted treatment results.

[0008] According to particular embodiments, the method comprises one or more of the following characteristics, taken in isolation or in all technically possible combinations.

[0009] - the method further comprises:

[0010] measuring, by the computer system, the at least one clinical sign of aging for the subject after an application of the skin care regimen; and updating, by the computer system, the at least one classifier based on a difference in the measurement of the at least one clinical sign of aging for the subject after the application of the skin care regimen;

[0011] - biological data types include two or more of data genomics, exomic data, transcriptomic data, epigenomic data, proteomic data, metabolomic data and microbiomic data;

[0012] - the at least one category of responders comprises at least one of:

[0013] a category of retinol responders;

[0014] a category of proxylane responders;

[0015] a category of vitamin C responders;

[0016] a category of hyaluronic acid responders;

[0017] a category of endolysin responders; and

[0018] a category of lipohydroxylic acid (LHA) responders;

[0019] - at least one responder category changes over time for the subject;

[0020] - the at least one category of responders that changes over time for the subject changes based on a date in a physiological cycle for the subject;

[0021] - the physiological cycle of the subject is a menstrual cycle.

[0022] The present invention also relates to a non-transitory computer-readable medium having stored thereon computer-executable instructions which, in response to execution by one or more processors of a computer system, cause the computer system to perform such a method.

[0023] The present invention also relates to a computer system, comprising:

[0024] a responder engine comprising computing circuits configured to: measure at least one clinical sign of aging for a subject;

[0025] receive a plurality of types of biological data for the subject; and

[0026] for each type of biological data, use at least one associated classifier to the type of biological data to determine whether the subject is in at least one responder category;

[0027] a treatment recommendation engine comprising computing circuits configured to:

[0028] predicting treatment outcomes for the at least one clinical sign of aging for the subject for a plurality of treatments based on the at least one responder category; and

[0029] determining a skin care regimen comprising at least one active ingredient based on the predicted treatment results.

[0030] According to a particular embodiment, the computer system comprises the following characteristic: the responder engine further comprises calculation circuits configured to:

[0031] measuring the at least one clinical sign of aging for the subject after application of the skin care regimen; and

[0032] updating the at least one classifier based on a difference in the measurement of the at least one clinical sign of aging for the subject after application of the skin care regimen. DESCRIPTION OF DRAWINGS

[0033] The foregoing aspects and numerous related advantages of this invention will be more readily appreciated as they are better understood with reference to the following detailed description, taken in conjunction with the accompanying drawings, in which:

[0034] [Fig.l] [Fig.l] is a block diagram illustrating aspects of a non-limiting exemplary embodiment of a processing enhancement computer system according to various aspects of the present disclosure.

[0035] [Fig.2] [Fig.2] illustrates a method 200 in accordance with one embodiment.

[0036] [Fig. 3] is a block diagram illustrating an exemplary embodiment non-limiting description of a computing device suitable for use as a computing device with embodiments of the present disclosure. DETAILED DESCRIPTION

[0037] In certain embodiments of the present disclosure, systems, devices and / or methods are provided for predicting the effectiveness of various skin care treatments to improve aging treatment outcomes based on information predicting a biological response to specific active ingredients determined from biological data obtained from subjects.

[0038] [Fig. 1] is a block diagram illustrating aspects of a non-limiting exemplary embodiment of a treatment enhancement computing system according to various aspects of the present disclosure. The illustrated treatment enhancement computing system 110 may be implemented by any computing device or set of computing devices, including but not limited to a desktop computing device, a portable computing device, a mobile computing device, a server computing device, a computing device of a cloud computing system, and / or combinations thereof. The treatment enhancement computing system 110 is configured to use classifiers to process biological data to determine ideal skin care regimens for subjects to treat clinical signs of aging.

[0039] As shown, the processing enhancement computer system 110 includes one or more processors 102, one or more communication interfaces 104, a data store 108, and a computer-readable medium 106.

[0040] In some embodiments, the processors 102 may comprise any suitable type of general-purpose computer processor. In some embodiments, the processors 102 may comprise one or more specialized computer processors or artificial intelligence accelerators optimized for specific computing tasks, including, but not limited to, processing units graphics processing units (GPUs), vision processing units (VPTs) and tensor processing units (TPUs).

[0041] In some embodiments, the communication interfaces 104 include one or more hardware and / or software interfaces adapted to provide communication links between components. The communication interfaces 104 may support one or more wired communication technologies (including but not limited to Ethernet, FireWire, and USB), one or more wireless communication technologies (including but not limited to Wi-Fi, WiMAX, Bluetooth, 2G, 3G, 4G, 5G, and LTE), and / or combinations thereof.

[0042]

[0010] As shown, the computer-readable medium 106 includes logic stored thereon which, in response to execution by the one or more processors 102, causes the treatment improvement computer system 110 to provide a responder engine 112 and a treatment recommendation engine 114.

[0043] As used herein, the term "computer-readable medium" refers to a removable or non-removable device that implements any technology capable of storing information in a volatile or non-volatile manner for reading by a processor of a computing device, including but not limited to: a hard disk drive; flash memory; a solid-state drive; random access memory (RAM); read-only memory (ROM); a CD-ROM, DVD, or other disk storage; a magnetic cassette; magnetic tape; and magnetic disk storage.

[0044] In some embodiments, the responder engine 112 is configured to determine whether a given subject is in a responder category or a non-responder category for various ingredients based on biological data obtained for the given subject. In some embodiments, the treatment recommendation engine 114 is configured to determine a skin care regimen for a given subject based on the responder categories determined by the responder engine 112. The responder engine 112 may use biological data stored in the data store 108, and / or classifiers stored in the data store 108. The treatment recommendation engine 114 may also use information stored in the data store 108 for processing.

[0045] A more detailed description of the configuration of each of these components is provided below.

[0046] As used herein, the term "engine" refers to logic implemented in hardware or software instructions, which may be written in one or more programming languages, including but not limited to C, C++, C#, COBOL, JAVA™, PHP, Perl, HTML, CSS, JavaScript, VBScript, ASPX, Go, and Python. An engine can be compiled into executable programs or written in interpreted programming languages. Software engines can be called from other engines or by themselves. In general, the engines described in this document refer to logic modules that can be merged with other engines, or that can be divided into sub-engines. Engines can be implemented by logic stored in any type of computer-readable medium or computer storage device and be stored on and executed by one or more general-purpose computers, creating a specialized computer configured to provide the engine or its functionality. Engines can be implemented by logic programmed into an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other hardware device.

[0047] As used herein, the term "data store" refers to any suitable device configured to store data that can be accessed by a computing device. An example of a data store is a highly reliable, high-speed relational database management system (DBMS) running on one or more computing devices and accessible over a high-speed network. Another example of a data store is a key-value store. However, any other suitable storage technique and / or device capable of quickly and reliably providing the stored data in response to queries may be used, and the computing device may be accessible locally rather than over a network, or may be provided as a cloud service.A data store may also comprise data stored in an organized manner on a computer-readable storage medium, such as a hard disk drive, flash memory, RAM, ROM, or any other type of computer-readable storage medium. A person skilled in the art will recognize that separate data stores described herein may be combined into a single data store, and / or that a single data store described herein may be separated into multiple data stores, without departing from the scope of the present disclosure.

[0048] [Fig. 2] is a flowchart that illustrates a non-limiting example embodiment of a method for improving aging treatment outcomes according to various aspects of the present disclosure.

[0049] In block 202, a computer system measures at least one clinical sign of aging for a subject. The clinical signs of aging may be any type of age-related change in skin condition that is clinically observable, including but not limited to shiny skin, rough skin, uneven skin tone, wrinkles around the eyes, photoaging, loss of elasticity, and enlarged pores. The computer system may measure the at least one clinical sign of aging. using any suitable technique, including but not limited to computer vision analysis of a three-dimensional image or scan of the subject, providing a questionnaire to be completed by the subject, and providing a questionnaire to be completed by a clinician observing the subject.

[0050] At block 204, the computer system receives a plurality of types of biological data for the subject. The computer system may collect the biological data itself, may receive the biological data from another device that samples the biological data from the subject, or may receive the biological data as entered by the subject or a clinician. Any type of biological data that reflects useful information about the subject may be used, including but not limited to the following:

[0051] • Genomic data

[0052] o Analysis of the structure of the genome of organisms as a whole.

[0053] o Can be collected using new sequencing technologies generation

[0054] * Exomic data

[0055] o Exomes are the protein-coding content of the genetic code, the part of the genome formed by exons. The exome constitutes 1 to 2% of the genome.

[0056] o Solution-based: In solution-based whole exome sequencing (WES), DNA samples are fragmented and biotinylated oligonucleotide probes (baits) are used to selectively hybridize to target regions of the genome.

[0057] o Chip-based: Chip-based methods are similar, except that the probes are linked to a high-density biochip.

[0058] • Transcriptomic data

[0059] o Analyzing all transcripts produced at a given time in an individual, in a pathological state or in a cell tells us which genes are activated or deactivated.

[0060] o Can be collected using cDNA microarrays or RNA-seq techniques.

[0061] o Are reflected in the different rates of transcription (synthesis of RNA molecules in a specific organism, tissue or cell type at a given time).

[0062] * Epigenomic data

[0063] o Chemical marks on DNA, which determine whether the gene is turned on or off.

[0064] * Proteomic data

[0065] o Proteins produced by a particular genome

[0066] • Metabolomic data

[0067] o Metabolites produced by a single organism

[0068] o Can be obtained by NMR spectroscopy.

[0069] • Microbiomic data

[0070] o Microorganisms (and their genes) living in a specific environment (e.g., intestine, skin)

[0071] • Metagenomic data

[0072] o The genes of microorganisms in a specific environment.

[0073] • Hormonal data

[0074] o May include estrogen, progesterone, testosterone, cortisol, melatonin, serotonin, growth hormone, leptin, ghrelin and insulin

[0075] o Can be measured using a blood test or saliva test

[0076] o Hormone levels change over time and indicate points in a physiological cycle, such as a menstrual cycle

[0077] In block 206, the computer system uses, for each biological data type, at least one classifier associated with the biological data type to determine whether the subject is in at least one responder category. A responder category indicates whether the subject will respond to a particular skin care product ingredient associated with the responder category. For example, the responder categories may include, but are not limited to, a retinol responder category, a proxylane responder category, a vitamin C responder category, a hyaluronic acid responder category, an endolysin responder category, and a lipohydroxylic acid (LHA) responder category.

[0078] In some embodiments, separate classifiers may be trained for each type of biological data and each responder category. In some embodiments, a single classifier may be trained to receive multiple types of biological data to determine a single responder category. In some embodiments, a single classifier may be trained for each type of biological data, but may provide categorization for multiple responder categories. Any suitable type or combination of types of classifiers may be used, including but not limited to decision trees, naive Bayes classifiers, k-nearest neighbor classifiers, support vector machines, and artificial neural networks.The classifiers may be trained using any suitable technique, including but not limited to determining a labeled training data set using subjects for which ground truth responder category information is known, and training the classifiers using the labeled training data by a technique including but not limited to gradient descent.

[0079] In addition to the above, other information may be used to determine responder categories. For example, in some embodiments, indications of time-dependent changes in methylation DNA (mDNA) are used to estimate molecular age relative to chronological age in human tissues. See, for example, Boroni, M., Zonari, A., Reis de Oliveira, C. et al. “Highly accurate skin-specific methylome analysis algorithm as a platform to screen and validate therapeutics for healthy aging,” Clin Epigenet 12, 105 (2020); available at https: / / doi.org / 10.1186 / sl3148-020-00899-l . As another example, in some embodiments, indications of genetic susceptibility to UV damage are used to classify an individual as a responder to sunscreen protection (see, e.g., Lear JT et al, “Detoxifying Enzyme Genotypes and Susceptibility to Cutaneous Malignancy,” Br J Dermatol. 2000 Jan; 142(1):8-15 doi: 10.1046 / j.l365-2133.2000.03339.x. PMID: 10651688; available at https: / / pubmed.ncbi.nlm.nih.gov / 10651688 / (describing the importance of polymorphisms in detoxifying enzyme genes in determining susceptibility to skin cancer). As yet another example, in some embodiments, one or more genomic, transcriptomic, proteomic, or metabolomic biomarkers of psoriasis are used to classify an individual as a responder to certain skin care products, (see, e.g., Jiang S et al, “Biomarkers of An Autoimmune Skin Disease—Psoriasis.” Genomics Proteomics Bioinformatics. 2015;13(4):224-233; doi: 10.1016 / j.gpb.2015.04.002).

[0080] At block 208, the computing system predicts treatment outcomes for the at least one clinical sign of aging for the subject for a plurality of treatments based on the at least one responder category. In some embodiments, the computing system may be configured with information regarding effects of various skin care treatments for various responder categories. For example, the computing system may be configured to know how a given skin care treatment will influence a given clinical sign of aging for subjects in a responder category for a given skin care treatment ingredient compared to subjects who are not in the responder category for the given skin care treatment ingredient.

[0081] At block 210, the computer system determines a skin care regimen based on the predicted treatment outcomes. For example, the computer system may determine one or more products having ingredients that were determined at block 208 to enhance treatment outcomes for the subject. In some embodiments, the computer system may provide an indication of the skin care regimen to the subject or a clinician to recommend products for use. In some embodiments, the computer system may provide an indication of the skin care regimen to a device for composing a skin care product. personalized skin that includes ingredients determined to enhance treatment results.

[0082] At optional block 212, the computer system measures the at least one clinical sign of aging for the subject after application of the skin care regimen. The computer system may use a technique similar to that used at block 202 to measure the at least one clinical sign of aging. At optional block 214, the computer system updates the at least one classifier based on a difference in the measurement of the at least one clinical sign of aging for the subject after application of the skin care regimen.For example, the difference in measurement of the at least one clinical sign of aging may be used to determine a ground truth indicating whether the subject was in a responder or non-responder category for the ingredients used in the skin care regimen (e.g., if there was improvement, the ground truth would be that the subject was in the responder category, and if there was no improvement or less improvement than expected, the ground truth would be that the subject was in the non-responder category). This ground truth may then be used with the subject's biological data to retrain the appropriate classifiers. Optional block 212 and optional block 214 are illustrated as optional because, in some embodiments, this data collection and retraining of the classifiers may not be performed.

[0083] [Fig. 3] is a block diagram that illustrates aspects of an exemplary computing device 300 suitable for use as a computing device of the present disclosure. Although multiple different types of computing devices have been discussed above, the exemplary computing device 300 describes various elements that are common to many different types of computing devices. Although [Fig. 3] is described with reference to a computing device that is implemented as a device on a network, the description below is applicable to servers, personal computers, mobile phones, smartphones, tablet computers, embedded computing devices, and other devices that may be used to implement portions of embodiments of the present disclosure.Some embodiments of a computing device may be implemented in or may include an application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other custom device. In addition, those skilled in the art and others will recognize that computing device 300 may be any of any number of devices currently available or yet to be developed.

[0084] In its most basic configuration, the computing device 300 comprises at least one processor 302 and one system memory 310 connected by a bus of communication 308. Depending on the exact configuration and device type, system memory 310 may be volatile or non-volatile memory, such as read-only memory (“ROM”), random access memory (“RAM”), EEPROM, flash memory, or similar memory technology. Those skilled in the art and others will recognize that system memory 310 typically stores data and / or program modules that are immediately accessible to and / or currently operating on processor 302. In this regard, processor 302 may serve as the computational center of computing device 300 by supporting instruction execution.

[0085] As further illustrated in [Fig. 3], the computing device 300 may include a network interface 306 including one or more components for communicating with other devices on a network. Embodiments of the present disclosure may access basic services that utilize the network interface 306 to perform communications using common network protocols. The network interface 306 may also include a wireless network interface configured to communicate via one or more wireless communication protocols, such as Wi-Fi, 2G, 3G, LTE, WiMAX, Bluetooth, Bluetooth Low Energy, and / or the like. As will be appreciated by those skilled in the art, the network interface 306 illustrated in [Fig. 3] may represent one or more of the wireless interfaces or physical communication interfaces described and illustrated above with respect to particular components of the computing device 300.

[0086] In the exemplary embodiment shown in [Fig. 3], the computing device 300 also includes a storage medium 304. However, it is possible to access services using a computing device that does not include means for retaining data on a local storage medium. Therefore, the storage medium 304 shown in [Fig. 3] is represented by a dotted line to indicate that the storage medium 304 is optional. In any event, the storage medium 304 may be volatile or non-volatile, removable or non-removable, implemented using any technology capable of storing information such as, but not limited to, a hard disk drive, a solid-state drive, a CD ROM, a DVD or other disk storage, magnetic cassettes, magnetic tape, magnetic disk storage, and / or other similar media.

[0087] Suitable implementations of computing devices that include a processor 302, a system memory 310, a communication bus 308, a storage medium 304, and a network interface 306 are known and commercially available. For ease of illustration and because it is not important to the understanding of the claimed subject matter, [Fig. 3] does not show some of the components typical of many computing devices. In this regard, computing device 300 may include input devices, such as a keyboard, keypad, mouse, microphone, touch input device, touchscreen, tablet, and / or the like. These input devices may be coupled to computing device 300 via wired or wireless connections including RF, infrared, serial, parallel, Bluetooth, Bluetooth Low Energy, USB, or other suitable connection protocols using wireless or physical connections. Similarly, computing device 300 may also include output devices such as a display, speakers, printer, etc. Because these devices are well known in the art, they are not illustrated or described further herein.

[0088] Although illustrative embodiments have been illustrated and described, it will be appreciated that various modifications may be made thereto without departing from the spirit and scope of the invention.

Claims

Claims

1. A computer-implemented method of improving aging treatment outcomes, the method comprising: measuring, by a computer system, at least one clinical sign of aging for a subject; receiving, by the computer system, a plurality of types of biological data for the subject; for each type of biological data, using, by the computer system, at least one classifier associated with the type of biological data to determine whether the subject is in at least one responder category; predicting, by the computer system, treatment outcomes for the at least one clinical sign of aging for the subject for a plurality of treatments based on at least one responder category;generating, by the computer system, a visualization of a predicted treatment outcome of at least one of the plurality of treatments based on other subjects having similar responder data; and determining, by the computer system, a skin care regimen based on the predicted treatment outcomes, the at least one responder category comprising at least one of: a retinol responder category; a proxylane responder category; a vitamin C responder category; a hyaluronic acid responder category; an endolysin responder category; and a lipohydroxylic acid (LHA) responder category.;

2. The computer-implemented method of claim 1, further comprising: measuring, by the computer system, the at least one clinical sign of aging for the subject after an application of the skin care regimen; and updating, by the computer system, the at least one classifier based on a difference in the measurement of the at least one a clinical sign of aging for the subject after application of the skin care regimen.

3. The computer-implemented method of claim 1, wherein the biological data types comprise two or more of genomic data, exomic data, transcriptomic data, epigenomic data, proteomic data, metabolomic data, and microbiomic data.

4. The computer-implemented method of claim 1, wherein at least one responder category changes over time for the subject.

5. The computer-implemented method of claim 4, wherein at least one responder category that changes over time for the subject changes based on a date in a physiological cycle for the subject.

6. The computer-implemented method of claim 5, wherein the physiological cycle of the subject is a menstrual cycle.

7. A non-transitory computer-readable medium having stored thereon computer-executable instructions which, in response to execution by one or more processors of a computer system, cause the computer system to perform a method as described in any one of claims 1 to 6.

8. A computer system, comprising: a responder engine having computational circuitry configured to: measure at least one clinical sign of aging for a subject; receive a plurality of biological data types for the subject; and for each biological data type, use at least one classifier associated with the biological data type to determine whether the subject is in at least one responder category; a treatment recommendation engine having computational circuitry configured to: predict treatment outcomes for the at least one clinical sign of aging for the subject for a plurality of treatments based on the at least one responder category; and determining a skin care regimen comprising at least one active ingredient based on the predicted treatment outcomes, the computer system further configured to generate a visualization of a predicted treatment outcome of at least one of the plurality of treatments based on other subjects having similar responder data, the at least one responder category comprising at least one of: a retinol responder category; a proxylane responder category; a vitamin C responder category; a hyaluronic acid responder category; an endolysin responder category; and a lipohydroxylic acid (LHA) responder category.

9. The computer system of claim 8, wherein the responder engine further comprises computational circuitry configured to: measure the at least one clinical sign of aging for the subject after an application of the skin care regimen; and update the at least one classifier based on a difference in the measurement of the at least one clinical sign of aging for the subject after the application of the skin care regimen.

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