KR102397683B1 - Methods and systems for microbial pharmacogenomics - Google Patents

Abstract

Embodiments of the method and system for microbial pharmacogenomics include: a sample handling system comprising: a sequencing system operable to collect a container comprising a biological sample from a set of users, and operable to determine a microbial sequence from the biological sample ; determine microbiome pharmacogenomic data based on the microbial sequence, collect supplemental data associated with the antibiotic-associated condition for the set of users, and extract from the supplemental data and the microbiome pharmacogenomic data a microbiome characterization system operable to transform characteristics into a characterization model associated with the antibiotic-associated condition; and a treatment system operable to facilitate user treatment for the antibiotic-related condition based on characterizing the user biological material with the characterization model with respect to the antibiotic-related condition.

Description

Methods and systems for microbial pharmacogenomics

FIELD OF THE INVENTION The present invention relates generally to the field of microbiology, and more particularly to new and useful methods and systems for microbial pharmacogenomics in the field of microbiology.

Cross-referencing of related applications

This application is a partial continuation application of US Patent Application No. 15/606,743 filed on May 26, 2017, and the partial continuation application is a continuation application of US Patent Application No. 14/919,614 filed on October 21, 2015. , the continuation applications are US Provisional Application No. 62/066,369, filed on October 21, 2014, US Provisional Application No. 62/087,551, filed on December 4, 2014, and U.S. Provisional Application filed on December 17, 2014 No. 62/092,999, U.S. Provisional Application No. 62/147,376, filed on April 14, 2015, U.S. Provisional Application No. 62/147,212, filed on April 14, 2015, United States filed on April 14, 2015 Benefit from the priority of Provisional Application No. 62/147,362, U.S. Provisional Application No. 62/146,855, filed on April 13, 2015, and U.S. Provisional Application No. 62/206,654, filed on August 18, 2015, each of which The application of is incorporated herein by reference in its entirety.

This application additionally enjoys the benefit of priority of U.S. Provisional Application No. 62/361,943, filed on July 13, 2016, which provisional application is incorporated by reference in its entirety.

The microbiome is an ecological community of commensal, symbiotic pathogenic microorganisms associated with an organism. Although the human microbiome contains as many microbial cells as the human cells present in the entire human body, the characterization of the human microbiome is still difficult due to limitations in sample processing technology, genetic analysis technology, and resources for processing large amounts of data. It is in an early stage. Nevertheless, the microbiome is suspected of playing at least a partial role in many health/disease-related conditions (eg, childbirth preparation, diabetes, autoimmune diseases, gastrointestinal disorders, rheumatic diseases, neurological disorders). Given the enormous impact of the microbiome on a subject's health, it is important to characterize the microbiome, gain insights from characterization, and develop therapies designed to correct the dysbiosis of the gut. effort must be pursued. However, current methods and systems for analyzing the human microbiome and providing therapeutic means based on the insights gained do not solve many questions. In particular, therapies tailored to a particular subject (e.g., probiotic/prebiotic therapy, fecal microbiota) based on a particular health condition and microbiome composition, functional features and/or pharmacogenomic characteristics. Methods for characterizing microbiota transplantation, etc.) could not be realized due to the limitations of current technology.

As such, there is a need in the field of microbiology for new and useful methods and systems for microbial pharmacogenomics in an individual and demographic manner. The present technology creates these new and useful methods and systems.

Degree 1a-1b depict representative flowcharts in modified embodiments of methods for microbial pharmacogenomics.
2 depicts a modified embodiment of a system and method for microbial pharmacogenomics.
3 depicts a variant of the process for model generation in an embodiment of the method and system for microbial pharmacogenomics.
4 is Depicts variants of the output of a therapeutic model in embodiments of methods and systems for microbial pharmacogenomics.
5 depicts a variant of the application of models in an embodiment of a method for microbial pharmacogenomics.
6 depicts a variant of the application of multiple models in an embodiment of a method for microbial pharmacogenomics.
7 is A modified embodiment of a method for microbial pharmacogenomics is depicted as a variant of facilitating therapy.
8 is Depicts variants of the mechanisms that actuate probiotic-based therapies in embodiments of methods for microbial pharmacogenomics.
9 depicts a variant of providing an alert in an embodiment of a method for microbial pharmacogenomics.
10 depicts a variant of the interface for providing antibiotic-related information in an embodiment of a method for microbial pharmacogenomics.
11 is Depicts variants of providing an alert in an embodiment of a method for microbial pharmacogenomics.
12 depicts a variant of changing a sample processing parameter in an embodiment of a method for microbial pharmacogenomics.

1. Overview

2 , characterizing (e.g., evaluating) an antibiotics-associated condition with respect to a user (e.g., human subject, patient, animal subject, environmental ecosystem, care provider, etc.) Implementations of the system 200 for ) are operable to collect containers comprising biological material (eg, biological material, such as a nucleic acid material) from a set of users, from one or more: a handling system 210 (eg, a sample handling system) comprising a sequencing system (eg, a sequencer system) operable to determine a microbial sequence; determine microbiome pharmacogenomic data (and/or at least one microbiome composition data and microbiome functional diversity data) based on the microbial sequence, and collect supplemental data associated with antibiotic-associated status for said set of users and transforming features extracted from the supplemental data and the microbiome pharmacogenomic data (and/or at least one microbiome composition data and microbiome functional diversity data) into a characterization model associated with an antibiotic-associated condition. a microbiome characterization system 220 operable to and based on characterizing the user biological material with the characterization model with respect to the antibiotic-associated condition, the user treatment (eg, therapy) for the antibiotic-related condition (eg, the treatment is the antibiotic- a treatment system 230 (eg, a therapy system) operable to facilitate adjustment of a user microbiome composition to improve a state, such as a related condition; can

The system 200 and method 100 characterize a user (eg, using characterization models for clinical diagnosis, companion diagnosis, etc.) according to a microbiome dataset associated with one or more antibiotic-associated conditions and/or Or it can serve to diagnose. The system 200 and method 100 may additionally or alternatively function to facilitate (eg, provide) treatment, such as a means of treatment, to a user and/or to perform any suitable function. . Variations of the system 200 and/or method 100 may be used to create and/or provide personalized antibiotic regimens in a rapid and efficient manner compared to antibiotic-based standard of care. Furthermore, variations of the system 200 and/or method 100 may provide for monitoring and/or modulation of such therapy provided to a subject, for example, through receipt, processing, or analysis of additional samples from the subject throughout the course of treatment. can facilitate

The system 200 and method 100 are preferably capable of generating and facilitating characterization and/or therapy for antibiotic-associated conditions (eg, antibiotic-treatable conditions), which may include any one or more of: symptoms , cause, disease, disorder, microbiome pharmacogenomics profile (describing, for example, antibiotic resistance and/or susceptibility to an antibiotic-treatable condition) and/or other appropriate aspects associated with an antibiotic-related condition may include Antibiotic-related conditions include any one or more of: gonorrhea, urinary tract infection, vaginal trichomoniasis, acne, appendicitis, atrial septal defect, ureteric cyst, urethral syndrome, urethritis, tuberculosis, bacterial arthritis, bacterial vaginosis, dizziness, imbalance, pressure Ulcer, bursitis, bronchitis, syphilis, tonsillitis, pharyngitis, sepsis, pyelonephritis, otitis media, deafness, peritonitis, pericarditis, pelvic inflammatory disease, meningitis, laryngitis, strep throat, sinusitis, other sexually transmitted diseases, other skin-related conditions, other ear-related conditions, other mouth-related conditions, other bacterial induced infections and/or other appropriate antibiotic-related conditions.

One or more examples of a method 100 and/or process described herein may be asynchronous (eg, sequentially), concurrently (eg, in parallel; sample processing and/or antibiotic-related conditions). concurrently on another thread for parallel computing to improve computational processing for characterizing and/or determining and/or providing therapies for; etc.), in temporal relation to the trigger event, and/or or in any other suitable order at any suitable time and frequency, and/or using one or more instances, elements, and/or entities described herein of the system 200 . However, the method 100 and/or system 200 may be configured in any suitable manner.

2. Advantages

Microbiome analysis may enable accurate and efficient characterization and/or provision of therapies for antibiotic-related conditions caused and/or otherwise associated with microorganisms. This technique may overcome several difficulties faced by existing approaches in characterizing and/or facilitating therapies for antibiotic-associated conditions. First, existing approaches require the patient to visit one or more care providers to characterize and/or recommend therapy for an antibiotic-related condition, which may lead to inefficiencies and health-related inefficiencies associated with the amount of time elapsed prior to diagnosis and/or treatment. can put you at risk. Second, existing gene sequencing and analysis techniques for sequencing the human genome may be incompatible and/or inefficient when applied to the microbiome (e.g., the human microbiome contains more than 10 times more microbial cells than human cells). may include; optimal sample handling techniques may differ; sequence reference databases may differ; microbiome characterization may include interpretation of different microbiome pharmacogenomic profiles across populations and/or individuals with different microbiome may include; different approaches may be different in microbiome pharmacogenomics analysis compared to human genome pharmacogenomics analysis; microbiome may be different for different body parts of the user; etc.). Third, the initiation of sequencing technology (eg, next-generation sequencing method) is a technical problem that does not exist (eg, data processing problem, information display problem, microbiome analysis problem, treatment prediction problem, treatment provision problems, etc.), but has brought unprecedented advances in the speed and data generation associated with the sequencing of genetic material. Embodiments of the system 200 and method 100 may provide a technologically rooted solution to at least the difficulties described above.

First, the technology facilitates computational capabilities of functions that were previously inoperable, thereby enabling computer-related technologies (eg, personalized characterization and/or modeling associated with determining treatment for antibiotic-associated conditions; the microbiome; Pharmacogenomics computer analysis; computer processing related to biological material processing; etc.) can be improved. For example, the technology is based on microbiome pharmacogenomics data derived from technologies that have recently become viable due to advances in sample processing techniques and sequencing techniques (e.g., the Genome Reference Consortium). Consortium) can be utilized to computationally generate microbiome characterizations and/or recommended therapies for antibiotic-associated conditions).

Second, the technique may lead to improvements in processing speed, microbiome characterization accuracy, microbiome-related therapy determination and facilitation, and/or other appropriate aspects with respect to antibiotic-related conditions. The technique provides a vast and potential (eg, extractable from vast microbiome data) feature pools for generating and/or applying characterization models and/or therapeutic models (eg, antibiotic therapy models). of features) to select an optimized subset of features (e.g., microbiome pharmacogenomic features, microbiome compositional features, microbiome functional diversity features, etc.) Feature-selection rules (eg, microbiome pharmacogenomics feature-selection rules; antibiotic-related feature-selection rules) may be created and applied. The potential size of the microbiome (e.g., human microbiome, animal microbiome, etc.) can be translated into vast amounts of data, which provide actionable microbiome insights with respect to antibiotic-associated states. It raises the question of how to process and analyze vast amounts of data to obtain it. However, the feature-selection rules and/or other suitable computer-executable rules reduce generation and execution time (eg, for generating and/or applying decision tree models), and Efficient interpretation of results through simplification of results, reduction of overfitting, and collection of microbiome datasets (e.g., microbiome pharmacogenomics dataset, microbiome composition dataset, microbiome functional diversity dataset) and improving data sources (to improve the predictive power of technologies); Improving identification and presentation (e.g., storing specific models, microbial sequences, characteristics, and/or other appropriate data associated with users and/or sets of users to improve personalized characterization and/or delivery of treatments) It allows for improved data storage and retrieval and other suitable improvements that facilitate characterization and/or rapid decisions of treatment.

third, The technology may transform an entity (eg, a user, a biological sample, a treatment system including a medical device, etc.) into another state or thing. For example, the system 200 and/or method 100 may provide a patient with a microbiome pharmacogenomic profile, microbiome composition, and/or microbiome functional to prevent and/or ameliorate an antibiotic-associated condition. Treatments to promote alteration of diversity may be identified, thereby modifying the microbiome and/or the health of the patient. In another embodiment, the technique can transform biological material collected from a patient (eg, via fragmentation, multiplex amplification, sequencing, etc.) into a microbiome dataset, which can later be used as a characterization model and/or therapy It can be transformed into features that correlate with antibiotic-associated status to create a model. In yet another embodiment, the technique may control a treatment system for facilitating therapy (eg, by generating control instructions (instructions) for the treatment system to execute), thereby modifying the treatment system. can In another embodiment, improvements in computer-related techniques may include microbiome pharmacogenomic characteristics (eg, mutations associated with antibiotic resistance or sensitivity), microbiome composition characteristics and/or microbiome associated with antibiotic-related conditions. Modifications to biological sample processing approaches, such as selecting a subset of primers compatible with genetic targets identified to correspond to biome functional diversity characteristics, can lead to modifications.

fourth, The technique corresponds to a creative distribution of functionality across a sample handling system, a microbiome characterization system, and a network comprising a plurality of users, wherein the sample handling system receives biological samples from the plurality of users substantially simultaneously (e.g., for antibiotic-associated conditions (eg, customized to the user's microbiome, such as the user's microbiome pharmacogenomic profile, medical history, demographics, behaviors, preferences, etc.) ) can be utilized by the microbiome characterization system to develop personalized characterizations and/or therapies.

Fifth, the technology may improve at least the field of computer modeling of antibiotic-related conditions with respect to microbiome digital medicine, digital medicine in general, gene sequencing and/or other related fields. Sixth, the technique is a computing device (e.g., a sample handling system such as a sequencing system; Ohmic characterization systems; processing systems; devices associated with the back) may be utilized. However, the technique may provide other suitable advantages in the context of using non-generalized computer systems for microbiome characterization and/or modulation.

3. System

The handling system 210 of the system 200 may function to receive and process (eg, fragment, amplify, sequence analysis, etc.) a biological sample. The handling system 210 may additionally or alternatively be configured for a plurality of users (eg, in response to a purchase order for the sample kit 250 ) via a postal delivery system and/or other suitable procedure. For example, it may function to provide and/or collect a sample kit 250 (including containers configured to receive biological material, self-sampling process guide instructions for users, etc.). In embodiments, the sample kit 250 includes materials for a user (eg, via a carton tip swab; aspiration of fluids; biopsy; etc.) for sample collection from one or more collection sites; Relevant guidance may be included. The collection site may include one or more: female genitalia, male genitalia, rectum, intestine, skin, mouth, nose, any mucous membranes, and/or other suitable sample providing site (eg, blood, sweat, urine, feces, semen, vaginal secretions). , tears, tissue samples, interstitial fluids, other bodily fluids, etc.). The handling system 210 may additionally or alternatively automatically prepare a biological sample to be sequenced by a sequencing system (eg, a next-generation sequencing platform) (eg, antibiotic-associated conditions, such as multiplexing, etc.) a library preparation system operable for fragmentation and/or amplification using primers compatible with the nucleic acid sequence associated with it; and/or any suitable elements. However, the handling system 210 and related elements may be configured in any suitable manner.

The microbiome characterization system 220 of the system 200 has the ability to determine and/or analyze a microbiome dataset and/or supplemental dataset for characterizing and/or determining a therapy for an antibiotic-associated condition. can do. In a variant, the microbiome characterization system 220 may set computer-executed rules (eg, feature selection rules; model generation rules; user preference rules; data storage, retrieval and/or display rules; microbial sequence generation rules). ; sequence alignment rules; and/or other suitable rules) may be obtained and/or applied. However, the microbiome characterization system 220 may be set up in any suitable manner.

The treatment system 230 of the system 200 provides a user (e.g., subject; reduce; change a user's microbiome pharmacogenomic profile to a treatment-sensitive condition for an antibiotic-treatable condition, etc.) to facilitate one or more therapies. The treatment system 230 may include any one or more of: (eg, for communicating treatment recommendations via interface 240 , or via notifying a care provider to recommend and/or provide treatment; telemedicine; a communication system, an application executable on the user device (e.g., an antibiotic-related status application to facilitate treatment; a drug reminder application; an application operable to communicate with an automatic drug dispenser; etc.); Antibiotic-related therapies such as antibiotics (eg, type, dosage, dosing schedule, etc.), auxiliary medical devices (eg, drug dispensers; antibiotic applicators for topical delivery, biodegradable antibiotic delivery systems, non-biodegradable Pharmaceutical devices comprising antibiotics, such as antibiotic delivery systems, antibiotic delivery agents, nanoparticle delivery systems, scaffold delivery systems, bead delivery systems, release control devices, dissolution devices, and/or other suitable drug delivery devices; of a diagnostic device; etc.), a user device (eg, including a biometric sensor), and/or other suitable elements. One or more treatment systems 230 may preferably be controlled by a microbiome characterization system 220 . For example, the microbiome characterization system 220 may be configured to transmit control instructions and/or control instructions to the treatment system 230 for activating and/or otherwise operating the treatment system 230 in facilitating therapy. You can create notifications. However, the treatment system 230 may be configured in any other manner.

As shown in FIG. 10 , the system 200 may additionally or alternatively provide antibiotic-related information (eg, characterization of an antibiotic-related condition; treatment recommendations; comparison with other users; interface 240 that can serve to improve the way in which treatment evaluations with respect to microbiome pharmacogenomic profiles are presented; microbiome composition; microbiome functional diversity; etc., as shown in FIG. may include In another embodiment, the interface 240 may include microbiome composition (eg, taxa), functional diversity (eg, relative abundance of genes associated with functions correlating with antibiotic-associated status, etc.); and/or groups of users that share demographic characteristics (e.g., patients who share a condition, smokers, exercisers, other diet users, probiotic consumers, antibiotic users, groups, etc.) may provide antibiotic-related information, including risk of one or more antibiotic-related conditions, such as preparedness. In yet another embodiment, the interface 240 may be configured to present a microbiome pharmacogenomic profile (and/or microbiome composition, microbiome functional diversity, etc.) over time with respect to the treatment and the antibiotic-related condition. may be operable to provide antibiotic-related information including changes. In a specific embodiment, the interface provides an indication of antibiotic-related information that is associated with an antibiotic-treatable status and derived based on a comparison between a group of users that share a user microbiome pharmacogenomic profile and demographic characteristics for the user. may be operable to improve. In another specific embodiment, the interface display of antibiotic-related information (e.g., a characterization element that satisfies a threshold condition; a user microbiome pharmacogenomics profile that matches a reference profile that exceeds a threshold similarity; exceeds a threshold value) antibiotic-related condition risk; based on other trigger events; etc.) through selection and provision of a subset of antibiotic-related information (e.g., highlighting and/or highlighting a subset of antibiotic-related information); can be improved. However, the interface 240 may display any suitable information and may be configured in any suitable manner.

The system 200 and/or elements of the system 200 may, in whole or in part, execute, host, communicate and/or otherwise: a remote computing system (eg, a server, at least one network computing system). , stateless, stateful), local computing system, database (eg, user database, microbiome dataset database, antibiotic-related state database, therapeutic database, etc.), user device (eg, user smartphone) , computer, laptop, assistive medical device, wearable medical device, care providing device, etc.) and/or any suitable element. For example, the system 200 may be configured to perform an appropriate portion of the method 100, such as determination of microbiome pharmacogenomics data, the handling system 210 (e.g., the handling system 210) a computing system operable to communicate with a next-generation sequencing platform of Although the elements of the system 200 are generally described as distinct elements, they may be physically and/or logically integrated in any manner. For example, a smartphone application may partially or fully implement the microbiome characterization system 220 (eg, apply a characterization model that generates a characterization of an antibiotic-associated condition in real time; sequence a biological sample) Analyze; process microbial sequences; extract features from microbiome datasets; etc.) and execute treatment system 230 (eg, communicate with a smartphone's calendar application to determine the antibiotic therapy model notify the user to take antibiotics according to parameters). Additionally or alternatively, the functionality of the system 200 may be distributed in any suitable manner among any suitable system elements. However, the elements of the system 200 may be configured in any suitable manner.

4. Method

1A and 1B , an embodiment of a method 100 for microbial pharmacogenomics for a user with respect to an antibiotic-associated condition comprises: at least one microbiome based on a biological sample obtained from a set of subjects. determine a pharmacogenomics dataset, a microbiome composition dataset, and a microbiome functional diversity dataset (S110); receive a supplemental dataset associated with at least a subset of a set of subjects, wherein the supplemental dataset informs an antibiotic-related status for the set of subjects (S120); and characterization associated with the antibiotic-associated condition based on features extracted from the supplemental dataset and at least one of the microbiome pharmacogenomics dataset, the microbiome composition dataset and the microbiome functional diversity dataset. It may include executing the process (S130).

Embodiments of the method 100 may additionally or alternatively be configured to modulate microbial abundance, distribution, functional diversity, and/or pharmacogenomic diversity in a subject being characterized according to the characterization process (e.g., as an antibiotic therapy model). ) to determine the treatment (S140); receiving a biological sample from the user (S150); and the antibiotic-associated state based on processing with the characterization process at least one microbiome pharmacogenomics dataset, a microbiome composition dataset and a microbiome functional diversity dataset derived from a biological sample obtained from the user; identify the relevant user's characterization (S160); promote treatment of the subject (eg, antibiotic therapy, etc.) based on the characterization (eg, and treatment model, etc.) (S170); and monitoring the effectiveness of the therapy for the subject at different time points based on processing the biological sample to evaluate at least one microbiome pharmacogenomic profile, microbiome composition, and microbiome functional diversity (S180). may include As such, the method 100 can be used for therapy monitoring and potentially for characterization as an intermediate step, in particular for evaluation of one or more antibiotic therapies.

4.1 Method-Dataset Processing

Block S110 characterizes microbiome composition, function, and/or pharmacogenomics for a full set of each biological sample associated with the subject population, thereby characterizing at least one microbiome composition diversity dataset for the subject population; Lists generating microbiome functional diversity datasets and microbiome pharmacogenomics datasets. Block S110 serves to each process the entire set of biological samples to determine composition, function, and/or pharmacogenomic aspects associated with the microbiome of each population of subjects. Composition, function, and pharmacogenomic aspects (e.g., as measured by the total abundance of each group, the relative abundance of each group, the total number of groups represented, etc.) of other groups of families, phyla, classes, orders, families, and genera , compositional aspects at the microbial level, including parameters related to the distribution of microorganisms across species, subspecies, strains, and/or other suitable infraspecies taxon. Composition, function, and pharmacogenomic aspects may also be expressed in operational taxonomic units (OTUs). Composition, function, and pharmacogenomic aspects may additionally or alternatively be determined at the genetic level (eg, multilocus sequence typing), regions, 16S rRNA sequences, 18S rRNA sequences, ITS sequences, protein-encoding sequence, other genetic markers, other phylogenetic markers, etc.). Composition, function, and pharmacogenomic aspects may include the presence or absence, or amount, of a gene associated with a particular function (eg, enzymatic activity, migratory function, immune activity, antibiotic resistance gene, etc.). Thus, the output of block S110 may be used to provide features of interest to the characterization process of block S130 , the treatment process of block S140 , and/or other suitable portions of the method 100 , wherein the features are microbial-based (e.g., the presence of a bacterial genus), gene-based (e.g., based on representativeness of a specific gene region and/or sequence), function-based (e.g., presence of a specific catalytic activity), pharmacogenomics-based ( for example, codon mutations, exon deletions or substitutions, gene rearrangements, positional shifts, etc.), and/or otherwise set.

Additionally or alternatively, the S130 block (eg, features associated with the S130 block) may include a nucleotide region or other nucleotide-derived functional characteristic (eg, structure or modulation) associated with resistance or metabolism of biologically active molecules. RNAs, messenger RNAs, proteins or peptides). In particular, biologically active molecules, regardless of their source (eg, diet, environment), may include, but are not limited to, antibiotics, antibodies, peptides, hormones, and other endogenous or exogenous molecules. In variations, the method 100 may generate (eg, complete) one or more entire metagenomes for any user (eg, for an individual user, for an entire set of subjects, such as a population of subjects). For microbial metagenome sequence analysis; without using a primer; in combination with a primer; etc.) amplification may be included. In a specific embodiment, the method 100 includes collecting a biological sample from a set of users, including self-sampling of users; processing (eg, isolating, amplifying, sequencing, aligning, etc.) the entire metagenome from the biological sample; at least one microbiome dataset, microbiome compositional diversity characteristics, microbiome functional diversity characteristics, and microbiome pharmacogenomic characteristics (eg, prevalence of genes associated with antibiotic resistance or sensitivity, etc.) decide; and characterizing (eg, antibiotic-associated condition) and/or determining a therapy based on the characteristics (and/or microbiome dataset). However, processing of the entire metagenome may be performed in any suitable manner.

In variants (eg, in the context of clinical diagnosis and treatment), block S110 may additionally or alternatively inform the characterization of a subsequent block of the method 100 and/or a therapy model (eg, analyzing and processing environmental samples (eg, from the subject's hospital environment, from the subject's home environment, etc.). For example, identification of microbes in a subject's environment can be used to inform a therapeutic model in encouraging a therapy that prevents a subject from having the type of microorganism present in the subject's environment. However, environmental samples may be used in any suitable manner to support the method 100 .

In variations, processing the sample at block S110 may include one or more of: lysis of the biological sample, disruption of cell membranes of the biological sample, isolation of unwanted elements (eg, RNA, protein) from the biological sample, nucleic acids in the biological sample (e.g., DNA) purification, amplification of nucleic acids from the biological sample (e.g., with a library preparation system), further purification of the amplified nucleic acids of the biological sample, and sequencing of the amplified nucleic acids of the biological sample. can

In variants of the S110 block, the amplification of the purified nucleic acid is preferably carried out by at least one or more: polymerase chain reaction (PCR)-based techniques (eg solid phase PCR, RT-PCR, qPCR, multiplex PCR, touchdown PCT (touchdown PCR), nano PCR, nested PCR, hot start PCR, etc.), helicase-dependent amplification (HDA), loop mediated isothermal amplification amplification (LAMP), self-sustained sequence replication (3SR), nucleic acid sequence based amplification (NASBA), strand displacement amplification (SDA), rolling circle amplification (rolling) circle amplification (RCA), ligase chain reaction (LCR), and other suitable amplification techniques. For amplification of purified nucleic acids, the primers used are preferably taxonomic, phylogenetic, diagnostic, formulation (eg, probiotic formulation), therapeutic, and/or other suitable The application is configured to amplify informative nucleic acid regions/sequences (eg, 16S region, 18S region, ITS region, antibiotic resistance gene, etc.), as well as selecting to avoid or minimize amplification bias do. Thus, universal primers set to avoid amplification bias (eg, F27-R338 primer set for 16S rRNA, F515-R806 primer set for 16S rRNA, etc.) can be used for amplification. The selected primers may additionally or alternatively contain one or more antibiotic-associated conditions, microbiome pharmacogenomic characteristics (e.g.,gyrA gene and/orparC microbiome mutations associated with antibiotic efficacy, such as primers compatible with antibiotic efficacy, such as point mutations in genes; microbiome compositional features (eg, microbiome associated with taxa associated with one or more sexually transmitted diseases); identified primers compatible with a gene target corresponding to compositional characteristics, etc.), functional diversity characteristics, complementary characteristics, and/or other characteristics associated with an antibiotic-associated condition (eg, a gene may be compatible with the target). In an embodiment, the primers may be complementary to a genetic target associated with a feature (eg, a gene sequence from which relative abundance features are derived). In a specific embodiment, the method 100 identifies a primer for a nucleic acid sequence associated with an antibiotic-associated condition, fragments a nucleic acid material derived from a biological sample associated with a user set, and based on the primer, the fragmented nucleic acid determining the microbial sequence based on amplifying the material (eg, multiplex amplification using a bridge amplification substrate of a next-generation sequencing platform, etc.); determining an alignment of the microbial sequence with a set of reference nucleic acid sequences associated with the antibiotic-associated condition (eg, comprising a microbiome pharmacogenomic biomarker associated with the antibiotic-associated condition, etc.); And, it may include determining microbiome pharmacogenomics data based on the alignment. Primers used in variants of the S110 block may additionally or alternatively include an integrated barcode sequence specific to each biological sample, which may facilitate identification of the biological sample after amplification. Primers used in variants of the S110 block may additionally or alternatively contain an adapter region configured to interact with a sequencing technique (e.g., Illumina Sequencing) that includes a complementary adapter. . Additionally or alternatively, the S110 block may execute other steps set to facilitate processing (eg, using a Nextera kit).

In variants of the S110 block, the purified nucleic acid sequencing is associated with targeted amplicon sequencing and/or metagenomic sequencing, and one or more: sequencing-by-synthesis techniques) (e.g. Illumina sequencing), capillary sequencing techniques (e.g. Sanger sequencing), pyrosequencing techniques and (e.g. , methods comprising nanopore sequencing techniques (using Oxford Nanopore technique) and/or other suitable sequencing techniques.

In a specific embodiment of block S110, amplification and sequencing of nucleic acids obtained from a biological sample of a set of biological samples comprises: solid-phase PCR associated with bridge amplification of a DNA fragment of said biological sample on a substrate having an oligo adapter, wherein the amplification is a forward index A primer with a forward index sequence (e.g., using an Illumina forward index corresponding to the MiSeq/NextSeq/HiSeq platform), a forward barcode sequence, (e.g., MiSeq/NextSeq) a transposase sequence (corresponding to the transposase binding site for the /HiSeq platform), a linker (e.g., a 0, 1 or 2-base fragment established to reduce homogeneity and improve sequencing results); Additional random bases, sequences for targeting specific target regions (eg, 16S rRNA region, 18S rRNA region, ITS region, antibiotic resistance gene, etc.), (eg, Illumina reverse to MiSeq/NextSeq/HiSeq platform) a reverse index sequence (corresponding to the Illumina reverse index), and a reverse barcode sequence. In a specific embodiment, sequencing comprises Illumina sequencing using synthetic sequencing techniques (eg, with HiSeq platform, with MiSeq platform, with NextSeq platform, etc.).

In variants, the computational process at block S110 may include any one or more of: identification of microbiome-derived sequences (eg, as opposed to subject sequences and contaminants), alignment and mapping of microbiome-derived sequences (eg, as opposed to subject sequences and contaminants). For example, one or more single-ended alignments, ungapped alignments, gapped alignments, such as to facilitate alignment with a reference sequence associated with the antibiotic-associated condition; pairing, and/or alignment of fragmented sequences using other suitable techniques), and generation of features derived from compositional, functional and pharmacogenomic aspects of the microbiome associated with the biological sample.

With respect to block S110, once a representative microbial group of a microbiome associated with a biological sample is identified, generation of features derived from compositional, functional, and pharmacogenomic aspects of the microbiome associated with the biological sample can be performed. In one variation, the generation of features may include generation of the features based on multilocus sequence typing (MLST), to identify markers useful for characterization in subsequent blocks of the method 100 . . Additionally or alternatively, the generated features may include the generation of features that describe the presence or absence of a particular microbial taxa and/or a ratio between published microbial taxa. Additionally or alternatively, the generation of the features may be determined by at least one or more: quantity of representative taxa, network of representative taxa, functional group and/or pharmacogenomic group, correlation with other representative taxa, functional group and/or pharmacogenomic group. , interactions between different taxa, products produced by different taxa, ratios between dead and surviving microorganisms (e.g., for other representative taxa based on RNA analysis), (e.g., Cantorovich-Rubinstein distance ( phylogenetic distances (in terms of Kantorovich-Rubinstein distances), Wasserstein distances, etc.), other appropriate taxon-related characteristic(s), other suitable genetic or functional characteristic(s), other suitable pharmacogenomics may include the creation of features that describe the feature(s).

With respect to block S110, additionally or alternatively, the generation of features (eg, for a microbiome dataset) may be performed, for example, to perform a maximum likelihood assessment of the relative abundance of one or more microbiome. , the sparCC approach, the Genome Relative Abundance and Average size (GAAS) approach, and/or the Genome Relative Abundance using Mixture Model theory (GRAMMy) approach, using sequence similarity data. can be used to generate features that describe the relative abundance of different microbiome. Additionally or alternatively, generating features may include generating statistical means of taxa diversity, such as derived from abundance metrics. Additionally or alternatively, generating features may include generating features that are derived from relative abundance factors (eg, related to changes in the abundance of one taxon that affect the abundance of another taxon). Additionally or alternatively, generating features may include generating qualitative features that separately and/or in combination describe the existence of one or more taxa. Additionally or alternatively, generating the features can include generating features associated with a genetic marker (eg, representative 16S, 18S and/or ITS sequences) that characterizes a microorganism of the microbiome associated with the biological material. there is. Additionally or alternatively, the generation of features may include the generation of features associated with a specific gene and/or a functional association of an organism having the specific gene. Additionally or alternatively, generation of characteristics may include generation of characteristics associated with pathogenicity of the taxa and/or products attributed to the taxa (eg, genes for virulence factors, genomic island markers, etc.). Additionally or alternatively, generating the characteristics may comprise generating characteristics associated with a pharmacogenomic marker (e.g., detection of a gene and/or mutation associated with antibiotic resistance of any kind) of a taxon or taxa of interest. there is.

However, block S110 and/or other suitable portions of method 100 may include generating other suitable feature(s) derived from sequencing and mapping to nucleic acids of a biological sample. For example, the feature(s) may be combined (eg, pairwise, triply), are correlated (eg, related to a correlation between other features), and/or (eg, For example, temporal changes, changes across sample locations, spatial changes, etc.) are associated with changes in characteristics. However, block S110 may be implemented in any other suitable manner.

Block S120 enumerates receiving a supplemental dataset associated with at least one subset of a set of subjects, wherein the supplemental dataset informs an antibiotic-related status for the set of subjects ( S120 ). Block S120 serves to obtain additional data associated with one or more subjects in the set of subjects, which may be used to train and/or validate the characterization process generated in block S130. In block S120, the supplemental dataset preferably includes survey-derived data, but additionally or alternatively, one or more: sensors and/or other suitable elements (eg, a treatment device; contextual data derived from elements of the system 200 (which may include user equipment, etc.), medical data (eg, current and past medical data, such as antibiotic medical history), antibiotic-related status informative data (eg, notification of the presence or absence of the condition, associated diagnosis, associated treatment, progress over time, etc.), and/or other suitable form of data. In variants of block S120 comprising receiving survey-derived data, the survey-derived data preferably provides information regarding the physiological, demographic and behavioral associated with the subject. Additionally or alternatively, blocks S110 and S120 may be implemented in any manner similar to US Application Serial No. 15/097,862, filed on April 13, 2016, which is incorporated by reference in its entirety. However, the supplemental dataset processing of block S120 may be performed in any other suitable manner.

4.2 Running the characterization process

Block S130 determines an antibiotic-associated state based on features extracted from the supplemental dataset and at least one of the microbiome pharmacogenomics dataset, the microbiome composition dataset, and the microbiome functional diversity dataset. It enumerates the execution of the associated characterization process. 3 , block S130 identifies features and/or feature combinations that can be used to characterize a subject or group based on the subject's microbiome composition, functional characteristics, and/or pharmacogenomic characteristics. function can be performed. As such, the characterization process can be used as a diagnostic tool capable of characterizing a subject (eg, in terms of a medical condition, etc.) based on the subject's microbiome composition, functional characteristics, and/or pharmacogenomic characteristics. there is. This characterization can then be used to suggest or provide personalized antibiotic therapy (and/or other therapies) via the therapeutic model of the S140 block.

In executing the characterization process, block S130 uses computer-based methods (eg, statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.) , can be characterized by expression characteristics that characterize groups of subjects with health conditions that can be effectively treated with personalized antibiotic therapy). In variations of block S130 , executing the characterization process may include generating one or more characterizations of one or more antibiotic-associated conditions. In embodiments, the characterization process of block S130 includes an antibiotic-associated state, a microbiome pharmacogenomic profile (eg, microbiome pharmacogenomic features correlating with an antibiotic-associated state, etc.), a microbial population (s) (eg, microbiome pharmacogenomic characteristics, taxa, microbiome compositional characteristics, etc.), (eg, Clusters of Orthologous Groups/Kyoto Incyclopedia upregulation related to microbiome functional diversity and/or other appropriate aspects associated with said microbiome (related to the Kyoto Encyclopedia of Genes and Genomes pathway, microbiome functional diversity characteristics, etc.); or It can facilitate identification of correlations between downregulation. Characterization of upregulation and/or downregulation may include any suitable taxonomic level (eg, line, phyla, class, order, family, genus, species, strain, etc.), any suitable granularity of functional diversity, Any suitable segmentation and/or any suitable segmentation (eg, chromosome, locus, gene, allele, locus, gene, nucleotide, etc.) of the microbiome pharmacogenomics profile may be made.

In another variation, in block S130, characterization of the antibiotic-associated condition is performed using a diagnostic assay (eg, assessing the risk of contracting the antibiotic-related condition; calculating the change in risk conferred by the identified treatment; microbiome pharmacogenomics characteristics and diagnosis of the presence or absence of an antibiotic-associated condition such as; diagnosis of the severity of an antibiotic-related condition over time associated with microbiome pharmacogenomics, composition, and/or functional diversity; resistance and/or sensitivity to treatment such as antibiotics, etc.) and/or generation of other assays. In another variation of block S130, the characterization of the antibiotic-associated condition may be based on one or more supplemental datasets. For example, the set of feature-selection rules may include one or more antibiotic-related conditions, biometric data indicative of an antibiotic-related condition (eg, gonorrhea, another sexually transmitted disease, urinary tract infection, and/or other appropriate optical data associated with an antibiotic-related condition; data collected in connection with an auxiliary medical device, such as parameters of antibiotic delivery by an antibiotic delivery device; supplemental data associated with a sample collection site; blood data; temperature data; user behavior data; cardiovascular data; one or more biometric characteristics derived from stool data, etc.). In yet other embodiments, the supplemental dataset may include sensors collected from user devices, assistive medical devices, and/or other suitable elements (eg, sample handling systems, etc.). In another embodiment, the characterization process execution is based on treatment promoted over time (eg, treatment data comprising antibiotic therapy data, probiotic therapy data, and/or other appropriate treatment data associated with a user population). may include a series of characterization decisions over time, where different therapeutic effects over time correlate with antibiotic-associated status, microbiome pharmacogenomics, microbiome composition, and/or insights associated with functional diversity can help to reveal However, the execution of the characterization process and/or the generation of a diagnostic analysis based on the supplemental dataset may be performed in any suitable manner.

Block S130 may additionally or alternatively include generation of features, which may be for a characterization process (eg, for use in generating a characterization model) and/or other suitable process of the method 100 above. may function to create one or more features for The characteristics may include one or more: microbiome pharmacogenomic characteristics, microbiome composition characteristics (eg, absolute and/or relative abundance of a taxon in the user's microbiome), microbiome functional diversity characteristics, and/or Other suitable features may be included. Microbiome pharmacogenomic characteristics may be one or more of: codon mutations, exon deletions, substitutions, gene rearrangements, translocations, microbial trait variability, microbial trait resistance or resistance to antibiotics, microbial trait interdependent behavior, microbial pharmacogenomic markers and/or characteristics (eg, related to antibiotic-associated status, related taxa, related functional diversity, etc.) associated with other suitable characteristics (eg, relative abundance of such characteristics, etc.). Microbiome functional diversity characteristics include one or more: Kyoto Encyclopedia of Genes and Genomes (KEGG) functional characteristics (eg, KEGG characteristics associated with flagellar biosynthesis, etc.), clusters of ortho Clusters of Orthologous Groups (COG) protein characteristics, L2, L3, L4-derived characteristics, genomic functional characteristics, taxa-associated and/or taxa-specific functional characteristics, chemical functional characteristics (e.g., cysteine metabolism, etc.), systemic functional characteristics (eg, systemic immune function; functions associated with systemic disease, etc.), and/or any suitable functional characteristics. The microbiome features may additionally or alternatively be determined in at least one of: relative abundance monotonic transformation, non-monotonic transformation, normalization, feature vectors such as those derived from at least one linear latent variable analysis and non-linear latent variable analysis, linear regression, It may be derived from and/or associated with non-linear regression, kernel methods, feature embedding methods, machine learning, statistical inference, and/or other suitable approaches.

With respect to block S130, the determination of the features is preferably based on processing the microbiome dataset according to one or more computer-executed rules (eg, feature-selection rules, user preference rules, etc.), although the features are It may be determined based on any suitable information. For example, the method 100 may include antibiotic-associated conditions, microbiome pharmacogenomic characteristics (eg, from a set of potential microbiome characteristics), microbiome composition characteristics, and/or microbiome obtain a set of feature-selection rules (eg, microbiome pharmacogenomics feature-selection rules, etc.) that associate a subset of biome functional diversity features; and generating features based on evaluating the microbiome dataset against a set of feature-selection rules, wherein the set of antibiotic-related feature-selection rules (eg, supplemental data and by reducing processing time, such as transforming features into a characterization model; by improving model storage, retrieval and/or execution speed; by improving characterization and/or treatment delivery accuracy; etc.) improving microbiome characterization systems. operable for In a specific embodiment, feature-selection rules may relate to a correlation between microbiome pharmacogenomic features and antibiotic efficacy in relation to one or more antibiotic-associated conditions (eg, wherein the correlation is may be determined based on processing biological samples over time and evaluating antibiotic efficacy in the context of a user's microbiome pharmacogenomic profile, microbiome composition, and/or microbiome functional diversity, etc.).

Block S130 and/or other portions of the method 100 preferably include applying computer-executed rules to process population-level data, but additionally or alternatively applying computer-executed rules to determine demographics. - on a specific basis (e.g., subgroups sharing demographic characteristics such as medical history associated with an antibiotic-related condition and/or past antibiotic treatment, ethnicity, age, sex, etc.), condition-specific on a systematic basis (e.g., a subgroup exhibiting a particular antibiotic-associated condition), on a sample type-specific basis (e.g., derived from samples collected at different collection sites by applying different computer-executed rules) processing the microbiome data; applying other computer-executed rules based on the type of supplemental data available to supplement the sample; etc.) and/or processing the microbiome-related data on other suitable criteria. there is. As such, block S130 assigns a user from the user population to one or more subgroups, and sets other computer-executed rules for determining characteristics for the other subgroups (eg, a set of feature types used; and applying the generated characterization model type; etc.). However, applying computer-executed rules may be performed in any suitable way and at any suitable level of granularity.

In a variant, block S130 contains feature-selection rules (eg, exhaustive, best first, simulated annealing, greedy forward, greedy filter and rank by applying a feature selection algorithm, such as greedy backward, and/or other suitable feature selection algorithm), and/or otherwise (e.g., one or more treatments with different degrees of efficacy); generate one or more characterization models, therapy models, and/or other suitable models) using rules that relate to the microbiome pharmacogenomics profile, microbiome composition, microbiome functional diversity, and/or other appropriate aspect of having selecting features for use in In a variant, application of feature-selection rules may impart microbiome-related insights that may be based on changes in sample processing (eg, descriptions in blocks S110-S120, S150, experimental conditions, etc.) there is. For example, the method 100 applies antibiotic-associated feature-selection rules to identify features that are correlated (eg, most correlated; etc.) with an antibiotic-associated state (eg, , presence, risk, treatment, etc.); selecting primers compatible with the genetic target associated with the characteristics identified above (eg, for use in amplification and sequencing to generate microbiome datasets; etc.). As such, the feature-selection rules and/or other computer-executed rules may additionally or alternatively, as shown in FIG. 12 (eg, described with respect to blocks S110-S120, S150, etc.) It may serve to determine sample processing parameters. However, any suitable number and/or type of feature-selection rules may be applied in any manner to define one or more feature sets.

In an embodiment, in block S130 , the feature-selection rules include a first group of subjects exhibiting a target condition (eg, a state of an antibiotic-associated state) and a group of subjects that do not exhibit the target state (eg, a “normal” state). ) applying a statistical analysis (eg, probability distribution analysis) of similarities and/or differences between the second group of subjects. In practicing this variant, one or more of a Kolmogorov-Smirnov (KS) test, a permutation test, a Cramer-von Mises test, and Other statistical tests (eg, t-test, Welch t-test, z-test, chi-squared test, distribution association test, etc.) may be used. In particular, one or more such statistical hypothesis tests may include a first group of subjects exhibiting a target condition (eg, diseased state; microbiome pharmacogenomic profile; etc.) and a first group of subjects not indicative of the target state (eg, in a steady state). It can be used to evaluate a set of features with varying degrees of richness in a second group of subjects. More specifically, the assessed set of features is based on a percentage abundance and/or other appropriate parameter that correlates with the diversity associated with the first and second groups of subjects to increase or decrease confidence in the characterization. may be limited. In a specific implementation of this example, a characteristic may be derived from a bacterial taxa that is enriched at a certain percentage in a first group of subjects and a second group of subjects, wherein the relative abundance of the taxa between the first and second groups of subjects is ( for example, in terms of p-values) and can be determined with the KS test. Thus, the output of block S130 indicates significance (e.g., a p-value of 0.0013) and may contain normalized relative abundance values (e.g., greater than 25% abundance in a taxon of diseased versus healthy subjects). Feature creation variants may additionally or alternatively implement or derive functional characteristics, pharmacogenomic characteristics or metadata characteristics (eg, non-bacterial markers). However, any suitable statistical analysis may be applied in any suitable manner.

In another variation, block S130 may additionally or alternatively include at least one microbiome compositional diversity dataset, microbiome functional diversity dataset, microbiome pharmacogenomic diversity dataset, supplemental dataset, and/or Input data from other suitable datasets can be transformed into feature vectors that can be tested for efficacy in predicting characterization (eg, diagnosis) of a population of subjects. Data from the supplemental dataset may additionally or alternatively be used to provide an indication of one or more characterizations of the characterization set, wherein the characterization process is at a high (or low) level in accurately predicting classification. is trained with a training dataset of candidate features and candidate classifications to identify features and/or feature combinations with predictive power of . As such, the training dataset and elaboration of the characterization process identify feature sets (eg, of subject features, of feature combinations) that are highly correlated with the specific classification of the subject.

In variants of block S130, the feature vectors effective for predicting the classification of the characterization process include one or more: (eg, with respect to distribution across taxa, distribution across archaea, bacteria, virus and/or eukaryotes with respect to) microbiome diversity metrics, the presence of taxa in the human microbiome, representativeness of specific gene sequences (eg, 16S sequences) in the human microbiome, the relative abundance of taxa in the human microbiome; A microbiome elasticity matrix (e.g., in response to small changes determined from a supplemental dataset), a protein or RNA with a specific function (e.g., an enzyme, a messenger, a protein from the immune system, a hormone, an interfering RNA, etc.) associated with the abundance of genes encoding for It may include features. Additionally or alternatively, combinations of features may be used in a feature vector, wherein features may be grouped and/or weighted in providing the combined feature as part of a feature set. For example, a feature or set of features may be a weighted aggregation of the number of bacterial types represented in the human microbiome, the presence of bacteria belonging to a particular genus in the human microbiome, and the human microbiome. representativeness of a particular 16S sequence in , and the relative abundance of bacteria of the first phylum to bacteria of the second phylum. However, the feature vectors and features may additionally or alternatively be determined in any other suitable manner.

Block S130 may additionally or alternatively include generating a characterization model, based on applying one or more features, a microbiome dataset, supplemental data, and/or other appropriate data, one or more antibiotic-related It may serve to generate one or more characterization models for the state. Characterization models (and/or therapy models or other suitable models) may include any one or more: a probabilistic characteristic, an empirical characteristic, a deterministic characteristic, and/or other suitable characteristic. Block S130 and/or other suitable portions of method 100 (eg, generation of therapy model S140 ) and/or system 200 as of 13 April 2016, incorporated by reference in its entirety. Any one or more algorithms described in or similar to filed US application Ser. No. 15/097,862 may be used and/or any suitable algorithm may be used. In a variant of block S130, the characterization process combines a random forest predictor with bagging (eg, bootstrap aggregation) and selection of a random set of features from the training dataset. Generated and trained according to a (random forest predictor; RFP) algorithm, it is possible to construct a set of decision trees, T , associated with a random set of features. In using the random forest algorithm, N cases from the set of decision trees are randomly sampled with replacement to make a subset of the decision tree, and for each node, m predictive features are all predictive features for evaluation. are selected from The predictive feature that gives the best partitioning (e.g., according to an objective function) at the node is used to perform the partitioning (e.g., as bifurcation at the node and trifurcation at the node). . By sampling multiple times from a large data set, the intensity of the characterization process can be substantially increased in identifying features that are robust to classification prediction. In this variant, measures to avoid bias (eg sampling bias) and/or account for the amount of bias may be included during processing to increase the robustness of the model. However, such model application may be implemented in any suitable manner.

In another variant of block S130, different demographic groups (eg, different characterization models for user subgroups associated with different antibiotic medical histories; etc.), antibiotic-related status (eg, other antibiotic- Other characterization models for treatable status; other characterization models for different microbiome pharmacogenomic profiles; etc.), individual subjects, supplemental data (e.g., biometric sensor data and/or survey response data derived from Other characterization models may be created for models that include features versus models that are independent of supplementary data, etc.) and/or other suitable criteria. As shown in FIG. 6 , the characterizations output from different characterization models can be compared to outputs derived from other characterizations (eg, for other sexually transmitted diseases, urinary tract infections, etc.) for different antibiotic-associated conditions (eg, the above can be used to determine and/or facilitate therapy, by input into a therapy model (creating a single therapy or multiple therapies tailored to treat other antibiotic-associated conditions, etc.).

In another embodiment, block S130 selects different characterization models for different user accounts; and, for each user account, storing a corresponding characterization model in association with the user account to enhance data storage and/or retrieval (eg, for executing a process of the method 100 , etc.). may include Creation of multiple characterization models suitable for different contexts (e.g., by adjusting analysis for a particular subject's demographics, antibiotic-related status, characteristics, etc.) speed of retrieval for suitable characterization models from a database (by associating them with . associated with a subset of the pool of potential features that are present, and the remaining unselected features have little correlation with antibiotic-associated status), and/or other pertinent aspects of the microbiome characterization system. Biome characterization systems can be improved.

In another variant of block S130, generation of a feature set for other characterization models (and/or other models) may be based on other feature-selection rules (eg, microbiome pharmacogenomics feature). - applying different sets of selection rules to generate different sets of features specific to different STDs; etc.). In general, an overlapping or identical set of feature-selection rules will be used to generate different characterization models (e.g., using the same microbiome pharmacogenomics feature to generate two different characterization models, etc.) can Additionally or alternatively, generating any number of characterization models may be performed in any suitable manner. However, the execution of the characterization process S130 may be performed in any suitable manner.

4.2.A Methods - Characterization of Antibiotic-Related Conditions

In one implementation, the characterization process of block S130 based on statistical analysis in a culture-free manner is characterized by the highest correlation with antibiotic-treatable status (eg, sexually transmitted disease, STD). Sets may be identified, and/or self-sampling by the patient may be performed to significantly reduce lags in which individuals receive treatment. In a specific embodiment of this practice, the characterization process of the S130 block is based on multiplexed PCR of the genetic material in the sample, and one or more: the mutagenicity of the strain (e.g., mutation type, mutation tendency, mutation rate etc.), antibiotic type-specific resistance by the strain, the presence or absence of other microorganisms present in the subject (e.g., in terms of interference in functional behavior, in terms of up-regulation, in terms of down-regulation, etc.) interdependent It can be used as a diagnostic test to identify and characterize the strain(s) of gonorrhea present in a sample, in view of its behavior, and other suitable aspects of interest in instructing models for the provision of personalized therapies. As such, the characterization process of block S130 may include characterizing the strain of the STD-associated microorganism (eg, gonorrhea) present in the sample while identifying the genetic characteristics of the microorganism.

In variants of block S130, the microbiome features may be associated with Neisseria gonorrhoeae (species). In a specific embodiment, the microbiome features include at least one gyrA gene and parC for a taxon (eg, N. gonorrhoeae ). point mutation characteristics associated with a point mutation in the gene, which may be associated with ciprofloxacin resistance. In another specific embodiment, the microbiome features may be associated with the presence of a mosaic penA allele, which may be associated with cephalosporin resistance. Such correlations and/or other suitable correlations between microbiome characteristics and therapeutic efficacy may be used to determine and/or facilitate treatment. However, any microbiome characteristics may include any suitable microbiome pharmacogenomic characteristics associated with any suitable taxa and/or any suitable antibiotic resistance or sensitivity. Characterization related to gonorrhea and/or other appropriate antibiotic-related conditions may additionally or alternatively be performed in a manner similar to the process described in U.S. Application Serial No. 15/097,862, filed on April 13, 2016, which is incorporated by reference in its entirety. can be executed as

4.3 Method - Personalization

The method 100 may additionally or alternatively include block S140, which determines a treatment set to modulate microbial abundance, distribution, functional diversity, and/or pharmacogenomic diversity in a subject characterized according to the characterization process. Lists what to do (S140). The S140 block functions to identify and/or predict a therapy (eg, antibiotic therapy, etc.) that corrects or otherwise treats the antibiotic-treatable state the subject has. Block S140 may additionally or alternatively include generating and/or applying a therapy model to determine a therapy. At block S140 , the treatment may additionally or alternatively include one or more: antibiotic-based therapy, probiotic-based therapy (eg, as shown in FIG. 8 ), antifungal therapy, phage-based therapy, prebiotic tic-based therapy, small molecule-based therapy, dose-based therapy, diet-related therapy, topical therapy, cognitive/behavioral therapy, physical therapy, clinical therapy, alternative medicine-based therapy, environment-based therapy (e.g., light -based therapies, temperature-based therapies, etc.), and/or other appropriate therapies designed to operate in other appropriate ways (eg, in promoting user health with respect to antibiotic-related conditions). can be Antibiotic-based therapy may include any one or more of: cell wall-based antibiotics (eg, penicillins, cephalosporins, etc.), bacterial enzyme-based antibiotics (eg, rifamycin, rifiamycin, quinolones, sulfonamides, etc.), cell membrane- based antibiotics (eg polymyxin, etc.), antimicrobial-based antibiotics, protein-synthesis-based antibiotics (eg macrolide, lincosamide, tetracycline, etc.), cyclic lipopeptides, glycylcyclines, oxazolidinone, therapies associated with ancillary medical devices, and/or other appropriate antibiotic-based therapies.

In the variants of block S140, the treatment model is, as shown in FIG. 4, a decision tree/ An antibiotic therapy model comprising the tree/graph arranging available antibiotics (eg, specific antibiotic type, antibiotic dose, etc.) at a node of a decision graph may be included. The arrangement of treatment steps may include positive outcomes (e.g., minimizing the effect on the subject's beneficial bacterial population) and/or negative outcomes (e.g., risk of fatal infections) when applying a particular antibiotic treatment to a subject. weighting, whereby the most effective and least harmful antibiotics targeting disease-associated strains are provided early in the treatment plan. When traversing the branches of the decision tree/graph, the patient indication and/or result after applying the antibiotic at the upstream node may be used to facilitate antibiotic application at the downstream node. Alternatively, once the patient has been treated, further facilitation of antibiotic treatment may be terminated, and reparative treatment (eg, probiotic supplements generated by modification of the therapeutic model) may be facilitated in the subject. However, the antibiotic therapy model may incorporate any suitable characteristic and/or apply any suitable algorithm in determining any suitable aspect associated with antibiotic therapy.

With respect to block S140, additionally or alternatively, the output of the antibiotic therapy model may include a predictive model describing predictive outcomes and/or outcomes according to application of other antibiotic treatments/dose to the subject, other entities ( entity) to make a decision to provide one or more antibiotics to a subject. With respect to the S140 block, as such, antibiotic therapy may include one or more of: the subject's condition (eg, microbial strain(s) present in association with disease; microbial gene sequences associated with antibiotic resistance and/or susceptibility); abundance/distribution of strains associated with the subject's condition; demographic and/or behavioral characteristics of the subject; the subject's other microbial populations; considerations derived from the subject's environment; and other appropriate factors. In a specific embodiment of phage-based therapy, one or more (eg, in terms of colony forming units) phage populations specific for a particular bacterium (or other microorganism) represented in the subject comprises a particular bacterium associated with the subject's disease or condition. It can be used to downregulate or otherwise eliminate a population. As such, phage-based therapies can be used to reduce the size(s) of the unwanted bacterial (or other microbial) population(s) represented in a subject. Complementarily, phage-based therapies can be used to increase the relative abundance of a bacterial population that is not targeted by the phage(s) used.

In variations, blocks S140 and/or S170 may include initiating a treatment system control signal to automatically facilitate therapy (eg, based on characterization, therapy model output, etc.), wherein the signal initiation is one or more : generate and transmit control instructions (commands) to the treatment system (e.g., control an antibiotic delivery device to provide antibiotics to the user, etc.); (e.g., inform the user about one or more characterizations and/or treatments); for, etc.) initiate providing notifications and/or other suitable actions in controlling the treatment system to facilitate therapy. In another variation, block S140 is a trigger event (e.g., characterizing an antibiotic-related condition risk that exceeds a threshold; manual request by the user and/or care provider; treatment below a threshold based on analysis of the biological sample after treatment) facilitating an interaction between the user and the care provider in response to and/or concurrently with the efficacy check; etc. (eg, scheduling an appointment with the care provider; wireless communication, as shown in FIG. 7 ) initiating a telemedicine conference over the channel; etc.). However, facilitating interaction between users may be implemented in any suitable manner.

In a variant of block S140, the generation and/or application of a therapy model may be based on one or more causes for an antibiotic-associated condition (eg, causes of risk for an antibiotic-related condition), and determined by the therapy model. Therapy may be operable to reduce the risk of the antibiotic-associated condition. With respect to block S140, processing of the therapy model may be similar to processing of the characterization model (eg, as described in block S130), and any number and/or type of therapy models may be generated for different purposes. there is. With respect to block S140, the therapy model may be derived with respect to identification of "normal" or reference microbiome makeup, functional characteristics, and/or pharmacogenomic characteristics evaluated from subjects in a population of subjects identified as healthy. there is. Identifying (e.g., using characteristics of the characterization process) a subset of subjects in a population of subjects that are characterized as healthy can lead to therapeutics that modulate microbiome composition, functional characteristics, and/or pharmacogenomic characteristics for a healthy subject. It may be generated in block S140. Block S140 is thus directed to identification of one or more reference microbiome composition, functional characteristics, and/or pharmacogenomic characteristics (eg, one reference microbiome for each demographic set), and a state of intestinal bacterial imbalance. Potential therapeutic formulations and treatment regimens that can alter the microbiome of a subject with an identified baseline microbiome composition, functional composition, and/or pharmacogenomic composition. However, the therapeutic model may be created and/or improved in any other suitable manner.

In variants, the S140 and/or S170 block comprises at least one microbiome pharmacogenomics (eg, elicitation of an antibiotic treatment designed to circumvent antibiotic resistance or to attack antibiotic sensitivity identified by a microbiome pharmacogenomic profile; etc.), microbiome composition and/or functional diversity (eg, extracted features). In an embodiment, the method 100 determines a modulator of a biomolecule associated with an antibiotic-associated condition (eg, a modulator of a biomolecule derived from a set of taxa associated with the antibiotic-associated condition); deriving a therapeutic composition for the antibiotic-associated condition based on the modulator; And, it may include promoting the therapeutic composition. However, treatment decisions and/or facilitation may be performed in any suitable manner and/or similar to US application Ser. No. 15/097,862, filed on April 13, 2016, which is incorporated by reference in its entirety.

The method 100 may additionally or alternatively include a block S150, which enumerates receiving a biological sample from a user. The S150 block may function to facilitate the creation of a microbiome dataset for a subject that may be used to derive input for the characterization process. As such, receiving, processing, and analyzing a biological sample preferably facilitates the generation of a microbiome dataset for a subject, which can be used to provide input to the characterization process. Processing and analyzing the biological sample from the subject is preferably carried out in a manner similar to one of the embodiments, variations and/or embodiments relating to receiving a sample described with respect to block S110 above. Additionally or alternatively, biological sample processing may be effected in any manner similar to US application Ser. No. 15/097,862, filed on April 13, 2016, which is incorporated by reference in its entirety. However, the receiving and processing of the biological sample at block S150 may alternatively be performed in any other suitable manner.

The method 100 may additionally or alternatively include block S160, which includes at least one microbiome pharmacogenomics dataset, a microbiome composition dataset, and a microbiome derived from a biological sample of the user. Based on processing of the functional diversity dataset, the characterization process enumerates identifying a characterization of a user with respect to an antibiotic-associated condition. Block S160 extracts features from the subject's microbiome-derived data (eg, based on microbiome dataset evaluation of computer-executed rules), and uses the features to the characterization described in block S130. It may serve as an implementation, variant or embodiment of a process, and/or as input to any suitable process of the method 100 . Accordingly, identifying the characterization in block S160 preferably identifies features and/or combinations of features associated with the microbiome composition, functional composition, and/or pharmacogenomic composition of the subject's microbiome sample, and characterizing the features in the process of characterization. and receiving output characterizing the subject as belonging to one or more behavioral groups, gender groups, dietary groups, disease-state groups, and other suitable groups identifiable by the characterization process. Block S160 may further include generating and/or outputting a confidence metric associated with the characterization of the subject. For example, the reliability metric may be derived from the number of features used to generate the characterization, the relative weight or rank of the features used to generate the characterization, a measure of bias in the characterization process, and/or other suitable parameters associated with aspects of the characterization process. In some variations, features extracted from the subject's microbiome dataset may be supplemented with investigation-derived and/or patient medical history-derived features, which may further improve the characterization process of block S130. can be used However, the subject's microbiome dataset may additionally or alternatively be used in any other suitable manner to improve the models of the method 100, and block S160 may be executed in any suitable manner.

4.4 Method-Treatment Facilitation and monitoring

The method 100 may additionally or alternatively include a block S170, which facilitates treatment (eg, antibiotic treatment, etc.) in a subject based on characterization (eg, and a therapy model, etc.). list things The S170 block may function to recommend and/or provide personalized therapy to the patient to change microbiome composition, functional characteristics, and/or pharmacogenomic characteristics of the subject into a desired equilibrium state. Block S170 may include providing a personalized therapy to the subject according to the microbiome composition, functional characteristics, and pharmacogenomic characteristics of the subject, as shown in FIG. 5 , wherein the personalized therapy is the identified characterization It is an antibiotic formulation designed to correct the condition of a subject with As such, the output of block S140 can be used to directly facilitate personalized therapy formulations and regimens (eg, dosing, instructions for use) based on the therapy model trained to the subject. Additionally or alternatively, the provision of therapy may include the recommendation of available therapeutic measures designed to alter microbiome composition, functional characteristics, and/or pharmacogenomic characteristics into a desirable state.

Providing antibiotic therapy at block S170 may include providing a notification to the patient regarding recommended therapy and/or other types of therapy. In one embodiment, a web interface on a personal computer or laptop associated with the subject may provide access by the subject to a user account of the subject, wherein the user account may include information regarding the characterization of the user, aspects of the user's microbiome. detailed characterization, and notifications regarding the proposed treatment means generated in blocks S140 and/or S170. In another embodiment, an application running on a personal electronic device (eg, a smartphone, a smart watch, a head-mounted smart device) sends a notification regarding a therapy proposal generated by the therapy model of block S170 (eg, , on the display, in a sensory, audible manner, etc.). Alerts and/or probiotic therapy may additionally or alternatively be provided directly through an entity associated with the subject (eg, a caregiver, spouse, significant other, health care professional, etc.). In some further variations, the notification may additionally or alternatively be provided to an entity associated with the subject (eg, a healthcare professional), the entity providing a means of treatment (eg, by prescription, for treatment). by conducting a session, etc.) can be managed. However, the reminder may be provided to the subject in any other suitable manner for treatment management.

The method 100 may additionally or alternatively include a block S180, which uses a biological sample to evaluate at least one microbiome pharmacogenomic profile, microbiome composition, and microbiome functional diversity. Based on treatment, monitoring of treatment efficacy for a subject at different time points is enumerated. Block S180 may function to collect additional data regarding the lack of effectiveness of a probiotic therapy proposed by the therapy model for a subject having a positive effect, a negative effect, and/or a given characterization, wherein the additional data is yes For example, it may be used to create, update, and/or execute one or more characterization models, therapy models, and/or other suitable models, and/or may be used in any suitable part of the method 100 . . For example, the method 100 may include microbiome pharmacogenomics profile, microbiome composition, microbiome functional diversity and/or microbiome data (eg, associated with antibiotic-associated conditions; pre- and post-treatment microbiome data). one or more microbiome datasets (e.g., from a biological sample after treatment of a user), such as updating the model based on observed modulation of other relevant microbiome aspects (such as determined based on comparison of sets) updating the model (eg, characterization model, therapy model, etc.) based on updated microbiome features extracted from the derived updated microbiome dataset; etc.). In yet another embodiment, the method 100, in response to updating a model, provides a user associated with the antibiotic-related condition (eg, a second user associated with a biological sample that was not based on the update). determining an update to (eg, to characterization, to therapy, etc.) based on the updated model. In another embodiment, the method 100 includes receiving a post-treatment biological sample from a user (eg, after facilitating therapy); based on the biological sample after treatment (eg, based on updated microbiome characteristics derived from the biological sample after treatment, wherein the microbiome characteristics can be used in conjunction with a characterization model) antibiotic-associated status and create a post-treatment characterization of the relevant user; characterize modulation of the antibiotic-associated condition based on said post-treatment characterization (eg, and one or more pre-treatment characterizations for said user, second user, etc.); and/or facilitating an updated therapy to the user based on the updated characterization. However, any suitable portion and/or any suitable operation of the method 100 may be performed in any suitable manner (eg, by any user number) can be implemented. Thus, during the course of treatment facilitated by the therapy model (eg, by receiving and analyzing a biological sample from the subject throughout treatment, by receiving research-derived data from the subject throughout treatment) Monitoring may be used to generate a therapy-effectiveness model for each characterization provided by the characterization process of block S130 and each recommended therapy action provided by blocks S140 and S170.

At block S180, the subject may be prompted to provide an additional biological sample at one or more key time points of a treatment regimen comprising the therapy, wherein the additional biological sample(s) are processed and analyzed (eg, at block S120) In a manner similar to that described with respect to) one can generate a matrix characterizing the modulation of a subject's microbiome composition, functional characteristics, and/or pharmacogenomic characteristics. For example, one or more: a change in the relative abundance of one or more taxa representative in the subject's microbiome at an initial time point, a change in the representativeness of a particular taxa to which the subject's microbiome belongs, a first bacterial taxa in the subject's microbiome ratios between the abundance of and the abundance of a second bacterial taxa, metrics related to changes in the relative abundance of one or more functional families in the subject's microbiome, and other suitable metrics include microbiome composition, functional characteristics and/or or to evaluate therapeutic efficacy from changes in pharmacogenomic characteristics. Additionally or alternatively, research-derived data from the subject, relating to the subject's experience (e.g., side effects experienced, personal assessment of improvement, etc.) while receiving treatment, can be used to determine the effectiveness of the treatment at block S180. can be used for However, monitoring the effectiveness of one or more treatments and/or executing a process based on the effectiveness may be implemented in any suitable manner.

The method 100 and/or the system of the above implementations may be embodied and/or executed as a machine configured to receive a computer-readable medium having at least partially stored thereon computer-readable instructions. The instructions may be computer-executed integrated with an application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software component of a patient computer or mobile device, or any suitable combination thereof. It can be executed by computer-executable components. Other systems and methods of the above embodiments may be embodied and/or executed as a machine configured to receive a computer-readable medium having, at least in part, computer-readable instructions stored thereon. The instructions may be executed by computer-executable elements integrated with networks and devices of the type described above. The computer-readable medium may be stored in any suitable computer-readable medium, such as RAMs, ROMs, hard memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device. . The computer-executable element may be a processor, although any suitable dedicated hardware device may (alternatively or additionally) execute the instructions.

The drawings illustrate possible executable structures, functionality and operation of systems, methods and computer program products according to preferred embodiments, configuration of embodiments, and variations thereof. In this regard, each block of a flowchart or block diagram may represent a module, segment, step, or portion of code, which includes one or more executable instructions for execution of the specified logical function(s). It should also be noted that, in some alternative implementations, the functions described in the blocks may occur out of the order described in the figures. For example, two blocks marked consecutively may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order depending on the function involved. Each block in the block diagrams and/or flowchart descriptions and combinations of blocks in the block diagrams and/or flowchart descriptions may be executed by special-purpose hardware-based systems or combinations of special-purpose hardware and computer instructions that perform specific functions or operations. It should also be noted that there may be The above implementations include all combinations and permutations of various system elements and various method processes, including any variations, embodiments and specific embodiments. As those skilled in the art will recognize from the preceding detailed description and from the drawings and claims, modifications and variations can be made in the embodiments of the invention without departing from the scope of the invention as defined in the claims below.

Claims (22)

A system for assessing an antibiotic-treatable status with respect to a user, the system comprising:
• a handling system, comprising a sequencer system for determining a microbial sequence from biological material obtained from a set of users comprising a plurality of users;
- determining at least one of (i) microbiome pharmacogenomic data and (ii) microbiome composition data and microbiome functional diversity data based on the microbial sequence;
collect supplemental data associated with the antibiotic-treatable status for the set of users; and
a microbiome characterization system that transforms features extracted from the supplemental data, the microbiome pharmacogenomics data, and the at least one data into a characterization model associated with the antibiotic-treatable condition; and
● providing treatment information on the antibiotic-treatable condition for the specific user based on characterizing the biological material of the specific user with the characterization model in relation to the antibiotic-treatable condition;
After treatment according to the treatment information, the specific user's microbiome pharmacogenomics data, microbiome composition data, and microbiome functional diversity data by evaluating at least one data to monitor the effect of the treatment system comprising a treatment system do,
The microbiome pharmacogenomics data includes information on the presence or absence of the genes or the amount of genes associated with antibiotic resistance or sensitivity in the plurality of users,
system. The system of claim 1 , wherein the handling system is operable to determine a microbial sequence based on amplifying a nucleic acid material from the biological material using a primer to a nucleic acid sequence associated with the antibiotic-treatable condition. . The method of claim 1 , wherein the handling system is operable to collect a user container comprising the user biological material, and wherein the treatment system is operable to facilitate treatment based on characterizing the user biological material; wherein the treatment is operable to modulate the user microbiome composition to ameliorate the state of the antibiotics-treatable condition. The system of claim 1 , wherein the features include microbiome pharmacogenomic features associated with at least one codon mutation, exon deletion, substitution, gene rearrangement, and displacement of a taxon associated with the antibiotic-treatable state. . 5. The system of claim 4, wherein the microbiome pharmacogenomic characteristics are associated with at least one microbial strain mutagenicity, microbial strain resistance to antibiotics and microbial strain interdependent behavior. 5. The system of claim 4, wherein the microbiome pharmacogenomic characteristics are associated with a relative abundance of a microbial pharmacogenomic marker associated with the antibiotic-treatable condition. The system of claim 1 , wherein the characteristics include a nucleotide-related functional characteristic associated with tolerance and metabolism of at least one of a biologically active molecule comprising at least one of structural RNA, regulatory RNA, messenger RNA, protein and peptide. . The microbiome of claim 1 , wherein the antibiotic-treatable condition comprises at least one urinary tract infection and a sexually transmitted disease, and wherein the microbiome characterization system comprises the supplemental data and the microbiome pharmacogenomic data and at least one of the microbiome. operable to transform features extracted from compositional data and the microbiome functional diversity data into the characterization model for at least one urinary tract infection and sexually transmitted disease. 9. The method of claim 8, wherein the antibiotic-treatable condition comprises a gonorrhea-associated condition, wherein the characteristics are extracted from the microbiome pharmacogenomic data and comprise a microbiome pharmacogenomic characteristic associated with a mosaic penA allele. system. 10. The method of claim 9, wherein said treatment system is operable to evaluate efficacy of a cephalosporin treatment based on said microbiome pharmacogenomic characteristics, and wherein said treatment system is operable to facilitate treatment based on said efficacy. In, system. The method of claim 1 , operable to improve display of antibiotic-related information associated with the antibiotic-treatable status, between a user microbiome pharmacogenomic profile for the user and a group of users sharing a demographic characteristic. The system further comprising an interface derived based on the comparison. A method for assessing an antibiotic-related condition in relation to a first user, the method comprising:
• determining microbiome pharmacogenomic data based on microbial sequences derived from a biological sample obtained from a set of subjects;
• receive a supplemental dataset informing of the antibiotic-related status for the set of subjects;
• determine a first set of microbiome pharmacogenomics characteristics based on the microbiome pharmacogenomic data and the supplemental dataset;
• generate a characterization model associated with the antibiotic-associated condition based on the first set of microbiome pharmacogenomic characteristics;
- determine a characterization for the first user associated with the antibiotic-related condition based on the characterization model; And
- facilitating a therapy for the first user based on the characterization, wherein the therapy is associated with the antibiotic-associated condition;
The microbiome pharmacogenomics data includes information on the presence or absence of genes or amounts of genes related to antibiotic resistance or susceptibility in a plurality of users included in the set of subjects. 13. The method of claim 12, wherein the microbiome pharmacogenomics data determination comprises:
• determining the microbial sequence based on amplifying the nucleic acid material from the biological sample;
• determining the alignment of said microbial sequence with a set of reference nucleic acid sequences associated with said antibiotic-associated condition;
• A method comprising determining the microbiome pharmacogenomics data based on the alignment. 14. The method of claim 13, wherein the amplification of the nucleic acid material comprises performing multiplex amplification with the nucleic acid material using a bridge amplification substrate of a next-generation sequencing platform, and the determination of the microbiome pharmacogenomics data is communicated with the next-generation sequencing platform. and determining the microbiome pharmacogenomics data in a computing system operable to 13. The method of claim 12, further comprising: obtaining a microbiome pharmacogenomics feature-selection rule associated with a correlation between the first set of microbiome pharmacogenomic features and an antibiotic efficacy associated with the antibiotic-associated condition; , wherein the determination of the microbiome pharmacogenomic characteristics of the first set is based on evaluating the microbiome pharmacogenomic data against the microbiome pharmacogenomics characteristic-selection rule. A method comprising determining genomic characteristics. 13. The method of claim 12, wherein the first set of microbiome pharmacogenomic characteristics comprises a nucleotide-related functional characteristic associated with at least one resistance and metabolism of a biologically active molecule. 13. The method of claim 12, wherein the therapy is operable to facilitate altering the microbiome pharmacogenomics profile of the first user, the method further comprising:
• receive a post-treatment biological sample from the first user;
• determine an updated set of microbiome pharmacogenomics characteristics based on the biological sample after said treatment;
- determine an updated characterization for the first user associated with the antibiotic-related condition based on the characterization model and the updated set of microbiome pharmacogenomic characteristics; And
- facilitating a first updated therapy to the first user based on the updated characterization. 18. The method of claim 17,
● create an updated antibiotic therapy model based on the updated characterization for the first user;
• The method further comprising promoting antibiotic therapy to a second user based on the updated antibiotic therapy model, wherein the antibiotic therapy is operable to modulate the microbiome composition of the second user. 19. The antibiotic therapy of claim 18, further comprising determining a second set of microbiome pharmacogenomic characteristics for the second user based on a second user biological sample, and wherein the antibiotic therapy is based on the updated antibiotic therapy model. wherein promoting comprises determining the antibiotic therapy based on processing the second set of microbiome pharmacogenomic features into a decision tree model. 13. The method of claim 12,
- generating the characterization model comprises generating the characterization model based on a set of microbiome features comprising the first set of microbiome pharmacogenomic features and at least one microbiome compositional diversity feature and a microbiome functional diversity feature. including, and
- the characterization determination for the first user is based on a set of user microbiome characteristics comprising a user microbiome pharmacogenomics characteristic and at least one user microbiome compositional diversity characteristic and a user microbiome functional diversity characteristic. and determining the characterization for a first user. 21. The microbiome of claim 20, wherein the antibiotic-associated condition comprises a gonorrhea-associated condition, the microbiome compositional diversity characteristic comprises a compositional characteristic for a taxon associated with the gonorrhea-associated condition, and wherein the first set of microbiome The method of claim 1 , wherein the scabies pharmacogenomic features comprises a set of point mutation features for genes associated with the taxa. 22. The method of claim 21, wherein said set of point mutation features is at least one gyrA gene for said taxon. and parC associated with a point mutation in a gene.

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