CN109475305B - Methods and systems for microbial pharmacogenomics - Google Patents

Abstract

Embodiments of methods and systems for pharmacogenomics of microorganisms can include: a sample processing system operable to collect containers containing substances from a collection of users, the processing system comprising a sequencing system operable to determine a sequence of a microorganism from a biological sample; a microbiome characterization system operable to determine microbiome pharmacogenomic data based on a microbial sequence, collect supplemental data associated with an antibiotic-associated condition of the set of users, and convert the supplemental data and features extracted from the microbiome pharmacogenomic data into a characterization model of the antibiotic-associated condition; and a treatment system that characterizes the user's biological material for an antibiotic-associated disorder based on using the characterization model, the treatment system operable to facilitate treatment of the antibiotic-associated disorder of the user.

Description

Methods and systems for microbial pharmacogenomics

Cross Reference to Related Applications

This application is a partially-filed application of U.S. patent application No.15/606,743 filed on 26/5/20157 (which is a continuation of U.S. patent application serial No. 14/919,614 filed on 21/10/2015), where the U.S. patent application serial No. 14/919,614 requires a U.S. provisional application serial No. 62/066,369 filed on 21/10/2014, a U.S. provisional application serial No. 62/087,551 filed on 04/2014, a U.S. provisional application serial No. 62/092,999 filed on 17/12/2014, a U.S. provisional application serial No. 62/147,376 filed on 14/2015, a U.S. provisional application serial No. 62/147,212 filed on 14/2015, a U.S. provisional application serial No. 62/147,362 filed on 14/2015, a U.S. provisional application serial No. 62/146,855 filed on 13/2015, and a U.S. provisional application serial No. 62 filed on 18/2015 The benefit of U.S. provisional application serial No./206,654, each of which is hereby incorporated by reference in its entirety.

This application also claims the benefit of U.S. provisional application serial No. 62/361,943 filed on 2016, 7, 13, which is incorporated herein by reference in its entirety.

Technical Field

The present invention relates generally to the field of microbiology, and more particularly to a new and useful method and system for characterizing microbial pharmacogenomics in the field of microbiology.

Background

Microbial communities are an ecological community of commensal, symbiotic, and pathogenic microorganisms associated with an organism. The human microbial population includes as many microbial cells as there are human cells present in the entire human body, but the characterization of the human microbial population is still in its infancy due to limitations in sample processing techniques, genetic analysis techniques, and resources used to process large amounts of data. Nonetheless, the microbiome is suspected to play at least in part a role in a number of health/disease-related states (e.g., preparation for childbirth, diabetes, autoimmune disorders, gastrointestinal disorders, rheumatoid disorders, neurological disorders, etc.). In view of the profound impact of microbiome on the health aspects of a subject, efforts should be expended in connection with characterization of microbiome, developing insights from that characterization, and generating therapies configured to recover from dysbiosis states. However, the current methods and systems for analyzing human microbial populations and providing therapeutic measures based on the knowledge obtained still leave a number of questions that have not yet been answered. In particular, methods of characterizing certain health conditions based on microbiome composition characteristics and/or functional characteristics, as well as treatments that are tailored to specific subjects (e.g., probiotic/prebiotic (prebiotic) treatments, fecal microbiota transplantation, etc.) have not been feasible due to limitations of current technology.

Thus, in the field of microbiology, there is a need for a new and useful method and system for microbial pharmacogenomics in an individualized and population-wide manner. The present invention provides such a new and useful method and system.

Drawings

FIGS. 1A-1B depict schematic flow diagrams of embodiment variations of a method for microbial pharmacogenomics;

figure 2 depicts an embodiment variation of a system and method for microbial pharmacogenomics;

FIG. 3 depicts a variation of a process for generating a model in an embodiment of a method of microbial pharmacogenomics;

figure 4 depicts variations in the output of a treatment model in embodiments of methods and systems for microbial pharmacogenomics;

FIG. 5 depicts a variation of the application model in an embodiment of the method for microbial pharmacogenomics;

FIG. 6 depicts a variation of applying multiple models in an embodiment of a method for microbial pharmacogenomics;

figure 7 depicts variations that facilitate treatment in variations of embodiments of the methods for microbial pharmacogenomics;

figure 8 depicts a variation of the mechanism by which probiotic-based therapies operate in embodiments of methods for microbial pharmacogenomics;

figure 9 depicts a variation of notification in an embodiment of a method for microbial pharmacogenomics;

figure 10 depicts a variation of an interface for providing information related to antibiotics in an embodiment of a method for microbial pharmacogenomics;

figure 11 depicts a variation of notification in an embodiment of a method for microbial pharmacogenomics;

figure 12 depicts variations in sample processing parameter changes in embodiments of methods for microbial pharmacogenomics.

Detailed Description

The following description of the embodiments of the present invention is not intended to limit the invention to these embodiments, but is intended to enable any person skilled in the art to make and use the invention.

1. Overview

As shown in fig. 2, one embodiment of a system 200 for characterizing (e.g., assessing) an antibiotic-associated condition for a user (e.g., a human subject, a patient, an animal subject, an environmental ecosystem, a care provider, etc.) can include one or more of: a processing system 210 (e.g., a sample processing system) operable to collect containers containing a substance (e.g., a biological sample such as a microbial nucleic acid substance, etc.) from a user collection, the processing system 210 comprising a sequencing system (sequencer system) operable to determine a microbial sequence by sequencing the substance; a microbiome characterization system 220 operable to determine microbiome pharmacogenomic data (and/or at least one of microbiome composition data and microbiome functional diversity data) based on a microbiome sequence, collect supplemental data related to the antibiotic-treatable condition of the user set, and convert the supplemental data and features extracted from the microbiome pharmacogenomic data (and/or at least one of microbiome composition data and microbiome functional diversity data) into a characterization model of an antibiotic-associated condition; and/or a treatment system (e.g., a medical treatment system) 230 that is operable to facilitate treatment (e.g., medical treatment) of a user for an antibiotic-associated disorder (e.g., treatment is operable to adjust a user microbiome composition to improve a state of the antibiotic-associated disorder, etc.) based on characterizing the user biological material with a characterization model associated with the antibiotic-associated disorder.

The system 200 and method 100 can function to characterize and/or diagnose a user (e.g., using characterization models for clinical diagnosis, companion diagnosis) based on a microbiota data set associated with one or more antibiotic-associated conditions. The system 200 and method 100 may additionally or alternatively function to facilitate (provide) treatment (e.g., therapeutic measures) to a user and/or perform appropriate functions. A wide variety of systems 200 and/or methods 100 may be used to generate or provide personalized antibiotic regimens in a quick and efficient manner as compared to current standard antibiotic-based therapies. The wide variety of systems 200 and/or methods 100 can further facilitate monitoring and/or adjusting the therapy provided to the subject (e.g., by performing a triage (treatment), processing, and analysis of additional samples from the subject throughout the course of treatment).

The system 200 and method 100 may preferably generate and facilitate characterization and/or treatment of an antibiotic-associated disorder (e.g., an antibiotic-treatable disorder), which may include any one or more of the following: symptoms, etiologies, diseases, disorders, microbiome pharmacogenomic profiles (e.g., describing resistance and/or sensitivity to antibiotics used to antibiotically treat a disorder), and/or any other suitable aspect associated with an antibiotic-associated disorder. The antibiotic-associated disorder may include any one or more of: gonorrhea, urinary tract infections, trichomoniasis, acne, appendicitis, atrial septal defects, ureteral cysts, urethral syndromes, urethritis, tuberculosis, bacterial arthritis, bacterial vaginosis, vertigo, balance disorders, pressure ulcers, bursitis, bronchitis, syphilis, tonsillitis, pharyngitis, sepsis, pyelonephritis, ear infections, hearing loss, peritonitis, pericarditis, pelvic inflammatory disease, meningitis, laryngitis, streptococcal laryngitis, sinus infections, other sexually transmitted diseases, other skin-related conditions, other ear-related conditions, other mouth-related conditions, other bacteria-induced infections, and/or any other suitable antibiotic-related condition.

One or more examples of the method 100 and/or processes described herein may be performed by and/or using one or more examples of the systems 200, components, and/or entities described herein, not simultaneously (e.g., sequentially), simultaneously (e.g., in parallel; executing simultaneously on different threads for parallel computations to improve sample processing and/or computational processing for determinations and/or to provide characteristics and/or treatment of antibiotic-related conditions, etc.), to trigger a temporal relationship of events, and/or at any suitable time and frequency. However, the method 100 and/or system 200 may be configured in any suitable manner.

2. Benefits of

Microbiome analysis can enable accurate and effective characterization and/or therapeutic provision of antibiotic-associated conditions caused by and/or otherwise associated with microorganisms. This technology can overcome several challenges faced by conventional approaches in characterizing and/or facilitating treatment of antibiotic-associated disorders. First, conventional approaches may require a patient to visit one or more care providers to receive characterization and/or treatment recommendations for an antibiotic-related disorder, which may amount to inefficiencies and health risks associated with the amount of time elapsed before diagnosis and/or treatment. Second, conventional gene sequencing and analysis techniques for human genome sequencing may be incompatible and/or inefficient when applied to microbiome (e.g., where a human microbiome may include more than 10-fold more microbial cells than human cells; where optimal sample processing techniques may be different; where sequence reference databases may be different; where microbiome characterization may include consideration of different microbiome pharmacogenomic profiles across populations and/or individuals; where different methods of microbiome pharmacogenomic analysis may be different from human genome pharmacogenomic analysis; where microbiome may vary between different body regions of a user, etc.). Again, the advent of sequencing technologies (e.g., next generation sequencing) raises technical issues (e.g., data processing issues, information display issues, microbiome analysis issues, treatment prediction issues, treatment supply issues, etc.) that would not otherwise exist with the unprecedented development of speed and data generation associated with sequencing genetic material. Embodiments of the system 200 and method 100 may provide a solution to at least the above challenges that is of technical origin.

First, the techniques can impart improvements to computer-related techniques (e.g., modeling related to determining personalized characterization and/or treatment of antibiotic-related disorders; microbiome pharmacogenomic computational analysis; computational processing related to biological sample processing, etc.) by facilitating computer performance of previously unexecutable functions. For example, based on microbiome pharmacogenomic data derived from techniques that computationally generate microbiome profiles and recommended therapies for antibiotic-associated conditions, e.g., using a database of microbiome Reference sequences, such as the Genome Reference Consortium, which have recently become available due to advances in sample processing and sequencing technologies.

Second, the techniques may confer improvements in treatment speed, microbiome characterization accuracy, determination and facilitation of microbiome-related therapy, and/or other suitable aspects related to antibiotic-related conditions. The techniques can generate and apply feature selection rules (e.g., microbiome pharmacogenomic feature selection rules; antibiotic-related feature selection rules) to select (e.g., can be extracted from a large population of microbiome data) an optimized feature subset (e.g., microbiome composition features, microbiome functional diversity features, etc.) from a large pool of potential features for use in generating and applying characterization models and/or therapy models (e.g., antibiotic therapy models). The potential size of a microbial community (e.g., a human microbial community, an animal microbial community, etc.) can be translated into a large amount of data, creating the following problems: how to process and analyze large data arrays to generate viable microbiome insights associated with antibiotic-associated disorders. However, feature selection rules and/or other suitable computer-implementable rules can enable shorter generation and execution times (e.g., for generating and/or applying decision tree models), model simplification to facilitate efficient interpretation of results, reduce overfitting, improvements in data sources (e.g., for collecting and processing microbiota coefficient data sets (such as microbiota pharmacogenomics data sets, microbiota composition data sets, microbiota functional diversity data sets, etc.)), identification and presentation of improvements in microbiota-related antibiotic-associated condition insights (e.g., by collecting and processing more and more data associated with more and more users to improve the predictive power of the technology), data storage and retrieval (e.g., storage of specific models, data associated with users and/or user sets, data storage and retrieval of specific models, data storage and/or retrieval of specific models, data storage, and/or data storage of specific models, and/or data storage of specific data storage, such, Microbial sequences, characteristics, and/or other suitable data to improve personalized characterization and/or delivery of therapy, etc.), and other suitable data to facilitate rapid determination of characterization and/or therapy.

Again, the techniques can transform an entity (e.g., a user, a biological sample, a therapeutic system including a medical device, etc.) into a different state or thing. For example, system 200 and/or method 100 can identify therapies to facilitate a patient to alter a microbiome pharmacogenomic profile, microbiome composition, and/or microbial functional diversity to prevent and/or ameliorate an antibiotic-associated condition, thereby altering the microbiome and/or health of the patient. In another example, the technique can transform (e.g., by fragmentation, multiplex amplification, sequencing, etc.) a biological sample received from a patient to a microbiota coefficient dataset, which can then be transformed into a feature associated with an antibiotic-associated disorder. For generating a characterization model and/or a treatment model. In another embodiment, the techniques may control the therapy system to facilitate therapy (e.g., by generating control instructions for the therapy system to perform), thereby transforming the therapy system. In another embodiment, improvements in computer-related technology can facilitate the transition of biological sample processing methods, such as selecting a subset of primers that are compatible with a relevant genetic target identified as corresponding to a microbiome pharmacogenomic signature (e.g., a mutation associated with antibiotic resistance or susceptibility), a microbiome composition signature, and/or a microbiome functional diversity signature associated with an antibiotic-related disorder.

Fourth, the techniques can enable functional, inventive distribution in a network that includes a sample processing system, a microbiome characterization system, and a plurality of users, where the sample processing system can simultaneously process biological samples from the plurality of users (e.g., in a multiplexed manner), which can be utilized in generating personalized characterization and/or treatment for antibiotic-related conditions by the microbiome characterization system (e.g., tailored to a user's microbiome (such as the user's microbiome pharmacogenomics profile, medical history, demographics, behavior, preferences, etc.).

Fifth, the techniques may improve the technical field of computational modeling of antibiotic-associated disorders related to at least microbiota digital medicine, general digital medicine (digital medicine), gene sequencing, and/or other related fields. Sixth, the techniques can utilize a dedicated computing device (e.g., a device associated with a sample processing system (such as a sequencing system; microbiota characterization system; treatment system, etc.)) to determine and process a microbiota dataset for characterization, and/or determine treatment for an antibiotic-associated disorder. However, the techniques may provide any other suitable benefit where a non-general purpose computer system for microbiome characterization and/or regulation is used.

3. System for controlling a power supply

The processing system 210 of the system 200 can be used to receive and process (e.g., fragment, amplify, sequence, etc.) biological samples. The processing system 210 may additionally or alternatively be used to provide and/or collect the sample kits 250 (e.g., including containers configured to receive biological material, instructions to a user directing a self-sampling process, etc.) for a plurality of users (e.g., responses to purchase orders for the sample kits 250), such as by mail delivery systems and/or other suitable processes. In some embodiments, the sample kit 250 may include materials and associated instructions for the user to collect samples from one or more collection sites (e.g., by swab swabbing, fluid aspiration, biopsy, etc.). The collection site may be associated with one or more of: female genitalia, male genitalia, rectum, digestive tract (gut), skin, oral cavity, nose, any mucous membrane, and/or any other suitable sample providing site (e.g., blood, sweat, urine, feces, semen, vaginal secretions, tears, tissue samples, interstitial fluid, other bodily fluids, etc.). The processing system 210 can additionally or alternatively include a library preparation system operable to automatically prepare a biological sample (e.g., for fragmentation and/or amplification (such as in a multiplexed manner, etc.) using primers compatible with nucleic acid sequences associated with antibiotic-associated disorders) for sequencing by a sequencing system (e.g., a next generation sequencing platform), and/or any suitable component. However, the processing system 210 and related components may be configured in any suitable manner.

The microbiota characterization system 220 of the system 200 may be used to determine and/or analyze microbiota coefficient data sets and/or supplemental data sets for characterizing and/or determining treatment of antibiotic-associated disorders. In one variation, the microbiome characterization system 220 may obtain and/or apply computer-implemented rules (e.g., 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 any other suitable rules). However, the microbiome characterization system 220 may be configured in any suitable manner.

Treatment system 230 of system 200 is used to facilitate one or more treatments for a user (e.g., a subject; a care provider facilitating treatment delivery, etc.) to treat an antibiotic-associated condition (e.g., reduce the risk of the condition; change the microbiome pharmacogenomic profile of the user to a state susceptible to treatment for an antibiotic-treatable condition, etc.). Treatment system 230 may include any one or more of the following: a communication system (e.g., by notifying a care provider to communicate treatment recommendations (such as through interface 240) to recommend and/or provide treatment; telemedicine capable, etc.), an application executable on a user device (e.g., an antibiotic-related disorder application for facilitating treatment; a medication reminder application; an application program operable to communicate with an automated medication dispenser, etc.), an antibiotic-related therapy (such as an antibiotic (e.g., type, dose, medication plan, etc.)), an auxiliary medical device (e.g., a medication dispenser; an antibiotic-containing medication device (such as an antibiotic applicator for local delivery, a biodegradable antibiotic delivery system, a non-biodegradable antibiotic delivery system, an antibiotic delivery agent, a rice particle delivery system, a stent delivery system, a bead delivery system, a controlled release device, an elution device, and/or other suitable medication delivery device; a diagnostic for an antibiotic-related disorder; a diagnostic for a diagnostic of an antibiotic-related disorder Equipment; etc.), a user device (e.g., including a biometric sensor), and/or any other suitable component. One or more treatment systems 230 are preferably controllable by the microbiome characterization system 220. For example, microbiome characterization system 220 may generate control instructions and/or notifications to communicate to treatment system 230 to activate and/or otherwise operate the treatment system for facilitating treatment. However, the therapy system 230 may be configured in any other manner.

As shown in fig. 10, the system 200 can additionally or alternatively include an interface 240, the interface 240 can be used to improve the display of antibiotic-related information (e.g., characterization of antibiotic-related conditions; treatment recommendations; comparisons with other users; as shown in fig. 11, assessment of treatment related to microbiome pharmacogenomic profiles; as shown in fig. 9, microbiome composition; microbiome functional diversity, etc.). In another embodiment, the interface 240 can display antibiotic-related information, including microbiome composition (e.g., taxonomic groups), functional diversity (e.g., relative abundance of genes related to functions associated with antibiotic-related disorders, etc.), and/or risk of one or more antibiotic-related disorders to the user (such as relative to a population of users sharing demographics (e.g., patients sharing disorders, smokers, exercisers, users of different dietary regimens, consumers of probiotics, antibiotic users, populations undergoing special therapies, etc.)). In another embodiment, the interface 240 can be used to present antibiotic-associated information, including changes over time in microbiome pharmacogenomic profiles (and/or microbiome composition, microbiome functional diversity, etc.) relating to treatment and antibiotic-associated disorders. In one embodiment, the interface is operable to improve an antibiotic associated with an antibiotically treatable condition and display relevant information based on a comparison between a user microbiome pharmacogenomic profile of the user relative to a population of users sharing a demographic. In another particular embodiment, the display of antibiotic-associated information of the interface can be improved by selection (e.g., based on the characterized components meeting a threshold condition; the microbiome pharmacogenomic profile of the user matching a reference profile that exceeds a threshold similarity; the antibiotic-associated condition risk exceeding a threshold; other triggering events, etc.) and presentation of a subset of antibiotic-associated information (e.g., highlighting and/or otherwise emphasizing the subset of antibiotic-associated information). However, interface 240 may display any suitable information and may be configured in any suitable manner.

System 200 and/or components of system 200 may be executed, hosted (host on), communicated with, and/or include, in whole or in part: a remote computing system (e.g., a server, at least one networked computing system, stateless, stateful), a local computing system, a database (e.g., a user database, a microbiota data set database, an antibiotic-related condition database, a therapy database, etc.), a user device (e.g., a user smartphone, a computer, a laptop, an auxiliary medical device, a wearable medical device, a care provider device, etc.), and/or any suitable component. For example, the system 200 may include a computing system operable to communicate with a processing system 210 (e.g., a next generation sequencing platform of the processing system 210) to perform appropriate portions of the method 100 (such as determining microbiome pharmacogenomic data). Although the components of system 200 are generally described as distinct components, they may be physically and/or logically grouped in any manner. For example, a smartphone application may partially or completely execute a microbiota characterization system 220 (e.g., apply a characterization model to generate a characterization of an antibiotic-associated condition in real-time; sequence a biological sample; process a microbial sequence; extract features from a microbiota data set, etc.) and a treatment system 230 (e.g., communicate with a calendar application of the smartphone to notify the user to take antibiotics according to parameters determined by an antibiotic treatment model, etc.). Additionally or alternatively, the functionality of system 200 may also be distributed in any suitable manner among any suitable system components. However, the components of system 200 may be configured in any suitable manner.

4. Method of producing a composite material

As shown in fig. 1A-1B, an embodiment of a method 100 for microbial pharmacogenomics of a user associated with an antibiotic-associated disorder can comprise: determining at least one of a microbiome pharmacogenomic dataset, a microbiome composition dataset, and a microbiome functional diversity dataset based on the biological samples from the set of subjects S110; receiving a supplemental data set associated with at least a subset of the group of subjects, wherein the supplemental data set provides information on antibiotic-associated conditions for the group of subjects S120; a characterization process related to the antibiotic-associated condition is performed based on the complementary dataset and the features extracted from at least one of the microbiome pharmacogenomic dataset, the microbiome composition dataset, and the microbiome functional diversity dataset S130. Embodiments of method 100 may additionally or alternatively include determining a treatment (e.g., with an antibiotic treatment model) configured to modulate microbial abundance, distribution, functional diversity, and/or pharmacogenomic diversity in a subject characterized according to a characterization process S140; receiving a biological sample from a user S150; identifying a characterization of the user related to the antibiotic-associated disorder using a characterization process based on at least one of a microbiome pharmacogenomic dataset, a microbiome composition dataset, and a microbiome functional diversity dataset processing a biological sample from the user S160; facilitating treatment (e.g., antibiotic treatment, behavior) of the subject based on the characterization (e.g., and treatment model, etc.) S170; and monitoring the effectiveness of the treatment of the subject at different time points based on processing the biological sample to assess at least one of a microbiome pharmacogenomic profile, microbiome composition, and microbiome functional diversity S180. Thus, the method 100 may be used for therapy monitoring, and may potentially be used for characterization as an intermediate step, in particular for evaluating one or more antibiotic therapies.

4.1, methods-processing datasets

Block S110 describes: characterizing microbiome composition, function and/or pharmacogenomics of each biological sample in a set of biological samples associated with a population of subjects, thereby generating at least one of a microbiome composition diversity dataset, a microbiome functional diversity dataset and a microbiome pharmacogenomics dataset for the population of subjects. Block S110 is for processing each biological sample in the set of biological samples to determine a compositional aspect, a functional aspect, and/or a pharmacogenomic aspect associated with the microbiome of each of the population of subjects. Compositional, functional, and/or pharmacogenomic aspects can include compositional aspects at the microbial level, including parameters related to the microbial profile of kingdom, phylum, class, order, family, genus, species, subspecies, strain, and/or any other suitable sub-taxonomic groups (e.g., as measured in total abundance of each group, relative abundance of each group, total number of groups shown, etc.). Compositional, functional and/or pharmacogenomic aspects can also be represented by Operational Taxonomic Units (OTUs). The compositional, functional, and/or pharmacogenomic aspects can additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multi-site sequence typing, 16S rRNA sequences, 18S rRNA sequences, ITS sequences, protein coding sequences, other genetic markers, other phylogenetic markers, and the like). The compositional, functional, and/or pharmacogenomic aspects can include the presence or absence of genes or amounts of such genes associated with a particular function (e.g., enzymatic activity, transport function, immunological activity, antibiotic resistance genes, etc.). Thus, the output of block S110 can be used to provide targeted features for the characterization process of block S130 and/or the treatment process of block S140, and/or other suitable portions of the method 100, where the features can be microorganism-based (e.g., the presence of a bacterial genus), genetically-based (e.g., based on the representation of a particular genetic region and/or sequence), functionally-based (e.g., the presence of a particular catalytic activity), pharmacogenomics-based (e.g., codon mutations, exon deletions or substitutions, gene rearrangements, translocations, etc.), and/or otherwise configured.

Additionally or alternatively, block S130 (e.g., features related to block S130) may include, but are not limited to: a region of nucleotides associated with metabolism or resistance of a biologically active molecule or any other functional characteristic of the nucleotide source (e.g., structural or regulatory RNA, messenger RNA, protein or peptide). In particular, bioactive molecules may include, but are not limited to: antibiotics, antibodies, peptides, hormones, and any other endogenous or exogenous molecule, regardless of its source (e.g., diet, environment). In one variation, the method 100 can include amplifying one or more complete metagenomes (e.g., for complete microbial metagenome sequencing; as opposed to utilizing primers; in combination with primers, etc.) for any number of users (e.g., for individual users, for a collection of subjects, such as a population of subjects). In particular embodiments, method 100 may include: collecting a biological sample from a collection of users (including self-sampling from users); processing (e.g., isolating, amplifying, sequencing, comparing, etc.) the complete metagenome from the biological sample; determining at least one of a microbiota data set, a microbiota compositional diversity characteristic, a microbiota functional diversity characteristic, and a microbiota pharmacogenomic characteristic (e.g., prevalence of genes associated with antibiotic resistance or susceptibility, etc.); determining a characterization (e.g., an antibiotic-associated disorder) and/or a treatment based on the features (and/or microbiota dataset). However, the complete metagenome can be processed in any suitable manner.

In some variations (e.g., in the context of clinical diagnosis and treatment), block S110 may additionally or alternatively include analyzing and processing environmental samples (e.g., from a hospital environment of the subject, from a home environment of the subject, etc.) in order to inform characterization and/or treatment models of subsequent blocks of method 100. For example, identification of microorganisms within the subject's environment can be used to inform a treatment model to facilitate treatment that allows the subject to prevent the type of harmful microorganisms present in the subject's environment. However, the environmental sample may be used in any other suitable manner to support the method 100.

In some variations, the sample processing in block S110 may include any one or more of: lysing the biological sample, disrupting cell membranes of the biological sample, isolating undesired components (e.g., RNA, protein) from the biological sample, purifying nucleic acids (e.g., DNA) in the biological sample, amplifying (e.g., using a library preparation system) nucleic acids from the biological sample, further purifying the amplified nucleic acids of the biological sample, and sequencing the amplified nucleic acids of the biological sample.

In some variations of block S110, amplifying the purified nucleic acid preferably comprises performing one or more of: polymerase Chain Reaction (PCR) -based techniques (e.g., solid phase PCR, RT-PCR, qPCR, multiplex PCR, touchdown PCR, nanopcr, nested PCR, hot start PCR, etc.), helicase-dependent amplification (HDA), loop-mediated isothermal amplification (LAMP), autonomous sequence replication (3SR), nucleic acid sequence-based amplification (NASBA), Strand Displacement Amplification (SDA), Rolling Circle Amplification (RCA), Ligase Chain Reaction (LCR), and any other suitable amplification technique. In amplifying the purified nucleic acid, the primers used are preferably selected to prevent or minimize amplification bias, and are configured to amplify nucleic acid regions/sequences (e.g., 16S region, 18S region, ITS region, antibiotic resistance genes, etc.) that provide information on taxonomy, phylogeny, diagnosis, formulation (e.g., probiotic formulation), therapy, and/or for any other suitable purpose. Thus, universal primers configured to avoid amplification bias (e.g., F27-R338 primer set for 16S rRNA, F515-R806 primer set for 16S rRNA, etc.) can be used in the amplification. The selected primers can additionally or alternatively be associated with one or more antibiotic-associated disorders, microbiome pharmacogenomic features (e.g., primers compatible with microbiome mutations associated with antibiotic efficacy (such as point mutations in the gyrA gene and/or parC gene), etc.), microbiome compositional features (e.g., identified primers compatible with genetic targets corresponding to microbiome compositional features associated with a set of taxa associated with one or more sexually transmitted diseases, etc.), functional diversity features, complementary features, and/or other features associated with antibiotic-associated disorders (such as compatibility with genetic targets). For example, a primer can be complementary to a genetic target associated with a feature (e.g., obtain a genetic sequence from a relative abundance feature, etc.). In a particular embodiment, the method 100 may include: determining the microbial sequence based on the following: identifying primers for nucleic acid sequences associated with antibiotic-associated conditions, fragmenting nucleic acid material derived from biological samples associated with a user pool, and amplifying the fragmented nucleic acid material based on the primers (e.g., multiplex amplification using bridge amplification substrates of a next generation sequencing platform, etc.); determining an alignment of a microbial sequence to a set of reference sequences associated with an antibiotic-associated disorder (e.g., including microbiome pharmacogenomic biomarkers associated with an antibiotic-associated disorder, etc.); and determining microbiome pharmacogenomic data based on the alignment. The primers used in some variations of block S110 may additionally or alternatively include an aggregate barcode sequence specific to each biological sample, which may facilitate identification of the biological sample after amplification. The primers used in some variations of block S110 may additionally or alternatively include an adapter region configured to mate with a sequencing technique involving a complementary adapter (e.g., Illumina sequencing). Additionally or alternatively, block S110 may implement any other steps configured to facilitate processing (e.g., using a Nextera kit).

In some variations of block S110, sequencing the purified nucleic acid may include methods involving targeted amplicon sequencing and/or metagenomic sequencing, implementing techniques including one or more of: sequencing-by-synthesis techniques (e.g., Illumina sequencing), capillary sequencing techniques (e.g., Sanger sequencing), pyrosequencing techniques, and nanopore sequencing techniques (e.g., using oxford nanopore techniques), or any other suitable sequencing technique.

In a particular embodiment of block S110, amplifying and sequencing nucleic acids from a biological sample in a set of biological samples comprises: solid phase PCR, which involves bridge amplification of DNA fragments of a biological sample on a substrate with an oligomeric linker, wherein amplification involves primers with the following sequences: forward index sequences (e.g., Illumina forward index corresponding to the MiSeq/NextSeq/HiSeq platform), forward barcode sequences, transposase sequences (e.g., transposase binding sites corresponding to the MiSeq/NextSeq/HiSeq platform), linkers (e.g., fragments of zero, one, or two bases configured to reduce homogeneity and improve sequence outcome), other random bases, sequences for targeting specific target regions (e.g., 16S rRNA regions, 18S rRNA regions, ITS regions, antibiotic resistance genes, etc.), reverse index sequences (e.g., Illumina reverse index corresponding to the MiSeq/NextSeq/HiSeq platform), and reverse barcode sequences. In particular embodiments, sequencing comprises Illumina sequencing using sequencing-by-synthesis techniques (e.g., using the HiSeq platform, using the MiSeq platform, using the NextSeq platform, etc.).

In some variations, the computational processing in block S110 may include any one or more of the following: identifying sequences from which the microbiome is derived (e.g., as opposed to subject sequences and contaminants); aligning and/or locating microbiome derived sequences (e.g., aligning fragmented sequences using one or more of a single-ended alignment, a no gap alignment, a pairing, and/or other suitable techniques (such as an alignment of reference sequences for facilitating association with antibiotic-associated disorders), and the like); and generating features derived from compositional, functional, and/or pharmacogenomic aspects of a microbiome associated with the biological sample.

With respect to block S110, upon identifying the indicated cohort of microorganisms of the microbiome associated with the biological sample, generation of characteristics derived from compositional and functional aspects of the microbiome associated with the biological sample may be performed. In one variation, generating the feature may include generating the feature based on multi-site sequence typing (MSLT) to identify markers that may be used for characterization in subsequent blocks of the method 100. Additionally or alternatively, generating the features may include generating features that describe the presence or absence of certain taxonomic groups of microorganisms and/or ratios between the represented taxonomic groups of microorganisms. Additionally or alternatively, generating the features may include generating at least one of the features describing one or more of: the number of taxonomic groups shown, the network of functional groups and/or pharmacogenomic groups, the different taxonomic groups shown, the relevance of the functional groups and/or pharmacogenomic groups, the interactions between different taxonomic groups, the products produced by different taxonomic groups, the interactions between products produced by different taxonomic groups, the ratio between dead and live microorganisms (e.g., RNA-based analysis for different shown taxonomic groups), the phylogenetic distance (e.g., in terms of kantovich-Rubinstein distance, Wasserstein distance, etc.), any other suitable feature associated with a taxonomic group, any other suitable genetic or functional feature, any other suitable pharmacogenomic feature.

With respect to block S110, additionally or alternatively, generating features (e.g., for a microbiota coefficient dataset) may include, for example, using a sparCC method, using a relative abundance and average size of Genome (GAAS) method, and/or using a mixed model theory (GRAMMy) method using relative abundance of genomes to generate features describing relative abundances of different microbiota, wherein the GRAMMy method uses sequence similarity data to make a most likely assessment of the relative abundance of one or more groups of microorganisms. Additionally or alternatively, generating the feature may include generating a statistical measure of the categorical change as derived from the abundance metric. Additionally or alternatively, generating the features can include generating features derived from relative abundance factors (e.g., associated with changes in abundance of a taxon that affect the abundance of other taxa). Additionally or alternatively, generating the features may include generating qualitative features describing the presence of one or more taxonomic groups, individually and/or in combination. Additionally or alternatively, generating the features can include generating features associated with genetic markers (e.g., representative 16S, 18S, and/or ITS sequences) that characterize microorganisms of a microbial lineage associated with the biological sample. Additionally or alternatively, generating a feature may include generating a feature associated with a functional association of a particular gene and/or organism having a particular gene. Additionally or alternatively, generating a feature can include generating a feature (e.g., a gene for a virulence factor, a genomic island marker, etc.) associated with the pathogenicity of the taxon and/or a product assigned to the taxon. Additionally or alternatively, generating the features can include generating features associated with pharmacogenomic markers of the taxon of interest (e.g., detecting genes and/or mutations associated with any kind of antibiotic resistance).

However, block S110 and/or other suitable portions of method 100 can include generating any other suitable features derived from sequencing and localization of nucleic acids of a biological sample. For example, the one or more features may be combinatorial (e.g., relating to pairings, triplets), related (e.g., relating to correlation between different features), and/or related to a change in a feature (e.g., temporal change, change in sample site, spatial change, etc.). However, block S110 may also be performed in any suitable manner.

Block S120 describes: a supplemental data set associated with at least a subset of the set of subjects is received, the supplemental data set providing information of antibiotic-associated conditions of the set of subjects S120. Block S120 provides for obtaining additional data related to one or more subjects in the set of subjects, which may be used to train (train) and/or validate the characterization process generated in block S130. In block S120, the supplemental data set preferably includes data derived from the survey, but may additionally or alternatively include any one or more of: omnidirectional data derived from sensors and/or other suitable components (e.g., components of system 200, which can include a therapy device, a user device, etc.), medical data (e.g., current and historical medical data such as an antibiotic medical history), data information for antibiotic-related conditions (e.g., whether a condition exists, a related diagnosis, a related therapy, an indication of progress over time, etc.), and/or any other suitable type of data. In some variations of block S120, which includes receiving survey-derived data, the survey-derived data preferably provides physiological, demographic, and behavioral information related to the subject. Additionally or alternatively, blocks S110 and S120 may be performed in a similar manner as in U.S. patent application No. 15/097,862 filed on 4/13/2016, which is incorporated herein by reference in its entirety. However, the process supplemental data set block S120 may be performed in other manners.

4.2, performing characterization processing

Block S130 describes: a characterization process related to the antibiotic-associated condition is performed based on the complementary dataset and the features extracted from at least one of the microbiome pharmacogenomic dataset, the microbiome composition dataset, and the microbiome functional diversity dataset S130. As shown in fig. 3, block S130 can be used to identify features and/or combinations of features that can be used to characterize a subject or population based on the subject' S microbiome composition, functional characteristics, and/or pharmacogenomic characteristics. Thus, the characterization process can be used as a diagnostic tool that can characterize a subject (e.g., in terms of medical conditions, etc.) based on the subject's microbiome composition, functional characteristics, and/or pharmacogenomic characteristics. Such characterization may then be used by the treatment model of block S140 to suggest or provide personalized antibiotic therapy (and/or other therapies).

In performing the characterization process, block S130 may use computational methods (e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.) to characterize the subject as exhibiting characteristic features of a group of subjects having a health condition that may be effectively treated by a personalized treatment regimen (e.g., a personalized antibiotic treatment regimen). In some variations of block S130, performing the characterization process may include generating one or more characterizations of the one or more antibiotic-associated conditions. In embodiments, the characterization process of block S130 can help identify correlations between antibiotic-associated disorders and up-or down-regulation associated with a microbiome pharmacogenomic profile (e.g., microbiome pharmacogenomic profile associated with antibiotic-associated disorders; etc.), a microbiome (e.g., microbiome pharmacogenomic profile, taxonomic group, microbiome composition profile, etc.), microbiome functional diversity (e.g., associated with the cluster/kyoto gene and genomic pathway encyclopedia of orthologous groups, microbiome functional diversity profile, etc.), and/or other suitable aspects associated with a microbiome. Characterization up-and/or down-regulation can be at any suitable taxonomic level (e.g., kingdom, phylum, class, order, family, genus, species, strain), at any suitable granularity of functional diversity, at any suitable granularity of pharmacogenomic profile of a microbiome, and/or at any suitable granularity (e.g., chromosome, locus, gene, allele, locus, gene, nucleotide, etc.).

In another variation, characterizing an antibiotic-associated disorder in block S130 can include producing a diagnostic analysis (e.g., assessing risk posed by an antibiotic-associated disorder; calculating changes in risk conferred by identified treatments; diagnosing the presence or absence of an antibiotic-associated disorder (such as the presence or absence of a microbiome pharmacogenomic signature), diagnosing the severity over time of antibiotic-associated disorders related to microbiome pharmacogenomics, compositional and/or functional diversity; resistance and/or susceptibility to a treatment such as an antibiotic, etc.) and/or other analysis. In another variation of block S130, characterizing the antibiotic-associated disorder may be based on one or more supplemental data sets. For example, the feature selection rule set may associate one or more antibiotic-associated conditions with one or more biometric features derived from biometric data that provide antibiotic-associated condition information (e.g., optical data related to diagnosing gonorrhea, another sexually transmitted disease, urinary tract infection, and/or other suitable antibiotic-associated conditions; data collected associated with an auxiliary medical device (such as antibiotic delivery parameters via an antibiotic delivery device); supplemental data related to a sample collection site; blood data; temperature data; user behavior data; cardiovascular data; stool data; etc.). In another embodiment, the supplemental data set may include sensor data collected at a user device, an auxiliary medical device, and/or other suitable component (e.g., a sample processing system, etc.). In another embodiment, performing the characterization process can include determining a series of characterizations over time based on the therapy advancing over time (e.g., the therapy data includes antibiotic therapy regimen data, probiotic regimen data, and/or other suitable therapy data associated with a population of users), wherein the effects of different therapies over time can help clarify insights related to microbiome pharmacogenomics, microbiome composition, and/or functional diversity associated with antibiotic-related disorders. However, performing the characterization process based on the supplemental data set, and/or generating the diagnostic analysis may be performed in any suitable manner.

Block S130 may additionally or alternatively generate features, the block S130 for generating one or more features for characterizing the process (e.g., use of generating a characterization model) and/or other suitable processes for the method 100. The features may include any one or more of the following: a microbiome pharmacogenomic profile, a microbiome composition profile (e.g., absolute and/or relative abundance of a taxonomic group in a microbiome of a user), a microbiome functional diversity profile, and/or other suitable profile. Microbiome pharmacogenomic features can include features associated with one or more of the following (e.g., antibiotic-associated disorders, associated taxa, associated functional diversity, etc.): codon mutations, exon deletions, substitutions, gene rearrangements, translocations, microbial strain mutability, resistance or susceptibility of a microbial strain to an antibiotic, microbial strain co-dependency behavior, microbial pharmacogenomic markers, and/or any other suitable characteristic (e.g., relative abundance of such characteristics, etc.). The microbiome functional diversity characteristic may include any one or more of the following: kyoto Encyclopedia of Genes and Genomes (KEGG) functional features (e.g., KEGG feature associated with flagellar biosynthesis, etc.), clusters of orthologous groups of protein features (COG), L2, L3, L4-derived features, genomic functional features, functional features associated with and/or specific to a taxonomic group, chemical functional features (e.g., cysteine metabolism, etc.), systemic functional features (e.g., systemic immune function; function associated with systemic disease, etc.), and/or any suitable functional features. The microbiome signature may additionally or alternatively be derived from and/or correlated with at least one of: monotonic transformation of relative abundance, non-monotonic transformation, normalization, feature vectors such as derived from at least one of linear latent variable analysis and non-linear latent variable analysis, linear regression, non-linear regression, kernel methods, feature embedding methods, machine learning, statistical inference methods, and/or any other suitable method.

With respect to block S130, the characteristics are preferably determined based on processing the microbiota data set according to one or more computer-implemented rules (e.g., characteristic selection rules, user preference rules, etc.), but may be determined based on any suitable information. For example, the method 100 can include obtaining a feature selection rule set (e.g., a microbiome pharmacogenomic feature selection rule, etc.), associating an antibiotic-associated disorder with a microbiome pharmacogenomic feature, a microbiome composition feature, and/or a subset of a microbiome functional diversity feature (e.g., from a potential microbiome feature library); and generating a feature based on evaluating the microbiota data set against the feature selection rule set, wherein the antibiotic-associated feature selection rule set is operable to improve the microbiota feature system (e.g., by facilitating reduced processing time (such as for transforming supplemental data and features into a characterization model); by increasing the speed at which the model is stored, retrieved, and/or executed; by improving characterization and/or therapy to provide accuracy). In one particular embodiment, the feature selection rule can be associated with a correlation between a microbiome pharmacogenomic feature and antibiotic efficacy related to one or more antibiotic-associated conditions (e.g., where the correlation can be determined based on processing a series of user biological samples over time to assess antibiotic efficacy, in the case 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-implemented rules to process the microbiome-level data, but may additionally or alternatively include applying computer-implemented rules to process microbiome-related data on the basis of: demographics-specific basis (e.g., a subset that shares demographic characteristics such as medical history, race, age, gender, etc. associated with antibiotic-associated conditions and/or historical antibiotic therapy adoption); a condition-specific basis (e.g., a subset exhibiting a particular antibiotic-associated condition), a sample type-specific basis (e.g., applying different computer-implemented rules to process microbiota data derived from samples collected at different collection sites; applying different computer-implemented rules based on the type of supplemental data available to supplement the sample, etc.), and/or any other suitable basis. As such, block S130 may include assigning users in the user population to one or more subgroups; and apply different computer-implemented rules to determine different subsets of features (e.g., the set of feature types used; the type of characterization model generated from the features, etc.). However, applying computer-implemented rules may be performed at any suitable level of granularity in any suitable manner.

In one variation, block S130 can include applying feature selection rules (e.g., such as exhaustive, best-first, simulated annealing, forward greedy, reverse greedy, and/or other suitable feature selection algorithms) to filter, sort, and/or otherwise select features for generating one or more characterization models, therapy models (e.g., using rules that correlate one or more therapies to varying degrees of efficacy, to microbiome pharmacogenomic profiles, microbiome compositions, microbiome functional diversity, and/or other suitable aspects, etc.), and/or other suitable models. In one variation, application of the feature selection rules can lead to a microbial community-related insight and make changes in sample processing (e.g., techniques, experimental conditions, blocks S110-S120, S150, etc.) based thereon. For example, the method 100 may include: applying an antibiotic-associated feature selection rule set to identify features (e.g., presence, risk, treatment, etc.) associated with (e.g., most relevant to, etc.) the antibiotic-associated condition; and selecting primers compatible with the genetic target associated with the identified characteristic (e.g., for amplification and sequencing to generate a microbiota dataset, etc.). As such, as shown in fig. 12, feature selection rules and/or other computer-implemented rules may additionally or alternatively be used to determine sample processing parameters (e.g., described with respect to blocks S110-S120, S150, etc.). 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 one embodiment, in block S130, the feature selection rule may include applying a statistical analysis (e.g., an analysis of a probability distribution) for similarity and/or dissimilarity between the first set of subjects and the second set of subjects. The first set of subjects exhibits a target state (e.g., an antibiotic-associated disorder state); the second set of subjects did not exhibit the target state (e.g., a "normal" state). In practicing this variation, one or more of the Kolmogorov-Smirnov (KS) test, the alignment test, the Cram mer-von Mises test, and any other statistical test (e.g., t-test, Welch t-test, z-test, chi-square test, distribution-related test, etc.) may be used. In particular, one or more such statistical hypothesis tests may be used to assess a set of features having different abundances in subjects as described below: a first set of subjects exhibiting a correlation to a target state (e.g., a poor state; microbiome pharmacogenomic profile; etc.) and a second set of subjects not exhibiting a target state (e.g., a normal state). In more detail, the assessed feature set may be constrained based on the abundance percentages associated with the first and second subject sets and/or any other suitable diversity-related parameter to increase or decrease the confidence interval of the characterization. In a specific implementation of this example, the features can be from a bacterial taxon that is present in abundance in a percentage of the first set of subjects and the second set of subjects, wherein the relative abundance of the taxon between the first set of subjects and the second set of subjects can be determined by the KS test, expressed in significance (e.g., in p-value). Thus, the output of block S130 may include a normalized relative abundance value showing significance (e.g., a p-value of 0.0013) (e.g., a 25% higher relative abundance of taxa in diseased subjects compared to healthy subjects). Variations in feature generation may additionally or alternatively be implemented or derived from functional features, pharmacogenomic features, or metadata features (e.g., non-bacterial markers). However, any suitable statistical analysis may be applied in any suitable manner.

In another variation, block S130 can additionally or alternatively convert input data from at least one of a microbiome composition diversity dataset, a microbiome functional diversity dataset, a microbiome pharmacogenomic diversity dataset, a complementary dataset, and/or other suitable dataset into a feature vector that can be tested for efficacy in predicting characterization (e.g., diagnosis) of a population of subjects. Data from the supplemental data set can additionally or alternatively be used to provide an indication of one or more characterizations of the feature set, where the characterization process is trained using a training data set of candidate features and candidate classifications to identify features and/or feature combinations that have a high (or low) predictive power for accurately predicting the classification. Thus, refinement of the characterization process using the training dataset results in the identification of a feature set (e.g., subject feature, combination of features) that is highly correlated to a particular classification of the subject.

In a variation of block S130, the feature vectors of the classes of active predictive characterization processes may include features related to one or more of: a microbiome diversity metric (e.g., with respect to distribution in each taxonomic group, with respect to distribution in archaebacteria, bacteria, viruses, and/or eukaryotes), the presence of a taxonomic group in one microbiome, a representation of a particular genetic sequence (e.g., a 16S sequence) in one microbiome, a relative abundance of a taxonomic group in one microbiome, microbiome suitability metrics (e.g., in response to perturbations determined from a complementary data set), abundance of genes encoding proteins or RNAs (enzymes, transporters, proteins from the immune system, hormones, interfering RNAs, etc.) with a given function, and other suitable characteristics derived from a microbiome diversity data set, a microbiome functional diversity data set, a microbiome pharmacogenomic characteristic data set, and/or a complementary data set. Additionally or alternatively, combinations of features may be used in the feature vector, where the features may be grouped and/or weighted when providing the combined features as part of the feature set. For example, a feature or set of features may include a weighted composition of the number of indicated classes of bacteria in a microbiome of one (weighted composite), the presence of a particular genus of bacteria in a microbiome of one (weighted composite), the showing of a particular 16S sequence in a microbiome of one (weighted composite), and the relative abundance of bacteria of a first phylum relative to bacteria of a second phylum. However, the feature vectors and features may additionally or alternatively be determined in any other suitable manner.

Additionally or alternatively, block S130 can include generating a characterization model for generating one or more characterization models for one or more antibiotic-associated conditions based on applying the one or more features, the microbiota data set, the supplemental data, and/or other suitable data. The characterization model (and/or therapy model or other suitable model) may include any one or more of the following: probabilistic properties, heuristic properties, deterministic properties, and/or any other suitable properties. Block S130 and/or other suitable portions of the method 100 (e.g., generating the treatment model S140) and/or the system 200 may employ one or more algorithms similar to the algorithms described in U.S. patent application No. 15/097,862 filed 4, 13, 2016, the entire contents of which are incorporated herein by reference, and/or any suitable algorithm may be employed. In one variation of block S130, the characterization process may be generated and trained according to a Random Forest Prediction (RFP) algorithm that combines bagging (i.e., bootstrap aggregation) and selecting a random feature set from a training data set to construct a decision tree set T that is correlated with the random feature set. When a random forest algorithm is used, N samples in the decision tree set are randomly selected and replaced to create a subset of the decision tree, and for each node, m predicted features are selected from all the predicted features for determination. Forking is performed using a predictive feature that provides the best fork at a node (e.g., according to an objective function) (e.g., bifurcating at a node, trifurcating at a node). By sampling multiple times from a large dataset, the strength of the characterization process in identifying strong features in the predictive classification can be greatly increased. In this variation, measures to prevent bias (e.g., sampling bias) and/or cause an amount of bias may be included during processing to increase the robustness of the model. However, the application of these models may be performed in any suitable manner.

In another variation of block S130, different characterization models can be generated for different demographic groups (e.g., different characterization models for a subset of users associated with different antibiotic drug histories, etc.), antibiotic-associated disorders (e.g., different characterization models for different antibiotic-treatable disorders; different characterization models for different microbiome pharmacogenomic profiles, etc.), individual subjects, supplemental data (e.g., models incorporating features derived from biometric sensor data and or survey response data vs independent of supplemental data, etc.), and or other suitable criteria. As shown in fig. 6, characterization with different characterization model outputs can be used to determine and or facilitate treatment, such as by inputting outputs derived from different characterizations for different antibiotic-associated disorders into a treatment model (e.g., to generate a customized single treatment or multiple therapies to treat different antibiotic-associated disorders, etc.).

In another embodiment, block S130 may include selecting different characterization models for different user accounts; and for each user account, a respective characterization model associated with the user account is stored to improve data storage and/or retrieval (e.g., for performing the processes of method 100, etc.). Generating multiple characterization models suitable for different environments can provide improvements to a microbiome characterization system by improving characterization accuracy (e.g., by customizing an analysis for demographics, antibiotic-related disorders, characteristics, etc. for a particular subject), appropriate speed of retrieval of characterization models from a database (e.g., by associating customized characterization models with particular user accounts and/or other identifiers), generation and/or execution of characterization models (e.g., where customized models are associated with a subset of potential feature pools related to antibiotic-related disorders and remaining unselected features are less relevant to antibiotic-related disorders), and/or other suitable aspects of the microbiome characterization system.

In another variation of block S130, generating a set of features for different characterization models (and/or other models) can be based on different feature selection rules (e.g., applying different microbiome pharmacogenomic feature selection rule sets to generate different sets of features specific to different sexually transmitted diseases; etc.). Alternatively, overlapping or identical feature set selection rules can be used to generate different characterization models (e.g., using the same microbiome pharmacogenomic features when generating two different characterization models, etc.). Additionally or alternatively, generating any number of characterization models may be performed in any suitable manner. However, performing the characterization process S130 may be performed in any suitable manner.

A, methods-characterization of antibiotic-associated disorders

In one embodiment, the characterization process of block S130 based on statistical analysis can identify the feature set with the highest correlation to an antibiotic-treatable disease (e.g., sexually transmitted disease, STD) in a manner that does not incubate and/or implement self-sampling of patients, thereby significantly reducing the time lag for an individual to receive treatment. In a particular example of this embodiment, the characterization process of block S130 can be used as a diagnostic test to identify and characterize gonococcal strains present in a sample, based on multiplex PCR of the genetic material of the sample, and in terminology, one or more of the following: mutability of the strain (e.g., mutation type, mutation tendency, mutation rate, etc.), resistance of the strain to various types of antibiotics, co-dependent behavior of the strain relative to the presence or absence of other microorganisms in the subject (e.g., in terms of interference with functional behavior, up-regulation, down-regulation, etc.), and other suitable aspects of interest in any notification model provided for personalized therapy. Thus, the characterization process of block S130 can include characterizing the strain of STD (e.g., gonorrhea) related microorganisms present in the sample based on the genetic characteristics identifying the microorganisms.

In variations of block S130, the microbiome signature may be associated with neisseria gonorrhoeae (species). In one particular embodiment, the microbiome signature may include a point mutation signature associated with a point mutation in at least one of the gyrA gene and the parC gene (which may be associated with ciprofloxacin resistance) of a taxon (e.g., neisseria gonorrhoeae). In another particular embodiment, the microbiome characteristic may be associated with the presence of a chimeric penA allele (which may be associated with cephalosporin resistance). This correlation and/or other suitable correlations between microbiome characteristics and therapeutic effects may be used to determine and/or facilitate treatment. However, any microbiome characteristic may include any suitable microbiome pharmacogenomic characteristic associated with any suitable taxonomic unit and/or any suitable resistance or susceptibility to an antibiotic. Characterization related to gonorrhea and/or other suitable antibiotic-associated disorders may additionally or alternatively be performed in a manner similar to the method described in U.S. application No. 15/097,862 filed 4/13/2016, which is incorporated herein by reference in its entirety.

4.3 method-personalization

The method 100 may additionally or alternatively include block S140, block S140 reciteing: determining a treatment configured to modulate microbial abundance, distribution, functional diversity, and/or pharmacogenomic diversity in the subject characterized according to the characterization process S140. Block S140 is for identifying and/or predicting a therapy (e.g., an antibiotic therapy regimen, etc.) that can correct or otherwise treat the antibiotic-treatable condition that the subject has. Block S140 may additionally or alternatively include generating and/or applying a therapy model for determining therapy. In block S140, the treatment may additionally or alternatively be selected from therapies comprising one or more of: antibiotic-based therapies, probiotic-based therapies (e.g., as shown in fig. 8), antifungal therapies, phage-based therapies, prebiotic-based therapies, small molecule-based therapies, drug-based therapies, diet-related therapies, topical therapies, cognitive/behavioral therapies, physical therapies, clinical therapies, alternative medicine-based therapies, environmental-based therapies (e.g., light-based therapies, temperature-based therapies, etc.), and/or any other suitable therapies designed to operate in any other suitable manner (e.g., to promote the health of a user with respect to an antibiotic-related disorder; etc.). The antibiotic-based therapy may include any one or more of: cell wall-based antibiotics (e.g., penicillins, cephalosporins, etc.), bacterial enzyme-based antibiotics (e.g., rifamycin, lipomycins, quinolones, sulfonamides, etc.), cell membrane-based antibiotics (e.g., polymyxins, etc.), bactericidal-based antibiotics, protein synthesis-based antibiotics (e.g., macrolide antibiotics, lincosamide antibiotics, tetracyclines, etc.), cyclic lipopeptides, glycylcyclines, oxazolidinones, adjunctive medical device-related therapies, and/or other therapeutic systems, and/or any other suitable antibiotic-based therapy.

In a variation of block S140, the treatment model may include an antibiotic treatment model that includes a decision tree/decision graph that prioritizes the available antibiotics (e.g., specific antibiotic types, antibiotic doses, etc.) at the nodes of the tree/graph to provide the most appropriate antibiotic and its dose at each stage of the treatment plan, as shown in fig. 4. The schedule of treatment phases may be based on a tradeoff of positive outcome (e.g., minimizing impact on the subject's beneficial bacterial population) and/or negative outcome (e.g., risk of malignant infection) for the administration of a particular antibiotic treatment to a subject, thereby facilitating the most effective but least harmful antibiotic targeting the disease-related strain early in the treatment plan. In traversing the branches of the decision tree/graph, patient instructions and/or results after administration of antibiotics at an upstream node may be used to facilitate administration of antibiotics at a downstream node. Alternatively, once the patient is treated, further promotion of antibiotic treatment may be terminated and restorative treatment of the subject may be promoted (e.g., probiotic supplements produced by a change in treatment model). However, the antibiotic treatment model may include any suitable properties and/or apply any suitable algorithms to determine any suitable aspects related to antibiotic treatment.

With respect to block S140, additionally or alternatively, the output of the antibiotic treatment model may include a predictive model that describes the predicted outcome and/or consequence of administering different antibiotic treatments/doses to the subject and allows another entity to make a decision in providing one or more antibiotics to the subject. For block S140, the antibiotic therapy may be customized for the subject as such based on one or more of: a disorder of the subject (e.g., the presence of a microbial strain associated with a disease); a microbial genetic sequence associated with resistance and/or susceptibility to an antibiotic; abundance/spread of the strain associated with the subject's condition; demographic and/or behavioral characteristics of the subject; other microbial populations of the subject; considerations derived from the subject's environment; and any other suitable factors. In particular embodiments of phage-based therapies, one or more populations of phage (e.g., in terms of colony forming units) specific for a particular bacteria (or other microorganism) shown in a subject can be used to down-regulate or otherwise eliminate populations of certain bacteria associated with a disease or disorder in the subject. Thus, phage-based therapies can be used to reduce the size of undesirable bacterial populations (or other microorganisms) shown in a subject. Additionally, phage-based therapies can be used to increase the relative abundance of bacterial populations not targeted by the phage used.

In one variation, blocks S140 and/or S170 may include automatically initiating a signal to control the therapy system to facilitate therapy (e.g., based on the characterization, the therapy model output, etc.), wherein the initiating signal may include one or more of: generate and send control instructions to the treatment system (e.g., control the antibiotic delivery device to provide antibiotics to the user, etc.), initiate notification provision (e.g., to notify the user about one or more characteristics and/or treatment, etc.), and/or control the treatment system to facilitate any other suitable operation of the treatment. In another variation, block S140 may include facilitating interaction between the user and the care provider (e.g., scheduling an appointment with the care provider as shown in fig. 7; initiating a remote medical conference over a wireless communication channel, etc.), such as in response to and/or concurrent with a trigger event (e.g., characterizing a risk of an antibiotic-related condition exceeding a threshold; being manually requested by the user and/or the care provider; identifying a therapeutic effect below a threshold based on analysis of a treated biological sample, etc.). However, the interaction between the users may be facilitated in any suitable manner.

In variations of block S140, generating and/or applying a therapy model may be based on one or more causes of the antibiotic-associated condition (e.g., causes of antibiotic-associated condition risk), wherein a therapy is determined by being able to operate the therapy model to reduce antibiotic-associated condition risk. With respect to block S140, a treatment model can be derived in connection with identifying a "normal" or baseline microbiome composition characteristic, functional characteristic, and/or pharmacogenomic characteristic, as assessed by subjects in a population of subjects identified as being in good health. Once a subset of subjects in the population of subjects characterized as being in good health are identified (e.g., using the features of the characterization process), a therapy can be generated in block S140 that modulates the microbiome composition features and/or functional features towards the microbiome composition features, functional features, and/or pharmacogenomic features of the subjects in good health. Thus, block S140 can include identifying one or more baseline microbiome composition, functional, and/or pharmacogenomic features (e.g., one baseline microbiome for each of the demographic groups) and potential therapeutic agents and treatment regimens that can divert the microbiome of the subject in the dysbiosis state toward one of the identified baseline microbiome composition, functional composition, and/or pharmacogenomic composition. However, the treatment model may be generated and/or refined in any other suitable manner.

In one variation, blocks S140 and/or S170 can include obtaining a therapeutic composition (e.g., antibiotic treatment) associated with at least one of microbiome pharmacogenomics (e.g., obtaining antibiotic treatment intended to circumvent antibiotic resistance or challenge antibiotic susceptibility through microbiome pharmacogenomic profiling, etc.), microbiome composition, and/or functional diversity (e.g., extracted features). In one embodiment, method 100 can include determining a modulator of a biomolecule associated with an antibiotic-associated disorder (e.g., a modulator of a biomolecule derived from a set of taxa associated with an antibiotic-associated disorder); obtaining a therapeutic composition for an antibiotic-associated condition based on the modulator; and promoting therapeutic composition. However, determining and/or promoting treatment may be performed in any manner similar to U.S. application No. 15/097,862 filed on 4/13/2016, which is incorporated by reference in its entirety, and/or may be performed in any suitable manner.

The method 100 may additionally or alternatively include block S150, block S150 recite: a biological sample is received from a user. Block S150 can be used to facilitate generation of a microbiota data set for a subject, which can be used to derive input for a characterization process. As such, receiving, processing and analyzing the biological sample preferably facilitates the generation of a microbiota dataset for the subject, which can be used to provide input for the characterization process. Processing and analyzing the biological sample from the subject is preferably performed in a manner similar to one of the embodiments, variations and/or examples described above with respect to block S110. Additionally or alternatively, the method of processing the biological sample may be performed in a manner similar to that in U.S. patent application No. 15/097,862 filed on 4/13/2016, which is incorporated herein by reference in its entirety. However, the biological sample reception and processing in block S150 may alternatively be performed in any other suitable manner.

Additionally or alternatively, the method 100 may include block S160, block S160 recite: using a characterization process, a characterization of the subject is determined based on at least one of a microbiome pharmacogenomic dataset, a microbiome composition dataset, and a microbiome functional diversity dataset that process a biological sample derived from the user. Block S160 provides for extracting features from the subject' S microbiome-derived data (e.g., evaluating the microbiome dataset based on computer-implemented rules) and using the features as input to the embodiments, variations or examples of the characterization process described in block S130 above and/or in any suitable process of the method 100. Thus, the characterizing in identification block S160 preferably comprises identifying a feature and/or combination of features associated with microbiome composition, functional composition and/or pharmacogenomic composition of the microbiome sample of the subject, inputting the feature into a characterization process, and receiving an output characterizing the subject as belonging to one or more of: behavioral groups, gender groups, diet groups, disease state groups, and any other suitable group that can be identified 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 confidence measure may be derived from a number of features used to generate the characterization, a relative weight or ranking of features used to generate the characterization, a measure of preference in the characterization process, and/or any other suitable parameter associated with an aspect of the characterization process. In some variations, features extracted from the microbiota data set of the subject may be supplemented with survey-derived and/or medical history-derived features from the subject, which may be used to further refine the characterization process of block S130. However, the microbiota data set of the subject may additionally or alternatively be used in any other suitable manner to enhance the model of the method 100, and block S160 may be performed in any suitable manner.

4.4, promoting and monitoring treatment

Additionally or alternatively, the method 100 may include block S170, the block S170 recites: based on the characterization (e.g., and treatment model, etc.) facilitating treatment (e.g., antibiotic treatment, etc.) for the subject, block S170 can be used to recommend and/or provide personalized treatment for the subject in order to shift the subject' S microbiome composition, functional characteristics, and/or pharmacogenomic characteristics to a desired equilibrium state. Block S170 can include providing a customized therapy to the subject according to the microbiome composition, the functional characteristics, and the pharmacogenomic characteristics of the subject, as shown in fig. 5, wherein the customized therapy is an antibiotic preparation configured to correct a disorder in the subject having the identified characteristics. As such, the output of block S140 can be used to facilitate customized therapeutic formulations and protocols (e.g., dosages, instructions for use) directly for the subject based on the trained therapy model. Additionally or alternatively, the treatment providing can include recommending available treatment measures configured to turn microbiome composition, functional characteristics, and/or pharmacogenomic characteristics to a desired state.

The provision of antibiotic therapy in block S170 may include providing a notification to the subject regarding recommended therapy and/or other forms of therapy. In one embodiment, a web interface of a personal computer or laptop associated with the subject may provide the subject with access to a user account for the subject, wherein the user account includes information regarding a characterization of the user, detailed characterizations of various aspects of the user' S microbiome, and a notification of suggested therapeutic measures generated in blocks S140 and/or S170. In another embodiment, an application executing on a personal electronic device (e.g., smartphone, smartwatch, head-mounted smart device) may be configured to provide a notification (e.g., on a display, haptically, audibly, etc.) regarding the therapy recommendation generated by the therapy model of block S170. The notification and/or probiotic therapy may additionally or alternatively be provided directly by an entity associated with the subject (e.g., a caregiver, spouse, important other person, healthcare professional, etc.). In some further variations, the notification may additionally or alternatively be provided to an entity (e.g., a healthcare professional) associated with the subject, wherein the entity is capable of administering the therapeutic measure (e.g., by prescription, by conducting a therapeutic conference, etc.). However, notification of treatment administration may be provided to the subject in any other suitable manner.

Additionally or alternatively, the method 100 may include block S180, block S180 recite: based on processing the biological sample, the effectiveness of treatment of the subject at different time points is monitored to assess at least one of a microbiome pharmacogenomic profile, microbiome composition, and microbiome functional diversity. Block S180 may be used to collect additional data regarding the positive, negative, and/or lack of effectiveness of a probiotic therapy for a given characterized treatment model of a subject (where the additional data may be used, for example, to generate, update, and/or execute one or more characterization models, treatment models, and/or other suitable models), and/or may be used in any suitable portion of method 100. For example, the method 100 can include updating a model (e.g., a characterization model, a treatment model, etc.) based on one or more microbiota datasets (e.g., updated microbiota characteristics extracted from an updated microbiota dataset obtained from a post-treatment biological sample of a user; etc.), such as updating a model based on observed adjustments regarding microbiota pharmacogenomic profiles, microbiota compositions, microbiota functional diversity, and/or other suitable microbiota aspects (e.g., associated with antibiotic-related conditions; e.g., determined based on a comparison of pre-treatment and post-treatment microbiota datasets). In another embodiment, the method 100 can include, in response to updating the model, based on the updated model, determining an update (e.g., to a characterization, to a treatment, etc.) of a user related to the antibiotic-associated disorder (e.g., a second user not associated with the biological sample based on the update). In another embodiment, the method 100 may include: receiving a post-treatment biological sample from a user (e.g., after facilitating treatment); generating a post-treatment characterization of the user associated with the antibiotic-associated disorder based on the post-treatment biological sample (e.g., based on updated microbiome characteristics obtained from the post-treatment biological sample, wherein the microbiome characteristics can be used with a characterization model, etc.); characterizing an adjustment of an antibiotic-associated disorder based on the post-treatment characterization (e.g., one or more pre-treatment characteristics for the user, for a second user, etc.); and/or promoting updated therapy to the user based on the updated characterization. However, any suitable portion of the method 100 and/or any suitable operation can be performed in any suitable manner based on the post-treatment biological sample and/or the updated microbiota data set. Monitoring the subject during the course of treatment facilitated by the treatment model (e.g., by receiving and analyzing a biological sample from the subject throughout the course of treatment, by receiving survey-derived data from the subject throughout the course of treatment) can thus be used to generate a treatment-effectiveness model for each feature provided by the characterization process of block S130 and for each recommended treatment provided in blocks S140 and S170.

In block S180, the subject can be prompted to provide additional biological samples at one or more key time points of the treatment regimen incorporating the treatment, and the additional biological samples can be processed and analyzed (e.g., in a manner similar to that described with respect to block S120) to generate a metric characterizing modulation of the subject' S microbiome composition, functional characteristics, and/or pharmacogenomic characteristics. For example, metrics relating to one or more of: changes in the relative abundance of one or more taxonomic groups shown in the subject's microbiome at earlier time points, changes in the representation of a particular taxonomic group of the subject's microbiome, the ratio of the abundance of a first taxonomic group to the abundance of a second taxonomic group of bacteria of the subject's microbiome, changes in the relative abundance of one or more functional families in the subject's microbiome, and any other suitable measures can be used to assess treatment effects from changes in microbiome composition, functional characteristics, and/or pharmacogenomic characteristics. Additionally or alternatively, data derived from the survey of the subject (relating to the subject' S experience while receiving treatment) (e.g., experienced side effects, improved personal assessment, etc.) may be used to determine the effectiveness of the treatment in block S180. However, the performance of the procedure based on the monitoring effectiveness of one or more treatments, and/or based on the effectiveness of the performance, may be performed in any suitable manner.

The method 100 and/or the system of an embodiment is at least partially embodied and/or implemented as a machine configured to receive a computer-readable medium storing computer-readable instructions. These instructions may be executed by computer-executable components integrated with an application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software element of a patient computer or mobile device, or any suitable combination thereof. Other systems and methods of the embodiments may be at least partially embodied and/or implemented as a machine configured to receive a computer-readable medium storing computer-readable instructions. These instructions may be executed by computer-executable components integrated with devices and networks of the type described above. The computer readable medium may be stored on any suitable computer readable medium such as RAM, ROM, flash memory, EEPROM, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device. The computer-executable component may be a processor, but any suitable dedicated hardware device may (alternatively or additionally) execute instructions.

The figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to preferred embodiments, exemplary configurations and variations thereof. In this regard, each block in the flowchart or block diagrams may represent a module, segment, step, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. Embodiments include each combination and permutation of the various system components and the various method processes, including any variations, examples, and specific examples. As those skilled in the art will recognize from the foregoing detailed description and from the accompanying drawings and claims, modifications and variations can be made to the embodiments of the invention without departing from the scope of the invention as defined in the following claims.

Claims (18)

1. A system for evaluating an antibiotically treatable condition for a user, the system comprising:

a processing system operable to collect containers containing biological material from a user collection, the processing system comprising a sequencing system operable to determine a sequence of microorganisms from the biological material;

a microbiota characterization system operable to:

determining at least one of i microbiome composition data and microbiome functional diversity data and ii microbiome pharmacogenomic data based on the microbial sequences,

collecting supplemental data relating to said antibiotically treatable condition of said set of users, and

transforming the supplementary data and the features extracted from i the at least one of microbiome composition data and microbiome functional diversity data and ii microbiome pharmacogenomic data into a characterization model associated with the antibiotic-treatable condition; and

a therapy system operable to:

facilitating the treatment of the antibiotically treatable condition of the user based on the characterization of the user's biological material using the characterization model for the above-mentioned antibiotically treatable condition,

wherein the characteristics comprise microbiome pharmacogenomic characteristics associated with at least one of codon mutation, exon deletion, substitution, gene rearrangement, and translocation for a taxon associated with an antibiotically treatable condition, or

A nucleotide-related functional characteristic associated with at least one of resistance and metabolism of the biologically active molecule.

2. The system of claim 1, wherein the processing system is operable to determine the microbial sequence based on amplifying nucleic acid material from the biological material using primers for nucleic acid sequences associated with the antibiotically treatable condition.

3. The system of claim 1, wherein the processing system is operable to collect a user container containing the user biological material, wherein the treatment system is operable to facilitate the treatment based on a characterization of the sampled user biological material, and wherein the treatment is operable to adjust a user microbiome composition to improve a state of the antibiotically treatable condition.

4. The system of claim 1, wherein the microbiome pharmacogenomic profile is associated with at least one of microbial strain mutability, microbial strain resistance to antibiotics, and microbial strain co-dependent behavior.

5. The system of claim 1 wherein the microbiome pharmacogenomic signature is correlated to the relative abundance of microbial pharmacogenomic markers for the antibiotic-treatable condition.

6. The system of claim 1, wherein the bioactive molecule comprises at least one of a structural RNA, a regulatory RNA, a messenger RNA, a protein, and a peptide.

7. The system of claim 1, wherein the antibiotically treatable condition includes at least one of a urinary tract infection and a sexually transmitted disease, and wherein the microbiome characterization system is operable to convert the supplemental data and the features extracted from the microbiome pharmacogenomic data and at least one of microbiome composition data and microbiome functional diversity data into a characterization model for the at least one of a urinary tract infection and a sexually transmitted disease.

8. The system of claim 1, wherein the antibiotically treatable condition comprises a gonorrhea-associated condition, and wherein the features comprise microbiome pharmacogenomic features extracted from the microbiome pharmacogenomic data and associated with the chimeric penA allele.

9. The system of claim 8, wherein the treatment system is operable to assess efficacy of a cephalosporin treatment based on the microbiome pharmacogenomic profile, wherein the treatment system is operable to facilitate the treatment based on the efficacy.

10. The system of claim 1, further comprising an interface operable to improve display of antibiotic-associated information associated with the antibiotic-treatable condition and derived based on a comparison of the user microbiome pharmacogenomic profile of the user relative to a population of users sharing demographic characteristics.

11. A method of generating a characterization model of an antibiotic-associated disorder, the method comprising:

determining microbiome pharmacogenomic data based on microbial sequences obtained from biological samples of a collection of subjects;

receiving a supplemental data set providing information of the antibiotic-associated condition of the collection of subjects;

determining a first microbiome pharmacogenomic feature set based on the microbiome pharmacogenomic data and the complementary data set; and

generating a characterization model related to the antibiotic-associated disorder based on the first microbiome pharmacogenomic feature set

Wherein the first microbiome pharmacogenomic feature set comprises microbiome pharmacogenomic features associated with at least one of codon mutation, exon deletion, substitution, gene rearrangement, and translocation for a taxon associated with an antibiotically treatable condition, or

A nucleotide-related functional characteristic associated with at least one of resistance and metabolism of the biologically active molecule.

12. The method of claim 11 wherein determining the microbiome pharmacogenomic data comprises:

determining the microbial sequence based on amplification of nucleic acid material from the biological sample;

determining an alignment of the microbial sequence with a set of reference sequences relating to an antibiotic-associated disorder; and

determining the microbiome pharmacogenomic data based on the alignment.

13. The method of claim 12, wherein amplifying the nucleic acid material comprises multiplex amplification with a bridge amplification substrate of a next generation sequencing platform with the nucleic acid material, and wherein determining the microbiome pharmacogenomic data comprises determining the microbiome pharmacogenomic data in a computing system operable to communicate with the next generation sequencing platform.

14. The method of claim 11 further comprising obtaining a microbiome pharmacogenomic feature selection rule related to a correlation between the first microbiome pharmacogenomic feature set and antibiotic therapy efficacy related to an antibiotic-associated disorder, wherein determining the first microbiome pharmacogenomic feature set comprises determining a first microbiome pharmacogenomic feature set based on evaluating the microbiome pharmacogenomic data against the microbiome pharmacogenomic feature selection rule.

15. The method of claim 11, wherein the bioactive molecule comprises at least one of a structural RNA, a regulatory RNA, a messenger RNA, a protein, and a peptide.

16. The method of claim 11, wherein the step of treating the sample with the acid is carried out in a single step,

wherein generating the characterization model comprises generating the characterization model based on a microbiome profile set comprising at least one of i a microbiome composition diversity characteristic and a microbiome functional diversity characteristic, and ii the first microbiome pharmacogenomic profile set.

17. The method of claim 16 wherein the antibiotic-associated disorder comprises a gonorrhea-associated disorder, wherein the microbiome compositional diversity feature comprises a compositional feature of a taxon associated with a gonorrhea-associated disorder, and wherein the first microbiome pharmacogenomic feature set comprises a feature set of point mutations of genes associated with the taxon.

18. The method of claim 17, wherein the set of point mutation characteristics is associated with point mutations in at least one of the gyrA gene and the parC gene of the taxon.

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