Insights from pharmacokinetic models of host-microbiome drug metabolism

Gut Microbiome Integration in Drug Discovery and Development of Small Molecules

Human microbiomes, particularly in the gut, could have a major impact on the efficacy and toxicity of drugs. However, gut microbial metabolism is often neglected in the drug discovery and development process. Medicen, a Paris-based human health innovation cluster, has gathered more than 30 international leading experts from pharma, academia, biotech, clinical research organizations, and regulatory science to develop proposals to facilitate the integration of microbiome science into drug discovery and development. Seven subteams were formed to cover the complementary expertise areas of 1) pharma experience and case studies, 2) in silico microbiome-drug interaction, 3) in vitro microbial stability screening, 4) gut fermentation models, 5) animal models, 6) microbiome integration in clinical and regulatory aspects, and 7) microbiome ecosystems and models. Each expert team produced a state-of-the-art report of their respective field highlighting existing microbiome-related tools at every stage of drug discovery and development. The most critical limitations are the growing, but still limited, drug-microbiome interaction data to produce predictive models and the lack of agreed-upon standards despite recent progress. In this paper we will report on and share proposals covering 1) how microbiome tools can support moving a compound from drug discovery to clinical proof-of-concept studies and alert early on potential undesired properties stemming from microbiome-induced drug metabolism and 2) how microbiome data can be generated and integrated in pharmacokinetic models that are predictive of the human situation. Examples of drugs metabolized by the microbiome will be discussed in detail to support recommendations from the working group. SIGNIFICANCE STATEMENT: Gut microbial metabolism is often neglected in the drug discovery and development process despite growing evidence of drugs' efficacy and safety impacted by their interaction with the microbiome. This paper will detail existing microbiome-related tools covering every stage of drug discovery and development, current progress, and limitations, as well as recommendations to integrate them into the drug discovery and development process.

High-throughput screening of the effects of 90 xenobiotics on the simplified human gut microbiota model (SIHUMIx): a metaproteomic and metabolomic study

The human gut microbiota is a complex microbial community with critical functions for the host, including the transformation of various chemicals. While effects on microorganisms has been evaluated using single-species models, their functional effects within more complex microbial communities remain unclear. In this study, we investigated the response of a simplified human gut microbiota model (SIHUMIx) cultivated in an in vitro bioreactor system in combination with 96 deep-well plates after exposure to 90 different xenobiotics, comprising 54 plant protection products and 36 food additives and dyes, at environmentally relevant concentrations. We employed metaproteomics and metabolomics to evaluate changes in bacterial abundances, the production of Short Chain Fatty Acids (SCFAs), and the regulation of metabolic pathways. Our findings unveiled significant changes induced by 23 out of 54 plant protection products and 28 out of 36 food additives across all three categories assessed. Notable highlights include azoxystrobin, fluroxypyr, and ethoxyquin causing a substantial reduction (log2FC < -0.5) in the concentrations of the primary SCFAs: acetate, butyrate, and propionate. Several food additives had significant effects on the relative abundances of bacterial species; for example, acid orange 7 and saccharin led to a 75% decrease in Clostridium butyricum, with saccharin causing an additional 2.5-fold increase in E. coli compared to the control. Furthermore, both groups exhibited up- and down-regulation of various pathways, including those related to the metabolism of amino acids such as histidine, valine, leucine, and isoleucine, as well as bacterial secretion systems and energy pathways like starch, sucrose, butanoate, and pyruvate metabolism. This research introduces an efficient in vitro technique that enables high-throughput screening of the structure and function of a simplified and well-defined human gut microbiota model against 90 chemicals using metaproteomics and metabolomics. We believe this approach will be instrumental in characterizing chemical-microbiota interactions especially important for regulatory chemical risk assessments.

Integrating the gut microbiome and pharmacology

The gut microbiome harbors trillions of organisms that contribute to human health and disease. These bacteria can also affect the properties of medical drugs used to treat these diseases, and drugs, in turn, can reshape the microbiome. Research addressing interdependent microbiome-host-drug interactions thus has broad impact. In this Review, we discuss these interactions from the perspective of drug bioavailability, absorption, metabolism, excretion, toxicity, and drug-mediated microbiome modulation. We survey approaches that aim to uncover the mechanisms underlying these effects and opportunities to translate this knowledge into new strategies to improve the development, administration, and monitoring of medical drugs.

Dissecting the human gut microbiome to better decipher drug liability: A once-forgotten organ takes center stage

BACKGROUND

The gut microbiome is a diverse system within the gastrointestinal tract composed of trillions of microorganisms (gut microbiota), along with their genomes. Accumulated evidence has revealed the significance of the gut microbiome in human health and disease. Due to its ability to alter drug/xenobiotic pharmacokinetics and therapeutic outcomes, this once-forgotten "metabolic organ" is receiving increasing attention. In parallel with the growing microbiome-driven studies, traditional analytical techniques and technologies have also evolved, allowing researchers to gain a deeper understanding of the functional and mechanistic effects of gut microbiome.

AIM OF REVIEW

From a drug development perspective, microbial drug metabolism is becoming increasingly critical as new modalities (e.g., degradation peptides) with potential microbial metabolism implications emerge. The pharmaceutical industry thus has a pressing need to stay up-to-date with, and continue pursuing, research efforts investigating clinical impact of the gut microbiome on drug actions whilst integrating advances in analytical technology and gut microbiome models. Our review aims to practically address this need by comprehensively introducing the latest innovations in microbial drug metabolism research- including strengths and limitations, to aid in mechanistically dissecting the impact of the gut microbiome on drug metabolism and therapeutic impact, and to develop informed strategies to address microbiome-related drug liability and minimize clinical risk.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We present comprehensive mechanisms and co-contributing factors by which the gut microbiome influences drug therapeutic outcomes. We highlight in vitro, in vivo, and in silico models for elucidating the mechanistic role and clinical impact of the gut microbiome on drugs in combination with high-throughput, functionally oriented, and physiologically relevant techniques. Integrating pharmaceutical knowledge and insight, we provide practical suggestions to pharmaceutical scientists for when, why, how, and what is next in microbial studies for improved drug efficacy and safety, and ultimately, support precision medicine formulation for personalized and efficacious therapies.

Physiologically based pharmacokinetic (PBPK) modelling of oral drug absorption in older adults - an AGePOP review

The older population consisting of persons aged 65 years or older is the fastest-growing population group and also the major consumer of pharmaceutical products. Due to the heterogenous ageing process, this age group shows high interindividual variability in the dose-exposure-response relationship and, thus, a prediction of drug safety and efficacy is challenging. Although physiologically based pharmacokinetic (PBPK) modelling is a well-established tool to inform and confirm drug dosing strategies during drug development for special population groups, age-related changes in absorption are poorly accounted for in current PBPK models. The purpose of this review is to summarise the current state-of-knowledge in terms of physiological changes with increasing age that can influence the oral absorption of dosage forms. The capacity of common PBPK platforms to incorporate these changes and describe the older population is also discussed, as well as the implications of extrinsic factors such as drug-drug interactions associated with polypharmacy on the model development process. The future potential of this field will rely on addressing the gaps identified in this article, which can subsequently supplement in-vitro and in-vivo data for more robust decision-making on the adequacy of the formulation for use in older adults and inform pharmacotherapy.

Effects of intestinal flora on pharmacokinetics and pharmacodynamics of drugs

Gut microbiota is known as unique collection of microorganisms (including bacteria, archaea, eukaryotes and viruses) that exist in a complex environment of the gut. Recently, this has become one of the most popular areas of research in medicine because this plays not only an important role in disease development, but gut microbiota also influences drug pharmacokinetics. These alterations in drug pharmacokinetic pathways and drug concentration in plasma and blood often lead to an increase in the incidence of toxicological events in patients. This review aims to present current knowledge of the most commonly used drugs in clinical practice and their dynamic interplay with the host's gut microbiota as well as the mechanisms underlying these metabolic processes and the consequent effect on their therapeutic efficacy and safety. These new findings set a foundation for the development of personalized treatments specific to each metabolism, maximizing drugs' therapeutic effects and minimizing the side effects because they are one of the major limiting factors in treating patients.

Pharmacomicrobiomics in Anticancer Therapies: Why the Gut Microbiota Should Be Pointed Out

Anticancer treatments have shown a variable therapeutic outcome that may be partly attributable to the activity of the gut microbiota on the pathology and/or therapies. In recent years, microbiota-drug interactions have been extensively investigated, but most of the underlying molecular mechanisms still remain unclear. In this review, we discuss the relationship between the gut microbiota and some of the most commonly used drugs in oncological diseases. Different strategies for manipulating the gut microbiota layout (i.e., prebiotics, probiotics, antibiotics, and fecal microbiota transplantation) are then explored in order to optimize clinical outcomes in cancer patients. Anticancer technologies that exploit tumor-associated bacteria to target tumors and biotransform drugs are also briefly discussed. In the field of pharmacomicrobiomics, multi-omics strategies coupled with machine and deep learning are urgently needed to bring to light the interaction among gut microbiota, drugs, and host for the development of truly personalized precision therapies.

Analyzing the metabolic fate of oral administration drugs: A review and state-of-the-art roadmap

The key orally delivered drug metabolism processes are reviewed to aid the assessment of the current in vivo/vitro experimental systems applicability for evaluating drug metabolism and the interaction potential. Orally administration is the most commonly used state-of-the-art road for drug delivery due to its ease of administration, high patient compliance and cost-effectiveness. Roles of gut metabolic enzymes and microbiota in drug metabolism and absorption suggest that the gut is an important site for drug metabolism, while the liver has long been recognized as the principal organ responsible for drugs or other substances metabolism. In this contribution, we explore various experimental models from their development to the application for studying oral drugs metabolism of and summarized advantages and disadvantages. Undoubtedly, understanding the possible metabolic mechanism of drugs in vivo and evaluating the procedure with relevant models is of great significance for screening potential clinical drugs. With the increasing popularity and prevalence of orally delivered drugs, sophisticated experimental models with higher predictive capacity for the metabolism of oral drugs used in current preclinical studies will be needed. Collectively, the review seeks to provide a comprehensive roadmap for researchers in related fields.

Probiotics: A gut response to the COVID-19 pandemic but what does the evidence show?

Since the global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), research has focused on understanding the etiology of coronavirus disease 2019 (COVID-19). Identifying and developing prophylactic and therapeutics strategies to manage the pandemic is still of critical importance. Among potential targets, the role of the gut and lung microbiomes in COVID-19 has been questioned. Consequently, probiotics were touted as potential prophylactics and therapeutics for COVID-19. In this review we highlight the role of the gut and lung microbiome in COVID-19 and potential mechanisms of action of probiotics. We also discuss the progress of ongoing clinical trials for COVID-19 that aim to modulate the microbiome using probiotics in an effort to develop prophylactic and therapeutic strategies. To date, despite the large interest in this area of research, there is promising but limited evidence to suggest that probiotics are an effective prophylactic or treatment strategy for COVID-19. However, the role of the microbiome in pathogenesis and as a potential target for therapeutics of COVID-19 cannot be discounted.

Label-Free Vibrational and Quantitative Phase Microscopy Reveals Remarkable Pathogen-Induced Morphomolecular Divergence in Tumor-Derived Cells

Delineating the molecular and morphological changes that cancer cells undergo in response to extracellular stimuli is crucial for identifying factors that promote tumor progression. Label-free optical imaging offers a potentially promising route for retrieving such single-cell information by generating detailed visualization of the morphology and determining alterations in biomolecular composition. The potential of such nonperturbative morphomolecular microscopy for analyzing microbiota-cancer cell interactions has been surprisingly underappreciated, despite the growing evidence of the critical role of dysbiosis in malignant transformations. Here, using a model system of breast cancer cells, we show that label-free Raman microspectroscopy and quantitative phase microscopy can detect biomolecular and morphological changes in single cells exposed to Bacteroides fragilis toxin (BFT), a toxin secreted by enterotoxigenicB. fragilis. Remarkably, using machine learning to elucidate subtle, but consistent, cellular differences, we found that the morphomolecular differences between BFT-exposed and control breast cancer cells became more accentuated after in vivo passage, corroborating our findings that a short-term BFT exposure imparts a long-term effect on cancer cells and promotes a more invasive phenotype. Complementing more classical labeling techniques, our label-free platform offers a global detection approach with measurements representative of the overall cellular phenotype, paving the way for further investigations into the multifaceted interactions between the cancer cell and the microbiota.

The disposition of polychlorinated biphenyls (PCBs) differs between germ-free and conventional mice

The disposition of toxicants, such as polychlorinated biphenyls (PCBs), in germ-free (GF) vs. conventional (CV) mice has received little attention to date. Here, we investigate PCB levels in three-month-old female CV and GF mice exposed orally daily for 3 days to 0, 6, or 30 mg/kg body weight of the Fox River Mixture (FRM), an environmental PCB mixture. We euthanized animals 24 h after the final dose. PCB profiles in tissues differed from the FRM profile but were similar in tissues across all 4 PCB exposure groups. PCB levels in CV but not GF mice followed the difference in PCB dose. Importantly, PCB levels were higher in CV than GF mice exposed to the same dose. Hepatic cytochrome P450 enzyme or lipid levels did not explain these trends in PCB tissue levels. Thus, toxicity studies with CV and GF animals need to assess the toxicokinetics of the toxicant investigated. CAPSULE: PCB levels are typically higher in conventional than germ-free mice exposed to the same dose of PCBs.

Reduced Enterohepatic Recirculation of Mycophenolate and Lower Blood Concentrations are Associated with the Stool Bacterial Microbiome After Hematopoietic Cell Transplantation

BACKGROUND

Mycophenolate mofetil (MMF) is an important immunosuppressant used after allogeneic hematopoietic cell transplant (HCT). MMF has a narrow therapeutic index and blood concentrations of mycophenolic acid (MPA), the active component of MMF, are highly variable. Low MPA concentrations are associated with risk of graft vs host disease (GvHD) while high concentrations are associated with toxicity. Reasons for variability are not well known and may be due, at least in part, to the presence of β-glucuronidase producing bacteria in the gastrointestinal tract which enhance MPA enterohepatic recirculation (EHR) by transforming MPA metabolites formed in the liver back to MPA.

OBJECTIVE

To determine if individuals with high MPA EHR have a greater abundance of β-glucuronidase producing bacteria in their stool and higher MPA concentrations relative to those with low EHR.

STUDY DESIGN

We conducted a pharmacomicrobiomics study in 20 adult HCT recipients receiving a myeloablative or reduced intensity preparative regimen. Participants received MMF 1g IV every 8 hours with tacrolimus. Intensive pharmacokinetic sampling of mycophenolate was conducted before hospital discharge. Total MPA, MPA glucuronide (MPAG) and acylMPAG were measured. EHR was defined as a ratio of MPA area under the concentration-versus-time curve (AUC)_4-8 to MPA AUC_0-8. Differences in stool microbiome diversity and composition, determined by shotgun metagenomic sequencing, were compared above and below the median EHR (22%, range 5-44%).

RESULTS

Median EHR was 12% and 29% in the low and high EHR groups, respectively. MPA troughs, MPA AUC_4-8 and acylMPAG AUC_4-8/AUC_0-8, were greater in the high EHR group vs low EHR group [1.53 vs 0.28 mcg/mL, p = 0.0001], [7.33 vs 1.79 hr*mcg/mL, p = 0.0003] and [0.33 vs 0.24 hr*mcg/mL, p = 0.0007], respectively. MPA AUC_0-8 was greater in the high EHR than the low EHR group and trended towards significance [22.8 vs. 15.3 hr*mcg/mL, p=0.06]. Bacteroides vulgatus, stercoris and thetaiotaomicron were 1.2-2.4 times more abundant (p=0.039, 0.024, 0.046, respectively) in the high EHR group. MPA EHR was positively correlated with B. vulgatus (⍴=0.58, p≤0.01) and B. thetaiotaomicron (⍴=0.46, p<0.05) and negatively correlated with Blautia hydrogenotrophica (⍴=-0.53, p<0.05). Therapeutic MPA troughs were achieved in 80% of patients in the high EHR group and 0% in the low EHR. There was a trend towards differences in MPA AUC_0-8 and MPA C_ss mcg/mL in high vs. low EHR groups (p=0.06).

CONCLUSION

MPA EHR was variable. Patients with high MPA EHR had greater abundance of Bacteroides species in stool and higher MPA exposure than patients with low MPA EHR. Bacteroides may therefore be protective from poor outcomes such as graft vs host disease but in others it may increase the risk of MPA adverse effects. These data need to be confirmed and studied after oral MMF.

Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods

The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.

Impact of Contaminants on Microbiota: Linking the Gut-Brain Axis with Neurotoxicity

Over the last years, research has focused on microbiota to establish a missing link between neuronal health and intestine imbalance. Many studies have considered microbiota as critical regulators of the gut–brain axis. The crosstalk between microbiota and the central nervous system is mainly explained through three different pathways: the neural, endocrine, and immune pathways, intricately interconnected with each other. In day-to-day life, human beings are exposed to a wide variety of contaminants that affect our intestinal microbiota and alter the bidirectional communication between the gut and brain, causing neuronal disorders. The interplay between xenobiotics, microbiota and neurotoxicity is still not fully explored, especially for susceptible populations such as pregnant women, neonates, and developing children. Precisely, early exposure to contaminants can trigger neurodevelopmental toxicity and long-term diseases. There is growing but limited research on the specific mechanisms of the microbiota–gut–brain axis (MGBA), making it challenging to understand the effect of environmental pollutants. In this review, we discuss the biological interplay between microbiota–gut–brain and analyse the role of endocrine-disrupting chemicals: Bisphenol A (BPA), Chlorpyrifos (CPF), Diethylhexyl phthalate (DEHP), and Per- and polyfluoroalkyl substances (PFAS) in MGBA perturbations and subsequent neurotoxicity. The complexity of the MGBA and the changing nature of the gut microbiota pose significant challenges for future research. However, emerging in-silico models able to analyse and interpret meta-omics data are a promising option for understanding the processes in this axis and can help prevent neurotoxicity.

Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota

Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug-microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs' susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug-microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients.

Quantifying host-microbiota interactions

Modeling the microbiome increases understanding of its role in drug metabolism

Liver tissue microbiota in nonalcoholic liver disease: a change in the paradigm of host-bacterial interactions

Nonalcoholic fatty liver disease (NAFLD) pathogenesis is explained by the complex relationship among diet and lifestyle-predisposing factors, the genetic variance of the nuclear and mitochondrial genome, associated phenotypic traits, and the yet not fully explored interactions with epigenetic and other environmental factors, including the microbiome. Despite the wealth of knowledge gained from molecular and genome-wide investigations in patients with NAFLD, the precise mechanisms that explain the variability of the histological phenotypes are not fully understood. Earlier studies of the gut microbiota in patients with NAFLD and nonalcoholic steatohepatitis (NASH) provided clues on the role of the fecal microbiome in the disease pathogenesis. Nevertheless, the composition of the gut microbiota does not fully explain tissue-specific mechanisms associated with the degree of disease severity, including liver inflammation, ballooning of hepatocytes, and fibrosis. The liver acts as a key filtration system of the whole body by receiving blood from the hepatic artery and the portal vein. Therefore, not only microbes would become entrapped in the complex liver anatomy but, more importantly, bacterial derived products that are likely to be potentially powerful stimuli for initiating the inflammatory response. Hence, the study of liver tissue microbiota offers the opportunity of changing the paradigm of host-NAFLD-microbial interactions from a "gut-centric" to a "liver-centric" approach. Here, we highlight the evidence on the role of liver tissue bacterial DNA in the biology of NAFLD and NASH. Besides, we provide evidence of metagenomic findings that can serve as the seed of further hypothesis-raising studies as well as can be leveraged to discover novel therapeutic targets.

Recent advances in the ontogeny of drug disposition

Developmental changes that occur throughout childhood have long been known to impact drug disposition. However, pharmacokinetic studies in the paediatric population have historically been limited due to ethical concerns arising from incorporating children into clinical trials. As such, much of the early work in the field of developmental pharmacology was reliant on difficult-to-interpret in vitro and in vivo animal studies. Over the last 2 decades, our understanding of the mechanistic processes underlying age-related changes in drug disposition has advanced considerably. Progress has largely been driven by technological advances in mass spectrometry-based methods for quantifying proteins implicated in drug disposition, and in silico tools that leverage these data to predict age-related changes in pharmacokinetics. This review summarizes our current understanding of the impact of childhood development on drug disposition, particularly focusing on research of the past 20 years, but also highlighting select examples of earlier foundational research. Equally important to the studies reviewed herein are the areas that we cannot currently describe due to the lack of research evidence; these gaps provide a map of drug disposition pathways for which developmental trends still need to be characterized.

Towards a mechanistic understanding of reciprocal drug-microbiome interactions

Broad-spectrum antibiotics target multiple gram-positive and gram-negative bacteria, and can collaterally damage the gut microbiota. Yet, our knowledge of the extent of damage, the antibiotic activity spectra, and the resistance mechanisms of gut microbes is sparse. This limits our ability to mitigate microbiome-facilitated spread of antibiotic resistance. In addition to antibiotics, non-antibiotic drugs affect the human microbiome, as shown by metagenomics as well as in vitro studies. Microbiome-drug interactions are bidirectional, as microbes can also modulate drugs. Chemical modifications of antibiotics mostly function as antimicrobial resistance mechanisms, while metabolism of non-antibiotics can also change the drugs' pharmacodynamic, pharmacokinetic, and toxic properties. Recent studies have started to unravel the extensive capacity of gut microbes to metabolize drugs, the mechanisms, and the relevance of such events for drug treatment. These findings raise the question whether and to which degree these reciprocal drug-microbiome interactions will differ across individuals, and how to take them into account in drug discovery and precision medicine. This review describes recent developments in the field and discusses future study areas that will benefit from systems biology approaches to better understand the mechanistic role of the human gut microbiota in drug actions.

Reflections on the Use of an Invertebrate Chordate Model System for Studies of Gut Microbial Immune Interactions

The functional ecology of the gastrointestinal tract impacts host physiology, and its dysregulation is at the center of various diseases. The immune system, and specifically innate immunity, plays a fundamental role in modulating the interface of host and microbes in the gut. While humans remain a primary focus of research in this field, the use of diverse model systems help inform us of the fundamental principles legislating homeostasis in the gut. Invertebrates, which lack vertebrate-style adaptive immunity, can help define conserved features of innate immunity that shape the gut ecosystem. In this context, we previously proposed the use of a marine invertebrate, the protochordate Ciona robusta, as a novel tractable model system for studies of host-microbiome interactions. Significant progress, reviewed herein, has been made to fulfill that vision. We examine and review discoveries from Ciona that include roles for a secreted immune effector interacting with elements of the microbiota, as well as chitin-rich mucus lining the gut epithelium, the gut-associated microbiome of adults, and the establishment of a large catalog of cultured isolates with which juveniles can be colonized. Also discussed is the establishment of methods to rear the animals germ-free, an essential technology for dissecting the symbiotic interactions at play. As the foundation is now set to extend these studies into the future, broadening our comprehension of how host effectors shape the ecology of these microbial communities in ways that establish and maintain homeostasis will require full utilization of "multi-omics" approaches to merge computational sciences, modeling, and experimental biology in hypothesis-driven investigations.

2020 White Paper on Recent Issues in Bioanalysis: BMV of Hybrid Assays, Acoustic MS, HRMS, Data Integrity, Endogenous Compounds, Microsampling and Microbiome (Part 1 - Recommendations on Industry/Regulators Consensus on BMV of Biotherapeutics by LCMS, Advanced Application in Hybrid Assays, Regulatory Challenges in Mass Spec, Innovation in Small Molecules, Peptides and Oligos)

The 14^th edition of the Workshop on Recent Issues in Bioanalysis (14^th WRIB) was held virtually on June 15-29, 2020 with an attendance of over 1000 representatives from pharmaceutical/biopharmaceutical companies, biotechnology companies, contract research organizations, and regulatory agencies worldwide. The 14^th WRIB included three Main Workshops, seven Specialized Workshops that together spanned 11 days in order to allow exhaustive and thorough coverage of all major issues in bioanalysis, biomarkers, immunogenicity, gene therapy, cell therapy and vaccine. Moreover, a comprehensive vaccine assays track; an enhanced cytometry track and updated Industry/Regulators consensus on BMV of biotherapeutics by Mass Spectrometry (hybrid assays, LCMS and HRMS) were special features in 2020. As in previous years, this year's WRIB continued to gather a wide diversity of international industry opinion leaders and regulatory authority experts working on both small and large molecules to facilitate sharing and discussions focused on improving quality, increasing regulatory compliance and achieving scientific excellence on bioanalytical issues. This 2020 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop and is aimed to provide the Global Bioanalytical Community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2020 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication covers the recommendations on (Part 1) Hybrid Assays, Innovation in Small Molecules, & Regulated Bioanalysis. Part 2A (BAV, PK LBA, Flow Cytometry Validation and Cytometry Innovation), Part 2B (Regulatory Input) and Part 3 (Vaccine, Gene/Cell Therapy, NAb Harmonization and Immunogenicity) are published in volume 13 of Bioanalysis, issues 5, and 6 (2021), respectively.

Harnessing machine learning for development of microbiome therapeutics

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.

Targeting gut microbiota for precision medicine: Focusing on the efficacy and toxicity of drugs

Intra- and interindividual variation in drug responses is one major reason for the failure of drug therapy, drug toxicity, and even the death of patients. Precision medicine, or personalized medicine, is a field of medicine that customizes an individual's medical diagnosis and treatment based on his/her genes, microbiomes, environments, etc. Over the past decade, a large number of studies have demonstrated that gut microbiota can modify the efficacy and toxicity of drugs, and the extent of the modification varies greatly from person to person because of the variability of the gut microbiota. Personalized manipulation of gut microbiota is an important approach to rectify the abnormal drug response. In this review, we aim to improve drug efficacy and reduce drug toxicity by combining precision medicine and gut microbiota. After describing the interactions between gut microbiota and xenobiotics, we discuss (1) the effects of gut microbiota on drug efficacy and toxicity and the corresponding mechanisms, (2) the variability of gut microbiota, which leads to variation in drug responses, (3) the biomarkers used for the patient stratification and treatment decisions before the use of drugs, and (4) the methods used for the personalized manipulation of gut microbiota to improve drug outcomes. Overall, we hope to improve the drug response by incorporating the knowledge of gut microbiota into clinical practice.