Principles of precision medicine and its application in toxicology

GlcNac produced by the gut microbiome enhances host influenza resistance by modulating NK cells

Microbiota are known to modulate the host response to influenza infection, but the mechanisms remain largely unknown. Gut metabolites are the key mediators through which gut microbes play anti-influenza effect. Transferring fecal metabolites from mice with high influenza resistance into antibiotic-treated recipient mice conferred resistance to influenza infections. By comparing the metabolites of different individuals with high or low influenza resistance, we identified and validated N-acetyl-D-glucosamine (GlcNAc) and adenosine showed strong positive correlations with influenza resistance and exerted anti-influenza effects in vivo or in vitro, respectively. Especially, GlcNAc mediated the anti-influenza effect by increasing the proportion and activity of NK cells. Several gut microbes, including Clostridium sp., Phocaeicola sartorii, and Akkermansia muciniphila, were positively correlated with influenza resistance, and can upregulate the level of GlcNAc in the mouse gut by exogenous supplementation. Subsequent studies confirmed that administering a combination of the three bacteria to mice via gavage resulted in similar modulation of NK cell responses as observed with GlcNAc. This study demonstrates that gut microbe-produced GlcNAc protects the host against influenza by regulating NK cells, facilitating the elucidation of the action mechanism of gut microbes mediating host influenza resistance.

Using human genetics to improve safety assessment of therapeutics

Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.

Phenobarbital use in benzodiazepine and z-drug detoxification: a single-centre 15-year observational retrospective study in clinical practice

Given the increase in benzodiazepine (BZD) and Z-drug (ZD) use disorder, this study described the use of phenobarbital (PHB) as detoxification in clinical practice. A 15-year observational retrospective study was performed on medical records of BZD-ZD use disorder patients detoxified with PHB at the Toxicology Unit and Poison Centre, Careggi University Hospital, Florence (Italy). A multivariate logistic regression was used to estimate odd ratios (ORs) and related 95% confidence intervals (CI) of "treatment failure" considering demographic and pharmacological characteristics. "Hospitalisation length", "PHB discharge dose", and "BZD-ZD free status" at discharge were also calculated. During detoxification, out of 355 patients (57% of men), with a mean age of 42.92 years, only 20 (5.6%) treatment failures were recorded: 19 were discharged against medical advice or due to misbehaviour, and only one for PHB-related non-serious skin rash. Analysis showed a higher probability to be BZD-ZD free at discharge for subjects who reported to be employed (OR 2.29; CI 95% 1.00-5.24), for those who abused oral drops of BZD-ZD (OR 2.16, CI 1.30-3.59), and for those treated with trazodone (OR 2.86, CI 1.14-7.17) during hospital stay. A hospitalisation length of > 7 days was observed for patients with opioid maintenance therapy (OR 2.07, CI 1.20-3.58) for substance use disorder, and for those treated with more than 300 mg/day of PHB equivalents at hospital admission (OR 1.68, CI 1.03-2.72). Our results suggested that PHB can be considered a valuable detoxification option for different types of BZD and ZD use disorder patients.

Utilizing preclinical models of genetic diversity to improve translation of phytochemical activities from rodents to humans and inform personalized nutrition

Mouse models are an essential tool in different areas of research, including nutrition and phytochemical research. Traditional inbred mouse models have allowed the discovery of therapeutical targets and mechanisms of action and expanded our knowledge of health and disease. However, these models lack the genetic variability typically found in human populations, which hinders the translatability of the results found in mice to humans. The development of genetically diverse mouse models, such as the collaborative cross (CC) or the diversity outbred (DO) models, has been a useful tool to overcome this obstacle in many fields, such as cancer, immunology and toxicology. However, these tools have not yet been widely adopted in the field of phytochemical research. As demonstrated in other disciplines, use of CC and DO models has the potential to provide invaluable insights for translation of phytochemicals from rodents to humans, which are desperately needed given the challenges and numerous failed clinical trials in this field. These models may prove informative for personalized use of phytochemicals in humans, including: predicting interindividual variability in phytochemical bioavailability and efficacy, identifying genetic loci or genes governing response to phytochemicals, identifying phytochemical mechanisms of action and therapeutic targets, and understanding the impact of genetic variability on individual response to phytochemicals. Such insights would prove invaluable for personalized implementation of phytochemicals in humans. This review will focus on the current work performed with genetically diverse mouse populations, and the research opportunities and advantages that these models can offer to phytochemical research.

Akkermansia muciniphila Improves Host Defense Against Influenza Virus Infection

Influenza virus infection can alter the composition of the gut microbiota, while its pathogenicity can, in turn, be highly influenced by the gut microbiota. However, the details underlying these associations remain to be determined. The H7N9 influenza virus is an emerging zoonotic pathogen which has caused the death of 616 humans and has incurred huge losses in the poultry industry. Here, we investigated the effects of infection with highly pathogenic H7N9 on gut microbiota and determined potential anti-influenza microbes. 16S rRNA sequencing results show that H7N9 infection alters the mouse gut microbiota by promoting the growth of Akkermansia, Ruminococcus 1, and Ruminococcaceae UCG-010, and reducing the abundance of Rikenellaceae RC9 gut group and Lachnoclostridium. Although the abundance of Akkermansia muciniphila is positively related to H7N9 infection, the oral administration of cultures, especially of pasteurized A. muciniphila, can significantly reduce weight loss and mortality caused by H7N9 infection in mice. Furthermore, oral administration of live or pasteurized A. muciniphila significantly reduces pulmonary viral titers and the levels IL-1β and IL-6 but enhances the levels of IFN-β, IFN-γ, and IL-10 in H7N9-infected mice, suggesting that the anti-influenza role of A. muciniphila is due to its anti-inflammatory and immunoregulatory properties. Taken together, we showed that the changes in the gut microbiota are associated with H7N9 infection and demonstrated the anti-influenza role of A. muciniphila, which enriches current knowledge about how specific gut bacterial strains protect against influenza infection and suggests a potential anti-influenza probiotic.

A decade of toxicological trends: what the papers say

Here we look at popular trends and concepts in toxicology over the decade 2009-2019. The top 10 concepts included methodological approaches such as zebrafish and genomics as well as broader concepts such as personalized medicine and adverse outcome pathways. The total number and rank order for each of the top 10 were tracked year by year via PubMed with >9500 papers contributing to the analysis. The data revealed a slow upward trend in the number of papers across all the concepts from 260 in 2009 to >1700 in 2019. Zebrafish, genomics and personalized medicine remained in the top four slots since 2009 with zebrafish dominating the rankings over the entire decade. Genomics was a strong second until 2013 when it was displaced first by the microbiome in 2014 and secondly by personalized medicine in 2015. Other notable trends were the ascendancy of the microbiome and adverse outcome pathways and the descendancy of hormesis and the 3Rs (replacement, reduction and refinement of animals in testing). The observation that the top four slots have been static over the past 4 years suggests that new ideas are introduced and increase in popularity until they find their place in scientific culture. This may suggest that relatively new concepts such as artificial intelligence and microphysiological systems have yet to find their steady state in the rankings. Similarly, as a relatively new player in toxicology, the full impact of the human microbiome on drug efficacy and safety remains to be seen.

Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI)

Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank database, which classifies DILI severity and potential. These classifications have been used by various research groups in generating computational predictions for this type of liver injury. Recently, groups from Pfizer and AstraZeneca have collated DILI in vitro data and physicochemical properties for compounds that can be used along with data from the FDA to build machine learning models for DILI. In this study, we have used these data sets, as well as the Biopharmaceutics Drug Disposition Classification System data set, to generate Bayesian machine learning models with our in-house software, Assay Central. The performance of all machine learning models was assessed through both the internal 5-fold cross-validation metrics and prediction accuracy of an external test set of compounds with known hepatotoxicity. The best-performing Bayesian model was based on the DILI-concern category from the DILIRank database with an ROC of 0.814, a sensitivity of 0.741, a specificity of 0.755, and an accuracy of 0.746. A comparison of alternative machine learning algorithms, such as k-nearest neighbors, support vector classification, AdaBoosted decision trees, and deep learning methods, produced similar statistics to those generated with the Bayesian algorithm in Assay Central. This study demonstrates machine learning models grouped in a tool called MegaTox that can be used to predict early-stage clinical compounds, as well as recent FDA-approved drugs, to identify potential DILI.

Influenza infection elicits an expansion of gut population of endogenous Bifidobacterium animalis which protects mice against infection

Background

Influenza is a severe respiratory illness that continually threatens global health. It has been widely known that gut microbiota modulates the host response to protect against influenza infection, but mechanistic details remain largely unknown. Here, we took advantage of the phenomenon of lethal dose 50 (LD₅₀) and metagenomic sequencing analysis to identify specific anti-influenza gut microbes and analyze the underlying mechanism.

Results

Transferring fecal microbes from mice that survive virulent influenza H7N9 infection into antibiotic-treated mice confers resistance to infection. Some gut microbes exhibit differential features to lethal influenza infection depending on the infection outcome. Bifidobacterium pseudolongum and Bifidobacterium animalis levels are significantly elevated in surviving mice when compared to dead or mock-infected mice. Oral administration of B. animalis alone or the combination of both significantly reduces the severity of H7N9 infection in both antibiotic-treated and germ-free mice. Functional metagenomic analysis suggests that B. animalis mediates the anti-influenza effect via several specific metabolic molecules. In vivo tests confirm valine and coenzyme A produce an anti-influenza effect.

Conclusions

These findings show that the severity of influenza infection is closely related to the heterogeneous responses of the gut microbiota. We demonstrate the anti-influenza effect of B. animalis, and also find that the gut population of endogenous B. animalis can expand to enhance host influenza resistance when lethal influenza infection occurs, representing a novel interaction between host and gut microbiota. Further, our data suggest the potential utility of Bifidobacterium in the prevention and as a prognostic predictor of influenza.

Recent Advances in Enabling Technologies in 3D Printing for Precision Medicine

Advances in areas such as data analytics, genomics, and imaging have revealed individual patient complexities and exposed the inherent limitations of generic therapies for patient treatment. These observations have also fueled the development of precision medicine approaches, where therapies are tailored for the individual rather than the broad patient population. 3D printing is a field that intersects with precision medicine through the design of precision implants with patient-directed shapes, structures, and materials or for the development of patient-specific in vitro models that can be used for screening precision therapeutics. Toward their success, advances in 3D printing and biofabrication technologies are needed with enhanced resolution, complexity, reproducibility, and speed and that encompass a broad range of cells and materials. The overall goal of this progress report is to highlight recent advances in 3D printing technologies that are helping to enable advances important in precision medicine.

Clinical protein science in translational medicine targeting malignant melanoma

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.

Obesity is associated with impaired responsiveness to once-daily low-dose aspirin and in vivo platelet activation

BACKGROUND

The prevalence and degree of obesity is rising worldwide, increases cardiovascular risk, modifies body composition and organ function, and potentially affects the pharmacokinetics and/or pharmacodynamics of drugs.

OBJECTIVES

To investigate the pharmacodynamics of once-daily low-dose aspirin in healthy obese subjects, and to assess whether body weight (BW) and body mass index (BMI) affect the pharmacology of aspirin.

PATIENTS/METHODS

Otherwise healthy, obese (BMI > 30 kg/m2 ) subjects were studied before and after 3-4 weeks of 100-mg once-daily aspirin intake. Aspirin pharmacodynamics were assessed according to serum thromboxane (TX) B2 levels measured at 4 hours, 24 hours (i.e., posologic interval) and 48 hours after the last witnessed intake; age-matched and sex-matched non-obese controls were included. A previously calibrated pharmacokinetic/pharmacodynamic in silico model of aspirin was used to fit serum TXB2 data from obese subjects. At baseline, the major urinary TXA2 and prostacyclin metabolites, urinary isoprostane and plasma inflammatory biomarkers were measured.

RESULTS

In 16 obese subjects (aged 47 ± 11 years; BMI of 39.4 ± 5.1 kg/m2 ), residual serum TXB2 values between 4 and 48 hours after aspirin intake were increased 3- to 5-fold as compared with controls. At 24 hours, the residual serum TXB2 level was log-linearly associated with body size over a wide range of BMI and BW values, without any apparent threshold. The in silico model predicted that reduced aspirin bioavailability would be inversely related to body size and rescued by 200 mg of aspirin once daily or 85 mg twice daily. Baseline urinary TXA2 metabolite, isoprostane and plasma C-reactive protein levels were significantly increased in obese subjects.

CONCLUSIONS

Obesity is associated with impaired aspirin responsiveness, largely because of body size. Impaired inhibition of platelet activation by conventional low-dose aspirin may affect antithrombotic efficacy.

Phase I study of PF‐04895162, a Kv7 channel opener, reveals unexpected hepatotoxicity in healthy subjects, but not rats or monkeys: clinical evidence of disrupted bile acid homeostasis

During a randomized Phase 1 clinical trial the drug candidate, PF‐04895162 (ICA‐105665), caused transaminase elevations (≥grade 1) in six of eight healthy subjects treated at 300 mg twice daily for 2‐weeks (NCT01691274). This was unexpected since studies in rats (<6 months) and cynomolgus monkeys (<9 months) treated up to 100 mg/kg/day did not identify the liver as a target organ. Mechanistic studies showed PF‐04895162 had low cytotoxic potential in human hepatocytes, but inhibited liver mitochondrial function and bile salt export protein (BSEP) transport. Clinical relevance of these postulated mechanisms of liver injury was explored in three treated subjects that consented to analysis of residual pharmacokinetic plasma samples. Compared to a nonresponder, two subjects with transaminase elevations displayed higher levels of miRNA122 and total/conjugated bile acid species, whereas one demonstrated impaired postprandial clearance of systemic bile acids. Elevated taurine and glycine conjugated to unconjugated bile acid ratios were observed in two subjects, one before the onset of elevated transaminases. Based on the affinity of conjugated bile acid species for transport by BSEP, the profile of plasma conjugated/unconjugated bile acid species was consistent with inhibition of BSEP. These data collectively suggest that the human liver injury by PF‐04895162 was due to alterations in bile acid handling driven by dual BSEP/mitochondrial inhibition, two important risk factors associated with drug‐induced liver injury in humans. Alterations in systemic bile acid composition were more important than total bile acids in the manifestation of clinical liver injury and may be a very early biomarker of BSEP inhibition.

Use of Titration as a Therapeutic Individualization Strategy: An Analysis of Food and Drug Administration-Approved Drugs

Selecting a dose regimen that is both safe and effective for patients is one of the most critical elements of a successful drug development program. Titrating the dose regimen of a drug based on patient response may help to identify safe and effective dosages at the individual patient level. Therefore, we quantified and characterized the use of response-guided titration for drugs recently approved by the US Food and Drug Administration (FDA) to assess how frequently this dosing strategy is used and how titration regimens are evaluated during drug development. Most of the 181 drugs approved from 2013-2017 (78%) had only one approved dosing regimen. Only 30 of 76 (39%) drugs that were considered amenable to response-guided dosing strategies had information in labeling about such strategies. These findings indicate that although response-guided titration can be found in labeling, this strategy is used in a minority of drugs for which it may be useful. Careful consideration should be made early in drug development as to whether a new drug is amenable to response-guided titration as an approach to reducing interpatient variability.