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The Pharmacogene Variation Consortium (PharmVar) catalogues star (*) allele nomenclature for the polymorphic human CYP2B6 gene. Genetic variation within the CYP2B6 gene locus impacts the metabolism or bioactivation of clinically important drugs. Of particular importance are efficacy and safety concerns regarding: efavirenz, which is used for the treatment of HIV type-1 infection; methadone, a mainstay in the treatment of opioid use disorder and as an analgesic; ketamine, used as an antidepressant and analgesic; and bupropion, which is prescribed to treat depression and for smoking cessation. This GeneFocus provides a comprehensive overview and summary of CYP2B6 and describes how haplotype information catalogued by PharmVar is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

The 12(th) International Society for the Study of Xenobiotics (ISSX) meeting, held in Portland, OR, USA from July 28–31, 2019, was attended by diverse members of the pharmaceutical sciences community. The ISSX New Investigators Group provides learning and professional growth opportunities for student and early career members of ISSX. To share meeting content with those who were unable to attend, the ISSX New Investigators herein elected to highlight the “Advances in the Study of Drug Metabolism” symposium, as it engaged attendees with diverse backgrounds. This session covered a wide range of current topics in drug metabolism research including predicting sites and routes of metabolism, metabolite identification, ligand docking, and medicinal and natural products chemistry, and highlighted approaches complemented by computational modeling. In silico tools have been increasingly applied in both academic and industrial settings, alongside traditional and evolving in vitro techniques, to strengthen and streamline pharmaceutical research. Approaches such as quantum mechanics simulations facilitate understanding of reaction energetics towards prediction of routes and sites of drug metabolism. Furthermore, in tandem with crystallographic and orthogonal wet lab techniques for structural validation of drug metabolizing enzymes, in silico models can aid understanding of substrate recognition by particular enzymes, identify metabolic soft spots and predict toxic metabolites for improved molecular design. Of note, integration of chemical synthesis and biosynthesis using natural products remains an important approach for identifying new chemical scaffolds in drug discovery. These subjects, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are discussed in this review.

FINDbase (http://www.findbase.org) is a comprehensive data resource recording the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants underlying genetic disorders as well as pharmacogenomic biomarkers that can guide drug treatment. Here, we report significant new developments and technological advancements in the database architecture, leading to a completely revamped database structure, querying interface, accompanied with substantial extensions of data content and curation. In particular, the FINDbase upgrade further improves the user experience by introducing responsive features that support a wide variety of mobile and stationary devices, while enhancing computational runtime due to the use of a modern Javascript framework such as ReactJS. Data collection is significantly enriched, with the data records being divided in a Public and Private version, the latter being accessed on the basis of data contribution, according to the microattribution approach, while the front end was redesigned to support the new functionalities and querying tools. The abovementioned updates further enhance the impact of FINDbase, improve the overall user experience, facilitate further data sharing by microattribution, and strengthen the role of FINDbase as a key resource for personalized medicine applications and personalized public health.

ATP-binding cassette (ABC) transporters constitute a superfamily of 48 structurally similar membrane transporters that mediate the ATP-dependent cellular export of a plethora of endogenous and xenobiotic substances. Importantly, genetic variants in ABC genes that affect gene function have clinically important effects on drug disposition and can be predictors of the risk of adverse drug reactions and efficacy of chemotherapeutics, calcium channel blockers, and protease inhibitors. Furthermore, loss-of-function of ABC transporters is associated with a variety of congenital disorders. Despite their clinical importance, information about the frequencies and global distribution of functionally relevant ABC variants is limited and little is known about the overall genetic complexity of this important gene family. Here, we systematically mapped the genetic landscape of the entire human ABC superfamily using Next-Generation Sequencing data from 138,632 individuals across seven major populations. Overall, we identified 62,793 exonic variants, 98.5% of which were rare. By integrating five computational prediction algorithms with structural mapping approaches using experimentally determined crystal structures, we found that the functional ABC variability is extensive and highly population-specific. Every individual harbored between 9.3 and 13.9 deleterious ABC variants, 76% of which were found only in a single population. Carrier rates of pathogenic variants in ABC transporter genes associated with autosomal recessive congenital diseases, such as cystic fibrosis or pseudoxanthoma elasticum, closely mirrored the corresponding population-specific disease prevalence, thus providing a novel resource for rare disease epidemiology. Combined, we provide the most comprehensive, systematic, and consolidated overview of ethnogeographic ABC transporter variability with important implications for personalized medicine, clinical genetics, and precision public health.

Background The efficacy and safety of tofacitinib, a Janus kinase inhibitor, in patients who are hospitalized with coronavirus disease 2019 (Covid-19) pneumonia are unclear. Methods We randomly assigned, in a 1:1 ratio, hospitalized adults with Covid-19 pneumonia to receive either tofacitinib at a dose of 10 mg or placebo twice daily for up to 14 days or until hospital discharge. The primary outcome was the occurrence of death or respiratory failure through day 28 as assessed with the use of an eight-level ordinal scale (with scores ranging from 1 to 8 and higher scores indicating a worse condition). All-cause mortality and safety were also assessed. Results A total of 289 patients underwent randomization at 15 sites in Brazil. Overall, 89.3% of the patients received glucocorticoids during hospitalization. The cumulative incidence of death or respiratory failure through day 28 was 18.1% in the tofacitinib group and 29.0% in the placebo group (risk ratio, 0.63; 95% confidence interval [CI], 0.41 to 0.97; P=0.04). Death from any cause through day 28 occurred in 2.8% of the patients in the tofacitinib group and in 5.5% of those in the placebo group (hazard ratio, 0.49; 95% CI, 0.15 to 1.63). The proportional odds of having a worse score on the eight-level ordinal scale with tofacitinib, as compared with placebo, was 0.60 (95% CI, 0.36 to 1.00) at day 14 and 0.54 (95% CI, 0.27 to 1.06) at day 28. Serious adverse events occurred in 20 patients (14.1%) in the tofacitinib group and in 17 (12.0%) in the placebo group. Conclusions Among patients hospitalized with Covid-19 pneumonia, tofacitinib led to a lower risk of death or respiratory failure through day 28 than placebo. (Funded by Pfizer; STOP-COVID ClinicalTrials.gov number, NCT04469114.)

High-aspect-ratio and hierarchically nanostructured surfaces are common in nature. Synthetic variants are of interest for their specific chemical, mechanic, electric, photonic, or biologic properties but are cumbersome in fabrication or suffer from structural collapse. Here, we replicated and directly biofunctionalized robust, large-area, and high-aspect-ratio nanostructures by nanoimprint lithography of an off-stoichiometric thiol-ene-epoxy polymer. We structured-in a single-step process-dense arrays of pillars with a diameter as low as 100 nm and an aspect ratio of 7.2; holes with a diameter of 70 nm and an aspect ratio of20; and complex hierarchically layered structures, all with minimal collapse and defectivity. We show that the nanopillar arrays alter mechanosensing of human hepatic cells and provide precise spatial control of cell attachment. We speculate that our results can enable the widespread use of high-aspect-ratio nanotopograhy applications in mechanics, optics, and biomedicine.

Statins are inhibitors of 3-hydroxy-3-methylglutaryl-CoA reductase, a key enzyme in cholesterol biosynthesis, that are highly effective in reducing plasma low-density lipoprotein (LDL) cholesterol and decreasing the risk of cardiovascular events. In recent years, a multitude of variants in genes involved in pharmacokinetics (PK) and pharmacodynamics (PD) have been suggested to influence the cholesterol-lowering response. However, the vast majority of studies have analyzed the pharmacogenetic associations in populations in Europe and the USA, whereas data in other populations, including Brazil, are mostly lacking. This narrative review provides an update of clinical studies on statin pharmacogenomics in Brazilian cohorts exploring lipid-lowering response, adverse events and pleiotropic effects. We find that variants in drug transporter genes (SLCO1B1 and ABCB1) positively impacted atorvastatin and simvastatin response, whereas variants in genes of drug metabolizing enzymes (CYP3A5) decreased response. Furthermore, multiple associations of variants in PD genes (HMGCR, LDLR and APOB) with statin response were identified. Few studies have explored statin-related adverse events, and only ABCB1 but not SLCO1B1 variants were robustly associated with increased risk in Brazil. Statin-related pleiotropic effects were shown to be influenced by variants in PD (LDLR, NR1H2) and antioxidant enzyme (NOS3, SOD2, MTHFR, SELENOP) genes. The findings of these studies indicate that statin pharmacogenomic associations are distinctly different in Brazil compared to other populations. This review also discusses the clinical implications of pharmacogenetic studies and the rising importance of investigating rare variants to explore their association with statin response.

Multidrug resistance due to facilitated drug efflux mediated by ATP-binding cassette (ABC) transporters is a main cause for failure of cancer therapy. Genetic polymorphisms in ABC genes affect the disposition of chemotherapeutics and constitute important biomarkers for therapeutic response and toxicity. Here we correlated germline variability in ABC transporters with disease-specific survival (DSS) in 960 breast cancer (BRCA), 314 clear cell renal cell carcinoma and 325 hepatocellular carcinoma patients. We find that variant burden in ABCC1 is a strong predictor of DSS in BRCA patients, whereas candidate polymorphisms are not associated with DSS. This association is highly drug-specific for subgroups treated with the MRP1 substrates cyclophosphamide (log-rank p = 0.0011) and doxorubicin (log-rank p = 0.0088) independent of age and tumor stage, whereas no association was found in individuals treated with tamoxifen (log-rank p = 0.13). Structural mapping of significant variants revealed multiple variants at residues involved in protein stability, cofactor stabilization or substrate binding. Our results demonstrate that BRCA patients with high variant burden in ABCC1 are less prone to respond appropriately to pharmacological therapy with MRP1 substrates, thus incentivizing the consideration of genomic germline data for precision cancer medicine.

Graphical abstract, In recent decades the identification of pharmacogenomic gene-drug associations has evolved tremendously. Despite this progress, a major fraction of the heritable inter-individual variability remains elusive. Higher-dimensional phenomena, such as gene-gene-drug interactions, in which variability in multiple genes synergizes to precipitate an observable phenotype have been suggested to account at least for part of this missing heritability. However, the identification of such intricate relationships remains difficult partly because of analytical challenges associated with the complexity explosion of the problem. To facilitate the identification of such combinatorial pharmacogenetic associations, we here propose a network analysis strategy. Specifically, we analyzed the landscape of drug metabolizing enzymes and transporters for 100 top selling drugs as well as all compounds with pharmacogenetic germline labels or dosing guidelines. Based on this data, we calculated the posterior probabilities that gene i is involved in metabolism, transport or toxicity of a given drug under the condition that another gene j is involved for all pharmacogene pairs (i, j). Interestingly, these analyses revealed significant patterns between individual genes and across pharmacogene families that provide insights into metabolic interactions. To visualize the gene-drug interaction landscape, we use multidimensional scaling to collapse this similarity matrix into a two-dimensional network. We suggest that Euclidian distance between nodes can inform about the likelihood of epistatic interactions and thus might provide a useful tool to reduce the search space and facilitate the identification of combinatorial pharmacogenomic associations.

Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.

Background Older adults are at the highest risk of severe disease and death due to COVID‐19. Randomized data have shown that baricitinib improves outcomes in these patients, but focused stratified analyses of geriatric cohorts are lacking. Our objective was to analyze the efficacy of baricitinib in older adults with COVID‐19 moderate‐to‐severe pneumonia. Methods This is a propensity score [PS]‐matched retrospective cohort study. Patients from the COVID‐AGE and Alba‐Score cohorts, hospitalized for moderate‐to‐severe COVID‐19 pneumonia, were categorized in two age brackets of age

Background/Objectives The safety and immunogenicity of the BNT162b2 coronavirus disease 2019 (COVID‐19) vaccine in older adults with different frailty and disability profiles have not been well determined. Our objective was to analyze immunogenicity of the BNT162b2 mRNA COVID‐19 vaccine in older adults across frailty and disability profiles. Design Multicenter longitudinal cohort study. Setting and participants A total of 134 residents aged ≥65 years with different frailty and disability profiles in five long‐term care facilities (LTCFs) in Albacete, Spain. Intervention and measurements Residents were administered two vaccine doses as per the label, and antibody levels were determined 21.9 days (SD 9.3) after both the first and second dose. Functional variables were assessed using activities of daily living (Barthel Index), and frailty status was determined with the FRAIL instrument. Cognitive status and comorbidity were also evaluated. Results Mean age was 82.9 years (range 65–99), and 71.6% were female. The mean antibody titers in residents with and without previous COVID‐19 infection were 49,878 AU/ml and 15,274 AU/ml, respectively (mean difference 34,604; 95% confidence interval [CI]: 27,699–41,509). No severe adverse reactions were observed, after either vaccine dose. Those with prevaccination COVID‐19 had an increased antibody level after the vaccine (B = 31,337; 95% CI: 22,725–39,950; p

Despite rigorous preclinical testing, clinical attrition rates in drug development remain high with drug-induced liver injury (DILI) remaining one of the most frequent causes of project failures. To understand DILI mechanisms, major efforts are put into the development of physiologically relevant cell models and culture paradigms with the aim to enhance preclinical to clinical result translation. While the majority of toxicogenomic studies have been based on cell lines, there are emerging trends toward the predominant use of stem cell-derived organoids and primary human hepatocytes in complex 3D cell models. Such studies have been successful in disentangling diverse toxicity mechanisms, including genotoxicity, mitochondrial injury, steatogenesis and cholestasis and can aid in distinguishing hepatotoxic from nontoxic structural analogs. Furthermore, by leveraging inter-individual differences of cells from different donors, these approaches can emulate the complexity of polygenic risk scores, which facilitates personalized drug-specific DILI risk analyses. In summary, toxicogenomic studies into drug-induced hepatotoxicity have majorly contributed to our mechanistic understanding of DILI and the incorporation of organotypic human 3D liver models into the preclinical testing arsenal promises to enhance biological insights during drug discovery, increase confidence in preclinical safety and minimize the translational gap.

The dual anticytokine and antiviral actions of baricitinib reduce SARS-CoV-2 infectivity in organoids and morbidity in people., Using AI, we identified baricitinib as having antiviral and anticytokine efficacy. We now show a 71% (95% CI 0.15 to 0.58) mortality benefit in 83 patients with moderate-severe SARS-CoV-2 pneumonia with few drug-induced adverse events, including a large elderly cohort (median age, 81 years). An additional 48 cases with mild-moderate pneumonia recovered uneventfully. Using organotypic 3D cultures of primary human liver cells, we demonstrate that interferon-α2 increases ACE2 expression and SARS-CoV-2 infectivity in parenchymal cells by greater than fivefold. RNA-seq reveals gene response signatures associated with platelet activation, fully inhibited by baricitinib. Using viral load quantifications and superresolution microscopy, we found that baricitinib exerts activity rapidly through the inhibition of host proteins (numb-associated kinases), uniquely among antivirals. This reveals mechanistic actions of a Janus kinase-1/2 inhibitor targeting viral entry, replication, and the cytokine storm and is associated with beneficial outcomes including in severely ill elderly patients, data that incentivize further randomized controlled trials.

Interindividual variability in drug response constitutes a major concern in pharmacotherapy. While polymorphisms in genes involved in drug disposition have been extensively studied, drug target variability remains underappreciated. By mapping the genomic variability of all human drug target genes onto high-resolution crystal structures of drug target complexes, we identified 1094 variants localized within 6 Å of drug-binding pockets and directly affecting their geometry, topology, or physicochemical properties. We experimentally show that binding site variants affect pharmacodynamics with marked drug- and variant-specific differences. In addition, we demonstrate that a common

Baricitinib, is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently predicted, using artificial intelligence (AI)‐algorithms, to be useful for COVID‐19 infection via a proposed anti‐cytokine effects and as an inhibitor of host cell viral propagation. We evaluated the in vitro pharmacology of baricitinib across relevant leukocyte subpopulations coupled to its in vivo pharmacokinetics and showed it inhibited signaling of cytokines implicated in COVID‐19 infection. We validated the AI‐predicted biochemical inhibitory effects of baricitinib on human numb‐associated kinase (hNAK) members measuring nanomolar affinities for AAK1, BIKE, and GAK. Inhibition of NAKs led to reduced viral infectivity with baricitinib using human primary liver spheroids. These effects occurred at exposure levels seen clinically. In a case series of patients with bilateral COVID‐19 pneumonia, baricitinib treatment was associated with clinical and radiologic recovery, a rapid decline in SARS‐CoV‐2 viral load, inflammatory markers, and IL‐6 levels. Collectively, these data support further evaluation of the anti‐cytokine and anti‐viral activity of baricitinib and supports its assessment in randomized trials in hospitalized COVID‐19 patients.

A 2016 study proposed induction of the cytochrome P450 (CYP) enzyme CYP3A5 as a key mechanism for chemotherapy resistance and suggested CYP3A5 as a promising therapeutic target. However, in the context of the available literature we are skeptical that CYP3A5 contributes significantly to cancer drug resistance.

High failure rates of drug candidates in the clinics, restricted-use warnings as well as withdrawals of drugs in postmarketing stages are of substantial concern for the pharmaceutical industry and drug-induced liver injury (DILI) constitutes one of the most frequent reasons for such safety failures. Importantly, as DILI cannot be accurately predicted using animal models, animal safety tests are commonly complemented with assessments in human in vitro systems. 3D spheroid cultures of primary human hepatocytes in chemically defined conditions, hereafter termed CD-spheroids, have recently emerged as a microphysiological model system in which hepatocytes retain their molecular phenotypes and hepatic functions for multiple weeks in culture. However, their predictive power for the detection of hepatotoxic liabilities has not been systematically assessed. Therefore, we here evaluated the hepatotoxicity of 123 drugs with or without direct implication in clinical DILI events. Importantly, using ATP quantifications as the single endpoint, the model accurately distinguished between hepatotoxic and nontoxic structural analogues and exceeded both sensitivity and specificity of all previously published in vitro assays at substantially lower exposure levels, successfully detecting 69% of all hepatotoxic compounds without producing any false positive results (100% specificity). Furthermore, the platform supports the culture of spheroids of primary hepatocytes from preclinical animal models, thereby allowing the identification of animal-specific toxicity events. We anticipate that CD-spheroids represent a powerful and versatile tool in drug discovery and preclinical drug development that can reliably flag hepatotoxic drug candidates and provide guidance for the selection of the most suitable animal models.

Enhancing the early detection of new therapies that are likely to carry a safety liability in the context of the intended patient population would provide a major advance in drug discovery. Microphysiological systems (MPS) technology offers an opportunity to support enhanced preclinical to clinical translation through the generation of higher-quality preclinical physiological data. In this review, we highlight this technological opportunity by focusing on key target organs associated with drug safety and metabolism. By focusing on MPS models that have been developed for these organs, alongside other relevant in vitro models, we review the current state of the art and the challenges that still need to be overcome to ensure application of this technology in enhancing drug discovery.

Genetic polymorphisms in cytochrome P450 (CYP) genes can result in altered metabolic activity toward a plethora of clinically important medications. Thus, single nucleotide variants and copy number variations in CYP genes are major determinants of drug pharmacokinetics and toxicity and constitute pharmacogenetic biomarkers for drug dosing, efficacy, and safety. Strikingly, the distribution of CYP alleles differs considerably between populations with important implications for personalized drug therapy and healthcare programs. To provide a global distribution map of CYP alleles with clinical importance, we integrated whole‐genome and exome sequencing data from 56,945 unrelated individuals of five major human populations. By combining this dataset with population‐specific linkage information, we derive the frequencies of 176 CYP haplotypes, providing an extensive resource for major genetic determinants of drug metabolism. Furthermore, we aggregated this dataset into spectra of predicted functional variability in the respective populations and discuss the implications for population‐adjusted pharmacological treatment strategies.

Obesity and insulin resistance are risk factors for nonalcoholic fatty liver disease (NAFLD), the most common chronic liver disease worldwide. Because no approved medication nor an accurate and noninvasive diagnosis is currently available for NAFLD, there is a clear need to better understand the link between obesity and NAFLD. Lipid accumulation during obesity is known to be associated with oxidative stress and inflammatory activation of liver macrophages (LMs). However, we show that although LMs do not become proinflammatory during obesity, they display signs of oxidative stress. In livers of both humans and mice, antioxidant nuclear factor erythroid 2– related factor 2 (NRF2) was down-regulated with obesity and insulin resistance, yielding an impaired response to lipid accumulation. At the molecular level, a microRNA-targeting NRF2 protein, miR-144, was elevated in the livers of obese insulin-resistant humans and mice, and specific silencing of miR-144 in murine and human LMs was sufficient to restore NRF2 protein expression and the antioxidant response. These results highlight the pathological role of LMs and their therapeutic potential to restore the impaired endogenous antioxidant response in obesity-associated NAFLD. Published (Publication status)

Immune-mediated drug hypersensitivity reactions are an important source of iatrogenic morbidity and mortality. Human leukocyte antigen (HLA)-B*57:01, HLA-B*15:02, HLA-A*31:01, and HLA-B*58:01 constitute established risk factors and preemptive genotyping of these HLA alleles in patients prior to the initiation of abacavir, carbamazepine, and allopurinol-based therapies can prevent toxicity and improve patient outcomes. However, the cost-effectiveness of preemptive HLA testing has only been evaluated in the United States and few countries in Europe and Asia. In this study, we consolidated HLA genotypes from 3.5-6.4 million individuals across up to 74 countries and modeled the country-specific cost-effectiveness of genetic testing. We find major ethnogeographic differences in risk allele prevalence, which translated into pronounced differences in the number of patients needed to test to prevent one case of severe hypersensitivity reactions between countries and populations. At incremental cost-effectiveness ratio thresholds of $40,000, testing of HLA-B*57:01 in patients initiating abacavir was cost-effective in the majority of countries with potential exceptions of East Asia, Saudi Arabia, Ghana, and Zimbabwe. For carbamazepine, preemptive genotyping of HLA-B*15:02 is only cost-effective across most of East and South Asia, whereas HLA-A*31:01 testing is likely to be cost-effective globally. Testing of HLA-B*58:01 is more likely to be cost-effective throughout Africa and Asia compared with Europe and the Americas. We anticipate that this data set can serve as an important resource for clinicians and health economists to guide clinical decision making and inform public healthcare strategies.

Checkpoint inhibitor therapy constitutes a promising cancer treatment strategy that targets the immune checkpoints to re-activate silenced T cell cytotoxicity. In recent pivotal trials, immune checkpoint blockade (ICB) demonstrated durable responses and acceptable toxicity, resulting in the regulatory approval of 8 checkpoint inhibitors to date for 15 cancer indications. However, up to ~85% of patients present with innate or acquired resistance to ICB, limiting its clinical utility. Current response biomarker candidates, including DNA mutation and neoantigen load, immune profiles, as well as programmed death-ligand 1 (PD-L1) expression, are only weak predictors of ICB response. Thus, identification of novel, more predictive biomarkers that could identify patients who would benefit from ICB constitutes one of the most important areas of immunotherapy research. Aberrant DNA methylation (5mC) and hydroxymethylation (5hmC) were discovered in multiple cancers, and dynamic changes of the epigenomic landscape have been identified during T cell differentiation and activation. While their role in cancer immunosuppression remains to be elucidated, recent evidence suggests that 5mC and 5hmC may serve as prognostic and predictive biomarkers of ICB-sensitive cancers. In this review, we describe the role of epigenetic phenomena in tumor immunoediting and other immune evasion related processes, provide a comprehensive update of the current status of ICB-response biomarkers, and highlight promising epigenomic biomarker candidates.

Recently, it was published that CYP3A5 contributes to chemotherapeutic drug resistance in a wide range of solid tumors, including hepatocellular carcinoma. However, CYP3A5 is highly polymorphic and 90% of Caucasians are homozygous for the loss-of-function allele CYP3A5*3. Here, we evaluate the relationship between CYP3A5 genotype and expression level of both CYP3A5 transcripts and protein in biopsies from 19 pairs of liver tumors and corresponding peritumoral tissue. We find that CYP3A5 transcript levels are reduced compared with peritumoral controls. Moreover, we do not detect CYP3A5 protein in homozygous CYP3A5*3 carriers and no relative increase of CYP3A5 in tumoral tissue of CYP3A5*1 carriers. We conclude that anticancer drug resistance is unlikely to be caused by increased CYP3A5 expression.

Assessing the potential of a new drug to cause drug-induced liver injury (DILI) is a challenge for the pharmaceutical industry. We therefore determined whether cell models currently used in safety assessment (HepG2, HepaRG, Upcyte and primary human hepatocytes in conjunction with basic but commonly used endpoints) are actually able to distinguish between novel chemical entities (NCEs) with respect to their potential to cause DILI. A panel of thirteen compounds (nine DILI implicated and four non-DILI implicated in man) were selected for our study, which was conducted, for the first time, across multiple laboratories. None of the cell models could distinguish faithfully between DILI and non-DILI compounds. Only when nominal in vitro concentrations were adjusted for in vivo exposure levels were primary human hepatocytes (PHH) found to be the most accurate cell model, closely followed by HepG2. From a practical perspective, this study revealed significant inter-laboratory variation in the response of PHH, HepG2 and Upcyte cells, but not HepaRG cells. This variation was also observed to be compound dependent. Interestingly, differences between donors (hepatocytes), clones (HepG2) and the effect of cryopreservation (HepaRG and hepatocytes) were less important than differences between the cell models per se. In summary, these results demonstrate that basic cell health endpoints will not predict hepatotoxic risk in simple hepatic cells in the absence of pharmacokinetic data and that a multicenter assessment of more sophisticated signals of molecular initiating events is required to determine whether these cells can be incorporated in early safety assessment. Electronic supplementary material The online version of this article (doi:10.1007/s00204-016-1745-4) contains supplementary material, which is available to authorized users.

Recent research highlighted the large extent of rare variants in pharmacogenes and, on this basis, it was estimated that rare variants account for 30–40% of the functional variability in pharmacogenes. It has been proposed that comprehensive next-generation sequencing (NGS)-based sequencing of pharmacogenes could soon be a cost-effective methodology for clinical routine genotyping. Yet, multiple challenges on technical, interpretative and ethical levels need to be overcome to enable the reasonable dissemination of comprehensive pharmacogenetic genotyping, that includes rare genetic variation, into clinical practice. We argue that current pre-emptive pharmacogenetic testing cannot be based on comprehensive approaches but needs to be restricted to validated variants. Rather, comprehensive strategies should only be used for retrospective analyses of patients exhibiting unanticipated drug responses. Thereby, subsequent to computational analyses and functional validations, emerging variants with confirmed functional relevance can be incorporated into candidate genotyping strategies, thus refining and enhancing future pre-emptive genetic testing.

In the version of this article initially published, author Volker M. Lauschke had affiliation number 13; the correct affiliation number is 12. The error has been corrected in the HTML and PDF versions of the article.

Pharmacogenomics aims to tailor pharmacological treatment to each individual by considering associations between genetic polymorphisms and adverse drug effects (ADEs). With technological advances, pharmacogenomic research has evolved from candidate gene analyses to genome-wide association studies. Here, we integrate deep whole-genome sequencing (WGS) information with drug prescription and ADE data from Estonian electronic health record (EHR) databases to evaluate genome- and pharmacome-wide associations on an unprecedented scale. We leveraged WGS data of 2240 Estonian Biobank participants and imputed all single-nucleotide variants (SNVs) with allele counts over 2 for 13,986 genotyped participants. Overall, we identified 41 (10 novel) loss-of-function and 567 (134 novel) missense variants in 64 very important pharmacogenes. The majority of the detected variants were very rare with frequencies below 0.05%, and 6 of the novel loss-of-function and 99 of the missense variants were only detected as single alleles (allele count = 1). We also validated documented pharmacogenetic associations and detected new independent variants in known gene-drug pairs. Specifically, we found that CTNNA3 was associated with myositis and myopathies among individuals taking nonsteroidal anti-inflammatory oxicams and replicated this finding in an extended cohort of 706 individuals. These findings illustrate that population-based WGS-coupled EHRs are a useful tool for biomarker discovery.

Purpose: Biomedical databases combining electronic medical records and phenotypic and genomic data constitute a powerful resource for the personalization of treatment. To leverage the wealth of information provided, algorithms are required that systematically translate the contained information into treatment recommendations based on existing genotype-phenotype associations. Methods: We developed and tested algorithms for translation of preexisting genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations. We compared the results obtained by genome sequencing, exome sequencing, and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia. Results: Our most striking result was that the performance of genotyping arrays is similar to that of genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions. Interestingly, 99.8% of all assessed individuals had a genotype associated with increased risks to at least one medication, and thereby the implementation of pharmacogenetic recommendations based on genotyping affects at least 50 daily drug doses per 1000 inhabitants. Conclusion: We find that microarrays are a cost-effective solution for creating preemptive pharmacogenetic reports, and with slight modifications, existing databases can be applied for automated pharmacogenetic decision support for clinicians.

Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.

Organic anion transporting polypeptides (OATP) encoded by the SLCO gene family constitute clinically important transporters involved in the disposition of endogenous compounds and many commonly prescribed drugs, including statins, methotrexate and antihypertensive medications. Common genetic polymorphisms in SLCO genes are known to affect OATP function and modulate efficacy and safety of OATP substrates. However, current frequency data of these variants and haplotypes is generally based on few rather heterogenous populations of relatively small sample size. Furthermore, the genetic variability beyond these selected pharmacogenetic biomarkers has not been systematically analyzed. Here, we provide a global consolidated map of SLCO variability by leveraging fully compatible Next Generation Sequencing data from 138,632 unrelated individuals across seven major human populations. Overall, we find 9811 exonic single nucleotide variants and 155 copy number variations of which 99.3% were rare with frequencies

PurposeBiomedical databases combining electronic medical records, phenotypic and genomic data constitute a powerful resource for the personalization of treatment. To leverage the wealth of information provided, algorithms are required that systematically translate the contained information into treatment recommendations based on existing genotype-phenotype associations.MethodsWe developed and tested algorithms for translation of pre-existing genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations. We compared the results obtained by whole genome sequencing, whole exome sequencing and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia.ResultsOur most striking result was that the performance of genotyping arrays is similar to that of whole genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions. Interestingly, 99.8% of all assessed individuals had a genotype associated with increased risks to at least one medication, and thereby the implementation of pharmacogenetic recommendations based on genotyping affects at least 50 daily drug doses per 1000 inhabitants.ConclusionWe find that microarrays are a cost-effective solution for creating pre-emptive pharmacogenetic reports, and with slight modifications, existing databases can be applied for automated pharmacogenetic decision support for clinicians.

The liver fulfills central roles in metabolic control and detoxification and, as such, is continuously exposed to a plethora of insults. Importantly, the liver has a unique ability to regenerate and can completely recoup from most acute, non-iterative insults. However, multiple conditions, including viral hepatitis, non-alcoholic fatty liver disease (NAFLD), long-term alcohol abuse and chronic use of certain medications, can cause persistent injury in which the regenerative capacity eventually becomes dysfunctional, resulting in hepatic scaring and cirrhosis. Calcium is a versatile secondary messenger that regulates multiple hepatic functions, including lipid and carbohydrate metabolism, as well as bile secretion and choleresis. Accordingly, dysregulation of calcium signaling is a hallmark of both acute and chronic liver diseases. In addition, recent research implicates calcium transients as essential components of liver regeneration. In this review, we provide a comprehensive overview of the role of calcium signaling in liver health and disease and discuss the importance of calcium in the orchestration of the ensuing regenerative response. Furthermore, we highlight similarities and differences in spatiotemporal calcium regulation between liver insults of different etiologies. Finally, we discuss intracellular calcium control as an emerging therapeutic target for liver injury and summarize recent clinical findings of calcium modulation for the treatment of ischemic-reperfusion injury, cholestasis and NAFLD.

BackgroundAdverse drug reactions are a major concern in drug development and clinical therapy. Genetic polymorphisms in genes involved in drug metabolism and transport are major determinants of treatment efficacy and adverse reactions, and constitute important biomarkers for drug dosing, efficacy and safety. Importantly, human populations and subgroups differ substantially in their pharmacogenetic variability profiles, with important consequences for personalised medicine strategies and precision public health approaches. Despite their long migration history, Ashkenazi Jews constitute a rather isolated population with a unique genetic signature that is distinctly different from other populations.ObjectiveTo provide a comprehensive overview of the pharmacogenetic profile in Ashkenazim.MethodsWe analysed next-generation sequencing data from 5076 Ashkenazim individuals and used sequence data from 117 425 non-Jewish individuals as reference.ResultsWe derived frequencies of 164 alleles in 17 clinically relevant pharmacogenes and derived profiles of putative functional consequences, providing the most comprehensive data set of Jewish pharmacogenetic diversity published to date. Furthermore, we detected 127 variants with an aggregated frequency of 20.7% that were specifically found in Ashkenazim, of which 55 variants were putatively deleterious (aggregated frequency of 9.4%).ConclusionThe revealed pattern of pharmacogenetic variability in Ashkenazi Jews is distinctly different from other populations and is expected to translate into unique functional consequences, especially for the metabolism of CYP2A6, CYP2C9, NAT2 and VKORC1 substrates. We anticipate that the presented data will serve as a powerful resource for the guidance of pharmacogenetic treatment decisions and the optimisation of population-specific genotyping strategies in the Ashkenazi diaspora.

Personalized drug therapy aims to optimize the efficacy of pharmacological treatments by considering genetic, pathophysiological, dietary, and environmental factors as well as comedications and compliance. A multitude of associations between the specific genetic constitution of the patient and drug pharmacokinetics and pharmacodynamics has been identified in the last decades that encompass mainly common single nucleotide variants (SNVs) and gene copy number variations (CNVs) of importance for the function of genes encoding drug-metabolizing enzymes and transporters involved in drug absorption, distribution, metabolism, and excretion (ADME). In addition, genetic variation in factors encoding the major histocompatibility complex have been helpful to predict immune-mediated drug toxicity. This knowledge has been translated into clinical applications through the implementation of pharmacogenomic biomarkers. Over 230 of such markers that can, to a certain extent, predict drug efficacy or the likelihood of adverse drug reactions (ADRs) are recognized by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) and are included in the drug labels of the respective medication. They are of particular value in cancer therapy to provide information of use to avoid ADRs and lack of response.

Background Variability in genes implicated in drug pharmacokinetics or drug response can modulate treatment efficacy or predispose to adverse drug reactions. Besides common genetic polymorphisms, recent sequencing projects revealed a plethora of rare genetic variants in genes encoding proteins involved in drug metabolism, transport, and response. Results To understand the global importance of rare pharmacogenetic gene variants, we mapped the variability in 208 pharmacogenes by analyzing exome sequencing data from 60,706 unrelated individuals and estimated the importance of rare and common genetic variants using a computational prediction framework optimized for pharmacogenetic assessments. Our analyses reveal that rare pharmacogenetic variants were strongly enriched in mutations predicted to cause functional alterations. For more than half of the pharmacogenes, rare variants account for the entire genetic variability. Each individual harbored on average a total of 40.6 putatively functional variants, rare variants accounting for 10.8% of these. Overall, the contribution of rare variants was found to be highly gene- and drug-specific. Using warfarin, simvastatin, voriconazole, olanzapine, and irinotecan as examples, we conclude that rare genetic variants likely account for a substantial part of the unexplained inter-individual differences in drug metabolism phenotypes. Conclusions Combined, our data reveal high gene and drug specificity in the contributions of rare variants. We provide a proof-of-concept on how this information can be utilized to pinpoint genes for which sequencing-based genotyping can add important information to predict drug response, which provides useful information for the design of clinical trials in drug development and the personalization of pharmacological treatment. Electronic supplementary material The online version of this article (10.1186/s40246-018-0157-3) contains supplementary material, which is available to authorized users.

Prediction of phenotypic consequences of mutations constitutes an important aspect of precision medicine. Current computational tools mostly rely on evolutionary conservation and have been calibrated on variants associated with disease, which poses conceptual problems for assessment of variants in poorly conserved pharmacogenes. Here, we evaluated the performance of 18 current functionality prediction methods leveraging experimental high-quality activity data from 337 variants in genes involved in drug metabolism and transport and found that these models only achieved probabilities of 0.1–50.6% to make informed conclusions. We therefore developed a functionality prediction framework optimized for pharmacogenetic assessments that significantly outperformed current algorithms. Our model achieved 93% for both sensitivity and specificity for both loss-of-function and functionally neutral variants, and we confirmed its superior performance using cross validation analyses. This novel model holds promise to improve the translation of personal genetic information into biological conclusions and pharmacogenetic recommendations, thereby facilitating the implementation of Next-Generation Sequencing data into clinical diagnostics.

In this issue of Clinical Science, Schueller et al. [Clin. Sci. (2017) 131, 1971-1987] evaluated the role of miR-223 across multiple etiologies of acute and chronic liver insults in murine models and clinical samples. The authors find that while miR-223 is not mechanistically involved in liver injury, its intracellular levels in hepatocytes are increased upon hepatic damage in a broad panel of mechanistically distinct injury models. Furthermore, the authors provide evidence that circulating miR-223 levels provide a promising minimally invasive biomarker for acute liver failure (ALF) that defines a distinct subset of ALF cases and correlates with clinical outcomes. Combined, the highlighted study suggests that miR-223 constitutes a promising biomarker whose clinical validity and utility warrant further investigations.

Responses to drugs and pharmacological treatments differ considerably between individuals. Importantly, only 50%–75% of patients have been shown to react adequately to pharmacological interventions, whereas the others experience either a lack of efficacy or suffer from adverse events. The liver is of central importance in the metabolism of most drugs. Because of this exposed status, hepatotoxicity is amongst the most common adverse drug reactions and hepatic liabilities are the most prevalent reason for the termination of development programs of novel drug candidates. In recent years, more and more factors were unveiled that shape hepatic drug responses and thus underlie the observed inter-individual variability. In this review, we provide a comprehensive overview of different principle mechanisms of drug hepatotoxicity and illustrate how patient-specific factors, such as genetic, physiological and environmental factors, can shape drug responses. Furthermore, we highlight other parameters, such as concomitantly prescribed medications or liver diseases and how they modulate drug toxicity, pharmacokinetics and dynamics. Finally, we discuss recent progress in the field of in vitro toxicity models and evaluate their utility in reflecting patient-specific factors to study inter-individual differences in drug response and toxicity, as this understanding is necessary to pave the way for a patient-adjusted medicine.

The liver is an organ with critical importance for drug treatment as the disposition and response to a given drug is often determined by its hepatic metabolism. Patient-specific factors can entail increased susceptibility to drug-induced liver injury, which constitutes a major risk for drug development programs causing attrition of promising drug candidates or costly withdrawals in postmarketing stages. Hitherto, mainly animal studies and 2D hepatocyte systems have been used for the examination of human drug metabolism and toxicity. Yet, these models are far from satisfactory due to extensive species differences and because hepatocytes in 2D cultures rapidly dedifferentiate resulting in the loss of their hepatic phenotype and functionality. With the increasing comprehension that 3D cell culture systems more accurately reflect in vivo physiology, in the recent decade more and more research has focused on the development and optimization of various 3D culture strategies in an attempt to preserve liver properties in vitro. In this contribution, we critically review these developments, which have resulted in an arsenal of different static and perfused 3D models. These systems include sandwich-cultured hepatocytes, spheroid culture platforms, and various microfluidic liver or multiorgan biochips. Importantly, in many of these models hepatocytes maintain their phenotype for prolonged times, which allows probing the potential of newly developed chemical entities to cause chronic hepatotoxicity. Moreover, some platforms permit the investigation of drug action in specific genetic backgrounds or diseased hepatocytes, thereby significantly expanding the repertoire of tools to detect drug-induced liver injuries. It is concluded that the development of 3D liver models has hitherto been fruitful and that systems are now at hand whose sensitivity and specificity in detecting hepatotoxicity are superior to those of classical 2D culture systems. For the future, we highlight the need to develop more integrated coculture model systems to emulate immunotoxicities that arise due to complex interactions between hepatocytes and immune cells.

This manuscript provides nomenclature recommendations developed by an international workgroup to increase transparency and standardization of pharmacogenetic (PGx) result reporting. Presently, sequence variants identified by PGx tests are described using different nomenclature systems. In addition, PGx analysis may detect different sets of variants for each gene, which can affect interpretation of results. This practice has caused confusion and may thereby impede the adoption of clinical PGx testing. Standardization is critical to move PGx forward.

Interindividual variability in drug metabolism and drug toxicity persists as a major problem for drug development and treatment. Increased or decreased capacity for drug elimination or drug action reduces drug efficacy and places substantial economic burdens on society (e.g., due to treatment of adverse drug reactions) [1]. To a great extent this variation is based on genetic differences, and indeed many drugs now carry pharmacogenomic labels regarding mandatory or informative genetic tests that have to/can be performed before prescription (http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm).

Pre-emptive pharmacogenetics (PGx) testing of a panel of germline genetic variants represents a new model for personalized medicine. Clinical impact of PGx testing is maximized when all variant alleles for which actionable clinical guidelines are available are included in the test panel. However, no such standardized panel has been presented to date, impeding adoption, exchange, and continuity of PGx testing. We, therefore, developed such a panel, hereafter called the PGx-Passport, based on the actionable Dutch Pharmacogenetics Working Group (DPWG) guidelines. Germline-variant alleles were systematically selected using predefined criteria regarding allele population frequencies, effect on protein functionality, and association with drug response. A PGx-Passport of 58 germline variant alleles, located within 14 genes (CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5, DPYD, F5, HLA-A, HLA-B, NUDT15, SLCO1B1, TPMT, UGT1A1, and VKORC1) was composed. This PGx-Passport can be used in combination with the DPWG guidelines to optimize drug prescribing for 49 commonly prescribed drugs.

Much of the inter-individual variability in drug efficacy and risk of adverse reactions is due to polymorphisms in genes encoding proteins involved in drug pharmacokinetics and pharmacodynamics or immunological responses. Pharmacogenetic research has identified a multitude of gene-drug response associations, which have resulted in genetically guided treatment and dosing decisions to yield a higher success rate of pharmacological treatment. The rapid technological developments for genetic analyses reveal that the number of genetic variants with importance for drug action is much higher than previously thought and that a true personalized prediction of drug response requires attention to millions of rare mutations. Here, we review the evolutionary background of genetic polymorphisms in drug-metabolizing enzymes, provide some important examples of current use of pharmacogenomic biomarkers, and give an update of germline and somatic genome biomarkers that are in use in drug development and clinical practice. We also discuss the current technology development with emphasis on complex genetic loci, review current initiatives for validation of pharmacogenomic biomarkers, and present scenarios for the future taking rare genetic variants into account for a true personalized genetically guided drug prescription. We conclude that pharmacogenomic information for patient stratification is of value to tailor optimized treatment regimens particularly in oncology. However, the routine use of pharmacogenomic biomarkers in clinical practice in other therapeutic areas is currently sparse and the prospects of its future implementation are being scrutinized by different international consortia.

Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.

Drug development is a failure-prone endeavour, and more than 85% of drugs fail during clinical development, showcasing that current preclinical systems for compound selection are clearly inadequate. Liver toxicity remains a major reason for safety failures. Furthermore, all efforts to develop pharmacological therapies for a variety of chronic liver diseases, such as non-alcoholic steatohepatitis (NASH) and fibrosis, remain unsuccessful. Considering the time and expense of clinical trials, as well as the substantial burden on patients, new strategies are thus of paramount importance to increase clinical success rates. To this end, human liver spheroids are becoming increasingly utilized as they allow to preserve patient-specific phenotypes and functions for multiple weeks in culture. We here review the recent application of such systems for i) predictive and mechanistic analyses of drug hepatotoxicity, ii) the evaluation of hepatic disposition and metabolite formation of low clearance drugs and iii) the development of drugs for metabolic and infectious liver diseases, including NASH, fibrosis, malaria and viral hepatitis. We envision that with increasing dissemination, liver spheroids might become the new gold standard for such applications in translational pharmacology and toxicology.

Drug-induced liver injury (DILI) is a major concern for the pharmaceutical industry and constitutes one of the most important reasons for the termination of promising drug development projects. Reliable prediction of DILI liability in preclinical stages is difficult, as current experimental model systems do not accurately reflect the molecular phenotype and functionality of the human liver. As a result, multiple drugs that passed preclinical safety evaluations failed due to liver toxicity in clinical trials or postmarketing stages in recent years. To improve the selection of molecules that are taken forward into the clinics, the development of more predictive in vitro systems that enable high-throughput screening of hepatotoxic liabilities and allow for investigative studies into DILI mechanisms has gained growing interest. Specifically, it became increasingly clear that the choice of cell types and culture method both constitute important parameters that affect the predictive power of test systems. In this review, we present current 3D culture paradigms for hepatotoxicity tests and critically evaluate their utility and performance for DILI prediction. In addition, we highlight possibilities of these emerging platforms for mechanistic evaluations of selected drug candidates and present current research directions towards the further improvement of preclinical liver safety tests. We conclude that organotypic and microphysiological liver systems have provided an important step towards more reliable DILI prediction. Furthermore, we expect that the increasing availability of comprehensive benchmarking studies will facilitate model dissemination that might eventually result in their regulatory acceptance.