Comprehensive translational assessment of human-induced pluripotent stem cell derived cardiomyocytes for evaluating drug-induced arrhythmias

Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) hold promise for assessment of drug-induced arrhythmias and are being considered for use under the comprehensive in vitro proarrhythmia assay (CiPA). We studied the effects of 26 drugs and 3 drug combinations on 2 commercially available iPSC-CM types using high-throughput voltage-sensitive dye and microelectrode-array assays being studied for the CiPA initiative and compared the results with clinical QT prolongation and torsade de pointes (TdP) risk. Concentration-dependent analysis comparing iPSC-CMs to clinical trial results demonstrated good correlation between drug-induced rate-corrected action potential duration and field potential duration (APDc and FPDc) prolongation and clinical trial QTc prolongation. Of 20 drugs studied that exhibit clinical QTc prolongation, 17 caused APDc prolongation (16 in Cor.4U and 13 in iCell cardiomyocytes) and 16 caused FPDc prolongation (16 in Cor.4U and 10 in iCell cardiomyocytes). Of 14 drugs that cause TdP, arrhythmias occurred with 10 drugs. Lack of arrhythmic beating in iPSC-CMs for the four remaining drugs could be due to differences in relative levels of expression of individual ion channels. iPSC-CMs responded consistently to human ether-a-go-go potassium channel blocking drugs (APD prolongation and arrhythmias) and calcium channel blocking drugs (APD shortening and prevention of arrhythmias), with a more variable response to late sodium current blocking drugs. Current results confirm the potential of iPSC-CMs for proarrhythmia prediction under CiPA, where iPSC-CM results would serve as a check to ion channel and in silico modeling prediction of proarrhythmic risk. A multi-site validation study is warranted

In silico methods for the prediction of drug-induced cardiotoxicity

Unexpected adverse reactions, especially unsafe cardiac effects, are a major concern of pharmaceutical companies that can prompt them to both discontinue drugs currently in development and withdraw drugs already on the market. Therefore, the safety assessment is a key stage of both the drug development process and the current regulatory framework of clinical trials. Given the importance of unforeseen acute electrophysiological effects in precipitating potentially lethal arrhythmias, the current preclinical testing stages of drug development are largely focused on their detection. However, a substantial number of drugs also affect cardiac function on many other levels, including contractility, mitochondria function and cell signalling. A number of in vitro, in vivo and in silico approaches capable of detecting different types of possible cardiovascular side effects have been proposed recently. Among those, human-based computational methods hold a great potential to increase the productivity of drug discovery pipelines, drive a more rational drug design and replace costly animal experiments that have limited translational ability for humans. Therefore, the goal of this thesis is to propose a computational approach to predict drug-induced cardiotoxicity. A multi-label machine learning classification approach is used to simultaneously predict multiple forms of clinical cardiac side effects and take into account relationships between those forms of toxicity. In the last part of this thesis, the effects of trafficking impairment, as one of the cardiotoxicity mechanisms, are then investigated using simulations of action potential models

Deconvoluting kinase inhibitor induced cardiotoxicity

Many drugs designed to inhibit kinases have their clinical utility limited by cardiotoxicity-related label warnings or prescribing restrictions. While this liability is widely recognized, designing safer kinase inhibitors (KI) requires knowledge of the causative kinase(s). Efforts to unravel the kinases have encountered pharmacology with nearly prohibitive complexity. At therapeutically relevant concentrations, KIs show promiscuity distributed across the kinome. Here, to overcome this complexity, 65 KIs with known kinome-scale polypharmacology profiles were assessed for effects on cardiomyocyte (CM) beating. Changes in human iPSC-CM beat rate and amplitude were measured using label-free cellular impedance. Correlations between beat effects and kinase inhibition profiles were mined by computation analysis (Matthews Correlation Coefficient) to identify associated kinases. Thirty kinases met criteria of having (1) pharmacological inhibition correlated with CM beat changes, (2) expression in both human-induced pluripotent stem cell-derived cardiomyocytes and adult heart tissue, and (3) effects on CM beating following single gene knockdown. A subset of these 30 kinases were selected for mechanistic follow up. Examples of kinases regulating processes spanning the excitation–contraction cascade were identified, including calcium flux (RPS6KA3, IKBKE) and action potential duration (MAP4K2). Finally, a simple model was created to predict functional cardiotoxicity whereby inactivity at three sentinel kinases (RPS6KB1, FAK, STK35) showed exceptional accuracy in vitro and translated to clinical KI safety data. For drug discovery, identifying causative kinases and introducing a predictive model should transform the ability to design safer KI medicines. For cardiovascular biology, discovering kinases previously unrecognized as influencing cardiovascular biology should stimulate investigation of underappreciated signaling pathways

Potassium currents in the heart: functional roles in repolarization, arrhythmia and therapeutics

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Genome-wide association analyses identify new Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility

Brugada syndrome (BrS) is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of SCN5A, encoding the cardiac sodium channel NaV1.5, susceptibility genes remain largely unknown. Here we performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with BrS and 10,001 controls, and identified 21 association signals at 12 loci (10 new). Single nucleotide polymorphism (SNP)-heritability estimates indicate a strong polygenic influence. Polygenic risk score analyses based on the 21 susceptibility variants demonstrate varying cumulative contribution of common risk alleles among different patient subgroups, as well as genetic associations with cardiac electrical traits and disorders in the general population. The predominance of cardiac transcription factor loci indicates that transcriptional regulation is a key feature of BrS pathogenesis. Furthermore, functional studies conducted on MAPRE2, encoding the microtubule plus-end binding protein EB2, point to microtubule-related trafficking effects on NaV1.5 expression as a new underlying molecular mechanism. Taken together, these findings broaden our understanding of the genetic architecture of BrS and provide new insights into its molecular underpinnings

Development of a non-mammalian, pre-clinical screening tool for the predictive analysis of drug toxicity

The failure to predict drug-induced toxicity reactions is still a major problem contributing to a high attrition rate and tremendous cost in drug development. Xenopus laevis embryos are amenable for the early stage medium to high throughput small molecule screens. We hypothesise Xenopus embryos can assist in vitro drug-induced toxicity safety assessment in the early phases of drug development before moving on to expensive preclinical trials in mammals. The objective of this study was to assess the use of Xenopus laevis embryos for the prediction of organ-specific toxicity. To do this I used drugs known to generate toxicity reactions in humans. First of all I determined that Xenopus embryos treated with a drug from the age of stage 38 until stage 45, was an appropriate assay for the prediction of drug-induced toxicity. The embryos expressed major drug metabolism enzymes including CYP2E1, CYP2D6, CYP3A4 and glutathione S-transferases, sulphotransferases and glucuronosyltransferases. They also expressed KCNH2, which encodes the α-subunit protein of the potassium ion channel KV11.1 that contributes to heart electrophysiology. For drug-induced liver injury, I used paracetamol treatment. Xenopus laevis embryos treated with paracetamol (0-5 mM) generated predicted paracetamol metabolites, had a dose-dependent depletion of free glutathione and increased expression of microRNA-122 (miR-122) in tissue that did not contain the liver. To investigate drug-induced cardiotoxicity, I treated Xenopus embryos with doxorubicin (0-100 µM) and terfenadine (050 µM). Embryo heart rates increased and decreased with these drugs respectively and arrhythmias occurred with both drug treatments. Embryos treated with doxorubicin had an increasing amount of arrhythmia that correlated with an increasing dose of doxorubicin treatment. Terfenadine treatment induced arrhythmia at a rate that was not concentration dependent. Wholemount in situ hybridisation (WISH) revealed the Xenopus embryos also express miR-208 specifically in the heart, similar to mammalian models. We conclude that Xenopus laevis embryos exhibit some similar characterisations of drug-induced hepatotoxicity and cardiotoxicity observed in mammalian models. These data indicate the Xenopus embryo could be a useful model to assess drug-induced toxicity and aid lead compound prioritisation in early drug development

The Pharmacoepigenomics Informatics Pipeline and H-GREEN Hi-C Compiler: Discovering Pharmacogenomic Variants and Pathways with the Epigenome and Spatial Genome

Over the last decade, biomedical science has been transformed by the epigenome and spatial genome, but the discipline of pharmacogenomics, the study of the genetic underpinnings of pharmacological phenotypes like drug response and adverse events, has not. Scientists have begun to use omics atlases of increasing depth, and inferences relating to the bidirectional causal relationship between the spatial epigenome and gene expression, as a foundational underpinning for genetics research. The epigenome and spatial genome are increasingly used to discover causative regulatory variants in the significance regions of genome-wide association studies, for the discovery of the biological mechanisms underlying these phenotypes and the design of genetic tests to predict them. Such variants often have more predictive power than coding variants, but in the area of pharmacogenomics, such advances have been radically underapplied. The majority of pharmacogenomics tests are designed manually on the basis of mechanistic work with coding variants in candidate genes, and where genome wide approaches are used, they are typically not interpreted with the epigenome. This work describes a series of analyses of pharmacogenomics association studies with the tools and datasets of the epigenome and spatial genome, undertaken with the intent of discovering causative regulatory variants to enable new genetic tests. It describes the potent regulatory variants discovered thereby to have a putative causative and predictive role in a number of medically important phenotypes, including analgesia and the treatment of depression, bipolar disorder, and traumatic brain injury with opiates, anxiolytics, antidepressants, lithium, and valproate, and in particular the tendency for such variants to cluster into spatially interacting, conceptually unified pathways which offer mechanistic insight into these phenotypes. It describes the Pharmacoepigenomics Informatics Pipeline (PIP), an integrative multiple omics variant discovery pipeline designed to make this kind of analysis easier and cheaper to perform, more reproducible, and amenable to the addition of advanced features. It described the successes of the PIP in rediscovering manually discovered gene networks for lithium response, as well as discovering a previously unknown genetic basis for warfarin response in anticoagulation therapy. It describes the H-GREEN Hi-C compiler, which was designed to analyze spatial genome data and discover the distant target genes of such regulatory variants, and its success in discovering spatial contacts not detectable by preceding methods and using them to build spatial contact networks that unite disparate TADs with phenotypic relationships. It describes a potential featureset of a future pipeline, using the latest epigenome research and the lessons of the previous pipeline. It describes my thinking about how to use the output of a multiple omics variant pipeline to design genetic tests that also incorporate clinical data. And it concludes by describing a long term vision for a comprehensive pharmacophenomic atlas, to be constructed by applying a variant pipeline and machine learning test design system, such as is described, to thousands of phenotypes in parallel. Scientists struggled to assay genotypes for the better part of a century, and in the last twenty years, succeeded. The struggle to predict phenotypes on the basis of the genotypes we assay remains ongoing. The use of multiple omics variant pipelines and machine learning models with omics atlases, genetic association, and medical records data will be an increasingly significant part of that struggle for the foreseeable future.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145835/1/ariallyn_1.pd

Induced pluripotent stem cells for therapy personalization in pediatric patients: Focus on drug-induced adverse events

Adverse drug reactions (ADRs) are major clinical problems, particularly in special populations such as pediatric patients. Indeed, ADRs may be caused by a plethora of different drugs leading, in some cases, to hospitalization, disability or even death. In addition, pediatric patients may respond differently to drugs with respect to adults and may be prone to developing different kinds of ADRs, leading, in some cases, to more severe consequences. To improve the comprehension, and thus the prevention, of ADRs, the set-up of sensitive and personalized assays is urgently needed. Important progress is represented by the possibility of setting up groundbreaking patient-specific assays. This goal has been powerfully achieved using induced pluripotent stem cells (iPSCs). Due to their genetic and physiological species-specific differences and their ability to be differentiated ideally into all tissues of the human body, this model may be accurate in predicting drug toxicity, especially when this toxicity is related to individual genetic differences. This review is an up-to-date summary of the employment of iPSCs as a model to study ADRs, with particular attention to drugs used in the pediatric field. We especially focused on the intestinal, hepatic, pancreatic, renal, cardiac, and neuronal levels, also discussing progress in organoids creation. The latter are three-dimensional in vitro culture systems derived from pluripotent or adult stem cells simulating the architecture and functionality of native organs such as the intestine, liver, pancreas, kidney, heart, and brain. Based on the existing knowledge, these models are powerful and promising tools in multiple clinical applications including toxicity screening, disease modeling, personalized and regenerative medicine

Genome-wide Association Analysis Identifies 14 New Risk Loci for Schizophrenia

Schizophrenia is a heritable disorder with substantial public health impact. We conducted a multi-stage genome-wide association study (GWAS) for schizophrenia beginning with a Swedish national sample (5,001 cases, 6,243 controls) followed by meta-analysis with prior schizophrenia GWAS (8,832 cases, 12,067 controls) and finally by replication of SNPs in 168 genomic regions in independent samples (7,413 cases, 19,762 controls, and 581 trios). In total, 22 regions met genome-wide significance (14 novel and one previously implicated in bipolar disorder). The results strongly implicate calcium signaling in the etiology of schizophrenia, and include genome-wide significant results for CACNA1C and CACNB2 whose protein products interact. We estimate that ∼8,300 independent and predominantly common SNPs contribute to risk for schizophrenia and that these collectively account for most of its heritability. Common genetic variation plays an important role in the etiology of schizophrenia, and larger studies will allow more detailed understanding of this devastating disorder