Predicting oncology drug-induced cardiotoxicity with donor-specific iPSC-CMs-a proof-of-concept study with doxorubicin.

Many oncology drugs have been found to induce cardiotoxicity in a subset of patients, which significantly limits their clinical use and impedes the benefit of lifesaving anticancer treatments. Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) carry donor-specific genetic information and have been proposed for exploring the interindividual difference in oncology drug-induced cardiotoxicity. Herein, we evaluated the inter- and intraindividual variability of iPSC-CM-related assays and presented a proof of concept to prospectively predict doxorubicin (DOX)-induced cardiotoxicity (DIC) using donor-specific iPSC-CMs. Our findings demonstrated that donor-specific iPSC-CMs exhibited greater line-to-line variability than the intraindividual variability in impedance cytotoxicity and transcriptome assays. The variable and dose-dependent cytotoxic responses of iPSC-CMs resembled those observed in clinical practice and largely replicated the reported mechanisms. By categorizing iPSC-CMs into resistant and sensitive cell lines based on their time- and concentration-related phenotypic responses to DOX, we found that the sensitivity of donor-specific iPSC-CMs to DOX may predict in vivo DIC risk. Furthermore, we identified a differentially expressed gene, DND microRNA-mediated repression inhibitor 1 (DND1), between the DOX-resistant and DOX-sensitive iPSC-CMs. Our results support the utilization of donor-specific iPSC-CMs in assessing interindividual differences in DIC. Further studies will encompass a large panel of donor-specific iPSC-CMs to identify potential novel molecular and genetic biomarkers for predicting DOX and other oncology drug-induced cardiotoxicity.

US FDA public meeting: identification of concepts and terminology for multicomponent biomarkers.

The US FDA convened a virtual public workshop with the goals of obtaining feedback on the terminology needed for effective communication of multicomponent biomarkers and discussing the diverse use of biomarkers observed across the FDA and identifying common issues. The workshop included keynote and background presentations addressing the stated goals, followed by a series of case studies highlighting FDA-wide and external experience regarding the use of multicomponent biomarkers, which provided context for panel discussions focused on common themes, challenges and preferred terminology. The final panel discussion integrated the main concepts from the keynote, background presentations and case studies, laying a preliminary foundation to build consensus around the use and terminology of multicomponent biomarkers.

Metabolomics as a Truly Translational Tool for Precision Medicine.

Metabolomics is unique among omics technologies in being applicable to metabolism and toxicity studies broadly across organisms (e.g., humans, other mammals, model organisms, and even bacteria) and across biological materials (e.g., blood, urine, saliva, biopsy, and stool), including cultured cells and subcellular fractions. Metabolomics can be used to characterize biologic response patterns in humans as well as to support mechanistic studies in model systems and ex vivo studies. A broad range of resources are available, including publicly accessible data repositories (e.g., Metabolomics Workbench), tools for biostatistics and bioinformatics (e.g., MetaboAnalyst), metabolite identification (e.g., Metlin), and pathway analysis (e.g., Kyoto Encyclopedia of Genes and Genomes). Thus, metabolomics is more than a promise of the future; metabolomics is already available as a translational approach to facilitate precision medicine. This ACT Symposium review will contain an introduction to metabolomics in toxicity studies followed by sections on translational metabolic networks, translational metabolite biomarkers of acetaminophen-induced acute liver injury, translational framework using high-resolution metabolomics for integrated pharmacokinetics and pharmacodynamics, and precision medicine applications: extracting actionable targets from untargeted metabolomics data following one year in space.

Nitrosative Stress and Lipid Homeostasis as a Mechanism for Zileuton Hepatotoxicity and Resistance in Genetically Sensitive Mice.

Zileuton is an orally active inhibitor of leukotriene synthesis for maintenance treatment of asthma, for which clinical usage has been associated with idiosyncratic liver injury. Mechanistic understanding of zileuton toxicity is hampered by the rarity of the cases and lack of an animal model. A promising model for mechanistic study of rare liver injury is the Diversity Outbred (J:DO) mouse population, with genetic variation similar to that found in humans. In this study, female DO mice were administered zileuton or vehicle daily for 7 days (i.g.). Serum liver enzymes were elevated in the zileuton group, with marked interindividual variability in response. Zileuton exposure-induced findings in susceptible DO mice included microvesicular fatty change, hepatocellular mitosis, and hepatocellular necrosis. Inducible nitric oxide synthase and nitrotyrosine abundance were increased in livers of animals with necrosis and those with fatty change, implicating nitrosative stress as a possible injury mechanism. Conversely, DO mice lacking adverse liver pathology following zileuton exposure experienced decreased hepatic concentrations of resistin and increased concentrations of insulin and leptin, providing potential clues into mechanisms of toxicity resistance. Transcriptome pathway analysis highlighted mitochondrial dysfunction and altered fatty acid oxidation as key molecular perturbations associated with zileuton exposure, and suggested that interindividual differences in cytochrome P450 metabolism, glutathione-mediated detoxification, and farnesoid X receptor signaling may contribute to zileuton-induced liver injury (ZILI). Taken together, DO mice provided a platform for investigating mechanisms of toxicity and resistance in context of ZILI which may lead to targeted therapeutic interventions.

Determination of structural factors affecting binding to mu, kappa and delta opioid receptors.

Addiction is a complex behavioral phenomenon in which naturally occurring or synthetic chemicals modulate the response of the reward system through their binding to a variety of neuroreceptors, resulting in compulsive substance-seeking and use despite harmful consequences to the individual. Among these, the opioid receptor (OR) family and more specifically, the mu-opioid receptor (MOR) subtype plays a critical role in the addiction to powerful prescription and illicit drugs such as hydrocodone, oxycodone, fentanyl, cocaine, and methamphetamine (Contet et al. in Curr Opin Neurobiol 14(3):370-378, 2004). Conversely, agonists binding to kappa (KOR) and antagonists binding to delta opioid receptors (DOR) have been reported to induce negative reinforcing effects. As more than 700 new psychoactive substances were illegally sold between 2009 and 2016 (DEA-DCT-DIR-032-18), most of them lacking basic toxicological and pharmacological profiles, molecular modeling approaches that could quickly and reliably fill the gaps in our knowledge would be highly desirable tools for determining the effects of these synthetics. Here, we report accurate 3D-spectrometric data-activity relationship classification models for large and diverse datasets of MOR, KOR and DOR binders with areas under the receiver operating characteristic curve for the "blind" prediction sets exceeding 0.88. Structural features associated with (selective) binding to MOR, KOR and/or DOR were identified. These models could assist regulatory agencies in evaluating the health risks associated with the use of unprofiled substances as well as to help the pharmaceutical industry in its search for new drugs to combat addiction.

Recent advances in understanding the hepatotoxicity associated with protein kinase inhibitors.

Introduction: As of October 2019, the U.S. Food and Drug Administration (FDA) has approved 53 small molecule kinase inhibitors (KI), which account for about 10% of all FDA-approved new molecular entities and new biologics in the past two decades. Yet, hepatotoxicity is a major safety concern with KIs, as reflected by 35 KIs having warnings for liver injury in drug labeling, among which seven are boxed warnings. In spite of that, KI hepatotoxicity remains a relatively under-investigated area.Areas covered: This review aims to summarize recent advances in the study of KI hepatotoxicity including the definition, mechanisms, and predictors of KI hepatotoxicity. Data sources include PubMed, LiverTox and the FDA official website.Expert opinion: The hepatotoxicity potential of many KIs has not yet been fully established and therefore the predictive power of in vitro or in silico models cannot be accurately assessed at present. Two KIs accumulated in the liver at concentrations of 10- to 25-fold blood levels, highlighting the importance of normalizing the test concentrations in in vitro models to tissue but blood levels. Pluripotent stem cell-derived hepatocyte-like cells and genotyping of leucocyte antigen (HLA) showed early promise in identifying the individuals who were highly susceptible to KI hepatotoxicity and warrant further investigation.

Dr. Daniel Acosta and In Vitro toxicology at the U.S. Food and Drug Administration's National Center for Toxicological Research.

For the past five years, Dr. Daniel Acosta has served as the Deputy Director of Research at the National Center for Toxicological Research (NCTR), a principle research laboratory of the U.S. Food and Drug Administration (FDA). Over his career at NCTR, Dr. Acosta has had a major impact on developing and promoting the use of in vitro assays in regulatory toxicity and product safety assessments. As Dr. Acosta nears his retirement we have dedicated this paper to his many accomplishments at the NCTR. Described within this paper are some of the in vitro studies that have been conducted under Dr. Acosta's leadership. These studies include toxicological assessments involving developmental effects, and the development and application of in vitro reproductive, heart, liver, neurological and airway cell and tissue models.

Comprehensive Identification and Characterization of Human Secretome Based on Integrative Proteomic and Transcriptomic Data.

Secreted proteins (SPs) play important roles in diverse important biological processes; however, a comprehensive and high-quality list of human SPs is still lacking. Here we identified 6,943 high-confidence human SPs (3,522 of them are novel) based on 330,427 human proteins derived from databases of UniProt, Ensembl, AceView, and RefSeq. Notably, 6,267 of 6,943 (90.3%) SPs have the supporting evidences from a large amount of mass spectrometry (MS) and RNA-seq data. We found that the SPs were broadly expressed in diverse tissues as well as human body fluid, and a significant portion of them exhibited tissue-specific expression. Moreover, 14 cancer-specific SPs that their expression levels were significantly associated with the patients' survival of eight different tumors were identified, which could be potential prognostic biomarkers. Strikingly, 89.21% of 6,943 SPs (2,927 novel SPs) contain known protein domains. Those novel SPs we mainly enriched with the known domains regarding immunity, such as Immunoglobulin V-set and C1-set domain. Specifically, we constructed a user-friendly and freely accessible database, SPRomeDB (www.unimd.org/SPRomeDB), to catalog those SPs. Our comprehensive SP identification and characterization gain insights into human secretome and provide valuable resource for future researches.

Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation.

A grid-based, alignment-independent 3D-SDAR (three-dimensional spectral data-activity relationship) approach based on simulated 13C and 15N NMR chemical shifts augmented with through-space interatomic distances was used to model the mutagenicity of 554 primary and 419 secondary aromatic amines. A robust modeling strategy supported by extensive validation including randomized training/hold-out test set pairs, validation sets, "blind" external test sets as well as experimental validation was applied to avoid over-parameterization and build Organization for Economic Cooperation and Development (OECD 2004) compliant models. Based on an experimental validation set of 23 chemicals tested in a two-strain Salmonella typhimurium Ames assay, 3D-SDAR was able to achieve performance comparable to 5-strain (Ames) predictions by Lhasa Limited's Derek and Sarah Nexus for the same set. Furthermore, mapping of the most frequently occurring bins on the primary and secondary aromatic amine structures allowed the identification of molecular features that were associated either positively or negatively with mutagenicity. Prominent structural features found to enhance the mutagenic potential included: nitrobenzene moieties, conjugated π-systems, nitrothiophene groups, and aromatic hydroxylamine moieties. 3D-SDAR was also able to capture "true" negative contributions that are particularly difficult to detect through alternative methods. These include sulphonamide, acetamide, and other functional groups, which not only lack contributions to the overall mutagenic potential, but are known to actively lower it, if present in the chemical structures of what otherwise would be potential mutagens.

Cytotoxicity of 34 FDA approved small-molecule kinase inhibitors in primary rat and human hepatocytes.

Of the 34 FDA approved oral small-molecule kinase inhibitors (KI), 23 (68%) have warnings for hepatotoxicity in product labeling. To better understand the mechanisms of KI hepatotoxicity and whether such effects can be predicted, we examined 34 KIs for cytotoxicity in primary rat and human hepatocytes. The hepatocytes were treated with KIs at ten concentrations normalized to maximal therapeutic blood levels (Cmax). At 5 and 24 h post treatment, lactate dehydrogenase or alanine aminotransferase leakage, caspase 3/7 activities and cellular adenosine triphosphate levels were measured. At 1 to 100-fold Cmax, while 5 KIs were neither toxic to human nor rat hepatocytes, 3 KIs showed similar cytotoxicity in both species and 26 KIs showed species-biased cytotoxicity, with 16 KIs being more toxic to human hepatocytes and 10 KIs being more toxic to rat hepatocytes. At concentrations of 1-, 2.5-, 5-, 10-, 100-fold Cmax, the number of cytotoxic KIs in human hepatocytes was 4, 8, 11, 14 and 27, respectively, and the corresponding number in rat hepatocytes was 1, 4, 9, 12 and 27, respectively. When hepatocyte cytotoxicity at 100-fold Cmax was used to predict KI clinical hepatotoxicity reflected in product labeling, the accuracy was 0.65 with human hepatocytes and 0.59 with rat cells. When the criterion of daily dose ≥100 mg or Cmax ≥1.1 µM was used to predict KI hepatotoxicity, the accuracy was 0.56 or 0.47, respectively. These results suggest both indirect and direct drug-induced hepatocyte toxicity may contribute to the mechanisms of KI-induced hepatotoxicity seen clinically and use of primary hepatocytes is a useful in vitro model to help predict such toxicity.

Multiple microRNAs function as self-protective modules in acetaminophen-induced hepatotoxicity in humans.

Acetaminophen (APAP) overdose is the leading cause of acute liver failure. Yet the mechanisms underlying adaptive tolerance toward APAP-induced liver injury are not fully understood. To better understand molecular mechanisms contributing to adaptive tolerance to APAP is an underpinning foundation for APAP-related precision medicine. In the current study, the mRNA and microRNA (miRNA) expression profiles derived from next generation sequencing data for APAP-treated (5 and 10 mM) HepaRG cells and controls were analyzed systematically. Putative miRNAs targeting key dysregulated genes involved in APAP hepatotoxicity were selected using in silico prediction algorithms, un-biased gene ontology, and network analyses. Luciferase reporter assays, RNA electrophoresis mobility shift assays, and miRNA pull-down assays were performed to investigate the role of miRNAs affecting the expression of dysregulated genes. Levels of selected miRNAs were measured in serum samples obtained from children with APAP overdose (58.6-559.4 mg/kg) and from healthy controls. As results, 2758 differentially expressed genes and 47 miRNAs were identified. Four of these miRNAs (hsa-miR-224-5p, hsa-miR-320a, hsa-miR-449a, and hsa-miR-877-5p) suppressed drug metabolizing enzyme (DME) levels involved in APAP-induced liver injury by downregulating HNF1A, HNF4A and NR1I2 expression. Exogenous transfection of these miRNAs into HepaRG cells effectively rescued them from APAP toxicity, as indicated by decreased alanine aminotransferase levels. Importantly, hsa-miR-320a and hsa-miR-877-5p levels were significantly elevated in serum samples obtained from children with APAP overdose compared to health controls. Collectively, these data indicate that hsa-miR-224-5p, hsa-miR-320a, hsa-miR-449a, and hsa-miR-877-5p suppress DME expression involved in APAP-induced hepatotoxicity and they contribute to an adaptive response in hepatocytes.

Regulatory landscapes for biomarkers and diagnostic tests: Qualification, approval, and role in clinical practice.

While the term 'biomarker' is relatively new, the concept is millennia old. However, with the introduction of new technologies to discover potential biomarkers comes the need to assess their utility and veracity for any given use. This is particularly true for the use of biomarkers to support regulatory decisions in medical product development. Hence the US Food and Drug Administration has developed processes for the qualification of biomarkers and other medical product development tools, processes that are underscored by recent legislation (i.e. the 21st Century Cures Act). In addition to these qualification processes, diagnostic tests that measure a biomarker may follow a process for regulatory decision through the processes that evaluate companion diagnostics. This mini-review provides an overview of these processes and their role in pharmaceutical development and clinical use. Impact statement This work summarizes very recent developments in the US FDA's biomarker qualification program. Furthermore, it contrasts biomarker qualification with companion diagnostic evaluation. As such, it will be highly informative for researches considering taking a biomarker discovery farther along the road to validation.

Spatial and Ontogenetic Variation in Mercury in Lake Superior Basin Sea Lamprey (Petromyzon marinus).

Mercury concentrations were measured in eggs, larvae, and adult spawning-phase sea lampreys (Petromyzon marinus) collected in tributaries of Lake Superior to investigate spatial and ontogenetic variation. There were significant differences in mercury concentrations between all three life stages, with levels highest in adults (mean = 3.01 µg/g), followed by eggs (mean = 0.942 µg/g), and lowest in larvae (mean = 0.455 µg/g). There were no significant differences in mercury concentrations by location for any life stage or by sex in adults. Mercury was not correlated with adult or larval lamprey length or mass. Mercury levels in adult lampreys exceeded U.S. and Canadian federal guidelines for human consumption. Mercury concentrations in all life stages exceeded criteria for the protection of piscivorous wildlife, posing a threat to local fish, birds, and mammals. High mercury levels in adult lampreys combined with their semelparous life history make them a potential source of lake-derived mercury to spawning streams.

Effects of 31 FDA approved small-molecule kinase inhibitors on isolated rat liver mitochondria.

The FDA has approved 31 small-molecule kinase inhibitors (KIs) for human use as of November 2016, with six having black box warnings for hepatotoxicity (BBW-H) in product labeling. The precise mechanisms and risk factors for KI-induced hepatotoxicity are poorly understood. Here, the 31 KIs were tested in isolated rat liver mitochondria, an in vitro system recently proposed to be a useful tool to predict drug-induced hepatotoxicity in humans. The KIs were incubated with mitochondria or submitochondrial particles at concentrations ranging from therapeutic maximal blood concentrations (Cmax) levels to 100-fold Cmax levels. Ten endpoints were measured, including oxygen consumption rate, inner membrane potential, cytochrome c release, swelling, reactive oxygen species, and individual respiratory chain complex (I-V) activities. Of the 31 KIs examined only three including sorafenib, regorafenib and pazopanib, all of which are hepatotoxic, caused significant mitochondrial toxicity at concentrations equal to the Cmax, indicating that mitochondrial toxicity likely contributes to the pathogenesis of hepatotoxicity associated with these KIs. At concentrations equal to 100-fold Cmax, 18 KIs were found to be toxic to mitochondria, and among six KIs with BBW-H, mitochondrial injury was induced by regorafenib, lapatinib, idelalisib, and pazopanib, but not ponatinib, or sunitinib. Mitochondrial liability at 100-fold Cmax had a positive predictive power (PPV) of 72% and negative predictive power (NPV) of 33% in predicting human KI hepatotoxicity as defined by product labeling, with the sensitivity and specificity being 62% and 44%, respectively. Similar predictive power was obtained using the criterion of Cmax ≥1.1 µM or daily dose ≥100 mg. Mitochondrial liability at 1-2.5-fold Cmax showed a 100% PPV and specificity, though the NPV and sensitivity were 32% and 14%, respectively. These data provide novel mechanistic insights into KI hepatotoxicity and indicate that mitochondrial toxicity at therapeutic levels can help identify hepatotoxic KIs.

Molecular Docking for Identification of Potential Targets for Drug Repurposing.

Using existing drugs for new indications (drug repurposing) is an effective method not only to reduce drug development time and costs but also to develop treatments for new disease including those that are rare. In order to discover novel indications, potential target identification is a necessary step. One widely used method to identify potential targets is through molecule docking. It requires no prior information except structure inputs from both the drug and the target, and can identify potential targets for a given drug, or identify potential drugs for a specific target. Though molecular docking is popular for drug development and repurposing, challenges remain for the method. In order to improve the prediction accuracy, optimizing the target conformation, considering the solvents and adding cobinders to the system are possible solutions.

The Promise of New Technologies to Reduce, Refine, or Replace Animal Use while Reducing Risks of Drug Induced Liver Injury in Pharmaceutical Development.

Drug induced liver injury (DILI) has contributed more to marketed pharmaceutical withdrawals and clinical development failures than any other human organ toxicity. DILI seen in animal studies also frequently leads to the discontinuation of promising drug candidates very early in the pipeline. This manuscript reviews and critically assesses the current regulatory expectations; the current drug development approaches, strategies, and gaps; and the numerous exciting opportunities becoming available to address these gaps through technological advances. Emerging integrated pharmaceutical development strategies, while far from uniform, have generally evolved to currently inform early DILI risk potential using supplemental assays for reactive metabolite formation, mitochondrial toxicity, inhibition of bile salt transport, and cellular imaging endpoints including cytotoxicity. Despite these approaches and robust animal testing, significant gaps in addressing human DILI remain. Increasingly sophisticated in vitro humanized test systems, new animal models, emerging computational models, and novel translational biomarkers are being introduced to improve our ability to more accurately predict DILI. Expectations are high for a future state with more predictive tools and problem solving strategies that will improve pharmaceutical discovery and development in relation to understanding human DILI risk potential and make it less dependent on animal studies for successfully developing safer drug candidates.

Circulating mitochondrial biomarkers for drug-induced liver injury.

Liver mitochondria affected by drugs can be released into circulation and serve as biomarkers for drug-induced liver injury (DILI). The tissue specificity of ALT was improved by differentiating cytosolic ALT1 and mitochondrial ALT2 isoforms released in circulation. Prior to ALT elevation, mitochondrial cytochrome c, OCT, GLDH, CPS1 and DNA were increased in circulation following DILI. The baseline expression of mt-Nd6 was predictive of individual DILI susceptibility in animals. As mitochondrial DILI biomarkers appeared to be drug or species dependent, they might have value in clinical scenarios when culprit drugs are established, but may not be ideal tools to assess DILI potentials of new drugs.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis.

BACKGROUND: As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. METHODS: Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. RESULTS: Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. CONCLUSIONS: Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

microRNAs as pharmacogenomic biomarkers for drug efficacy and drug safety assessment.

Much evidence has documented that microRNAs (miRNAs) play an important role in the modulation of interindividual variability in the production of drug metabolizing enzymes and transporters (DMETs) and nuclear receptors (NRs) through multidirectional interactions involving environmental stimuli/stressors, the expression of miRNA molecules and genetic polymorphisms. MiRNA expression has been reported to be affected by drugs and miRNAs themselves may affect drug metabolism and toxicity. In cancer research, miRNA biomarkers have been identified to mediate intrinsic and acquired resistance to cancer therapies. In drug safety assessment, miRNAs have been found associated with cardiotoxicity, hepatotoxicity and nephrotoxicity. This review article summarizes published studies to show that miRNAs can serve as early biomarkers for the evaluation of drug efficacy and drug safety.