Emerging efforts for discovering new biomarkers of liver disease and hepatotoxicity

Reconstitution of human tissue barrier function for precision and personalized medicine

Tissue barriers in a body, well known as tissue-to-tissue interfaces represented by endothelium of the blood vessels or epithelium of organs, are essential for maintaining physiological homeostasis by regulating molecular and cellular transports. It is crucial for predicting drug response to understand physiology of tissue barriers through which drugs are absorbed, distributed, metabolized and excreted. Since the FDA Modernization Act 2.0, which prompts the inception of alternative technologies for animal models, tissue barrier chips, one of the applications of organ-on-a-chip or microphysiological system (MPS), have only recently been utilized in the context of drug development. Recent advancements in stem cell technology have brightened the prospects for the application of tissue barrier chips in personalized medicine. In past decade, designing and engineering these microfluidic devices, and demonstrating the ability to reconstitute tissue functions were main focus of this field. However, the field is now advancing to the next level of challenges: validating their utility in drug evaluation and creating personalized models using patient-derived cells. In this review, we briefly introduce key design parameters to develop functional tissue barrier chip, explore the remarkable recent progress in the field of tissue barrier chips and discuss future perspectives on realizing personalized medicine through the utilization of tissue barrier chips.

A proteomic approach of biomarker candidate discovery for alcoholic liver cirrhosis

Alcoholic liver disease (ALD) progresses from steatosis to alcoholic hepatitis to fibrosis and cirrhosis. Liver biopsy is considered as the gold standard method for diagnosis of liver cirrhosis and provides useful information about damaging process which is an invasive procedure with complications. Existing biomarkers in clinical practice have narrow applicability due to lack of specificity and lack of sensitivity. The objective of this article is to identify proteomic biomarker candidates for alcoholic liver cirrhosis by differential expression analysis between alcoholic liver cirrhotic and healthy subjects. Blood samples were collected from 20 subjects (10 alcoholic liver cirrhosis and 10 healthy) from R. L. Jalapa Hospital and Research Centre, Kolar, Karnataka, India. Differential protein analysis was carried out by two-dimensional electrophoresis after albumin depletion, followed by liquid chromatography–mass spectrometry. The image analysis found 46 spots in cirrhotic gel and 69 spots in healthy gel, of which 14 spots were identified with significant altered expression levels. Based on the protein score and clinical significance, among 14 spots, a total of 28 protein biomarker candidates were identified: 13 with increased expression and 15 with decreased expression were categorized in alcoholic liver cirrhosis compared to healthy subjects. Protein biomarker candidates identified by “-omics” approach based on differential expression between alcoholic liver cirrhotic subjects and healthy subjects may give better insights for diagnosis of ALD. Prioritization of candidates identified is a prerequisite for validation regimen. Biomarker candidates require verification that demonstrates the differential expression will remain detectable by assay to be used for validation.

Evolution of proteomic biomarker for chronic liver disease: Promise into reality

Liver is the vital organ for synthesis of proteins whose concentration in blood reflects liver dysfunction. Variations in protein domain can generate clinically significant biomarkers. Biomarker pipeline includes discovery of candidates, qualification, verification, assay optimization, and validation. Advances in proteomic approach can discover protein biomarker candidates based on “up-or-down” regulation or fold change in expression which is correlated with disease state. Despite numerous biomarker candidates been discovered, only few are useful in clinical practice which indicates the need for well-established validation regimen. Hence, the main purpose of this review is to understand the protein biomarker development and pitfalls. Companion diagnostics provide insights into potential cost-effective diagnosis for chronic liver disease.

Metabolomic approaches in the discovery of potential urinary biomarkers of drug-induced liver injury (DILI)

Drug-induced liver injury (DILI) is a major safety issue during drug development, as well as the most common cause for the withdrawal of drugs from the pharmaceutical market. The identification of DILI biomarkers is a labor-intensive area. Conventional biomarkers are not specific and often only appear at significant levels when liver damage is substantial. Therefore, new biomarkers for early identification of hepatotoxicity during the drug discovery process are needed, thus resulting in lower development costs and safer drugs. In this sense, metabolomics has been increasingly playing an important role in the discovery of biomarkers of liver damage, although the characterization of the mechanisms of toxicity induced by xenobiotics remains a huge challenge. These new-generation biomarkers will offer obvious benefits for the pharmaceutical industry, regulatory agencies, as well as a personalized clinical follow-up of patients, upon validation and translation into clinical practice or approval for routine use. This review describes the current status of the metabolomics applied to the early diagnosis and prognosis of DILI and in the discovery of new potential urinary biomarkers of liver injury.

Concurrent Inflammation Augments Antimalarial Drugs-Induced Liver Injury in Rats

Purpose: Accumulating evidence suggests that drug exposure during a modest inflammation induced by bacterial lipopolysaccharide (LPS) might increase the risk of drug-induced liver injury. The current investigation was designed to test if antimalarial drugs hepatotoxicity is augmented in LPS‑treated animals. Methods: Rats were pre-treated with LPS (100 µg/kg, i.p). Afterward, non-hepatotoxic doses of amodiaquine (25, 50 and 100 mg/kg, oral) and chloroquine (25, 50 and 100 mg/kg, oral) were administered. Results: Interestingly, liver injury was evident only in animals treated with both drug and LPS as estimated by pathological changes in serum biochemistry (ALT, AST, LDH, and TNF-α), and liver tissue (severe hepatitis, endotheliitis, and sinusoidal congestion). An increase in liver myeloperoxidase enzyme activity, lipid peroxidation, and protein carbonylation, along with tissue glutathione depletion were also detected in LPS and drug co-treated animals. Conclusion: Antimalarial drugs rendered hepatotoxic in animals undergoing a modest inflammation. These results indicate a synergistic liver injury from co-exposure to antimalarial drugs and inflammation.

Alignment of Short Reads: A Crucial Step for Application of Next-Generation Sequencing Data in Precision Medicine

Precision medicine or personalized medicine has been proposed as a modernized and promising medical strategy. Genetic variants of patients are the key information for implementation of precision medicine. Next-generation sequencing (NGS) is an emerging technology for deciphering genetic variants. Alignment of raw reads to a reference genome is one of the key steps in NGS data analysis. Many algorithms have been developed for alignment of short read sequences since 2008. Users have to make a decision on which alignment algorithm to use in their studies. Selection of the right alignment algorithm determines not only the alignment algorithm but also the set of suitable parameters to be used by the algorithm. Understanding these algorithms helps in selecting the appropriate alignment algorithm for different applications in precision medicine. Here, we review current available algorithms and their major strategies such as seed-and-extend and q-gram filter. We also discuss the challenges in current alignment algorithms, including alignment in multiple repeated regions, long reads alignment and alignment facilitated with known genetic variants.

Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science

Improved biomarker-based assessment of drug safety is needed in drug discovery and development as well as regulatory evaluation. However, identifying drug safety-related biomarkers such as genes, proteins, miRNA and single-nucleotide polymorphisms remains a big challenge. The advances of 'omics' and computational technologies such as genomics, transcriptomics, metabolomics, proteomics, systems biology, network biology and systems pharmacology enable us to explore drug actions at the organ and organismal levels. Computational and experimental systems pharmacology approaches could be utilized to facilitate biomarker-based drug safety assessment for drug discovery and development and to inform better regulatory decisions. In this article, we review the current status and advances of systems pharmacology approaches for the development of predictive models to identify biomarkers for drug safety assessment.

Influence of ethnicity on the distribution of genetic polymorphisms associated with risk of chronic liver disease in South American populations

BACKGROUND

The global burden of chronic liver disease is rising. Besides environmental, behavioral, viral and metabolic factors, genetic polymorphisms in patatin-like phospholipase-3 (PNPLA3) and vitamin D receptor (VDR) genes have been related to the development of chronic liver disease and progression towards liver cancer. Although their prevalence differs remarkably among ethnic groups, the frequency of these polymorphisms in South American populations -whose genetic background is highly admixed- has been poorly studied. Hence, the aim of this study was to characterize polymorphisms related to chronic liver disease and their association with the genetic ancestry of South American populations.

RESULTS

DNA samples from 258 healthy unrelated male volunteers were analyzed. The frequencies of G and C alleles of rs738409 polymorphism (PNPLA3 gene) were 74 % and 26 %, respectively; whereas the bAt (CCA) haplotype (VDR gene) was observed in 32.5 % of the samples. The GG genotype of PNPLA3 rs738409 and the bAt (CCA) haplotype -associated with an increased risk of chronic liver disease and progression towards liver cancer- were significantly more frequent among samples exhibiting maternal and paternal Native American haplogroups (63.7 % and 64.6 %), intermediate among admixed samples (45.1 % and 44.9 %; p = 0.03) and the lowest for Non-native American ancestry (30.1 % and 29.6 %; p = 0.001 and p = 0.0008).

CONCLUSIONS

These results suggest that individuals with Native American ancestry might have a high risk of chronic liver disorders and cancer. Furthermore, these data not only support the molecular evaluation of ancestry in multi-ethnic population studies, but also suggest that the characterization of these variants in South American populations may be useful for establishing public health policies aimed at high risk ethnic communities.

Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure

The U.S. Tox21 and EPA ToxCast program screen thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors, then used supervised machine learning to predict in vivo hepatotoxic effects. A set of 677 chemicals was represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PaDEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector machines (SVM), classification and regression trees (CART), k-nearest neighbors (KNN), and an ensemble of these classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure descriptors, ToxCast bioactivity descriptors, and hybrid descriptors. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.84 ± 0.08), injury (0.80 ± 0.09), and proliferative lesions (0.80 ± 0.10). Though chemical and bioactivity classifiers had a similar balanced accuracy, the former were more sensitive, and the latter were more specific. CART, ENSMB, and SVM classifiers performed the best, and nuclear receptor activation and mitochondrial functions were frequently found in highly predictive classifiers of hepatotoxicity. ToxCast and ToxRefDB provide the largest and richest publicly available data sets for mining linkages between the in vitro bioactivity of environmental chemicals and their adverse histopathological outcomes. Our findings demonstrate the utility of high-throughput assays for characterizing rodent hepatotoxicants, the benefit of using hybrid representations that integrate bioactivity and chemical structure, and the need for objective evaluation of classification performance.

Screening and verification of linearly dependent biomarkers with acute toxicity induced by Aconiti Radix based on liquid chromatography-mass spectrometry-based metabolite profiling

We built a method that has three parts: first is to screen the biomarkers with metabolic profiling analysis; second is to determine the linear dependence with acute toxicity biomarkers; third is to validate the biomarkers with different Aconiti Radix involved medicine.