Given the complexity of DILI and the length of time it has been an active field of research, it comes as no surprise that each research group has tackled the problem differently,
according to their area of expertise and availability of resources. Many methodological approaches have been utilised in the study of iDILI, ranging from in vitro studies on cell lines (6) to the use of microfluidic liver biochips (85), in vivo animal models (e.g. zebra-fish (86) and rabbits (87)), bioinformatics approaches (88) and epidemiological approaches (89) (Figure 1.4).
Figure 1.4 Idiosyncratic Drug Induced Liver Injury (iDILI) has been studied through a multitude of methodologies, yet many mysteries remain regarding the mechanisms of toxicity and the bioloical targets and pathways involved. Part of the figure sourced from http://www.clker.com/; http://en.wikipedia.org/.
14 While in vitro and in vivo models of iDILI exist, they are far from perfect (90). Unless models are able to capture and account for the large number of risk factors and proteins associated with iDILI, it is likely that a generalizable, predictive model of iDILI will remain elusive.
Hartung recently described the frustrations associated with translating in vitro and animal studies into humans in his Food for Thought article aptly titled: Look Back in Anger – What Clinical Studies Tell Us About Preclinical Work (91). It is this disparity in results between preclinical and clinical studies, and indeed between clinical trials and post-marketing surveillance, that is responsible for the billions of dollars ‘wasted’ in pharmaceutical research and development. With the deficiencies in in vivo models, researchers are aiming at reducing reliance on animal models, moving instead towards in silico screening and the use of bioinformatics tools (91,92).
1.5.1.1 In vitro methods
In vitro assays have long been established as efficient and cost effective alternatives to in vivo toxicological studies. Typically, the reaction of interest is studied under controlled conditions, and various factors are varied in a systematic manner to determine their influence on the reaction.
While in vitro methods, particularly high throughput assays, are commonly used to
investigate iDILI, debates ensue over the suitability of different cell types for these kinds of studies (93). The three most commonly used cell lines are primary hepatocytes,
immortalised cell lines and hepatocellular carcinoma cell lines. Primary hepatocytes are thought to be the gold standard, as they are the closest representation of the human liver out of the three options. However, primary hepatocytes are short lived, hard to culture and difficult to obtain since sacrificing the animal is required. Another disadvantage of human primary hepatocytes is a lack of reproducibility across various donors. Moreover, the health of the donors can affect results. HepG2 (liver hepatocellular carcinoma) is a commonly used hepatocellular cell line used in toxicological studies. While these cells are relatively
inexpensive and easy to obtain, they have a low expression of phase I enzymes, and as such, may not accurately reflect the situation in vivo, especially if metabolic activation is required.
15 testing (94).
To overcome some of the problems associated with using single cell lines, researchers have attempted co-culture models, where two or more cell lines are cultured simultaneously.
Atienzar et al. developed a co-culture approach where dog primary hepatocytes were uniformly dispersed with stromal cells. Cell viability and glutathione concentrations were used to quantify the level of toxicity. The resultant co-culture system maintained metabolic capabilities for up to two weeks, which they suggest is sufficient for use in long term
metabolism studies (95).
Liver cells have a particular architecture that facilitates their role in detoxification. The move towards 3D models of liver cells comes amid increasing recognition that simple toxicological assays using single cell lines or even a co-culture of cells are unable to replicate in vivo toxicity (96). Since iDILI is influenced by a multitude of risk factors, a working model must be able to capture and distil the complexity of the various pathways involved in iDILI.
As a step towards this, Ramaigari et al. developed a liver 3D model where HepG2 carcinoma cells were organised into multiple polarised spheroids. HepG2 cells in spheroid form
regained some metabolic capability, as well as other functions such as storage of glycogen and transportation of bile salts. This increase in functionality appeared to translate into increased sensitivity in DILI screening (97). Similarly, Aritomi et al. found that although conventional cultures of HepG2 cells were unsuitable for modelling paracetamol DILI, 3D cultures of HepG2 cells in nano-culture plates were able to replicate key features of paracetamol DILI, including the creation of reactive metabolites (98).
Adding another level of complexity, newer 3D models allow cells to aggregate and develop structures and communication networks. Bhushan et al. created a 3D co-culture of cells that effectively mimics a functional unit of the human liver. This liver-on-a-chip technology comprises of 4 different cell types: hepatocytes, endothelial, stellate and Kupffer cells.
Preliminary work to prepare the model for use in early DILI detection appears to be
promising (99). Similarly, Larson et al. also reports the use of liver microtissues for long term toxicity studies (100).
16 which focus on a single reaction and towards big picture approaches which better reflect the complexity of in vivo systems.
1.5.1.2 In vivo methods
One frustrating aspect of iDILI is the inability to reproduce the human manifestations of toxicity in animal models. In part, this may be due to differences between the test animal and humans, but it is also likely that, just as in humans, iDILI is rare and idiosyncratic in animals - i.e. host related susceptibility factors are required to enhance the intrinsic toxicity of a drug that will otherwise pass unnoticed. Knowledge of the susceptibility factors,
proteins and pathways involved in the toxicity will be critical to developing an animal model that can replicate iDILI in humans (13).
There are many difficulties with animal studies, including poor reproducibility and
difficulties translating the results into humans or even between species (91). Traditionally, regulatory sciences have used high doses in animals to test for toxicity. This approach may be of limited benefit for toxicity which does not appear to be dose dependent.
One method by which researchers have attempted to improve translatability between animals and humans is to make use of chimeric rodents with humanized livers. These are created by transplanting human hepatocytes into immunocompromised mice. The metabolic capabilities of these models are similar to that in humans, and so have been utilised in a number of pharmacological studies, as well as in the study of liver infections (101). However, chimeric mice failed to reproduce the liver injury caused by troglitazone, which was withdrawn due to iDILI. This may be due to the lack of B- and T- lymphocytes in chimeric mice, and hence their inability to model immune-mediated DILI (102).
Given the limitations of animal models and with continual improvements in technology, the FDA is looking to replace animal studies with in silico and in vitro toxicity assays in the near future (92).
17 Rapid advancements in computing technology and capacity have seen many researchers turn to in silico modelling as a cost effective alternative to in vitro and in vivo studies. For DILI, computational modelling has proven to be very versatile, with applications ranging from developing structural alerts for identifying hepatotoxic drugs by using the formation of reactive metabolites (49) to Quantitative Structure Toxicity Studies and mathematical modelling of mechanistic interactions between compounds and the liver (103). In addition, in silico technologies are often used in conjunction with bioinformatics and systems
approaches in order to make sense of the large volumes of data generated by these techniques.
One disadvantage with in silico methodologies is that when hypotheses are generated, there is still a need to test the hypothesis with in vitro or in vivo assays. Hence in silico
technologies are most helpfully applied to preliminary screenings or in a setting where the technology has already been validated by another method.
1.5.2 Bioinformatics approaches
Bioinformatics or so-called ‘omics’ approaches have recently become popular because of the large amount of data that can be generated using these approaches, and the increasing availability of the technology to analyse such data. Bioinformatics techniques attempt to capture a snapshot of the biological content in the sample at a moment in time. By
comparing the data captured during periods of disease and the ‘normal’ state, it is possible to use these snapshots as biomarkers of various diseases.
Proteomics qualitatively and quantitatively measures the proteins that are present in a cell or in the extra cellular matrix. A sample of biological fluid is taken and then put through liquid chromatography-mass spectrometry (LC-MS). Identification of constituents is done by comparing the results to the spectra of known proteins. However, this is by no means an easy task since the level and range of proteins present in biological fluids can vary based on the timing and site of collection. Rodriguez-Suarez et al. analysed the proteomes of
extracellular vesicles secreted by primary hepatocytes (104). By cataloguing the proteins secreted by hepatocytes that have been exposed to hepatotoxins, these researchers
18 hepatocytes which could be developed into novel non-invasive biomarkers of DILI (104).
The Centre for Omic Sciences defines transcriptomics as ‘the study of transcriptomes – the complete set of RNA transcripts produced by the genome at any one time’
(omicscentre.com). The contribution of transcriptomics in the study of ADRs has been summarised in a recent review by Fernandez et al. (105). In short, researchers have found patterns in gene expression which differ between samples obtained from intoxicated and healthy patients. Blood borne gene expression markers can then serve as biomarkers of ADRs, including DILI (105). Similarly, Benet et al. investigated transcription factor expression profiles for cells exposed to known steatotic drugs and were able to identify three
transcription factors as predictive biomarkers of drug induced hepatic steatosis (106).
Although omics technologies are now commonly used in academia and industry, relative few research groups have utilised approaches combining multiple methods (107,108). In a recent review, Khan et al. argue that a holistic approach, combining proteomics,
transcriptomics, metabolomics etc. is useful as a means of gathering comprehensive information, and that the use of bioinformatics tools may eventually revolutionise the drug discovery paradigm (88).
1.5.3 Epidemiological approaches
Due to the rare incidence of DILI, there is a need to identify cases by which to study the pathophysiology of the disease. Post-marketing data collected by pharmaceutical companies or regulatory authorities consists of adverse drug reaction (ADR) reports, and typically contain information on patient demographics, information on suspected drugs and details of the experienced ADR. Using case/non-case methodology, researchers can measure the strength of an association between particular ADRs (e.g. DILI) and drug exposure and hence identify DILI cases out of population data. Networks and registries have also been set up specifically to collect DILI cases for study.
Table 1.1 lists some examples of DILI associations that have been found by analysing pharmacovigilance databases around the world.
19
Data source Study focus
Portuguese, French, Spanish, Italian
pharmacovigilance system
Hepatotoxicity with agometaline and newer antidepressents (109)
Spanish DILI registry Characteristics of DILI cases (10) U.S. DILI network Characteristics of statin DILI (110) U.S. DILI network DILI due to herbal products (111)