Evaluation of a Three-Dimensional Primary Human Hepatocyte Spheroid Model: Adoption and Industrialization for the Enhanced Detection of Drug-Induced Liver Injury

Drug-induced liver injury is a leading cause of compound attrition during both preclinical and clinical drug development, and early strategies are in place to tackle this recurring problem. Human-relevant in vitro models that are more predictive of hepatotoxicity hazard identification, and that could be employed earlier in the drug discovery process, would improve the quality of drug candidate selection and help reduce attrition. We present an evaluation of four human hepatocyte in vitro models of increasing culture complexity (i.e., two-dimensional (2D) HepG2 monolayers, hepatocyte sandwich cultures, three-dimensional (3D) hepatocyte spheroids, and precision-cut liver slices), using the same tool compounds, viability end points, and culture time points. Having established the improved prediction potential of the 3D hepatocyte spheroid model, we describe implementing this model into an industrial screening setting, where the challenge was matching the complexity of the culture system with the scale and throughput required. Following further qualification and miniaturization into a 384-well, high-throughput screening format, data was generated on 199 compounds. This clearly demonstrated the ability to capture a greater number of severe hepatotoxins versus the current routine 2D HepG2 monolayer assay while continuing to flag no false-positive compounds. The industrialization and miniaturization of the 3D hepatocyte spheroid complex in vitro model demonstrates a significant step toward reducing drug attrition and improving the quality and safety of drugs, while retaining the flexibility for future improvements, and has replaced the routine use of the 2D HepG2 monolayer assay at GlaxoSmithKline.

Proteomic analysis of filaggrin deficiency identifies molecular signatures characteristic of atopic eczema

BACKGROUND

Atopic eczema (AE) is characterized by skin barrier and immune dysfunction. Null mutations in filaggrin (FLG), a key epidermal barrier protein, strongly predispose to AE; however, the precise role of FLG deficiency in AE pathogenesis remains incompletely understood.

OBJECTIVES

We sought to identify global proteomic changes downstream of FLG deficiency in human epidermal living skin-equivalent (LSE) models and validate findings in skin of patients with AE.

METHODS

Differentially expressed proteins from paired control (nontargeting control short hairpin RNA [shNT]) and FLG knockdown (FLG knockdown short hairpin RNA [shFLG]) LSEs were identified by means of proteomic analysis (liquid chromatography-mass spectrometry) and Ingenuity Pathway Analysis. Expression of key targets was validated in independent LSE samples (quantitative RT-PCR and Western blotting) and in normal and AE skin biopsy specimens (immunofluorescence).

RESULTS

Proteomic analysis identified 17 (P ≤ .05) differentially expressed proteins after FLG knockdown, including kallikrein-7 (KLK7; 2.2-fold), cyclophilin A (PPIA; 0.9-fold), and cofilin-1 (CFL1, 1.3-fold). Differential protein expression was confirmed in shNT/shFLG LSEs; however, only KLK7 was transcriptionally dysregulated. Molecular pathways overrepresented after FLG knockdown included inflammation, protease activity, cell structure, and stress. Furthermore, KLK7 (1.8-fold) and PPIA (0.65-fold) proteins were differentially expressed in lesional biopsy specimens from patients with AE relative to normal skin.

CONCLUSIONS

For the first time, we show that loss of FLG in the absence of inflammation is sufficient to alter the expression level of proteins relevant to the pathogenesis of AE. These include proteins regulating inflammatory, proteolytic, and cytoskeletal functions. We identify PPIA as a novel protein with levels that are decreased in clinically active AE skin and show that the characteristic upregulation of KLK7 expression in patients with AE occurs downstream of FLG loss. Importantly, we highlight disconnect between the epidermal proteome and transcriptome, emphasizing the utility of global proteomic studies.

Development of a multivariate statistical model to predict peroxisome proliferation in the rat, based on urinary 1H-NMR spectral patterns

A previous report of this work (Ringeissen et al. 2003) described the use of nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical data analysis (MVDA) to identify novel biomarkers of peroxisome proliferation (PP) in Wistar Han rats. Two potential biomarkers of peroxisome proliferation in the rat were described, N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY). The inference from these results was that the tryptophan-nicotinamide adenine dinucleotide (NAD(+)) pathway was altered in correlation with peroxisome proliferation, a hypothesis subsequently confirmed by TaqMan analysis of the relevant genes encoding two key enzymes in the pathway, aminocarboxymuconate-semialdehyde decarboxylase (EC 4.1.1.45) and quinolinate phosphoribosyltransferase (EC 2.4.2.19). The objective of the present study was to investigate these data further and identify other metabolites in the NMR spectrum correlating equally with PP. MVDA Partial Least Squares (PLS) models were constructed that provided a better prediction of PP in Wistar Han rats than levels of 4PY and NMN alone. The resulting Wistar Han rat predictive models were then used to predict PP in a test group of Sprague Dawley rats following administration of fenofibrate. The models predicted the presence or absence of PP (above on arbitrary threshold of >2-fold mean control) in all Sprague Dawley rats in the test group.

Tryptophan-NAD+ pathway metabolites as putative biomarkers and predictors of peroxisome proliferation

The present study was designed to provide further information about the relevance of raised urinary levels of N-methylnicotinamide (NMN), and/or its metabolites N-methyl-4-pyridone-3-carboxamide (4PY) and N-methyl-2-pyridone-3-carboxamide (2PY), to peroxisome proliferation by dosing rats with known peroxisome proliferator-activated receptor alpha (PPARalpha) ligands [fenofibrate, diethylhexylphthalate (DEHP) and long-chain fatty acids (LCFA)] and other compounds believed to modulate lipid metabolism via PPARalpha-independent mechanisms (simvastatin, hydrazine and chlorpromazine). Urinary NMN was correlated with standard markers of peroxisome proliferation and serum lipid parameters with the aim of establishing whether urinary NMN could be used as a biomarker for peroxisome proliferation in the rat. Data from this study were also used to validate a previously constructed multivariate statistical model of peroxisome proliferation (PP) in the rat. The predictive model, based on 1H nuclear magnetic resonance (NMR) spectroscopy of urine, uses spectral patterns of NMN, 4PY and other endogenous metabolites to predict hepatocellular peroxisome count. Each treatment induced pharmacological (serum lipid) effects characteristic of their class, but only fenofibrate, DEHP and simvastatin increased peroxisome number and raised urinary NMN, 2PY and 4PY, with simvastatin having only a transient effect on the latter. These compounds also reduced mRNA expression for aminocarboxymuconate-semialdehyde decarboxylase (ACMSDase, EC 4.1.1.45), the enzyme believed to be involved in modulating the flux of tryptophan through this pathway, with decreasing order of potency, fenofibrate (-10.39-fold) >DEHP (-3.09-fold) >simvastatin (-1.84-fold). Of the other treatments, only LCFA influenced mRNA expression of ACMSDase (-3.62-fold reduction) and quinolinate phosphoribosyltransferase (QAPRTase, EC 2.4.2.19) (-2.42-fold) without any change in urinary NMN excretion. Although there were no correlations between urinary NMN concentration and serum lipid parameters, NMN did correlate with peroxisome count (r2=0.63) and acyl-CoA oxidase activity (r2=0.61). These correlations were biased by the large response to fenofibrate compared to the other treatments; nevertheless the data do indicate a relationship between the tryptophan-NAD+ pathway and PPARalpha-dependent pathways, making this metabolite a potentially useful biomarker to detect PP. In order to strengthen the observed link between the metabolites associated with the tryptophan-NAD+ pathway and more accurately predict PP, other urinary metabolites were included in a predictive statistical model. This statistical model was found to predict the observed PP in 26/27 instances using a pre-determined threshold of 2-fold mean control peroxisome count. The model also predicted a time-dependent increase in peroxisome count for the fenofibrate group, which is important when considering the use of such modelling to predict the onset and progression of PP prior to its observation in samples taken at autopsy.