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Evaluation of a human in vitro skin test for predicting drug hypersensitivity reactions
Toxicology and Applied Pharmacology 369 (2019) 39–48
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Evaluation of a human in vitro skin test for predicting drug hypersensitivity reactions
T
Ahmed S.S.a, Whritenour J.c, Ahmed M.M.b, Bibby L.a,b, Darby L.b, Wang X.N.b, Watson J.b, ⁎ Dickinson A.M.a,b, a
Alcyomics Ltd, Bulman House, Regent Centre, Gosforth, Newcastle-upon-Tyne NE3 3LS, United Kingdom Haematological Sciences, Institute of Cellular Medicine, Newcastle University, Newcastle-upon-Tyne NE2 4HH, United Kingdom c Pfizer Inc., Drug Safety Research and Development, Eastern Point Rd, Groton, CT 06340, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Low molecular weight drugs Drug hypersensitivity Drug allergy Drug induced T cell responses Hazard identification
The occurrence of drug hypersensitivity reactions (DHRs) following administration of low molecular weight (LMW) drugs is an important health concern. However, in vivo animal models which could be used as tools for the prediction of DHRs are lacking. As a result, research has focused on development of in vitro tools for predicting DHRs. In this study a novel human in vitro pre-clinical skin explant test was used to predict T cellmediated hypersensitivity responses induced by LMW drugs. Responses in the skin explant test for 12 LMW drugs associated with T cell-mediated hypersensitivity in the clinic (abacavir, amoxicillin, carbamazepine, diclofenac, lamotrigine, lapatinib, lumiracoxib, nevirapine, ofloxacin, phenytoin, propranolol, sulfamethoxazole) were compared with responses for 5 drugs with few/no reports of T cell-mediated hypersensitivity reactions (acetaminophen, cimetidine, flecainide, metformin, verapamil). Changes in skin histology following in vitro exposure to the drugs as well as T cell proliferation and interferon gamma (IFNγ) production were studied. The results of the skin explant assays showed a good positive correlation (r = 0.77, p < .001) between the test outcome (prediction of positive or negative) and the clinical classification of the tested drugs. The T cell proliferation assay showed a correlation of r = 0.60 (p < .01) and the IFNγ assay r = 0.51 (p < .04). The data suggest that the skin explant model could be a useful tool to predict the potential of LMW drugs to induce DHRs.
1. Introduction A large number of low molecular weight (LMW) drugs fail in clinical development. Lack of efficacy along with drug hypersensitivity reactions (DHRs) are a major cause of failure or withdrawal of new LMW drugs (Hay et al., 2014). Despite a battery of clinical and pre-clinical tests used for evaluating the safety of drugs, DHRs are often undetected until drugs are tested in large clinical trials or marketed (Pourpak et al., 2008). This late failure of drugs due to DHRs clearly indicates the need for predictive tools during the early stages of pre-clinical drug development. DHRs can be categorized based on mechanism (Baldo, 2013) and may include allergic reactions caused by adaptive immune mechanisms or non-allergic reactions caused by pharmacologic interaction of the drug with immune cells (e.g., pseudoallergy) (Pichler and Hausmann, 2016; Corsini et al., 2018). Pseudoallergic drug reactions are typically dose dependent and can be observed in animals during pre-clinical
development, whereas allergic drug reactions are rarely observed during pre-clinical drug development (Whritenour et al., 2016). There are currently no in vitro pre-clinical tools available which have been validated to screen for potential allergic DHRs. However, there are some in vivo models such as the modified popliteal lymph node assay (PLNA) (Warbrick et al., 2001; Pieters, 2001), the lymph node proliferation assay (LNPA) (Weaver et al., 2005) and the mouse allergy model (Whritenour et al., 2014; Zhu et al., 2015), which show some potential for predicting DHRs. Drug hypersensitivity can be related to genetic predisposition (Pirmohamed, 2006) occurring in only a minority of the population and therefore can be difficult to predict, especially using animal models (Bala et al., 2005). Consequently, in vitro testing using human material may be an attractive alternative. Tests developed more specifically for determining sensitisation potential of topically administered chemicals/compounds such as the human cell line activation test (h-CLAT), or the direct peptide reactivity assay (DPRA), could be further investigated as suitable tools for predicting
⁎ Corresponding author at: Haematological Sciences, Institute of Cellular Medicine, Medical School, University of Newcastle upon Tyne, Newcastle upon Tyne NE2 4HH, UK. E-mail address: [email protected] (A.M. Dickinson).
https://doi.org/10.1016/j.taap.2019.02.005 Received 4 October 2018; Received in revised form 4 February 2019; Accepted 11 February 2019 Available online 12 February 2019 0041-008X/ © 2019 Elsevier Inc. All rights reserved.
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tests. 1 Fisher Scientific, Hampton, NH; 2Sigma Aldrich, St Louis, MO; 3 Pfizer in-house; 4LC Laboratories, Woburn, MA; 5Toronto Research Chemicals, Inc.; 6AK Scientific Inc., Union City, CA.
DHRs for systemically administered compounds given that there is likely overlap in mechanisms driving both responses (Corsini et al., 2018).Since we have used a human skin explant assay for a similar assessment of chemical compounds (Ahmed et al., 2016) with a sensitivity and specificity of 95%, we used the same skin explant assay in the present study to predict potential DHRs with systemically administered LMW drugs. The objective of the current study was to evaluate the human in vitro skin explant assay for predicting DHRs to LMW drugs. The skin explant assay used in these studies was modified from the original predictive test, which has been extensively used to understand the underlying mechanism and immunobiology of systemic graft versus host disease (GvHD) (Dickinson et al., 1999; Dickinson et al., 1988; Vogelsang et al., 1985), (Dickinson et al., 2002; Jarvis et al., 2002; Ruffin et al., 2011; Mavin et al., 2012; Ahmed et al., 2015). The modified assay has also been used as a method for predicting skin sensitisation potential of chemical compounds (Ahmed et al., 2016). The skin explant model offers the potential to study the initiation of an immune response through interaction of dendritic cells and T cells and resulting T cell activation. In the current study we have extended the use of this assay to test a panel of systemically administered LMW drugs that are associated (or not) with hypersensitivity reactions in the clinic. As previously described by Ahmed and colleagues, the assay was used in an autologous setting, in this instance autologous refers to when the different cell types and skin used in the assay are derived from the same donor. The assay involves activation of dendritic cells, assessment of T cell proliferation, cytokine production, and histopathological assessment of damage to skin tissue as an indication of the degree of damage caused by activation of T cells. The skin reaction is scored in severity from grade I-IV (Lerner et al., 1974) and a score of a grade II or above is used to predict a positive DHR. Seventeen LMW drugs were evaluated in the skin explant assay; this included 5 drugs with low or no association with DHRs (acetaminophen, cimetidine, flecainide, metformin and verapamil) and 12 drugs (abacavir, amoxicillin, carbamazepine, diclofenac, lamotrigine, lapatinib, lumiracoxib, nevirapine, ofloxacin, phenytoin, propranolol and sulfamethoxazole) that have been reported to be associated with DHRs in the clinic. In the same assay, the effects of the drugs on T cell proliferation and IFNγ production were also investigated. The objective of the study was to determine if the skin explant assay could be used as a pre-clinical test to predict the potential of LMW drugs to cause DHRs.
2.2. Human samples & tissue culture Local Research Ethics Committee (LREC) approval was granted prior to study commencement. Healthy volunteers were screened using an allergy questionnaire and eliminated from the study if they had allergies or were unwell or on prescribed medication. Peripheral blood (60 mL) and abdominal skin punch biopsies (2 × 4 mm) were obtained from healthy volunteers on the day of use, following informed consent. Biopsies were processed by washing in PBS (Lonza) and removing any residual excess fat before dissection for the skin explant assay. Standard culture conditions of 5% CO2 at 37 °C were used. Peripheral blood mononuclear cells (PBMCs) were isolated using density-gradient centrifugation using the Lymphoprep™ (Axis Shield, UK) method and then used for positive selection of CD14+ monocytes (MACS®, Miltenyl Biotec). The CD14- fraction was also collected and used as a source of autologous T cells for further tests. Monocyte derived dendritic cells (MoDCs) were generated as previously described (Ahmed et al., 2016). Briefly, the CD14 + monocytes were cultured with GM-CSF (50 ng/mL) and IL-4 (50 ng/mL) for 24 h. This was followed by a 24 h culture with maturation stimuli (LPS 0.1 μg/mL, IL-6 10 ng/mL, IL-1β 10 ng/mL, TNFα 10 ng/mL, PGE2 1 μM (Immunotools and Sigma-Aldrich) resulting in a DC phenotype (Kvistborg, 2009). Unless specified, cells were cultured in RF10 media. Skin biopsies were collected in X-Vivo™ 10 (Lonza) media.
2.3. Cell viability test Viability of PBMCs following exposure to drugs for 24 h was determined using the Trypan Blue Exclusion Test. Drugs were cultured with PBMCs (2 × 105 cells) at several test concentrations in duplicate (Table 1 and Supplementary data). A 1:1 volume of cells was added to 0.4% Trypan Blue and the number of viable (unstained) and dead (stained) cells were counted using a hemocytometer.
2.4. Skin explant test Following a 24 h exposure of 0.3–0.5 × 105 MoDCs to the drug at the evaluation test concentrations (Table 1), cells were harvested and co-cultured with autologous T cells (CD14- fraction) at concentrations of 3–5 × 105 MoDCs and 3–5 × 106 T cells for 4 days in RPMI 1640 (Gibco, UK) containing 100 IU/mL penicillin, 100 μg/mL streptomycin (Gibco UK) and 2 mM L-glutamine (Gibco UK) supplemented with 10% v/v heat inactivated human AB serum (Sigma-Aldrich, UK). Skin biopsies were co-cultured with the primed T cells (1 × 106/well) for 3 days. Skin was formalin fixed and then paraffin embedded, sectioned, and stained with haematoxylin and eosin for histopathological evaluation. A blind analysis was performed by two independent examiners. Histological damage was graded from 0 to IV according to the Lerner criteria (Lerner et al., 1974; Vogelsang et al., 1985) and characterized as grade 0 no observable damage to skin keratinocytes, grade I mild vacuolisation of basal cells, grade II vacuolisation of basal cells and dyskeratotic bodies, grade III sub-epidermal cleft formation at the dermal epidermal junction and grade IV complete epidermal separation. Based on previous results using this grading system, grades 0 and I are regarded as negative results and a grade of ≥II as a positive result. In each assay, skin explants cultured in media alone were used as a background control. Autologous T cells cultured with 0.001% triton-x served as a negative control and 0.1 μM DNCB as a positive control, as previously described (Ahmed et al., 2016).
2. Materials & methods 2.1. Test drugs Drugs used in this study included abacavir1, acetaminophen2, amoxicillin2, carbamazepine3, cimetidine2, diclofenac2, flecainide2, lamotrigine3, lapatinib4, lumiracoxib5, metformin2, nevirapine2, ofloxacin6, phenytoin3, propranolol3, sulfamethoxazole3 (SMX), and verapamil3 (Table 1). Drugs were classified as positive or negative for hypersensitivity reactions based on information contained within their labels in addition to available clinical and/or mechanistic data suggestive of an adaptive immune response. Drug solubility was achieved using either RF10 media (RPMI 1640 [Gibco, UK] containing 100 IU/ mL penicillin, 100 μg/mL streptomycin [Gibco, UK] and 2 mM L-glutamine [Gibco, UK] supplemented with 10% v/v heat inactivated fetal calf serum [Sera Lab]) or 10% dimethyl sulfoxide (DMSO [Fisher Scientific, UK]) in phosphate buffered saline (PBS). Some drugs required incubation at 37 °C (10–20 min) for complete solubility in the chosen vehicle. Drug solutions were prepared fresh on the day of use. Drug concentrations were selected after performing viability studies (Table 1) at a number of concentrations. 2, 4-dinitrochlorobenzene (DNCB; 0.1 μM) (Sigma-Aldrich) was used as a positive control and 0.001% triton-x (Sigma-Aldrich) was used as a negative control in all 40
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Table 1 List of drugs tested and concentrations used. The drug name and drug classification is given as positive (association with DHR) or negative (low or no association with DHR). Drug label information is categorized as boxed warning (risk of a serious adverse reaction), warning (risk of an adverse reaction), adverse reaction (adverse reaction reported), none (no reference on label information) (dailymed.nlm.nih.gov). Vehicle used (DMSO or RPMI media), drug concentrations (μg/ml) used for viability tests to determine test concentrations and selected test concentrations for the skin explant, T cell proliferation, and IFNγ production assays are given. Drug
Classification
Drug hypersensitivity label information
Vehicle
Cell viability test concentrations (μg/ mL)
Evaluation test concentrations (μg/ mL)⁎⁎
Abacavir
Positive
Boxed warning, (hypersensitivity reactions)
500, 250, 100, 50, 25
100, 50, 25
Amoxicillin
Positive
500, 250, 100, 50, 25
200, 100, 50
Carbamazepine Diclofenac Lamotrigine Lapatinib
Positive Positive Positive Positive
500, 500, 500, 100,
200, 200, 200, 100,
Lumiracoxib Nevirapine Ofloxacin Phenytoin Propranolol Sulfamethoxazole Acetaminophen Cimetidine Flecainide Metformin
Positive Positive Positive Positive Positive Positive Negative Negative Negative Negative
Warning, (hypersensitivity (anaphylactic) reactions) Boxed warning, (hypersensitivity reactions) Warning (anaphylactic reactions) Boxed warning (hypersensitivity reactions) Warning, (hypersensitivity (anaphylactic) reactions) Withdrawn from market Boxed warning, (hypersensitivity reactions) Warning (hypersensitivity reactions) Warning (hypersensitivity reactions) Warning (hypersensitivity reactions) Warning (hypersensitivity reactions) None Rare None None
Media (RPMI) 10% DMSO⁎
Verapamil
Negative
Rare
⁎ ⁎⁎
10% 10% 10% 10%
DMSO⁎ DMSO⁎ DMSO⁎ DMSO⁎
10% DMSO⁎ 10% DMSO⁎ Media (RPMI 10% DMSO⁎ 10% DMSO⁎ 10% DMSO⁎ 10% DMSO⁎ 10% DMSO⁎ 10% DMSO⁎ Media (RPMI) 10% DMSO⁎
250, 100, 50 250, 100, 50 250, 100, 50 50, 10
100, 50, 10 100, 50, 25 500, 250, 100, 100, 50, 10 500, 250, 100, 200, 100, 50 500, 250, 100, 500, 250, 100, 25, 10, 5 500, 250, 100, 50, 25, 10
50 50, 25, 10 50 50 50
100, 50 100, 50 100, 50 50, 10
100, 50, 10 100, 50, 25 200, 100, 50 100, 50, 10 100, 50, 25 200, 100, 50 200, 100, 50 200, 100, 50 25, 10, 5 200, 100, 50 25, 10, 5
Final concentration of DMSO in the assays was 0.1%. Cell viability 70–80% at highest concentration (with exception to Propranolol) and > 80% at intermediate and lower concentrations.
overall prediction was determined by evaluating the mean responses to the high, intermediate and low drug concentrations. If two or more concentrations gave a mean positive response, then the drug was considered to be positive. If however, two or more concentrations gave a mean negative response then the drug was considered to be negative. This overall prediction criterion was applied to the skin explant grades, where a positive response was considered grade ≥ IIT cell proliferation assays where a positive response was considered an SI ≥ 3 (Log2 1.58) and to the IFNγ assays where a positive response was considered a fold increase ≥ 3 (Log2 1.58). Statistical analysis was performed using SPSS Statistics V21. Multiple comparison analysis was performed using univariate, post-hoc Bonferroni tests. Pearson's correlation was performed to test for strength of association between test methods. Receiver operating characteristic (ROC) curve analysis was performed by SPSS Statistics V21 and used to determine the predictive ability of the tests evaluated in this study. Sensitivity is plotted on the y-axis and 1-specificity on the x-axis. The area under the curve (AUC) is a measure of the test accuracy where a value of 1 demonstrates the test is 100% accurate in predicting if the LMW drugs will cause a DHR and an inaccurate test would have an AUC below 0.5. The p value assesses the null hypothesis that the AUC is 0.5.
2.5. Analysis of T cell proliferation and IFNγ production MoDCs generated from peripheral blood were exposed to the test drugs at the selected concentrations for 24 h (Table 1). Cells were then cultured with autologous T cells (CD14- fraction) at a ratio of 1 × 104 MoDC: 1 × 105 T cells for 5 days. All tests were performed in triplicate. Positive (DNCB), negative (triton-x) or/and vehicle (0.1% DMSO prepared in RPMI) controls were included in each assay. Supernatants were collected for IFNγ analysis prior to [3H] thymidine addition on day 5. [3H] thymidine pulsed cells were harvested after 16–18 h and subsequently counted using a β-scintillation counter. IFNγ was measured by flow cytometry using a CBA Flex Kit (BD Biosciences). Data were analysed using Prism Graph Pad software (V5). 2.6. Data and statistical analysis Skin explant assays were performed using samples collected from 4 individual healthy volunteers. Tissue histopathology was analysed and grading scores (0-IV) were assigned. A response was considered positive if observed changes in skin tissue were assigned a grade ≥ II based on the histopathological damage. A grade I response was considered a negative response. T cell proliferation and IFNγ assays were performed on each compound using samples collected from 6 individual healthy volunteers (4 of which were the same individuals from which the skin biopsies were collected). T cell proliferation was measured by [3H]Thymidine incorporation in counts per minute (cpm). Analysis was performed by calculating a stimulation index (SI) of T cell proliferation by dividing the cpm value of cells treated with each drug with the cpm value of cells treated with vehicle only (0.1% DMSO). IFNγ (pg/mL) analysis was performed by calculating a fold increase in IFNγ concentrations by dividing the value obtained from cells treated with each drug with the value obtained from cells treated with vehicle only (0.1% DMSO). The cut off value of a 3-fold increase (Basketter et al., 1999) was considered to be a positive response for the T cell proliferation and IFNγ endpoints. Results for each test method are given as an overall prediction as positive or negative for the potential of each drug to cause a DHR. The
3. Results 3.1. Determining non-toxic test concentrations Drugs were initially tested at several concentrations to determine effects on PBMC viability after 24 h of exposure (Table 1 and Supplemental Table 1). Viability > 70% was considered to be low drug toxicity, however, for most compounds the viability was > 80%. 3.2. Evaluation of human in vitro drug responses using the skin explant assay The capacity of the skin explant assay to predict the potential of LMW drugs to induce DHRs was evaluated using a panel of drugs with known associations (or not) with DHRs in the clinic. A set of 12 positive 41
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Fig. 1. Representative images of skin explant sections showing histopathology of skin after exposure to LMW drugs or controls. Haematoxylin and Eosin stained 3 μm skin sections. Drugs classified as negative, A-D: acetaminophen (200 μg/mL), cimetidine (200 μg/mL), metformin (200 μg/mL) and flecainide (25 μg/mL), respectively, showing a grade I negative response. Drugs classified as positive, E-H: carbamazepine (200 μg/mL), ofloxacin (200 μg/mL), lapatinib (100 μg/mL), and nevirapine (100 μg/mL), respectively, showing a grade III positive response. I: Negative control, triton-X (0.001%) grade I. J: Positive control, DNCB (0.1 μM), grade III. Arrows indicate damage to the dermal/epidermal junction and formation of clefts following 24 h exposure of MoDC to the drugs followed by co-culture of MoDC with autologous T cells and then further co-culture of primed T cells with autologous skin.
negative response at the low concentration. Lumiracoxib gave a mean positive response at the high concentration and a mean negative response at the intermediate and low concentrations. Phenytoin elicited a mean positive response at the high and low concentrations and a mean negative response at the intermediate concentration. Sulfamethoxazole did not cause a mean positive response at any concentration tested. Four of the five LMW drugs clinically classified as negative (acetaminophen, cimetidine, flecainide and metformin) gave a grade I mean negative response to all 3 concentrations. Verapamil, however, showed a mean positive response at the high concentration and a mean negative response at the intermediate and low concentrations. In summary, an overall prediction for each LMW drug was determined by considering the mean positive or mean negative responses observed at each concentration using the criterion that if a mean positive response was observed in at least 2 of 3 concentrations then the
and 5 negative drugs were selected and each drug was tested at 3 concentrations (Table 1). A minimum of 4 independent skin explant assays were performed per drug. Histopathological grading was used to determine if a drug gave a positive or negative response. Representative images of positive and negative responses are shown in Fig. 1. The mean drug responses for each compound showing a mean positive (grade ≥ II, represented by vacuolisation of keratinocytes on the dermal/epidermal junction) or a mean negative response (grade 0–I, no damage on the dermal/epidermal junction) in response to high, intermediate and low test concentrations (Fig. 2) were calculated. Seven of the LMW drugs clinically classified as positive (abacavir, amoxicillin, carbamazepine, diclofenac, lapatinib, ofloxacin and propranolol) gave mean positive responses (grade II ≥) in the skin explant test at all 3 test concentrations. Lamotrigine and nevirapine gave a mean positive response at the high and intermediate concentrations and a mean 42
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Fig. 2. LMW drug responses in the skin explant assay. Responses of 12 drugs tested at high (1), intermediate (2) and low (3) concentrations. Graph shows mean skin explant grade responses for each drug at each concentration. The black line denotes the cut-off grade for a positive response (≥II). A mean response below this cut off signifies no tissue damage and indicates a negative response.
3.3. Analysis of T cell proliferation responses
test drug was considered positive for DHR. Equally, a mean negative response to at least 2 concentrations determined the test drug as negative for DHR. Using these criteria, 15 (abacavir, acetaminophen, amoxicillin, carbamazepine, cimetidine, diclofenac, flecainide, lamotrigine, lapatinib, metformin, nevirapine, ofloxacin, propranolol, phenytoin and verapamil) of 17 drugs (88%) gave a correct predictive response using the skin explant. Lumiracoxib and sulfamethoxazole gave false negative responses. Next, we wanted to determine if the mean grade response produced was due to independent factors such as the drug (independent of the test concentration) or the concentration or alternatively due to the effect of a combined interaction between the drug and concentration (both factors together). A two-way Anova was performed and showed that the drug (p < .0005) and the concentration (p < .0005) as independent factors had a statistically significant effect on the graded response. No significant effect was observed for the interaction between drug and concentration on the graded response. Furthermore, we analysed if the graded responses observed were significantly different between high, intermediate and low drug concentrations. A post-hoc multiple comparison analysis showed a significant difference with regards to mean grade for the high concentration when compared to the intermediate (p < .009) or low concentrations (p < .0005). We measured the strength of the correlation between the overall skin explant prediction and clinical outcome and determined a Pearson's correlation coefficient r = 0.77 (p < .001) (Table 2).
Six individual experiments were performed for each test drug at low, intermediate and high concentration and results were calculated as SI of T cell proliferation responses in comparison to responses observed for vehicle control samples. While the magnitude of the proliferative response varied between volunteers, the results for each drug (negative or positive) were generally consistent across study volunteers. T cell proliferation responses to the drugs were dichotomised as positive or negative using a cut-off for a significant stimulation index set at 1.58Log2 which corresponded to an SI of a 3-fold change. Based on this cut-off, the mean drug responses for each LMW drug showing a positive (≥1.58Log2) or a negative (< 1.58Log2) response in presence of high, intermediate and low test concentrations were studied. From the LMW drugs clinically classified as positive, amoxicillin (2.08, 1.95 and 2.00), diclofenac (1.95, 1.87 and 1.75), lamotrigine (2.00, 1.82 and 1.84), ofloxacin (1.99, 1.89 and 1.75) and propranolol (2.07, 2.07 and 1.91) showed a mean positive response to high intermediate and low concentrations, respectively (Fig. 3). Abacavir (1.62 and 1.62), carbamazepine (1.81 and 1.74), lumiracoxib (1.81 and 1.69), nevirapine (2.14 and 2.03) and phenytoin (1.89 and 1.76) showed a positive mean response to high and intermediate concentrations respectively and a negative mean response (1.56, 1.54, 1.54, 1.51 and 1.51, for each molecule respectively) at the low concentration. Lapatinib showed a positive response to the high concentration (1.83) and negative mean response to the intermediate and low concentrations (1.56 and 1.18) and sulfamethoxazole showed a negative mean response to all 3 test concentrations (1.39, 1.37 and 1.33, respectively).
Table 2 Performance analysis of test assays. Assay
Overall predictive outcome
Correlation overall prediction to clinical classification
Inter-assay correlation
Skin Explant T cell proliferation IFNγ production
88% 82% 76%
r = 0.77 (p < .001) r = 0.60 (p < .01) r = 0.51 (p < .04)
T cell assay r = 0.87 (p < .0005) IFNγ r = 0.88 (p < .0005) Skin Explant Assay r = 0.77 (p < .0005)
Table shows the overall predictive outcome of each assay as a percentage for the 12 positive and 5 negative LMW drugs tested, correlation of the overall prediction of each assay to the clinical classification and inter assay correlations. 43
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Fig. 3. T cell proliferation responses following exposure to LMW drugs. Graph shows stimulation indices of T cells relative to vehicle control samples following treatment with 17 LMW drugs. Data show mean responses of 6 independent experiments for each test drug at high (1), intermediate (2) and low (3) concentrations. Data are expressed as SI Log2. The black line denotes the cut-off for a 3 fold (SI Log2 1.58) increase in stimulation indices of T cell proliferation above baseline levels which indicates a positive response.
Of the five LMW drugs clinically classified as negative, four produced a mean negative response to all 3 concentrations: acetaminophen, cimetidine, flecainide and metformin, however, verapamil produced a mean positive response to the high and intermediate concentrations (1.75 and 1.74, respectively) and a mean negative response (1.52) at the low concentration. To summarise the overall prediction capacity of the T cell proliferation assays using the criteria described earlier, 14 (abacavir, acetaminophen, amoxicillin, carbamazepine, cimetidine, diclofenac, flecainide, lamotrigine, lumiracoxib, metformin, nevirapine, ofloxacin, propranolol and phenytoin) of 17 drugs (82%) were predicted correctly. There were false negative responses with lapatinib and sulfamethoxazole and verapamil produced a false positive response. The data show a statistically significant effect of the specific drug (p < .005) and also a statistically significant effect of concentration (p < .005), as independent factors on T cell proliferation responses. However, analysis to determine the effect of both factors combined showed no statistically significant effect of a drug and concentration interaction on T cell proliferation responses. Comparison of the difference in T cell proliferation responses between high, intermediate and low concentrations showed a statistically significant mean difference between the high and the low concentration (p < .0005) and between the intermediate and the low concentration (p < .007). No significant mean difference was observed between the intermediate and the high concentrations. Association of overall T cell proliferation prediction outcome to clinical outcome showed a Pearson's correlation coefficient of r = 0.60 (p < .01) (Table 2).
(1.96, 1.92 and 1.96), lamotrigine (2.32, 2.21 and 2.21), ofloxacin (1.81, 1.74 and 1.85) and propranolol (1.90, 1.91 and 2.01) showed a mean positive fold increase (≥1.58Log2) at the high, intermediate and low concentrations, respectively. Abacavir (1.92 and 1.72) lumiracoxib (1.87 and 1.75) and nevirapine (1.97 and 1.78) produced a mean positive fold increase at the high and intermediate concentrations and a mean negative response (1.45, 1.31 and 1.42 for the three molecules, respectively) at the low concentration. Lapatinib and phenytoin produced a mean positive fold increase (2.15 and 1.96, respectively) at the high concentration only and sulfamethoxazole did not produce a mean positive fold increase at any concentration. From the LMW drugs clinically classified as negative; acetaminophen, cimetidine, flecainide and metformin all had a mean negative response at all 3 concentrations. However, a mean positive fold increase was seen with verapamil at the high and intermediate concentrations (1.78 and 1.59, respectively) and a mean negative response (1.41) at the low concentration. Using the criteria described earlier for the overall prediction, 13 (abacavir, acetaminophen, amoxicillin, carbamazepine, cimetidine, diclofenac, flecainide, lamotrigine, lumiracoxib, metformin, nevirapine, ofloxacin and propranolol) of 17 drugs (76%) were correctly predicted in the IFNγ assay; lapatinib, phenytoin and sulfamethoxazole showed a false negative response and verapamil showed a false positive response. The data show a statistically significant effect of the drug on IFNγ levels (p < .0005) in cell culture supernatants. A statistically significant effect of concentration (p < .005) on IFNγ levels was also observed. However, no statistically significant effect was observed when the combination of drug and concentration were analysed, suggesting that the two are independent factors. Comparison of the difference in IFNγ levels between high, intermediate and low concentrations showed a statistically significant mean difference between low and intermediate concentrations (p < .03), low and high concentrations (p < .0005) and between intermediate and high concentrations (p < .04). A Pearson's correlation coefficient of r = 0.51 (p < .04) was determined between the overall IFNγ prediction outcome and clinical outcome (Table 2).
3.4. IFNγ cytokine production Supernatants collected from cell culture during the T cell proliferation assays were analysed for IFNγ as a marker of immune activation. Inter-donor variability was observed in the range of responses in IFNγ (pg/ml). Six individual experiments were performed for each test. The fold increase of IFNγ production in cell culture supernatants relative to vehicle control samples was considered as positive or negative using a cut-off of a 3-fold increase (1.58Log2). Results are presented as mean fold increase (Log 2) (Fig. 4). From the LMW drugs clinically classified as positive, amoxicillin (2.13, 2.01 and 1.89), carbamazepine (2.17, 2.11 and 2.02), diclofenac
3.5. ROC curve analysis and inter-assay correlation To quantify how accurately the tests could discriminate between positive and negative LMW drug DHR responses, ROC curve analysis 44
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Fig. 4. IFNγ levels in cell culture supernatants following exposure to LMW drugs. Fold increase of cytokine levels in comparison to basal levels following treatment with 17 LMW drugs. Graph shows mean responses of 6 independent experiments for each test drug at high (1), intermediate (2) and low (3) concentrations. The black line denotes the cut-off for a 3 fold increase (SI Log2 1.58) in IFNγ concentrations above vehicle control values which indicates a positive response.
Fig. 5. ROC curve analysis showing accuracy of each test to discriminate between LMW drugs which do or do not cause DHR. Performance of the 3 tests is plotted and represented by the ROC curves. The accuracy of each test is detailed in the table which gives the area under the curve (AUC), standard error statistical significance of each test, sensitivity and specificity.
significantly (p < .045) discriminate between the LMW drugs which do or do not cause DHR. The IFNγ assay showed AUC = 0.78 with 75% sensitivity and 80% specificity, while this indicates the test performed well it did not show a statistically significant discriminatory power (p < .08) to separate the LMW drugs between positive and negative events for DHR. To determine the association of responses between the tests, a Pearson's correlation coefficient was determined (Table 2). A strong
was performed (Fig. 5) using the drug clinical classifications (positive or negative) against the prediction outcome of each test. The AUC indicates the predictive accuracy of each test. For the skin explant assay the AUC was 0.86 with 92% sensitivity and 80% specificity, indicating the skin explant assay significantly (p < .02) discriminated between the LMW drugs which cause DHR from those that do not. For the T cell proliferation assay, the AUC was 0.82 with 83% sensitivity and 80% specificity, indicating the T cell proliferation assay is able to 45
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test is more sensitive at correctly predicting the drug DHR outcome at lower concentrations while the T cell proliferation and IFNγ endpoints lose their ability to determine positivity at the low concentration. Additionally, the overall prediction (as described in methods) for each test showed the skin explant test had the strongest predictive capacity which was supported by the ROC curve data. All 3 tests showed a false negative response for sulfamethoxazole which is generally associated with a relatively high DHR incidence once metabolised (Sanderson et al., 2007). We believe this result was probably related to a reduced presence of the metabolite SMX-NO, and therefore the full potential of this drug to elicit an immune response was dampened. We have previously tested the metabolite SMX-NO in the skin explant assay and obtained strong positive results (data not shown). Moreover, we have shown this response to be antigen-specific (Ahmed et al., 2016). The positive response of the test drugs was evident from the histopathological analysis of skin tissue. Some variation in the degree of tissue damage (ranging between grades II–IV) caused by the same drug between volunteers was observed. This is likely to be related to donor variability and the specific intensity of the reaction determined by the donor's genetic variability and the drug structure or composition. A similar variation in responses was also observed for T cell proliferation and IFNγ production, however, it was still possible to clearly discriminate which test drugs were able to induce an increase in T cell responses in association with IFNγ production. Responses at the high concentration for each drug tested in the skin explant, T cell proliferation and IFNγ assays are provided in the appendices (Table A1). Overall, a strong correlation between the different test methods was observed. This suggests that, despite the different features or endpoints of the tests, the mechanisms occurring in the tests to drive the response are closely related. In conclusion, the skin explant assay offers a human in vitro model with a novel endpoint to predict the potential of a drug to cause DHRs which standard toxicology tests do not offer. The skin damage observed in response to drug exposure is reflective of immune activation and the production of cytokine and other cytotoxic molecules by the activated T cells. The study outcome was encouraging and demonstrates that the skin explant assay could be used as a valuable predictive tool either as an independent test or in conjunction with in vivo assays mentioned earlier. Given that the n-value of the study was low and that predictive sensitivity and specificity based clinical classification can be presumptive, a further validation study with additional test compounds and a larger healthy donor cohort would be of value. However, the 3 tests used in this study could be used collectively to further predict and understand the mechanism for DHRs induced by LMW drugs.
positive correlation was observed between the T cell proliferation and the IFNγ data (r = 0.88, p < .0005), between the skin explant and T cell proliferation data (r = 0.87, p < .0005) and between the skin explant and IFNγ data (r = 0.77, p < .0005). 4. Discussion The use of human primary cells to predict the potential for adverse drug responses has become routine in pre-clinical development (Dunne et al., 2009; Eglen and Reisine, 2011). Common endpoints of these assays include, but are not limited to, metabolite measurements (Lake et al., 2009), membrane integrity (Cook and Mitchell, 1989), lysosomal and mitochondrial functions (Varga et al., 2015) and measure of cell death or apoptosis (Elzagallaai et al., 2013). However, there still remains a lack of validated and available pre-clinical tools for identifying drugs that have the potential to induce DHRs. There is a number of available or validated sensitisation tests which could potentially be adapted for assessment of DHRs. These are mainly cell-based assays focused on the use of cells involved in key events of contact hypersensitivity reactions such as dendritic cells and T cells. The hCLAT (Nukada et al., 2012), Myeloid U927 Skin Sensitisation Test (MUSST) (Urbisch et al., 2015) and THP1 Activation Assay (Mitjans et al., 2010; Mitjans et al., 2008) have been used to test some pharmaceutical compounds. However, because of the small number of drugs tested in these assays, it is difficult to determine how successful the use of these assays would be for predicting DHR. Therefore, they still need to be accurately validated for their use in assessment of predicting hypersensitivity of systemically administered pharmaceuticals. Since the mechanisms driving DHRs presumably share some commonalities with the mechanism of contact sensitisation, in particular the involvement of dendritic and T cells, these assays could possibly be developed to predict DHRs. The current study has explored the suitability of the skin explant test for use as a pre-clinical in vitro predictive tool for hazard assessment (as opposed to risk assessment) to discriminate between LMW drugs which cause DHRs from those that do not. Full thickness skin biopsies and immune cells taken from the same individual were used to perform each individual test. The test method as previously described is characteristic of a cutaneous immune response and includes observational skin damage representative of in vivo rashes and blisters. It also demonstrates antigen specific T cell responses as reported by Ahmed and colleagues (Ahmed et al., 2016). Seventeen drugs known to be clinically associated or not associated with DHRs were used to evaluate the ability of the skin explant test to predict DHR. The skin explant test showed promising results as a potential predictive assessment tool for DHR (88% correct prediction and 0.72 correlations to clinical classification for DHR). The overall potential of each drug to cause a DHR was determined using a criterion based on considering if a positive response of a compound was observed at two or more drug concentrations. We believe observing a positive response in only one out of three drug concentrations as a criterion would not be predictive enough of the true damaging potential of the drug. On the other hand, establishing the criterion of three out of three positive reactions to the high, intermediate and low concentrations will not give a true indicator of the potency threshold of the testing drug. Therefore, establishing the criterion of two out of three positive responses to the drug concentrations for an overall predictive outcome will minimize the likelihood of false results and improve the accuracy of predictive DHR potential of the test chemical. In addition, we compared the skin explant results to the T cell proliferation and IFNγ results. Table 2 summarises the predictive capacity of each test relative to the effect of a drug concentration response and overall prediction. The results show that, of the 3 test assays, the skin explant test has the highest correct predictive outcome when considering the concentration response. The data show the skin explant
Authorship contributions Ahmed, S.S., Ahmed M.M., Burgess, M., Bibby L., Darby L., Watson, J. performed experiments and analysed results. Ahmed S.S. carried out the statistical analysis. Ahmed S.S., Dickinson, A.M. and Whritenour J. wrote the report. Dickinson, A.M. and Wang, X.N. performed the histopathological analysis. Dickinson, A.M. and Whritenour J. designed the research.
Conflict of interest disclosure The authors have no conflicts of interest.
Submission declaration and verification The work described has not been previously published or submitted for publication elsewhere. The publication is approved by all authors. 46
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Funding
Acknowledgments
This work was supported by Pfizer Inc. who also contributed to the study design and report writing.
The authors thank Elizabeth Douglas and Patricia Schneider (Pfizer Inc.) for technical support and Kim Pearce for Statistical support.
Appendix A Table A1 Test results for skin explant tests, T cell proliferation and IFNγ concentrations in response to the highest test concentration for each drug. Results for each drug in response to the high concentration showing percentage of positive (grade ≥ II) skin explant tests, mean SI value for T cell proliferation and mean fold increase in IFNγ concentration responses. (Highest test concentration)
Clinical Classification % of positive skin explant tests (n = 4) T Cell proliferation mean SI (n = 6) IFNγ concentration mean fold increase (n = 6)
Abacavir (100 μg/mL) Amoxicillin (200 μg/mL) Carbamazepine (200 μg/mL) Diclofenac (200 μg/mL) Lamotrigine (200 μg/mL) Lapatinib (100 μg/mL) Lumiracoxib (100 μg/mL) Nevirapine (100 μg/mL) Ofloxacin (200 μg/mL) Phenytoin (100 μg/mL) Propranolol (100 μg/mL) Sulfamethoxazole (200 μg/mL) Acetaminophen (200 μg/mL) Cimetidine (200 μg/mL) Flecainide (25 μg/mL) Metformin (200 μg/mL) Verapamil (25 μg/mL)
Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive
75 100 100 100 75 75 50 75 100 50 100 50
3.10 4.40 3.50 3.90 4.00 3.7 3.60 4.50 4.00 4.10 4.40 2.70
3.84 4.57 4.72 3.98 5.12 5.53 3.75 4.13 3.66 4.15 3.95 3.10
Negative Negative Negative Negative Negative
25 0 25 0 75
2.70 2.50 2.50 2.50 3.60
1.93 2.24 3.07 2.70 3.90
Table A2 Test results for skin explant tests, T cell proliferation and IFNγ concentrations in response to the intermediate test concentration for each drug. Results for each drug in response to the high concentration showing percentage of positive (grade ≥ II) skin explant tests, mean SI value for T cell proliferation and mean fold increase in IFNγ concentration responses. Intermediate test concentration Clinical classification % positive skin explant tests (n = 4) T Cell proliferation mean SI (n = 6) IFNγ concentrations mean fold increase (n = 6) Abacavir (50 μg/mL) Amoxicillin (100 μg/mL) Carbamazepine (100 μg/mL) Diclofenac (100 μg/mL) Lamotrigine (100 μg/mL) Lapatinib (50 μg/mL) Lumiracoxib (50 μg/mL) Nevirapine (50 μg/mL) Ofloxacin (100 μg/mL) Phenytoin (50 μg/mL) Propranolol (50 μg/mL) Sulfamethoxazole (100 μg/mL) Acetaminophen (100 μg/mL) Cimetidine (100 μg/mL) Flecainide (10 μg/mL) Metformin (100 μg/mL) Verapamil (10 μg/mL)
Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Negative Negative Negative Negative Negative
75 100 100 100 75 75 50 75 100 50 100 25 25 0 25 0 50
3.10 4.00 3.40 3.80 3.80 3.10 3.40 4.30 3.70 3.60 4.30 2.60 2.70 2.40 2.20 2.50 3.60
3.38 4.08 4.50 3.82 4.75 3.09 3.41 3.76 3.59 3.16 3.95 2.83 1.81 1.84 3.02 2.00 3.30
Table A3 Test results for skin explant tests, T cell proliferation and IFNγ concentrations in response to the lowest test concentration for each drug. Results for each drug in response to the high concentration showing percentage of positive (grade ≥ II) skin explant tests, mean SI value for T cell proliferation and mean fold increase in IFNγ concentration responses. Low test concentration
Clinical class
% positive skin explant tests (n = 4)
T Cell proliferation mean SI (n = 6)
IFNγ concentrations mean fold increase (n = 6)
Abacavir (25 μg/mL) Amoxicillin (50 μg/mL) Carbamazepine (50 μg/mL) Diclofenac (50 μg/mL) Lamotrigine (50 μg/mL) Lapatinib (10 μg/mL)
Positive Positive Positive Positive Positive Positive
100 100 100 100 75 75
3.10 4.10 3.00 3.40 3.60 2.50
3.14 3.75 4.23 3.92 4.76 2.62
(continued on next page) 47
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Table A3 (continued) Low test concentration
Clinical class
% positive skin explant tests (n = 4)
T Cell proliferation mean SI (n = 6)
IFNγ concentrations mean fold increase (n = 6)
Lumiracoxib (10 μg/mL) Nevirapine (25 μg/mL) Ofloxacin (50 μg/mL) Phenytoin (10 μg/mL) Propranolol (25 μg/mL) Sulfamethoxazole (50 μg/mL) Acetaminophen (50 μg/mL) Cimetidine (50 μg/mL) Flecainide (5 μg/mL) Metformin (50 μg/mL) Verapamil (5 μg/mL)
Positive Positive Positive Positive Positive Positive Negative Negative Negative Negative Negative
50 50 100 50 100 25 25 0 25 25 75
3.00 3.00 3.40 3.10 3.90 2.50 2.40 2.20 2.10 2.30 3.20
2.76 3.07 3.92 2.26 4.24 2.21 1.51 1.62 2.78 1.84 2.75
Appendix B. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.taap.2019.02.005.
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Evaluation of a human in vitro skin test for predicting drug hypersensitivity reactions
T
Ahmed S.S.a, Whritenour J.c, Ahmed M.M.b, Bibby L.a,b, Darby L.b, Wang X.N.b, Watson J.b, ⁎ Dickinson A.M.a,b, a
Alcyomics Ltd, Bulman House, Regent Centre, Gosforth, Newcastle-upon-Tyne NE3 3LS, United Kingdom Haematological Sciences, Institute of Cellular Medicine, Newcastle University, Newcastle-upon-Tyne NE2 4HH, United Kingdom c Pfizer Inc., Drug Safety Research and Development, Eastern Point Rd, Groton, CT 06340, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Low molecular weight drugs Drug hypersensitivity Drug allergy Drug induced T cell responses Hazard identification
The occurrence of drug hypersensitivity reactions (DHRs) following administration of low molecular weight (LMW) drugs is an important health concern. However, in vivo animal models which could be used as tools for the prediction of DHRs are lacking. As a result, research has focused on development of in vitro tools for predicting DHRs. In this study a novel human in vitro pre-clinical skin explant test was used to predict T cellmediated hypersensitivity responses induced by LMW drugs. Responses in the skin explant test for 12 LMW drugs associated with T cell-mediated hypersensitivity in the clinic (abacavir, amoxicillin, carbamazepine, diclofenac, lamotrigine, lapatinib, lumiracoxib, nevirapine, ofloxacin, phenytoin, propranolol, sulfamethoxazole) were compared with responses for 5 drugs with few/no reports of T cell-mediated hypersensitivity reactions (acetaminophen, cimetidine, flecainide, metformin, verapamil). Changes in skin histology following in vitro exposure to the drugs as well as T cell proliferation and interferon gamma (IFNγ) production were studied. The results of the skin explant assays showed a good positive correlation (r = 0.77, p < .001) between the test outcome (prediction of positive or negative) and the clinical classification of the tested drugs. The T cell proliferation assay showed a correlation of r = 0.60 (p < .01) and the IFNγ assay r = 0.51 (p < .04). The data suggest that the skin explant model could be a useful tool to predict the potential of LMW drugs to induce DHRs.
1. Introduction A large number of low molecular weight (LMW) drugs fail in clinical development. Lack of efficacy along with drug hypersensitivity reactions (DHRs) are a major cause of failure or withdrawal of new LMW drugs (Hay et al., 2014). Despite a battery of clinical and pre-clinical tests used for evaluating the safety of drugs, DHRs are often undetected until drugs are tested in large clinical trials or marketed (Pourpak et al., 2008). This late failure of drugs due to DHRs clearly indicates the need for predictive tools during the early stages of pre-clinical drug development. DHRs can be categorized based on mechanism (Baldo, 2013) and may include allergic reactions caused by adaptive immune mechanisms or non-allergic reactions caused by pharmacologic interaction of the drug with immune cells (e.g., pseudoallergy) (Pichler and Hausmann, 2016; Corsini et al., 2018). Pseudoallergic drug reactions are typically dose dependent and can be observed in animals during pre-clinical
development, whereas allergic drug reactions are rarely observed during pre-clinical drug development (Whritenour et al., 2016). There are currently no in vitro pre-clinical tools available which have been validated to screen for potential allergic DHRs. However, there are some in vivo models such as the modified popliteal lymph node assay (PLNA) (Warbrick et al., 2001; Pieters, 2001), the lymph node proliferation assay (LNPA) (Weaver et al., 2005) and the mouse allergy model (Whritenour et al., 2014; Zhu et al., 2015), which show some potential for predicting DHRs. Drug hypersensitivity can be related to genetic predisposition (Pirmohamed, 2006) occurring in only a minority of the population and therefore can be difficult to predict, especially using animal models (Bala et al., 2005). Consequently, in vitro testing using human material may be an attractive alternative. Tests developed more specifically for determining sensitisation potential of topically administered chemicals/compounds such as the human cell line activation test (h-CLAT), or the direct peptide reactivity assay (DPRA), could be further investigated as suitable tools for predicting
⁎ Corresponding author at: Haematological Sciences, Institute of Cellular Medicine, Medical School, University of Newcastle upon Tyne, Newcastle upon Tyne NE2 4HH, UK. E-mail address: [email protected] (A.M. Dickinson).
https://doi.org/10.1016/j.taap.2019.02.005 Received 4 October 2018; Received in revised form 4 February 2019; Accepted 11 February 2019 Available online 12 February 2019 0041-008X/ © 2019 Elsevier Inc. All rights reserved.
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tests. 1 Fisher Scientific, Hampton, NH; 2Sigma Aldrich, St Louis, MO; 3 Pfizer in-house; 4LC Laboratories, Woburn, MA; 5Toronto Research Chemicals, Inc.; 6AK Scientific Inc., Union City, CA.
DHRs for systemically administered compounds given that there is likely overlap in mechanisms driving both responses (Corsini et al., 2018).Since we have used a human skin explant assay for a similar assessment of chemical compounds (Ahmed et al., 2016) with a sensitivity and specificity of 95%, we used the same skin explant assay in the present study to predict potential DHRs with systemically administered LMW drugs. The objective of the current study was to evaluate the human in vitro skin explant assay for predicting DHRs to LMW drugs. The skin explant assay used in these studies was modified from the original predictive test, which has been extensively used to understand the underlying mechanism and immunobiology of systemic graft versus host disease (GvHD) (Dickinson et al., 1999; Dickinson et al., 1988; Vogelsang et al., 1985), (Dickinson et al., 2002; Jarvis et al., 2002; Ruffin et al., 2011; Mavin et al., 2012; Ahmed et al., 2015). The modified assay has also been used as a method for predicting skin sensitisation potential of chemical compounds (Ahmed et al., 2016). The skin explant model offers the potential to study the initiation of an immune response through interaction of dendritic cells and T cells and resulting T cell activation. In the current study we have extended the use of this assay to test a panel of systemically administered LMW drugs that are associated (or not) with hypersensitivity reactions in the clinic. As previously described by Ahmed and colleagues, the assay was used in an autologous setting, in this instance autologous refers to when the different cell types and skin used in the assay are derived from the same donor. The assay involves activation of dendritic cells, assessment of T cell proliferation, cytokine production, and histopathological assessment of damage to skin tissue as an indication of the degree of damage caused by activation of T cells. The skin reaction is scored in severity from grade I-IV (Lerner et al., 1974) and a score of a grade II or above is used to predict a positive DHR. Seventeen LMW drugs were evaluated in the skin explant assay; this included 5 drugs with low or no association with DHRs (acetaminophen, cimetidine, flecainide, metformin and verapamil) and 12 drugs (abacavir, amoxicillin, carbamazepine, diclofenac, lamotrigine, lapatinib, lumiracoxib, nevirapine, ofloxacin, phenytoin, propranolol and sulfamethoxazole) that have been reported to be associated with DHRs in the clinic. In the same assay, the effects of the drugs on T cell proliferation and IFNγ production were also investigated. The objective of the study was to determine if the skin explant assay could be used as a pre-clinical test to predict the potential of LMW drugs to cause DHRs.
2.2. Human samples & tissue culture Local Research Ethics Committee (LREC) approval was granted prior to study commencement. Healthy volunteers were screened using an allergy questionnaire and eliminated from the study if they had allergies or were unwell or on prescribed medication. Peripheral blood (60 mL) and abdominal skin punch biopsies (2 × 4 mm) were obtained from healthy volunteers on the day of use, following informed consent. Biopsies were processed by washing in PBS (Lonza) and removing any residual excess fat before dissection for the skin explant assay. Standard culture conditions of 5% CO2 at 37 °C were used. Peripheral blood mononuclear cells (PBMCs) were isolated using density-gradient centrifugation using the Lymphoprep™ (Axis Shield, UK) method and then used for positive selection of CD14+ monocytes (MACS®, Miltenyl Biotec). The CD14- fraction was also collected and used as a source of autologous T cells for further tests. Monocyte derived dendritic cells (MoDCs) were generated as previously described (Ahmed et al., 2016). Briefly, the CD14 + monocytes were cultured with GM-CSF (50 ng/mL) and IL-4 (50 ng/mL) for 24 h. This was followed by a 24 h culture with maturation stimuli (LPS 0.1 μg/mL, IL-6 10 ng/mL, IL-1β 10 ng/mL, TNFα 10 ng/mL, PGE2 1 μM (Immunotools and Sigma-Aldrich) resulting in a DC phenotype (Kvistborg, 2009). Unless specified, cells were cultured in RF10 media. Skin biopsies were collected in X-Vivo™ 10 (Lonza) media.
2.3. Cell viability test Viability of PBMCs following exposure to drugs for 24 h was determined using the Trypan Blue Exclusion Test. Drugs were cultured with PBMCs (2 × 105 cells) at several test concentrations in duplicate (Table 1 and Supplementary data). A 1:1 volume of cells was added to 0.4% Trypan Blue and the number of viable (unstained) and dead (stained) cells were counted using a hemocytometer.
2.4. Skin explant test Following a 24 h exposure of 0.3–0.5 × 105 MoDCs to the drug at the evaluation test concentrations (Table 1), cells were harvested and co-cultured with autologous T cells (CD14- fraction) at concentrations of 3–5 × 105 MoDCs and 3–5 × 106 T cells for 4 days in RPMI 1640 (Gibco, UK) containing 100 IU/mL penicillin, 100 μg/mL streptomycin (Gibco UK) and 2 mM L-glutamine (Gibco UK) supplemented with 10% v/v heat inactivated human AB serum (Sigma-Aldrich, UK). Skin biopsies were co-cultured with the primed T cells (1 × 106/well) for 3 days. Skin was formalin fixed and then paraffin embedded, sectioned, and stained with haematoxylin and eosin for histopathological evaluation. A blind analysis was performed by two independent examiners. Histological damage was graded from 0 to IV according to the Lerner criteria (Lerner et al., 1974; Vogelsang et al., 1985) and characterized as grade 0 no observable damage to skin keratinocytes, grade I mild vacuolisation of basal cells, grade II vacuolisation of basal cells and dyskeratotic bodies, grade III sub-epidermal cleft formation at the dermal epidermal junction and grade IV complete epidermal separation. Based on previous results using this grading system, grades 0 and I are regarded as negative results and a grade of ≥II as a positive result. In each assay, skin explants cultured in media alone were used as a background control. Autologous T cells cultured with 0.001% triton-x served as a negative control and 0.1 μM DNCB as a positive control, as previously described (Ahmed et al., 2016).
2. Materials & methods 2.1. Test drugs Drugs used in this study included abacavir1, acetaminophen2, amoxicillin2, carbamazepine3, cimetidine2, diclofenac2, flecainide2, lamotrigine3, lapatinib4, lumiracoxib5, metformin2, nevirapine2, ofloxacin6, phenytoin3, propranolol3, sulfamethoxazole3 (SMX), and verapamil3 (Table 1). Drugs were classified as positive or negative for hypersensitivity reactions based on information contained within their labels in addition to available clinical and/or mechanistic data suggestive of an adaptive immune response. Drug solubility was achieved using either RF10 media (RPMI 1640 [Gibco, UK] containing 100 IU/ mL penicillin, 100 μg/mL streptomycin [Gibco, UK] and 2 mM L-glutamine [Gibco, UK] supplemented with 10% v/v heat inactivated fetal calf serum [Sera Lab]) or 10% dimethyl sulfoxide (DMSO [Fisher Scientific, UK]) in phosphate buffered saline (PBS). Some drugs required incubation at 37 °C (10–20 min) for complete solubility in the chosen vehicle. Drug solutions were prepared fresh on the day of use. Drug concentrations were selected after performing viability studies (Table 1) at a number of concentrations. 2, 4-dinitrochlorobenzene (DNCB; 0.1 μM) (Sigma-Aldrich) was used as a positive control and 0.001% triton-x (Sigma-Aldrich) was used as a negative control in all 40
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Table 1 List of drugs tested and concentrations used. The drug name and drug classification is given as positive (association with DHR) or negative (low or no association with DHR). Drug label information is categorized as boxed warning (risk of a serious adverse reaction), warning (risk of an adverse reaction), adverse reaction (adverse reaction reported), none (no reference on label information) (dailymed.nlm.nih.gov). Vehicle used (DMSO or RPMI media), drug concentrations (μg/ml) used for viability tests to determine test concentrations and selected test concentrations for the skin explant, T cell proliferation, and IFNγ production assays are given. Drug
Classification
Drug hypersensitivity label information
Vehicle
Cell viability test concentrations (μg/ mL)
Evaluation test concentrations (μg/ mL)⁎⁎
Abacavir
Positive
Boxed warning, (hypersensitivity reactions)
500, 250, 100, 50, 25
100, 50, 25
Amoxicillin
Positive
500, 250, 100, 50, 25
200, 100, 50
Carbamazepine Diclofenac Lamotrigine Lapatinib
Positive Positive Positive Positive
500, 500, 500, 100,
200, 200, 200, 100,
Lumiracoxib Nevirapine Ofloxacin Phenytoin Propranolol Sulfamethoxazole Acetaminophen Cimetidine Flecainide Metformin
Positive Positive Positive Positive Positive Positive Negative Negative Negative Negative
Warning, (hypersensitivity (anaphylactic) reactions) Boxed warning, (hypersensitivity reactions) Warning (anaphylactic reactions) Boxed warning (hypersensitivity reactions) Warning, (hypersensitivity (anaphylactic) reactions) Withdrawn from market Boxed warning, (hypersensitivity reactions) Warning (hypersensitivity reactions) Warning (hypersensitivity reactions) Warning (hypersensitivity reactions) Warning (hypersensitivity reactions) None Rare None None
Media (RPMI) 10% DMSO⁎
Verapamil
Negative
Rare
⁎ ⁎⁎
10% 10% 10% 10%
DMSO⁎ DMSO⁎ DMSO⁎ DMSO⁎
10% DMSO⁎ 10% DMSO⁎ Media (RPMI 10% DMSO⁎ 10% DMSO⁎ 10% DMSO⁎ 10% DMSO⁎ 10% DMSO⁎ 10% DMSO⁎ Media (RPMI) 10% DMSO⁎
250, 100, 50 250, 100, 50 250, 100, 50 50, 10
100, 50, 10 100, 50, 25 500, 250, 100, 100, 50, 10 500, 250, 100, 200, 100, 50 500, 250, 100, 500, 250, 100, 25, 10, 5 500, 250, 100, 50, 25, 10
50 50, 25, 10 50 50 50
100, 50 100, 50 100, 50 50, 10
100, 50, 10 100, 50, 25 200, 100, 50 100, 50, 10 100, 50, 25 200, 100, 50 200, 100, 50 200, 100, 50 25, 10, 5 200, 100, 50 25, 10, 5
Final concentration of DMSO in the assays was 0.1%. Cell viability 70–80% at highest concentration (with exception to Propranolol) and > 80% at intermediate and lower concentrations.
overall prediction was determined by evaluating the mean responses to the high, intermediate and low drug concentrations. If two or more concentrations gave a mean positive response, then the drug was considered to be positive. If however, two or more concentrations gave a mean negative response then the drug was considered to be negative. This overall prediction criterion was applied to the skin explant grades, where a positive response was considered grade ≥ IIT cell proliferation assays where a positive response was considered an SI ≥ 3 (Log2 1.58) and to the IFNγ assays where a positive response was considered a fold increase ≥ 3 (Log2 1.58). Statistical analysis was performed using SPSS Statistics V21. Multiple comparison analysis was performed using univariate, post-hoc Bonferroni tests. Pearson's correlation was performed to test for strength of association between test methods. Receiver operating characteristic (ROC) curve analysis was performed by SPSS Statistics V21 and used to determine the predictive ability of the tests evaluated in this study. Sensitivity is plotted on the y-axis and 1-specificity on the x-axis. The area under the curve (AUC) is a measure of the test accuracy where a value of 1 demonstrates the test is 100% accurate in predicting if the LMW drugs will cause a DHR and an inaccurate test would have an AUC below 0.5. The p value assesses the null hypothesis that the AUC is 0.5.
2.5. Analysis of T cell proliferation and IFNγ production MoDCs generated from peripheral blood were exposed to the test drugs at the selected concentrations for 24 h (Table 1). Cells were then cultured with autologous T cells (CD14- fraction) at a ratio of 1 × 104 MoDC: 1 × 105 T cells for 5 days. All tests were performed in triplicate. Positive (DNCB), negative (triton-x) or/and vehicle (0.1% DMSO prepared in RPMI) controls were included in each assay. Supernatants were collected for IFNγ analysis prior to [3H] thymidine addition on day 5. [3H] thymidine pulsed cells were harvested after 16–18 h and subsequently counted using a β-scintillation counter. IFNγ was measured by flow cytometry using a CBA Flex Kit (BD Biosciences). Data were analysed using Prism Graph Pad software (V5). 2.6. Data and statistical analysis Skin explant assays were performed using samples collected from 4 individual healthy volunteers. Tissue histopathology was analysed and grading scores (0-IV) were assigned. A response was considered positive if observed changes in skin tissue were assigned a grade ≥ II based on the histopathological damage. A grade I response was considered a negative response. T cell proliferation and IFNγ assays were performed on each compound using samples collected from 6 individual healthy volunteers (4 of which were the same individuals from which the skin biopsies were collected). T cell proliferation was measured by [3H]Thymidine incorporation in counts per minute (cpm). Analysis was performed by calculating a stimulation index (SI) of T cell proliferation by dividing the cpm value of cells treated with each drug with the cpm value of cells treated with vehicle only (0.1% DMSO). IFNγ (pg/mL) analysis was performed by calculating a fold increase in IFNγ concentrations by dividing the value obtained from cells treated with each drug with the value obtained from cells treated with vehicle only (0.1% DMSO). The cut off value of a 3-fold increase (Basketter et al., 1999) was considered to be a positive response for the T cell proliferation and IFNγ endpoints. Results for each test method are given as an overall prediction as positive or negative for the potential of each drug to cause a DHR. The
3. Results 3.1. Determining non-toxic test concentrations Drugs were initially tested at several concentrations to determine effects on PBMC viability after 24 h of exposure (Table 1 and Supplemental Table 1). Viability > 70% was considered to be low drug toxicity, however, for most compounds the viability was > 80%. 3.2. Evaluation of human in vitro drug responses using the skin explant assay The capacity of the skin explant assay to predict the potential of LMW drugs to induce DHRs was evaluated using a panel of drugs with known associations (or not) with DHRs in the clinic. A set of 12 positive 41
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Fig. 1. Representative images of skin explant sections showing histopathology of skin after exposure to LMW drugs or controls. Haematoxylin and Eosin stained 3 μm skin sections. Drugs classified as negative, A-D: acetaminophen (200 μg/mL), cimetidine (200 μg/mL), metformin (200 μg/mL) and flecainide (25 μg/mL), respectively, showing a grade I negative response. Drugs classified as positive, E-H: carbamazepine (200 μg/mL), ofloxacin (200 μg/mL), lapatinib (100 μg/mL), and nevirapine (100 μg/mL), respectively, showing a grade III positive response. I: Negative control, triton-X (0.001%) grade I. J: Positive control, DNCB (0.1 μM), grade III. Arrows indicate damage to the dermal/epidermal junction and formation of clefts following 24 h exposure of MoDC to the drugs followed by co-culture of MoDC with autologous T cells and then further co-culture of primed T cells with autologous skin.
negative response at the low concentration. Lumiracoxib gave a mean positive response at the high concentration and a mean negative response at the intermediate and low concentrations. Phenytoin elicited a mean positive response at the high and low concentrations and a mean negative response at the intermediate concentration. Sulfamethoxazole did not cause a mean positive response at any concentration tested. Four of the five LMW drugs clinically classified as negative (acetaminophen, cimetidine, flecainide and metformin) gave a grade I mean negative response to all 3 concentrations. Verapamil, however, showed a mean positive response at the high concentration and a mean negative response at the intermediate and low concentrations. In summary, an overall prediction for each LMW drug was determined by considering the mean positive or mean negative responses observed at each concentration using the criterion that if a mean positive response was observed in at least 2 of 3 concentrations then the
and 5 negative drugs were selected and each drug was tested at 3 concentrations (Table 1). A minimum of 4 independent skin explant assays were performed per drug. Histopathological grading was used to determine if a drug gave a positive or negative response. Representative images of positive and negative responses are shown in Fig. 1. The mean drug responses for each compound showing a mean positive (grade ≥ II, represented by vacuolisation of keratinocytes on the dermal/epidermal junction) or a mean negative response (grade 0–I, no damage on the dermal/epidermal junction) in response to high, intermediate and low test concentrations (Fig. 2) were calculated. Seven of the LMW drugs clinically classified as positive (abacavir, amoxicillin, carbamazepine, diclofenac, lapatinib, ofloxacin and propranolol) gave mean positive responses (grade II ≥) in the skin explant test at all 3 test concentrations. Lamotrigine and nevirapine gave a mean positive response at the high and intermediate concentrations and a mean 42
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Fig. 2. LMW drug responses in the skin explant assay. Responses of 12 drugs tested at high (1), intermediate (2) and low (3) concentrations. Graph shows mean skin explant grade responses for each drug at each concentration. The black line denotes the cut-off grade for a positive response (≥II). A mean response below this cut off signifies no tissue damage and indicates a negative response.
3.3. Analysis of T cell proliferation responses
test drug was considered positive for DHR. Equally, a mean negative response to at least 2 concentrations determined the test drug as negative for DHR. Using these criteria, 15 (abacavir, acetaminophen, amoxicillin, carbamazepine, cimetidine, diclofenac, flecainide, lamotrigine, lapatinib, metformin, nevirapine, ofloxacin, propranolol, phenytoin and verapamil) of 17 drugs (88%) gave a correct predictive response using the skin explant. Lumiracoxib and sulfamethoxazole gave false negative responses. Next, we wanted to determine if the mean grade response produced was due to independent factors such as the drug (independent of the test concentration) or the concentration or alternatively due to the effect of a combined interaction between the drug and concentration (both factors together). A two-way Anova was performed and showed that the drug (p < .0005) and the concentration (p < .0005) as independent factors had a statistically significant effect on the graded response. No significant effect was observed for the interaction between drug and concentration on the graded response. Furthermore, we analysed if the graded responses observed were significantly different between high, intermediate and low drug concentrations. A post-hoc multiple comparison analysis showed a significant difference with regards to mean grade for the high concentration when compared to the intermediate (p < .009) or low concentrations (p < .0005). We measured the strength of the correlation between the overall skin explant prediction and clinical outcome and determined a Pearson's correlation coefficient r = 0.77 (p < .001) (Table 2).
Six individual experiments were performed for each test drug at low, intermediate and high concentration and results were calculated as SI of T cell proliferation responses in comparison to responses observed for vehicle control samples. While the magnitude of the proliferative response varied between volunteers, the results for each drug (negative or positive) were generally consistent across study volunteers. T cell proliferation responses to the drugs were dichotomised as positive or negative using a cut-off for a significant stimulation index set at 1.58Log2 which corresponded to an SI of a 3-fold change. Based on this cut-off, the mean drug responses for each LMW drug showing a positive (≥1.58Log2) or a negative (< 1.58Log2) response in presence of high, intermediate and low test concentrations were studied. From the LMW drugs clinically classified as positive, amoxicillin (2.08, 1.95 and 2.00), diclofenac (1.95, 1.87 and 1.75), lamotrigine (2.00, 1.82 and 1.84), ofloxacin (1.99, 1.89 and 1.75) and propranolol (2.07, 2.07 and 1.91) showed a mean positive response to high intermediate and low concentrations, respectively (Fig. 3). Abacavir (1.62 and 1.62), carbamazepine (1.81 and 1.74), lumiracoxib (1.81 and 1.69), nevirapine (2.14 and 2.03) and phenytoin (1.89 and 1.76) showed a positive mean response to high and intermediate concentrations respectively and a negative mean response (1.56, 1.54, 1.54, 1.51 and 1.51, for each molecule respectively) at the low concentration. Lapatinib showed a positive response to the high concentration (1.83) and negative mean response to the intermediate and low concentrations (1.56 and 1.18) and sulfamethoxazole showed a negative mean response to all 3 test concentrations (1.39, 1.37 and 1.33, respectively).
Table 2 Performance analysis of test assays. Assay
Overall predictive outcome
Correlation overall prediction to clinical classification
Inter-assay correlation
Skin Explant T cell proliferation IFNγ production
88% 82% 76%
r = 0.77 (p < .001) r = 0.60 (p < .01) r = 0.51 (p < .04)
T cell assay r = 0.87 (p < .0005) IFNγ r = 0.88 (p < .0005) Skin Explant Assay r = 0.77 (p < .0005)
Table shows the overall predictive outcome of each assay as a percentage for the 12 positive and 5 negative LMW drugs tested, correlation of the overall prediction of each assay to the clinical classification and inter assay correlations. 43
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Fig. 3. T cell proliferation responses following exposure to LMW drugs. Graph shows stimulation indices of T cells relative to vehicle control samples following treatment with 17 LMW drugs. Data show mean responses of 6 independent experiments for each test drug at high (1), intermediate (2) and low (3) concentrations. Data are expressed as SI Log2. The black line denotes the cut-off for a 3 fold (SI Log2 1.58) increase in stimulation indices of T cell proliferation above baseline levels which indicates a positive response.
Of the five LMW drugs clinically classified as negative, four produced a mean negative response to all 3 concentrations: acetaminophen, cimetidine, flecainide and metformin, however, verapamil produced a mean positive response to the high and intermediate concentrations (1.75 and 1.74, respectively) and a mean negative response (1.52) at the low concentration. To summarise the overall prediction capacity of the T cell proliferation assays using the criteria described earlier, 14 (abacavir, acetaminophen, amoxicillin, carbamazepine, cimetidine, diclofenac, flecainide, lamotrigine, lumiracoxib, metformin, nevirapine, ofloxacin, propranolol and phenytoin) of 17 drugs (82%) were predicted correctly. There were false negative responses with lapatinib and sulfamethoxazole and verapamil produced a false positive response. The data show a statistically significant effect of the specific drug (p < .005) and also a statistically significant effect of concentration (p < .005), as independent factors on T cell proliferation responses. However, analysis to determine the effect of both factors combined showed no statistically significant effect of a drug and concentration interaction on T cell proliferation responses. Comparison of the difference in T cell proliferation responses between high, intermediate and low concentrations showed a statistically significant mean difference between the high and the low concentration (p < .0005) and between the intermediate and the low concentration (p < .007). No significant mean difference was observed between the intermediate and the high concentrations. Association of overall T cell proliferation prediction outcome to clinical outcome showed a Pearson's correlation coefficient of r = 0.60 (p < .01) (Table 2).
(1.96, 1.92 and 1.96), lamotrigine (2.32, 2.21 and 2.21), ofloxacin (1.81, 1.74 and 1.85) and propranolol (1.90, 1.91 and 2.01) showed a mean positive fold increase (≥1.58Log2) at the high, intermediate and low concentrations, respectively. Abacavir (1.92 and 1.72) lumiracoxib (1.87 and 1.75) and nevirapine (1.97 and 1.78) produced a mean positive fold increase at the high and intermediate concentrations and a mean negative response (1.45, 1.31 and 1.42 for the three molecules, respectively) at the low concentration. Lapatinib and phenytoin produced a mean positive fold increase (2.15 and 1.96, respectively) at the high concentration only and sulfamethoxazole did not produce a mean positive fold increase at any concentration. From the LMW drugs clinically classified as negative; acetaminophen, cimetidine, flecainide and metformin all had a mean negative response at all 3 concentrations. However, a mean positive fold increase was seen with verapamil at the high and intermediate concentrations (1.78 and 1.59, respectively) and a mean negative response (1.41) at the low concentration. Using the criteria described earlier for the overall prediction, 13 (abacavir, acetaminophen, amoxicillin, carbamazepine, cimetidine, diclofenac, flecainide, lamotrigine, lumiracoxib, metformin, nevirapine, ofloxacin and propranolol) of 17 drugs (76%) were correctly predicted in the IFNγ assay; lapatinib, phenytoin and sulfamethoxazole showed a false negative response and verapamil showed a false positive response. The data show a statistically significant effect of the drug on IFNγ levels (p < .0005) in cell culture supernatants. A statistically significant effect of concentration (p < .005) on IFNγ levels was also observed. However, no statistically significant effect was observed when the combination of drug and concentration were analysed, suggesting that the two are independent factors. Comparison of the difference in IFNγ levels between high, intermediate and low concentrations showed a statistically significant mean difference between low and intermediate concentrations (p < .03), low and high concentrations (p < .0005) and between intermediate and high concentrations (p < .04). A Pearson's correlation coefficient of r = 0.51 (p < .04) was determined between the overall IFNγ prediction outcome and clinical outcome (Table 2).
3.4. IFNγ cytokine production Supernatants collected from cell culture during the T cell proliferation assays were analysed for IFNγ as a marker of immune activation. Inter-donor variability was observed in the range of responses in IFNγ (pg/ml). Six individual experiments were performed for each test. The fold increase of IFNγ production in cell culture supernatants relative to vehicle control samples was considered as positive or negative using a cut-off of a 3-fold increase (1.58Log2). Results are presented as mean fold increase (Log 2) (Fig. 4). From the LMW drugs clinically classified as positive, amoxicillin (2.13, 2.01 and 1.89), carbamazepine (2.17, 2.11 and 2.02), diclofenac
3.5. ROC curve analysis and inter-assay correlation To quantify how accurately the tests could discriminate between positive and negative LMW drug DHR responses, ROC curve analysis 44
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Fig. 4. IFNγ levels in cell culture supernatants following exposure to LMW drugs. Fold increase of cytokine levels in comparison to basal levels following treatment with 17 LMW drugs. Graph shows mean responses of 6 independent experiments for each test drug at high (1), intermediate (2) and low (3) concentrations. The black line denotes the cut-off for a 3 fold increase (SI Log2 1.58) in IFNγ concentrations above vehicle control values which indicates a positive response.
Fig. 5. ROC curve analysis showing accuracy of each test to discriminate between LMW drugs which do or do not cause DHR. Performance of the 3 tests is plotted and represented by the ROC curves. The accuracy of each test is detailed in the table which gives the area under the curve (AUC), standard error statistical significance of each test, sensitivity and specificity.
significantly (p < .045) discriminate between the LMW drugs which do or do not cause DHR. The IFNγ assay showed AUC = 0.78 with 75% sensitivity and 80% specificity, while this indicates the test performed well it did not show a statistically significant discriminatory power (p < .08) to separate the LMW drugs between positive and negative events for DHR. To determine the association of responses between the tests, a Pearson's correlation coefficient was determined (Table 2). A strong
was performed (Fig. 5) using the drug clinical classifications (positive or negative) against the prediction outcome of each test. The AUC indicates the predictive accuracy of each test. For the skin explant assay the AUC was 0.86 with 92% sensitivity and 80% specificity, indicating the skin explant assay significantly (p < .02) discriminated between the LMW drugs which cause DHR from those that do not. For the T cell proliferation assay, the AUC was 0.82 with 83% sensitivity and 80% specificity, indicating the T cell proliferation assay is able to 45
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test is more sensitive at correctly predicting the drug DHR outcome at lower concentrations while the T cell proliferation and IFNγ endpoints lose their ability to determine positivity at the low concentration. Additionally, the overall prediction (as described in methods) for each test showed the skin explant test had the strongest predictive capacity which was supported by the ROC curve data. All 3 tests showed a false negative response for sulfamethoxazole which is generally associated with a relatively high DHR incidence once metabolised (Sanderson et al., 2007). We believe this result was probably related to a reduced presence of the metabolite SMX-NO, and therefore the full potential of this drug to elicit an immune response was dampened. We have previously tested the metabolite SMX-NO in the skin explant assay and obtained strong positive results (data not shown). Moreover, we have shown this response to be antigen-specific (Ahmed et al., 2016). The positive response of the test drugs was evident from the histopathological analysis of skin tissue. Some variation in the degree of tissue damage (ranging between grades II–IV) caused by the same drug between volunteers was observed. This is likely to be related to donor variability and the specific intensity of the reaction determined by the donor's genetic variability and the drug structure or composition. A similar variation in responses was also observed for T cell proliferation and IFNγ production, however, it was still possible to clearly discriminate which test drugs were able to induce an increase in T cell responses in association with IFNγ production. Responses at the high concentration for each drug tested in the skin explant, T cell proliferation and IFNγ assays are provided in the appendices (Table A1). Overall, a strong correlation between the different test methods was observed. This suggests that, despite the different features or endpoints of the tests, the mechanisms occurring in the tests to drive the response are closely related. In conclusion, the skin explant assay offers a human in vitro model with a novel endpoint to predict the potential of a drug to cause DHRs which standard toxicology tests do not offer. The skin damage observed in response to drug exposure is reflective of immune activation and the production of cytokine and other cytotoxic molecules by the activated T cells. The study outcome was encouraging and demonstrates that the skin explant assay could be used as a valuable predictive tool either as an independent test or in conjunction with in vivo assays mentioned earlier. Given that the n-value of the study was low and that predictive sensitivity and specificity based clinical classification can be presumptive, a further validation study with additional test compounds and a larger healthy donor cohort would be of value. However, the 3 tests used in this study could be used collectively to further predict and understand the mechanism for DHRs induced by LMW drugs.
positive correlation was observed between the T cell proliferation and the IFNγ data (r = 0.88, p < .0005), between the skin explant and T cell proliferation data (r = 0.87, p < .0005) and between the skin explant and IFNγ data (r = 0.77, p < .0005). 4. Discussion The use of human primary cells to predict the potential for adverse drug responses has become routine in pre-clinical development (Dunne et al., 2009; Eglen and Reisine, 2011). Common endpoints of these assays include, but are not limited to, metabolite measurements (Lake et al., 2009), membrane integrity (Cook and Mitchell, 1989), lysosomal and mitochondrial functions (Varga et al., 2015) and measure of cell death or apoptosis (Elzagallaai et al., 2013). However, there still remains a lack of validated and available pre-clinical tools for identifying drugs that have the potential to induce DHRs. There is a number of available or validated sensitisation tests which could potentially be adapted for assessment of DHRs. These are mainly cell-based assays focused on the use of cells involved in key events of contact hypersensitivity reactions such as dendritic cells and T cells. The hCLAT (Nukada et al., 2012), Myeloid U927 Skin Sensitisation Test (MUSST) (Urbisch et al., 2015) and THP1 Activation Assay (Mitjans et al., 2010; Mitjans et al., 2008) have been used to test some pharmaceutical compounds. However, because of the small number of drugs tested in these assays, it is difficult to determine how successful the use of these assays would be for predicting DHR. Therefore, they still need to be accurately validated for their use in assessment of predicting hypersensitivity of systemically administered pharmaceuticals. Since the mechanisms driving DHRs presumably share some commonalities with the mechanism of contact sensitisation, in particular the involvement of dendritic and T cells, these assays could possibly be developed to predict DHRs. The current study has explored the suitability of the skin explant test for use as a pre-clinical in vitro predictive tool for hazard assessment (as opposed to risk assessment) to discriminate between LMW drugs which cause DHRs from those that do not. Full thickness skin biopsies and immune cells taken from the same individual were used to perform each individual test. The test method as previously described is characteristic of a cutaneous immune response and includes observational skin damage representative of in vivo rashes and blisters. It also demonstrates antigen specific T cell responses as reported by Ahmed and colleagues (Ahmed et al., 2016). Seventeen drugs known to be clinically associated or not associated with DHRs were used to evaluate the ability of the skin explant test to predict DHR. The skin explant test showed promising results as a potential predictive assessment tool for DHR (88% correct prediction and 0.72 correlations to clinical classification for DHR). The overall potential of each drug to cause a DHR was determined using a criterion based on considering if a positive response of a compound was observed at two or more drug concentrations. We believe observing a positive response in only one out of three drug concentrations as a criterion would not be predictive enough of the true damaging potential of the drug. On the other hand, establishing the criterion of three out of three positive reactions to the high, intermediate and low concentrations will not give a true indicator of the potency threshold of the testing drug. Therefore, establishing the criterion of two out of three positive responses to the drug concentrations for an overall predictive outcome will minimize the likelihood of false results and improve the accuracy of predictive DHR potential of the test chemical. In addition, we compared the skin explant results to the T cell proliferation and IFNγ results. Table 2 summarises the predictive capacity of each test relative to the effect of a drug concentration response and overall prediction. The results show that, of the 3 test assays, the skin explant test has the highest correct predictive outcome when considering the concentration response. The data show the skin explant
Authorship contributions Ahmed, S.S., Ahmed M.M., Burgess, M., Bibby L., Darby L., Watson, J. performed experiments and analysed results. Ahmed S.S. carried out the statistical analysis. Ahmed S.S., Dickinson, A.M. and Whritenour J. wrote the report. Dickinson, A.M. and Wang, X.N. performed the histopathological analysis. Dickinson, A.M. and Whritenour J. designed the research.
Conflict of interest disclosure The authors have no conflicts of interest.
Submission declaration and verification The work described has not been previously published or submitted for publication elsewhere. The publication is approved by all authors. 46
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Funding
Acknowledgments
This work was supported by Pfizer Inc. who also contributed to the study design and report writing.
The authors thank Elizabeth Douglas and Patricia Schneider (Pfizer Inc.) for technical support and Kim Pearce for Statistical support.
Appendix A Table A1 Test results for skin explant tests, T cell proliferation and IFNγ concentrations in response to the highest test concentration for each drug. Results for each drug in response to the high concentration showing percentage of positive (grade ≥ II) skin explant tests, mean SI value for T cell proliferation and mean fold increase in IFNγ concentration responses. (Highest test concentration)
Clinical Classification % of positive skin explant tests (n = 4) T Cell proliferation mean SI (n = 6) IFNγ concentration mean fold increase (n = 6)
Abacavir (100 μg/mL) Amoxicillin (200 μg/mL) Carbamazepine (200 μg/mL) Diclofenac (200 μg/mL) Lamotrigine (200 μg/mL) Lapatinib (100 μg/mL) Lumiracoxib (100 μg/mL) Nevirapine (100 μg/mL) Ofloxacin (200 μg/mL) Phenytoin (100 μg/mL) Propranolol (100 μg/mL) Sulfamethoxazole (200 μg/mL) Acetaminophen (200 μg/mL) Cimetidine (200 μg/mL) Flecainide (25 μg/mL) Metformin (200 μg/mL) Verapamil (25 μg/mL)
Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive
75 100 100 100 75 75 50 75 100 50 100 50
3.10 4.40 3.50 3.90 4.00 3.7 3.60 4.50 4.00 4.10 4.40 2.70
3.84 4.57 4.72 3.98 5.12 5.53 3.75 4.13 3.66 4.15 3.95 3.10
Negative Negative Negative Negative Negative
25 0 25 0 75
2.70 2.50 2.50 2.50 3.60
1.93 2.24 3.07 2.70 3.90
Table A2 Test results for skin explant tests, T cell proliferation and IFNγ concentrations in response to the intermediate test concentration for each drug. Results for each drug in response to the high concentration showing percentage of positive (grade ≥ II) skin explant tests, mean SI value for T cell proliferation and mean fold increase in IFNγ concentration responses. Intermediate test concentration Clinical classification % positive skin explant tests (n = 4) T Cell proliferation mean SI (n = 6) IFNγ concentrations mean fold increase (n = 6) Abacavir (50 μg/mL) Amoxicillin (100 μg/mL) Carbamazepine (100 μg/mL) Diclofenac (100 μg/mL) Lamotrigine (100 μg/mL) Lapatinib (50 μg/mL) Lumiracoxib (50 μg/mL) Nevirapine (50 μg/mL) Ofloxacin (100 μg/mL) Phenytoin (50 μg/mL) Propranolol (50 μg/mL) Sulfamethoxazole (100 μg/mL) Acetaminophen (100 μg/mL) Cimetidine (100 μg/mL) Flecainide (10 μg/mL) Metformin (100 μg/mL) Verapamil (10 μg/mL)
Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Negative Negative Negative Negative Negative
75 100 100 100 75 75 50 75 100 50 100 25 25 0 25 0 50
3.10 4.00 3.40 3.80 3.80 3.10 3.40 4.30 3.70 3.60 4.30 2.60 2.70 2.40 2.20 2.50 3.60
3.38 4.08 4.50 3.82 4.75 3.09 3.41 3.76 3.59 3.16 3.95 2.83 1.81 1.84 3.02 2.00 3.30
Table A3 Test results for skin explant tests, T cell proliferation and IFNγ concentrations in response to the lowest test concentration for each drug. Results for each drug in response to the high concentration showing percentage of positive (grade ≥ II) skin explant tests, mean SI value for T cell proliferation and mean fold increase in IFNγ concentration responses. Low test concentration
Clinical class
% positive skin explant tests (n = 4)
T Cell proliferation mean SI (n = 6)
IFNγ concentrations mean fold increase (n = 6)
Abacavir (25 μg/mL) Amoxicillin (50 μg/mL) Carbamazepine (50 μg/mL) Diclofenac (50 μg/mL) Lamotrigine (50 μg/mL) Lapatinib (10 μg/mL)
Positive Positive Positive Positive Positive Positive
100 100 100 100 75 75
3.10 4.10 3.00 3.40 3.60 2.50
3.14 3.75 4.23 3.92 4.76 2.62
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Table A3 (continued) Low test concentration
Clinical class
% positive skin explant tests (n = 4)
T Cell proliferation mean SI (n = 6)
IFNγ concentrations mean fold increase (n = 6)
Lumiracoxib (10 μg/mL) Nevirapine (25 μg/mL) Ofloxacin (50 μg/mL) Phenytoin (10 μg/mL) Propranolol (25 μg/mL) Sulfamethoxazole (50 μg/mL) Acetaminophen (50 μg/mL) Cimetidine (50 μg/mL) Flecainide (5 μg/mL) Metformin (50 μg/mL) Verapamil (5 μg/mL)
Positive Positive Positive Positive Positive Positive Negative Negative Negative Negative Negative
50 50 100 50 100 25 25 0 25 25 75
3.00 3.00 3.40 3.10 3.90 2.50 2.40 2.20 2.10 2.30 3.20
2.76 3.07 3.92 2.26 4.24 2.21 1.51 1.62 2.78 1.84 2.75
Appendix B. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.taap.2019.02.005.
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