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A Multiplatform Approach for the Discovery of Novel Drug-Induced Kidney Injury Biomarkers Liuxi Chen,†,§ James Smith,† Jaromir Mikl,† Ryan Fryer,† Frank Pack,† Brad J. Williams,‡ Jonathan A. Phillips,†,⊥ and Vladimir V. Papov, Jr.*,† †

Boehringer Ingelheim Pharmaceuticals, Ridgefield, Connecticut 06877, United States Waters Corporation, Milford, Massachusetts 01757, United States

‡

S Supporting Information *

ABSTRACT: Drug-induced kidney injury (DIKI) is a common toxicity observed in pharmaceutical development. We demonstrated the use of label-free liquid chromatography−mass spectrometry (LC−MS) and multiplex liquid chromatography-single reaction monitoring (LC-SRM) as practical extensions of standard immunoassay based safety biomarker assessments for identification of new toxicity marker candidates and for improved mechanistic understanding. Two different anticancer drugs, doxorubicin (DOX) and cisplatin (cis-diamminedichloridoplatinum, CDDP), were chosen as the toxicants due to their different modes of nephrotoxicity. Analyses of urine samples from toxicant treated and untreated rats were compared to identify biochemical analytes that changed in response to toxicant exposure. A discovery (label-free LC−MS) and targeted proteomics (multiplex LC-SRM) approach was used in combination with well established immunoassay experiments for the identification of a panel of urinary protein markers related to drug induced nephrotoxicity in rats. The initial generation of an expanded set of markers was accomplished using the label-free LC−MS discovery screen and ELISA based analysis of six nephrotoxicity biomarker proteins. Diagnostic performance of the expanded analyte set was statistically compared to conventional nephrotoxicity biomarkers. False discovery rate (FDR) analysis revealed 18 and 28 proteins from the CDDP and DOX groups, respectively, exhibiting significant differences between the vehicle and treated groups. Multiplex SRM assays were constructed to more precisely quantify candidate markers selected from the discovery screen and immunoassay experiments. To evaluate the sensitivity and specificity for each of the candidate biomarkers, histopathology severity scores were used as a benchmark for renal injury followed by receiveroperating characteristic (ROC) curve analysis on selected biomarkers. Further examination of the best performing analytes revealed relevant biological significance after consideration of anatomical localization and functional roles. In summary, the inclusion of mass spectrometry together with conventional ELISA based assays resulted in the identification of an expanded set of biomarkers with a realistic potential for providing additional beneficial information in mechanistic investigations of drug induced kidney injury and with similar responsiveness to conventionally applied indicators of renal injury.

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INTRODUCTION Experimental drugs are routinely challenged to negotiate narrower therapeutic indices throughout the course of development. In the absence of exhaustive investigative studies, predicting toxicity from nonclinical safety evaluations into firstin-man studies can further diminish confidence. Several precompetitive, public/private efforts have been launched to better understand the functional translatability of emerging safety biomarkers that are conserved at the molecular level.1 These efforts aim to accelerate scientific consensus among R&D and regulatory stakeholders through a formalized qualification process.2 Rapid discovery and application of mechanistic safety biomarkers offers new opportunities to more precisely navigate narrower therapeutic indices while providing deeper insight into a drug candidate’s pathophysiology.3 A primary target for toxicity is the kidney due to its function as a filtration organ. It is estimated that one-quarter to one© 2017 American Chemical Society

third of hospital acute kidney injury (AKI) cases requiring dialysis are related to drug nephrotoxicity.4 Strides have been made in the area of identifying new biomarkers for drug induced AKI both in nonclinical models and in the clinic.5,6 In fact, much of the early focus on novel biomarker regulatory qualification has been on translational kidney injury biomarkers.7 Historically, blood urea nitrogen (BUN) and serum creatinine (sCr) have been used to monitor AKI.8 These traditional clinical chemistries require loss of approximately 50% of renal function before they reliably detect injury.9 Unfortunately, significant kidney injury can take place before any change is detectable in these routinely used biomarkers for renal function. This delay in detecting early kidney injury leaves histopathology as the only nonclinical approach for reliably identifying structural damage before functional impact. Received: June 6, 2017 Published: September 8, 2017 1823

DOI: 10.1021/acs.chemrestox.7b00159 Chem. Res. Toxicol. 2017, 30, 1823−1834

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anticancer therapies doxorubicin (DOX) and cisplatin (CDDP) were selected as nonclinical models of nephrotoxicity, each of which has oxidative stress-mediated nephrotoxicities. DOX induces glomerular injury, which results in increased glomerular capillary permeability, followed by tubular atrophy due to protein overload.25 CDDP is taken up by cells in the proximal convoluted tubule (PCT) where it has direct cytotoxic effects.26 The conventional biomarkers listed in the paper are generally known with mechanism of action and performance characterized. Multiplex LC-SRM methods are then developed for targeted measurement of each newly identified marker candidate to improve quantitation. A number of studies have been reported that identified candidate biomarkers for specific kidney injuries using various proteomics approaches including LC−MS,27,28 capillary electrophoresis (CE)-MS,29,30 and 2DPAGE with matrix-assisted laser desorption/ionization-time-offlight (MALDI-TOF)-MS.31−33 To the best of our knowledge, this is the first report utilizing the combination of discovery and targeted proteomics workflow, guided by benchmark immunoassays, to characterize the rat urinary proteome for AKI biomarker discovery.

Furthermore, potentially nephrotoxic compounds in nonclinical development can be difficult to advance into the clinic due to the impracticality of monitoring for iatrogenic renal injury at the structural level in human patients. In contrast to BUN and sCr (i.e., traditional biomarkers), urinary biomarkers are often generated at or near the site of renal injury. Consequently, urinary biomarkers tend to show greater sensitivity and specificity when compared to traditional serum or functional markers.10−13 Increased acceptance by regulatory authorities of urinary nephrotoxicity biomarkers, coupled with the growing demand for additional mechanistic information from nonclinical drug safety studies, has resulted in renewed interest in discovery of new safety markers and monitoring technology. Conventional safety biomarkers are typically measured using technology available as ligand binding (e.g., ELISA, RIA, etc.), enzymatic, or nonenzymatic colorimetric assays. Sensitivity is the main advantage of these approaches with lower limits of quantitation routinely reaching into the picomolar range. An additional advantage is scalability, which allows large numbers of samples to be measured efficiently. However, a major limitation of these approaches is the lack of availability of high quality reagents needed to specifically detect the desired analyte, for example, conventional ligand binding assays have not been developed for the majority of known biomolecules. The utility of ligand binding assays for small molecules, such as 25-hydroxyvitamin D14 and testosterone15,16 may be limited by low accuracy and interference.17−19 In these cases, liquid chromatography−mass spectrometry (LC−MS) has improved confidence in quantitative measurement of small molecule biomarkers. Quantitative LC−MS based assays have been used in clinical laboratories for decades for the accurate and precise measurement of small molecules. Concerns about throughput and automation of LC−MS approaches are also dissipating.20 In addition to the gains in accuracy and specificity, application of MS based approaches to large molecule biomarker development is attractive due to generally straightforward and rapid LC−MS method development, independence from antibody reagents, and the ability of LC−MS to provide relative or absolute quantitation together with the flexibility to measure multiple target analytes simultaneously.21,22 Furthermore, steady improvement in MS technology has allowed MS to begin to realistically compete with the sensitivity of immunoassays,23 which in turn is renewing interest in using MS as a primary biomarker discovery and analysis platform. The objective of this study was to design and evaluate a workflow for investigating safety biomarkers using label-free LC−MSE and multiplex liquid chromatography-single reaction monitoring (LC-SRM) as practical extensions of standard safety biomarker immunoassay assessments. This approach aims to improve mechanistic information as well as identify new marker candidates that may be relevant to the mode of toxicity. LC−MSE is a specific type of LC−MS discovery investigation utilizing alternating MS (intact peptide measurement) and MS/ MS (peptide fragment measurement) modes without any precursor peptide selection during the MS/MS mode. LC− MSE is attractive because of its reported ability to extend the lower limit of sensitivity for peptides that may be missed when using the more traditional data dependent methods.24 We chose to investigate common nonclinical models of nephrotoxicity and measure conventional urinary biomarkers while surveying the urinary proteome using a proteomic approach to identify new nephrotoxicity marker candidates. The

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MATERIALS AND METHODS

In Vivo Studies. All animal work was conducted according to relevant national and international guidelines and performed under protocols approved by the Boehringer Ingelheim Pharmaceuticals, Inc. Institutional Animal Care and Use Committee (IACUC) and according to the United States Animal Welfare Act. Doxorubicin Study. A 14-day study was conducted in male Sprague−Dawley rats (Charles River) 7−8 weeks old at the start of the study. Rats were housed in individual metabolism cages for a 24-h acclimation and a 24-h baseline period and had access to drinking water and Purina rodent chow ad libitum. Rats were weighed at baseline and days 1, 5, 8, 12, and 14. Doxorubicin (DOX; marketed name: Adriamycin; Toronto Research Chemicals) at 0 mg/kg/wk (vehicle) or 5 mg/kg/wk in 0.9% NaCl (USP, Baxter Healthcare) was administered intravenously at 5 mL/kg on day 1 and day 8, n = 8/ group. At baseline and each day of the study, a 24-h urine sample was collected under refrigerated conditions for biomarker assessment. Urine volume was measured by gravimetric analysis. Urine samples were clarified by spinning at 2500 × g and 4 °C for 10 min, aliquoted, and then stored at −80 °C prior to clinical chemistry and biomarker analysis. Blood was collected via tail vein on days 8 and 14 and processed to serum for clinical chemistry analysis. Animals were euthanized at the end of day 14, and the kidneys and other organs were weighed and fixed in 10% neutral buffered formalin for processing to histological HE stained sections. Cisplatin Study. A 14-day study was conducted in male Sprague− Dawley rats that were housed as described above. Rats were weighed at baseline and days 1, 8, and 14. Cisplatin (CDDP; marketed name: Platinol; Spectrum Chemical) at 0 mg/kg/day (vehicle) or 1 mg/kg/ day in 0.9% NaCl (USP, Baxter Healthcare) was administered intraperitoneally at 5 mL/kg on day 1 and day 8, n = 8/group. Urine samples were collected daily, processed, and stored as above. Blood was collected via tail vein on days 7 and 14 and processed to serum for clinical chemistry analysis. Animals were euthanized at the end of day 14, and the organs processed to sections and examined microscopically as described above. Histopathological Evaluation. Representative sections of kidneys and liver were processed routinely, embedded in paraffin, and stained with HE for all animals. The right kidney was prepared in cross-section, and the left kidney was prepared in longitudinal section; both kidneys were sectioned at 3 μm. In addition, 3 μm sections of kidneys were stained with PAS stain and evaluated microscopically. Four lobes of the liver (left lateral, right lateral, caudate, and medial lobes) were trimmed, embedded, sectioned at 5 μm, and stained with hematoxylin and eosin (HE). However, only the left the lateral lobe 1824

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Label-Free LC−MSE Experiments. Prior to LC−MS analysis, each digested sample was spiked with a predigested yeast alcohol dehydrogenase (ADH) standard (Waters, Massachusetts, USA) at a level of 25 fmol per 1 μL injection. Urinary tryptic digests were analyzed in duplicate using a nanoACQUITY UPLC and Synapt G2 HDMS mass spectrometer equipped with a nanolockspray ion source (Waters, Massachusetts, USA). Peptides were separated with a nanoAcquity UPLC BEH C18 75 μm id × 150 mm column packed with 1.7 μm particles (Waters Corp, Milford, MA) using a 60 min linear gradient at a flow rate of 400 nL/min. Mobile phase A was water with 0.1% formic acid, while mobile phase B was acetonitrile with 0.1% formic acid. The Synapt G2 mass spectrometer was operated in the LC−MSE mode, with alternating 0.5 s scans of low (15 V) and high (40 V) collision energies used to generate intact peptide ions and peptide product ions, respectively. Glu-fibrinopeptide (Waters, MA, USA) at a concentration of 300 fmol/μL (m/z 785.8426) was infused via the nanolockspray source at a flow rate of 500 nL/min and acquired every 30 s as the external mass calibrant. Protein Identification and Quantitation. Each raw data file was processed using ProteinLynx Global Server (PLGS) V2.5 software (Waters, MA, USA) to generate charge state reduced and deisotoped precursor mass lists as well as associated product ion mass lists for subsequent protein identification and quantification. Database searching was performed against the Uniprot rats protein sequence (reviewed entries with canonical and isoform) database (downloaded on 5/15/2013, http://www.uniprot.org). The yeast ADH sequence was added to the rat database to enable protein quantification by PLGS using the added ADH internal standard. PLGS parameters for the database search were set as follows. Carbamidomethylated cysteine was set as a fixed and methionine oxidation as a variable amino acid modification with only one missed tryptic cleavage site allowed. The default “automatic” setting for mass accuracy (10 ppm for precursor ions and 15 ppm for product ions) was used, with a minimum of three peptide matches per protein, a minimum of three consecutive product ion matches per protein, and a minimum of seven total product ion matches per protein. The proteins were identified at a maximum of 4% false-discovery rate at the protein level. Label-free protein quantification was performed using the TransOmics Informatics for Proteomics software (Waters Corporation, Manchester, UK and NonLinear Dynamics, Newcastle, UK). Each LC−MSE (data independent acquisition) experiment was imported and chromatographically aligned using TransOmics. Following the database search, identified proteins and their amounts were compared across injections to determine dose and time dependent secretion profiles. Missing data (i.e., proteins not identified in a given injection or treatment) were replaced with a value representing the limit of detection as determined by the smallest detected protein amount within the data set (3 fmol). Outlier quantification values between technical replicates (3 replicates for each sample) were manually corrected by normalizing peptide intensities of the protein of interest against the intensities of peptides from the spiked internal standard. Statistical Analysis. The primary end point analyzed was the foldchange from baseline on days 12 and 14 of this study for each biomarker. All data were log2 transformed prior to analysis. The principal statistical analysis consisted of false discovery rate (FDR) and receiver operating characteristic (ROC) analysis. FDR analysis was performed to adjust for multiple testing,35 while not necessarily controlling for familywise error rate, and was designed to control the proportion of incorrect rejection of the null hypotheses of no difference between control and treated. The FDR statistical analysis consisted of three steps: (i) traditional t test to identify statistically significant differences in biomarker expression between treated and untreated (vehicle controls) animals within each study group and for each biomarker, (ii) FDR analysis to adjust for multiple testing35 using the p-values from step 1 as input, and (iii) the creation of volcano plots to identify the biomarkers with the biggest differential expression between control and treated animals within each treatment group and each biomarker. For the volcano plots, the p-values (y-axis) from the t test procedure were transformed by −Log10 resulting in the most

was evaluated microscopically. The scores and criteria used to score microscopic findings in the kidney are presented in Table 1.34

Table 1. Individual Animal Test Article Microscopic Observationsa DOX treatment

overall glomernephrop score

overall PCT necrosis score

0 1 2 3 4 0 1 2 3 4

CDDP treatment

vehicle (n = 8)

5 mg/kg (n = 8)

vehicle (n = 8)

1 mg/kg (n = 8)

8 0 0 0 0 8 0 0 0 0

1 2 1 4 0 1 2 5 0 0

8 0 0 0 0 8 0 0 0 0

8 0 0 0 0 0 5 2 0 1

a

0 = no damage, 1 = minimal, 2 = mild, 3 = moderate, 4 = marked. 5 mg/kg/wk group in DOX treatment and 1 mg/kg/day group were selected for biomarker analysis.

Clinical Chemistry Analyses. The urine and serum were analyzed using a Roche Cobas Integra 400 Plus clinical chemistry analyzer; creatinine (Jaffe kinetic method) was measured in urine and serum, and blood urea nitrogen (BUN; Urease/GLDH method) was measured in serum only. The Cobas Integra was calibrated and quality controlled according to the manufacturer’s instructions. Samples were processed undiluted unless quantification could not be obtained due to limits of the assay, in which case, further dilutions were performed. Immunoassay Experiments. Urine concentrations of GST-α (GSTA), albumin (ALBU), clusterin (CLUS), lipocalin-2 (NGAL), kidney injury marker (KIM1), and β2-microglobulin (B2MG) were measured using the rat kidney injury panel (KIP)-1 or KIP-2 electrochemiluminescent (ECL) kits and the Rat β2 M Kit (Meso Scale Diagnostics) according to the manufacturer’s instructions. A four-parameter logistic fit model was used to calculate a standard curve from the signal values of the standards. Signal values for the urine samples were compared to the standard curve to determine the calculated concentration. The upper and lower limits of quantification (ULOQ and LLOQ, respectively) were defined for each plate as follows: the highest or lowest expected (nominal) concentration of the standard at which the % recovery for at least one of the duplicates was between 75% and 125% and the % CV for the duplicate measurements was 20% for duplicate readings were excluded. Sample Preparation for Label-Free LC−MSE. The urine samples were thawed immediately before the proteomics sample preparation. Each sample was precipitated with 100% ethanol (−20 °C) at a ratio of 1:9 (v/v) urine/ethanol, incubated at −20 °C for 2 h, and centrifuged at 12 000 × g for 30 min at 4 °C. The pellet was washed once with cold ethanol, air-dried, and resolubilized in 50 mM ammonium bicarbonate with 0.1% (w/v) Rapigest SF surfactant (Waters, Massachusetts, USA). The resuspended protein solution was mixed with 100 mM DTT to a final concentration of 5 mM and incubated at 60 °C for 1 h. The denatured sample was then alkylated by adding 100 mM iodoacetamide stock solution to a final concentration of 15 mM and incubated at room temperature in the dark for 1 h. Sequencing grade modified trypsin (Promega, Wisconsin, USA) was added to each sample (enzyme to protein ratio 1:100, w/w) and incubated at 37 °C for 6 h. The digested samples were acidified with 10% (v/v) formic acid to the final concentration of 0.5% (v/v) formic acid at room temperature for 1 h and centrifuged at 12 000 × g for 10 min to remove the hydrolysis products of Rapigest SF. Digested samples were desalted with Supel-Tips C18 (Sigma-Aldrich). 1825

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Chemical Research in Toxicology significant of the results at the top of the plot. The summary of the selected proteins is shown in Supporting Information Table S2. The ROC analysis included the calculation of the AUC and associated 95% confidence intervals for each treatment group, biomarker, and histopathological end point. The AUC lower 95% confidence interval value was used to select the best potential nephrotoxicity biomarker candidates; the biomarker was selected only if the lower boundary met or exceeded 80%. Additional confidence intervals for sensitivity given fixed a specificity (80%) were generated.36 To further clarify the potential usefulness of the selected biomarkers, predicted probabilities from the ROC models (EFFECT plots) for each histopathological assessment were plotted using model predicted probability against observed fold change from baseline. The level of significance was fixed at α = 5% with a p-value of ≤ 0.05 considered to be statistically significant. The statistical analysis was carried out with the software product SAS (SAS Institute, Cary, NC USA), version 9.3. Target Selection for LC-SRM Experiments. For each peptide of interest and based on LC−MSE data, the six most abundant singly or doubly charged N-terminal fragment (b) or C-terminal fragment (y) product ions37 were evaluated by SRM to determine the best transitions to use for the targeted quantitative analysis. A Waters Acquity UPLC (Waters Corp, Milford, MA) was used in conjunction with an Applied Biosystems/Sciex API 5000 triple stage quadrupole mass spectrometer for all of the SRM analyses. Mobile phases were the same as those used for the LC−MSE experiments. Peptides were separated with an Acquity UPLC BEH C18 2.1 × 150 mm2 column packed with 1.7 μm particles (Waters Corp, Milford, MA) and a 7 min linear gradient at a flow rate of 400 μL/min. Dwell time was fixed at 25 ms with 300 ms as the maximum cycle time (12 transitions) for each scouting run. The collision energy (CE) was calculated based on the equation CE = 0.043 m/z + 4.756.38 and the MS1 and MS2 resolution set to “unit”. The resulting data files were manually inspected and the top two transitions (highest signals) for each peptide selected for the final SRM method. Single scheduled SRM methods were constructed with the transitions scheduled using a retention time window of ±30 s around the observed retention time. Two transitions from one peptide from the internal standard (yeast ADH protein) were also monitored for each run. The detailed SRM assay method is included in Supporting Information Table S3. Fold change is calculated as follows = log2 (end point value/pretreatment value)dosed group − log2 (end point value/pretreatment value)vehicle group.

Figure 1. Overview of the study design.

anistic biomarker information to verify the suitability of candidate biomarkers in part by using what is known about the mechanisms associated with the qualified markers. Although the candidate biomarkers may have similar biological roles, they also have the potential to provide for a different specificity or sensitivity unique to the type of renal injury. Histopathology and Serum Clinical Chemistry Analyses. Test article related microscopic observations were not observed in liver. For kidneys, microscopic findings were observed in animals dosed with DOX or CDDP but not in either vehicle group (Table 1). Although the severity scores for individual animals in the groups varied, both 5 mg/kg/wk DOX and 1 mg/kg/day CDDP resulted in similar mean severity PCT damage as evaluated microscopically. Renal injury was considered test article related and dose responsive in all cases. To test the hypothesis that certain biomarkers can discriminate different modes of toxicity, we chose to focus analysis on the 5 mg/kg/wk DOX-treated and 1 mg/kg/day CDDP-treated animals because the overall PCT severity score distributions were reasonably comparable. Average BUN and sCr levels at days 7−8 and days 14−15 were within historical ranges for all study groups (Figure 2). Accordingly, BUN and sCr were not statistically different between dosed and vehicle groups at either time point. Longitudinal Profile of Conventional Nephrotoxicity Biomarkers by Immunoassay. All conventional urinary biomarkers tested (ALBU, B2MG, CLUS, GSTA, KIM1, and NGAL) were observed at increased levels for at least one of the treatments as compared to vehicle controls (Figure 3). Albuminuria appeared in 5 mg/kg/wk DOX-treated animals starting at day 6 and continued to elevate until peaking at an average of >1000-times the pretreatment values on day 14 (Figure 3A). In contrast to DOX treatment, animals treated with 1 mg/kg/day CDDP did not exhibit a strong of a urinary ALBU signal (Figure 3B). Urinary B2MG behaved similarly to urinary ALBU with a slightly delayed temporal response. Urinary B2MG rose in rats treated with 5 mg/kg/wk DOX from day 10 and elevated consistently until peaking at approximately 10-times pretreatment values on day 14 (Figure 3C). Likewise, B2MG levels remained low with 1 mg/kg/day CDDP treatment group until the latest sampling time (Figure 3D). In response to both treatments, the mean urinary CLUS values were elevated as compared to concurrent controls. Urinary CLUS values starting increasing at day 10 for DOX and

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RESULTS To identify biomarkers that are useful for delivering richer mechanistic evidence and the potential to monitor injury, we developed a three-step experimental workflow to rapidly identify the most relevant mechanistic indicators of doxorubicin-induced glomerular and cisplatin-induced tubular injury in the urinary proteome. An overview of the study designs and the associated biomarker selection process is shown in Figure 1. Histopathological evaluation and serum clinical chemistry analysis were performed in addition to the ELISA based immunoassay of six conventional nephrotoxicity biomarkers. Label-free LC−MSE experiments were used for discovery screening of the urinary proteome to identify new candidate markers. All data from terminal urine collections from DOX and CDDP studies were used in FDR analysis to identify candidates that changed in response to treatment. The LCSRM assays were then generated for the selected candidates and six conventional biomarkers to obtain the longitudinal response. Next, FDR analysis and ROC curve analysis were performed on all candidates to yield biomarkers exhibiting predictive power on the basis of area under the curve (AUC > 0.90) and changes in the histopathology findings. The experimental results from each step are illustrated in the following sections. This workflow incorporates known mech1826

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Figure 2. Levels of (A) serum BUN and (B) serum creatinine at day 7 and day 14.

different proteins identified by mass spectrometry in the DOX and CDDP dosed groups, respectively. Among biomarkers that were measured by conventional immunoasssys, ALBU and B2MG were detected in both DOX and CDDP data sets, while CYTC was found only with DOX and CLUS and NGAL only with CDDP treatment. DOX induced glomerular injury resulted in significant protein level changes in comparison to the pretreatment and vehicle group values with high concentrations of abundant serum proteins like haptoglobin, hemopexin, and serotransferrin observed (changes were similar in magnitude to ALBU). Candidate biomarkers were selected from the LC−MSE data using FDR analysis to adjust for multiplicity. The average levels of each protein at two time points (days 12 and 14) were taken as the end point values and compared with the pretreatment values. Figure 4 shows a volcano plot of the fold change and FDR p-value calculated for each identified protein. The comparison resulted in the identification of 32 proteins showing statistically significant changes during the course of the experiment (p < 0.05). Of these 32 proteins, three came from the CDDP data sets, 24 from the DOX data sets, and five were identified in both treatments (Figure 4B). Two of these candidate biomarker proteins (ALBU and B2MG) were also conventional markers that were analyzed by immunoassay. A summary of this data is in the Supporting Information Table S2 with all 32 proteins shown to be differentially regulated with pvalues less than 0.05.

day 8 for CDDP and peaked at approximately 500 and 200 ng/ mg sCr at day 14 for each group, respectively (Figure 3E,F). Urinary NGAL levels at the end of the study are similar in magnitude for the DOX and CDDP treated animals (Figure 3G,H); however, initial urinary NGAL elevations appear 8 days earlier in CDDP treated animals as compared to DOX treated animals (day 4 vs day 12). Urinary GSTA transiently increased at day 8 to approximately twice the pretreatment values in DOX treated animals (Figure 3I). In CDDP treated animals, urinary GSTA increased from day 2, peaked at approximately twice the pretreatment values by day 4, and remained elevated until the end of the study (Figure 3J). In the DOX treated animals, nominal increases in urinary KIM1 levels were observed at day 14 (approximately twice the pretreatment values as shown in Figure 3K). In contrast, urinary KIM1 appeared elevated on day 2 in CDDP treated animals, which was the earliest observed biomarker excursion. Urinary KIM1 levels continued to increase after day 2 with a peak change of approximately 100-times pretreatment values at day 14. Nephrotoxicity Biomarker Discovery by Label-Free LC−MSE Analysis. Urine samples from rats treated with either vehicle, 5 mg/kg/wk DOX or 1 mg/kg/day CDDP for 0, 12, or 14 days, were analyzed by proteomics profiling using mass spectrometry to determine the differential modulation of the urinary proteome. Individual protein abundances were corrected for urine volume and creatinine level. The variety and abundance of the proteins observed increased significantly at the later stage of the study for both drugs with 93 and 129 1827

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Figure 4. (A) Volcano plot summarizing urinary protein modulation on drug treatment (CDDP and DOX) showing FDR p-value plotted against protein fold change. (B) Venn diagram depicting number of unique candidate biomarkers (excluding conventional urinary biomarkers) identified in each treatment group.

LC−MSE data) were the conventional nephrotoxicity biomarkers ALBU and CLUS. The remaining four conventional nephrotoxicity biomarkers (NGAL, B2MG, GSTA, and KIM1) were also included in the SRM assay list bringing the total number of proteins analyzed by SRM to 36. An optimized scheduled SRM method was developed to simultaneously measure and quantify the 36 proteins (30 new candidates from LC−MSE and 6 conventional biomarkers) using 36 proteotypic peptides and 72 transitions. Longitudinally collected urine samples from vehicle, 5 mg/kg/wk DOX, or 1 mg/kg/day CDDP treated rats (treatment days 2, 4, 6, 8, 10, 12 and 14) were analyzed by targeted SRM analysis. The technical repeatability of the optimized SRM method was determined by eight serial injections of a representative sample (Supporting Information Figure S1). The SRM results indicated that reproducibility decreased marginally with decreasing SRM signal intensity, but all individual peptides displayed a CV below 20%. The SRM data sets of the 36 proteins were further evalulated by FDR analysis. Biomarkers with a significance level of p < 0.05 in both DOX and CDDP treatment groups are listed in Table 2. Detailed descriptive statistics from the SRM and FDR analysis of these 36 proteins can be found in Supporting Information Table S4. For all 36 proteins, 28 were upregulated at a significance level of p < 0.05, with 28 and 18 proteins identified in DOX and CDDP groups, respectively. Temporal response profiles of these biomarkers are illustrated in Figure 5. Distinct patterns were observed for DOX and CDDP treatment groups. TRFE increased as early as day 4. Plasma proteins (HPT, A1AT, KNT1, AFAM,, PLMN, CERU, MUG1, and CO3) increased at day 6. Smaller plasma proteins like TTHY (15 kDa) and A1AG (23 kDa) did not significantly elevate until day 10 and 8, respectively. A different pattern of biomarker response was observed for the tubular toxicant CDDP indicating, as expected, a different mechanism of toxicity from DOX. For example, the levels of ALBU as well as other plasma protein candidate biomarkers (including TTHY, A1AG, HPT, A1AT, KNT1, AFAM, PLMN, CERU, MUG1, and CO3) were

Figure 3. Box plots of biomarker levels obtained by immunoassay. Data are shown according to treatment group with time matched vehicle controls. All values are corrected by urine volume and urinary creatinine level. The values for the box plots are the minimum, 25th percentile, media, 75th percentile, and the maximum. Mean values and outliers are also indicated on the plot. Outliers are defined as any value that lays more than one and a half times the range of the box.

Targeted Proteomics−SRM Experiments. Candidate biomarkers selected for targeted SRM were identified from the FDR analysis results of the LC−MSE data sets. Included in the 32 candidate biomarkers (selected independently from 1828

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Chemical Research in Toxicology Table 2. List of Potential Biomarkers for Both DOX and CDDP Treatment Groups Confirmed by SRM Assaysa treatment DOX

treatment CDDP glomer-nephrop score

PCT score

biomarker

fold change

FDR p-value

AUC

lower 95% CL

Somers’ D

biomarker

fold change

ALBU_RAT MUG1_RAT CYTC_RAT GSTA1_RAT CLUS_RAT CO3_RAT SPA3L_RAT NGAL_RAT HPT_RAT TIMD1_RAT AFAM_RAT FETUA_RAT KNT1_RAT VTDB_RAT CERU_RAT A1AT_RAT TRFE_RAT PLMN_RAT EST1C_RAT IGG2A_RAT TTHY_RAT HEMO_RAT A1AG_RAT UP2_RAT AMBP_RAT SODC_RAT B2M_RAT KACB_RAT

8.16 9.04 3.76 2.22 4.68 8.26 5.40 2.27 5.79 3.38 9.08 10.21 9.65 8.97 8.92 8.59 8.46 8.19 8.04 7.64 6.76 6.41 6.29 3.61 3.19 1.95 3.24 1.53

1.50 0.00 3.08 1.02 3.38 0.00 1.00 4.73 1.10 6.83 0.00 0.00 4.70 8.28 0.00 0.00 7.60 0.00 4.70 5.00 8.60 2.50 3.11 2.72 1.08 2.81 2.58 3.72

× 10−03 × 1000 × 10−03 × 10−03 × 10−04 × 1000 × 10−07 × 10−03 × 10−03 × 10−03 × 1000 × 1000 × 10−06 × 10−05 × 1000 × 1000 × 10−06 × 1000 × 10−06 × 10−07 × 10−06 × 10−04 × 10−04 × 10−04 × 10−04 × 10−02 × 10−02 × 10−02

1.000 0.952 0.952 0.946 0.946 0.937 0.937 0.938 0.921 0.911 0.905 0.889 0.889 0.889 0.889 0.889 0.889 0.889 0.889 0.889 0.889 0.889 0.889 0.889 0.889 0.873 0.841 0.794

1.000 0.849 0.848 0.843 0.830 0.805 0.805 0.801 0.782 0.763 0.716 0.671 0.671 0.671 0.671 0.671 0.671 0.671 0.671 0.671 0.671 0.671 0.671 0.671 0.671 0.655 0.563 0.521

1.000 0.905 0.905 0.893 0.893 0.873 0.873 0.875 0.841 0.821 0.810 0.778 0.778 0.778 0.778 0.778 0.778 0.778 0.778 0.778 0.778 0.778 0.778 0.778 0.778 0.746 0.683 0.587

FETUA_RAT KNT1_RAT VTDB_RAT TRFE_RAT EST1C_RAT A1AG_RAT AMBP_RAT SODC_RAT CLUS_RAT CERU_RAT HEMO_RAT HPT_RAT IGG2A_RAT TTHY_RAT UP2_RAT PLMN_RAT CO3_RAT AFAM_RAT

3.31 2.70 3.74 3.12 2.77 2.17 2.46 2.03 4.29 2.32 1.77 2.60 1.72 2.74 1.69 1.83 1.72 1.56

FDR p-value

AUC

lower 95% CL

Somers’ D

× 10−03 × 10−04 × 10−04 × 10−05 × 10−05 × 10−04 × 10−02 × 10−04 × 10−04 × 10−04 × 10−04 × 10−04 × 10−04 × 10−03 × 10−02 × 10−02 × 10−02 × 10−02

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.984 0.984 0.984 0.969 0.922 0.922 0.875 0.859 0.844 0.828

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.941 0.941 0.941 0.896 0.762 0.762 0.702 0.638 0.634 0.607

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.969 0.969 0.969 0.938 0.844 0.844 0.750 0.719 0.688 0.656

3.07 2.21 9.49 5.03 1.29 1.16 1.97 1.53 1.63 1.49 7.24 4.74 7.24 2.41 1.67 1.01 3.41 2.77

a

Biomarkers with FDR p-value 0.80. Biomarkers are shown using Uniprot ID. GSTA1_RAT = GSTA; TIMD1_RAT = KIM1; B2M_RAT = B2MG.

SRM), which indicates SRM method may be less sensitive than immunoassay in some cases. Table 4 contains the ratio to pretreatment values predicted that represents the possibility of 50%, 80% and 90% of damage for each treatment. For example in the DOX group, ALBU, CO3 and MUG require significant elevation in ratio to pretreatment (588, 1782, 776, respectively) to show a 90% chance of damage, while CLUS, CYTC and NGAL only require the ratios of 16, 10.6 and 1.9, respectively, for the same 90% chance of damage. It is interesting to note that CLUS shows similar predictive power for damage in both DOX and CDDP cases where the ratio of 16 and 13 to pretreatment value yield a prediction of about 90% in DOX and CDDP groups, respectively. These results by mass spectrometry also match very well with the immunoassay measurements for CLUS and together indicate that CLUS is a responsive biomarker to PCT injury independent of a direct toxicity or indirect damage to the tubular epithelium (as shown in Table 3).

significantly lower in the CDDP than in the DOX treatment group at days 8−14. The candidate biomarkers were further evaluated for sensitivity and specificity via ROC analysis, which is a method for correlating the observed protein changes with histopathology findings. Predictive power was reported as ROC area under the curve (AUC) using overall glomerular nephrophathy and proximal convoluted tubule (PCT) necrosis scores as the classification variables. ROC analysis revealed 8 and 12 biomarkers (highlighted in bold in Table 2) exhibiting predictive power (AUC > 0.90) for overall glomerular (DOX) and PCT damage (CDDP), respectively. ROC analysis was also applied to directly compare the predictive power of SRM assays versus immunoassay methods (Table 3). ROC analysis revealed that the six biomarkers measured by both SRM and immunoassay had good predictive power (AUC > 0.90) for glomerular nephropathy in the DOX treated group (when the lower 95% wald confidence limit for the AUC threshold was set to 0.80) regardless of which method was used. However, the SRM assays did not result in good predictive power for identifying cases of PCT necrosis in CDDP treated animals with the exception of CLUS (the only conventional biomarker yielding an AUC greater than 0.90 by

■

DISCUSSION

We have developed a simple experimental workflow to rapidly identify proteins that are mechanistically relevant to drugrelated AKI. This workflow also incorporates known mechanistic biomarker information to simultaneously verify the 1829

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Figure 5. LC-SRM determined temporal response profiles of qualified biomarkers selected by FDR analysis. Biomarkers are ranked by molecular weight, least to greatest, from left to right. Values indicate response multiples corrected to pretreatment values (day 0) as fold-change (log2). All values are corrected to urinary creatine. Fold change is calculated as follows = log2 (end point value/pretreatment value)dosed group − log2 (end point value/pretreatment value)vehicle group.

To benchmark conventional biomarker responses, we measured the timed onset of well-established nephrotoxicity urinary biomarkers using immunoassays. These markers were also either recently qualified or in review to be qualified for use in clinical trial enabling rat toxicology studies.10 In DOXtreated animals, significant changes in ALBU (day 6) and B2MG (day 10) were observed (Figure 3). These markers are known indicators of glomerular permselectivity, which are often used to identify cases of impaired glomerular filtration.39 Earlier onset of ALBU at day 6, followed by appearance of B2MG at day 10 is expected based on their mechanisms. With glomerular injury, ALBU leaks from the bloodstream into the nephron. As this progresses, the larger sized ALBU (66 kDa) competes with tubular reuptake of smaller, freely filtered molecules, such as B2MG (14 kDa).40,41 The later appearance of B2MG is consistent with ALBU competing with its reuptake in the proximal tubule, suggesting impact to tubular function. Also consistent with temporal onset of secondary tubular injury were the inducible marker responses from CLUS, NGAL and KIM1 observed at time points following the earliest appearance of ALBU and B2MG (days 11−14). In comparison to DOX-treated rats, those treated with CDDP did not experience significant elevations of urinary ALBU or B2MG. This is consistent with the microscopic absence of observed glomerular injury. However, CDDP treated animals had relatively early elevations of urinary KIM1 and GSTA, first seen at days 2 and 4, respectively

suitability of candidate biomarkers. Furthermore, this approach can conceivably translate to target organs and sampling matrices other than the kidney and urinary proteome. Here we report outcomes of testing this approach with two models of drug-induced nephrotoxicity in rats: doxorubicin-induced glomerular injury and cisplatin-induced tubular injury. The glomerular injury caused by DOX increases concentrations of high and low molecular weight proteins, which overwhelms the reabsorptive capacity of the renal tubules which in turn can lead to significant proteinuria.25 We also used CDDP as a comparator with a different mode of action predominantly affecting the proximal tubule epithelium. CDDP is taken up by cells in the proximal convoluted tubule (PCT) where it has direct cytotoxic effects on the tubular epithelium.26 We observed histopathological evidence that DOX treatment illicited the expected glomerular injury, with associated secondary injury to renal tubules. We also observed the expected direct renal tubular injury from CDDP treatment (Table 1). Routine serum clinical chemistry analysis of renal function did not signal renal injury when assessed by BUN or serum creatinine levels (Figure 2). The histopathology observations positively establish the two types of drug-induced kidney injury we aimed to study. These models also reflect typical scenarios where clinical development of a new molecular entity may be impeded by nonclinical renal histopathology findings not readily monitored by routine serum creatinine and BUN chemistries. 1830

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Using what is known about the physiological roles of previously characterized biomarkers, we can explore other protein indicators that may have similar roles, but potentially provide specificity and sensitivity to glomerular or tubular injuries. Temporal profiles of new identified biomarkers showed distinct patterns for DOX and CDDP groups (Figure 5). Among new identified biomarkers, blood plasma proteins TTHY, A1AG, HPT, A1AT, KNT1, AFAM, PLMN, CERU, MUG1, and CO3 were elevated in the urine in DOX and CDDP groups to a different extent. Within the DOX group, the levels of smaller plasma proteins like TTHY and A1AG were elevated later than the larger plasma proteins (e.g., HPT, A1AT, KNT1, AFAM, PLMN, CERU, MUG1, and CO3). The delay in response of the small plasma proteins suggests that at high filtered loads, albumin decreases the reuptake of low molecular weight proteins by competition for a common uptake mechanism.41 Furthermore, the level of plasma proteins were significantly lower in the CDDP group especially toward the end of the study. The absence of significant proteinuria and microstructural glomerular damage suggest direct injury to the tubular epithelium. Identification of these blood plasma proteins in the urine, without appreciable glomerlular injury, supports the idea that direct tubular epithelial injury can allow freely filtered blood plasma proteins to pass into the urine rather than be returned to the blood through epithelial reuptake. Our three-step workflow identifies the most relevant mechanistic indicators of glomerular or tubular injury in the urinary proteome (Figure 1). In the first nontargeted LC−MSE step, all detectable marker candidates are surveyed for a change in response to treatment. The newly identified (from LC− MSE) together with the already known markers are then subjected to a more rigorous quantitative analysis using targeted LC-SRM with more sample time points to provide additional information about progressive changes over time. Finally, context is added to the mass spectrometry derived identifications and observed changes in terms of treatmentrelated toxicity as identified by histopathology. From the final list of treatment-related injury biomarkers, we can infer mechanistic information based on what is already known about the anatomical distribution and function of each candidate (Table 2). This process of progressively refining biomarker candidates allows more information to be gained at each step while narrowing the discovery focus to biomarkers that will provide the most relevant information related to the observed toxicity. After closely comparing the temporal response profiles of each biomarker in DOX treatment versus CDDP treatment, and considering the two different modes of toxicity, we identify two general biomarker mechanism categories: (i) Direct nephrotoxicity markers: Responsiveness to renal injury in this category is similar among biomarkers, but reflects direct injury to the kidney, which may be agnostic to mode of action. In this case, identification of blood plasma proteins that would otherwise be returned to the blood are considered an indicator of tubular epithelial dysfunction rather than mass leakage into the tubular lumen (see next category). These are characterized by lower magnitude change in the urine and often are lower molecular weight proteins. These biomarkers tend to exhibit similar magnitude changes in both DOX and CDDP treatments or slightly higher increase in CDDP

Table 3. Existing Biomarker Characterization by SRM and Immunoassay Methodsa CDDP - PCT overall necrosis 1% Sen at 80% Spec 95% CI

AUC biomarker

SRM

ALBU B2MG CLUS NGAL KIM1 GSTA

0.78 0.63 0.98 0.73 0.66 0.53

IMMUNO

SRM

IMMUNO

0.98 25−91 0.91 0.3−53 1.00 63−100 1.00 24−91 1.00 16−84 1.00 9−76 DOX - glomerular nephropathy

63−100 35−97 63−100 63−100 63−100 63−100

p-value 0.08 0.07 0.48 0.07 0.02 0.004

2% Sen at 80% Spec 95% CI

AUC biomarker

SRM

IMMUNO

SRM

IMMUNO

p-value

ALBU B2MG CLUS NGAL KIM1 GSTA

1.00 0.84 0.95 0.94 0.91 0.95

1.00 1.00 0.89 0.83 0.66 1.00

59−100 42−100 42−100 36−100 29−96 42−100

59−100 59−100 42−100 42−100 18−90 59−100

n/a 0.26 0.37 0.34 0.22 0.31

a

AUC and percent sensitivity were chosen for the comparison. 2% Sen at 80% Spec 95%CI: Percent sensitivity at per-specified 80% specificity, 95% confidence interval (Binomial exact). n/a, Not available.

Table 4. Ratio of Pretreatment Values for Each Selected Candidate by ROC Analysis in Both Treatment Groups treatment DOX

treatment CDDP ratio to pretreament value at probability level of

ratio to pretreament value at probability level of biomarker

50%

80%

90%

biomarker

50%

80%

90%

ALBU CLUS C03 CYTC GSTA MUG NGAL SPA3L

42.2 2.3 90.5 1.9 1.5 27.9 0.6 6.1

194.0 7.5 675.6 4.9 2.8 238.9 1.4 21.1

588.1 16.0 1782.9 10.6 4.0 776.0 1.9 42.2

A1AG AMBP CERU CLUS EST1C FETUA HEMO HPT KNT1 SODC TRFE VTDB

4.9 1.5 2.6 3.7 2.3 2.5 4.9 7.0 2.6 1.0 2.6 3.0

7.0 1.9 4.0 8.0 3.7 3.7 6.5 12.1 4.0 1.4 4.3 4.9

8.6 2.3 5.3 13.0 5.3 5.7 7.5 17.1 5.3 1.7 5.7 7.5

(Figure 3L and J, respectively). KIM1 is known to be a sensitive indicator of direct tubular epithelial injury, whereas GSTA is an indicator of structural damage where this protein is shed from the brush border when cell integrity is altered.42 Together, these measurements indicate a direct and relatively rapid injury to the tubular epithelial cells, which is also consistent with the microscopic findings. On a similar time scale to DOX-treated animals, but with lesser magnitude, CLUS also appeared toward the later half of the study period in CDDP treated rats (Figure 3F). This comparison suggests CLUS may not be selective for mechanism of injury (direct or indirect toxicity) but may support the idea that CLUS is a general indicator of tubular epithelial injury.43 1831

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Chemical Research in Toxicology

dynamic range is required to qualify or validate the biomarker panel selected by the discovery approach. Multiplex SRM assays are much easier to develop (compared to immunoassays) and can quickly be put into place for testing putative biomarker panels. It should be noted that the lower dynamic range (compared to LC-SRM) of LC−MSE does place a limit on the achievable sensitivity for very low level analytes which imposes an additional challenge for routine preclinical safety biomarker evaluations. On the other hand, although immunoassays are generally very sensitive, antibody quality can vary dramatically and there are examples in the literature of poor specificity.48

treatment. This category also includes conventional biomarkers (GSTA, KIM1, NGAL, and CLUS). (ii) Blood−urine leakage markers: These biomarkers tend to significantly increase in DOX but not CDDP treated animals. This category includes most of the plasma proteins identified. These increases are consistent with glomerular injury where changes in biomarker levels may be low initially (while the tubular reuptake capacity can compensate), but as the injury persists the reuptake capacity becomes overwhelmed resulting in dramatic increases in biomarker levels. While glomerular injury is likely a significant cause of the elevation of these types of markers, specificity or exclusivity to a renal injury mechanism may not be as confidently claimed. For example, other factors and in particular, cardiovascular changes may also contribute to the observed elevated levels of these markers. On the basis of the above two categories, the majority of the new biomarkers observed in this study belong to the category (ii). A possible exception is fetuin-A (FETUA). Although the function of fetuin-A in acute kidney injury is unknown, it has been reported that increased urinary exosomal fetuin-A might be secreted from injured proximal tubule cells.44 It may also appear in the urine as a result of incomplete proximal tubule processing in proteinuria states (a form of overflow proteinuria) or released during tubular cell apoptosis. In addition, the temporal response profiles of the FETUA are consistent with CLUS in the CDDP treated group, which suggests a similar toxicodynamic mechanism of FETUA with CLUS. Among the new nephrotoxicity markers observed in this study, candidates like superoxide dismutase (SODC) provide some mechanistic insight into the molecular basis for toxicity. The superoxide dismutase system converts peroxide to hydrogen peroxide and oxygen during oxidative stress.45 Elevation of SODC has been shown in diabetic rats, implicating superoxide in the mediation of renal and vascular injury in diabetes.46 Another interesting example of a biomarker from category (ii) is vitamin D-binding protein (VTDB). The main function of VTDB is to transport vitamin D metabolites through circulation. VTDB is filtered by the glomerulus and subsequently reabsorbed by proximal tubular cells through the major endocytic receptor, that is, megalin.47 VTDB has been reported to be significantly up-regulated in rat models with renal interstitial inflammation and fibrosis.46 As a discovery screen, the strength of utilizing an approach such as LC−MS E for profiling lies in the ability to simultaneously identify and quantify hundreds to thousands of unknown species at once without reliance on antibodies for detection. Although it requires expertise as well as sophisticated instrumentation and software for data analysis, mass spectrometry can provide a major advantage due to the ability to query all proteins present in a given sample and is limited only by the intrinsic detection limit of individual proteins rather than the availability of an antibody. Furthermore, although immunoassays are widely used in routine analysis, development of multiplex immunoassays for profiling or new biomarker validation can also be very difficult and time-consuming. Consequently, once the investment has been made and a platform for LC−MSE profiling successfully established, it should then be possible to routinely use this approach for screening. After the LC−MSE analysis is concluded, a faster and higher throughput method (multiplex LC-SRM) with a wider

■

CONCLUSIONS This paper presents a proof-of-principle study of the integration of the traditional immunoassay used in toxicology together with a mass spectrometry based proteomics to establish a new platform for investigation of new drug candidates for nephrotoxicity risk. The combination of discovery and targeted MS based proteomics approaches provides a powerful workflow for biomarker discovery and validation.49 The selection of putative markers for the targeted SRM experiments was guided by the discovery screen and the immunoassay experiments. Multiplex SRM assays were developed to measure 30 candidate biomarkers selected from the discovery stage (LC−MSE) and six conventional biomarkers by immunoassay. FDR analysis revealed that 28 biomarkers in the DOX and 18 biomarkers in the CDDP treatment groups were upregulated at a significance level p < 0.05 at the end points. ROC analysis further revealed that eight biomarkers including five existing biomarkers from the DOX group and 12 biomarkers including one existing biomarker from CDDP group showed good predictive power (AUC > 0.90). In addition, there were no significant differences in the results generated by SRM and immunoassay platforms for the five existing biomarkers in the DOX group. On the other hand, differences between SRM and the immunoassay results were observed for KIM1 and GSTA in the CDDP group, which in this case was most likely due to the inherent lower precision when measuring less abundant proteins by mass spectrometry methods. This finding, however, only further highlights the strength of using orthogonol methods in biomarker discovery research. We have demonstrated the utility of mass spectrometry in the search for proteomics biomarkers as well as translation of this information into highthroughput SRM assays that facilitate the verification of putative markers. Although the immunoassay and mass spectrometry results do not always strictly agree, the use of both platforms in tandem brings together important new information as well as confirmatory evidence that ultimately adds credibility to this biomarker discovery approach. While these DOX and CDDP represent single examples of glomerular and tubular injury models, respectively, additional nephrotoxicants tested in this manner would improve mechanistic resolution. Additional glomerular and tubular toxiciants would aid in classifying newly identified markers as general nephrotoxicants capable of detecting most types of nephrotoxicity. Separately, some biomarkers may be specific to the injured anatomical region (e.g., proximal tubular vs glomerular injury). With further iteration, with additional compounds of varying nephrotoxic mechanisms, biomarkers may be further stratified as compound- or mechanism-specific indicators of nephrotoxicity. Despite the small sample size and necessity of additional experiments to fully validate these findings, the workflow revealed several new biomarkers, 1832

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(4) Vaidya, V. S., Ferguson, M. A., and Bonventre, J. V. (2008) Biomarkers of acute kidney injury. Annu. Rev. Pharmacol. Toxicol. 48, 463−493. (5) Nguyen, M. T., and Devarajan, P. (2008) Biomarkers for the early detection of acute kidney injury. Pediatr. Nephrol. 23, 2151−2157. (6) Parikh, C. R., and Devarajan, P. (2008) New biomarkers of acute kidney injury. Crit. Care Med. 36, S159−165. (7) Dieterle, F., Sistare, F., Goodsaid, F., Papaluca, M., Ozer, J. S., Webb, C. P., Baer, W., Senagore, A., Schipper, M. J., Vonderscher, J., Sultana, S., Gerhold, D. L., Phillips, J. A., Maurer, G., Carl, K., Laurie, D., Harpur, E., Sonee, M., Ennulat, D., Holder, D., AndrewsCleavenger, D., Gu, Y. Z., Thompson, K. L., Goering, P. L., Vidal, J. M., Abadie, E., Maciulaitis, R., Jacobson-Kram, D., Defelice, A. F., Hausner, E. A., Blank, M., Thompson, A., Harlow, P., Throckmorton, D., Xiao, S., Xu, N., Taylor, W., Vamvakas, S., Flamion, B., Lima, B. S., Kasper, P., Pasanen, M., Prasad, K., Troth, S., Bounous, D., RobinsonGravatt, D., Betton, G., Davis, M. A., Akunda, J., McDuffie, J. E., Suter, L., Obert, L., Guffroy, M., Pinches, M., Jayadev, S., Blomme, E. A., Beushausen, S. A., Barlow, V. G., Collins, N., Waring, J., Honor, D., Snook, S., Lee, J., Rossi, P., Walker, E., and Mattes, W. (2010) Renal biomarker qualification submission: a dialog between the FDA-EMEA and Predictive Safety Testing Consortium. Nat. Biotechnol. 28, 455− 462. (8) Devarajan, P. (2010) The use of targeted biomarkers for chronic kidney disease. Adv. Chronic Kidney Dis 17, 469−479. (9) Pfaller, W., and Gstraunthaler, G. (1998) Nephrotoxicity testing in vitro–what we know and what we need to know. Environ. Health Perspect 106 (Suppl 2), 559−569. (10) Bonventre, J. V., Vaidya, V. S., Schmouder, R., Feig, P., and Dieterle, F. (2010) Next-generation biomarkers for detecting kidney toxicity. Nat. Biotechnol. 28, 436−440. (11) Yu, Y., Jin, H., Holder, D., Ozer, J. S., Villarreal, S., Shughrue, P., Shi, S., Figueroa, D. J., Clouse, H., Su, M., Muniappa, N., Troth, S. P., Bailey, W., Seng, J., Aslamkhan, A. G., Thudium, D., Sistare, F. D., and Gerhold, D. L. (2010) Urinary biomarkers trefoil factor 3 and albumin enable early detection of kidney tubular injury. Nat. Biotechnol. 28, 470−477. (12) Dieterle, F., Perentes, E., Cordier, A., Roth, D. R., Verdes, P., Grenet, O., Pantano, S., Moulin, P., Wahl, D., Mahl, A., End, P., Staedtler, F., Legay, F., Carl, K., Laurie, D., Chibout, S. D., Vonderscher, J., and Maurer, G. (2010) Urinary clusterin, cystatin C, beta2-microglobulin and total protein as markers to detect druginduced kidney injury. Nat. Biotechnol. 28, 463−469. (13) Vaidya, V. S., Ozer, J. S., Dieterle, F., Collings, F. B., Ramirez, V., Troth, S., Muniappa, N., Thudium, D., Gerhold, D., Holder, D. J., Bobadilla, N. A., Marrer, E., Perentes, E., Cordier, A., Vonderscher, J., Maurer, G., Goering, P. L., Sistare, F. D., and Bonventre, J. V. (2010) Kidney injury molecule-1 outperforms traditional biomarkers of kidney injury in preclinical biomarker qualification studies. Nat. Biotechnol. 28, 478−485. (14) Tsugawa, N., Suhara, Y., Kamao, M., and Okano, T. (2005) Determination of 25-hydroxyvitamin D in human plasma using highperformance liquid chromatography–tandem mass spectrometry. Anal. Chem. 77, 3001−3007. (15) Cawood, M. L., Field, H. P., Ford, C. G., Gillingwater, S., Kicman, A., Cowan, D., and Barth, J. H. (2005) Testosterone measurement by isotope-dilution liquid chromatography-tandem mass spectrometry: validation of a method for routine clinical practice. Clin. Chem. 51, 1472−1479. (16) Thienpont, L. M., Van Uytfanghe, K., Blincko, S., Ramsay, C. S., Xie, H., Doss, R. C., Keevil, B. G., Owen, L. J., Rockwood, A. L., Kushnir, M. M., Chun, K. Y., Chandler, D. W., Field, H. P., and Sluss, P. M. (2008) State-of-the-art of serum testosterone measurement by isotope dilution-liquid chromatography-tandem mass spectrometry. Clin. Chem. 54, 1290−1297. (17) Wang, C., Catlin, D. H., Demers, L. M., Starcevic, B., and Swerdloff, R. S. (2004) Measurement of total serum testosterone in adult men: comparison of current laboratory methods versus liquid

including VTDB, FETUA, and SODC, that exhibited exhibiting a good correlation and predicting power for mechanisms of DIKI and potential nephrotoxicity.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.chemrestox.7b00159. Individual animal test article microscopic observations; selected candidate marker based on the false discovery rate analysis of log2 transferred LC−MSE data sets adjusted by multiplicity testing; list of selected candidate biomarkers and their LC-SRM assay transitions; technical reproducibility for peptide measurement by SRM assays (PDF)

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: 203-798-4115. ORCID

Liuxi Chen: 0000-0003-1803-8835 Present Addresses §

(L.C.) Drug Metabolism & Pharmacokinetics, Genentech, 1 DNA Way, South San Francisco, California 94080, United States. ⊥ (J.A.P.) Vertex Pharmaceuticals Incorporated, Boston, Massachusetts 02110, United States. Notes

The authors declare no competing financial interest.

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ABBREVIATIONS DOX, doxorubicin; CDDP, cis-diamminedichloridoplatinum or cisplatin; LC−MS, liquid chromatography−mass spectrometry; SRM, selected reaction monitoring; DIKI, drug-induced kidney injury; FDR, false discovery rate; ROC, receiver-operating characteristic; AKI, acute kidney injury (AKI); BUN, blood urea nitrogen; sCr, serum creatinine; IACUC, Institutional Animal Care and Use Committee; GSTA, GST-α (GSTA); ALBU, albumin; CLUS, clusterin; NGAL, Lipocalin-2; KIM1, kidney injury marker; B2MG, β2-microglobulin; ULOQ, upper limit of quantification; LLOQ, lower limit of quantification; ADH, alcohol dehydrogenase; PLGS, ProteinLynx Global Server

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REFERENCES

(1) Goodsaid, F., and Frueh, F. (2007) Biomarker qualification pilot process at the US Food and Drug Administration. AAPS J. 9, E105− 108. (2) Sistare, F. D., Dieterle, F., Troth, S., Holder, D. J., Gerhold, D., Andrews-Cleavenger, D., Baer, W., Betton, G., Bounous, D., Carl, K., Collins, N., Goering, P., Goodsaid, F., Gu, Y. Z., Guilpin, V., Harpur, E., Hassan, A., Jacobson-Kram, D., Kasper, P., Laurie, D., Lima, B. S., Maciulaitis, R., Mattes, W., Maurer, G., Obert, L. A., Ozer, J., PapalucaAmati, M., Phillips, J. A., Pinches, M., Schipper, M. J., Thompson, K. L., Vamvakas, S., Vidal, J. M., Vonderscher, J., Walker, E., Webb, C., and Yu, Y. (2010) Towards consensus practices to qualify safety biomarkers for use in early drug development. Nat. Biotechnol. 28, 446−454. (3) Fuchs, T. C., and Hewitt, P. (2011) Biomarkers for drug-induced renal damage and nephrotoxicity-an overview for applied toxicology. AAPS J. 13, 615−631. 1833

DOI: 10.1021/acs.chemrestox.7b00159 Chem. Res. Toxicol. 2017, 30, 1823−1834

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DOI: 10.1021/acs.chemrestox.7b00159 Chem. Res. Toxicol. 2017, 30, 1823−1834