Computational In Vitro Toxicology Uncovers Chemical Structures

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Computational In Vitro Toxicology Uncovers Chemical Structures Impairing Mitochondrial Membrane Potential David Dreier, Nancy D. Denslow, and Christopher J Martyniuk J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00433 • Publication Date (Web): 15 Jan 2019 Downloaded from http://pubs.acs.org on January 19, 2019

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Journal of Chemical Information and Modeling

Computational In Vitro Toxicology Uncovers Chemical Structures Impairing Mitochondrial Membrane Potential David A. Dreier1, Nancy D. Denslow1, Christopher J. Martyniuk1 1Center

for Environmental & Human Toxicology, Department of Physiological Sciences,

College of Veterinary Medicine, University of Florida, Gainesville, FL, 32611 USA

ACS Paragon Plus Environment

1

Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Abstract Technological advances in molecular biology enable high-throughput screening (HTS) of large chemical libraries. These approaches have provided valuable toxicity data for many physiological responses, including mitochondrial dysfunction. While several quantitative structure-activity relationship (QSAR) models have been developed for mitochondrial dysfunction, there remains a need to identify specific chemical features associated with this response. Thus, the objective of this study was to identify chemical structures associated with altered mitochondrial membrane potential (MMP). To achieve this, we developed computational models to examine the relationship between specific chemotypes (e.g., ToxPrints) and bioactivity in

ToxCast/Tox21

HTS

assays

"bond:COH_alcohol_aromatic",

for

altered

MMP.

The

analysis

revealed

"bond:COH_alcohol_aromatic_phenol",

the and

"ring:aromatic_benzene" ToxPrints had the highest average correlation (phi coefficient) with ToxCast/Tox21 assay component endpoints for decreased MMP. These structures also comprised a “core” group of ToxPrints for decreased MMP in a force-directed network model and were the most important chemotypes in a random forest (RF) classification model for the “TOX21_MMP_ratio_down” assay component endpoint. Based on multiple lines of evidence, these structures, which are present in numerous chemicals (e.g., aromatic hydrocarbons, pesticides, industrial chemicals), are likely involved in mitochondrial dysfunction. Due to the hierarchical structure of ToxPrints, these chemotypes were highly convergent and, when excluded from training data, had limited effects on classification performance as related structures compensated for predictor loss. These results highlight the flexibility of the RF algorithm and ToxPrints for QSAR modeling, which is useful to identify chemicals affecting mitochondrial function.


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Introduction Mitochondria are essential organelles in eukaryotic cells. These organelles play a role in multiple biochemical and physiological functions, including ATP production, sterol biosynthesis, calcium regulation, and apoptotic signaling, among others. In recent years, there is growing evidence many chemicals affect mitochondrial function and bioenergetics. Specifically, mitochondria are vulnerable to many toxicants due to their attraction for lipophilic compounds and metals, cytochrome P450 activity, mtDNA repair mechanisms, and proximity to reactive oxygen species (ROS) generation from the electron transport chain (ETC).1 Moreover, mitochondrial dysfunction has been associated with several diseases such as cancer, Parkinson’s and Alzheimer’s disease, as well as aging.2–4 Thus, this mechanism provides a conduit between environmental chemical exposures and human diseases. While there remains a need to identify causal pathways for these outcomes, there is momentum towards identifying which environmental chemicals affect mitochondrial structure and function. A major advance in mitochondrial toxicology has been the development of high-throughput screening (HTS) assays. Unlike more traditional toxicological approaches (e.g., in vivo animal testing), HTS assays can be utilized to screen thousands of chemicals across multiple conditions (i.e., concentration, time, experimental models) and different modes of action. Put together, HTS data offer unique insights that can be used to predict apical outcomes and prioritize chemicals for additional testing. Such an approach has already been leveraged by various programs, such as ToxCast and Tox21 in the United States.5–7 As mitochondria play an important role in chemicalinduced toxicity, several HTS assays have been developed for mitochondrial dysfunction. For example, multiple studies have used mitochondrial membrane potential (MMP) reporter assays, as well as functional measurements of mitochondrial-linked oxygen consumption rate (OCR) to


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determine how chemicals affect respiration.8–10 Over the years, these assays have been used to screen large chemicals libraries, including those from Library of Pharmacologically Active Compounds (LOPAC), the National Toxicology Program (NTP), ToxCast, and Tox21.8,11–15 Mitochondrial toxicity data from HTS assays have been used to construct numerous quantitative structure-activity relationship (QSAR) models. These models typically require large data sets, such as those provided by the Tox21 MMP screen, for machine learning and model construction.15 Over the years, several robust QSAR models have been developed for mitochondrial dysfunction using various approaches, such as naïve-Bayes classifiers, similaritybased learning, and neural networks.16–19 However, while these approaches offer excellent model performance, they remain limited in their interpretation (i.e., “black box” methodologies) and ability to identify toxic chemical features leading to mitochondrial dysfunction. This is an important knowledge gap that must be addressed, as such information can be used to articulate chemical design rules for reduced toxicity.20 Moreover, most QSAR models focus on a single assay or outcome, and it is important to systematically evaluate structure-activity relationships across multiple assays to identify unique and consensus responses. The objectives of this research were two-fold. First, we aimed to identify chemical features associated with mitochondrial dysfunction, specifically altered MMP. Next, we aimed to compare the importance of these chemical features across multiple assays to identify a “core” group of structures leading to mitochondrial dysfunction. We hypothesize ToxPrint chemotypes will be associated with assays based on the mode of action (i.e., decreased or increased MMP). Furthermore, we expect to observe clustering of assays based on their overarching design (i.e., cell type, detection technology). To address these objectives, we leveraged HTS data from the ToxCast/Tox21 to identify mitochondrial toxicants in several assays. Using various statistical


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modeling approaches and machine learning, we linked structural data to bioactivity in each assay and developed a network model to compare feature importance across assays.

Methods ToxCast/Tox21 data collection High-throughput screening assays from the ToxCast and Tox21 programs were used to identify chemicals altering the MMP. The latest ToxCast summary files (October 2015 data release) were downloaded from the U.S. Environmental Protection Agency website. Assays measuring altered MMP were identified in the “Assay_Summary_151020.csv” file by selecting assays with “membrane potential reporter” as the “assay_design_type”. This query returned 3 assays (“APR_Hepat_MitoFxnI”, “APR_HepG2_MitoMembPot”, “TOX21_MMP_ratio”) with 14 total assay component endpoints (Table 1). These assays comprise unique organisms, cell types, and examine different chemical libraries. All assays measured the MMP with fluorescent reporters but used unique detection technologies and dyes; APR assays used high content imaging with MitoTracker Red while the Tox21 assays used a homogenous MMP reporter assay with MitoMPS.8,9 The component endpoints of each assay provide additional time and direction information about the MMP response and, to maximize the value of this information, were examined separately in this analysis. Accordingly, hit calls were extracted for assay component endpoints from the “hitc_Matrix_151020.csv”

file

and

joined

with

CASRN

identifiers

from

the

“Chemical_Summary_151020.csv” matrix. Here, we acknowledge Tox21 bioactivity data were available from other sources, but we elected to extract this information from the ToxCast database because these data were processed using a common pipeline (i.e., tcpl), which subsequently improves the consistency and quality of downstream analyses.21


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Table 1. Description of ToxCast/Tox21 assay component endpoints for altered mitochondrial membrane potential. Tested chemicals

Hits

Percent active

hepatocyte

310

57

18.39

rat

hepatocyte

310

0

0.00

24

rat

hepatocyte

310

46

14.84

up

24

rat

hepatocyte

310

0

0.00

APR_Hepat_MitoFxnI_48hr_dn

down

48

rat

hepatocyte

310

45

14.52

APR_Hepat_MitoFxnI_48hr_up

up

48

rat

hepatocyte

310

11

3.55

APR_HepG2_MitoMembPot_1h_dn

down

1

human

HepG2

310

26

8.39

APR_HepG2_MitoMembPot_1h_up

up

1

human

HepG2

310

0

0.00


down

24

human

HepG2

1061

192

18.10


up

24

human

HepG2

1061

0

0.00


down

72

human

HepG2

1046

136

13.00


up

72

human

HepG2

1046

9

0.86

TOX21_MMP_ratio_down

down

1

human

HepG2

5592

914

16.34

TOX21_MMP_ratio_up

up

1

human

HepG2

5592

124

2.22

Assay component endpoint

Direction


down


up


Time (h)

Organism

Cell type

1

rat

1

down


ToxPrint generation To identify chemical structures altering the MMP, we generated ToxPrints for chemicals examined in ToxCast/Tox21. ToxPrints are a set of chemotypes (i.e., binary fingerprints) that symbolize unique chemical structures, including generic structural fragments, Ashby-Tennant genotoxic carcinogen rules, and cancer thresholds of toxicological concern categories.22–24 These chemotypes have been used in several studies with ToxCast/Tox21 data and represent many structures present in this large chemical landscape.17,25–28 ToxPrints were generated using the Chemotyper application by matching structures from the “TOX21S_v2a_8193_22Mar2012.sdf”


6

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file with 729 chemotypes outlined in “toxprint_v2.0_r711.xml”. Chemicals were identified by CASRN, exported into a matrix format, and then joined with the hit call matrix for selected assays. Following curation, 589 ToxPrints were present in one or more chemicals comprising the chemical-assay space in this analysis (5372 total chemicals).

Correlation and machine learning analysis Various statistical modeling approaches were used to identify chemical structures associated with bioactivity in each of the ToxCast/Tox21 assays. All analyses were performed with R version 3.5.0 “Joy in Playing” and documented in an R script provided in the supporting information. First, the binary correlation (phi coefficient) was calculated between the presence of a ToxPrint and bioactivity (i.e., hit call) for all chemicals tested in an assay. Using the BenjaminiHochberg procedure, a false discovery rate (FDR) was also calculated to adjust significance values for multiple comparisons.29 To compare assay component endpoints, a hierarchical cluster analysis (Euclidean distance) was performed using ToxPrint correlations. Several linkage methods (e.g., Ward’s, single, complete, average) were compared using the agglomerative coefficient from the agnes function (R package: cluster) and plotted with ggdendrogram (R package: ggdendro). To identify ToxPrints associated with bioactivity in multiple assays, a network analysis (R package: igraph) was performed for significant correlations (FDR < 0.05) above a positive threshold ( > 0.1). A Fruchterman-Reingold force-directed layout was used to visualize clustering of similar assay component endpoints (colored nodes) based on selected correlations (edges) with ToxPrints (gray nodes).30 Finally, for each ToxPrint, the average correlation was calculated across all assay component endpoints to identify the top chemical structures leading to decreased MMP. For assays with multiple component endpoints, ToxPrint correlations were also analyzed as a function of time.


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In addition to the correlation analysis, we leveraged machine learning to identify chemical structures important for bioactivity classification. Specifically, we constructed a random forest (RF) classification model for the “TOX21_MMP_ratio_down” assay component endpoint. Random forest models are common for QSAR development and provide excellent classification accuracy with categorical data.31 Moreover, unlike other machine learning approaches, RF can be used to identify the importance of predictor variables. A RF classification model is constructed by generating an ensemble of decision trees that “vote” on a predicted classification. Each decision tree is unique and “grown” from a bootstrapped group of observations. Following model construction, the algorithm can calculate predictor importance by imputing random numbers for each predictor variable and subsequently measuring changes to model accuracy (mean decrease in accuracy, MDA). Thus, predictors decreasing model accuracy with random imputation are considered important.32 In our analysis, we used ToxPrints as predictor variables to classify bioactivity (i.e., hit calls) in the “TOX21_MMP_ratio_down” assay component endpoint. This assay was selected because it provided the largest number of observations (i.e., chemicals) for machine learning. Furthermore, the data set includes numerous molecules from the Tox21 chemical library, which represents diverse structural features exhibited in many environmental chemicals (e.g., pesticides, industrial chemicals). First, observations were split into training (80%) and test (20%) data sets (R package: rsample) for model validation. Next, the training data set was balanced for active and inactive compounds. Imbalanced data sets can lead to poor model performance, specifically high false positive rates. While there are several popular techniques to handle imbalanced data (e.g., cost-sensitive learning, minority class oversampling, majority class undersampling), we used a cluster-selection strategy that has been shown to improve QSAR classification performance.33


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Briefly, a binary distance matrix was generated for the majority class (inactive compounds) and structures were clustered using the Ward’s agglomeration method. The hierarchical cluster analysis was trimmed to have the same number of clusters as the minority class (i.e., active compounds) from the training data, and compounds were randomly sampled from these clusters to reduce the size of the majority class while retaining chemical diversity. Next, this balanced data set was used to construct a RF classification model with n = 100 decision trees using the randomForest package in R. The model was validated with each data set (i.e., clustered training, training, and test) using several metrics, including sensitivity, specificity, balanced accuracy, and receiver operating characteristic area under the curve (ROC-AUC) for ensemble classification votes (R package: pROC). Following model validation, the predictor importance (MDA) was calculated for all ToxPrints and compared with their correlation values to identify chemical structures associated with decreased MMP. To aid with this comparison, upper and lower correlation significance thresholds were established based on an FDR < 0.05 for the phi coefficient, and a 95th percentile threshold was established for predictor importance in the RF classification model. An overview of this machine learning workflow is outlined in Figure S1. In addition, we examined the redundancy of ToxPrints for classification model construction. ToxPrints are arranged in a hierarchical structure, which introduces data convergence at different levels of organization.24 Based on the original classification model, we constructed additional RF classifiers that excluded all possible combinations of the 3 most important ToxPrints (identified below). As before, we systemically evaluated classification performance and predictor importance to determine whether all ToxPrints are necessary for robust classification.


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Results Correlation analysis In this analysis, there were 14 assays component endpoints measuring altered MMP. However,

among

the

assays,

there

were

4

“APR_Hepat_MitoFxnI_1hr_up”,

component

endpoints

(i.e.,

“APR_Hepat_MitoFxnI_24hr_up”,

“APR_HepG2_MitoMembPot_1h_up”, “APR_HepG2_MitoMembPot_24h_up”) with no active hit calls, which consequently had to be excluded in subsequent analyses. Interestingly, all 4 component endpoints comprised early time points in the APR assays and were examining increased MMP. However, for the remaining 10 assay component endpoints, the phi coefficient was successfully calculated for available ToxPrints (Figure 1, Table S1). The highest correlations were recorded in the “APR_Hepat_MitoFxnI_48hr_up” assay component endpoint for “bond:NN_hydrazine_alkyl_generic”

(

=

0.296,

n

=

292,

FDR

0.1) above a stringent significance criteria (FDR < 0.05). In this model, there were several ToxPrints associated with multiple ToxCast/Tox21 assay component endpoints for decreased MMP.

For

example,

both

the

“bond:COH_alcohol_aromatic”

and

“bond:COH_alcohol_aromatic_phenol” ToxPrints met the significance criteria for 4 assays component endpoints measuring decreased MMP. Similarly, the “bond:CX_halide_alkylX_dihalo_(1_1-)”, “bond:CX_halide_alkyl-X_trihalo_(1_1_1-)”, and “bond:X[any]_halide” ToxPrints met the significance criteria for 2 component endpoints in the positive direction. In the


12

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overall model, there was strong clustering of component endpoints for decreased MMP due to similar ToxPrint correlations. Likewise, there was separation and clustering of the ToxCast/Tox21 assay component endpoints measuring increased MMP. Two of the component endpoints (“APR_Hepat_MitoFxnI_1hr_dn” and “APR_Hepat_MitoFxnI_48hr_up”) did not join the larger network due to their unique correlation profiles. Put together, these data indicate specific chemical structures play an important role in predicting diverging mitochondrial responses (i.e., decreased or increased MMP).

Figure 3. Force-directed network model illustrating correlations between ToxPrints (gray nodes) and ToxCast/Tox21 assay component endpoints for altered mitochondria membrane potential. Only strong positive correlations (phi coefficient > 0.1) meeting a stringent significance criterion (false discovery rate < 0.05) were included in the model.

In addition to the network model, summary statistics were calculated for each ToxPrint to identify those associated with decreased MMP. It was found the "bond:COH_alcohol_aromatic",


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"bond:COH_alcohol_aromatic_phenol", and "ring:aromatic_benzene" ToxPrints had the highest average correlation with selected ToxCast/Tox21 assay component endpoints (Figure 4). While there was a large range in correlation values for each ToxPrint, the mean correlations for these ToxPrints were markedly higher than others. Among the top ToxPrints, there were numerous structures representing bonds or rings associated with aromatic compounds. These ToxPrints are common for many Tox21 chemicals, such as aromatic hydrocarbons, pesticides, and industrial chemicals (Table S2). In addition, the importance of these ToxPrints changed as a function of time (Figure S2). For example, in the APR_HepG2_MitoMembPot assay for decreased MMP, “ring:aromatic_phenyl” had the strongest correlation among the top 8 ToxPrints at 1 hour, yet was largely uncorrelated at 24 and 72 hours. In general, the correlation of the selected ToxPrints decreased as a function of time.

Figure 4. Correlations (phi coefficient) between the top 8 ToxPrints and ToxCast/Tox21 assay component endpoints for decreased mitochondrial membrane potential. ToxPrints were ranked by mean correlation across all assays (vertical bars).


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Correlation and machine learning comparison A RF model for the “TOX21_MMP_ratio_down” assay component endpoint was constructed and validated, and predictor variables (i.e., ToxPrints) were analyzed for their classification importance. Several validation metrics were used to measure classification performance (Table 2), and it was found the RF model had good classification performance for the clustered training, training, and test data sets. Classification sensitivity was similar between the clustered training (0.940) and training (0.937) data sets, although there was a decrease in specificity between the clustered training (0.924) and training (0.823) data sets. This was expected, as more compounds were included in the majority class (i.e., inactive compounds). In the test data set, there was good sensitivity (0.733) and specificity (0.746), leading to a balanced accuracy of 0.740. Classification votes from the ensemble model were also used to calculate the ROC-AUC, which was 0.972, 0.942, and 0.837 for the clustered training, training, and test data sets, respectively (Figure S3). When evaluating predictor importance, it was found the “ring:aromatic_benezene” (MDA = 12.181), “bond:COH_alcohol_aromatic_phenol” (MDA = 7.840), and “bond:COH_alcohol_aromatic” (MDA = 7.827) were the most important ToxPrints for classification performance (i.e., accuracy) in the RF model.

Table 2. Random forest (RF) classification performance for clustered training, training, and test data sets from the “TOX21_MMP_ratio_down” assay component endpoint. The receiver operating characteristic area under the curve (ROC-AUC) was generated based on the proportion of votes in the RF ensemble. Data set Clustered training Training Test

Sensitivity

Specificity

Balanced accuracy

ROC-AUC from RF votes

0.940 0.937 0.733

0.924 0.823 0.746

0.932 0.880 0.740

0.972 0.942 0.837


15


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For each ToxPrint, predictor importance was compared to the phi coefficient to identify chemical structures associated with decreased MMP in both analyses. Significance criteria were established for both correlation (FDR < 0.05) and variable importance (MDA > 95th percentile) to identify thresholds for discussion. In this comparison, it was the found that the top 3 ToxPrints in the

RF

classification

model

also

had

the

strongest

correlations

with

the

“TOX21_MMP_ratio_down” assay component endpoint (Figure 5, Table S3). There were 23 additional ToxPrints meeting both significance criteria in the positive direction (26 ToxPrints significant overall); no ToxPrints were significant in the negative direction. Interestingly, several ToxPrints were important in the RF classification model but had little to no correlation (e.g., “chain:alkaneLinear_hexyl_C6”, “chain:alkaneLinear_octyl_C8”,

“chain:alkaneLinear_butyl_C4”, “chain:alkaneLinear_decyl_C10”).

In

general,

however,

ToxPrints that were highly correlated with bioactivity in the “TOX21_MMP_ratio_down” assay component endpoint were also important in the RF classification model.


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Figure 5. Comparison between correlation (phi coefficient) and random forest predictor importance for ToxPrints in the “TOX21_MMP_ratio_down” assay component endpoint. Significance thresholds (dotted lines) include: (1) Correlation false discovery rate < 0.05; (2) Predictor importance in classifier (mean decrease in accuracy) > 95th percentile.

Additional RF models excluding selected ToxPrints were constructed to examine the redundancy of these chemotypes for classification. Due to the hierarchical structure of ToxPrints, there were significant overlaps between the top chemotypes (Figure 6). For chemicals examined in the “TOX21_MMP_ratio_down” assay component endpoint, there were 481 compounds with the “bond:COH_alcohol_aromatic” ToxPrint, and among these compounds 469 included the “bond:COH_alcohol_aromatic_phenol” ToxPrint. Similarly, these compounds also included the


17


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“ring:aromatic_benzene” ToxPrint, illustrating the convergence of these chemotypes. To determine whether these ToxPrints are necessary for classification, we constructed additional RF classifiers for “TOX21_MMP_ratio_down” excluding all possible combinations of these chemotypes (7 models total). Test data sets were used to examine classification performance, and it was found ToxPrint exclusion had negligible effects on sensitivity, specificity, balanced accuracy, or ROC-AUC (Table S4). In addition, we examined predictor importance in each of these models and discovered that related ToxPrints compensated for predictor loss (Figure 7, Table S5). For example, when the “bond:COH_alcohol_aromatic” ToxPrint was excluded from the RF classifier, the “bond:COH_alcohol_aromatic” and “bond:COH_alcohol_generic” ToxPrints became more important in the model. This was a common effect in each exclusion scenario, where the top ToxPrints were most responsive to predictor loss.


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Figure 6. Venn diagram of ToxPrint representation in the Tox21 chemical space for selected aromatic structures.


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Figure 7. Heat map of ToxPrint importance in random forest classifiers excluding selected ToxPrints.


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Discussion The advent of HTS has provided an enormous quantity of toxicity data for mitochondrial dysfunction. Several HTS assays have been developed to measure mitochondrial responses.34 For example, Sakamuru and colleagues optimized a fluorescence MMP assay with HepG2 cells for 1536-well plates and screened 1280 compounds from the LOPAC library.8 This assay was later used to screen 1408 compounds from the NTP at 14 concentrations for multiple time points.12 Active compounds at both time points (1 hour and 5 hours) were used in a similarity-based cluster analysis, and a group of substances comprising the active chemical space were verified using additional fluorescent, high content imaging (HCI), and respirometric assays, demonstrating reproducible findings using different methodologies. Furthermore, the assay was adapted to screen the Tox21 chemical library (approximately 8300 unique compounds) for altered MMP, and it revealed that approximately 11% of chemicals decreased the MMP after 1 hour of exposure.15 Similarly, HCI has been used to measure the MMP for large chemical libraries, including ToxCast.13 These assays measure multiple endpoints, including the MMP, to identify cellular “tipping points” departing from homeostasis. Taken together, HCI provides an integrated approach to illustrate connections between mitochondrial function, cellular trajectories, and toxicity with adequate experimental throughput that leverages large chemical libraries.9 In addition to MMP assays, other HTS applications have been developed to measure mitochondrial dysfunction. For example, advances in cellular respirometry have enabled HTS for functional measurements of mitochondrial-linked respiration (i.e., OCR) in primary and immortalized cells. These assays are highly adaptable and, in many cases, add pharmacological inhibitors of mitochondrial components to measure specific mitochondrial parameters (i.e., ATP production, maximal respiration, spare capacity).10 Recently, respirometry-based assays have been


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used to screen 1760 compounds from the LOPAC and ChemBridge DIVERSet libraries in renal proximal tubular cells (RPTCs).11 In addition, this assay has screened the ToxCast Phase II library (676 compounds) and identified 376 ETC inhibitors and 5 uncouplers in RPTCs. Interestingly, when a subgroup of these compounds were screened in a similar assay with immortalized cells (i.e., HK2), only a small proportion of compounds caused similar responses.14 Thus, while cellular respirometry offers powerful insight, there remain important design and interpretation criteria that need to be further addressed with such assays. Alongside assay development, the advance of HTS has led to the construction of many QSAR models for mitochondrial dysfunction. A recent competition called the “Tox21 data challenge” solicited for accurate QSAR models predicting bioactivity in 12 different HTS assays.35 Indeed, several QSARs were presented for the Tox21 MMP assay, and the winning group developed the most accurate QSAR (balanced accuracy = 0.904) with associative neural networks.18,36 Similarly, other accurate QSARs for the MMP assay leveraged neural networks, as these models offer improved accuracy over other methods.19,37 In addition, other QSAR model designs have been leveraged with the MMP assay, such as Bayesian and similarity-based approaches, with comparable performance.17 A powerful utility of QSAR modeling is the ability to identify chemical features leading to toxicity. While most QSAR models for mitochondrial dysfunction have focused on performance, a few have identified chemical classes and toxicophores associated with altered MMP and OCR. For example, Attene-Ramos and colleagues clustered the Tox21 chemical library by structural similarity and identified groups enriched for decreased MMP.15 Representative scaffolds were identified in these clusters and included diverse molecules, such as nitrobenzenes, flavonoids, rodenticides, phenols, and chlorinated organic insecticides, among others. In addition, Wills and


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colleagues identified toxicophores decreasing OCR based on structural clustering of selected chemicals from the LOPAC and ChemBridge DIVERSet libraries.11 Similarly, an expert-guided structure activity relationship model for mitochondrial uncoupling has been developed.38 This model identified 11 toxicophores and other chemical features (e.g., pKa, logP) leading to uncoupling in a data set of 2085 compounds. While these studies outline a step forward to identify chemical features associated with toxicity, there remained a need to systematically evaluate multiple HTS assays for mitochondrial dysfunction. In the present study, we identified chemical structures (i.e., ToxPrints) associated with bioactivity in ToxCast/Tox21 assays for mitochondrial dysfunction, specifically altered MMP. In total, there were 3 assays included in this analysis measuring 10 unique component endpoints (e.g., several time points, direction of MMP change). When these component endpoints were clustered based on their structure-activity correlation with ToxPrints, there was strong grouping based on the direction of altered MMP (i.e., up or down) and assay design. This confirmed our hypothesis, which stated that structure-activity relationships would be most similar between assays with a similar mode of action (i.e., decreased or increased MMP). While the assays had similar time points, there was no apparent clustering of component endpoints as a function of time, suggesting other factors contribute to structure-specific bioactivity in these assays. For decreased MMP, it was found the “bond:COH_alcohol_aromatic”, “bond:COH_alcohol_aromatic_phenol”, and “ring:aromatic_benzene” ToxPrints had the highest average correlation across all assay component endpoints. Similarly, 2 of these ToxPrints were associated with 4 component endpoints for decreased MMP in a network model with stringent correlation criteria (φ > 0.1, FDR < 0.05). These ToxPrints represented a “core” for decreased MMP in the network model, indicating these structures are involved in mitochondrial dysfunction.


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To further validate these predictions, we constructed a RF classification model using ToxPrints to predict hit calls in the “TOX21_MMP_ratio_down” assay component endpoint. After validating the model with internal and external data sets, the predictor importance was examined and compared with correlations for each ToxPrints. It was found 26 ToxPrints met significance criteria in both assessments, indicating their association with decreased MMP. The majority of these chemotypes included aromatic structures that are present in many chemicals, such as hydrocarbons, pesticides, and industrial chemicals. Furthermore, the machine learning models presented in this work were trained with a diverse chemical library from the Tox21 program. Thus, it is expected these QSAR models will have a large domain of applicability, as Tox21 includes chemicals from many groups, including industrial chemicals, solvents, plasticizers, flame retardants, pesticides, food additives, natural products, and therapeutic agents, among others.28 Additional steps will be required to address the specific domain of applicability for novel compounds. The computational approach outlined in this study represents a significant step forward in QSAR modeling for mitochondrial dysfunction. While numerous studies have employed ToxPrints, we examined the redundancy of these chemotypes and found that excluding important ToxPrints prior to RF construction had little effect on classification performance for mitotoxicants. When predictor importance was examined in these classifiers, it was found that related ToxPrints compensated for predictor loss, suggesting only a selection of chemotypes might be necessary for robust classification performance. These results highlight the flexibility of the RF algorithm for QSAR applications, especially when coupled with the hierarchical structure of ToxPrints. Moreover, these modeling approaches are useful to identify chemical structures associated with mitochondrial dysfunction. A few studies have determined general scaffolds and toxicophores


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enriched within structural clusters including mitotoxicants, yet these models do not examine specific chemical structures that alone, or in combination with other structures, lead to toxicity.11,15 The top ToxPrints identified in this study, which also comprised the “core” ToxPrints in the network model, offer valuable information to identify and predict mitochondrial toxicants. While these structures were identified using multiple lines of evidence, further validation will improve the utility of this information. Altered MMP is a common mitochondrial endpoint, but there are other hallmarks of mitochondrial dysfunction. For example, the Tox21 MMP assay has been used in a tiered testing approach to prioritize chemicals for testing with additional endpoints, including ROS generation, p53 and Nrf2/ARE regulation, mitochondrial OCR, Parkin translocation, and ATP content.39 Ultimately, integrated models that incorporate multiple mitochondrial endpoints will be required to fully appreciate the complex relationship between environmental chemicals and mitochondrial health. Long-term, these models may also be useful to develop sustainable molecular design guidelines to reduce toxicity. Toxic structures – such as those identified in this study - are ideal design rule candidates and may be useful to design less hazardous substances.20,40 In a separate context, these findings are also useful to identify substances likely to elicit toxicity at higher levels of biological organization. For example, bioactivity data from the Tox21 MMP assay has been used to predict acute toxicity thresholds in fish, daphnia, and rats (intravenous exposure). Coupled with absorption and CYP-based metabolism data, oral rat acute toxicity could also be predicted for a selected group of chemicals.41 In a separate study, a panel of Tox21 assays (including the MMP assay) was used to build predictive models for 72 in vivo toxicity endpoints. Using a cluster-based approach, it was found these assays predict human toxicity endpoints better than animal toxicity, especially when structural data were made


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available.42 With such approaches, structure-activity data will become increasingly important to improve toxicity predictions across multiple taxa. Finally, identifying chemical structures associated with mitochondrial dysfunction and other molecular initiating events (MIEs) will be important to refine Adverse Outcome Pathways (AOPs). AOPs consist of one or more MIE(s) linked to subsequent key events (KEs) through causal key-event relationships.43,44 Ultimately, this framework can be used to predict apical responses with quantitative AOPs, as has been done with aromatase inhibition leading to reduced fecundity and population decline in fish.45,46 In the present study, altered membrane potential and mitochondrial dysfunction represent early KEs leading to apical responses. For example, mitochondrial dysfunction has been associated with impaired growth in fish, as well as parkinsonian motor deficits in mammalian taxa.47,48 Structure-activity data, such as those presented here, may be useful to refine these AOPs by identifying specific MIEs (e.g., ETC inhibition, uncoupling, mitochondrial DNA mutations) based on structural clusters. While AOPs are not chemical-specific, this information may be useful to identify general structural features leading to MIEs and subsequent biological responses.43,49,50 Ultimately, these improvements will enhance our capability to assess mode of action and predict apical responses useful for risk assessment and other management decisions.51

Conclusions Many environmental chemicals affect mitochondrial function, and there is a growing need to identify mitochondrial toxicants. Robust QSAR model development is a necessary step to address this research need, and it is important to interpret the contribution of chemical features within these models to identify novel structures leading to toxicity. The present work identified


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several ToxPrints associated with decreased MMP across multiple ToxCast/Tox21 HTS assays with unique designs. This is an important step forward and provides useful information to identify mitochondrial toxicants and design rules for reduced toxicity. In the AOP framework, this information may also be useful to classify the mode of action of environmental chemicals. In order to develop a more comprehensive understanding of structure-mediated toxicity, future research should integrate these findings with additional modes of action. Further, it will be necessary to identify chemical features leading to functional changes in mitochondrial bioenergetics (i.e., OCR) and related physiological responses. These steps will be important to improve the capacity of QSAR modeling to predict downstream responses in the AOP framework. Author Information *Corresponding

author

Christopher J. Martyniuk Email: [email protected] Phone: 352-294-4636 Fax: 352-392-4707 2187 Mowry Rd., Bldg. 471 P.O. Box 110885 Gainesville, FL 32611

Conflict of Interest Statement The authors declare no competing financial interest.


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Acknowledgement We would like to acknowledge the National Science Foundation supporting D. Dreier through the Graduate Research Fellowship Program under Grant No. DGE-1315138.

Abbreviations AOP, adverse outcome pathway; ETC, electron transport chain; FDR, false discovery rate; HCI, high-content imaging; HTS, high-throughput screening; KE, key event; LOPAC, Library of Pharmacologically Active Compounds; MDA, mean decrease in accuracy; MIE, molecular initiating event; MMP, mitochondrial membrane potential; NTP, National Toxicology Program; OCR, oxygen consumption rate; QSAR, quantitative-structure activity relationship; RF, random forest; ROC-AUC, receiver operating characteristic area under the curve; ROS, reactive oxygen species; RPTC, renal proximal tubular cells

Supporting Information Figure S1, Machine learning workflow for the “TOX21_MMP_ratio_down” random forest classification model; Figure S2, Time-series analysis of correlations between ToxPrints and ToxCast/Tox21 assay component endpoints for decreased mitochondrial membrane potential; Figure S3, Receiver operating characteristic curves for data sets in the “TOX21_MMP_ratio_down” random forest classification model; Table S1, Correlations between ToxPrints and ToxCast/Tox21 assay component endpoints for mitochondrial dysfunction; Table S2, ToxPrint representation for most significantly correlated structures across all assays; Table S3, Comparison between correlation and random forest predictor importance for ToxPrints in the “TOX21_MMP_ratio_down” assay component endpoint; Table S4,


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Performance of random forest classifiers excluding selected ToxPrints for the “TOX21_MMP_ratio_down” assay component endpoint; Table S5, Importance of ToxPrints in random forest classification models excluding selected ToxPrints; Script S1, R script outlining all analyses in this study

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