Comparative analysis of OECD guideline data and Tox21 assays to improve reproductive and developmental toxicity prediction

Abstract

Reproductive and developmental toxicities pose major challenges in chemical safety evaluation because adverse effects can span generations. Conventional in vivo OECD Test Guideline (TG) assays provide robust hazard characterization but require large animal cohorts and extended study periods. In this study, datasets from OECD TG-compliant studies were compiled to identify the mechanistic drivers of toxicity. To complement these data, we integrated high-throughput in vitro results from the U.S. Tox21 program, which evaluates > 10,000 chemicals in human cell-based assays targeting nuclear receptor modulation, oxidative stress, and DNA damage. By aligning OECD TG outcomes with Tox21 profiles, we assessed the associations between in vivo reproductive/developmental toxicity and in vitro mechanistic endpoints using the Toxicity Predictor program. Our analysis suggested that OECD TG 414-, TG 421-, and TG 422-positive chemicals were negatively associated with thyroid-stimulating hormone receptor (TSHR) antagonists, farnesoid X receptor (FXR) antagonists, peroxisome proliferator-activated receptor delta (PPAR δ) antagonists, transforming growth factor beta (TGFβ) agonists, and endoplasmic reticulum stress response (ERsr) agonists in the Tox21 assays. Conversely, OECD TG 416- and TG 443-positive chemicals showed a positive association with histone deacetylase (HDAC) antagonists. Overall, the regulation of miRNA transcription, nuclear receptor activity, and transcription pathways showed significant associations that may indicate key mechanistic links between in vitro and in vivo datasets. These associations may provide supportive insights for considering how alternative data sources could complement existing regulatory toxicology frameworks.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-33419-3.

Introduction

The reproductive system is essential for species survival. Damage to this system, including pathological lesions in reproductive organs or disruptions of the hypothalamic-pituitary-gonadal axis, often results in developmental impairment in offspring. Thus, chemicals suspected of inducing developmental and reproductive toxicity (DART) should be effectively assessed and prioritized for regulatory consideration.

However, OECD test guidelines (TG) for DART are time-intensive, expensive, and require expert evaluation because of the numerous and complex endpoints involved. The OECD TG for DART includes TG 414 (prenatal developmental toxicity study), TG 416 (two-generation study), TG 421 (DART screening test), TG 422 (combined repeated-dose toxicity study with the DART screening test), TG 443 (extended one-generation reproductive toxicity study), and TG 426 (developmental neurotoxicity study)¹. It is impractical to conduct DART studies on the numerous chemicals that require assessment.

To address this challenge, new approach methodologies (NAMs) have been described, including in vitro, in silico, and omics-based strategies, which aim to provide mechanistic insights into chemical toxicity². These approaches are efficient, ethical, and mechanistically informative alternatives to conventional animal models. Among NAMs, quantitative structure-activity relationships (QSARs) are commonly applied³. The QSAR Toolbox, for example, has been increasingly utilized to predict toxicological properties based on chemical structures by assessing structure–activity relationships. However, because DART encompasses numerous and complex endpoints, QSAR predictions remain limited. To overcome these challenges, the adverse outcome pathway (AOP) framework has been proposed as a structured approach to integrate mechanistic information on toxicity⁴.

The Tox21 program, launched as a collaborative initiative by the United States National Institutes of Health, Environmental Protection Agency, and Food and Drug Administration, aims to implement high-throughput screening approaches using in vitro cell-based assays for over 10,000 chemicals. It focuses on mechanistic endpoints, including nuclear receptor activation, oxidative stress, and DNA damage, enabling rapid and cost-effective evaluation of large-scale chemicals⁵. Tox21 assay datasets have been used to construct databases for prediction models^6,7.

In this study, we investigated which MIEs correlated with DART outcomes based on the Tox21 assay datasets to predict the DART chemicals.

Results

MIEs associated with OECD TG 414 data

Significant associations between the OECD TG 414 data (1,242 chemicals) and the MIE models are summarized in Table 1. A total of 1,242 SMILES were entered into the toxicity predictor and analyzed for prediction values in each model. Among 108 models, forty-one models were significant. Duplicates corresponding to the same entity across the normalized_1 and _40 models were treated as one. Thus, significant 34 MIEs negatively associated with the OECD TG 414 data. Six stress- and damage-related response factors, including activator protein-1 (AP1), antioxidant response element (ARE), histone variant H2AX (H2AX), hypoxia-induced factor 1 (HIF1), nuclear factor kappa B (NFκB), and p53 agonists, showed negative associations with positive chemicals in OECD TG 414 data (developmental toxicity). Additionally, two apoptosis-related caspases-3/7 (CaspH and CaspC) inducers and heat shock response (HSR) activator showed negative associations with positive chemicals in OECD TG 414 data (developmental toxicity).

Table 1.

Association of Tox21 assay results with OECD TG 414 data.

Tox21 assays	Fisher’s exact test
	Positive	Negative	Non-directional
AP1_ago_normalized_1	0.98568	0.02015	0.036114
ARE_ago_normalized_40	0.991172	0.012952	0.023512
ARfull_ant_normalized_1	0.999013	0.001544	0.002906
Arom_ant_normalized_40	0.996232	0.00578	0.010269
CAR_ant_normalized_40	0.99994	0.000126	0.000212
CaspC_ind_normalized_40	0.999939	0.000108	0.000211
CaspH_ind_normalized_1	0.990159	0.015155	0.030152
CaspH_ind_normalized_40	0.99381	0.01003	0.018408
ERb_ago_normalized_1	0.997617	0.004374	0.008355
ERlbd_ant_normalized_40	0.998744	0.002019	0.003611
ERRPGC_ant_normalized_40	0.999972	6.02E-05	0.000121
ERsr_ago_normalized_1	0.996518	0.005455	0.009658
FXR_ago_normalized_1	0.999864	0.000262	0.000526
FXR_ago_normalized_40	0.999696	0.00059	0.001041
GR_ago_normalized_1	0.998663	0.002235	0.004542
GR_ago_normalized_40	0.998518	0.004907	0.00863
H2AX_ago_normalized_1	0.992659	0.011547	0.023303
HIF1_ago_normalized_1	0.999802	0.000349	0.000611
HSR_act_normalized_1	0.989894	0.014653	0.025475
NFkB_ago_normalized_1	0.999936	0.000152	0.00028
NFkB_ago_normalized_40	1	1.17E-06	1.86E-06
p53_ago_normalized_40	0.997139	0.005278	0.009188
PPARd_ago_normalized_1	0.999675	0.000546	0.001019
PPARd_ant_normalized_1	0.999881	0.000208	0.000425
PPARg_ago_normalized_40	0.999487	0.000933	0.0019
PPARg_ant_normalized_1	0.99778	0.003306	0.006105
RAR_ant_normalized_40	0.997196	0.004512	0.007614
ROR_ant_normalized_40	0.998675	0.002172	0.003901
RXR_ago_normalized_1	0.983445	0.023204	0.040778
Shh_ant_normalized_1	0.983403	0.022616	0.043306
TGFb_ago_normalized_1	0.998779	0.002472	0.004517
TGFb_ago_normalized_40	0.998839	0.002812	0.004582
TRHR_ago_normalized_1	0.99744	0.004532	0.00929
TRHR_ago_normalized_40	0.986601	0.019329	0.037726
TRHR_ant_normalized_1	0.997545	0.003973	0.007557
TSHR_ago_normalized_40	0.98407	0.024967	0.049873
TSHR_agoant_normalized_40	1	3.00E-07	5.66E-07
TSHR_ant_normalized_1	0.998943	0.001741	0.003382
TSHR_ant_normalized_40	0.999661	0.000567	0.001095
VDR_ago_normalized_1	0.999366	0.00106	0.001879
VDR_ant_normalized_40	0.998107	0.002868	0.005041

ant, antagonist; ago, agonist; normalized_1 or _40, cutoff level.

Positive and negative associations correspond to right- and left-tailed Fisher’s exact tests, respectively. Non-directional refers to the two-tailed test.

Among endocrine system-related factors, positive developmental toxicity chemicals were negatively associated with chemicals binding to the thyrotropin-releasing hormone receptor (TRHR), thyroid-stimulating hormone receptor (TSHR), and aromatase (Arom) antagonists. Multiple nuclear receptors and metabolomic factors, including the constitutive androstane receptor (CAR) antagonists, farnesoid X receptor (FXR) agonists, peroxisome proliferator-activated receptor delta (PPARδ) agonists and antagonists, PPAR gamma (PPARγ) agonists and antagonists, retinoid X receptor alpha (RXR) agonists, vitamin D receptor (VDR) agonist and antagonists, retinoic acid receptor (RAR) antagonists, retinoid-related orphan receptor gamma (ROR) antagonists, estrogen receptor alpha ligand binding domain (ERlbd) antagonists, estrogen receptor beta (ERb) agonists, estrogen-related receptor with PGC (ERRPGC) antagonists, endoplasmic reticulum stress response (ERsr) agonists, and glucocorticoid receptor (GR) agonists, were negatively associated with the OECD TG 414 data. Developmental signaling pathways, including Sonic hedgehog (Shh) antagonists and transforming growth factor beta (TGFβ) agonists, were also negatively associated with OECD TG 414 data.

MIEs associated with OECD TG 416 data

Significant associations between the OECD TG 416 data (265 chemicals) and the MIE models are listed in Table 2. Ten models (9 MIEs) were positively associated with OECD TG 416 data. Significantly positive associations were observed for ARE, ERR, and PPARγ agonists, as well as CAR, ER full, FXR, histone deacetylase (HDAC), progesterone receptor (PR), and Shh antagonists. ARE agonists, CAR antagonists, and PPARγ agonists showed differing statistical association patterns between the OECD TG 414 (negative association) and TG 416 data (positive association), although the underlying biological basis for this difference cannot be determined from these analyses.

Table 2.

Association of Tox21 assay results with OECD TG 416 data.

Tox21 assays	Fisher’s exact test
	Positive	Negative	Non-directional
ARE_ago_normalized_40	0.02028	0.99086	0.03395
CAR_ant_normalized_40	0.01546	0.99501	0.02205
ERfull_ant_normalized_1	0.04092	0.98193	0.06109
ERR_ago_normalized_40	0.01022	0.99755	0.01233
FXR_ant_normalized_40	0.03052	0.98657	0.04817
HDAC_ant_normalized_1	0.00446	0.99902	0.00594
HDAC_ant_normalized_40	0.04022	0.98994	0.05255
PPARg_ago_normalized_40	0.01773	0.99337	0.02687
PR_ant_normalized_40	0.02404	0.99054	0.03107
Shh_ant_normalized_40	0.03041	0.98538	0.05676

ant, antagonist; ago, agonist; normalized_1 or _40, cutoff level.

Positive and negative associations correspond to right- and left-tailed Fisher’s exact tests, respectively. Non-directional refers to the two-tailed test.

MIEs associated with OECD TG 421 data

Significant associations between OECD TG 421 outcomes (723 chemicals) and the MIE models are shown in Table 3. A total of 29 models (27 MIEs) showed negative associations, whereas one model (PR agonists) showed positive associations, with the OECD TG 421 data. Odds ratios (OR) of these models are shown in Fig. 1A. PR agonists showed an OR of > 1, suggesting that these chemicals were associated with increased odds of DART occurrence. Proteins showing negative associations overlapped with OECD TG 414 results. However, androgen receptor ligand-binding domain (ARlbd) agonists and antagonists, ER alpha with stimulator (ERfulls) antagonists, and FXR antagonists showed significantly negative associations only in the OECD TG 421 data. Chemical interactions with sex hormone receptor-related proteins were statistically associated with reproductive toxicity in the OECD TG datasets.

Table 3.

Association of Tox21 assay results with OECD TG 421 data.

Tox21 assays	Fisher’s exact test
	Positive	Negative	Non-directional
ARfulls_ant_normalized_40	0.987514	0.023927	0.046225
ARlbd_ago_normalized_1	0.999932	0.000222	0.000379
ARlbd_ant_normalized_40	0.997344	0.006539	0.013455
Arom_ant_normalized_40	0.997664	0.004858	0.007547
CaspC_ind_normalized_1	0.997749	0.005189	0.010597
CaspC_ind_normalized_40	0.996336	0.007303	0.014853
ERb_ago_normalized_1	0.995429	0.011731	0.024333
ERfull_ant_normalized_40	0.99333	0.013022	0.023710
ERfulls_ant_normalized_40	0.999717	0.000711	0.001241
ERRPGC_ant_normalized_40	0.999748	0.000717	0.001220
ERsr_ago_normalized_1	0.995815	0.008593	0.016008
FXR_ago_normalized_40	0.998793	0.002963	0.004875
FXR_ant_normalized_40	0.999820	0.000458	0.000903
HIF1_ago_normalized_1	0.999824	0.000455	0.000835
p53_ago_normalized_40	0.993461	0.014591	0.028355
PPARd_ago_normalized_1	0.999607	0.000936	0.001542
PPARd_ant_normalized_1	0.993507	0.012359	0.020873
PPARg_ago_normalized_1	0.996763	0.006903	0.014337
PPARg_ant_normalized_40	0.998795	0.003079	0.005422
PR_ago_normalized_1	0.004707	0.995108	0.047068
RAR_ant_normalized_40	0.993142	0.013561	0.022016
ROR_ant_normalized_40	0.999854	0.000386	0.000766
Shh_ant_normalized_40	0.985468	0.026495	0.043732
TGFb_ago_normalized_1	0.999732	0.000955	0.001774
TGFb_ago_normalized_40	0.999940	0.000371	0.000621
TRHR_ago_normalized_40	0.987702	0.023199	0.038891
TSHR_agoant_normalized_40	0.998462	0.004063	0.008372
TSHR_ant_normalized_40	0.998160	0.003828	0.005922
VDR_ago_normalized_1	0.999905	0.000245	0.000459
VDR_ant_normalized_40	0.999932	0.00017	0.000256

ant, antagonist; ago, agonist; normalized_1 or _40, cutoff level.

Positive and negative associations correspond to right- and left-tailed Fisher’s exact tests, respectively. Non-directional refers to the two-tailed test.

Fig. 1.

Volcano plots of OECD TGs. Odds ratios of toxicity predictors and OECD TG 421 (A), TG 422 (B), and TG 443 data (C).

MIEs associated with OECD TG 422 data

Significant associations between the OECD TG 422 data (1,456 chemicals) and the MIE models are shown in Table 4. Significant proteins against OECD TG 422 data showed patterns similar to those of OECD TG 414 and TG 421 data. No proteins were uniquely associated with OECD TG 422 data. PR agonists with OR > 1 (-log P value > 1.3) were suggested to be associated with DART (Fig. 1B).

Table 4.

Association of Tox21 assay results with OECD TG 422 data.

Tox21 assays	Fisher’s exact test
	Positive	Negative	Non-directional
ARE_ago_normalized_1	0.984473	0.023318	0.040307
ERsr_ago_normalized_1	0.990011	0.016141	0.031895
FXR_ant_normalized_40	0.998312	0.002995	0.005202
GR_ago_normalized_40	0.999438	0.002375	0.004499
PPARd_ant_normalized_1	0.99738	0.004462	0.00813
TGFb_ago_normalized_40	0.999062	0.002551	0.004025
TSHR_agoant_normalized_40	0.999014	0.002203	0.004079
TSHR_ant_normalized_40	0.997542	0.00415	0.007306

ant, antagonist; ago, agonist; normalized_1 or _40, cutoff level.

Positive and negative associations correspond to right- and left-tailed Fisher’s exact tests, respectively. Non-directional refers to the two-tailed test.

MIEs associated with OECD TG 443 data

Significant associations between the OECD TG 443 data (201 chemicals) and the MIE models are shown in Table 5. Aryl hydrocarbon receptor (AhR) agonists, HDAC antagonists, and ERβ antagonists showed significantly positive associations with OECD TG 443-positive chemicals. An OR > 1 was observed for AhR agonists and HDAC antagonists, suggesting that these chemicals are associated with reproductive toxicity (Fig. 1C).

Table 5.

Association of Tox21 assay results with OECD TG 443 data.

Tox21 assays	Fisher’s exact test
	Positive	Negative	Non-directional
AhR_ago_normalized_1	0.004710	0.998948	0.005937
ERb_ant_normalized_1	0.031016	0.988854	0.050139
HDAC_ant_normalized_40	0.004352	0.999695	0.004352

ant, antagonist; ago, agonist; normalized_1 or _40, cutoff level.

Positive and negative associations correspond to right- and left-tailed Fisher’s exact tests, respectively. Non-directional refers to the two-tailed test.

Networks of key proteins involved in MIE associated with DART chemicals

Results of MIEs associated with OECD TG data (Tables 1, 2, 3, 4 and 5) were visualized using a Venn diagram (Fig. 2) to examine the overlap of MIEs among different OECD TGs. Twenty-one common MIEs were shared between OECD TG 414 and TG 421 data, five MIEs (including TGFb agonists, ERsr agonists, PPARd antagonists, TSHR antagonists, and FXR antagonists) of which were also common to OECD TG 422 data. In addition, four MIEs were shared between OECD TG 416 and OECD TG 421, while OECD TG 414 and OECD TG 416 shared five MIEs, including three (FXR agonists, PPARγ agonists, and Shh antagonists) that overlapped. OECD TG 416 and OECD TG 443 had one common MIE, HDAC antagonists. Based on these findings, the interactions among the common MIEs were further analyzed using STRING.

Fig. 2.

MIE models associated with OECD TGs. OECD TG 414, yellow; OECD TG 416, blue; OECD TG 421, purple; OECD TG 422, light green; OECD TG 443, green. Agonists are shown in red, antagonists in blue, and others (inducer or activator) in black. In OECD TG 421, PR, as indicated in the boxed area, showed an association opposite to that of the other MIEs.

Protein networks of key MIE-associated proteins statistically associated with DART chemicals are shown in Fig. 3. Protein-protein interaction enrichment was significant (p-value = 1.66e-07). In biological processes, the negative regulation of growth, cell growth, cell population proliferation, heart development, reproductive structure development, and hormone-mediated signaling pathways were enriched (false discovery rate < 0.00008). The nuclear receptor transcription pathway and SUMOylation of intracellular receptors were significantly associated with DART (false discovery rate, < 0.00002) (Supplementary Data_S1).

Fig. 3.

Predicted protein-protein interactions generated by the SPRING algorithm. Multiple protein queries were used to identify key factors commonly associated with both OECD TGs and MIEs. The heatmap presents normalized SPRING interaction scores, with higher values indicating stronger predicted association. Average local clustering coefficient, 0.637. Protein-protein interaction enrichment p-value, 1.66e-07.

Discussion

Risk assessment of DART is crucial for the preservation of species in the environment. However, the experimental validation of the vast number of chemicals is limited. Thus, NAMs are increasingly being applied to the prediction of DART. Reliable prediction requires careful experimental and contextual validation. Reproductive toxicity is linked to disruption of biological activities of the reproductive system, including the ER, PR, and AR pathways^8–10. Developmental toxicity, in turn, is associated with hormones that regulate homeostasis, cell damage, and growth during organogenesis^11,12. In this study, the association between these pathways and DART results based on in vivo experimental data from OECD TG assays is analyzed.

Our results broadly align with previously proposed predictive mechanisms and highlight several candidate factors that merit further investigation. In silico validation of the prediction was performed using the AUC/ROC analysis, with AUC values ranging from 0.7 to 0.98 (Supplementary Data _S2). In Fig. 2, proteins mapped to each OECD TG assay. The overlap indicates putative associations between selected protein and assay endpoints but does not alone establish causal relationships. OECD TG 414 primarily reflects developmental toxicity, while OECD TG 416 and TG 443 converge on reproductive toxicity; however, OECD TG 421 and 422 support the evidence for DART.

As shown in Fig. 1, several proteins showed statistically significant associations with assay-derived endpoints. Interestingly, these commonly related proteins were negatively correlated with developmental toxicity in OECD TG 414 and positively correlated with reproductive toxicity in OECD TG 416.

In the prediction of reproductive toxicity, HDAC antagonists were significantly correlated with both OECD TG 416 and TG 443 levels. HDACs regulate gene expression and chromatin stability; their inhibition has been linked to cell death, teratogenesis, and female reproductive toxicity^13–15. Our results suggest that HDAC antagonists may be associated with reproductive rather than developmental toxicity. PR, a key receptor in the maintenance of pregnancy, antagonists and agonists are positively correlated with OECD TG416 and OECD TG 421, respectively. This suggests that complex receptor-level regulatory effects, rather than a direct PR signaling pathway, are associated with DART. Consequently, PR has been incorporated into deep learning models of female reproductive toxicity^16,17. AhR agonists and ERβ antagonists showed a significant positive association in OECD TG 443. AhR, activated by environmental pollutants, functions as a receptor for endocrine-disrupting chemicals, indicating its role as a key protein in male reproductive toxicity^18,19.

ARE, FXR, and PPARγ agonists, as well as Shh antagonists, showed statistical associations with DART. These finding may indicate potential involvement of oxidative stress, metabolism, and inflammatory response pathways, although causal interpretation is beyond the scope of the current dataset. Additionally, at the molecular level, AP1, H2AX, p53, HIF1, NF-kB, RXR, ROR, CaspC, and CaspH were significantly correlated with DNA damage, gene expression, and cell death pathways, suggesting the induction of developmental toxicity. These proteins are involved in the nuclear receptor signaling and SUMOylation pathways. Their association in this study may suggest potential links to transcriptional regulation and cellular homeostasis, although direct causality with DART cannot be established from association data alone.

TRHR and TSHR are known to be involved in thyroid hormone-related developmental processes, including nervous¹¹, cardiovascular, and skeletal systems²⁰. In our results, TRHR and TSHR agonist and antagonist activities were negatively associated with OECD TG 414 and OECD TG 421 outcomes. However, these statistical associations may reflect complex receptor-level regulatory patterns rather than a direct mechanistic effects.

In conclusion, our results obtained by binary classification of MIE models and OECD TG data are consistent with those of previous reports on reproductive and developmental toxicity and related factors. Although binary classification may reduce the granularity of the data and cannot fully capture dose–response relationships, potency, or partial activation, this approach was adopted to facilitate comparative screening across diverse endpoints. Our results suggest that this approach may be useful for preliminary prioritization of chemicals that could warrant further evaluation. Unfortunately, our results could not fully address crosstalk between signaling pathways and establish the association between key proteins and DART endpoints. MIE may omit metabolism, pharmacokinetics, species differences, exposure timing, and mixture effects. Nevertheless, these results suggest the broader implementation of in silico and non-animal methods for DART assessment by linking in vitro and in vivo data through statistically derived associations. This approach may help prioritize candidate DART chemicals for further evaluation and may help inform early-stage safety considerations. However, any translation into regulatory decision-making would requires substantial additional validation, contextual exposure information (pharmacokinetics), and integration with in vivo data.

Materials and methods

Data collection

Chemicals were collected from the database of test results included in the OECD QSAR toolbox (version 4.6) and were tested according to OECD TG 414, TG 416, TG 421, TG 422, and TG 443 depending on the specific endpoints. The chemicals were classified as positive or negative based on reference data from the database. Chemicals that lacked definitive positive or negative classification or had missing CAS registry numbers were excluded from the prediction dataset in this study. The dataset included IUPAC names, CAS registry numbers, and the result from OECD TG 414, TG 416, TG 421, TG 422, and TG 443.

Construction of datasets for collecting and analyzing toxicity and structural information

Chemical structures of each substance were obtained from PubChem using Python scripts with the corresponding CAS numbers. Only compounds containing at least one carbon atom, had a molecular weight below 1500 Da, and excluding coordination complexes, were retained for analysis. The simplified molecular input line-entry system (SMILES) strings were desalted and standardized to canonical SMILES forms using the Toxicity Predictor²¹; http://mmi-03.my-pharm.ac.jp/tox1/prediction_groups/new) utility, as described below. Duplicate SMILES entries were merged to obtain the final dataset.

MIE activity prediction with toxicity predictor

The Toxicity Predictor²¹, a QSAR platform built on the Tox21 10 K library^22–24, was used to predict the agonistic or antagonistic activities of MIEs in the nuclear receptor and stress response pathways^25,26. The Tox21 assay data were accessed from the U.S. EPA’s CompTox chemicals dashboard and the PubChem bioassay database.

PubChem activity scores (range: 0–100)²⁷ were binarized prior to model training using two alternative activity score thresholds: 40 (PubChem default) and 1 (lower threshold). For each of the 59 MIE assays, a separate binary classification model was trained at each threshold, yielding 118 candidate models²¹. Models built using a threshold of 1 minimized false-negative errors. Models built with a threshold of 40 minimized the false-positive errors. Nine models had an area under the receiver operating characteristic (ROC) curve (AUC) below 0.70 and were excluded, leaving 109 models for prediction (Supplementary Data_S2).

For each model, the decision boundary of the predictive probabilities was determined by maximizing the Youden index²⁸. The probabilities were then rescaled such that the boundary was equal to 0.5, thereby establishing a common prediction probability cutoff. Compounds with a normalized predicted probability greater than 0.5 were labeled as “active,” and those with a value less than 0.5 were labeled as “inactive.” Each MIE assay was modeled separately for agonist and antagonist responses. The binary classification of “active” and “inactive” in each model represented functional activity specific to either agonism or antagonism, not a uniform biological activity. When experimental MIE measurements from the Tox21 10 K library were available, these data were obtained directly from the PubChem Bioassay repository and converted to binary active/inactive calls according to the PubChem activity score (≥ 40, active; <40, inactive), thereby superseding the model-based labels. All SMILES in the analysis dataset were submitted to the Toxicity Predictor and their normalized probabilities and activity labels were assigned as described above.

Analysis of functional protein association networks

Protein-protein interactions were predicted from the Homo sapiens database using the Search Tool for Retrieving Interacting Genes (STRING) v12²⁹ https://string-db.org). Proteins showing statistically significant associations (p < 0.05) between OECD TG endpoints and Tox21 MIEs were identified, and only those that were commonly observed in two or more OECD TGs were selected for network analysis. The common protein list identified across multiple OECD TGs was entered into the STRING database to derive the functional associations among these proteins. The edges indicated both functional and physical protein associations. The minimum required interaction score was set to 0.4 (medium confidence), and disconnected were excluded. Active interaction sources included experimental data, curated database, co-occurrence, neighborhood, gene fusion and text mining.

Statistical analysis

For each compound, the presence or absence of toxicity in the OECD TG 414, TG 416, TG 421, TG 422, and TG 442 studies served as response variables, and the predicted MIE activity labels served as explanatory variables. OECD TG outcomes represent expert-interpreted in vivo endpoints that are categorized as positive and negative in the reference database, while Tox21 assay results were modeled using the Toxicity Predictor to yield binary active or inactive output for each assay. Because both datasets were represented in binary form, Fisher’s exact test was applied to evaluate associations between toxicity outcomes and individual MIEs. In this analysis, a right-tailed test corresponds to a positive association, where the co-occurrence of toxic and active outcomes exceeds random expectation, whereas a left-tailed test corresponds to a negative association, where toxic outcomes are less frequent among MIE-active compounds than expected by chance. For ease of interpretation, results are presented in the tables as “positive” or “negative association,” rather than “right-” or “left-tailed” tests. All analyses were performed using JMP Pro 18 (SAS Institute Inc., Cary, NC, USA) with a two-sided significance level of 0.05. The odds ratio (OR) was calculated using Eq. 1³⁰.

1

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AhR

Aryl hydrocarbon receptor

AOP

Adverse outcome pathway

AP-1

Activator protein-1

AR

Androgen receptor

ARE

Antioxidant response element

ARfulls_ant

Androgen receptor with stimulator antagonist

ARlbd

Androgen receptor ligand binding domain

Arom

Aromatase

ATAD5_ind

ATAD5 genotoxic inducer

AUC

Area under the curve

CAR

Constitutive androstane receptor

CaspC

Caspase-3/7 in CHO-K1

CaspH

Caspase-3/7 in HepG2

DART

Developmental and reproductive toxicity

ER

Estrogen receptor

ERa

Estrogen receptor alpha

ERb

Estrogen receptor beta

ERfulls_ant

Estrogen receptor alpha with stimulator antagonist

ERlbd

Estrogen receptor alpha ligand binding domain

ERR

Estrogen-related receptor

ERRPGC

Estrogen related receptor with PGC

ERsr

Endoplasmic reticulum stress response

FXR

Farnesoid X receptor

GR

Glucocorticoid receptor

H2AX

H2A histone family member X

HDAC

Histone deacetylase

HIF-1a

Hypoxia-inducible factor-1a

HSR

Heat shock response activator

MIE

Molecular initiating events

MMP_disr

Mitochondrial membrane potential disruptor

NAMs

New approach methodologies

NFkB

Nuclear factor kappa B

OECD TG

Organisation for economic co-operation and development test guideline

PPARd

Peroxisome proliferator-activated receptor delta

PPARg

Peroxisome proliferator-activated receptor gamma

PR

Progesterone receptor

QSAR

Quantitative structure-activity relationships

PXR

Human pregnane X receptor

RAR

Ratinoic acid receptor

ROR

Retinoid-related orphan receptor gamma

RXR

Retinoid X receptor-alpha

Shh

Sonic hedgehog signaling

SMILES

Simplified molecular-input line-entry system

STRING

Search tool for retrieving interacting genes

TGFb

Transforming growth factor beta

TNF-a

Tumor necrosis factor a

Tox21

Toxicology in the 21st Century

TR

Thyroid receptor

TRHR

Thyrotropin-releasing hormone receptor

TSHR

Thyroid-stimulating hormone receptor

VDR

Vitamin D receptor

Author contributions

Conceptualization: Joohee Jung, Hyomin Lee, Yoshihiro Uesawa; Methodology: Yoshihiro Uesawa, Joohee Jung; Formal analysis and investigation: Hee Jung Kwon, Sunyi Lee, Woori Ko, Shin Jea Yun; Writing-original draft preparation: Joohee Jung, Hee Jung Kwon; Writing-review and editing: Joohee Jung; Funding acquisition: Joohee Jung and Hyomin Lee; Resources: Sunyi Lee, Woori Ko, Shin Jea Yun; Supervision: Joohee Jung.

Funding

This work was supported by the Korea Evaluation Institute of Industrial Technology (KEIT) through the Core Technology Development for the Knowledge Service Industry [No. 20023658], funded by the Ministry of Trade, Industry & Energy (MOTIE), Korea.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files (Supplementary Data_S1: Analysis of protein-protein interaction, Supplementary Data_S2: AUC of Toxicity predictor models). The curated datasets and analysis scripts generated during this study have been deposited in Zenodo and are available at DOI: 10.5281/zenodo.17851563.A FAIRsharing metadata record for this dataset is available.All original public data sources are accessible through the URLs provide below.Toxicity Predictor: [http://mmi-03.my-pharm.ac.jp/tox1/prediction_groups/new](http:/mmi-03.my-pharm.ac.jp/tox1/prediction_groups/new)PubChem Bioassay (Tox21 data): [https://pubchem.ncbi.nlm.nih.gov/](https:/pubchem.ncbi.nlm.nih.gov)EPA CompTox Chemicals Dashboard: [https://comptox.epa.gov/dashboard](https:/comptox.epa.gov/dashboard)OECD QSAR toolbox: [https://qsartoolbox.org/](https:/qsartoolbox.org)STRING: [https://string-db.org/cgi/input? sessionId=bgBtAw6Ap2bu&input_page_show_search=on](https:/string-db.org/cgi/input?sessionId=bgBtAw6Ap2bu&input_page_show_search=on)FAIRsharing: https://fairsharing.org/7462.

Declarations

Competing interests

The authors declare no competing interests.