Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment

Abstract

Current animal-free methods to assess teratogenicity of drugs under development still deliver high numbers of false negatives. To improve the sensitivity of human teratogenicity prediction, we characterized the TeraTox test, a newly developed multilineage differentiation assay using 3D human-induced pluripotent stem cells. TeraTox produces primary output concentration-dependent cytotoxicity and altered gene expression induced by each test compound. These data are fed into an interpretable machine-learning model to perform prediction, which relates to the concentration-dependent human teratogenicity potential of drug candidates. We applied TeraTox to profile 33 approved pharmaceuticals and 12 proprietary drug candidates with known in vivo data. Comparing TeraTox predictions with known human or animal toxicity, we report an accuracy of 69% (specificity: 53%, sensitivity: 79%). TeraTox performed better than 2 quantitative structure-activity relationship models and had a higher sensitivity than the murine embryonic stem cell test (accuracy: 58%, specificity: 76%, and sensitivity: 46%) run in the same laboratory. The overall prediction accuracy could be further improved by combining TeraTox and mouse embryonic stem cell test results. Furthermore, patterns of altered gene expression revealed by TeraTox may help grouping toxicologically similar compounds and possibly deducing common modes of action. The TeraTox assay and the dataset described here therefore represent a new tool and a valuable resource for drug teratogenicity assessment.

To assess the teratogenic potential of drug candidates, pharmaceutical companies are currently obliged to perform embryo-fetal-development (EFD) studies in at least 1 rodent and 1 nonrodent species (Beck et al., 1995; ICH, 2020). There is an urgent need to develop alternative, animal-free assays for early assessment of teratogenicity. The use of humanized in vitro assays could potentially better mimic human physiology, reduce animal use, and lower the cost of drug development by filtering out potential teratogens early (Barrow, 2016; ICH, 2020).

Animal-free approaches based on in silico prediction or in vitro assays have been widely adopted. For instance, the CAESAR model (Cassano et al., 2010) and the P&G model (Wu et al., 2013) are 2 quantitative structure-activity relationship (QSAR) models for reproductive and developmental toxicity prediction. Well-established in vitro models for the detection of teratogenicity include the DevTox^qp assay from Stemina and the mouse embryonic stem cell test (mEST). DevTox^qp uses human-induced pluripotent stem cells (hiPSCs) to predict teratogenicity based on the ratio of ornithine and cysteine in medium supernatants (Adler et al., 2008; Augustyniak et al., 2019; Burridge et al., 2011; Dreser et al., 2020; Palmer et al., 2013, 2017; Shinde et al., 2015; Worley et al., 2018). The mEST assay uses the beating of stem cell-derived cardiomyocytes as a functional readout for teratogenicity prediction (Genschow et al., 2000,2004; Scholz et al., 1999a,b; Whitlow et al., 2007).

Despite their wide adoption and relatively good performance, all these methods share major limitations. In silico models fail to consider the complexity and adaptiveness of biological systems. Also, their performance a priori for new chemical spaces that were not used for training the model is uncertain. On the other hand, existing in vitro models, which rely on a single readout for prediction, have limited capability to probe the complex biological processes underlying drug-induced teratogenicity.

To overcome these limitations, we developed a new, humanized in vitro teratogenicity assay for the preselection of pharmaceutical candidates. The new assay, which we call TeraTox, uses ethically nonrestricted hiPSC-derived embryoid bodies (EBs) that differentiate spontaneously into all 3 germ layers, with expression of representative early developmental markers of each layer. The new assay gave promising results in a preliminary evaluation (Jaklin et al., 2020). This article describes the subsequent characterization and critical assessment of TeraTox, including a detailed predictive algorithm. We tested a panel of 45 pharmaceuticals with evidence of human teratogenicity profiles based on FDA classification using a 6-point concentration-response, generating the largest dataset so far in a single study about in vitro modeling of pharmaceutical teratogenicity with reference to clinical or animal data. We adapted an amplicon-based RNA sequencing technique (a technology known as Molecular Phenotyping) to quantify the expression of germ-layer genes as well as pathway reporter genes. We benchmarked a variety of architectures of machine-learning models and identified the best-performing predictive model using factor analysis and random-forest regression. TeraTox performed favorably compared with other models, ie, the prediction accuracy was comparable with that of both Stemina DevTox^qp and mEST assays (higher sensitivity, lower specificity) and higher than that of 2 QSAR models (higher sensitivity and specificity). More importantly, TeraTox offers insights into a multitude of changes caused by the compounds on gene, pathway, and germ-layer levels, some of which corroborated their teratogenicity potential.

MATERIALS AND METHODS

 

 

hiPSCs-derived TeraTox assay

The TeraTox assay is built upon a commercially available hiPSC line (Gibco, A18945), which has indistinguishable gene expression profiles compared with embryonic stem cells (Burridge et al., 2011; Quintanilla et al., 2014). The cells form 3D EBs and undergo multilineage differentiation into all 3 germ layers (Jaklin et al., 2020). Prior to the assay, the hiPSCs were tested with the TaqMan ScoreCard assay (Thermo Fisher) to confirm sufficient levels of pluripotency (Tsankov et al., 2015a). The EBs were spontaneously differentiated and treated with each reference substance over a time course of 7 days in Elplasia 96w micro-well plates (Corning, 4442) using the ViaFlo 96 automated microplate pipetting device (Integra) for liquid handling. Compounds were applied to the EBs on days 0, 3, and 5 at 6 concentrations, together with EB medium and 0.25% DMSO solvent controls as the negative reference. To test for batch-to-batch variation, we included several positive reference compounds in multiple runs (eg, hydroxyurea, valproic acid, SB431542, etc.). Cell viability was determined prior to gene expression studies on day 7 by measuring ATP release in supernatants with the CellTiter-Glo 3D assay (Promega, G9681) according to the manufacturer’s protocol to prespecify appropriate testing ranges. Concentrations that showed <80% cell viability were excluded from the subsequent gene expression studies. CellTiter-Glo reagent (100 μl) was added and incubated for 5 min on a shaker to lyse the EBs. The plates were kept for an additional 25 min in the dark at room temperature for binding of the released ATP to the luminescent dye. ATP release in supernatants was measured with the spectrophotometer (Biotek, Vermont). All cell culture media and reagents were obtained from Gibco (Thermo Fisher) unless otherwise specified. The overall cell culture and cytotoxicity protocols have been described previously in detail by Jaklin et al. (2020). Targeted gene expression profiling was performed in biological duplicates at 6 subcytotoxic concentrations using the molecular phenotyping platform described previously (Drawnel et al., 2017; Zhang et al., 2014, 2015). The resulting 1055 samples of differentiated EBs were lysed after 7 days in 350 μl MagNA Pure LC RNA Buffer (Roche Diagnostics) and purified using an automated MagNA Pure 96 system (Roche Diagnostics). The total RNA was quantified using the Qubit RNA Assay Kit (Thermo Fisher) on the Fluorometer Glomax (Promega). Total RNA, with a maximum of 10 ng from each biological replicate, was reverse transcribed to cDNA using Superscript IV Vilo (Thermo Fisher). Libraries were generated with the AmpliSeq Library Plus Kit (Illumina) according to the reference guide. Pipetting steps for target amplification, primer digestion, and adapter ligation were done with a miniature mosquito automatic pipettor (SPT Labtech). For the purifications before and after final library amplification, solid-phase reversible immobilization magnetic bead purification (Clean NGS, LABGENE Scientific SA) was performed on a multidrop automated pipetting station (Thermo Fisher). We measured both amplicon sizes and cDNA concentrations using an Agilent High Sensitivity DNA Kit (Agilent Technologies) according to the manufacturer’s recommendation. Prior to sequencing, cDNA contents of the samples were normalized and pooled to 2 nM final concentration on a Biomek FXP workstation. The libraries were sequenced on the NovaSeq 6000 Instrument (Illumina) using sequencing-by-synthesis technology. All 75 cycles ended up with a minimum of 2 Mio sequencing reads per sample for analysis. We used molecular phenotyping with 1215 detectable pathway reporter genes, including a subset of 87 early developmental markers (germ-layer genes, Supplementary Table 4), and genes representative of toxicological pathways to identify differentially expressed genes induced at prespecified concentration levels (Tsankov et al., 2015a,b).

Assessing characteristics of differentiated hiPSCs with BioQC

We applied the BioQC software developed previously to characterize the identity of the differentiated samples across all treated compound concentrations (including vehicle controls) on day 7 (Zhang et al., 2017). We used raw data of gene expression derived from molecular phenotyping and compared these profiles with tissue-preferential gene signatures derived from organ, tissue, and cell-type-specific gene expression data compiled from public compendia (Ljosa et al., 2013; Young et al., 2008). The BioQC performs Wilcoxon-Mann-Whitney tests comparing expression of genes in a set, eg, genes preferentially expressed in 1 tissue, versus genes that are not in the set. The enrichment scores (log-10 transformed p values) reported by BioQC are used to assess the similarity between the expression profile of interest and cell-type- and tissue-specific expression profiles.

Analysis and modeling of the TeraTox data

We performed differential gene expression (DGE) analysis comparing compound-treated samples with DMSO controls using the generalized linear model implemented in the edgeR package in R/Bioconductor (Robinson et al., 2010). To generate features for machine-learning models, we transformed the p values associated with the coefficients of compound treatment to z-scores by the inverse of the quantile function of Gaussian distribution, multiplied by the sign of log₂ fold-change (logFC). The vectors of z-scores of all genes (n = 1215) were used as raw features for machine-learning models, based on which further feature selection and engineering work were performed. We also tested the possibility of using the effect size, logFC, as a feature.

Besides the raw feature set of z-scores of all genes, we used 3 knowledge- and data-driven approaches to engineer the features in order to improve the performance of the machine-learning algorithms. First, we confined ourselves to the subset of germ-layer genes, because our and other’s work confirmed that their expression is specific to germ layers of embryogenesis, and their expression is modulated by teratogenic compounds (Supplementary Table 4; Bock et al., 2011; Jaklin et al., 2020; Tsankov et al., 2015a,b). Second, we used the germ-layer associations reported by Tsankov et al. to derive a reduced feature set defined by 5 germ-layer classes, including both germ layers (ectoderm, endoderm, mesoderm, and mesendoderm) and pluripotency, by taking the median z-scores of germ-layer genes associated with each germ-layer class (Tsankov et al., 2015a). Finally, we used factor analysis, a dimension-reduction approach that derives latent variables from the correlation structure of observed variables, to identify latent biological, germ-layer factors (germ-layer factors for short), which reflect linear combinations of transcription factors, epigenetics, and other gene regulatory mechanisms that control embryogenesis.

We predicted teratogenicity potential in 2 ways. One way was to treat teratogenicity as a binary variable and to perform binary classification. The other way was to convert concentration-response teratogenicity into numeric metrics and to construct regression models. For the latter case, we defined a compound-specific Teratogenicity Score (TS hereafter). For nonteratogens, the TS was defined as 0 independent of the tested concentration. For teratogens, the TS was defined as the 0-1-bounded cosine similarity between the differential expression profile induced by a given concentration of a compound and the differential expression profile induced by the highest noncytotoxic concentration of the same compound. The noncytotoxic concentration was determined as the highest tested concentration associated with an average viability equal or larger than 80%.

The models were trained and validated using the Leave-One-Out (LOO) scheme. The full panel of compounds was assessed successively, leaving out 1 compound at a time and then used to build machine-learning models. We then compared TS predicted by the models with the observation of each left-out compound using the Spearman correlation coefficient. As an alternative to LOO, we also assessed repeated 80%/20% splitting of data into training sets and test sets.

In short, we considered 2 types of features (z-scores and logFC), 4 sets of factors (all genes/germ-layer genes/median z-scores or logFC of germ-layer classes defined by Tsankov et al./median z-scores or logFC of germ-layer factors defined by factor analysis), 2 methods (linear regression with elastic net regularization and random forest, implemented in the caret package, version 6.0-88), 2 types of target variables (binary classification and regression), and 2 training/testing schemes (LOO and 80%/20% splitting). We tested all combinations exhaustively to build machine-learning models for TS and identified the best-performing models.

Besides predicting TS, we also comprehensively probed all options to build regression models for cytotoxicity (100%-viability), which was measured as part of the TeraTox assay. The same set of model architectures was tested; however, the combinations giving best performing models differed from that for TS (further discussed in Results section). All data analysis was performed with R (version 4.0.1) or Python (version 3.8.1) unless otherwise specified.

Test chemicals for validation

In total, we tested 28 positive and 17 negative reference substances in 6-point concentrations in the mEST (see Supplementary Material and Methods, Supplementary Figs. 1a, 1b, and Supplementary Table 1) and the human TeraTox assay (Table 1). This compound panel consisted of both commercial and developmental pharmaceuticals with known teratogenicity potential (ie, positive or negative) available from either human data, as reported in FDA drug labels, or from in vivo EFD studies in rats and/or rabbits (ICH, 2020). Some compounds without existing human or in vivo animal data were classified as teratogens based on a known teratogenic hazard associated with their mode of action (Belair et al., 2020; Chen et al., 2002; Cusack et al., 2017; Evans, 2007; Kameoka et al., 2014; Lipinski et al., 2008; Sakata and Chen, 2011; Wang et al., 2013; Worley et al., 2018). Compounds that did not result in increased incidences of birth defects in an adequate prospective cohort study accepted by health authorities were considered as nonteratogenic in humans, at least at the therapeutically relevant exposure levels (Adams et al., 1969; Daniel et al., 2019; Dashe and Gilstrap, 1997; Etwel et al., 2014; Muanda et al., 2017; Rumbold et al., 2015; Supplementary Table 2).

Table 1.

Reference Compounds in the Human TeraTox Assay

Reference Compound	Teratogenicity Classification	Test Concentrations (Human Model) [µM]	CAS Number
Acitretin	Positive	0.08–2.5 (1:2)	55079-83-9
Amoxicillin	Negative	6.25–200 (1:2)	26787-78-0
Artesunate	Positive	0.13–8 (1:2)	88495-63-0
Ascorbic Acid	Negative	28–900 (1:2)	62624-30-0
Bosentan	Positive	4.7–150 (1:2)	147536-97-8
Busulfan	Positive	0.06–4 (1:2)	55-98-1
Carbamazepine	Positive	4.7–300 (1:2)	298-46-4
Cetirizine	Negative	9.3–600 (1:2)	83881-51-0
Cyclopamine	Positive	0.3–20 (1:2)	4449-51-8
Cyproheptadine	Negative	0.47–30 (1:2)	129-03-3
Dabrafenib	Positive	0.03–2 (1:2)	1195765-45-7
DAPT	Positive	0.05–3 (1:2)	208255-80-5
Dasatinib	Positive	0.3–20 (1:2)	302962-49-8
Dexamethasone	Positive	4.7–300 (1:2)	50-02-2
Dorsomorphin	Positive	0.2–14 (1:2)	866405-64-3
Doxycycline	Negative	0.3–20 (1:2)	564-25-0
5-Fluorouracil	Positive	0.004–0.25 (1:2)	51-21-8
Hydroxyurea	Positive	3.12–200 (1:2)	127-07-1
Ibuprofen	Negative	1.9–1400 (1:3)	15687-27-1
Isotretinoin	Positive	4.7–300 (1:2)	4759-48-2
Imatinib	Positive	1.6–100 (1:2)	152459-95-5
IWP-2	Positive	0.0015–0.1 (1:2)	686770-61-6
Lazabemide	Negative	1.6–100 (1:2)	103878-84-8
Metformin	Negative	7.8–500 (1:2)	657-24-9
Methotrexate	Positive	0.0025–40 (1:5)	59-05-2
Misoprostol	Positive	0.02–1.3 (1:2)	59122-46-2
Penicillin G	Negative	9.3–600 (1:2)	61-33-6
Progesterone	Negative	0.63–40 (1:2)	57-83-0
Retinoic Acid	Positive	0.0005–0.035 (1:2)	302-79-4
RO-1*	Positive	1.6–100 (1:2)	n/a
RO-2*	Negative	7.8–500 (1:2)	n/a
RO-3*	Positive	4.7–300 (1:2)	n/a
RO-4*	Negative	3.1–200 (1:2)	n/a
RO-5*	Negative	0.8–50 (1:2)	n/a
RO-6*	Negative	6.25–400 (1:2)	n/a
RO-7*	Negative	9.3–600 (1:2)	n/a
RO-8*	Positive	1:25–80 (1:2)	n/a
RO-9*	Positive	0.08–5 (1:2)	n/a
RO-10*	Positive	0.23–15 (1:2)	n/a
RO-11*	Negative	0.6–40 (1:2)	n/a
RO-12*	Negative	1.6–100 (1:2)	n/a
SB431542	Positive	0.31–20 (1:2)	301836-41-9
(±) Thalidomide	Positive	0.0007–0.5 (1:3)	50-35-1
Valproic Acid	Positive	15.6–1000 (1:2)	99-66-1
Warfarin	Positive	0.9–60 (1:2)	81-81-2

Compounds for assay validation, with human teratogenicity classification and test concentration according to noncytotoxic concentrations. Dilution ratios in brackets covering 6 concentrations. Teratogenicity classification was based on FDA classification (Supplementary Table 2) or in vivo EFD data (indicated with asterisks*, Supplementary Table 3).

Abbreviation: EFD, embryo-fetal-development.

The commercial compounds were obtained from Merck, Germany. The 12 investigational small molecule drug candidates RO-1 to RO-12 were provided by F. Hoffmann—La Roche, Switzerland (compound structures are not disclosed due to confidentiality and intellectual property issues). No human pregnancy data were available for the investigational drug candidates, but in vivo data were available from EFD studies in rats, and/or in rabbits (Supplementary Table 3). RO-1, RO-3, RO-8, RO-9, and RO-10 were teratogenic in EFD studies; RO-2, RO-4, RO-5, RO-6, RO-7, RO-11, and RO-12 did not induce teratogenicity.

All compounds were serially diluted in DMSO (0.25%) from a stock solution to 6 test concentrations and tested at appropriate noncytotoxic concentration ranges in the TeraTox and mEST assays. We used the following metrics to compare the performance of the TeraTox and mEST assays. Sensitivity was calculated as the proportion of correctly predicted teratogens. Assay specificity was calculated as the proportion of correctly predicted nonteratogens. Overall accuracy was taken as the proportion of all correct predictions. F₁ scores were calculated as the harmonic mean of precision and recall. True positive, true negative, false positive, and false negative are denoted with TP, TN, FP, and FN, respectively, and the performance metrics are defined in Supplementary equations 1–5. To identify the threshold of TS that maximizes the performance (F₁ score) of the TeraTox Score, we used a grid search between 0 and 1 with a step size of 0.01. The best threshold (TS = 0.38) was chosen manually by inspecting the performance metrics.

To benchmark the performance of TeraTox, we applied 2 regulatory-accepted structure-based models to predict teratogenicity of commercially available compounds: the CAESAR model (version 2.1.8, Cassano et al., 2010) and the P&G model (version 1.1.2, Wu et al., 2013) implemented in the VEGA platform (version 1.3.10, Marzo et al., 2016). For the benchmark, we used 20 compounds (15 teratogens and 5 nonteratogens) that were not part of the training set of the CAESAR model.

Model explainability and interpretation

We used the type I importance measure of features (mean decrease in accuracy) of random-forest models to compare the importance of germ-layer genes in the teratogenicity model and in the cytotoxicity model.

Pharmacology data of publicly available compounds were downloaded from ChEMBL (version 26). We only used human targets and affinities derived from high-quality dose-response data. Binary distances were used to cluster the compounds by their pharmacological profiles.

To construct a Bayesian network model of regulations between factors, we first discretized DGE data of the first 6 germ-layer factors into 3 levels using the Hartemink’s pairwise mutual information method implemented in the bnlearn package (Scutari, 2010). We generated 1000 bootstrap replicates using Hill Climbing, a score-based learning algorithm, and the Bayesian Dirichlet equivalent (uniform) score (bde, with the imaginary sample size set to 10). Edges that persisted in more than 85% bootstrap samples were deemed as significant and reported.

The beta regression model used for sensitivity analysis was built with the glmmTMB package (Brooks et al., 2017). Scores outside the boundaries [0.01, 0.99] are set to the boundary values to allow beta regression. All 10 factors and significant interaction terms identified in the Bayesian network were used as the model input and compounds were modeled as random effects to capture between-concentration correlations. For better interpretability, input variables were scaled to zero mean and SD. Simulation was performed using the ggeffects package (Lüdecke, 2018).

RESULTS

 

Gene Expression Quantification by Molecular Phenotyping

We previously described that differential expression of a set of 87 genes preferentially expressed in different germ layers is in principle able to distinguish between teratogenic and nonteratogenic compounds (Jaklin et al., 2020). These germ-layer genes both determine and reflect embryonic development (Tsankov et al., 2015a). To validate our findings, we compiled a large set of well-documented teratogens and nonteratogens that are challenging to predict and/or known to cause FP in animal studies (Supplementary Tables 2 and 3). The compounds cover a broad spectrum of chemical classes and a wide range of effective concentrations.

We evaluated the performance of our human stem-cell model by testing the panel of compounds, adapting the experimental workflow developed previously (Figs. 1A and 1B). We identified the assay throughput as a major challenge due to the high number of samples for gene expression profiling (>1000). It would be particularly cost- and labor-intensive to use the digital PCR technique established in our previous work to quantify gene expression (Jaklin et al., 2020). To address this challenge, we used molecular phenotyping as an alternative readout. Molecular phenotyping is based on amplicon-based targeted sequencing and is able to deliver quantitative expression data of 1215 predefined genes, including both pathway reporter genes, ie, genes that are specifically modulated by pathway perturbations, as well as germ-layer genes that we reported in our previous study. In this way, we were able to characterize both general pathway activity modulations and germ layer-specific changes as potential features associated with teratogenicity (Drawnel et al., 2017; Zhang et al., 2014, 2015).

Figure 1.

The human TeraTox assay: workflow and quality control. A, hiPSCs differentiate over 7 days and form EBs. Compounds are added on days 0, 3, and 5. Single wells of differentiated EBs are lysed for 1 sample. B, TeraTox score is calculated based on cell viability, gene expression data, and machine-learning model. C, DGE from molecular phenotyping correlated with data from RT-PCR assay represented in log2 fold change (log2FC). Dots represent germ-layer genes. R = Spearman correlation coefficient between 2 sets of measurements in all compounds. D, BioQC of raw gene expression data (DMSO controls) revealed the biological identity of hiPSCs and showed significantly enriched cell-type signatures (median p < .10). Each dot = 1 sample. Violins show distributions of BioQC scores (absolute log10 transformed p values of the Wilcoxon-Mann-Whitney test) from each gene set, vertical lines indicate median values. The larger the BioQC score, the more enriched is the expression of the genes. Abbreviations: hiPSCs, human-induced pluripotent stem cells; EBs, embryoid bodies; DGE, differential gene expression.

We performed extensive quality control of the data. Here we address the questions whether results of molecular phenotyping are comparable with those of qRT-PCR, and whether the hiPSCs used show expected cell identity based on their gene expression profile. We compared the differential expression profiles of germ-layer genes obtained by qRT-PCR in previous studies with newly generated data of molecular phenotyping and observed highly consistent results (Pearson correlation coefficient R = 0.9, p < 2.2E16; Figure 1C).

Molecular phenotyping requires far fewer cells and delivers much higher throughput than qRT-PCR, allowing a marked improvement in the productivity of the TeraTox assay. A unique advantage of quantifying pathway reporter genes along with germ-layer genes is the identification of cell-type-specific gene expression patterns. To this end, we applied BioQC analysis, a method that we developed to identify sample heterogeneity and tissue comparability using gene sets preferentially expressed in cells and tissues (Zhang et al., 2017). We observed that the expression profiles of the cells used in the TeraTox assay at day 7 resemble a mix of those gene signatures specific for astrocytes, epithelial cells, and iPSC-derived neurons (Figure 1D). This suggests that the hiPSCs used for the assay has a preferred differentiation propensity into the neuroectodermal lineage, which is in agreement with previous time-series gene expression studies that demonstrated pronounced expression of ectodermal markers at day 7, followed by meso- and endodermal expression (Jaklin et al., 2020; Tsankov et al., 2015a).

Unsupervised Learning From Gene Expression Data With Factor Analysis

Before applying supervised learning techniques to differentiate teratogens from nonteratogens, we applied several unsupervised learning algorithms to explore the gene expression data, including principal component analysis (PCA) and factor analysis. PCA revealed experimental plate effects that we could successfully correct with linear regression models for DGE (data not shown). Unexpectedly, factor analysis revealed both biological insights and suggested a technique for feature engineering to produce the best-performing model (see below, also a brief introduction to factor analysis is given in Supplementary Materials and Methods).

We applied factor analysis to raw gene expression data and identified intriguing patterns. Since factor analysis is based on intergene correlations, we visualized the correlation matrix of germ-layer genes in Figure 2A (the full matrix is visualized in Supplementary Figure 2a). Genes that strongly correlate with each other form clusters, which correspond to latent factors. Despite that factor analysis is a correlation-based statistical method in which we injected no prior knowledge, biologically meaningful patterns emerged. Using the maximum likelihood method, we decomposed the covariance matrix of gene expression into factors. The heatmap in Figure 2B shows loadings, ie, how strong factors influence the expression of germ-layer genes, of the first 10 factors that collectively explain more than 70% of the covariance (Supplementary Figs. 2b–d). Left to the heatmap we use colors to indicate germ-layer classes that were distilled from biological knowledge. We found that the first 6 factors (ranked by explained covariance of the data) are significantly enriched with signatures of individual germ layers or signatures of stem-cell self-renewal (Figure 2C, p < .01, Fisher’s exact test).

Figure 2.

Identification of latent factors that are associated with germ layers. A, Germ-layer genes show correlated expression and form clusters of coexpression. The heatmap represents pairwise Pearson correlation coefficients of germ-layer gene expression in all samples. Genes are split by latent germ-layer factors for representative genes (full matrix is shown in Supplementary Figure 2a). B, Loadings of factor analysis (germ-layer genes in rows and linear combinations of latent germ-layer factors in columns). Loadings equal to or near +1 or −1 indicate that the factor positively or negatively influences the gene, while loadings near 0 means that the factor has little effect on the gene (full matrix in Supplementary Figure 2c). C, Germ-layer factors are not equivalent to, but significantly associated with, germ-layer classes. The heatmap visualizes the number of genes shared by each pair of germ-layer classes (in rows) and germ-layer factors (in columns).

This significant enrichment is intriguing, because while it is established that germ-layer genes are highly expressed at different stages of embryogenesis, factor analysis reveals for the first time that their expressions are strongly correlated in 3D EBs formed by hiPSCs, with or without compound treatment. Given that the cells used in TeraTox are cultured up to day 7, it is unlikely that the correlations are caused by temporal changes of embryogenesis. Instead, factor analysis suggests that besides being correlated across time in development, expression of germ-layer genes is also correlated across treatment conditions in 7-day spontaneously differentiated EBs.

Detailed analysis of the results from the factor analysis revealed more insights. The strongest correlation of the germ-layer genes was observed among genes in Factor 1, many of which are markers of the ectodermal layer, eg, WNT1, POU4F1, OLFM3, CDH9, LMX1A, DMBX1, PAX3, MAP2, and TRPM8 (Figure 2A). Although BioQC analysis revealed that ectodermal genes are highly expressed at the endpoint on day 7, factor analysis further indicated that their expression is strongly correlated across conditions, too, which is neither sufficient nor necessary for their high expression. Factors 2–6 mainly consist of genes representing the mesodermal layer (factor 2), stem-cell self-renewal (factor 3), and the endoderm layer (factors 4–6), respectively. The remaining factors (factors 7–10) are of smaller sizes and more heterogeneous (Figure 2B). Genes associated with each factor are associated mainly, but not exclusively, with other genes of the same germ-layer class. In summary, factor analysis revealed that germ-layer genes form coregulated gene modules in TeraTox that are significantly enriched by germ-layer- or stem-cell-specific markers.

Training and Testing of a Predictive Model for the TeraTox Assay

To build a quantitative predictive model of concentration-dependent teratogenicity potential with gene expression, we explored all combinations of the following options (Figure 3A):

Figure 3.

Construction of machine-learning models predicting concentration-dependent teratogenicity potentials based on differential gene expression as input. A, Overview of investigated model architectures. B, Definition of TS from lowest (left) to highest (right) concentrations. TS of teratogens = 1 for the highest noncytotoxic concentration, TS for other concentrations = cosine similarity of differential gene expression profiles between each concentration and the highest noncytotoxic concentration. TS of nonteratogens = 0, independent of the concentration level (from Lazabemide to Amoxicillin). Negative TS = 0. C, Spearman correlation coefficients between observed teratogenicity scores, calculated on a per-compound basis, and predicted teratogenicity scores, which are derived from models trained by LOO testing. D, Mean (dots) and SDs (error bars) of teratogenicity scores of nonteratogens. Median teratogenicity score of each compound is derived from 6 concentrations. The average scores of nonteratogens are lower than those of teratogens, but not strictly zero, because they are predicted values by LOO testing with our machine learning model instead of assigned values as in (B). Abbreviations: TS, Teratogenicity Score; LOO, leave-one-out.

1. Feature type: We tested both logFC, the point-estimate of the effect size, and z-scores transformed from the sign of logFC and p value reported by the edgeR model, which considers both effect size and variance of DGE.

2. Feature engineering: We used all detectable pathway reporter genes (n = 1215), detectable germ-layer genes (n = 87), germ-layer classes defined by Tsankov et al. (n = 7), and germ-layer factors derived from factor analysis (n = 10). For both germ-layer classes and factors, we used the median value of the genes belonging to each group as the engineered feature.

3. Model construction: We used and benchmarked 2 methods of different nature, Elastic Net (linear regression with regularization) and Random Forest (ensemble decision trees), to construct machine-learning models. These methods were chosen based on the size of the dataset and the relatively good explainability of both methods (Badillo et al., 2020).

4. Target variable: We used both binary classification (teratogen or nonteratogen) and regression (the TS, defined below and further detailed in the Materials and Methods section) for teratogenicity and regression alone for cytotoxicity.

5. Data splitting: we tried both repeated splitting of 80% training and 20% test set, and the LOO scheme. For data splitting, we used 80% of compounds (stratified sampling from nonteratogens and teratogens) as the training set to train a model, which was used to predict the TS using the remaining 20% compounds as the test set. For LOO, the model was trained by assessing the panel of compounds minus one, which predicted the TS for the left-out compound. The procedure was repeated until all compounds had been left out. The performance of both models was assessed by F₁ scores in case of binary classification models, and Spearman correlation coefficients of TS for teratogens in case of regression models. The best model parameters were searched by 10-fold cross-validations of the training set.

The TSs of teratogens are defined between 0 and 1, and those of nonteratogens are fixed as 0 at all concentrations (Figure 3B). By defining TS, we effectively transformed the binary classification problem into a regression problem. We refer readers interested in the motivation of developing the TS and in the mathematical details to the section on Teratogenicity Score in Supplementary Materials and Methods.

We observed the following patterns as we tried all options of model building:

1. The feature type has minimal impact on the performance, though models trained with z-scores perform better on the test set than models trained with logFC (data not shown).

2. The combination of feature engineering and machine-learning model is important and the best combination depends on the prediction task (Figs. 3C and 3D). For teratogenicity prediction, the combination of germ-layer factors and random-forest regression worked best.

3. With regard to the target variable, the performance of the regression-based teratogenicity-score prediction model is slightly better than binary classification (data not shown).

4. Performance is comparable between 2 modes of data splitting (data not shown). However, the LOO training-testing scheme is preferable because it allows us to set up a single threshold of TS, which can be applied to all compounds, and is not conditioned by whether or not a compound is included in the training set or in the test set as in the case of 80%/20% data splitting.

Based on these observations, we decided to use germ-layer factors as features, random-forest regression as the machine-learning model, and TS as the target variable to build the predictive model for teratogenicity with gene expression data.

Performance of the TeraTox Assay and Benchmarking With Other Models

Based on the best-performing machine-learning model, we defined the following predictive model for teratogenicity. First, we considered the maximal noncytotoxic threshold concentration (NCC_max) for cell viability of at least 80%, measured by the CellTiter Glo assay. Next, we defined the minimal teratogenic concentration (TC_min) as the concentration at which the threshold of the TS was met (TS = 0.38, defined by grid search; Figure 4A). If no NCC_max or TC_min could be determined because values did not exceed these thresholds, the maximal tested concentrations were used for NCC_max and TC_min. The predictive score, which we named TeraTox Score, is defined by the logarithmic ratio between threshold concentrations at 20% viability impairment (NCC_max) and teratogenic concentrations (TC_min). Negative TeraTox scores classify the compounds as negative whereas positive scores classify compounds as positive (Figure 4B).

Figure 4.

Prediction of teratogenicity with the human TeraTox assay. A, Results of a grid search to select the optimal threshold of the TS using the best identified model architecture. The best threshold (TS = 0.38) was chosen based on performance metrics defined in Supplementary equations 1–5. B, Visual definition of the TeraTox score based on minimal teratogenic concentration (TC_min) and maximal noncytotoxic concentration (NCC_max). Compounds with TeraTox scores ≤0 are classified as negative and compounds with scores >0 are classified as positive. C, Examples of concentration-response curves reported by the TeraTox assay of 4 selected nonteratogens (top panels) and 4 selected teratogens (bottom panels). The “+” indicates predicted TS (n = 2) and measured cytotoxicity (n = 3). D, ROS curve of LOO tests based on 45 reference compounds. Abbreviations: TS, Teratogenicity Score; ROC, receiver operating characteristics; LOO, leaving-one-out.

We plotted the concentration-response curves of measured cytotoxicity and predicted TS induced by each compound (Figure 4C; see Supplementary Figure 4 for all compounds). In general, teratogenicity levels increased while cell viability decreased with rising concentrations. Correctly predicted negative compounds were unlikely to induce teratogenicity within noncytotoxic concentrations, which means the calculated TeraTox score was negative or zero (eg, Doxycycline, RO-4, RO-6). Positive compounds (eg, Bosentan, Carbamazepine, Retinoic Acid, RO-1) or FP predicted compounds (eg, Cetirizine) were more likely to induce teratogenicity under noncytotoxic concentrations, as indicated by positive TeraTox scores (Figure 4C).

We compared the TeraTox prediction scores with classifications from FDA or in vivo EFD studies for 45 reference compounds (Supplementary Table 5). Classification with TeraTox Scores achieved an overall accuracy of 69% and outperformed mEST (58%). The 2 assays show different sensitivity and specificity profiles: Although mEST is more specific (specificity 76%), TeraTox is more sensitive (sensitivity/recall 79%). Among 17 negative reference compounds, 8 were classified as FP by TeraTox, and only 4 by the mEST. Whereas from 28 positive reference compounds, 22 were predicted as TP by TeraTox and only 13 by the mEST (Table 2 and Figure 4D). It is noteworthy that among the 26 compounds misclassified in total, the following 7 compounds are wrongly predicted by both assays: cyproheptadine, RO-11, 5-FU, methotrexate, misoprostol, RO-8, and warfarin. Given the distinct sensitivity and specificity profiles of the 2 assays, we asked whether we can achieve even better prediction results by using the 2 tests in sequence. Therefore, if we first run the mEST on the full panel, the substances with negative mEST results would then be retested by TeraTox to benefit from the high specificity of mEST and the high sensitivity of TeraTox.

Table 2.

Overview of Assay Performance for mEST and Human TeraTox Assay

Model	TP	TN	FP	FN	Accuracy (%)	Precision (%)	Recall (%)	Specificity (%)	F1 (%)
TeraTox	22	9	8	6	69	73	79	53	76
mEST	13	13	4	15	58	76	46	76	57
mEST + TeraTox	22	13	4	6	78	85	79	76	82

Values were calculated based on 45 compounds (according to Supplementary equations 1–5).

Abbreviations: mEST, mouse embryonic stem cell test; TP, true positive; TN, true negative; FP, false positive; FN, false negative.

Indeed, we found that overall accuracy of the combined prediction increased to 78%, better than either TeraTox or mEST alone. This suggests that it may be possible to achieve better prediction results by combining the existing mEST assay with the novel TeraTox assay.

Furthermore, we compared 18 pharmaceutical compounds that were both tested in TeraTox and DevTox^qp by Stemina, and observed the identical accuracy (78%), whereas balanced accuracy was 73% for TeraTox and 87% for DevTox^qp assay. The DevTox^qp assay delivered a higher specificity (100%) compared with TeraTox (67%), whereas TeraTox was more sensitive (80%) than DevTox^qp (73%; Supplementary Tables 6 and 7).

We also compared TeraTox with in silico predictions of developmental and reproductive toxicity using 2 widely used QSAR models: CAESAR and P&G, both implemented in the VEGA software. Among the compounds that we tested, 20 compounds are new to the CAESAR model, the P&G model, and the LOO versions of the TeraTox model, namely these compounds were not used to train these models and therefore, the prediction results for them present a fair comparison of the performance (Supplementary Table 8). Among these 20 compounds, TeraTox performed better (85% accuracy) than both the CAESAR model (75% accuracy) and the P&G model (35% accuracy; Supplementary Table 9).

In short, TeraTox delivers comparable or more favorable performance with alternative assays, and combining TeraTox with other assays can further increase the prediction accuracy for drug-induced teratogenicity.

Leveraging TeraTox Data and Model to Gain Biological Insights

A model’s interpretability and explainability is crucial to allow for inspection and further improvement (Barredo Arrieta et al., 2020). We performed additional in-depth analysis of the cytotoxicity and gene expression data and collected additional data orthogonal to TeraTox, thereby implementing 4 independent approaches to interpret and explain how the machine-learning model works and to explore what differs teratogens from nonteratogens.

First, we followed up on previous work and asked the question whether compound-induced cytotoxicity quantified by the phenotypic assay can be predicted by gene expression data as well, and whether TS are confounded by general cytotoxicity (Waldmann et al., 2014, 2017). For this purpose, we followed the same scheme as described in Figure 3A using cytotoxicity instead of TS as the target variable. Interestingly, a comprehensive search showed that using all pathway reporter genes and the elastic net model, instead of using germ-layer factors and random forest as in the case of teratogenicity prediction, give the best result (Figure 5A, contrasted with Figs. 3C and 3D).

Figure 5.

Biological interpretation of the model. A, Prediction of cytotoxicity based on DGE (same workflow as Figure 3A) using all pathway reporter genes as features and an elastic net machine-learning model. Dots: Spearman correlations between observed and predicted cytotoxicity using LOO testing. B, Difference in feature importance of germ-layer genes for prediction of cytotoxicity (upper left) or teratogenicity (lower right) or both (middle). C, Clustering analysis: pharmaceutical target profiles of compounds alone are not sufficient to determine teratogenicity (based on annotated compound target profile, i.e., quantitative affinity to protein targets from ChEMBL database). D, Structure of the DAG to model relationships between teratogenicity score and germ-layer factors with a generalized linear model. Input variables: germ-layer factors and significant interactions between germ-layers identified by Bayesian networks (Factor 1-4 and 6). Model fitting: Supplementary Figure 6. E, Generalized linear model with beta-regression for sensitivity analysis, to test model behavior with tuned input variables. Each panel shows the result of 1 tuning parameter, eg, ectoderm germ-layer factor (top-left) while keeping all other parameters fixed. Lines = average prediction, Areas = 95% confidence intervals of prediction. Input variables are scaled to 0 mean and SD. Abbreviations: DGE, differential gene expression; LOO, leaving-one-out; DAG, directed acyclic graph.

Given that the combination of germ-layer genes and random forest gives reasonable performance in both cases, and that random forest allows inquiry of feature importance by accuracy, we compared the feature importance of germ-layer genes in predicting both target variables (Figure 5B). The prediction of cytotoxicity and teratogenicity by molecular phenotyping relies on expression changes of distinct genes. The distinction shows that (1) teratogenicity of a compound is not a determinant for cytotoxicity, (2) a compound that shows cytotoxicity at a specific concentration can still be teratogenic at lower concentrations, and (3) genes and pathways associated with cytotoxicity and teratogenicity can be regulated independently from each other. This concurs with several previous findings (Krug et al., 2013; Rempel et al., 2015; Shinde et al., 2017).

The second approach addressed the question whether a compound’s pharmacology, in this context its target profile (protein targets and binding affinities), suffices to predict its teratogenicity potential. If so, one may hope to predict teratogenicity potential based on target profiles and/or even based on the chemical structure alone. Although some teratogens indeed have similar target profiles, we observe close clustering of teratogens and nonteratogens that have similar target profiles as well (Figure 5C andSupplementary Figure 5a). The potential of teratogenicity, therefore, may be associated with off-target effects or effects through targets that are not captured in ChEMBL, especially at the relatively high concentrations approaching cytotoxicity levels that we tested. Corroborating this, we found almost no correspondence between clustering of average DGE across concentration per compound and that of pharmacological profile (Supplementary Figure 5b). Therefore, we conclude that while knowing the target- and off-target profile of a compound is essential for de-risking its safety liabilities including teratogenicity, pharmacology data alone cannot currently predict a compound’s teratogenicity potential. This conclusion concurs with the superior performance of TeraTox over 2 QSAR models, which consider the chemical structure alone. The hiPSCs-based in-vitro assays, such as TeraTox and other advanced cellular models, are therefore indispensable for assessing the potential for human teratogenicity.

The third approach was to use a simpler generalized linear regression model for sensitivity analysis, which would allow us to analyze how the model responds to changes of the input. Given that random forest is an ensemble method and the contribution of each germ-layer factor can be therefore difficult to interpret, we built an alternative model using generalized linear regression. To identify interaction terms in the linear regression, we made the assumption that germ-layer factors regulate each other by forming a directed acyclic graph. Under this assumption, we built a Bayesian network using the differential expression data of germ-layer factors (Figure 5D). The network reveals potential influences on both mesoderm and endoderm by the ectoderm, influences on endoderm by mesoderm, and influences on stem-cell renewal by endoderm.

The Bayesian network topology prompted us to build a beta regression model including all germ-layer factors and interactions identified in the Bayesian network (Figure 5E andSupplementary Figure 6). The model provides both interpretable coefficients of the model and a tool for sensitivity analysis, because we can quantify prediction uncertainty more easily with a generalized linear model than with a random forest model, by paying the price of assuming linear regulation relationship. For the sensitivity analysis, we kept all other parameters fixed and tuned one input parameter at a time to simulate its impact on predicted TSs. We observed that the model is likely sensitive to impairment of either ectoderm layer or stem-cell self-renewal, while being relatively robust to changes to either mesoderm or endoderm (Figure 5E). The results of sensitivity analysis further underlined the prominent ectodermal nature of the model at the endpoint on day 7.

Last but not least, we applied gene-set enrichment analysis to each compound with BioQC and compared median gene-set enrichment results over concentrations of each compound between teratogens and nonteratogens. We identified multiple gene-sets that are potentially differentially regulated by teratogens and nonteratogens (p < .10; Supplementary Figure 7). Interestingly, target genes of several transcription factors that are involved in organ development and cell differentiation, eg, POU4F1, GATA1, and NODAL, were impacted by teratogens differently from nonteratogens. Besides, teratogens also regulate genes involved in biological processes that are not restricted to embryogenesis, such as cleavage of cell adhesion proteins as well as lipid metabolism genes induced by SRBEF/SREBP. Although teratogens do not form a homogeneous group and have distinct pharmacological profiles (Figure 5C andSupplementary Figure 5a), these results suggest that transcriptional regulation by teratogens can manifest in changes of several biological processes, potentially mediated by key transcriptional factors. Gene-set enrichment analysis based on TeraTox data also revealed molecular insights of teratogenic effects at yet another level of gene expression regulation (Supplementary Figure 7).

In summary, we explain how the TeraTox model operates by complementing the machine-learning model with feature importance analysis, pharmacological profiling clustering, sensitivity analysis, and gene-set enrichment analysis.

DISCUSSIONS

This study characterizes the optimization of TeraTox and its application in the context of preclinical teratogenicity assessment of drugs. TeraTox extends and standardizes the previously published embryoid-body models and fully leverages the predictive potential of these models by adding a toxicological prediction model (Krug et al., 2013; Shinde et al., 2016). It exploits an explainable machine-learning approach to predict teratogenicity potential induced by drug-like molecules.

Only a few pharmaceutical compounds have been recognized as human teratogens based on high-quality data. Therefore, we decided to limit the training data of the TeraTox model to a compound set with well-described evidence without losing rigor in the teratogenicity classification (n = 45; Table 1). Although alternative compound collections are available, for instance the ToxCast data set applied to the DevTox^qp assay from Stemina (Zurlinden et al., 2020), our compilation was specifically related to pharmaceuticals with teratogenicity profiles supported by either labeling information or strong evidences.

Despite the limited number of reference compounds available, we report that TeraTox outperforms 2 QSAR models and performs comparably with state-of-the-art in-vitro models. TeraTox and DevTox^qp showed the same accuracy to predict 18 pharmaceutical compounds: the DevTox^qp assay showed a higher specificity and TeraTox a higher sensitivity. Combining TeraTox and DevTox^qp led to a higher prediction accuracy, suggesting a synergistic effect of using complementary assays for teratogenicity prediction.

Similarly, we observed comparable performance of TeraTox and mEST, with TeraTox being more sensitive and mEST more specific. Again, combining both assays led even to an increased accuracy, which would improve overall predictivity.

Due to rich working experience with the mEST assay, we could also offer a first-hand comparison between TeraTox and mEST with regard to manual work, cost, and throughput (Supplementary Table 10). We conclude that the overall effort and cost entailed by TeraTox is comparable to that of the mEST. However, TeraTox offers a much higher throughput thanks to automation and miniaturization, and it generates quantitative gene expression data that can be used to compare new chemical series with existing ones and to further refine the model.

The prediction model presented here is geared towards hazard identification, similar to animal studies, where maximum tolerated doses are to be used for studies testing DART. In the context of an overall risk assessment, one immediate step would be to consider compound potency and to evaluate whether the in vitro toxic concentrations would be relevant to human exposure situations. To exemplify this, we highlight here the FP (Supplementary Tables 2 and 5).

Two of them, progesterone and cetirizine, have a minimal transcriptome-altering concentration that is larger than 2 orders of magnitude higher than the human max plasma concentration. A third compound, RO-2, is likely to be similarly “overdosed” in vitro. And also, ascorbic acid gives positive signals only at clearly higher concentrations than usually found in human plasma. These considerations could be made more exactly on the basis of physiologically based pharmacokinetic models and considerations of free drug concentrations. However, the principle is exemplified here, and if eg, 4 of the FP would be negative at clinically realistic concentrations, the test specificity would be 76%.

TeraTox offers additional benefits that are not yet available in any existing models. First, TeraTox is more sensitive than either the mEST or the DevTox^qp assay, especially when we consider maximum plasma concentrations (C_max) from human data whenever possible or model species otherwise (Supplementary Tables 2 and 3). Detecting human-specific teratogens are critical for drug discovery and development, as illustrated by phthalimide-based series of molecules, which includes thalidomide (Belair et al., 2020; Donovan et al., 2018; Matyskiela et al., 2018; Smith and Mitchell, 2018). Thalidomide was correctly identified as positive by TeraTox. Second, TeraTox reveals concentration-response teratogenicity and cytotoxicity relationship. This can be integrated with pharmacokinetic and exposure data to better estimate teratogenic risk in the clinic. Third, TeraTox generates quantitative gene expression data. Here, we used these data to reveal germ-layer factors, to build a predictive model, and to identify pathways and gene-sets that are regulated by teratogens differently than by nonteratogens. Gene expression data can be also used to explore mechanisms of action and to prioritize drug candidates for preclinical development.

What sets TeraTox apart from other models is that it is less of a phenotypic black box but rather an interpretable and explainable model that provides mechanistic insights into gene, pathway, and germ-layer modulations. TeraTox informs predictions not only based on statistical data patterns but builds upon biological mechanisms and thus may reflect disturbed functionalities, similar to those leading to teratogenicity in vivo. These features put TeraTox conceptually in a group of other assays that use phenotypic changes or disturbed functionalities as readouts (Dreser et al., 2020; Hoelting et al., 2016; Meisig et al., 2020; Pallocca et al., 2016). The model consolidates our previous intention to “focus on germ layers” and corroborates recent work exploring gastruloid models that profiles morphological changes of germ-layers for teratogenicity prediction (Jaklin et al., 2020; Moris et al., 2020).

Interpretability and explainability analysis shed light on both the strengths and the limitations of the TeraTox model. Most importantly, we could distinguish cytotoxicity from teratogenicity. We explored machine-learning model variants for both teratogenicity and cytotoxicity predictions and made the observation that the best models depend on the target variable. Whereas germ-layer factors and random forest performed best for teratogenicity prediction, the combination of all pathway reporter genes and regularized linear regression with elastic nets showed the best prediction for cytotoxicity (Supplementary Figs. 3a and 3b). There are 2 likely reasons. First, the molecular phenotyping platform contains well-curated genes that reflect cytotoxicity and cell death, which were highlighted in a previous drug screening study using iPS-derived cardiomyocytes (Drawnel et al., 2017). Therefore, we anticipate that these genes are used by linear regression to predict cytotoxicity. Second, teratogenicity is notably complex. It can be induced in many different ways, with different perturbations leading to different down-stream changes that are collectively known as teratogenicity. Therefore, a change in the total output of the germ-layer regulatory network is probably a more robust readout of teratogenicity than individual genes. Random forest, which is an ensemble learning method, is better at detecting such heterogeneous signals than linear regression.

Further studies are warranted to explore several parallel paths for further optimization of the TeraTox assay. These can be divided into 4 categories: (1) paths leading to better characterization of EB differentiation, (2) paths leading to testing of larger chemical spaces beyond pharmaceuticals, (3) paths leading to better predictive and explanatory algorithms, and (4) paths leading to better biological models of human embryo development. These lines of research could broaden the applicability domain and increase the robustness of the TeraTox assay. To better characterize EBs, multimodal characterizations of the EBs using bulk and single-cell omics, morphological profiling, and time-series experiments could be used. Extension of the assay duration to more than 7 days or using other differentiation protocols may further improve TeraTox capacity to model mesoderm and endoderm development.

There are several options to further improve the predictivity and the interpretability of the TeraTox model. To better distinguish between nonteratogens and teratogens, we may try to test the compounds with the TeraTox assay at lower concentrations (especially for nonteratogens), where the lowest concentration should be predicted to have a TS equal to or close to zero. In this context, it could be feasible to apply the exposure-based validation approach described by Daston et al. (2014), based on minimal and maximal concentration-dependent effects of teratogenicity. Another option could be to include the TeraTox assay in a test battery to preselect those compounds that show high cytotoxic interference with weak teratogenicity. Multimodel data could be used to identify further relevant features beyond germ-layer genes and factors. As more data are collected, we may also optimize the prediction algorithm, for instance using the nearest-neighbor prediction or other variants. Finally, the TeraTox assay may benefit from a better modeling of human embryo development. We may use alternative morphology-based assays of gastruloids to complement the TeraTox readout (Baillie-Benson et al., 2020; Moris et al., 2020). Alternatively, sophisticated microphysiological systems may better mimic the maternal-placenta-embryo axis and hence, may recapitulate true embryo exposure levels and give insights into active drug metabolism although drugs do not need to cross the placental barrier to cause fetal harm (Blundell et al., 2016, 2018; Boos et al., 2021). In the future, they may replace the 3D EBs in TeraTox. In the current throughput, though, such systems will probably be more powerful as a secondary assay to spot check a few compounds of particular interest. For this purpose, a continuous integration and modeling of data of human embryogenesis, for instance from omics, imaging, and perturbation studies, is required to guide further optimization of the TeraTox assay (Canzler et al., 2020; Mantziou et al., 2021; Yan et al., 2013).

CONCLUSION

We demonstrate that the TeraTox is a novel predictive human in vitro assay for pharmaceutical teratogenicity prediction that addresses several limitations of alternative assays regarding sensitivity, species-specificity, and interpretability. We believe that its adoption in drug discovery empowers preclinical teratogenicity assessment. Further optimization of the TeraTox assay and its routine use in drug-screening processes will lead us towards better preclinical assessment of teratogenicity. Thus, we solicit the community for helping us with further refining and validating TeraTox in drug discovery and other contexts.

SUPPLEMENTARY DATA

Supplementary data are available at Toxicological Sciences online.

DATA AVAILABILITY

Sequencing files and gene counts are available at the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE183534; last access May 3, 2022). Differential gene expression data of commercially available compounds as well as the composition of germ-layer factors are available on the Zenodo platform (https://zenodo.org/record/6143691; last access May 3, 2022). All data are shared with the Creative Commons CC BY 4.0 license.