# Identification of new PFAS for severe interference with thyroid hormone transport: A combined in vitro/silico approach

Anita Sosnowska, Michalina Mudlaff, Enrico Mombelli, Peter Behnisch, Szymon Zdybel, Harrie Besselink, Jochen Kuckelkorn, Natalia Bulawska, Kacper Kepka, Dominika Kowalska, et al.

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## ► To cite this version:

Anita Sosnowska, Michalina Mudlaff, Enrico Mombelli, Peter Behnisch, Szymon Zdybel, et al. Identification of new PFAS for severe interference with thyroid hormone transport: A combined in vitro/silico approach. Journal of Hazardous Materials, 2025, 491, pp.137949. 10.1016/j.jhazmat.2025.137949. hal-05157063

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# Identification of new PFAS for severe interference with thyroid hormone transport: A combined in vitro/silico approach

Anita Sosnowska<sup>a,b,*</sup>, Michalina Mudlaff<sup>a, ID</sup>, Enrico Mombelli<sup>c</sup>, Peter Behnisch<sup>d, ID</sup>, Szymon Zdybel<sup>a,b, ID</sup>, Harrie Besselink<sup>d</sup>, Jochen Kuckelkorn<sup>e, ID</sup>, Natalia Bulawska<sup>a,b</sup>, Kacper Kepka<sup>a,b</sup>, Dominika Kowalska<sup>a</sup>, Abraham Brouwer<sup>d</sup>, Tomasz Puzyn<sup>a,b,*</sup>

## HIGHLIGHTS

* A set of 45 PFAS was tested via TTR-TRß-CALUX bioassay.

* Three in silico approaches were developed.

* 12,654 PFAS were screened for in vitro toxicity to disrupt thyroid hormone transport.

* PFAS activity depends mostly on molecular mass and molecular complexity.

* Eight potentially toxic PFAS (covered by two approaches, RPF > 1) were distinguished.

## GRAPHICAL ABSTRACT

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

**Keywords:**

PFAS CALUX

PFAS

Thyroid hormone transport competition

QSAR

Predictive models

New-approach methodologies (NAM)

Relative potency factors (RPF)

## ABSTRACT

A tiered in vitro/in silico approach was developed to screen 12,654 per- and polyfluoroalkyl substances (PFAS) for their potential to disrupt the thyroid hormone transport. Initially, a set of 45 PFAS was tested using TTR-TRß-CALUX bioassay, which was subsequently employed to develop a classification model, distinguishing active and inactive PFAS. The model fulfills all good practices for QSAR model validation and can predict whether a given PFAS can disrupt plasma transport of the thyroid hormone (T4). Subsequently, active compounds were used to develop two regression approaches: (i) multiple linear regression MLR, and (ii) second approach aimed at identifying multiple valid QSAR models based on different data-splitting strategies. Finally, a comprehensive virtual screening of a large PFAS dataset was conducted to assess their potency in disrupting thyroid hormone transport. The predictions indicated that more than 7500 compounds were active with over 100 PFAS potentially causing even greater adverse effects than PFOA. These findings highlight the critical role of integrating New

<footnote><sup>a</sup> QSAR Lab Ltd., Trzy Lipy 3 St., Gdańsk, Poland</footnote>

<footnote><sup>b</sup> University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, Gdansk 80-308, Poland</footnote>

<footnote><sup>c</sup> INERIS, Parc Technologique Alata BP 2, Verneuil-en-Halatte 60550, France</footnote>

<footnote><sup>d</sup> BioDetection Systems B.V., Science Park 406, Amsterdam 1098 XH, the Netherlands</footnote>

<footnote><sup>e</sup> UBA (German Environment Agency), Section of Toxicology of Drinking Water and Swimming Pool Water, Heinrich-Heine-Str. 12, Bad Elster 08645, Germany</footnote>

<footnote>* Corresponding author at: QSAR Lab Ltd., Trzy Lipy 3 St., Gdańsk, Poland.</footnote>

<footnote>E-mail addresses: a.sosnowska@qsarlab.com, anita.sosnowska@ug.gda.pl (A. Sosnowska), t.puzyn@qsarlab.com, tomasz.puzyn@ug.edu.pl (T. Puzyn).</footnote>
Approach Methodologies (NAM)-based in vitro toxicity testing with multifaceted molecular modeling in assessing the risks associated with PFAS contamination in environmental matrices.

# 1. Introduction

Per- and polyfluoroalkyl substances (PFAS) are a class of over 12,000 anthropogenic chemicals [1,2] known for their resistance to heat, microbes, and chemical degradation. Their fluorinated carbon structure imparts valuable properties such as hydrophobicity and oleophobicity, making them widely used in both consumer and industrial applications [3,4]. Due to their extensive use and high environmental persistence, PFAS compounds are detectable across various environmental compartments [5]. Their accumulation in plants, animals, and humans has led to extensive research on their toxicological effects on human health. Given that PFAS concentrations in many areas often exceed recommended health advisory levels, there is an urgent need to comprehensively understand their behavior in ecosystems. Sun et al. [6] examined the occurrence of both legacy and emerging PFAS in the Cape Fear River watershed, emphasizing the ineffectiveness of conventional water treatment methods in removing these contaminants. Additionally, recent studies have highlighted the development of comprehensive solutions for the removal of complex organic contaminants, including pharmaceuticals and personal care products from the environmental compartments [7-9]. This understanding is crucial for developing effective strategies to remediate PFAS contamination [10-12].

PFAS compounds have been associated with various adverse effects on endocrine function, particularly thyroid function. They've been identified as potential endocrine-disrupting chemicals (EDC) and linked to other detrimental effects on human health [13,14]. Sunderland et al [15]. highlighted the complex pathways of human exposure to PFAS and the significant health effects particularly on immune function and metabolic outcomes. Furthermore, they underscored the urgent need for further research to address existing knowledge gaps and to better understand the emerging health concerns related to PFAS exposure. Increasing evidence suggests that PFAS exposure affects thyroid hormone function, with variability observed among different compounds (e.g. long and short-chain PFAS), age at exposure, and sex [16]. Several epidemiological studies in populations with varying exposure levels (high and low) have reported sex-specific associations between exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) and the development of hypothyroidism and hyperthyroidism [16]. A more detailed understanding of the mechanisms underlying thyroid disruption is crucial, as thyroid dysfunction significantly affects multiple physiological systems. Impaired thyroid function can lead to disturbances in ovulation and the ovarian cycle, though the direct molecular mechanisms remain to be further elucidated. Hypothyroidism can result in decreased metabolic clearance rates of androstenedione and estrone in women as well as increase in peripheral aromatization [17]. Experimental studies have demonstrated that PFAS, such as PFOA, can reduce thyroid hormone levels by competitively binding to the thyroid transport protein transthyretin (TTR) [18,19]. Alteration in T4 levels may be attributed to interactions with transport proteins, increased glucuronidation in the liver, or by augmented conversion of T4 to T3 due to the action of type I deiodinase in the liver [20,21]. In addition to the effect of PFAS on T4 level changes, the researchers also describe positive links between the action of PFOA and total T3 and TSH levels [22]. PFAS reporter gene assays have demonstrated that the relative potencies of most PFAS align with in vivo relative potency factors RPFs, as currently discussed in Europe using in vivo/in silico read-across derived RPFs (see at EU-COM 540, 2022 as well as Bil et al., 2021 [23]). However, significant discrepancies exist for certain compounds, such as Perfluorononanoic acid (PFNA) and Perfluorododecanoic acid (PFDoDA), which may stem from differences in toxicokinetics. The rapid and reproducible CALUX human cell line-based bioassay enables monitoring

of thyroid hormone transport competition. This is achieved by combining the TTR binding assay with the TRβ CALUX detection assay (TTR-TRβ CALUX), which is applied to several PFAS congeners [24,25].

Experimental data obtained from the TTR-TRβ CALUX bioassay, combined with a systematic computational approach, provide insights into the TTR-mediated thyroid hormone transport-disrupting potential of a broad range of PFAS. In this study, we proposed a comprehensive *in vitro/in silico* approach for screening a large dataset (above 12,000 [26]) of PFAS in terms of their *in vitro* toxicity potency to disrupt the thyroid hormone transport using the RPF as an endpoint. To do this, we have experimentally tested a set of 45 PFAS using the TTR-TRβ-CALUX bioassay. Based on the results, we developed three QSAR models: one classification model to distinguish active and inactive compounds, and two regression approaches to estimate PFAS potency in interfering with the T4-TTR binding. All developed models meet the OECD's gold standards for model validation [27]. In the next phase, these models were employed for comprehensive virtual screening of over 12,000 PFAS (hereafter referred to as the PFAS db) [26] enabling further analysis of their potential to disrupt the thyroid hormone transport and establish structure-activity relationship based on predicted outcomes. As a result, we identified more than 7500 compounds exhibiting activity in the TTR-TRβ CALUX assay. The RPFs for active compounds were determined using the regression models, revealing that over 100 PFAS-like compounds (with RPF values exceeding 1) may pose even greater adverse effects on human health than the reference compound PFOA (RPF = 1). These findings confirm that thyroid-related pathways investigated in this study should be considered when assessing the toxicity mechanisms of PFAS.

# 2. Materials and methods

The *in vitro/in silico* approach proposed in this study was divided into two main phases, as schematically illustrated in Fig. 1. **PHASE I** consists of (i) performing the TTR-TRβ CALUX measurements for 45 PFAS and, based on the experimental data (ii) developing predictive models-a decision tree classification model (DTC), a multiple linear regression model (MLR), and multiple regression models (MRM)-to enable the prediction of RPF values for TTR disruption by PFAS. During **PHASE II**, the models developed in **PHASE I** were applied to predict RPF values for an extensive list of PFAS (PFAS db) [1]. In this phase, it was crucial to apply the developed tools in the correct order- first classifying the compounds as positive or negative based on the classification model and then applying the MLR and MRM models to estimate quantitative RPF values for compounds classified as positive. The workflow summarizing the overall approach is presented in Fig. 1., while a detailed description of each step is provided in the following section.

## 2.1. PFAS

A detailed list of all PFAS, including their abbreviation, chemical formula, CAS numbers, and SMILES (Simplified Molecular Input Line Entry System) notations is available in Electronic Supplementary Information 1 (ESI1, sheet: "Input data").

## 2.2. *In vitro* bioassays

**Materials:** 45 PFAS compounds and one technical product (ADONA) were obtained from Campro Scientific and Da Vinci.

**Methods:** The PFAS (TTR TR) CALUX bioassay was carried out under conditions described in detail previously [25,28].

PFAS (TTR TR) CALUX bioassay measurements were performed at
BioDetection Systems (Amsterdam, Netherlands) and carried out under conditions described in detail previously [25,28]. In short, this bioassay is based on competition between PFAS and the thyroid hormone T4 for binding sites on transthyetretin (TTR) transport proteins, in combination with the TRβ CALUX bioassay as a functional readout. The TTR-binding assays were performed by incubating serial dilution of 45 individual PFAS (5 µL; minimal 8 dilutions with log-increments of 0.5) with 100 µL of T4 (0.08 µM stock) and 50 µL TTR (0.15 µM stock) overnight at 4°C. Next, TTR-unbound T4 was separated from TTR-bound T4 on Bio-Gel P-6DG columns (Bio-Rad, product no.: 150-0739) following centrifugation for 1 minute at 120 g. One hundred and forty µL of the collected fractions were mixed with 500 µL of exposure medium after which they were tested in the TRβ CALUX bioassay to determine the amount of TTR-bound T4.

For determination of TRβ CALUX activities, CALUX cells were seeded in 96 wells plates in assay medium, pre-incubated for 24 hours and exposed for another 24 hours to exposure medium containing the eluates collected after the TTR-binding assay under standardized conditions (5 % CO₂; 37 °C; 100 % humidity). After incubation, cells were lysed and luciferase activity in cellular lysates was measured following addition of the substrate luciferin using a luminometer (Tristar, Berthold). Each analysis consisted of triplicate well-plate testing and each PFAS dilution series was tested in duplicate. Dose-response analysis was performed as described by Behnisch et al. (2021) [25]. Only analysis results that fulfill the BDS quality criteria as described in Collet et al. (2020) [28] are used for the final evaluation and derivation of RPF values. In this case, RPF values are the ratio of the toxic potency of selected PFAS to the toxic potency of PFOA, which is well characterized and its toxicity is well known PFOA serves as reference substance and is set as RPF= 1.

## 2.3. *In silico* approach

### 2.3.1. *Generation of the structure of PFAS and calculating molecular descriptors*

**2.3.1.1. DTC and MLR models.** For the development of DTC and MLR models, PFAS structures were generated using SMILES notation. Molecular descriptors were calculated using the Alvadesc software [29] based on 2D structures. For further modeling, 0D, 1D, and 2D descriptors including constitutional descriptors, topological indices,

connectivity indices, burden eigenvalues, etc. were utilized. In total, 4179 2D descriptors were obtained. Before modeling, the descriptors were preprocessed using Standard Scaler from the Scikit-learn Python package, which standardizes features by subtracting the mean and dividing them by the standard deviation.

**2.3.1.2. MRM model.** For the development of the MRM model, SMILES codes corresponding to PFAS with experimentally detectable RPF activity were normalized using the workflow proposed by Gadaleta et al [30], which generates two types of SMILES as output: canonical and kekulized. The latter format was retained, and molecular descriptors were computed using Mordred v 1.2.0 [31], yielding 1613 0D, 1D, and 2D descriptors. The descriptors were preprocessed, retaining only those without missing values, those with a pairwise correlation coefficient below 0.98, and those that did not contain more than 85 % repeated values, resulting in a final set of 419 descriptors. Finally, the dataset (ESI1; 'MRM Dataset') and RPF values were converted to decadic logarithms before modeling.

Additionally, a PFAS database for prediction (screening) was created using a similar approach. The same methodology applied to structure generation for model development was used. The list of PFAS was retrieved from the literature [26]. A comprehensive overview of the PFAS used to develop the QSAR models, along with the PFAS database created, is provided in ESI1 ('Classification Model Dataset', 'Regression Model Dataset', and 'MRM Dataset').

## 2.4. Classification tree QSAR model development

One of the most commonly used tree-based methods for modeling the relationship between structure and activity is the Decision Tree Classifier (DTC) method [32]. This approach enables the selection of the most relevant 'X' variables, which are crucial for the precise determination of the 'Y' response. The procedure is mapped through the construction of the tree, beginning at the "root node," and progressing upward through descendant nodes until a final decision is reached at a "leaf node" or terminal node.

The dataset used for modelling was pre-processed by removing constant descriptor values, descriptors with low variability, and missing values. Additionally, the data were autoscaled before constructing the decision tree. Each sample in the dataset was assigned to an appropriate

<figure><img src="image_5.png" /><figcaption>**PHASE I: Models developing based on experimental data**</figcaption></figure>

**Fig. 1.** Workflow summarizing the overall approach adopted to derive a PFAS prioritization scheme for TTR disruption. MLR - Multiple Linear Regression; MRM - Multiple Regression Models.

<figure><img src="image_6.png" /><figcaption>**PHASE II: Screening for more than 12 000 PFAS**</figcaption></figure>
class (active or inactive). Sample with an experimentally determined RPF value were classified as 'Active'; while those without this value were labeled as 'Inactive'. To ensure a balanced distribution, the samples were ranked by activity class and split into training and validation sets using the Z:1 algorithm, where Z = 4. In this process, all values were sorted in ascending order, and every Zth (third) compound in the group was assigned to the validation set. A summary of the dataset split is presented in Table 1, while a detailed breakdown of the assigned items can be found in ESI1, 'Classification Model Dataset', Tables S1 and S2.

We utilized the Genetic Algorithm (GA)[32–35] to select the optimal set of descriptors to predict RPF classes. The definitions and hyper-parameter values of GA used in this study is presented in ESI1, 'Classification Model Dataset', Table S3. The interpretation of the selected variables and the statistical analysis of the resulting descriptive model are provided in the Results section, with a summary of all results available in the ESI, 'Classification Model Dataset', Table S4.

The final step involved assessing the applicability domain (AD) of the developed model. For this, we applied the boundary box method[33], which defines the AD based on the boundary values of the variables included in the model. All scripts used in this research have been provided in the Zenodo[36] repository. The developed model complies with OECD principles, and its QMRF (QSAR Model Reporting Format) documentation is provided in ESI2.

## 2.5. QSAR models development

After verifying PFAS activities using a decision tree, we predicted the numerical values of RPF. To achieve this, we developed QSAR regression models based on the two approaches described below. In both approaches, to ensure a normal distribution of the data, the experimental RPF values were transformed using a decadic logarithm.

### 2.5.1. MLR-QSAR model – approach 1

The first QSAR model was developed using Multiple Linear Regression (MLR). Python 3.12 was employed for the modeling and implementation all algorithms. Compounds were sorted in ascending order based on their RPF values and were divided according to the 1:3 algorithm, where every third compound was allocated to the validation set, while the remaining compounds were used for training. This resulted in 24 PFAS being assigned to the training set and 11 to the validation set. Descriptors were selected using a GA.

A crucial stage in QSAR model calibration is to verify the goodness-of-fit by calculating appropriate parameters[37]. Model selection was guided by the coefficients of determination (R²) and the root mean square error of calibration (RMSEC), based on the prediction for the training set, which assessed the statistical fit of the model to the experimental data based on training set predictions. Internal validation, using the same dataset as model construction, involved calculating Q²LOO (leave-one-out cross-validation coefficient), RMSEC (root mean square error of cross-validation), and CCC (concordance correlation coefficient)[37]. Additionally, for the validation set, Q²F1, F2, and F3 coefficients, MAE (mean absolute error), RMSE (root mean square error), and CCC (concordance correlation coefficient) were calculated to demonstrate the predictive ability of the obtained model[37,38]. Furthermore, Y²SCR scrambling validation was conducted to rule out chance correlation[39].

To validate the predictions, the model's applicability domain was examined using a Williams plot, which depicted the relationship between standardized residuals and leverage values for compounds in both

<table><caption>**Table 1**
Overview of set division and class assignment (total number of PFAS in the set).</caption><thead><tr><td>Class</td><td>Training set</td><td>Validation set</td></tr></thead><tbody><tr><td>Inactive</td><td>7 (33)</td><td>2 (11)</td></tr><tr><td>Active</td><td>26 (33)</td><td>9 (11)</td></tr></tbody></table>

the training and validation sets. QMRF documentation is available in ESI2.

### 2.5.2. Multiple regression models (MRM) – approach 2

This approach was designed to address the lack of robustness that often compromises the evaluation of QSAR models calibrated on small datasets. Indeed, minor perturbations in the composition of such datasets can lead to significant variations in the estimated predictive performance across slightly different datasets and selected molecular descriptors. To mitigate this issue, a modeling strategy was implemented to identify multiple valid QSAR models based on different data splits. All computations were performed using R software (v 4.2.2) [40] and the libraries referenced below (script_01 available on Zenodo [36]).

The logic of the MRM approach is illustrated in Figure S4 in ESI1, 'MRM approach'. Multiple test sets were generated by randomly including 30% of PFAS, following the systematic inclusion of the compound with the highest and lowest activity in the corresponding training sets. The regsubsets function from the R library "leaps" v3.1 (Regression Subset Selection) was used to perform an exhaustive search [41] to identify the optimal regression model, limiting the maximum number of molecular descriptors according to a Topliss ratio of 5 [42] (i.e., the ratio of the training set compounds to molecular descriptors). The final retained regression model was the one with the lowest Bayesian Information Criterion (BIC). All models were developed using centered and scaled molecular descriptors. Notably, during automatic selection of molecular descriptors, no information from the test set was utilized by the regsubsets function, which exclusively relied on data from the training sets. Consequently, no data leakage occurred, ensuring that external validations remained independent of model calibrations.

Over 10⁵ cycles only models characterized by: Q²loo ≥ 0.7, Q²iso ≥ 0.7, Q²EXT F3 ≥ 0.7, CCCEXT ≥ 0.85 were retained. Here, compared to the MLR, an additional parameter is specified: Q²ISO (leave-some-out cross-validation coefficient). Moreover, two additional validation criteria were added: 1) the absolute difference between Q²mean = (Q²loo + Q²iso)/2 and Q²EXT F3 had to be lower than 0.15, and, 2) only models whose original Q²loo was higher than the 97.5th percentile of 700 scrambling iterations [39] were retained as valid models (i.e., scrambling validation). The selection of molecular descriptors was applied ex novo at each scrambling iteration to increase the stringency of the scrambling validation. Thirty-one different models were considered as valid after these evaluations. Finally, this set of QSAR models led to complete QSAR models (script_02 on Zenodo [36]) implemented as a function of the full dataset (i.e., training and test sets). This last set of models is referred to as the final set of models. All necessary information about developed models is attached in the QMRF documentation (ESI2).

## 2.6. Virtual screening of PFAS in terms of analysis of their potency to disrupt the thyroid hormone transport

As a final step, we screened a large dataset - PFAS db [26] for in vitro toxicity related to thyroid hormone transport disruption. After preprocessing, which included discarding compounds without CAS numbers or SMILES notation and normalizing CAS identifiers, we established a database comprising 12,654 PFAS compounds. Molecular descriptors were then computed for this dataset, and the compounds were classified using the binary model (DTC model) to identify active compounds. The results of this step are summarized in ESI1, Tables S9, and S10; 'Activity Classes Predictions'. For active compounds, we predicted LogRPF values using MLR and the MRM approach.

### 2.6.1. Evaluation of prediction for MLR approach

The accuracy of predictions for the MLR approach was rigorously evaluated using the Insubria Plot method[43], a crucial tool for assessing the applicability domain (AD) of the QSAR model and determining the reliability of predictions for new compounds. Predictions were
generated using the previously described MLR model. The Insubria Plot was constructed by plotting leverage values (which quantify the distance of each compound from the training set) on the x-axis and the predicted activity values on the y-axis. Compounds within the applicability range were plotted within a defined boundary or convex shape. Compounds with leverage values close to or slightly above the critical threshold h* (h* = (3(p + 1))/n where p is the number of independent variables in the model and n is the total number of observations used to develop the model) or with predicted values outside the range of experimental data were categorized as extrapolations and interpreted with caution. Compounds falling outside the applicability range were identified as potentially unreliable, suggesting that predictions for these compounds should be treated with increased skepticism. Predictions of active compounds from the MLR model are provided in ESI1, Table S11.

### 2.6.2. Integrated predictive strategy for the MRM approach

The final set of QSAR models was used to predict PFAS activity within the PFAS db (ESI1; 'PFAS_db') (script_03 on Zenodo [36]). Predictions generated by this subset of models were considered valid only if the query PFAS fell within the AD of at least three different QSAR models. Initially, predictions were made for all compounds in the database, after which those classified as active were extracted and analyzed.

Special emphasis was placed on defining a strict AD to minimize predictive extrapolations, given the low cardinality of the dataset. A query PFAS was considered within the AD of a QSAR model if it met the following criteria:

1) The predicted RPF value fell within the activity range defined by the training set,

2) The leverage value did not exceed the critical threshold (h* = 3 (p + 1)/n), where p is the number of descriptors used in the model and n denotes the number of compounds,

3) The query PFAS contained at least one structural PFAS-specific fingerprint [26] that was also present in the training set.

The final integrated prediction for a query PFAS was computed as a weighted mean of individual predictions, following Eq. 1. Here, N represents the number of QSAR models whose AD was compatible with the query PFAS, and weights ($w_i$) were defined as the reciprocal of the absolute prediction error (AE) of the closest neighbor, according to the equation: $w_i = 1/AE_{closest\ neighbor}$. The closest neighbor was identified based on Euclidean distances in the descriptor space of each QSAR model.

In addition, the integrated prediction for PFAS belonging to the AD of the models was only considered valid if the prediction uncertainty did

not exceed 0.5 logarithmic units.

$$ Integrated\ Prediction = \frac{\sum_{i}^{N} w_i Prediction_i}{\sum_{i}^{N} w_i} \qquad (1) $$

## 3. Results and discussion

### 3.1. PHASE I - experimental measurements

Several toxic effects of PFAS on humans are mediated through interactions with nuclear hormone receptors (NHRs). To date, only a limited number of PFAS have been reported to possess endocrine-disrupting potential, interfering with both the thyroid and steroid hormone systems [25]. Cytotoxicity (Cytotox CALUX) and PFAS CALUX activities have been assessed in cells for 45 selected PFAS compounds and one technical product (ADONA).

For the 45 PFAS compounds and ADONA tested on the selected CALUX battery, the TTR-TRβ or PFAS CALUX showed to be the bioassay on which most compounds elicited activities (33 active compounds). Fig. 2 presents representative dose-response curves for several PFAS using the PFAS reporter gene assay.

Table 2 lists the *in vitro* established RPF of the tested PFAS relative to PFOA (RPF = 1) in the PFAS CALUX activities ranged from 0.00015 (6:2 FTAB) to 2.3 (P37DMOA). Here, we present the various *in vitro* derived RPF-values obtained from the CALUX panel used in this study, as well as the *in vivo/in silico* read-across derived RPFs currently under discussion in Europe (EU-COM 540, 2022; see also Bil et al., 2021 [23]). The PFAS reporter gene assays show relative potencies for most PFAS in the same order of magnitude as the *in vivo* RPFs. However, significant discrepancies exist for certain compounds, such as for PFNA to PFDoDA, which may be due to e.g., differences in kinetics, including diffusion and transport processes [44]. Additionally, highly active thyroid hormone disrupting chemicals such as P37DMOA (RPF = 2.3) and PFOSA (RPF=0.75) are still absent from the EU-COM proposal.

Our findings demonstrate that sensitive effect-based and human cell-based *in vitro* toxicity bioanalysis tools can play an important role in assessing the *in vitro* toxicity of the whole group of the few known and many unknown PFAS (mono constituents and mixtures). The data indicate that the PFAS CALUX is a promising NAM- and effect-based bioanalysis tool, leveraging the common ability of PFAS to bind to specific thyroid hormone transport proteins and thereby disrupt the thyroid hormone system, potentially leading to adverse health effects. *In vitro* RPF values determined through PFAS CALUX bioanalysis for a broad set of PFAS have highlighted the relevance of previously

<figure><img src="image_7.png" /><figcaption>Fig. 2. Typical concentration-response curves of some selected PFAS by PFAS reporter gene CALUX bioanalysis. PFAS reporter gene activity is expressed as induction relative to the maximum induction of the PFOA reference series (relative induction RI%).</figcaption></figure>
<caption>**Table 2**</caption>

Overview of the 45 PFAS congeners and industrial mixtures (ADONA, GenX) incl. CAS RN, relative potency factors (RPFs) characterized by the *in vitro* PFAS CALUX and by *in vivo* studies from EU COM 540 (2022).

<table><thead><tr><th>PFAS congener</th><th>CAS</th><th>PFAS CALUX in vitro RPF</th><th>EU-COM 540/2022 in vivo RPF</th></tr></thead><tbody><tr><td>Reference compound PFOA</td><td>335-67-1</td><td>1.0</td><td></td></tr><tr><td>PFBA</td><td>375-22-4</td><td>0.0019</td><td>0.05</td></tr><tr><td>PFPeA</td><td>2706-90-3</td><td>0.083</td><td>0.03</td></tr><tr><td>PFHxA</td><td>307-24-4</td><td>0.19</td><td>0.01</td></tr><tr><td>PFHpA</td><td>375-85-9</td><td>1.5</td><td>0.5</td></tr><tr><td><strong>PFOA</strong></td><td><strong>335-67-1</strong></td><td><strong>1.0</strong></td><td><strong>1</strong></td></tr><tr><td>PFNA</td><td>375-95-1</td><td>0.34</td><td>10</td></tr><tr><td>PFDcA</td><td>335-76-2</td><td>0.13</td><td>7</td></tr><tr><td>PFDoDA</td><td>307-55-1</td><td>0.025</td><td>3</td></tr><tr><td>PFUnDA</td><td>2058-94-8</td><td>0.001</td><td></td></tr><tr><td>PFBS</td><td>375-73-5</td><td>0.054</td><td>0.001</td></tr><tr><td>PFPeS</td><td>2706-91-4</td><td>0.082</td><td>0.3</td></tr><tr><td>PFHxS</td><td>355-46-4</td><td>1.6</td><td>0.6</td></tr><tr><td>PFHpS</td><td>375-92-8</td><td>1.0</td><td>1.3</td></tr><tr><td>PFOS</td><td>1763-23-1</td><td>2.1</td><td>2</td></tr><tr><td>PFNS</td><td>98789-57-2</td><td>0.0045</td><td></td></tr><tr><td>PFDS</td><td>335-77-3</td><td>&lt; 0.0001</td><td></td></tr><tr><td>P37DMOA</td><td>172155-07-6</td><td>2.3</td><td></td></tr><tr><td>ADONA</td><td>919005-14-4</td><td>0.55</td><td>0.03</td></tr><tr><td>H4-PFUnDA</td><td>34598-33-9</td><td>0.018</td><td></td></tr><tr><td>PFMOPrA</td><td>377-73-1</td><td>0.0072</td><td></td></tr><tr><td>PFPrOPrA*</td><td>13252-13-6</td><td>0.0035</td><td></td></tr><tr><td>PFMOBA</td><td>863090-89-5</td><td>0.034</td><td></td></tr><tr><td>MeFBSA</td><td>68298-12-4</td><td>0.043</td><td></td></tr><tr><td>PFBSA</td><td>30334-69-1</td><td>0.011</td><td></td></tr><tr><td>HpFHpA</td><td>1546-95-8</td><td>0.071</td><td></td></tr><tr><td>MeFOSA</td><td>31506-32-8</td><td>0.015</td><td></td></tr><tr><td>EtFOSA</td><td>4151-50-2</td><td>0.054</td><td></td></tr><tr><td>PFOSA</td><td>754-91-6</td><td>0.75</td><td></td></tr><tr><td>PFMOAA</td><td>674-13-5</td><td>0.00044</td><td></td></tr><tr><td>N-MeFBSAA</td><td>159381-10-9</td><td>0.033</td><td></td></tr><tr><td>2:2 FTOH</td><td>54949-74-5</td><td>&lt; 0.0001</td><td></td></tr><tr><td>4:2 FTOH</td><td>2043-47-2</td><td>0.0003</td><td></td></tr><tr><td>6:2 FTOH</td><td>647-42-7</td><td>&lt; 0.0001</td><td>0.02</td></tr><tr><td>8:2 FTOH</td><td>678-39-7</td><td>&lt; 0.0001</td><td>0.04</td></tr><tr><td>10:2 FTOH</td><td>865-86-1</td><td>&lt; 0.0001</td><td></td></tr><tr><td>MeFOSAA</td><td>2355-31-9</td><td>&lt; 0.0001</td><td></td></tr><tr><td>EtFOSAA</td><td>2991-50-6</td><td>&lt; 0.0001</td><td></td></tr><tr><td>FOSSA</td><td>2806-24-8</td><td>&lt; 0.0001</td><td></td></tr><tr><td>6:2 FTAB</td><td>34455-29-3</td><td>0.00015</td><td></td></tr><tr><td>6:2 diPAP</td><td>57677-95-9</td><td>0.001</td><td></td></tr><tr><td>6:2 FTSAM</td><td>80475-32-7</td><td>&lt; 0.0001</td><td></td></tr><tr><td>8:2 FTUCA</td><td>70887-84-2</td><td>0.026</td><td></td></tr><tr><td>4:2 FTSA et H4-PFHxS</td><td>757124-72-4</td><td>0.018</td><td></td></tr><tr><td>6:2 FTSA et H4-PFOS</td><td>27619-97-2</td><td>0.02</td><td>0.02</td></tr><tr><td>GenX*</td><td>62037-80-3</td><td>0.0039</td><td></td></tr></tbody></table>

* The results for the compounds PFPrOPrA (C6HF11O3, CAS RN 13252-13-6) and GenX (C6H4F11NO3, CAS RN 62037-80-3) were averaged because these two chemical compounds, despite structural differences related to the presence of an ammonium group, are virtually indistinguishable from a QSAR perspective. Molecular descriptors for QSAR calculations are generated for the neutral form of the compounds, so that differences in the presence of salts do not significantly affect the computational properties. Therefore, the similar RPF values (0.0035 for PFPrOPrA and 0.0039 for GenX) obtained in the two independent analyses confirm their convergence. To avoid duplication of data, we decided to use the averaged RPF value as the activity score for these compounds.

unidentified PFAS compounds. Our studies suggest that, in addition to the 24 PFAS currently proposed (EU-COM 540, 2022), other PFAS, such as PFOSA and P37DMOA, should also be given significant consideration due to their in vitro toxic properties.

## 3.2. PHASE I - classification model

Based on the CALUX experimental data, we developed a

classification model to distinguish PFAS as either active or inactive. Using a GA, we identified the optimal combination of descriptors for model development, which consisted of SM4_D (spectral moment of order 4 form topological distance matrix) and GATS3m (Geary autocorrelation function weighted by atomic mass). The predictive accuracy of the model was assessed using a confusion matrix (Table 3). Key performance metrics, including sensitivity, specificity, accuracy, precision, and F-score, were used to characterize the model’s predictive ability (Table 4). A graphical representation of the model’s prediction is provided in ESI1; ‘Activity Classes Predictions’, Figure S1.

One of the OECD principles for the robust validation of QSAR models [27] is a mechanistic interpretation of the model. Transthyretin (TTR), PDBid 2F7I, is a globular protein comprised of four monomers, each consisting of 127 amino acids. Each monomer adopts a beta-barrel structure. The tertiary structure is primarily stabilized by beta-sheets, which forms the protein’s outer surface, while the interior is predominantly hydrophobic, with mostly aliphatic side chains. Additionally, five salt bridges contribute to structural stability: LYS15-GLU54, ASP18-ARG21, LYS35-ASP39, LYS70-GLU72, and LYS76-GLU89. The ligand-binding site of TTR is located on a beta-sheet and is predominantly hydrophobic, with a lysin residue at one basic extremity. As a result, an optimal ligand is expected to have a hydrophobic chain on one end and a hydrophilic, possibly acidic group on the other, which is the case in most of the studied PFAS. In the developed QSAR model, two descriptors were identified as key factors in modulating TTR activity in the presence of PFAS. The first descriptor is SM4_D[45], which describes the length of the molecule and its structural complexity. In general, smaller and less branched molecules exhibit lower value and tends to be inactive (e.g. CAS 54949-74-5, SM4_D= 12.72) (Fig. 3A). Conversely, larger molecules with greater structural complexity yield higher SM4_D value and demonstrate increased activity (e.g. CAS 57677-95-9, SM4_D= 24,05) (Fig. 3B).

The second descriptor under consideration is GATS3m[46], which increases in value with the presence of a greater diversity of atoms within a molecule, thereby influencing molecule activity. For instance, molecule 2-(perfluorodecyl)ethanol (CAS 865-86-1) (depicted in Fig. 3C) exhibits a lower GATS3m value of 0.55, attributed to its elongated PFAS chain terminating in a hydroxyl group. In contrast, the molecule MeFBSAA (CAS 159381-10-9) (illustrated in Fig. 3D) presents a higher GATS3m value of 1.16, due to its comparatively shorter PFAS chain and a structurally complex terminal moiety containing sulfur, nitrogen, oxygen, and hydrogen. Notably, an analysis of these descriptors indicates that molecules with simpler structural configurations generally do not impact TTR activity.

Molecular size plays a significant role in determining activity. Generally, smaller molecules exhibit inactivity, except for those with a highly intricate configuration, exemplified by PFMOAA (CAS 674-13-5) (SM4_D= 13.44, GATS3m= 0.80), as illustrated in Fig. 3E. Interestingly, most molecules containing the hydroxyl group, including larger ones like 2-(perfluorodecyl)ethanol (Fig. 3C), remain inactivity. This inactivity is likely due to the presence of a basic amino acid (LYS15) at the TTR binding site. More acidic PFAS molecules are attracted to the protein, inhibiting its function by blocking the T4 binding 3.3. PHASE I - QSAR models development

<caption>**Table 3**</caption>

Confusion matrix for the training and validation sets.

<table><thead><tr><th rowspan="3"></th><th rowspan="3"></th><th colspan="2">Training Set</th><th colspan="2">Validation Set</th></tr><tr><th colspan="2">Predicted</th><th colspan="2">Predicted</th></tr><tr><th>Inactive</th><th>Active</th><th>Inactive</th><th>Active</th></tr></thead><tbody><tr><td rowspan="3">True</td><td>Inactive</td><td>TP</td><td>FN</td><td>TP</td><td>FN</td></tr><tr><td rowspan="2">Active</td><td>7</td><td>0</td><td>2</td><td>0</td></tr><tr><td>FP</td><td>TN</td><td>FP</td><td>TN</td></tr><tr><td></td><td></td><td>2</td><td>26</td><td>1</td><td>8</td></tr></tbody></table>
<table><caption>**Table 4**
The summary of the statistical parameters obtained from the TTR-TRß DTC model.</caption><thead><tr><th rowspan="2"></th><th colspan="4">Training Set</th><th colspan="4">Validation Set</th></tr><tr><th>Precision</th><th>Recall</th><th>F1-score</th><th>Support</th><th>Precision</th><th>Recall</th><th>F1-score</th><th>Support</th></tr></thead><tbody><tr><td>Inactive</td><td>0.88</td><td>1.00</td><td>0.93</td><td>7</td><td>1.00</td><td>0.50</td><td>0.67</td><td>2</td></tr><tr><td>Active</td><td>1.00</td><td>0.96</td><td>0.98</td><td>26</td><td>0.9</td><td>1.00</td><td>0.95</td><td>9</td></tr><tr><td>Accuracy</td><td>-</td><td>-</td><td>0.97</td><td>33</td><td>-</td><td>-</td><td>0.91</td><td>11</td></tr><tr><td>Macro Avg</td><td>0.94</td><td>0.98</td><td>0.96</td><td>33</td><td>0.95</td><td>0.75</td><td>0.81</td><td>11</td></tr><tr><td>Weighted Avg</td><td>0.97</td><td>0.97</td><td>0.97</td><td>33</td><td>0.92</td><td>0.91</td><td>0.90</td><td>11</td></tr></tbody></table>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 3.** The structure of CAS 54949–74–5 l (A) and CAS 57677–95–9 (B), molecules that have the smallest and the biggest SM4_D value, CAS 865–86–1 (C) and CAS 159381–10–9 (D), molecules that got the smallest and the biggest GATS3m value and CAS 674–13–5 (E).</figcaption></figure>

### 3.2.1. PHASE I – MLR-QSAR model – approach 1

Next, for the active compounds, we developed a QSAR model to estimate the potency of PFAS in interfering with T4-TTR binding. The MLR model included three descriptors: AMW (average molecular weight), GATS7p (Geary autocorrelation of lag 7 weighted by polarizability), and B10[F-F] (presence/absence of F - F at topological distance 10). The correlation between the descriptors was less than 0.6. The model effectively explained the dependent variable, as evidenced by the high coefficient of determination R² and visually demonstrated by the strong fit of the trend line in Fig. 4.

Additionally, the model exhibited a high Q² and low RMSE values,

indicating minimal predictive error. Statistical validity was further supported by the low Y²SCR values. The detailed statistics of the model are presented in Table 5.

The Williams Plot (Fig. 4B), used to visually representing points categorized as not belonging to the AD of the model, indicated the absence of such points. All data points used to develop and validate the MLR model remained within the AD, without exceeding the threshold of three standard deviations or the leverage threshold h* = 0.5.

An indispensable step in QSAR modeling is the interpretation of the obtained model. In this case, three descriptors were identified and utilized (Table S8 in ESI1; ‘Compound analyst’). Among these descriptors,

<figure><img src="image_13.png" /><figcaption>**Fig. 4.** A) visual representation of the values predicted from experimental values of the obtained MLR model for LogRPF and B) Williams Plot defining the applicability domain of PFAS compounds of the obtained MLR model.</figcaption></figure>
<caption>**Table 5**</caption>

Summary of statistics obtained from the MLR model for LogRPF values. The validity thresholds proposed by Chirico et al. (2012) are respected [47].

<table><caption>MLR - QSAR model for LogRPF</caption><thead><tr><th>Equation</th><th>LogRPF = -1.4348 (± 0.124) + 1.0243(± 0.137) x AMW + 0.5737 (± 0.134) x GATS7p - 0.6249 (± 0.128) x B10[F-F]</th></tr></thead><tbody><tr><td>n</td><td>24</td><td>R<sup>2</sup> - Q<sup>2</sup><sub>LOO</sub></td><td>0.034</td><td>Q<sup>2</sup><sub>F3</sub></td><td>0.814</td></tr><tr><td>k</td><td>11</td><td>RMSE<sub>CV</sub></td><td>0.501</td><td>RMSE<sub>EXT</sub></td><td>0.501</td></tr><tr><td>R<sup>2</sup></td><td>0.770</td><td>MAE<sub>CV</sub></td><td>0.396</td><td>MAE<sub>EXT</sub></td><td>0.396</td></tr><tr><td>RMSE<sub>C</sub></td><td>0.555</td><td>CCC<sub>CV</sub></td><td>0.808</td><td>CCC<sub>EXT</sub></td><td>0.857</td></tr><tr><td>MAE<sub>C</sub></td><td>0.426</td><td>Q<sup>2</sup><sub>F1</sub></td><td>0.766</td><td>R<sup>2</sup>Y<sub>SCR</sub></td><td>0.131</td></tr><tr><td>Q<sup>2</sup><sub>LOO</sub></td><td>0.766</td><td>Q<sup>2</sup><sub>F2</sub></td><td>0.766</td><td>-</td><td>-</td></tr></tbody></table>

AMW represents the average molecular weight of the atoms constituting the molecule [29]. A higher average molecular weight typically correlates with increased ligand activity, as exemplified by perfluorohexanesulfonic acid (CAS 355–46–4) (predicted RPF= 3.01, AMW= 16.67) (Fig. 6A). Conversely, molecules with lower average molecular weight tend to exhibit reduced activity, e.g. CAS 2043–47–2 (predicted RPF= 0.0002, AMW= 12.58) (Fig. 4B).

An exception is observed in PFAS characterized by a B10[F-F] descriptor value of 1. This descriptor is indicative of molecules possessing a pair of F atoms separated by a topological distance of 10 bonds [29], thereby excluding those with longer chains. For instance, sodium perfluorononanesulfonate (CAS 98789–57–2) (predicted RPF=0.0046, AMW=17.34, B10[F-F]=1) (Fig. 5C) meets this criterion, whereas others compounds in the dataset exhibit a descriptor value of 0.

The third descriptor, GATS7p, reflects the polarity of the atoms within a molecule, with higher value indicating a greater presence of polar atoms [29]. Elevated GATS7p values correlate positively with molecular activity. For example, perfluorohexanesulfonic acid (CAS 355–46–4) (predicted RPF= 3.0149, AMW= 16.67, GATS7p= 0.16) exhibits higher activity compared to a molecule with a similar AMW value, such as 1- perfluoropentanesulfonic acid (CAS 2706–91–4) (predicted RPF= 0.1219, AMW= 16.67, GATS7p= 1.16) (Figs. 5A, 5D).

In conclusion, regarding the second QSAR model, the mechanistic interpretation suggests that PFAS structures containing heavier and more polar atoms tend to exhibit higher activity. Consequently, PFOS has higher RPF value than PFOA, as the -SO3H group is more polar than -COOH group. However, when comparing PFAS across different structural groups, the number, type and polarity of functional groups plays an important role. For example, PFAS containing an acidic group are more likely to interact with the TTR binding site, thereby inhibiting its activity [48].

### 3.2.2. PHASE I – regression model – approach 2

Thirty-one QSAR models, utilizing overlapping but not identical sets of molecular descriptors, met all the validation requirements described in the methodological section (Table 6). The corresponding equations

and training/test sets are provided in ESI1; 'MRM equations' and 'MRM sets' respectively).

All these QSAR models comply with the first four QSAR validation principles [27], as the modeled endpoint is well-defined (i.e., a robust and widely used in vitro system), the underlying algorithm is explicitly described and reproducible, an applicability domain is established, and appropriate measures of goodness-of-fit, robustness, and predictivity are provided. Regarding the fourth principle, it is important to note that the validity thresholds proposed by Chirico et al [47]. are respected.

The fifth validation principle concerns mechanistic interpretation, which can be explored by analyzing the 55 different molecular descriptors adopted by the final set of models (Table 6). In this respect, a principal component analysis (PCA) based on these molecular descriptors (Fig. 6) reveals that the PCA space is partitioned between regions enriched in low, medium, and high activity PFAS. The three activity classes are defined according to the 33rd and 66th percentiles of the modeled endpoint.

Three molecular descriptors stand out among all the retained descriptors due to their frequency of occurrence across the QSAR models: JGI10 (10-ordered mean topological charge), ATSC7c (autocorrelation of lag 7 weighted by Gasteiger charge) and MATS6i [49] (Moran coefficient of lag 6 weighted by ionization potential) with frequencies of 100 %, 61 %, and 61 % respectively. The partitioning of the chemical space for these three descriptors is shown in Fig. 6.

The chemical structure of a high-activity PFAS is generally characterized by high MATS6i values for and low/moderate ATSC7c values. P37DMOA is a typical example of this trend as it exhibits very low ATSC7c values and high MATS6i values. Indeed, this compound is characterized by numerous partial charges of different signs (C and F atoms) at a topological distance of 7 and many C-F bonds (high ionization potential) at a topological distance of 6. Conversely, low-medium activity PFAS are typically associated with moderate/high JGI10 values (which are higher in long-chain PFAS), as well as moderate-to-high MATS6i, and ATSC7c values (e.g., 6:2 diPAP, RPF = 1 10⁻³).

### 3.3. PHASE II – virtual screening of PFAS in terms of their potency to interfere with the T4-TTR binding

In the next step, we applied the computational models developed in this study for the virtual screening of PFAS db, focusing on compounds that had not been experimentally measured for their potency to interfere with T4-TTR binding.

Firstly, we used the classification model developed in PHASE I to categorize compounds according to their activity. The results of these activity class predictions for the external dataset, along with applicability domain determination, are summarized in Figure S2 in ESI1; 'Virtual Screening'. A total of 7685 compounds were predicted as active and found to be within the AD of the model. After identifying the active compounds, we proceeded on predicting their RPF values using the

<figure><img src="image_14.png" /><figcaption>**Fig. 5.** The structure of CAS 355–46–4 A) and CAS 89807–81–8 B), molecules that have the highest average molecular weight and the smallest, CAS 98789–57–2 C) and CAS 2706–91–4 D).</figcaption></figure>
<caption>**Table 6**</caption>

Molecular descriptors and validation performance for the QSAR models underpinning the MRM approach. The term fm (full models) refers to QSAR models calibrated on the whole dataset.

<table><thead><tr><th>Model ID</th><th>Descriptors</th><th>Q<sup>2</sup><sub>Ioo</sub></th><th>Q<sup>2</sup><sub>Iso</sub></th><th>Q<sup>2</sup><sub>EXT F3</sub></th><th>CCC<sub>EXT</sub></th><th>MAE<sub>EXT</sub></th><th>Q<sup>2</sup><sub>Ioo fm</sub></th><th>Q<sup>2</sup><sub>Iso fm</sub></th></tr></thead><tbody><tr><td>193_a_noweights</td><td>ATSC7p ATSC5i Xp-7dv MIC4 JGI10</td><td>0.79</td><td>0.78</td><td>0.78</td><td>0.86</td><td>0.42</td><td>0.78</td><td>0.78</td></tr><tr><td>1024_a_noweights</td><td>ATSC5i MATS6v Xp-7dv piPC6 JGI10</td><td>0.8</td><td>0.79</td><td>0.79</td><td>0.85</td><td>0.54</td><td>0.78</td><td>0.78</td></tr><tr><td>1026_a_noweights</td><td>ATSC7c ATSC3p MATS6i MIC3 JGI10</td><td>0.78</td><td>0.77</td><td>0.77</td><td>0.89</td><td>0.45</td><td>0.80</td><td>0.79</td></tr><tr><td>1602_a_noweights</td><td>AATS2v ATSC7c ATSC8i MATS6i JGI10</td><td>0.78</td><td>0.77</td><td>0.77</td><td>0.88</td><td>0.37</td><td>0.78</td><td>0.77</td></tr><tr><td>2027_a_noweights</td><td>ATSC7c ATSC3p MATS6i BCUTc-1h JGI10</td><td>0.81</td><td>0.8</td><td>0.8</td><td>0.86</td><td>0.45</td><td>0.80</td><td>0.81</td></tr><tr><td>2527_a_noweights</td><td>AATS6v MATS6p BCUTse-11 GGI9 JGI10</td><td>0.8</td><td>0.79</td><td>0.79</td><td>0.89</td><td>0.34</td><td>0.80</td><td>0.80</td></tr><tr><td>5164_a_noweights</td><td>AATS4v GATS1i GATS6i JGI7 JGI10</td><td>0.82</td><td>0.82</td><td>0.82</td><td>0.86</td><td>0.47</td><td>0.81</td><td>0.80</td></tr><tr><td>5827_a_noweights</td><td>ATSC8c MATS5dv BCUTv-11 AXp-5d JGI10</td><td>0.79</td><td>0.78</td><td>0.78</td><td>0.87</td><td>0.46</td><td>0.75</td><td>0.74</td></tr><tr><td>8767_a_noweights</td><td>VR3_A AATS0v ATSC8c ATSC2Z JGI10</td><td>0.79</td><td>0.78</td><td>0.78</td><td>0.89</td><td>0.50</td><td>0.77</td><td>0.77</td></tr><tr><td>9068_a_noweights</td><td>ATSC7c MATS3p MATS6i AETA_eta FL JGI10</td><td>0.79</td><td>0.78</td><td>0.78</td><td>0.85</td><td>0.46</td><td>0.79</td><td>0.80</td></tr><tr><td>9893_a_noweights</td><td>AATS6v MATS6p BCUTse-11 Xc-4d JGI10</td><td>0.81</td><td>0.8</td><td>0.8</td><td>0.85</td><td>0.45</td><td>0.80</td><td>0.79</td></tr><tr><td>11460_a_noweights</td><td>ATSC7c ATSC3p MATS6i JGI10</td><td>0.79</td><td>0.79</td><td>0.79</td><td>0.85</td><td>0.44</td><td>0.77</td><td>0.77</td></tr><tr><td>14179_a_noweights</td><td>ATSC7c MATS3p MATS6i AETA_eta_L JGI10</td><td>0.8</td><td>0.79</td><td>0.79</td><td>0.87</td><td>0.49</td><td>0.80</td><td>0.80</td></tr><tr><td>22159_a_noweights</td><td>AATS3m ATSC7c MATS6i GATS1i JGI10</td><td>0.78</td><td>0.78</td><td>0.78</td><td>0.87</td><td>0.47</td><td>0.78</td><td>0.77</td></tr><tr><td>962_b_noweights</td><td>ATSC7c ATSC8c MATS6i BCUTc-1h JGI10</td><td>0.79</td><td>0.79</td><td>0.79</td><td>0.85</td><td>0.47</td><td>0.78</td><td>0.78</td></tr><tr><td>1361_b_noweights</td><td>ATSC7c ATSC0Z ATSC3i MATS6i JGI10</td><td>0.8</td><td>0.79</td><td>0.79</td><td>0.87</td><td>0.52</td><td>0.78</td><td>0.78</td></tr><tr><td>9453_b_noweights</td><td>AATS5v ATSC7c MATS2d MATS6i JGI10</td><td>0.77</td><td>0.77</td><td>0.77</td><td>0.86</td><td>0.42</td><td>0.77</td><td>0.77</td></tr><tr><td>742_c_noweights</td><td>ATSC7c ATSC3p MATS6i GATS4m JGI10</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.85</td><td>0.53</td><td>0.78</td><td>0.77</td></tr><tr><td>780_c_noweights</td><td>VR3_A AATS1v ATSC8c ATSC2Z JGI10</td><td>0.77</td><td>0.77</td><td>0.77</td><td>0.87</td><td>0.44</td><td>0.79</td><td>0.78</td></tr><tr><td>4489_c_noweights</td><td>AATS0v ATSC7c ATSC3se MATS6i JGI10</td><td>0.78</td><td>0.77</td><td>0.77</td><td>0.89</td><td>0.43</td><td>0.79</td><td>0.79</td></tr><tr><td>4604_c_noweights</td><td>AATS6v MATS6p AXp-5d GGI9 JGI10</td><td>0.82</td><td>0.8</td><td>0.8</td><td>0.86</td><td>0.46</td><td>0.78</td><td>0.78</td></tr><tr><td>5116_c_noweights</td><td>AATS3s ATSC7c MATS6i GATS1i JGI10</td><td>0.76</td><td>0.76</td><td>0.76</td><td>0.87</td><td>0.48</td><td>0.76</td><td>0.76</td></tr><tr><td>5452_c_noweights</td><td>ATSC7c ATSC3p AATSC6dv MATS6i JGI10</td><td>0.8</td><td>0.79</td><td>0.79</td><td>0.85</td><td>0.48</td><td>0.79</td><td>0.78</td></tr><tr><td>5693_c_noweights</td><td>AATS4dv ATSC7c ATSC3i MATS6i JGI10</td><td>0.79</td><td>0.79</td><td>0.79</td><td>0.87</td><td>0.43</td><td>0.78</td><td>0.77</td></tr><tr><td>6700_c_noweights</td><td>nS AATS6v AATS1p MATS6p JGI10</td><td>0.79</td><td>0.79</td><td>0.79</td><td>0.88</td><td>0.49</td><td>0.80</td><td>0.80</td></tr><tr><td>10058_c_noweights</td><td>AATS0v ATSC7c MATS6i GATS3se JGI10</td><td>0.79</td><td>0.79</td><td>0.79</td><td>0.86</td><td>0.51</td><td>0.77</td><td>0.77</td></tr><tr><td>21503_c_noweights</td><td>AATS4v ATSC7c ATSC3i MATS6i JGI10</td><td>0.77</td><td>0.76</td><td>0.76</td><td>0.86</td><td>0.41</td><td>0.80</td><td>0.81</td></tr><tr><td>21551_c_noweights</td><td>AATS0v ATSC7c MATS6i GATS3s JGI10</td><td>0.77</td><td>0.76</td><td>0.76</td><td>0.87</td><td>0.47</td><td>0.79</td><td>0.79</td></tr><tr><td>11560_d_noweights</td><td>AATS6v ATSC1d MATS6p MPC9 JGI10</td><td>0.81</td><td>0.8</td><td>0.8</td><td>0.88</td><td>0.47</td><td>0.77</td><td>0.77</td></tr><tr><td>16570_d_noweights</td><td>AATS1pe ATSC7c ATSC3i MATS6i JGI10</td><td>0.81</td><td>0.8</td><td>0.8</td><td>0.86</td><td>0.50</td><td>0.78</td><td>0.78</td></tr><tr><td>17023_d_noweights</td><td>AATS6dv ATSC8c ATSC4i GATS2d JGI10</td><td>0.81</td><td>0.79</td><td>0.79</td><td>0.87</td><td>0.46</td><td>0.79</td><td>0.78</td></tr></tbody></table>

<figure><img src="image_15.png" /><figcaption>**Fig. 6.** A) Scores of the first three components of a PCA conducted on the normalized molecular descriptors adopted by the QSAR models; B) Scatter plots of the three most frequent molecular descriptors characterizing the QSAR models that define the MRM approach. Red points indicate high activities, orange points medium activities, and green points low activities. The red ellipses/boxes/ indicated the regions enriched in high/medium activity PFAS and the green boxes region enriched in low/medium activities PFAS.</figcaption></figure>

regression models developed in PHASE I. Here we applied two modeling approaches: MLR and MRM models. The same descriptors used to model training were calculated for the active compounds to generate predictions.

For the MLR model, predictions were assessed using the Insubria Plot (Figure S3 in ESI1, 'Virtual Screening'). The evaluation revealed that up to 4650 of 7685 (61 %) compounds fell within the MLR model AD, as defined by leverage values. The distribution of these predictions on a linear scale is presented in Figure S5 in ESI1; sheet: "Distribution of

predictions". Among the 4650 active compounds – 2683 compounds were in the range of 0–0.01 RPF (57.70 %), while 155 compounds1 (3.33 %) exhibited RPF values greater than. A detailed analysis of the distribution of the results is provided in ESI1 "Distribution on predictions".

After applying the MRM approach to the PFAS db, it was possible to obtain 2251 predictions (ESI1, tab "MRMpredictions") whose quality requirements comply with the conditions described in the methodological section (e.g., predictions with the AD) could be obtained. Firstly,
predictions were computed on the entire set of PFAS to ensure
comprehensive data coverage and avoid bias in the selection process,
and then the active compounds classified by the classifier were extracted
from them. This resulted in 1057 PFAS considered as active by the
classifier and within the AD of the MRM models (The results can be
found in the MRM_active sheet in ESI1). The distribution of these pre-
dictions on a linear scale is reported in ESI1 on Figure S6 in ESI1; sheet:
"Distribution of predictions". It is characterized by a majority of pre-
dictions (50.0 %) below 0.01 and only a minority of predictions
(1.32 %) were characterized by a predicted RPF value greater than 1
(potentially very high hazard PFAS). Notably, from a conservative point
of view, if an average precision of 0.5 Log units is considered as sug-
gested by MAE values, it would be appropriate to consider predictions
above 0.32 on a linear scale (11.16 % of predictions, ESI1; 'MRM_pre-
dictions') as alerts for a potential TTR disruption.

The proposed modeling approaches (MLR and MRM) align with the
typical expectations for *in silico* screening of large chemical datasets for
hazard prioritization: identifying a reasonable number of chemicals to
submit to further characterization. Experimental resources should be
cost-effectively directed toward a limited subset of chemicals to facili-
tate a more thorough hazard assessment.

When comparing the distributions of the MLR and MRM models,
notable differences and similarities emerge in the RPF prediction results.
Both models exhibit similar distribution shapes, indicating comparable
overall prediction patterns. However, the MLR model operates on a
larger number of compounds, as evidenced on the Y-axis, where the
frequency of most values is clearly higher than in the MRM model. In
Panel A (ESI1; sheet: "Distribution of predictions"), both models display
a strongly skewed distribution, with a high concentration of compounds
having very low predicted RPF values. The MRM model includes an even
smaller number of compounds with values above 1 compared to the MLR
model, suggesting that the MRM model applies a more stringent
threshold for predicting higher RPF values. In Panel B, both models
show a more even distribution within the 0–2 range, implying that for
this subset of compounds both MLR and MRM have more diverse pre-
dictions. Nevertheless, the MRM model comprises a smaller number of
compounds, which may influence the interpretation of the results (a
prediction made on a small amount of data may lead to less accurate
estimates). Panel C reveals a similar distribution for both models, but the
X-axis range is much narrower (0–0.01), indicating that both models
predict low RPF values for most PFAS in this group. Again, the MRM
model includes fewer compounds, reflecting differences in sample sizes
between the two methods. Variations in distribution of PFAS RPF values
stem from their chemical properties, toxicokinetic, and environmental
behavior. Differences in carbon chain lengths and functional groups
influence their biological activity and toxicity. Long-chain PFAS (e.g.,
PFOA, PFOS) tend to be more lipophilic, leading to strong bio-
accumulation and slow excretion, whereas short-chain PFAS are
generally more mobile but less persistent in organisms. Additionally, the
presence of carboxyl (-COOH), sulfonyl (-SO3H), or ether (-O-) groups
affects RPF values. Finally, the environmental impact of PFAS also dif-
fers between linear and branched isomers. Considering the two ap-
proaches, we identified 4 compounds (i.e. CAS 1550–24–9, CAS
16557–94–1, CAS 95639–15–9, NOCAS_1035375) with RPF values > 1.
We also took the MSE error range into account to classify a further 4
compounds into the RPF > 1 group (these were CAS 1071817–33–8,
CAS 375–84–8, CAS 378–90–5, NOCAS_892364), respectively.

Fifty-six compounds achieved RPF values below 0.01. All results
available are in Table S16 in ESI1; sheet "Results comparison Com-
pounds with higher activity (RPF > 1) typically have longer and more
branched chains, which increases their lipophilicity and ability to
interact with proteins such as TTR. Conversely, compounds with lower
activity (RPF < 0.01) are rather short-chain PFAS with less polar groups,
such as alcohols or esters, which decrease the ability to bind to
receptors.

4. Discussion

The results of the TTR-TR (PFAS) CALUX test for 45 PFAS (mainly
carboxylic and sulfonic acids) were utilized to develop a systematic
approach for estimating activity across an extensive PFAS dataset
comprising more than 12,000 chemicals. Experimental tests provided
critical information on PFAS activity, which can be used in two ways.
The obtained RPF values describe the level of activity relative to the
reference compound (PFOA; RPF = 1), while the absence of RPF values
simultaneously indicates the lack of observed experimental activity. Our
findings demonstrated that 33 out of 44 tested PFAS exhibited activity in
T4-TTR binding. Furthermore, our experimental studies identified more
than 24 PFAS from the EU-COM 540, 2022 dataset that display in vitro
toxic properties, with particular emphasis on PFOSA and P37DMOA,
which require careful consideration.

Since it is not possible to experimentally test the entire group of
PFASs (12,654 compounds to date) for their potential to interfere with
TTR-TRβ, we used the experimental data to develop QSARs that can not
only be used to classify a large number of compounds but also to predict
accurate RPF values and to characterize structure-activity relationships
for this toxicological phenomenon. The *in silico* approach was divided
into two primary phases. The first involved developing predictive
models based on experimental data. The creation of both a classification
model and two regression approaches were intended to make the best
use of the information – available, both in terms of the active/inactive
response itself (classification model) and the exact activity value
(regression models). The classification model results suggest that PFAS
activity in disrupting TTR-TRβ is influenced by molecular size and
structural complexity of the PFAS-like compounds. Generally, smaller
molecules tend to be inactive. Moreover, a comparison of molecular
descriptors adopted by the two regression approaches highlights the
significance of atomic charge properties at a topological distance of 10
(molecular descriptors B10[F-F] and JGI10) and 7 (molecular de-
scriptors GATS7p and ATSC7c). The analysis also indicates that shorter
PFAS structures containing heavier and more polar atoms tend to exhibit
higher activity compared to longer PFAS molecules with lighter and less
polar atoms. This phenomenon is likely due to the presence of polar
atoms with acidic functional groups, which interact with the TTR
binding site, thereby inhibiting its activity.

The second phase involved the application of the developed models
to estimate activity for 12,654 PFAS. During this phase, the classification
model was used first, and only those compounds that showed activity
were used for screening by applying the developed regression ap-
proaches. Our study indicated that 7685 PFAS out of 12,654 (60 %) can
be considered active thyroid hormone disrupting chemicals. For those
active compounds, we also estimated RPF values using developed QSAR
approaches.

The results of the LogRPF comparison indicate that the MLR model
generates a broader range of values (-3.80–0.33) than the MRM models
(-3.62–0.29), reflecting greater variability in predicted outcomes. While
the MLR-QSAR model is well-suited for detailed analysis within the
applicability domain, providing a larger set of compounds (4650) and
identifying a significant number of high-risk compounds (RPF > 1), the
MRM-QSAR model excels in large-scale virtual screening and hazard
prioritization. The latter focuses on a smaller, more conservative subset
of compounds (1057), enabling a more precise identification of critical
high-risk compounds. Both models are effective but serve different
purposes, depending on the objectives of the analysis.

Additionally, the independent implementation of rigorously vali-
dated QSAR models by different researchers, employing complementary
strategies and varying descriptor computations and variable selection
algorithms, enhances further value of the analysis. These models high-
light key structural features influencing the propensities of PFAS to
disrupt TTRβ, such as molecular mass and complexity. These primary
drivers are further refining by atomic charge properties, particularly
those associated with atoms at topological distances of 10 and 7, further
demonstrating the nuanced insights offered by both QSAR approaches.

## 5. Conclusion

The findings of this analysis demonstrate that the TTR-TRβ (PFAS) CALUX assay is a robust and precise method for determining PFAS activity relative to the reference chemical, PFOA. The integration of this *in vitro* approach with an *in silico* methodology, comprising classification and regression models, enabled the prediction of activity for more than 12,000 PFAS. The most significant structural features affecting PFAS activity are molecular mass and molecular complexity, as confirmed by independently developed QSAR models. These results underscore the importance of accurate and multi-faceted molecular modeling in PFAS hazard assessment. Based on our analyses, we identified eight potentially toxic PFAS (covered by two approaches) having RPF values greater than 1.

The presented results indicate that it is possible to leverage the information from relatively small *in vitro* datasets utilizing a specifically tailored *in silico* approaches to screen large chemical databases. From a broader perspective, the combined *in vitro/in silico* toxicity assessment of total PFAS content in the environment, food, or public health related matrices using *in vitro* bioassays represents a promising and effective strategy for managing known and yet-to-be-identified PFAS. The detection of potentially high PFAS levels, coupled with the challenges of effectively removing legacy PFAS through conventional water treatment processes suggest the need for expanded discharge control and contaminant monitoring. These findings emphasize the importance of monitoring PFAS occurrence across various environmental compartments to assess the actual risks. The PFAS (TTR TR) CALUX bioassay is useful in detecting effects on thyroid hormone binding to the related transport protein that can be induced by a variety of PFAS. The test can therefore be used as a process control when removing PFAS or in general monitoring and can be a starting point for identifying (new) PFAS.

## Environmental implication

PFAS are synthetic chemical compounds that are considered pollutants due to their persistence in the environment and consequent difficulty in terms of degradation. They have the capacity to permeate groundwater and surface water, leading to contamination of drinking water sources. Additionally, there is an increasing body of evidence that exposure to PFAS can affect thyroid hormone function, with heterogeneity observed between compounds (e.g. long- and short-chain PFAS). Therefore, in this study, a tiered in vitro/in silico approach was developed to screen 12,654 per- and polyfluoroalkyl substances (PFASs) for their in vitro toxicity to disrupt thyroid hormone transport.

## CRediT authorship contribution statement

**Zdybel Szymon:** Writing – original draft, Methodology, Formal analysis.
**Besselink Harrie:** Writing – review & editing, Methodology, Formal analysis.
**Kuckelkorn Jochen:** Writing – review & editing.
**Sosnowska Anita:** Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization.
**Puzyn Tomasz:** Writing – review & editing, Supervision.
**Mudlaff Michalina:** Writing – original draft, Methodology, Formal analysis.
**Mombelli Enrico:** Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization.
**Bulawska Natalia:** Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization.
**Behnisch Peter:** Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization.
**Kowalska Dominika:** Writing – review & editing.
**Brouwer Abraham:** Writing – review & editing.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This work has received funding from the European Union's Horizon 2020 research and innovation programme via the PROMISCES project under grant agreement N°101036449.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jhazmat.2025.137949.

## Data availability

Data will be made available on request.

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