# New approach methodologies for risk assessment of urinary occurring toxicants

Maximilian Jobst, Irís Guerreiro, Delphine Payros, Isabelle P Oswald, Ana S Fernandes, Benedikt Warth, Giorgia del Favero

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Maximilian Jobst, Irís Guerreiro, Delphine Payros, Isabelle P Oswald, Ana S Fernandes, et al. New approach methodologies for risk assessment of urinary occurring toxicants. Ecotoxicology and Environmental Safety, 2025, 302, 10.1016/j.ecoenv.2025.118649. hal-05198796

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Review

New approach methodologies for risk assessment of urinary
occurring toxicants

Maximilian Jobst<sup>a,b,c</sup>, Irís Guerreiro<sup>d,e</sup>, Delphine Payros<sup>f,g</sup>, Isabelle P. Oswald<sup>f</sup>,
Ana S. Fernandes<sup>d</sup>, Benedikt Warth<sup>a,b</sup>, Giorgia Del Favero<sup>a,b,✉</sup>

ARTICLE INFO

Edited by Dr G Liu

Keywords:

Urinary tract

Contaminants

Bladder

kidney

New approach methodologies (NAMs)

Next generation risk assessment (NGRA)

A B S T R A C T

The reduction, refinement, and replacement of animal-based toxicological studies is becoming an essential requirement at academic, industrial, and governmental levels, turning new approach methodologies (NAMs) from alternatives into necessities. New strategies for hazard characterization and exposure assessment have been developed in this wake, including among others, sophisticated organ-on-chip platforms, or holistic human bio-monitoring programs. This review summarizes state-of-the-art methods of NAMs for hazard characterization and advances in exposure assessment in the context of toxicants occurring in human urine. Considering that urine collects a wide variety of excretion products, it can harbor chemically diverse compounds including heavy metals and metalloids, foodborne mycotoxins and phytotoxins, as well as synthetic and process chemicals. Hence, urine analysis is a precious element for exposure assessment and the urinary excretory organs are highly relevant toxicological targets. Especially for environmental or foodborne contaminants, the classical regulatory framework is challenged by natural variability of the producing organisms, climate change, global trade, and obviously heterogeneous exposure landscape related to an individual's lifestyle and diet. In this light, NAMs represent an unprecedented opportunity to support and speed up risk assessment. The aim of this review is to summarize current knowledge, in order to identify gaps on hazard profiling for selected urinary occurring contaminants, as well as for their exposure assessment. Furthermore, it aims at identifying the challenges in the field from both the toxicological and analytical perspectives in light of next generation risk assessment (NGRA).

**Abbreviations:** 3-MCPD, 3-monochloropropane-1,2-diol; ADME, Absorption, Distribution, Metabolism and Excretion; AdSCs, Adult Stem Cells; AI, Artificial Intelligence; AOP, Adverse Outcome Pathways; BCG, Bacillus Calmette-Guerin; BMI, Body Mass Index; CKD, Chronic Kidney Disease; EC, European Commission; ECHA, European Chemicals Agency; ECM, Extracellular Matrix; EFSA, European Food Safety Authority; EU, European Union; FDA, United States Food and Drug Administration; HBM, Human Biomonitoring; IPSC, Induced pluripotent stem cell; NAMs, New Approach Methodologies; NGRA, Next Generation Risk Assessment; NHANES, National Health and Nutrition Examination Survey; NIH, National Institutes of Health; OECD, Organization for Economic Co-operation and Development; PAH, Polycyclic Aromatic Hydrocarbons; PARC, Partnership for the Assessment of Risks from Chemicals; PBPK, Physiologically Based Pharmacokinetic Modelling: PCB, Polychlorinated Biphenyl; PDX, Patient derived Xenografts; PFAS, Perfluoroalkyl Substances; PFHxS, Perfluorohexanesulfonic Acid; PFNA, Perfluorononanoic Acid; PFOA, Perfluorooctanoic Acid; PFOS, Perfluorooctanesulfonic Acid; PSCs, Pluripotent Stem Cells; RPTEC, Renal Proximal Tubule Epithelial Cells; RASFF, Rapid Alert System for Food and Feed; REACH, Regulation on the Registration, Evaluation, Authorization and Restriction of Chemicals; WHO, World Health Organization.

* Corresponding author at: Department of Food Chemistry and Toxicology, University of Vienna Faculty of Chemistry, Währinger Str. 38-40, Vienna 1090, Austria.
E-mail address: giorgia.del.favero@univie.ac.at (G. Del Favero).

<footnote><sup>a</sup> Department of Food Chemistry and Toxicology, University of Vienna Faculty of Chemistry, Währinger Str. 38-40, Vienna 1090, Austria</footnote>

<footnote><sup>b</sup> Core Facility Multimodal Imaging, University of Vienna Faculty of Chemistry, Josef-Holaubek-Platz 2, Vienna 1090, Austria</footnote>

<footnote><sup>c</sup> University of Vienna, Vienna Doctoral School in Chemistry (DoSChem), Währinger Str. 42, Vienna 1090, Austria</footnote>

<footnote><sup>d</sup> CBIOS, Universidade Lusófona's Research Center for Biosciences & Health Technologies, Lisboa, Portugal</footnote>

<footnote><sup>e</sup> Universidad de Alcalá de Henares, Alcalá de Henares, Madrid, Spain</footnote>

<footnote><sup>f</sup> Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France</footnote>

<footnote><sup>g</sup> IHAP, Université de Toulouse, INRAE, ENVT, Toulouse 31076, France</footnote>

<footnote><sup>h</sup> Exposome Austria, Research Infrastructure and National EIRENE Node, Vienna, Austria</footnote>
# 1. Introduction

Adhering to the “Directive 2010/63/EU on the protection of animals used for scientific purposes”, and building on the “Regulation on the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH (EC) 1907/2006)”, there has been an increasing multisectoral push towards the development and integration of new methods for assessing the risk associated with exposure to xenobiotics (Adashi et al., 2023; Bronzwaer et al., 2025; Carnesecchi et al., 2023; Hartung, 2009; Miccoli et al., 2022; Wood et al., 2025). To enable a high level of protection for humans and the environment, while concomitantly reducing animal-based testing, alternatives started to develop with growing priority and are nowadays collectively known as new approach methodologies (NAMs). These span through different disciplines, and innovative approaches are proposed to evaluate different endpoints of toxicity. Yet, with their scientific novelty comes the need to regulate, evaluate, and standardize the existing solutions to reach a broad consensus among the different stakeholders (Marx-Stoelting et al., 2023; Piergiovanni et al., 2024; Schmeisser et al., 2023). Hence, method harmonization and validation are necessary to overcome the understandable uncertainties regarding the reliability of the results, especially when compared to decades of established animal-centric approach. The latter has been increasingly scrutinized due to its numerous shortcomings; mainly ethical concerns, cost, but also limited transferability of results to humans (Bailey et al., 2014).

NAMs prioritize and propose innovative approaches, mostly animal free, that can provide information on chemical hazards or risk assessment (Dent et al., 2018; van der Zalm et al., 2022). These are also necessary elements of the “Next Generation Risk Assessment (NGRA)” (Cote et al., 2016; Iacono et al., 2022; Krewski et al., 2014) as NAMs enable obtaining information on hazard characterization by applying *in silico*, *in chemico*, *ex vivo*, and *in vitro* techniques. Similarly, for exposure assessment, methodological advances continuously extend and refine the evaluations for single individuals up to entire populations. First leaps have been made in omics-type human biomonitoring studies (HBM) working towards elucidating the complete exposome. While multiple definitions of the exposome exist, most contain the same three components. Firstly, the external exposome, including for instance the contribution of environmental contamination, radiation, lifestyle, and diet. Secondly, the internal exposome, encompassing the inner processes of the human body, such as metabolism, inflammation, and oxidative stress among other factors. Finally, more general external factors are also included, such as social-capital, education, financial status, and the overall living situation (for example rural vs urban) (Bridges et al., 2013; Rietjens et al., 2022; Wild, 2005, 2012). All these elements can influence urine composition and exposure of the excretory organs to potentially noxious chemicals. In this view, urine is a valuable tool to assess single individuals up to population exposures. However, its chemical complexity also raises the question of a rapid evaluation of the toxic events potentially targeting the urinary tract, such as for instance the kidney or the bladder. In this context, HBM data can be used to identify most relevant exposure scenarios to harmful chemicals or mixtures, for which customized NGRA can be performed. NAMs promise to offer an advanced workflow to increase data quality and speed up risk assessment which is particularly pertinent for the evaluation of urinary occurring xenobiotics and pathophysiological sequelae related to exposure.

## 1.1. Aim of the Study

This review aims at identifying and summarizing the current knowledge and existing gaps for xenobiotics excreted through the urinary tract. Additionally, this work introduces currently available NAM-based solutions for hazard characterization and approaches for exposure assessment. Although the importance of NAMs is hardly questionable, research fields developed in different directions and speeds,

jeopardizing the available solutions. Until now, notably less attention has been given to the development of NAMs predicting toxicity in the urinary tract (https://www.oecd-ilibrary.org, https://tsar.jrc.ec.europa.eu/). To conclude, the critical point of implementation will be discussed highlighting the need of combining technological innovation with regulatory standards.

# 2. Materials and Methods

## 2.1. Literature Review Methodology

### 2.1.1. Inclusion Criteria for Compounds

To the best of our knowledge no comprehensive overview of urinary contaminants and toxin exposure, together with its resulting toxicological effects on the bladder and kidneys was published so far. To identify toxicants relevant for the urinary tract, a literature research was conducted and allowed the identification of four main classes (metals and metalloids, mycotoxins, phytotoxins, synthetic and process chemicals). These adhere to the definition of contaminants proposed by Rietjens and colleagues (Rietjens et al., 2025) based on i) unintentional presence, ii) harmfulness, iii) existence of regulatory limits and iv) stakeholder perception. Further, compound libraries and classification were compiled based on the Commission Regulation (EU) 2023/915 on contaminants to serve as reference for the existence of regulatory limits and harmfulness. Notifications in the Rapid Alert System for Food and Feed - RASFF lists were taken as reference to evaluate the “unintentional presence” (Fürst et al., 2019) (2020–2024; Figs. 1–4). Namely, as the RASFF identifies compounds occurring in food commodities above legal thresholds, this data guided the identification of potential exposure through the diet and consequently urinary occurrence. This was complemented by cases described in the context of occupational exposures (i.e. metals and metalloids) and of emerging relevance, as for instance described in recent literature for lithium (Bolan et al., 2021; Robinson et al., 2018; Vera et al., 2023).

### 2.1.2. Inclusion Criteria for Biomonitoring Studies

Studies on urinary concentrations were chosen from multiple sources. Firstly, major governmental initiatives (e.g. National Health and Nutrition Examination Survey, NHANES) were included. Secondly and complementary to these sources, a non-exhaustive PubMed search was performed to enable global representation of otherwise underrepresented regions (i.e. parts of Asia, parts of Sub-Saharan Africa etc.), outside of the USA and the European Union, in which food safety problems also present public health issues (Anukul et al., 2013; Chilaka et al., 2022; Kwoba et al., 2023). Additionally, this enables the inclusion of data on specific risk groups (pregnant women, workers, smokers etc.). Along this line, if the participants of a study do not represent the general population (e.g. dental workers), it is specified so in the table. Search terms were compiled as follows: "<compound name> urine" (e.g. lead urine). Only studies conducted after the year 2000 were included to give an overview representative of the current exposure scenarios. To ensure the robustness and reliability of the selected studies, only those including more than 50 participants were included to ensure representative results. The selection was further limited to studies using methods of high sensitivity and selectivity (i.e. MS, ELISA, FAAS). Publications that did not report method details or did not include blank and/or positive controls were also excluded.

Reading across the HBM studies, these are not without challenges themselves: one aspect complicating the comparisons, is the lack of a unified normalization strategy. In the scope of the manuscripts selected for this review, some laboratories reported absolute values in their publications, while others normalized against creatinine (Tables 1–4). Normalization is performed to account for the variation in urine volume and concentration (Cocker et al., 2011; Sands and Layton, 2009), under the assumption that urinary creatinine excretion is constant. Yet, creatinine excretion is dependent on age, sex, body mass index (BMI)
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Review of RASFF notifications regarding selected metals and metalloids, on the left the number of notifications per compound in the years 2020–2024. On the right is a summary of the categories mentioned in the notification.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Review of RASFF notifications regarding selected mycotoxins, on the left the number of notifications per compound in the years 2020–2024. On the right is a summary of the categories mentioned in the notifications.</figcaption></figure>

(Forni Ogna et al., 2015), and kidney function (i.e. glomerular filtration rate) (Waikar et al., 2010). Consequently, this might be relevant for the monitoring of toxins; some compounds modulate the filtration rate, through their nephrotoxic nature (Barbier et al., 2005; Hou et al., 2021; Liu et al., 2000; Weaver et al., 2014), potentially altering observed biomarker levels in urine following excretion (Weaver et al., 2016). Alternative methods might alleviate this issue, for example the use of specific gravity (Edmands et al., 2014) or osmolality (Yeh et al., 2015). Nevertheless, the lack of harmonization between the laboratories remains a dominant issue. Finding a unified solution would ease comparison and combination of numerous datasets in multinational projects.

### 2.1.3. Inclusion Criteria for Regulatory Limits

For simplification of the regulatory limits only regulations in the EU are listed, in cases where multiple levels exist (i.e. infant, adult etc.) only the lowest one was included to follow the most conservative approach.

### 2.1.4. Inclusion Criteria for Toxicological Studies

For collection of toxicological studies PubMed search terms were selected as follows `<compound name>` and `<target>` (bladder, kidney) and effect `<toxicity, cancer, nephrotoxicity>`. A selection based on

general relevance was performed where a multitude of studies were found, as the goal is not to exhaustively represent all studies presented in the urinary tract for well characterized toxins, but rather to demonstrate the major adverse outcomes in the urinary tract. Furthermore, identifying the lack of such data for other toxins and recognizing gaps in the current state of knowledge.

## 3. Results

### 3.1. Urinary Occurring Contaminants and Toxins: Overview and Relevance

#### 3.1.1. Metals and Metalloids

Historically, due to the tight link to occupational exposure, regulation on heavy metals was among the first to be implemented in the EU (lead, cadmium, and mercury (EC) No 466/2001). To this day metal and metalloid contamination is an ongoing issue, as indicated through numerous RASFF notifications in recent years related to the occurrence of the metals in the food/feed commodities, and with high likelihood occurring in the urine (Fig. 1). Nowadays as in the past, cadmium, mercury, and lead remain the most concerning compounds, while fish
<figure><img src="image_5.png" /><figcaption>Fig. 3. Review of RASFF notifications regarding selected phytotoxins, on the left the number of notifications per compound in the years 2020–2024. On the right is a summary of the categories mentioned in the notification.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Review of RASFF notifications regarding selected synthetic and process chemicals, on the left the number of notifications per compound in the years 2020–2024. On the right is a summary of the categories mentioned in the notification.</figcaption></figure>

and seafood are the commodities with the most frequent notifications. Additionally, occupational exposure scenarios (i.e. metal workers, dental workers, miners) can be associated to the highest levels of exposure (Table 1). Urinary concentrations of multiple µg/L were found, which might pose concern when compared to the limits set for drinking water (5–25 µg/L). To correlate hazards more closely with human exposure, urine has been extensively used for biomonitoring programs. In relation to metal and metalloids exposures, bladder cancer and general nephrotoxicity can be regarded as the most prevalent toxicological endpoints in the urinary tract (Table 1). In addition to the many historically known metal contaminants, lithium contamination is an emerging issue, a byproduct of its rapidly rising demand for use in batteries. While sparsely soluble when geogenic, lithium ions when released into soil during extraction are highly mobile and can leach into groundwater (Bolan et al., 2021; Robinson et al., 2018). Lithium nephrotoxicity is well known, as a side effect from its medical use (Glen et al., 2000), yet as a contaminant it is only just emerging. The unprecedented increase in lithium usage for batteries of electronic vehicles (Mohammadi and Saif, 2023) makes monitoring and regulation of this compound a priority for the upcoming future (Kowalczyk et al., 2022).

### 3.1.2. *Mycotoxins*

Among urinary contaminants potentially stemming from dietary exposures, mycotoxins (Table 2) encompass a large heterogeneous group of molecules. Yet only a small number of them were included in the scope of this review, since both exposure and toxicological data for the urinary tract are scarcely available, especially for compounds of emerging concern. In line, looking at the RASFF notifications (Fig. 2) a clear trend crystallizes as aflatoxins account for almost 2000 notifications, with nuts and seeds accounting for most of the contaminated commodities. Moving the focus towards other compounds, many so called “emerging mycotoxins” were only recently detected in food and feed, thanks to modern analytical techniques (Gruber-Dorninger et al., 2017). In line, regulation for this subclass is similarly still developing, despite their common occurrence in food (Mihalache et al., 2023). Another particularly important factor for this class is the consideration of the combined effects of several mycotoxins, as demonstrated by studies on other tissues (Alassane-Kpembi et al., 2013; Beisl et al., 2020; Crudo et al., 2019; Kolf-Clauw et al., 2013; Sobral et al., 2018), ranging from synergy to additivity or antagonism (Ji et al., 2018; Willoquet et al., 2024). This aspect is all the more important given the complex
<table><caption>**Table 1**
Overview of heavy metals and metalloids known to elicit toxicity in the urinary tract.</caption><thead><tr><th>METALS</th><th>Urine concentration</th><th>Regulatory limits (EU)</th><th>Toxicological studies (only considering endpoints in the urinary tract)</th></tr></thead><tbody><tr><td>Arsenic</td><td>67 (12–399) µg/L (De Loma et al., 2020);<br>16.8 (7.7–32.3) µg arsenite /L (Hata et al., 2012);<br>101.8 ± 141.6 µg/L (Choi et al., 2022);<br>9.90 (9.06–10.8) µg/g creatinine (NHANES, 2009–2010)<br>(Centers for Disease Control and Prevention, 2022)</td><td>10 µg/L drinking water (EU) 2020/2184<br>0.02 mg/kg for infant formula (EU) 2023/915</td><td>Bladder cancer (Chen et al., 2010; Cohen et al., 2013; EFSA, 2009; Schrenk et al., 2024; Steinmaus et al., 2000, 2013);<br>Nephrotoxicity (mice) (Li et al., 2010; Liu et al., 2000);<br>Kidney disease (Schrenk et al., 2024; Zheng et al., 2014)</td></tr><tr><td>Cadmium</td><td>0.18 (0.08, 0.36) µg/L (female), 0.20 (0.10, 0.33) µg/L (male) (Sears et al., 2021);<br>HBM4U, lowest (Portugal) 0.109 (0.098–0.120) µg/L, highest (Poland) 0.408 (0.369–0.450) µg/L (Snoj Tratnik et al., 2022);<br>0.072 µg/L (3–17 year old) (Vogel et al., 2021);<br>0.145 (0.135–0.155) µg/g creatinine (NHANES, 2017–18)<br>(Centers for Disease Control and Prevention, 2022)</td><td>5 µg/L drinking water (EU) 2020/2184<br>0.005 mg/kg for infant formula (EU) 2023/915</td><td>Bladder cancer (Awadalla et al., 2020; Kellen et al., 2007; Siemiatycki et al., 1994);<br>Membrane permeability increased in T24 cells (Li et al., 2014);<br>Nephrotoxic (Goyer, 1989; Wang et al., 2016)</td></tr><tr><td>Chromium</td><td>0.393 µg/L (3–17 year old) (Vogel et al., 2021);<br>0.5 µg/L (Rooney et al., 2022);<br>6.86 ± 1.93 µg/g creatinine (printing workers) (Decharat, 2015);<br>1.13 (0.71–1.28) µg/g creatinine (Pokhrel et al., 2020);<br>0.5 ± 0.5 µg/L (Vimercati et al., 2017);<br>1.00 (0.98, 1.02) µg/L (pregnant women) (Peng et al., 2018);<br>0.07–0.76 µg/g creatinine (6–10 years old) (Alimonti et al., 2000);<br>0.95 ± 1.89 µg/g creatinine (workers) (Gil et al., 2011);<br>0.145 (0.135–0.155) µg/g creatinine (NHANES, 2017–18)<br>(Centers for Disease Control and Prevention, 2022)</td><td>25 µg/L drinking water (EU) 2020/2184<br>HBM-GV<sub>GenPop</sub> of 1 µg/g creatinine (HBM4EU) (Lamkarkach et al., 2021)</td><td>Oxidative stress (Mazdak et al., 2009);<br>Chromosome instability (Wise et al., 2016);<br>Promotes EMT (Sheng et al., 2021);<br>Higher Cr levels in bladder tumors (Feki-Tounsi et al., 2014; Golabek et al., 2017);<br>Potential role in chronic kidney disease (Tsai et al., 2017; Wedeen and Qian, 1991)</td></tr><tr><td>Lead</td><td>1.50 µg/g creatinine (Lin et al., 2020);<br>3.5 ± 0.4 µg/L (children) (Kim et al., 2020b);<br>29.4 ± 1.1 µg/dL (pregnant women) (Lin et al., 2020)</td><td>5 µg/L drinking water (EU) 2020/2184<br>0.01 mg/kg for infant formula (EU) 2023/915</td><td>Pb blood content correlated to bladder cancer (Golabek et al., 2009; Li et al., 2018);<br>Lead-related nephrotoxicity</td></tr></tbody></table>

<table><caption>**Table 1 (continued)**</caption><thead><tr><th>METALS</th><th>Urine concentration</th><th>Regulatory limits (EU)</th><th>Toxicological studies (only considering endpoints in the urinary tract)</th></tr></thead><tbody><tr><td></td><td>Adekunle et al., 2010;<br>1.06 µg/L (Fukui et al., 2004);<br>0.319 (0.294–0.346) µg/g creatinine (NHANES, 2015–16)<br>(Centers for Disease Control and Prevention, 2022)</td><td></td><td>/chronic kidney disease (Ekong et al., 2006; Goyer, 1989; Song et al., 2024)</td></tr><tr><td>Lithium</td><td>26.77 µg/g creatinine (Gonzalez-Martín et al., 2023);<br>9.64 ppb (Karakis et al., 2021)</td><td></td><td>Nephrotoxicity (Glen et al., 2000; Grünfeld and Rossier, 2009);<br>Risk factor for chronic kidney disease during Lithium therapy (Golic et al., 2023; Parsaik et al., 2024)</td></tr><tr><td>Manganese</td><td>&lt; 0.120 µg/L (mining/industry) (Molina-Villalba et al., 2015);<br>0.93 ± 4.00 µg/g creatinine (exposed workers) (Gil et al., 2011);<br>0.140 (0.126–0.155) µg/g (NHANES, 2011–12)<br>(Centers for Disease Control and Prevention, 2022)</td><td></td><td>Bladder stones, nephrotoxicity (rats) (Ponnapakkam et al., 2003)</td></tr><tr><td>Mercury</td><td>Total mercury: 1.72 (1.61–1.85) µg/g creatinine (Seo et al., 2020);<br>0.64 ± 1.14–69.40 ± 288 µg/L (miners) (Bose-O'Reilly et al., 2017);<br>19.41 ± 5.18 µg/g creatinine (dental workers),<br>2.15 ± 0.07 µg/g creatinine (unexposed control) (Shirkhanloo et al., 2017);<br>2.75 µg/L dental workers<br>2.66 µg/L healthcare workers (Sahani et al., 2016);<br>0.067 µg/L (3–17 year old) (Vogel et al., 2021);<br>0.283 (0.260–0.309) µg/g creatinine (NHANES, 2013–14)<br>(Centers for Disease Control and Prevention, 2022)</td><td>0.1 mg/kg (Salt) 1 µL/L drinking water (EU) 2020/2184</td><td>Nephrotoxic (Murphy et al., 1979; Samuels et al., 1982; Yasutake et al., 1989)</td></tr><tr><td>Nickel</td><td>5.05 µg/g creatinine (pregnant women) (Chen et al., 2018);<br>4.852 ± 5.365 µg/g creatinine (women)<br>3.435 ± 2.384 µg/g creatinine (men) (Mercan et al., 2022);<br>2.19 µg/L (Wei et al., 2018);<br>0.20–1.23 Range (6–10 years old) µg/g creatinine (Alimonti et al., 2000)</td><td>20 µg/L drinking water (EU) 2020/2184</td><td>Nephrotoxicity (rats) (Horak and Sunderman, 1980; Vyskocil et al., 1994)</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>METALS</th><th>Urine concentration</th><th>Regulatory limits (EU)</th><th>Toxicological studies (only considering endpoints in the urinary tract)</th></tr></thead><tbody><tr><td></td><td>1.26 (&lt;0.5–15) µg/L (Children (GerES IV)) (Wilhelm et al., 2013); 1.22 (1.15–1.30) µg/g creatinine (NHANES, 2017–18) (Centers for Disease Control and Prevention, 2022)</td><td></td><td></td></tr></tbody></table>

chemical mixture represented by urine, a fluid concentrating numerous xenobiotics at the same time. Hence overlooking additive effects may lead to an underestimation of the overall hazard, yet risk assessors face potentially endless combinations, further complicated by insufficient human exposure data and unknown ratios between the substances in vivo (Bloch et al., 2023). Therefore, a clear framework for prioritization and grouping of substances, which are of the most immediate concern, is necessary and the topic of ongoing discussion in agencies such as the EFSA (Dorne et al., 2022). For the mycotoxins selected in the scope of this work (Table 2), urinary concentrations were mostly in the ng/mL range. From the regulatory perspective, the EU limits for food commodities ((EU) 2023/915), mostly refer to baby food where the most stringent measures were employed to protect this vulnerable population yet exist at higher levels for multiple other food commodities (Table 2). Biomonitoring of mycotoxins is additionally complicated by the biotransformation of the compounds. Exemplary for this, although Aflatoxin B1 is rarely found in urine, AFM1 the major metabolite produced by CYP1A2, can be detected in exposed humans and used as a urinary biomarker of exposure (Cullen and Hsieh, 1987; Rushing and Selim, 2019). For other mycotoxins, such as ochratoxin A, the parent compound can be found in urine and DNA adducts in the targeted tissue can act as additional biomarkers of effect (Duarte et al., 2011). Another aspect is the masking of mycotoxins through plant metabolism and detoxification, the products of which are rarely screened for and toxicological effects in humans are often unknown (Berthiller et al., 2013). Furthermore, in contrast to metals (Table 1) for which toxicity was described in both the kidney and bladder, studies on mycotoxins were mostly found for renal toxicity (Table 2), if this is indicative of an increased sensitivity of the kidney or a simple lack of studies performed on the bladder remains to be elucidated in future studies.

### 3.1.3. Phytotoxins

For phytotoxins, only a relatively small number of RASFF notifications (Fig. 3), most of which regard herbs and spices (59 %), could be identified. Pyrrolizidine alkaloids (PAs) were the compounds more frequently detected. For this class (Table 3), multiple studies reported nephrotoxicity, yet there are almost no reports on urinary concentrations for the individual toxicants complicating exposure assessment. This gap might be explained by the fact that adverse effects for PAs are mostly acute and chronic exposure scenarios are less of a concern, or the toxins are masked in urine as metabolites. Regulations of these compound groups exist on the EU level, yet heterogeneity complicates risk assessment. For instance, 28 PAs are considered by the EFSA in a scientific opinion, of which they highlighted 17 for further monitoring (Knutsen et al., 2017), still observing such a wide range of substances is time and cost intensive. Consequently, toxicological data is only available for a limited number of substances (i.e. lasiocarpine) (EFSA, 2011). Likewise, varieties exist for other phytotoxin classes. Hence, hazard and exposure assessment for these substances presents a difficult and ongoing challenge.

Another important aspect concerning risk assessment of phytotoxins

<table><caption>**Table 2**

Overview of mycotoxins known to elicit toxicity in the urinary tract.</caption><thead><tr><th>Mycotoxins</th><th>Urine concentration</th><th>Regulatory limits (EU)</th><th>Toxicological studies (only considering endpoints in the urinary tract)</th></tr></thead><tbody><tr><td>Aflatoxin B1</td><td>AFM1: 0.66 (± 0.35) ng/mg creatinine (Foerster et al., 2022); 8.8 ± 12.9 pg/mL (Children) (Ali et al., 2020); 0.023 ± 0.048 ng/mL (Xia et al., 2020)</td><td>0.10 µg/kg in baby food and processed cereal-based food for infants and young children (EU) 2023/915</td><td>Potential renal carcinogen (Epstein et al., 1969); Nephrotoxic (Rodents) (Hussain et al., 2009; Jha et al., 2014)</td></tr><tr><td>Citrinin</td><td>2.16 ng/mL (children) and 0.70 ng/mL (infants) (Ali and Degen, 2020); 0.3 ng/mL (0.04–3.5 ng/mL) (Degen et al., 2022); 0.45 ± 0.24 ng/mL (Ouhibi et al., 2020); 0.06–0.97 ng/mL (DH-CIT) (Schmidt et al., 2021); 0.29 ng/mg creatinine (Narváez et al., 2021b); 0.03 ng/mL (20.2 ng/g creatinine) (Ali et al., 2015)</td><td>100 µg/kg In food supplements based on rice fermented with Monascus purpureus (EU) 2023/915</td><td>Nephrotoxic: Dog (Krejci et al., 1996); Mouse (Jordan et al., 1977; Wang et al., 2024); Rat (Phillips et al., 1980)</td></tr><tr><td>Deoxynivalenol</td><td>7.93 ng/mL (De Santis et al., 2019); 37.95 ± 37.21 nmol (27.69 ± 26.64 nmol) (Vidal et al., 2020); 34.8 ng/mg (Chen et al., 2022); 16.0 ± 21.7 ng/mL (Warensjö Lemming et al., 2020); 15 ng/mL (2.01–166) (children) (Ayeni et al., 2023)</td><td>150 µg/kg in baby food (EU) 2023/915</td><td>Cytotoxicity in bovine kidney cells (Bailey et al., 2014); Modulates proliferation, ER adaptation and alters biomechanical compliance of T24 cells (DON-3-Sulf) (Del Favero, 2021b; Warth, 2016)</td></tr><tr><td>Fumonisins</td><td>FB1: 50.1 ± 51.5 pg/mg creatinine (Elmore et al., 2021); FB1: 0.24 µg/g creatinine (Martins et al., 2019); FB1: 4.6 ± 2.8 µg/L FB2: 1.0 µg/L (Ezekiel et al., 2014); FB1: 0.33 (&lt;LOQ–9.54) µg/g creatinine (Abia et al., 2013); FB1: 1.12 ± 0.40 ng/mL (Huang et al., 2021); FB1: 0.004 ± 0.022 ng/mL FB2: 0.01 ± 0.06 ng/mL (Wallin et al., 2015)</td><td>200 µg/kg in baby food (EU) 2023/915</td><td>Nephrotoxic: HK–2 cells (Hou et al., 2021); Renal tumors (Rats) (Müller et al., 2012)</td></tr><tr><td>Ochratoxin A</td><td>0.020 ng/mL (29.41 ng/g of creatinine) (Silva et al., 2020);</td><td>0.5 µg/kg in baby food (EU) 2023/915</td><td>Renal carcinogen (rodents) (Mally, 2012); DNA damage (Lebrun et al., 2006); (continued on next page)</td></tr></tbody></table>
<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Mycotoxins</th><th>Urine concentration</th><th>Regulatory limits (EU)</th><th>Toxicological studies (only considering endpoints in the urinary tract)</th></tr></thead><tbody><tr><td rowspan="2">Patulin</td><td>0.13 ng/mL (children) and 0.08 ng/mL (infants) (Ali and Degen, 2020); 0.018 ± 0.014 ng/mL (Duarte et al., 2010); 0.013 (0.006–0.065) ng/mL (Fazekas et al., 2005); 0.21 ± 0.31 ng/mL (Ali et al., 2017); 0.32 (0.06–2.93) ng/mL (Kyei et al., 2022); 0.03 (&lt;LOQ–0.04) ng/mL (children) (Ayeni et al., 2023)</td><td rowspan="2">10 µg/kg in baby food (EU) 2023/915</td><td>Induces unscheduled DNA synthesis (Dörrenhaus et al., 2000); Cytotoxic, genotoxic, and induces apoptosis in Vero kidney cells (Costa et al., 2016); Nephrotoxicity (Khoi et al., 2021)</td></tr><tr><td>Not detected (Ouhibi et al., 2020)</td><td>Nephrotoxic (mice) (Lu et al., 2017; Speijers et al., 1988); Acute kidney injury through ferroptosis pathway (mice and human renal tubular epithelial cell) (Hou et al., 2022)</td></tr><tr><td>T2 Toxin</td><td>1.34 ng/mg creatine (range: 0.22–6.54 ng/mg creatinine) (Narváez et al., 2021a); 39 ± 81 µg/L (Duringer et al., 2021)</td><td>-</td><td>Nephrotoxin (Oxidative stress and apoptosis in human renal tubular epithelial cells (Lei et al., 2017) and mice (Zhang et al., 2021)</td></tr><tr><td>Zearalenone (ZEN)</td><td>6.70 ± 7 (&lt;LOQ–29.01) ng/mL (Carballo et al., 2021); 0.022 ± 0.041 ng/mL (Zhang et al., 2020); 0.10 ± 0.05 ng/mL (Ali and Degen, 2018); 0.11 ng/mL (children) (Ayeni et al., 2023)</td><td>20 µg/kg in baby food (EU) 2023/915</td><td>Renal toxicity, DNA damage, possible oxidative immunomodulatory pathway (Ben Salah-Abbès et al., 2020)</td></tr></tbody></table>

is the field of botanicals, commonly used as herbal medicines or supplements (Xu et al., 2020a). Here instead of the single purified compounds the mixture is often investigated. One main advantage of this approach is simplification, since at least in theory only one mixture must be tested, instead of all potentially active substances. Yet, finding a single representative mixture may be difficult, due to differing compositions (Patel et al., 2023) resulting from varying growth conditions (Melito et al., 2016) or extraction techniques (Huie, 2002). Despite the evident validity and importance of this approach, for the present study botanicals were not included, mainly since an excellent summary on the topic of their renal toxicity, which additionally exhaustively describes the potential use of NAMs in their toxicity screening, was already published (Pearson et al., 2022).

### 3.1.4. Synthetic and Process Chemicals

The urinary tract is not only exposed to naturally occurring compounds, additionally human-made chemicals can be found in the

<table><caption>**Table 3**

Overview of phytotoxins known to elicit toxicity in the urinary tract.</caption><thead><tr><th>Phytotoxins</th><th>Urine concentration</th><th>Regulatory limits (EU)</th><th>Toxicological studies (only considering endpoints in the urinary tract)</th></tr></thead><tbody><tr><td>Aristolochic acids</td><td>-</td><td>-</td><td>Nephrotoxic (Aristolochic acid-associated nephropathy (AAN)) (Hong et al., 2006; Jadot et al., 2017; Yang et al., 2007)</td></tr><tr><td>Erucic acid</td><td>-</td><td>50 g/kg fat/oil (EU) 2023/915</td><td>Detrimental effects on rat kidneys by high erucic acid rapeseed (HEAR) oil (Knutsen et al., 2016)</td></tr><tr><td>Hydrocyanic acid, including hydrocyanic acid bound in cyanogenic glycosides</td><td>-</td><td>Linseed (150 mg/kg); almonds (35 mg/kg); apricot kernels (20 mg/kg), cassava flour and tapioca flour (10 mg/kg) (EU) 2023/915</td><td>Nephrotoxicity via casava consumption (dogs) (Kamalu, 1993)</td></tr><tr><td>Isothiocyanates and glucosinolates</td><td>-</td><td>Some plant products controlled in feed (i.e. Camellia sativa, Brassica juncea, B. nigra etc) (EU) 1275/2013</td><td>Inhibits bladder cancer cell growth (Abbaoui et al., 2018); Reduces kidney cell motility (Erucin) (Guerreiro et al., 2023)</td></tr><tr><td>Oxalate</td><td>Normal urine levels are considered &lt; 0.5 mmol/1.73 m² body surface area per day (Hoppe, 2012)</td><td>-</td><td>Kidney stones (Mitchell et al., 2019)</td></tr><tr><td>Pyrrolizidine alkaloids (PA)</td><td>-</td><td>Combined content of 21 PAs specified in, lowest level is 1 µg/kg in tea (Camellia sinensis) and herbal infusions intended for infants (EU) 2023/915</td><td>Nephrotoxic (riddelliine, nonneoplastic lesions kidney, rodents) (NTP, 2003)</td></tr><tr><td>Tropane alkaloids</td><td>Mostly acute cases known. i.e. Datura in Slovenia 2003 (Perharić et al., 2013)</td><td>Ongoing evaluation (Arcella et al., 2018; EFSA, 2013)</td><td>-</td></tr></tbody></table>

environment and process contaminants can derive from food preparation. For this review, the selection of substances was based on Commission Regulation (EU) 2023/915. Starting from this initial list, the relevance for the urinary tract was verified (i.e. known toxicity), together with the number of RASFF notifications. For synthetic chemicals, their production is intended and subject to permission and regulation under REACH in the EU Regulation (EC) No 1907/2006. Compared to the other substance classes discussed, such as natural
<table><caption>**Table 4**
Overview of synthetic and process chemicals known to elicit toxicity in the urinary tract.</caption><thead><tr><th>SYNTHETIC AND PROCESS CHEIMCALS</th><th>Urine concentration</th><th>Regulatory limits (EU)</th><th>Toxicological studies (only considering endpoints in the urinary tract)</th></tr></thead><tbody><tr><td>Dioxins (PCBs)</td><td>Largely excreted through feces, only a fraction is excreted through urine. Mostly metabolized, depending on the compound (Gasiewicz et al., 1983; Keke Hu, 1999)</td><td>Sum of Dioxins: 0.1 pg/g wet weight food infant/young children (EU) 2023/915</td><td>Agent orange (Dioxin) potential link to bladder cancer (Mossanen et al., 2017; Williams et al., 2023) Blood dioxin level possibly related to CKD (Huang et al., 2016)</td></tr><tr><td>Perfluoroalkyl substances (PFOS, PFOA, PFNA and PFHxS)</td><td>PFOS: below LOD<br>PFOA: below LOD<br>PFNA: below LOD<br>PFHxS: below LOD (NHANES, 2013–14) (Centers for Disease Control and Prevention, 2022)</td><td>Sum of PFOS, PFOA, PFNA and PFHxS: 1.3 µg/kg Meat of bovine animals, pig and poultry (EU) 2023/915</td><td>PFAS inversely associated with kidney function (Conway et al., 2018; Kataria et al., 2015; Niu et al., 2024); Associated with kidney cancer occurrence (Bartell and Vieira, 2021); Correlation between PFOS and bladder cancer (workers) (Alexander et al., 2003)</td></tr><tr><td>Polycyclic aromatic hydrocarbons (PAH)</td><td>Metabolites/Parent:<br>9-Hydroxyfluorene:<br>245 (226–265) ng/L<br>1-Hydroxypyrene:<br>111 (101–123) ng/L<br>4-Hydroxyphenanthrene: 126 (117–135)<br>1-Hydroxynaphthalene: 1.50 (1.31–1.71) µg/L (NHANES, 2011–12/2015–16) (Centers for Disease Control and Prevention, 2022); Benzo[a]pyrene: < 0.015 (<0.015–0.073) µg/L (Coke oven workers) (Rossella et al., 2009)</td><td>Benzo(a)pyrene: 1.0 µg/kg infant formulae<br>Sum of PAHs: benzo(a)pyrene, benz (a)anthracene, benzo(b) fluoranthene and chrysene: 1.0 µg/ kg (EU) 2023/915</td><td>Correlation between bladder cancer and PAH exposure (Bonassi et al., 1989; Clavel et al., 1994); DNA damage (Benzo[a]pyrene; bladder, porcine) (Wolf et al., 2005)</td></tr><tr><td>3-MCPD</td><td>-</td><td>12 µg/kg in infant formula (EU) 2023/915</td><td>Nephrotoxicity (Barocelli et al., 2011; Liu et al., 2021); Altered DNA methylation (rat kidney cells) (Senyildiz et al., 2017); Genotoxicity (Ozcagli et al., 2016)</td></tr><tr><td>Glycidyl fatty acid esters (glycidol)</td><td>-</td><td>6 µg/kg liquid infant formula (EU) 2023/915</td><td>Altered DNA methylation (rat kidney cells) (Senyildiz et al., 2017); Nephrotoxicity (3-MCPD co-exposure) (Liu et al., 2021); Carcinomas of the urinary bladder (rat) (NTP, 1990)</td></tr><tr><td>Nitrates</td><td>Baseline value: 47.6 ppm of NO<sub>3</sub><sup>-</sup> (SD = 17.3) standard diet (Radomski et al., 1978); 43.7 (42.1–45.3) mg/g creatinine (NHANES, 2009–2010) (Centers for Disease Control and Prevention, 2022)</td><td>200 mg NO<sub>3</sub><sup>-</sup>/kg food baby/infant/ young children (EU) 2023/915</td><td>Suspected link to end-stage renal disease (Chen et al., 2024) and bladder cancer (Jones et al., 2016)</td></tr><tr><td>Melamine</td><td>2.97 µg/g creatinine (Shi et al., 2020); 27.8 ± 143.2 ng/mL (Children) (Sathyanarayana et al., 2019)</td><td>0.15 mg/kg liquid infant formula (EU) 2023/915</td><td>Nephrotoxicity (Kidney stones, reduced renal vascular function) (Gossner et al., 2009; Hau et al., 2009)</td></tr></tbody></table>

contaminants, risk assessment is already a prerequisite to their use and commercialization.

For process chemicals, there is a rather fragmentary understanding of toxicity related to the urinary tract, with mostly unknown urinary-relevant concentrations and wide chemical variety of compounds. In the Regulation (EU) 2023/915, these compounds have been categorized into multiple subgroups (i.e. dioxins, perfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), etc.), or identified as single substances of concern (e.g. 3-MCPD, melamine). Their importance is also reflected in the respective RASFF notifications (Fig. 4): dioxins, PAHs, 3-MCPD and melamine account for most of the mentions and could be found in numerous food commodities. For PAHs, 3-MCPD and melamine around half of the notifications were declared as “serious”, highlighting a potential risk for the public. The toxicological sequelae of the compounds vary, especially regarding the target organs. For dioxins, PAHs, glycidol and nitrate a potential link between exposure and bladder cancer is discussed (Table 4). PFAS, 3-MCPD and melamine toxicity was primarily reported for the kidneys. In the scope of this review, only limited HBM data on urinary concentrations can be found (Table 4). Along this line, 3-MCPD and glycidol highlighted a typical challenge of urinary biomonitoring, since studies in rats revealed that only a small portion of the parent compound is excreted, but rather the mercapturate 2,3-dihydroxypropylmercapturic acid (DHPMA) (Barocelli et al., 2011). Yet, the use of DHPMA as a biomarker is also under debate, since DHPMA seems to be excreted at relatively high constant rate (Andreoli et al., 2015). Additional exposure to 3-MCPD or glycidol seems to only minutely increase this parameter (Abraham et al., 2021). Also, for nitrate a baseline level exists, varying with individual

diet (Radomski et al., 1978), making the determination of harmful nitr ate exposure and the potential threshold of toxicity difficult. The situation is similar for dioxins, where HBM is challenged by an already large number of dioxin compounds. The complexity of this group is further increased by numerous biotransformation pathways (Gasiewicz et al., 1983; Keke Hu, 1999). Similar complexities, due to the large number of individual compounds, are faced by researchers studying the currently intensely investigated per- and polyfluoroalkyl substances (Fenton et al., 2021). Selection of the most relevant molecules will be difficult and in addition some of the individual metabolites might occur at concentrations not detectable with the current analytical techniques. Taken together, estimation of exposure through urine, therefore needs a clear understanding of the target substance and its fate in the body, in order to determine exposure directly or to select suitable biomarkers (Dor et al., 1999).

### 3.1.5. Urinary Contaminants and Toxins: Summary and Problem Statement

Overall, a noteworthy gap can be identified in our understanding of the underlying pathways of bladder and kidney toxicity. Most available literature focuses on the correlation between exposure and disease, lacking on more mechanistic considerations. For example, for heavy metals a strong connection with bladder cancer was apparent through occupational exposure, particularly in men (Siegel et al., 2022). Still, even for bladder cancer there is a lack of knowledge on molecular mechanisms, which would be prerequisites for the formulation of adverse outcome pathways (AOPs, Chapter 3.1). This gap could be filled by future studies based on NAMs, allowing authorities to make more
informed and confident decisions (Burley et al., 2022; Hernandez-Jerez et al., 2023; OECD, 2017a).

Furthermore, AOPs promise to assist the prioritization of substances for combinatorial toxicological studies. Potentially synergistic or antagonistic effects could be identified in this way, for compounds acting directly on the same molecular initiating event (MIE), but also when triggering different pathways (MIEs and KEs) converging in a single adverse outcome (AO). On these molecular premises, compounds acting on the same AOPs could be identified and subsequently prioritized for NAM-based testing (Fig. 5).

A further critical aspect is related to the fact that our understanding of urinary exposure is also incomplete. The urinary tract is exposed to a complex mixture which can vary widely between individuals, as evident by the numerous toxins found present in HBM studies and the corresponding RASFF notifications. Suggesting concerning levels of exposure of sensitive urinary tract organs including the bladder and kidneys (Figs. 1-4 and Tables 1-4). Furthermore, the toxins listed (Tables 1-4) only represent a fraction of our actual exposome and even those are not fully understood yet and subject of interindividual heterogeneities. Filling the gaps in exposure assessment presents a pressing and fascinating challenge for which a new generation of risk assessment (NGRA) will be needed.

The current developments and challenges of the emerging new approach methodologies (NAMs) on hazard identification (3.2) and methods for exposure assessment (3.3) tackling these open questions will be discussed in the following sections.

## 3.2. New Approach Methodologies for Hazard Identification

According to the previous sections, toxicological studies to elucidate potential harmful effects related to the exposure to urinary occurring contaminants can be performed with the help of *in vivo* and *in vitro* approaches. Exemplarily, a correlation between urine or blood content of a particular contaminant (i.e. cadmium) and the occurrence rate of a pathological endpoint (i.e. bladder cancer) is identified (Awadalla et al., 2020; Kellen et al., 2007). Building on this evidence, (direct) correlation between the two events could be established in controlled experimental settings, as for instance by *in vivo* study in rodents (e.g. OECD TG451 (OECD, 2018b)). While this animal-based approach has worked successfully for decades there are several drawbacks of animal studies, besides the obvious ones such as cost and ethical concerns. Firstly, tested doses are often multiple factors higher than doses experienced by humans in real life exposure scenarios. Secondly, there is the scarce reproducibility of some animal testing strategies (Meigs et al., 2018; Van

Norman, 2019) and problematic translatability of the results to humans (Bailey et al., 2014; Paglialunga et al., 2019). The sum of these issues raised concerns of researchers as early as 1962 when Litchfield and Part critically thematized these issues for one of the first times (Litchfield Jr, 1962). These concepts evolved over the years and ultimately resulted in the current combined scientific, public, and regulatory push towards novel animal free methods (EFSA (Miccoli et al., 2022), FDA (Adashi et al., 2023; Avila et al., 2020), ECHA (ECHA, 2016)), with the FDA and the NIH recently announcing their intent to increasingly phase out animal methods in preclinical studies (FDA, 2025; NIH, 2025). The promise of these new approaches can already be seen in the innovative *in vitro* studies performed in the investigation of urinary tract infections using bladder-on-chip (Sharma et al., 2021a) or organoid models (Sharma et al., 2021b), yet broad application and validation in toxicological context remain a pressing issue (Schmeisser et al., 2023). Indeed, the individual NAMs will only represent parts of the overall mechanistic pathways. Therefore, to combine these into a unified model for the mechanistic explanation of toxicity processes, adverse outcome pathways (AOP) are being established (Mally and Jarzina, 2022). Exemplary for this approach, Cho and colleagues (AOP 296, OECD ID 1.76 (Cho et al., 2023)) have recently developed an AOP for oxidative DNA damage leading to chromosomal aberrations and mutations. In its basic form an AOP contains a molecular initiating event (MIE, i.e. increase, oxidative DNA damage) consequent to exposure. The MIE is followed by one or several key events (KE1: Increase, DNA strand breaks; KE2: Inadequate repair) which finally manifest in the adverse outcomes (AO1, Increase, Mutations; AO2: Increase chromosomal aberrations). Importantly, the AOP is always independent of the compound, therefore if mechanistic data on a specific MIE or KE is known, the toxicological relevance of the compound can, in theory, be predicted. AOPs are collected in the AOP-DB (https://aopwiki.org/) hosted by the Society for the Advancement of Adverse Outcome Pathways (SAAOP) under the guidance of the OECD Expert Advisory Group on Molecular Screening and Toxicogenomics (EAGMST), or can be found in the AOP knowledge base (https://aopkb.oecd.org/index.html). While the creation of new AOPs that are submitted to the AOP-DB has been relatively constant over the years (Fig. 6), following an initial push in 2016 (AOP-wiki release). Yet, as for data available during the preparation of this manuscript only five kidney toxicity specific AOPs are listed in the AOP-wiki database, as under development with OECD status (AOP IDs: 128, 256, 257, 258, 437; OECD Project IDs: 1.40, 1.43, and 1.99). Furthermore, multiple of these AOPs were combined in a unified network identifying critical common KEs (mitochondrial dysfunction, oxidative stress, and tubular necrosis), which could provide focus points for future NAM development

<figure><img src="image_7.png" /><figcaption>Fig. 5. Flowchart representing a potential prioritization strategy for mixture toxicology based on AOPs.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** AOP knowledge base entries per year, 2015 (Date of creation) –2023 (https://aopkb.oecd.org/index.html).</figcaption></figure>

(Barnes et al., 2024). For the urinary bladder no AOP with OECD status is listed, yet two are under development; AOP 335, on urothelial carcinogenesis due to chemical cytotoxicity by mitochondrial impairment and a second one AOP 121 on bladder calculi leading to urothelial papilloma and carcinoma. While this is a starting point, as part of NGRA, a paradigm shift in toxicity testing towards a more mechanistic, hypothesis-driven, evaluation will be needed in the regulatory application of NAMs (Hernandez-Jerez et al., 2023) and consequently the development of robust OECD-approved bladder AOPs can be seen as a priority. In other fields, AOP-like frameworks are already in use for hazard prioritization (i.e. Endocrine disruptors, novel flame retardants) (Bajard et al., 2023, 2019; Browne et al., 2017).

Deepening on specific *in vitro* assays for risk assessment, a few success stories already exist. For instance, both the comet and micronucleus cytome assay have been widely used to determine genotoxicity, without the use of animal models (Bonassi et al., 2011; Collins et al., 2023; Collins, 2004; Honza et al., 2024; Thomas et al., 2009). Furthermore, bridging the gap between exposure and effect, these NAMs are also being applied in biomonitoring studies, since buccal, nasal or blood cell sampling act as minimal invasive methods to assess aggregated exposure to genotoxic agents in humans (Collins et al., 2023; Esteves et al., 2025; Fenech et al., 2024). Leading experts in the development and application of these methods formulated reporting and scoring guidelines on the use of these assays improving reproducibility across laboratories that can be used as blueprints for other emerging NAMs (Fenech et al., 2003; Møller et al., 2020). Despite their use for hazard characterization, these NAMs still face some challenges: for example, the common use of cells in monolayer or single cells does not fully represent the *in vivo* environment of the tissues (Honza et al., 2024). Development of more sophisticated 3D cell models, together with the addition of physical factors such as physiological fluid shear stress and substrate deformation and physiological stiffness, may bring us even closer to the tissue architecture (Bergen et al., 2025; Jobst et al., 2024; Karasová et al., 2025; Rebhahn et al., 2022). Omics-type studies are often used in experimental toxicology but their use in regulatory toxicology is still developing (Marx-Stoelting et al., 2015; Tralau et al., 2015). Recently the EFSA (Bronzwaer et al., 2025) published an editorial highlighting the opportunities stemming from the rapid technological advances in this scientific sector, allowing for in-depth profiling of proteomics,

transcriptomics, metabolomics, metagenomics and even microbiomics data. Yet, they also highlight current challenges, like the need for the development of robust methods, including *in vitro* to *in vivo* extrapolation, also identified by other authors (Jochum et al., 2024), fostering mechanistic insights needed for the incorporation into AOPs.

Together, these methods show the significant impact of NAMs on regulatory toxicology and risk assessment, further stressing the need for the development of methods specific to the urinary tract to keep up with the ongoing evolution of toxicology. The following chapters will discuss the current state of research, summarizing existing models supporting hazard characterization in target organs of the urinary tract, focusing on the bladder and kidney highlighting advantages and limitations of the different strategies. The overall aim being to give a current overview of the technical state of the field (Fig. 7).

### 3.2.1. *New Approach Methodologies for Organ Toxicity: Bladder*

*In silico* methods are highly efficient in terms of cost and speed, yet a possible drawback of these methods is their dependency on the already available data, yet with the growing trend towards “big data” and artificial intelligence, the importance of this approach is growing (Mellor et al., 2019; Pawar et al., 2019). With ever increasing computational power and availability of protein structures (Berman et al., 2000), it has become possible to model protein-toxin interactions *in silico*. For instance, as already used in bladder cancer research, molecular docking studies are performed to screen potential candidate molecules before clinical application (Ganji et al., 2023). A translation for use against targets of toxicological concern should be feasible, as these methods are used for activity profiling of natural derived compounds, such as ursolic acid derivatives, against molecular targets of the NF-κB pathway (Yadav et al., 2019). They could be used to screen the ever-growing amount of emerging toxins, identified by more and more sensitive analytical techniques (Chapter 3.3), and help prioritize the selection of compounds for more efficient hazard characterization.

In addition, efforts are being made to improve the overall quality and specifically the inter-laboratory reproducibility of *in vitro* test systems (OECD, 2018a; Zucco et al., 2004), since validation of the methods predictive power is a prerequisite for regulatory acceptance (Chapter 5). As far as the urinary bladder is concerned, several cell lines originating from this tissue are commercially available, including the most commonly used T24, RT4, 5637 cells among others (for an excellent systematic review of the please refer to (Zuiverloon et al., 2018)). These are generally cost-effective solutions and can be used for a detailed description of the molecular mechanisms of toxicity as well as for screening purposes (Jobst et al., 2025, 2024; Warth et al., 2016). On the other hand, limitations common to *in vitro* assays must be considered, potentially complicating method validation and regulatory acceptance. For instance, increasing heterogeneity in the cell models, cross contamination between cell lines (Dirks et al., 1999; Masters et al., 1988; O'Toole et al., 1983), genetic adaptation with increasing passaging numbers (Capes-Davis et al., 2013) and their altered metabolism and biomechanical properties compared to non-transformed cells (Gruber et al., 2023; Hernandez-Jerez et al., 2023).

To address some of these issues, the use of human primary cells, while more complicated, can be worthwhile. Multiple cell types can be collected and cultivated this way, for example epithelial cells displaying functional cytokeratin filaments (Flieger et al., 2008). Tissue harvested from patients undergoing urological procedures such as cystectomy during bladder cancer, act as readily available sources of such cells. Yet, this process demands close cooperation between researchers, clinicians and patients. Furthermore, cells derived from bladders in pathological conditions may not be representative of healthy tissue (Subramaniam et al., 2011). In this respect, primary or primary expanded cultures derived from animal tissues can be used as alternative. For instance, porcine bladder epithelial cells are harvested to assess genotoxic effects (Föllmann et al., 2000; Guhe and Föllmann, 1994).

Increasing the complexity of the models from monocellular to
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Summary of the currently described in vitro models most relevant for both bladder and kidney toxicological studies. Shown are the main advantages and disadvantages, depending on the sources of cell (human and animal) and the chosen model (2D, 2,5D, 3D and microfluidics). Parts of the figure were drawn by using pictures taken and adapted from Servier Medical Art (https://smart.servier.com/). Servier Medical Art by Servier is licensed under a Creative Commons Attribution 4.0 Unported License (https://creativecommons.org/licenses/by/4.0/).</figcaption></figure>

multicellular and/or differentiated cultures, it is worth mentioning that an essential function of the urothelium is the maintenance of the blood-urine barrier. This relies on a tight-junction layer of umbrella cells (Lasič et al., 2015; Negrete et al., 1996) covered by proteoglycans and glycoproteins referred to as the glycosaminoglycan (GAG) layer (Hurst et al., 2007; Hurst and Zebrowski, 1994), supported by the underlying intermediate and basal cells. This structure defines the possibility of toxins (re-)entering the body from urine (Lasič et al., 2015). Hence, recreating these functional properties in an *in vitro* system could help create a more concise picture of bladder toxicology. Previous and ongoing research in

this aspect has focused on interstitial cystitis/painful bladder syndrome (IC/PBS) a disease in which the integrity of the bladder epithelium is weakened and its permeability increases (Grundy et al., 2018). However, the impact of toxins in its maintenance and stability is not well understood. While the concept of chemical cystitis exists (Petca et al., 2021), to our knowledge this is scarcely covered by a broad understanding of xenobiotics' effects on bladder permeability. For example, for mycotoxins, deoxynivalenol was previously described to modulate tight junctions and barrier function in other tissues (i.e. colon or skin) (Pinton et al., 2012; Xue et al., 2022). Even though a study describing
the effects of deoxynivalenol on bladder barrier integrity is still lacking, T24 bladder cancer cells are sensitive to the mycotoxin and its metabolites, including the cell proliferation, capacity to regulate autophagic competence and motility (Del Favero et al., 2021a, 2021b; Jobst et al., 2024; Warth et al., 2016). One possibility to investigate the effects of xenobiotics on cells' barrier function is provided by the Transwell® based *in vitro* systems: in this case, cells are seeded and differentiated on membranes and transepithelial electrical resistance (TEER) is used to assess the tightness of the cell layer, which has already been successfully used to investigate the blood-brain barrier, gastrointestinal tract, and pulmonary tissue (Chen et al., 2015; Luo et al., 2019; Pinton et al., 2009; Srinivasan et al., 2015). Furthermore, penetration of fluorescent dyes, such as lucifer yellow, are additionally used to assess its integrity (Liebe et al., 2022; Zhao et al., 2019). For the bladder this can be performed using commercially available human urinary bladder epithelial cell lines (Díaz et al., 2020).

A recreation of the blood-urine barrier *in vitro* by a single cell type will possibly not be sufficient to fully represent the complexity of the bladder epithelium. Therefore, even more elaborate approaches are emerging, shifting from the single cell or monolayers, towards multicellular models, aiming to display the *in vivo* physiology and environment of the cells more accurately. One such model (Zupancić et al., 2018) uses porcine bladder epithelial cells cultured on top of fibroblasts and/or smooth muscle cells. Additional co-culture models exist, using cells of human origin, mixing cancer and non-cancer cells (Liu et al., 2015).

A further approach is the use of organoids, a 3D cell culture system partially recreating the architecture of human organs. Organoids may be grown from pluripotent (PSCs) or adult stem cells (AdSCs), the approach is still less common compared to 2D culture, but has already found widespread research application (Kim et al., 2020a). Human bladder cancer organoids have already been established, tissue extracted during the radical cystectomy of bladder cancer patients was used, for which the group performed functional profiling as well as drug sensitivity testing (Mullenders et al., 2019). Organoids allow research in unprecedented conditions, where toxin uptake, metabolism and effect could be studied in a closer to *in vivo* model. The organoid system enables toxin uptake from multiple sides, not just the top of the monolayer. Furthermore, cells are less constrained by the hard cell culture plastic and the close connection of cells inside the organoid allows for multidimensional cell-cell interaction, representative of the *in vivo* tissue architecture. One current downside of this method is the widespread use of cancer cells, which may not correctly translate to the physiology of normal tissues, yet similar issues are also faced by 2D cultures. A short summary of comparative literature revealed that sensitivity differed between 2D and 3D systems. Bladder organoids were used for chemotherapeutic testing and possessed higher resistance to treatment (Table 5). This trend aligns with data collected for other cell models (breast (Breslin and O'Driscoll, 2016) and colon (Abbas et al., 2023)), suggesting a general decrease in the cell sensitivity related to the differentiation status. Adaption in sensitivity might be highly relevant for toxicologists since this could potentially enable data-based decisions among results obtained with the different models. Yet, if it is also reflective of the *in vivo* response to toxins remains to be seen, as not many toxicological screenings are currently employing these 3D systems.

The use of *ex vivo* explants (or organ culture) is also classified as an alternative approach, importantly independent of cancerous cells. It was developed as an intermediate tool to study the homeostasis of the target organ and its behavior under physiological and pathological conditions (Maresca et al., 2021). The explant system offers all the advantages of an *in vitro* system, while containing all cell types in the correct proportions, maintaining tissue relationships, and potentially preserving the complex patterns of differentiation observed *in vivo*. Although this system still uses animal materials, it dramatically reduces the use of animals, as well as their discomfort and suffering during the performance of the studies. The explant system offers a more controlled environment for experimental manipulation compared to *in vivo* models, the ability to perform cross-species studies under reproducible conditions, and the ability to obtain multiple explants from a single donor, increasing the statistical power of any investigation. Although improvements in culture conditions allow preservation of the tissue, a remaining limitation of this technique is that the explant undergoes progressive and rapid degeneration after removal from the animal/the patient (Randall et al., 2011).

Human and animal intestinal explants have been used to assess the toxicity of chemical contaminants. Because pigs show digestive physiology very similar to that of humans, porcine intestinal explants have been widely used to investigate the toxic effect of mycotoxins through different parameters such as histology (García et al., 2018; Lucioli et al., 2013; Pierron et al., 2016), immune markers (Cano et al., 2013; Mastrogiovanni et al., 2019) (e.g. pro- or anti-inflammatory cytokines) and omics analysis (i.e. transcriptomics, proteomics, metabolomics) (Pierron et al., 2022; Soler et al., 2022, 2020; Tremblay-Franco et al., 2021). Kidney explants from animals and humans have also been used as an experimental model to understand the mechanisms involved in renal fibrosis, drug metabolism, transplant-related ischemic-reperfusion injury, and diabetic nephropathy (Gómez-Jaramillo et al., 2022; van Furth et al., 2022). The porcine renal system is functionally similar to the human renal system, and the preparation of porcine kidney and bladder cultures could be an important experimental tool in the extrapolation of animal toxicity data to humans. Porcine kidney explants have already been used to study the toxicity of gentamycin (Suhett et al., 2023) and porcine bladder explants are being developed to study the combined toxicity of mycotoxins and heavy metals (Payros D., Durant A., et al. *unpublished data*).

Another defining feature of the urothelium is the mechanical stress experienced by the cells; shear stress from urine flow can occur in ranges of 0.2–2 dyn/cm² based on theoretical models (Cai et al., 2000) and the mechanical stretch during the filling and voiding cycles of the bladder. Through mechanotransduction the physical cues can be translated into biochemical signals and activate multiple downstream responses (Dalghi et al., 2021; Di et al., 2023; Jafari and Rohn, 2022; Jobst et al., 2025, 2023; Li et al., 2022), for instance through the mobilization of mechanosensitive transcription factors (Jobst et al., 2023). In turn, exposure to toxicants and/or activation of endoplasmic reticulum stress response can modify mechanical compliance of bladder cells (Del Favero et al., 2021b; Gruber et al., 2023). Another possibility is the use of pump-based systems, which use commercially available microfluidic slides, increasing cost and complexity but enabling more precise control of the fluids parameters (Del Favero et al., 2021b; Jobst et al., 2023). Microfluidics systems can also be integrated in organ-on-chip platforms

<table><caption>**Table 5**

Comparative studies on drug sensitivity of 2D and 3D in vitro systems. (PDX: Patient derived xenografts, BCG: Bacillus Calmette-Guérin).</caption><thead><tr><th rowspan="2">Cell line</th><th rowspan="2">3D model</th><th rowspan="2">Tissue</th><th rowspan="2">Drug</th><th colspan="2">Sensitivity</th><th rowspan="2">Ref</th></tr><tr><th>2D</th><th>3D</th></tr></thead><tbody><tr><td>RT4</td><td>Spheroids</td><td>Bladder</td><td>Cisplatin</td><td>Higher</td><td>Lower</td><td>(Miranda et al., 2021)</td></tr><tr><td>PDX</td><td>Spheroids</td><td>Bladder</td><td>Cisplatin</td><td>Higher</td><td>Lower</td><td>(Miranda et al., 2021)</td></tr><tr><td>5637/T24</td><td>3D bioprinting</td><td>Bladder</td><td>Rapamycin/BCG</td><td>Higher</td><td>Lower</td><td>(Kim et al., 2019)</td></tr><tr><td>RT4/5637</td><td>Spheroids</td><td>Bladder</td><td>Cisplatin/Gemcitabine</td><td>Higher</td><td>Lower</td><td>(Yoshida et al., 2019)</td></tr><tr><td>RT4/5637</td><td>Spheroids</td><td>Bladder</td><td>Cisplatin/Gemcitabine/Mitomycin C/Dasatinib</td><td>Higher</td><td>Lower</td><td>(Whyard et al., 2020)</td></tr></tbody></table>
or implemented with systems for the deformation of the extracellular matrix (ECM); also, in this case multiple systems are commercially available, some even enabling live cell imaging during the stretching processes (Constantinou and Bastounis, 2023; Hou et al., 2023; Zhao et al., 2023). For the bladder this was used exemplary in a bladder-on-chip model (Sharma et al., 2021a), which consists of both endothelial (HMVEC-Bd) and epithelial cells (HTB-9), cultured under urine flow and mechanically recreated filling (linear increase in strain over 2 h), full bladder (constant stretch 2–4 h), voiding (fast release of stretch in 2 min), and empty bladder (no stress 4 h) cycles. This work primarily applied this system for the evaluation of urinary tract infections. Yet, the translation of these methods for use in toxicology could be envisioned; the bladder-on-chip would allow controlled investigation of toxic effects in near in vivo conditions, allowing the application of toxins in urine instead of cell culture medium as well as the recreation of the physical environment through stretch and shear stress. Investigation of permeability and cell viability should be possible through standard assays. Another group developed an bladder-on-chip focusing not on the cellular recreation of the bladder tissue itself, but rather asses the invasion capabilities of a panel of bladder cancer cell lines (T24, J82, and RT4), quantifying migratory capacities inside a Matrigel® matrix (Ewell et al., 2024). An open question is the reproducibility and inter-lab comparability of the results as with many in vitro systems. For microfluidics this is further complicated by the usage varying geometry, incubation times and shear stress values (0.2 – 12 dyn/cm², (Jobst et al., 2023; Lee et al., 2018; Raghavan et al., 2014; Xu et al., 2020b)) across models and laboratories. Obviously, much remains to be clarified for the application of these methods for regulatory purposes (Chapter 5) though the standardization of procedures that are currently applied for tailored research questions.

### 3.2.2. *New Approach Methodologies for Organ Toxicity: Kidney*

Substances present in urine (Tables 1–4) may disrupt renal physiology at different levels. In contrast to the bladder, correlation between urinary concentrations of toxins and adverse outcomes is less straightforward; while urinary contaminants have passed through the kidney, the kidney itself may experience a different toxicological mixture and variable exposure times. For example, through the effects of tubular reabsorption, preventing the excretion of a toxin into urine, thereby masking it from urinary HBM analysis. In addition, xenobiotics may undergo intracellular accumulation or biotransformation within the kidney cells (Guerreiro et al., 2022).

At a molecular level, the interactions of toxicants with transporters can be partly modulated in silico for instance to allow the rapid screening of multiple drug transporters existing in the kidney (i.e. OAT1, OAT2, OAT3, OAT4, OATP4C1, etc.) through molecular docking studies (Zou et al., 2021) and molecular dynamic simulations (Louisse et al., 2023, 2024). This approach might even allow the identification of previously unknown links between kidney disease and contaminant exposure, as shown for chronic kidney disease of unknown etiology (CKDu) and pesticides, through docking studies between the toxicants and enzymes relevant for kidney function (i.e. AMP-activated protein kinase, etc.) (Rajapaksha et al., 2021).

The kidney is a highly complex organ. In vitro models should retain the physiological characteristics of kidney cells, namely the polar architecture and junctional assembly of epithelia, the proper expression of enzymes and transporters, the vectorial transport of solutes and water, the transepithelial electrophysiological characteristics, the uptake of xenobiotics from both the apical and basolateral sides, the hormone responsiveness, and the retention of nephron segment-specific features (Pfaller et al., 2001). The proximal tubule is particularly vulnerable to nephrotoxicants, making it a suitable target when addressing renal toxicity (Hall et al., 2022; Van der Hauwaert et al., 2014). Renal proximal tubule epithelial cells (RPTEC) commonly used include continuous RPTEC lines such as the porcine LLC-PK1, the marsupial OK, the monkey JTC-12, and the human HK-2 cell lines (Pfaller et al., 2001; Prieto,

2002). The canine MDCK cells are often used as a model of distal tubular cells (Prieto, 2002). Human-derived PTEC (HPTEC) avoid the interspecies differences and can be of different origins, including immortalized cell lines, primary cultures, or iPSC-derived PTEC-like cells (Nieskens and Sjögren, 2019). Regarding human immortalized cell lines, although HK-2 cells are widely used, they lack the expression of important markers (Jenkinson et al., 2012). Another epithelial renal cell line, HEK-293, derived from human embryonic kidney cells, has been widely used in cell biology and toxicological studies (Le et al., 2018) for its fast and reliable growth and transfection capacity. RPTEC/telomerase reverse transcriptase (RPTEC/TERT1) (Simon-Friedt et al., 2015) and conditionally immortalized proximal tubule epithelial cells (ciPTEC) (Jansen et al., 2014) are two established cell lines that retain a greater degree of HPTEC characteristics and markers (Faria et al., 2019). Despite many advantages of cell lines, these models present some drawbacks, including the possibility of dedifferentiation (Loss of differentiation, return to an earlier less specialized cell type) or transdifferentiation (transformation of one adult somatic cell type in another) in culture, and functional alterations (Faria et al., 2019; Jenkinson et al., 2012; Pfaller et al., 2001; Prieto, 2002; Van der Hauwaert et al., 2014). While not easy to obtain and handle, renal primary cultures better mimic the physiological in vivo situation, especially during the early period after culture initiation. Primary cultures of PTEC have been established from various species, such as human, mouse, rat, rabbit, and pig (Pfaller et al., 2001; Van der Hauwaert et al., 2014). Renal cells derived from human induced pluripotent stem cells (h-iPSC) have also been proposed as a model for evaluating nephrotoxic compounds. Bajaj and colleagues differentiated h-iPSC into multicellular structures containing proximal tubule cells and podocytes, with improved expression of key renal transporters, that could constitute a useful model for predicting nephrotoxicity (Bajaj et al., 2018).

Typical renal cell cultures only include a single type, usually PTEC, grown in static monolayer (2D) cultures (Nieskens and Sjögren, 2019). Some limitations of these conventional models can be overcome by growing cells in microporous supports. Such systems allow access of nutrients to both the apical and basolateral membranes, better retention of cell polarization, and help in maintaining differentiation (Nieskens and Sjögren, 2019; Prieto, 2002). The support membrane may be coated with ECM molecules, resembling the physiological matrix (Nieskens and Sjögren, 2019; Pfaller et al., 2001). Given the limitations of 2D cell cultures, and to increase the physiological relevance and predictivity of kidney in vitro models, 3D systems have been developed. These may be divided into spheroids, organoids, tissue-engineered models, and kidney-on-chip models (Yu et al., 2022). Different cell types have been used in 3D culture renal models, including animal cell lines (e.g., MDCK, LLC-PK1, VERO), animal primary cultures, human-derived immortalized cell lines (e.g., HK2, ciPTEC, RPTEC/TERT1), human primary cultures (fetal and biopsy-derived), and human stem cells (e.g., iPSC) (Faria et al., 2019; Yu et al., 2022). Renal spheroids consist of cells (often RPTEC) embedded in hydrogel or ECM to form hollow spherical cysts with an apical membrane facing the surrounding medium/tubular lumen while the basolateral side is exposed to hypoxia (Yu et al., 2022). Spheroids introduce the third dimension into the tissue and allow interaction between cells and the ECM. Kidney organoids can be developed from several types of stem cells and have the advantage of incorporating multiple cell types, such as tubular, endothelial, and interstitial cells (Faria et al., 2019; Yu et al., 2022). Furthermore, organoids are able to retain transporter expression, a critical feature to study nephrotoxicity (Kishi et al., 2021). Different differentiation protocols have been developed to generate kidney organoids from human embryonic stem cells, h-iPSC, and renal stem cells/progenitors (Kishi et al., 2021; Yu et al., 2022). For example, h-iPSC can generate organoids with various nephron segment cell types. However, the iPSC-derived tissues still develop into premature kidney structures and therefore, might not properly predict the situation in human adults. (Faria et al., 2019; Nieskens and Sjögren, 2019; Yu et al., 2022) Additionally,
multistep procedures for organoid differentiation lead to reproducibility issues (Kishi et al., 2021). To recreate different nephron segments, allowing the detection of segment-specific toxicity, the morphology of nephron-like structures and the expression of site-specific molecular markers must be assured (Kishi et al., 2021). However, available bio-markers still present low specificity and sensitivity, making it difficult to distinguish renal cell types within organoids and to recognize cell type-specific damage. (Yu et al., 2022) The current kidney organoids present additional limitations: they do not recreate the functional nephron, with renal corpuscle, tubules, and collecting ducts merging into a single collecting system, nor do they reflect the fluid shear stress of the urinary flow in the kidney tubules. In addition, test compound administration is limited by the difficulty of perfusing organoids directionally. (Yu et al., 2022) Some of these limitations could be overcome by 3D-engineered kidney models, which incorporate various renal cells within a scaffold that recreates the tubular architecture, allowing the spatial organization of cells and thus mimicking the physiological geometry of the nephron. Bioengineered kidney models have been developed using a variety of scaffolds, including polyethersulfone hollow fiber membranes, hydrogels, ECM, or even decellularized kidneys (Faria et al., 2019; Yu et al., 2022). Organs-on-chip are microfluidic devices that further increase the physiological relevance of cultured cells. Various kidney-on-chip devices have been developed to mimic the glomerulus and especially the different segments of the tubule (Nieskens and Sjögren, 2019; Wang et al., 2022). Mono- or co-culture microfluidic devices have been established using different types of renal cells of human or animal origin, including proximal tubule cells, podocytes, endothelial cells, hiPSCs and mesangial cells, cultured inside a perfusable microchamber (Formica et al., 2025; Wang et al., 2022). In addition to the 3D architecture, kidney-on-chip devices may incorporate additional features to better recreate the *in vivo* environment, including ECM and fluid shear stress (Nieskens and Sjögren, 2019; Wang et al., 2022). Renal cells are supported by ECM networks which composition and stiffness varies according to cell type. As ECM affects cell behavior and signaling, this aspect should be taken into account in kidney chips (Wang et al., 2022). Kidney cells are exposed to other mechanical stimuli, such as shear stress, compression, and stretch, that play a relevant role in cell regulation (Miceli et al., 2020; Wang et al., 2022). Microfluidic devices allow incorporating vasculature and fluid flow that facilitate kidney maturation and provide a more physiological environment (Faria et al., 2019; Yu et al., 2022). Different renal cell types, including glomerular endothelial cells, podocytes, and tubule cells, are highly sensitive to fluid shear stress (FSS). Importantly, FSS varies profoundly within the nephron segments. For example, while glomerular endothelial cells are subject to apical FSS from blood flow that can be 30–50 dyn/cm², FSS on proximal tubular epithelial cells is considerably lower, with values of 2–4 dyn/cm². FSS is often applied using an external syringe pump or peristaltic pump and should recreate the physiological values (Wang et al., 2022). Organ-on-chip devices may be further improved by integrating sensors (e.g., electrical, optical, pH and oxygen sensors), for real-time monitoring of cell functions. In the case of kidney-on-chip, integration of TEER measurement has been used to monitor epithelial barrier function (Wang et al., 2022). Finally, a microfluidic device with interconnected compartments combining a liver-on-chip with a kidney-on-chip in one single model has also been developed (Theobald et al., 2018). This model allows studying inter-dependent responses between liver and kidney tissues and could provide a useful tool for the study of contaminants that undergo hepatic metabolism. Theobald et al. demonstrated that the system was able to replicate the biotransformation and consequent liver-mediated toxicity of Aflatoxin B1 to kidney cells, uplifting the *in vitro* toxicity testing (Theobald et al., 2018).

As kidney contains over 20 different cell types, reproducing *in vitro* 2D or 3D models that consider the complexity of the kidney could be complicated. To overcome this problem, kidney explants provide an alternative that preserves renal cell diversity and tissue integrity. As

with liver slices (Hasuda et al., 2023, 2022) or intestinal explants, *ex vivo* kidney slices or explants could be used to assess tissue damage, gene expression, inflammation, and metabolic status. For example, Suhett et al., used porcine kidney explants to assess tissue damage, oxidative stress, and inflammatory markers in response to antibiotic treatment (Suhett et al., 2023). This model could also be relevant to differentiate the effect of contaminants on specific areas of the kidney (i.e. cortex versus medulla).

### 3.3. New Approaches for Exposure Assessment

#### 3.3.1. Targeted Human Biomonitoring (HBM) and untargeted Exposomics Approaches

Urine as an easily accessible biological matrix can be sampled in a more straight-forward manner than matrices with invasive sampling. The cooperation of test subjects is easier achieved when compared to plasma/serum, breastmilk, or feces, which might be accompanied with invasion of privacy or feelings of shame, depending on cultural pre-notions. Therefore, it has been historically frequently used for assessing environmental, food, and occupational exposures. Recent advances in analytical techniques and instrumentation, notably chromatographic separation connected to mass spectrometric detectors, allow for an unprecedented number of concurrent exposure measurements with high sensitivity and specificity, also expanding the possibilities in biological matrices accessible for analysis (Ayeni et al., 2024; Brunmair et al., 2022, 2021a, 2021b; Krausová et al., 2023). This facilitates a better understanding of human co-exposure, bringing the idea of (partially) measuring the exposome, i.e. the life-course environmental exposures (including lifestyle factors), from the prenatal period onwards (Wild, 2005).

Traditionally, however, environmental exposure is still mostly assessed using targeted biomonitoring approaches, where a certain well-known contaminant/toxicant or a certain class of chemicals is quantified. Human biomonitoring aims to improve our understanding of human exposure to hazardous chemicals. Multiple biomatrices are collected in national and multinational projects, to provide a wide picture of the chemicals we are exposed to regularly and provide governmental agencies with the scientific framework to make policy decisions based on monitoring data. In Europe major initiatives included COPHES/DEMOCOPHES (2009–2012), HBM4EU (2016–2022), and the “Partnership for the Assessment of Risks from Chemicals” (PARC, 2022–2029) (Audebert et al., 2023; Braeuning et al., 2023; De Castelbajac et al., 2023; Gilles et al., 2021; Ramhøj et al., 2023; Schindler et al., 2014). These large-scale collaborative projects provide a highly relevant basis for advancing and harmonizing HBM across Europe and are supported by the European Chemicals Agency, the European Environment Agency and the European Food Safety Authority. For ensuring the sustainable long-term advancement of exposome research in Europe, the implementation of the “Environmental Exposure Assessment Research Infrastructure” (EIRENE RI; https://www.eirene-ri.eu/) was initiated in 2022. The mission of this distributed European Strategy Forum on Research Infrastructures (ESFRI) infrastructure is to bring together complementary capacities available in the member states, harmonize them and upgrade them for addressing current scientific and societal challenges in the areas of chemical exposures and population health and prevention.

Multiple naturally occurring toxicants relevant for the bladder and discussed within this review were included in the projects. As an example, already in the COPHES/DEMOCOPHES project, mercury exposure could be related to fish consumption, and urinary cadmium to overall air and water quality (Smolders et al., 2015). The HBM4EU study confirmed the cadmium exposure of the European population, and additionally revealed a correlation between a vegetarian diet and indicated a connection to exposure to Cd-containing mineral phosphate fertilizer (Snoj Tratnik et al., 2022). Furthermore, deoxynivalenol was found in the urine of adults in 5 participating countries with a
quantification frequency of 97% (Govarts et al., 2023). For other naturally occurring contaminants such as chromium exposure was primarily occupational (i.e. welders, platers), in this case work safety standards such as the wearing of gloves were identified as simple prevention strategies (Viegas et al., 2022). Together these results highlight the importance of large-scale multinational initiatives in public safety, which is also recognized by stakeholders, evident by the increasing scope of the projects. Yet, some biomonitoring studies are limited to the only one sampling time point, potentially overlooking changes in exposure over time. Longitudinal studies can alleviate this uncertainty enabling the more realistic demonstration of long-term exposure. This shift in current risk assessment would help to address chronic toxicity more reliably. For this purpose, the evaluation of self-sampled urine at multiple time-points is particularly suitable, making it likely that more robust data on the occurrence of non-persistent urinary-occurring contaminants will increase in the future. Currently developed exposome methodologies aim at increasing throughput while decreasing cost and sample material. Thereby, the assessment of longitudinal data for a multitude of simultaneously assessed biomarkers of exposure should become more feasible and standard.

While targeted HBM data for single contaminants or individual groups can be highly relevant for risk assessment, it does not consider co-exposure (Oesterle et al., 2021; Vermeulen et al., 2020). Consequently, innovative Next-Generation HBM approaches are currently trying to close this gap and provide multi-exposure data from LC-MS experiments following comparatively generic sample extraction procedures. Recently, two methods have been proposed that successfully applied this targeted multi-class approach (Jagani et al., 2022; Jamnik et al., 2022).

Comprehensively measuring (parts of) the chemical exposome can be regarded as one of the grand challenges in current bioanalytical chemistry. Targeted assays, even if covering hundreds of relevant exposures, can only account for targets that are known and where an authentic reference standard is available. Consequently, non-targeted analysis (NTA) and suspect screening approaches are becoming more popular in recent years, especially when integrating the exposome concept into a multi-omics framework (Miller, 2021; Price et al., 2022). While technically not straight-forward to establish, operate, and evaluate, it holds great promise in investigating emerging contaminants, known and unknown biotransformation products of xenobiotics, and biological responses (effects) following exposure events (David et al., 2021; Musatadi et al., 2022; Tkalec et al., 2022). New results indicate that these high-resolution MS (HRMS) based workflows are ready to be applied for xenobiotics that are frequently detected at relatively high concentration levels in human urine (Oesterle et al., 2023). However, for chronic low-dose exposure, targeted low-resolution approaches that are typically more sensitive still appear to be the preferred option. In a recent study, the limit of quantification of a targeted approach for > 100 xenobiotics and endogenous hormones was on average six times lower than HRMS in urine (Flasch et al., 2023).

While there has been significant progress with regards to exposomic workflow development, reporting and FAIR data sharing practice (thus meeting the principles of findability, accessibility, interoperability, and reusability) in recent years, many challenges remain unsolved to date. It needs to be understood that the exposome can only be systematically accessed, if researchers from various fields and domains join forces to define common minimum standards to allow better data evaluation and comparability. The broad chemical diversity and huge concentration and structural differences of exogenous molecules (naturally and human-made) in biological samples that needs to be tackled are enormous and cannot be solved without fresh lines of thought. An important example are the bioanalytical validation guidelines for LC-MS-based workflows that need pragmatism and the courage to leave gaps when applied to exposomics (Gu et al., 2023). Only with rigorous analytical and bioinformatic workflows providing high-quality data on the one hand, but also pragmatism towards solving biological/toxicological

research questions on the other hand, the impact of complex and dynamic chemical co-exposure can be elucidated at some point in the future. Exposomic data clearly indicate that humans and animals are simultaneously exposed to multiple toxicants; thus, there is a need for an update of the traditional single contaminant risk assessment approach. Indeed, different toxicants, which may interact in ways that are difficult to predict when only considering each component individually. Simultaneous exposure to different toxicants could result in antagonistic, additive, or synergistic effects (Alassane-Kpembi et al., 2017). The customary chemical-by-chemical approach to risk assessment is in danger of underestimating the risk of chemicals to health (Martin et al., 2021). Indeed, evidence of synergism increases concerns about health risks (Alassane-Kpembi et al., 2015), similarly, the toxicity of some contaminants could be exacerbated by non-toxic compounds (Garofalo et al., 2023; Payros et al., 2017). Therefore, it is of utmost importance to analyze the combined toxicity of contaminants simultaneously. However, with this approach new challenges come forth; as the testing of all possible combinations in vitro is not feasible, priorities have to be defined, as outlined by multiple frameworks (European-Commission, 2012; WHO/IPCS, 2011). Chemical combinations of high concern are intended to be identified by exposure levels, production already in mixtures, persistence, likelihood of adverse effect, information of known interaction, quantitative Structure-Activity Relationship (QSAR), threshold of toxicological concern (TTC), specific classification (i.e genotoxic carcinogens), etc. (European-Commission, 2012). Another approach is the combination of aggregate exposure and adverse outcome pathways into a combined AEP-AOP networks (Price and Leonard, 2019; Price et al., 2020). This data intensive approach of combining exposome studies with multiple effects observed in vitro could be integrated into the AOP based approach already discussed in chapter 3.2. (Fig. 5). Based on real life exposure data combinations of chemicals could be identified which not only co-occur, but also act on the same AOP, prioritizing them for in vitro confirmation of their potential combinatorial effect. The strength of this approach would be that it also includes (co-)exposure preventing the potentially unnecessary testing of combinations which do not even occur in humans and would help bridge the gap between exposure and causality of the health effects observed in epidemiological studies. In sum, this emphasizes the critical need for toxicologists, epidemiologists, analytical chemists, and food/environmental scientists to work across traditional disciplinary boarders for the exposome paradigm to become a reality that can deliver the answers the field is looking for.

### 3.3.2. Modeling of Internal Exposure through Physiologically Based Pharmacokinetic Modelling (PBPK)

Physiologically based pharmacokinetic (PBPK) models enable the *in silico* prediction of toxicants absorption, distribution, metabolism and excretion (ADME); while they are generally not used to quantify exposure on their own, PBPK-models are still highly relevant to describe chemical distribution in the different body compartments and to estimate internal exposure of the individual organs and tissues. Mathematical models have been used for a long time to predict drug behavior, in this field PBPK models are built by incorporating known physiology of the body and pharmacokinetic parameters of the individual compounds. These variables can be derived from kinetic *in vivo* data and physicochemical properties of compounds, but also knowledge on active or passive transport among many others (Jones and Rowland-Yeo, 2013). Furthermore, biotransformation can also be included, from CYP450-mediated metabolism in the liver (Gertz et al., 2010) to microbial metabolism in the intestine (Stevanoska et al., 2024). The sum of these mathematical parameters allows for the creation of models able to simulate a compounds distribution across multiple compartments representative of the individual tissues of the human body (Jones and Rowland-Yeo, 2013). Importantly, excretion pathways such as the urinary tract can also be incorporated and these models may therefore be used to estimate internal exposure (concentrations in the kidney or
urine) based on external (e.g. oral) exposure and vice versa (Cohen Hubal et al., 2019; Dede et al., 2018; EPA, 2024; Hartung et al., 2025; Karakoltzidis et al., 2025). PPBK models calculate the tissue specific PK parameters clearance, volume of distribution and half-life, enabling the prediction of a chemicals redistribution among the human body as concentration-time profiles. (Jones and Rowland-Yeo, 2013) When coupled to PK data, margins of exposure may be determined when combined with *in vitro* to *in vivo* extrapolation (Clewell et al., 2020; El-Masri et al., 2016). Toxicologists are becoming more and more aware of the advantages of these techniques; for example, reduced need of testing material, which for rare toxins is a limiting factor. Furthermore, once established a PBPK model can be used for multiple concentrations with relative ease, independent of the limit of detection of analytical methods. Allowing an overall reduction in the number of *in vivo* and *in vitro* studies needed. According to the application, a limitation of PBPK models can be the dependency on existing data, more specifically they require human or animal time dependent concentration data (i.e. plasma concentrations after exposure), to initially calibrate the models (Tan et al., 2018). As *in vivo* kinetic data is often unknown for toxins, this complicates the development of reliable models.

The EFSA fostered the implementation of toxicokinetic and toxicodynamic modelling of chemicals for risk assessment through the introduction of the TKPlate platform, integrating multiple physiologically-based kinetic models and toxicokinetic-toxicodynamic models for open-access usage (Dorne et al., 2023; Testai et al., 2021). The large amount of data collected in human biomonitoring studies may be combined with these models, linking biomarker and contaminant levels found in urine to the dietary exposure, as is already possible for some toxic metals (Dede et al., 2018) and other xenobiotics such as perchlorate (Yang et al., 2012).

Besides predicting tissue specific internal exposure, such as a contaminants distribution, accumulation and excretion through the kidneys (Ogungbenro and Aarons, 2014) and urinary bladder (Knutsen et al., 2013), there is another important use of PBPK modelling for the urinary tract; specific models allow us to describe the contribution of renal diseases, predicting drug and toxin PKs variability in case of impaired excretion pathways (Heimbach et al., 2021). Currently multiple organ specific PBPK models are being developed; for the kidney, special interest is focused on PFOAs which may be reabsorbed in the kidney requiring unique models (Karakoltzidis et al., 2025; Worley and Fisher, 2015). Another relevant aspect is that of the pediatric kidney, enabling the exploration of age dependent differences in renal transport (Salem et al., 2022), or even simulating in a combined approaches the renal transport in pediatric patients with renal diseases (Khalid et al., 2023). These models enable the representation of vulnerable groups, which may be underrepresented in clinical testing and exposure assessment, making it a potentially precious element also for the NGRA toolbox.

## 3.4. Implementation

The final chapter of this review summarizes the most pressing challenges for the use of NAMs and their implementation in risk assessment with focus on urinary occurring toxicants. Currently for the EU, the recent EFSA roadmap on NAMs in risk assessment recognized that “whole animal studies are not readily suited to mechanistic underpinning of toxicological events at a molecular level” and acknowledges that NAMs are promising options to fill this gap (Escher et al., 2022). In this light, large scale projects such as the Partnership for the Assessment of Risks from Chemicals (PARC) (Herzler et al., 2025) or ONTOX (Vinken et al., 2021) support the integration of NAMs into risk-assessment. However, the rapid progression on methodological development (Chapters 3 and 4) brings forth the challenges of low inter-laboratory reproducibility and technical differences in custom-made prototypes. To be used by risk assessors NAMs need to be validated to ensure their reproducibility, reliability, and accuracy, as summarized by the “Solna Principles of validation” (OECD GD34,

## General Principles of Validation

<figure><img src="image_10.png" /><figcaption>Fig. 8. General principles of validation (Based on OECD GD34).</figcaption></figure>

Fig. 8).

The proof of repeatability and reproducibility is especially challenging for *in vitro* studies. Traditionally performed in inter-laboratory ring studies, the length, expense, and coordination of which may cause delays between development and implementation. In this light, some suggest moving away from this practice and consider, for some NAMs, intra-laboratory based reproducibility testing instead (van der Zalm et al., 2022). The OECD Guidance Document on Good *In Vitro* Method Practices (GIVIMP) (OECD, 2018a) gives further recommendations to help improve the robustness of the results. For example, the ongoing quality control of the cells (i.e. confirming identity, checking for contamination, ensuring morphological, genetic, and functional stability etc.), can help reduce known issues with reproducibility of *in vitro* assays, occurring from differences between passages (model aging), handling errors, or heterogeneity in the sources of primary cells. The materials used in cell culture are another source of uncertainty (OECD, 2018a), to name one example among others, the use of animal-derived serum. Through its natural origin and unpredictable composition, it introduces measurable variations in the biological responses (Niederstaetter et al., 2021). The use of chemically defined mediums may be a solution; options vary from serum-free, to fully excluding all undefined components of animal origin (e.g. albumin) from the formulation (i.e. Xeno-free) (Hua et al., 2022; Lindroos et al., 2009; Usta et al., 2014). Yet, these options are not available for all commonly used cell lines.

Previously validated and accepted replacement methods are listed for instance in the “OECD Guidelines for the Testing of Chemicals - https://www.oecd-ilibrary.org” or in the “TSAR - Tracking System for Alternative methods towards regulatory acceptance - https://tsar.jrc.ec.europa.eu/”. Fig. 9 collects recent validated/approved methods qualifying as NAMs (none or reduced use of animal testing; timeframe 2002–2023) from these databases. In sum data reveals that most validated NAMs focus on the skin (Fig. 9). This can be interpreted in light of the ban on animal testing for cosmetics implemented by the European
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Recent trends in regulatory approval of NAMs for the testing of chemicals; list of accepted methods was generated from the Tracking System for Alternative methods towards Regulatory acceptance (TSAR) and the OECD Guidelines for the Testing of Chemicals, Section 4 Health Effects. On the left are the numbers of approved NAMs per tissue/endpoints, the pie chart depicts the percentage of in silico, in vitro, in vivo/ex vivo and in chemico methods respectively. On the right is a chronological representation of NAM approval with a focus on the ban on animal testing in cosmetics (steps of the ban indicated via red arrows, skin specific NAMs in pink).</figcaption></figure>

Commission (Regulation (EC) No 1223/2009). This was accompanied by a general increase in the number of NAMs implemented for this scientific sector. Other areas are currently undergoing a transition phase: for example, for acute toxicity a weight of evidence approach is already applicable for substances of low toxicity (LD50 > 2000 mg/kg), where data from an alternative test or test battery exists (OECD GD 237). In line, one validated method for acute oral toxicity is the 3T3 neutral red uptake (NRU) cytotoxicity assay using the BALB/c3T3 mouse embryonic fibroblast cell line (https://tsar.jrc.ec.europa.eu/test-method/tm2008-14). Additionally, while animal models are still compulsory for most acute toxicity testing, their usage can be reduced and refined via *in vitro* to *in vivo* extrapolation. For this, guidance documents (ICCVAM, 2001; OECD, 2010) have been published on the application of the NRU assay to determine the starting dose of the *in vivo* acute oral toxicity testing. For carcinogenicity exists the cell transformation assay based on the Bhas 42 and SHE Cell Line (OECD, 2015, 2017b). Together these methods demonstrate that regulatory acceptance is possible for *in vitro* studies if validation criteria are adhered to. Despite this, it seems unlikely that single NAMs will be able to replace the complexity of the animal models. Instead, the design of integrated approaches for testing and assessment, seems more probable and endows the combination of existing and new data stemming for example from *in chemico*, *in vitro*, *ex vivo* methods using AOPs as a framework (Hernández-Jerez et al., 2021). Promisingly, multiple methods and testing batteries are in the validation process for regulatory use (Barroso et al., 2023); for example, for non-genotoxic carcinogens, a OECD expert group is developing an integrated approach to the testing and assessment (IATA). Using an AOP as a framework they compiled results from multiple cancer models, identifying overarching mechanisms and modes of actions (Barroso et al., 2023; Jacobs et al., 2020). This could serve as a blueprint for the description of bladder and kidney toxicity. Indeed, data collected in this review reveal the great effort of the scientific community in this sector and offer fertile ground for future developments. Among the open issues for bladder/kidney-on-chip models, there is for example the high variability in equipment geometries or shear stress values utilized in the different laboratories (Jobst et al., 2023; Lee et al., 2018; Raghavan et al., 2014; Xu et al., 2020b), in addition to the known general limitations of *in vitro* assays (OECD, 2018a). To further enable regulatory use of NAMs, a consensus between researchers, regulators, and industry must be reached to define standard values not only for cell model compositions and chemical features, but also for key physical

parameters such as fluid shear stress and organ deformations (Del Favero and Kraegeloh, 2020). As a starting point, a roadmap (Pamies et al., 2024) for microphysiological systems (e.g. organ-on-a-chip), was published by stakeholders from various sectors, formulating recommendations on fit-for-purpose criteria for quality management and reproducibility monitoring. Addressing key challenges specific to quality control of the cell culture, experimental design, scaling of the “organ” and its physiological environment (fluid flow etc.), but also regarding the technical setup of the system, such as tightness, cleanliness, absence of bubbles, among others (Pamies et al., 2024; Piergiovanni et al., 2024). A similar effort was made to foster the use of imaging-based approaches for regulatory toxicology, focusing on aspects of image acquisition, analysis, and data management, in the light of increased data transferability, reliability and robustness (Piergiovanni et al., 2025).

Another aspect that is currently entering the discussion not just in risk assessment, but our society is the use of AI (Hartung, 2023; Kleinstreuer and Hartung, 2024; Lui, 2024). When fully embraced by researchers it will change the field forever, yet it will require responsible implementation and regulation. This tool could help us take the leap to fully unlock the potential of NAMs to tackle the current challenges regarding the replacement of animal-based methods (Kleinstreuer et al., 2018). Some areas of toxicology may be faster to adapt, especially those using *in silico* approaches, such as PBPK, QSAR or molecular modelling (Trovato et al., 2022). Unquestionably, the difficulty is to keep pace with the exponential use of AI, due to its low technological threshold compared to other methods, which require expensive instrumentation. So far, an EFSA external scientific report (Blümmel et al., 2024) explores the use of AI in the context of NAMs especially for integration of research results into AOP-like networks. While the authors highlight the potential of AI in this application scenario, they also urge caution indicating that an expert needs to be involved in the process for quality control (Blümmel et al., 2024). To promote ethical use of AI, researchers and developers should follow the ethical principles of risk assessment (Bhuller et al., 2025). For instance address the risk of bias, which may stem from the selection of training data, lack of transparency (“black box” nature of AI), lack of equity (restrained access to AI models), and many more (Hartung and Kleinstreuer, 2025). To address bias, multiple studies report the critical nature of quality in training data and published frameworks for its evaluation and assessment (i.e. METRIC-framework for medical data) (Schwabe et al., 2024; Vokinger et al., 2021); such strategies can be envisioned also for toxicological
data. To enable transparency in AI usage, a debate on explainable AI (XAI) has started, which focuses on the human interpretability of AI results and their generation (Adadi and Berrada, 2018; Bao and Zeng, 2024; Jiménez-Luna et al., 2020). This approach could allow for the reproducibility and accountability needed for regulatory application of AI methods. For a more general view the OECD published the executive summary "Artificial Intelligence in Science" (OECD, 2023), that insightfully presents further challenges, possible pitfalls but also opportunities of the coming paradigm shift in science. These considerations are becoming increasingly relevant for toxicologists, since AI usage in exposure and hazard assessment is already emerging; for example, information generated by AI-based approaches might be used to guide fertilizer, pesticide and fungicide usage, with the potential to increase plant health and limit contamination occurrence (e.g. prevent/treat infestation with toxigenic fungi) (Deshmukh et al., 2025; Liakos et al., 2018). Additionally, machine learning models may be applied to detect or even predict mycotoxin occurrence in agriculture (Camardo Leggieri et al., 2021; Castano-Duque et al., 2025; Torelli et al., 2012). Furthermore, by applying AI-powered statistical analysis, deeper insights into HBM data can be gained, disaggregating population-level into individual-level exposure-responses (Cox et al., 2025) and assist in investigating the connection between exposure and the development of chronic disease (Isola et al., 2024).

Hence, it is clear that NAMs especially in the combination with AI have great potential when compared to the animal models, and it will be key to align methods to the human biology for reference in their validation process (van der Zalm et al., 2022). Before NAMs can reach broad regulatory acceptance and wider use, these challenges must be addressed, to enable the evolution into NGRA. This would in turn also improve the quality and robustness of results obtained in research labs (Baker, 2016) if the principles of validation (OECD GD34, Fig. 8) are followed and are clearly defined from an early development stage.

## 4. Conclusions and Future Perspectives

The urinary tract is highly relevant in public health management, as evident by numerous previous studies, describing important toxicological endpoints such as bladder cancer or decreasing kidney function (Tables 1–4). Some of these pathologies may be credited to urinary toxicants, as indicated by studies linking exposure to these toxicological endpoints. However, an investigation of the literature revealed some significant data gaps, and this lack of certainty may hinder precise risk assessment and effective preventive strategies (Fig. 10). As a potential solution to these challenges and an answer to the ongoing critical evaluation of animal-based methods, NAMs need to be rapidly developed, validated, and implemented. Additionally, mass spectrometry is used to evaluate individual exposure and toxicant biotransformation patterns in large-scale human biomonitoring studies, ranging from targeted measurements of a limited number of contaminants, to the untargeted analysis of at least part of the chemical exposome. Moreover, integrated effect measurements can be performed. In vitro models are also increasing in complexity moving from 2D cell monolayers to 3D organoids or even the recreation of one or more organs on microfluidic chips. As a matter of fact, while these in vitro cell culture systems reproduce physiological conditions in vivo more closely, with complexity comes a potential lack of reproducibility and transferability, which are essential prerequisites for their validation (OECD GD34, Fig. 8). Accordingly, the next big challenge will be to adapt these methods, through improving reproducibility, reliability, and standardization especially for regulatory use. Additionally, to the value for prioritization and screening, further research is needed to tailor models on structural features and metabolic competence typical for the genitourinary tract. Even though the issue of urinary occurring contaminants might appear less pressing in comparison to the evaluation of other toxicity endpoints (i.e. cardiotoxic or neurotoxic insults), the research on models capable of reproducing acute and chronic toxicity in the urinary tract remains of

## Key knowledge gaps, limiting factors and possible solution:

<figure><img src="image_12.png" /></figure>

**RASFF notifications report the widespread presence of toxicants, which may associate with significant human exposure and accumulation in urine, as corroborated by HBM data. This observation does not parallel with the development of NAMs/ AOPs focusing on bladder and kidney toxicity.**

<figure><img src="image_13.png" /></figure>

**Mechanistic studies are scarce, especially for the bladder as target organ. This knowledge would be a prerequisite for NGRA and AOP development. Therefore, increased NAM-based research to elucidate molecular mechanisms of toxicity affecting the urinary tract is needed.**

<figure><img src="image_14.png" /></figure>

**Regulatory acceptance for NAM is increasing, yet implementation is still slow, despite multiple road maps and guidance documents supporting their application for risk assessment. Even more communication and collaboration between researchers, regulators, and industry needs to be fostered, as no sector alone will be able to tackle this issue holistically.**

<figure><img src="image_15.png" /></figure>

**Method development often lacks on reproducibility and reliability, which are prerequisites for their validation. Initial guidelines defining fit-for-purpose requirements exist, therefore among the big challenges ahead there will be adaptation or standardization of methods for regulatory use**

<figure><img src="image_16.png" /></figure>

**Many state-of-the-art methods such as bladder/kidney-on-chip or organoids were developed for other purposes (e.g. studies on urinary tract infections or cancer research) yet could be translated to the field of toxicology.**

<figure><img src="image_17.png" /></figure>

**HBM studies act as useful tools to assess the human exposome, yet interlaboratory comparison is currently difficult. Requiring global harmonization strategies (i.e. consensus in normalization) to enable worldwide exchange and comparison of exposure data.**

<figure><img src="image_18.png" /></figure>

**AI is in the process of transforming toxicology; its use may raise the field to unprecedented levels of efficiency and could truly push a new generation of risk assessment. Human oversight and knowledge-based trust can support implementation of this technology at its full potential for research and regulation.**

<caption>**Fig. 10.** Summary of key knowledge gaps, limiting factors and possible solutions, concerning the development, implementation and use of NAMs for risk assessment of toxicants in the urinary tract.</caption>

great importance; this relates to potentially life threatening (e.g. kidney failure (Jadot et al., 2017) or bladder cancer (Bonassi et al., 1989)) and chronic pathologies accompanied by high discomfort and reduced quality of life (e.g. chronic kidney disease requiring dialysis/transplant (Flythe and Watnick, 2024; Zheng et al., 2014) or cystitis/painful bladder syndrome (Grundy et al., 2018)). Enhanced development of NAMs for hazard characterization in combination with next generation biomonitoring programs, promises to open new frontiers for personalized medicine and risk assessment capable of depicting exposure scenarios representative of individual environmental and lifestyle contributions.

## Licensing of third-party sources

Parts of the graphical abstract and Fig. 7 were drawn by using pictures taken and adapted from Servier Medical Art (https://smart.servier.com/). Servier Medical Art by Servier is licensed under a Creative
Commons Attribution 4.0 Unported License (https://creativecommons.org/licenses/by/4.0/). Parts of the graphical abstract (3D protein structures) were taken from the RCSB PDB (RCSB.org (Berman et al., 2000; Burley et al., 2022), www.wwpdb.org (Berman et al., 2003)) of PDB IDs: 6LQI (Geng et al., 2020) and 8SDZ (Dou et al., 2023).

## CRediT authorship contribution statement

**Giorgia Del Favero:** Writing – review & editing, Writing – original draft, Conceptualization. **Benedikt Warth:** Writing – review & editing, Writing – original draft. **Ana S. Fernandes:** Writing – review & editing, Writing – original draft. **Isabelle P. Oswald:** Writing – review & editing, Writing – original draft. **Delphine Payros:** Writing – review & editing, Writing – original draft. **Irís Guerreiro:** Writing – review & editing, Writing – original draft. **Maximilian Jobst:** Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Conceptualization.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Isabelle P. Oswald reports financial support was provided by INRAE-INSERM Uritox project. Iris Guerreiro reports financial support was provided by Fundação para a Ciência e a Tecnologia (FCT, Portugal). If there are other authors, they 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, including open access publication, was supported by the University of Vienna. (intramural funding). Additionally, the authors, acknowledge the support of the “INRAE-INSERM Uritox project” and Fundação para a Ciência e a Tecnologia (FCT, Portugal) for the PhD grant (2020.07813.BD).

## Data availability

Data will be made available on request.

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