Dr. Arif Dönmez

Toxicological test methods generate raw data and provide instructions on how to use these to determine a final outcome such as a classification of test compounds as hits or non-hits. The data processing pipeline provided in the test method description is often highly complex. Usually, multiple layers of data, ranging from a machine-generated output to the final hit definition, are considered. Transition between each of these layers often requires several data processing steps. As changes in any of these processing steps can impact the final output of new approach methods (NAMs), the processing pipeline is an essential part of a NAM description and should be included in reporting templates such as the ToxTemp. The same raw data, processed in different ways, may result in different final outcomes that may affect the readiness status and regulatory acceptance of the NAM, as an altered output can affect robustness, performance, and relevance. Data management, pro­cessing, and interpretation are therefore important elements of a comprehensive NAM definition. We aim to give an overview of the most important data levels to be considered during the devel­opment and application of a NAM. In addition, we illustrate data processing and evaluation steps between these data levels. As NAMs are increasingly standard components of the spectrum of toxi­cological test methods used for risk assessment, awareness of the significance of data processing steps in NAMs is crucial for building trust, ensuring acceptance, and fostering the reproducibility of NAM outcomes.

Plain language summary
Toxicological test methods initially generate raw data. These need to be further processed to determine a final outcome, such as the classification of test compounds as hits or non-hits. The pro­cessing of the raw data is often highly complex and proceeds stepwise. This process generates many layers of data connected by several processing steps. Any change to these processing steps can impact the final output of new approach methods (NAMs). This means that the same raw data, processed in different ways, may result in different final outcomes. Data management, processing and interpretation are therefore considered important elements of a comprehensive NAM definition. We illustrate data processing and evaluation steps that play an important role. Awareness of the significance of data processing steps in NAMs is crucial for building trust, ensuring acceptance, and fostering the reproducibility of NAM outcomes.

Hormone signaling plays an essential role during fetal life and is vital for brain development. Endo­crine-disrupting chemicals can interfere with the hormonal milieu during this critical time-period, disrupting key neurodevelopmental processes. Hence, there is a need for the development of assays that evaluate developmental neurotoxicity (DNT) induced by an endocrine mode of action. Herein, we evaluated the neural progenitor C17.2 cell line as an in vitro test system to aid in the detection of endocrine disruption-induced DNT. For this, C17.2 cells were exposed during 10 days of dif­ferentiation to agonists and antagonists of the thyroid hormone (THR), glucocorticoid (GR), retinoic acid (RAR), retinoic x (RXR), oxysterol (LXR), estrogen (ER), androgen (AR), and peroxisome prolif­erator activated delta (PPARβ/δ) receptors, as well as to the agonist of the vitamin D (VDR) receptor. Upon exposure and differentiation, neuronal morphology (neurite outgrowth and branching) and the percentage of neurons in culture were assessed by immunofluorescence. For this, the cells were stained for βIII-tubulin (neuronal marker). C17.2 cells decreased neurite outgrowth and branching in response to RAR, RXR and PPARβ/δ agonists. Exposure to the GR agonist increased the number of cells differentiating into neurons, while exposure to the RXR agonist had the opposite effect. With this approach, we demonstrate that C17.2 cells are responsive to GR, RAR, RXR, and PPARβ/δ agonists and hence could be useful to develop a test system for hazard assessment of endocrine disruption-induced DNT.

Plain language summary
Hormones play a vital role for an organism’s development, including brain development. Endo­crine disrupting chemicals (EDCs) interfere with the hormone system. Exposure to EDCs while a fetus is developing can cause a toxic effect on the nervous system called developmental neurotoxicity (DNT). In Europe, the use of chemicals shown to be EDCs may be restricted. Animal tests for devel­opmental neurotoxicity require many animals but cannot determine whether a chemical causes DNT via endocrine disruption or other mechanisms. We have developed a method to identify endocrine disruption-caused DNT using a mouse nerve cell line. In culture, these cells extend long processes called neurites that branch out, and we can measure the length and branching rate of the neurites. We show that this maturation process is dependent on hormonal signals and can therefore be used to identify chemicals that interfere with nerve cell maturation via these signals.

Proper brain development is based on the orchestration of key neurodevelopmental processes (KNDP), including the for­mation and function of neural networks. If at least one KNDP is affected by a chemical, an adverse outcome is expected. To enable a higher testing throughput than the guideline animal experiments, a developmental neurotoxicity (DNT) in vitro testing battery (DNT IVB) comprising a variety of assays that model several KNDPs was set up. Gap analysis revealed the need for a human-based assay to assess neural network formation and function (NNF). Therefore, we established the human NNF (hNNF) assay. A co-culture comprised of human induced pluripotent stem cell (hiPSC)-derived excitatory and inhibitory neurons as well as primary human astroglia was differentiated for 35 days on microelectrode arrays (MEA), and spontaneous electrical activity, together with cytotoxicity, was assessed on a weekly basis after washout of the compounds 24 h prior to measurements. In addition to the characterization of the test system, the assay was challenged with 28 com­pounds, mainly pesticides, identifying their DNT potential by evaluating specific spike-, burst-, and network parameters. This approach confirmed the suitability of the assay for screening environmental chemicals. Comparison of benchmark con­centrations (BMC) with an NNF in vitro assay (rNNF) based on primary rat cortical cells revealed differences in sensitivity. Together with the successful implementation of hNNF data into a postulated stressor-specific adverse outcome pathway (AOP) network associated with a plausible molecular initiating event for deltamethrin, this study suggests the hNNF assay as a useful complement to the DNT IVB.