Safety Evaluation Of Chemicals Research Articles

Applications of 3D organoids in toxicological studies: a comprehensive analysis based on bibliometrics and advances in toxicological mechanisms.

A mechanism exploration is an important part of toxicological studies. However, traditional cell and animal models can no longer meet the current needs for in-depth studies of toxicological mechanisms. The three-dimensional (3D) organoid derived from human embryonic stem cells (hESC) or induced pluripotent stem cells (hiPSC) is an ideal experimental model for the study of toxicological effects and mechanisms, which further recapitulates the human tissue microenvironment and provides a reliable method for studying complex cell-cell interactions. This article provides a comprehensive overview of the state of the 3D organoid technology in toxicological studies, including a bibliometric analysis of the existing literature and an exploration of the latest advances in toxicological mechanisms. The use of 3D organoids in toxicology research is growing rapidly, with applications in disease modeling, organ-on-chips, and drug toxicity screening being emphasized, but academic communications among countries/regions, institutions, and research scholars need to be further strengthened. Attempts to study the toxicological mechanisms of exogenous chemicals such as heavy metals, nanoparticles, drugs and organic pollutants are also increasing. It can be expected that 3D organoids can be better applied to the safety evaluation of exogenous chemicals by establishing a standardized methodology.

AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks

Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained significant attention, driven by the need for efficient and cost-effective toxicity assessments. In this paper, we proposed AMPred-CNN, an innovative Ames mutagenicity prediction model based on Convolutional Neural Networks (CNNs), uniquely employing molecular structures as images to leverage CNNs’ powerful feature extraction capabilities. The study employs the widely used benchmark mutagenicity dataset from Hansen et al. for model development and evaluation. Comparative analyses with traditional ML models on different molecular features reveal substantial performance enhancements. AMPred-CNN outshines these models, demonstrating superior accuracy, AUC, F1 score, MCC, sensitivity, and specificity on the test set. Notably, AMPred-CNN is further benchmarked against seven recent ML and DL models, consistently showcasing superior performance with an impressive AUC of 0.954. Our study highlights the effectiveness of CNNs in advancing mutagenicity prediction, paving the way for broader applications in toxicology and drug development.

Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine).

The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured-a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.

Reproducibility and Robustness of a Liver Microphysiological System PhysioMimix LC12 under Varying Culture Conditions and Cell Type Combinations.

The liver is one of the key organs for exogenous and endogenous metabolism and is often a target for drug- and chemical-driven toxicity. A wide range of experimental approaches has been established to model and characterize the mechanisms of drug- and chemical-induced hepatotoxicity. A number of microfluidics-enabled in vitro models of the liver have been developed, but the unclear translatability of these platforms has hindered their adoption by the pharmaceutical industry; to achieve wide use for drug and chemical safety evaluation, demonstration of reproducibility and robustness under various contexts of use is required. One of these commercially available platforms is the PhysioMimix LC12, a microfluidic device where cells are seeded into a 3D scaffold that is continuously perfused with recirculating cell culture media to mimic liver sinusoids. Previous studies demonstrated this model's functionality and potential applicability to preclinical drug development. However, to gain confidence in PhysioMimix LC12's robustness and reproducibility, supplementary characterization steps are needed, including the assessment of various human hepatocyte sources, contribution of non-parenchymal cells (NPCs), and comparison to other models. In this study, we performed replicate studies averaging 14 days with either primary human hepatocytes (PHHs) or induced pluripotent stem cell (iPSC)-derived hepatocytes, with and without NPCs. Albumin and urea secretion, lactate dehydrogenase, CYP3A4 activity, and metabolism were evaluated to assess basal function and metabolic capacity. Model performance was characterized by different cell combinations under intra- and inter-experimental replication and compared to multi-well plates and other liver platforms. PhysioMimix LC12 demonstrated the highest metabolic function with PHHs, with or without THP-1 or Kupffer cells, for up to 10-14 days. iPSC-derived hepatocytes and PHHs co-cultured with additional NPCs demonstrated sub-optimal performance. Power analyses based on replicate experiments and different contexts of use will inform future study designs due to the limited throughput and high cell demand. Overall, this study describes a workflow for independent testing of a complex microphysiological system for specific contexts of use, which may increase end-user adoption in drug development.

Stimulation of insect vectors of pathogens by sublethal environmental contaminants: A hidden threat to human and environmental health?

Sublethal stimulation and hormetic responses are increasingly identified and acknowledged in scientific research. However, the occurrence and characteristics of such responses in insect vectors of pathogens are little explored and poorly understood. Here, we collate significant evidence from the scientific literature showing that sublethal doses of environmental contaminants, such as pesticides, microplastics, and plasticizers, induce stimulation and hormetic responses in insect vectors of pathogens of agricultural and public health importance, including mosquitoes, other dipterans, psyllids, aphids, and planthoppers. Physiological, behavioral, and demographic traits can be enhanced by exposure to lower subtoxic contaminant doses while being inhibited by higher toxic doses. Energetic trade-offs can also occur, especially at sublethal doses higher than the no-observed-adverse-effect level (NOAEL). The relevant literature is limited and so are the number of doses commonly included in the studies, precluding firm conclusions and enhanced understanding. Nevertheless, these effects are significant and could undermine human and environmental health, and thus sustainability agendas, if ultimately the transmission of pathogens and disease spread and severity are increased. Further research is urgently needed to tackle these phenomena, especially under field conditions. The findings discussed here are relevant to chemical risk assessment and chemical safety evaluations, in which all possible effects from the lowest to higher doses should be considered.

Nervous system development related gene expression regulation in the zebrafish embryo after exposure to valproic acid and retinoic acid: A genome wide approach

The evaluation of chemical and pharmaceutical safety for humans is moving from animal studies to New Approach Methodologies (NAM), reducing animal use and focusing on mechanism of action, whilst enhancing human relevance. In developmental toxicology, the mechanistic approach is facilitated by the assessment of predictive biomarkers, which allow mechanistic pathways perturbation monitoring at the basis of human hazard assessment. In our search for biomarkers of maldevelopment, we focused on chemically-induced perturbation of the retinoic acid signaling pathway (RA-SP), a major pathway implicated in a plethora of developmental processes. A genome-wide expression screening was performed on zebrafish embryos treated with two teratogens, all-trans retinoic acid (ATRA) and valproic acid (VPA), and a non-teratogen reference compound, folic acid (FA). Each compound was found to have a specific mRNA expression profile with 248 genes commonly dysregulated by both teratogenic compounds but not by FA. These genes were implicated in several developmental processes (e.g., the circulatory and nervous system). Given the prominent response of neurodevelopmental gene sets, and the crucial need to better understand developmental neurotoxicity, our study then focused on nervous system development. We found 62 genes that are potential early neurodevelopmental toxicity biomarker candidates. These results advance NAM-based safety assessment evaluation by highlighting the usefulness of the RA-SP in providing early toxicity biomarker candidates.

Evaluation of DNA adduct damage using G-quadruplex-based DNAzyme

Toxicity assessment is a major problem in pharmaceutical candidates and industry chemicals development. However, due to the lack of practical analytical methods for DNA adduct analysis, the safety evaluation of drug and industry chemicals was severely limited. Here, we develop a DNAzyme-based method to detect DNA adduct damage for toxicity assessment of drugs and chemicals. Among 18 structural variants of G4 DNAzyme, EA2 DNAzyme exhibits an obvious DNA damaging effect of styrene oxide (SO) due to its unstable structure. The covalent binding of SO to DNAzyme disrupts the Hoogsteen hydrogen bonding sites of G-plane guanines and affects the formation of the G4 quadruplex. DNA damage chemicals reduce the peroxidase activity of the G4 DNAzyme to monitor the DNA adduct damage by disrupting the structural integrity of the G4 DNAzyme. Our method for genotoxic assessment of pharmaceutical candidates and industrial chemicals can elucidate the complex chemical pathways leading to toxicity, predict toxic effects of chemicals, and evaluate possible risks to human health.

Toxicity prediction using target, interactome, and pathway profiles as descriptors

In silico methods are essential to the safety evaluation of chemicals. Computational risk assessment offers several approaches, with data science and knowledge-based methods becoming an increasingly important sub-group. One of the substantial attributes of data science is that it allows using existing data to find correlations, build strong hypotheses, and create new, valuable knowledge that may help to reduce the number of resource intensive experiments.In choosing a suitable method for toxicity prediction, the available data and desired toxicity endpoint are two essential factors to consider. The complexity of the endpoint can impact the success rate of the in silico models. For highly complex endpoints such as hepatotoxicity, it can be beneficial to decipher the toxic event from a more systemic point of view.We propose a data science-based modelling pipeline that uses compounds` connections to tissue-specific biological targets, interactome, and biological pathways as descriptors of compounds. Models trained on different combinations of the collected, compound-target, compound-interactor, and compound-pathway profiles, were used to predict the hepatotoxicity of drug-like compounds. Several tree-based models were trained, utilizing separate and combined target, interactome and pathway level variables. The model using combined descriptors of all levels and the random forest algorithm was further optimized. Descriptor importance for model performance was addressed and examined for a biological explanation to define which targets or pathways can have a crucial role in toxicity. Descriptors connected to cytochromes P450 enzymes, heme degradation and biological oxidation received high weights. Furthermore, the involvement of other, less discussed processes in connection with toxicity, such as the involvement of RHO GTPase effectors in hepatotoxicity, were marked as fundamental. The optimized combined model using only the selected descriptors yielded the best performance with an accuracy of 0.766.The same dataset using classical Morgan fingerprints for compound representation yielded models with similar performance measures, as well as the combination of systems biology-based descriptors and Morgan fingerprints. Consequently, adding the structural information of compounds did not enhance the predictive value of the models. The developed systems biology-based pipeline comprises a valuable tool in predicting toxicity, while providing novel insights about the possible mechanisms of the unwanted events.

From Protein Sequence to Structure: The Next Frontier in Cross-Species Extrapolation for Chemical Safety Evaluations.

Computational screening for potentially bioactive molecules using advanced molecular modeling approaches including molecular docking and molecular dynamic simulation is mainstream in certain fields like drug discovery. Significant advances in computationally predicting protein structures from sequence information have also expanded the availability of structures for nonmodel species. Therefore, the objective of the present study was to develop an analysis pipeline to harness the power of these bioinformatics approaches for cross-species extrapolation for evaluating chemical safety. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool compares protein-sequence similarity across species for conservation of known chemical targets, providing an initial line of evidence for extrapolation of toxicity knowledge. However, with the development of structural models from tools like the Iterative Threading ASSEmbly Refinement (ITASSER), analyses of protein structural conservation can be included to add further lines of evidence and generate protein models across species. Models generated through such a pipeline could then be used for advanced molecular modeling approaches in the context of species extrapolation. Two case examples illustrating this pipeline from SeqAPASS sequences to I-TASSER-generated protein structures were created for human liver fatty acid-binding protein (LFABP) and androgen receptor (AR). Ninety-nine LFABP and 268 AR protein models representing diverse species were generated and analyzed for conservation using template modeling (TM)-align. The results from the structural comparisons were in line with the sequence-based SeqAPASS workflow, adding further evidence of LFABL and AR conservation across vertebrate species. The present study lays the foundation for expanding the capabilities of the web-based SeqAPASS tool to include structural comparisons for species extrapolation, facilitating more rapid and efficient toxicological assessments among species with limited or no existing toxicity data. Environ Toxicol Chem 2023;42:463-474. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Structural characterization of 1,3-bis-tert-butyl monocyclic benzene derivatives with agonistic activity towards retinoid X receptor alpha

Retinoid X receptor alpha (RXRα) plays pivotal roles in multiple biological processes, but limited information is available on the structural features of chemicals that show low affinity for RXRα, but nevertheless cause significant activation, though these may represent a human health hazard. We recently discovered that several industrial chemicals having 1,3-bis-tert-butylbenzene as a common chemical structure exhibit agonistic activity towards rat RXRα. In this study, we explored the structure-activity relationship of 1,3-bis-tert-butyl monocyclic benzene derivatives for RXRα activation by means of in vitro and in silico analyses. The results indicate that a bulky substituent at the 5-position is favorable for agonistic activity towards human RXRα. Since 1,3-bis-tert-butyl monocyclic benzene derivatives with bulky hydrophobic moieties differ structurally from known RXRα ligands such as 9-cis-retinoic acid and bexarotene, our findings may be helpful for the development of structural alerts in the safety evaluation of industrial chemicals for RXRα-based toxicity to living organisms.

A cell-based evaluation of human tyrosinase-mediated metabolic activation of leukoderma-inducing phenolic compounds

BackgroundChemical leukoderma is a skin depigmentation disorder induced through contact with certain chemicals, most of which have a p-substituted phenol structure similar to the melanin precursor tyrosine. The tyrosinase-catalyzed oxidation of phenols to highly reactive o-quinone metabolites is a critical step in inducing leukoderma through the production of melanocyte-specific damage and immunological responses. ObjectiveOur aim was to find an effective method to evaluate the formation of o-quinone by human tyrosinase and subsequent cellular reactions. MethodsHuman tyrosinase-expressing 293T cells were exposed to various phenolic compounds, after which the reactive o-quinones generated were identified as adducts of cellular thiols. We further examined whether the o-quinone formation induces reductions in cellular GSH or viability. ResultsAmong the chemicals tested, all 7 leukoderma-inducing phenols/catechol (rhododendrol, raspberry ketone, monobenzone, 4-tert-butylphenol, 4-tert-butylcatechol, 4-S-cysteaminylphenol and p-cresol) were oxidized to o-quinone metabolites and were detected as adducts of cellular glutathione and cysteine, leading to cellular glutathione reduction, whereas 2-S-cysteaminylphenol and 4-n-butylresorcinol were not. In vitro analysis using a soluble variant of human tyrosinase revealed a similar substrate-specificity. Some leukoderma-inducing phenols exhibited tyrosinase-dependent cytotoxicity in this cell model and in B16BL6 melanoma cells where tyrosinase expression was effectively modulated by siRNA knockdown. ConclusionWe developed a cell-based metabolite analytical method to detect human tyrosinase-catalyzed formation of o-quinone from phenolic compounds by analyzing their thiol-adducts. The detailed analysis of each metabolite was superior in sensitivity and specificity compared to cytotoxicity assays for detecting known leukoderma-inducing phenols, providing an effective strategy for safety evaluation of chemicals.

P12-54 Establishment of developmental toxicity test based on the integration of FGF signal disruption effects for safety evaluation of drugs and chemicals using human iPS cells

P12-54 Establishment of developmental toxicity test based on the integration of FGF signal disruption effects for safety evaluation of drugs and chemicals using human iPS cells

Monte Carlo Models for Sub-Chronic Repeated-Dose Toxicity: Systemic and Organ-Specific Toxicity.

The risk-characterization of chemicals requires the determination of repeated-dose toxicity (RDT). This depends on two main outcomes: the no-observed-adverse-effect level (NOAEL) and the lowest-observed-adverse-effect level (LOAEL). These endpoints are fundamental requirements in several regulatory frameworks, such as the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) and the European Regulation of 1223/2009 on cosmetics. The RDT results for the safety evaluation of chemicals are undeniably important; however, the in vivo tests are time-consuming and very expensive. The in silico models can provide useful input to investigate sub-chronic RDT. Considering the complexity of these endpoints, involving variable experimental designs, this non-testing approach is challenging and attractive. Here, we built eight in silico models for the NOAEL and LOAEL predictions, focusing on systemic and organ-specific toxicity, looking into the effects on the liver, kidney and brain. Starting with the NOAEL and LOAEL data for oral sub-chronic toxicity in rats, retrieved from public databases, we developed and validated eight quantitative structure-activity relationship (QSAR) models based on the optimal descriptors calculated by the Monte Carlo method, using the CORAL software. The results obtained with these models represent a good achievement, to exploit them in a safety assessment, considering the importance of organ-related toxicity.

A weight optimization method for chemical safety evaluation indicators based on the bipartite graph and random walk

Abstract In the chemical safety evaluation system, the optimization of indicator weights needs to take both experts’ evaluations and the feedback on accident influences into account. Thus, this paper proposes a comprehensive weighting method based on the association bipartite graph (ABG). The accident influences and correlation intensity between the accident and the evaluation indicators are calculated on the ABG. A random walk algorithm, which integrates the objective influences of the accidents and the subjective evaluations of experts, is designed to realize the weight optimization. Experiments prove the effectiveness of the proposed method from the perspectives of weight ranking and fitting degree.

Comprehensive microRNA-seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats

Rat is one of the most widely-used models in chemical safety evaluation and biomedical research. However, the knowledge about its microRNA (miRNA) expression patterns across multiple organs and various developmental stages is still limited. Here, we constructed a comprehensive rat miRNA expression BodyMap using a diverse collection of 320 RNA samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats with four biological replicates per group. Following the Illumina TruSeq Small RNA protocol, an average of 5.1 million 50 bp single-end reads was generated per sample, yielding a total of 1.6 billion reads. The quality of the resulting miRNA-seq data was deemed to be high from raw sequences, mapped sequences, and biological reproducibility. Importantly, aliquots of the same RNA samples have previously been used to construct the mRNA BodyMap. The currently presented miRNA-seq dataset along with the existing mRNA-seq dataset from the same RNA samples provides a unique resource for studying the expression characteristics of existing and novel miRNAs, and for integrative analysis of miRNA-mRNA interactions, thereby facilitating better utilization of rats for biomarker discovery.

Advances in alternative methods upon the vision for toxicity testing in the 21st century

With the increase of global chemical production and the aggravation of population exposure and health risks, higher requirements are put forward for chemical toxicity testing and safety evaluation.'Toxicity testing in the 21st century: a vision and a strategy' has greatly promoted the reform of toxicity testing. Toxicity testing in the new era has made great progress by using new models, new methods and new strategies, combined with interdisciplinary and high-tech advantages. While improving the efficiency of chemical toxicity testing, it also realizes more comprehensive, multi-level and high-quality data acquisition and toxicity evaluation, which provides strong support for the exploration of toxicity mode, toxicity mechanism and toxicity pathway. Focusing on the current alternative new methods of toxicity testing, this issue invites many scholars to introduce and summarize high-content analysis, three-dimensional (3D) cell culture technology, Ex vivo test, single cell sequencing and zebrafish experimental methods, in order to promote the leapfrog development of chemical toxicity testing and evaluation in China.

Early detection of urinary bladder carcinogens in rats by immunohistochemistry for γ-H2AX: a review from analyses of 100 chemicals.

In safety evaluations of chemicals, there is an urgent need to develop short-term methods to replace long-term carcinogenicity tests. We have reported that immunohistochemistry for γ-H2AX, a well-established biomarker of DNA damage, can detect bladder carcinogens at an early stage using histopathological specimens from 28-day repeated-dose oral toxicity studies in rats. Given the markedly low level of γ-H2AX formation in the bladder urothelium of untreated rats, an increase in γ-H2AX-positive cells following chemical exposure can be relatively easy to identify. Among the 100 compounds examined to date, bladder carcinogens can be detected with high sensitivity (33/39; 84.6%) and specificity (58/61; 95.1%). As expected, γ-H2AX formation levels tended to be high following exposure to genotoxic bladder carcinogens, whereas nongenotoxic bladder carcinogens also increased the number of γ-H2AX-positive cells, probably through secondary DNA damage associated with sustained proliferative stimulation. γ-H2AX formation in the bladder urothelium reflects species differences in susceptibility to bladder carcinogenesis between rats and mice and shows a clear dose-dependency associated with the intensity of tumor development as well as high reproducibility. Some of the bladder carcinogens that showed false-negative results in the evaluation of γ-H2AX alone could be detected by combined evaluation with immunostaining for bladder stem cell markers, including aldehyde dehydrogenase 1A1. This method may be useful for the early detection of bladder carcinogens, as it can be performed by simple addition of conventional immunostaining using formalin-fixed paraffin-embedded tissues from 28-day repeated-dose toxicity studies in rodents, which are commonly used in safety evaluations of chemical substances.

Impact of in vitro experimental variation in kinetic parameters on physiologically based kinetic (PBK) model simulations.

In vitro toxicokinetic data are critical in meeting an increased regulatory need to improve chemical safety evaluations towards a better understanding of internal human chemical exposure and toxicity. In vitro intrinsic hepatic clearance (CLint), the fraction unbound in plasma (fup), and the intestinal apparent permeability (Papp) are important parameters as input in a physiologically based kinetic (PBK) model to make first estimates of internal exposure after oral dosing. In the present study we explored the experimental variation in the values for these parameters as reported in the literature. Furthermore, the impact that this experimental variation has on PBK model predictions of maximum plasma concentration (Cmax) and the area under the concentration time curve (AUC0-24h) was determined. As a result of the experimental variation in CLint, Papp, and fup, the predicted variation in Cmax for individual compounds ranged between 1.4- to 28-fold, and the predicted variation in AUC0-24h ranged between 1.4- and 23-fold. These results indicate that there are still some important steps to take to achieve robust data that can be used in regulatory applications. To gain regulatory acceptance of in vitro kinetic data and PBK models based on in vitro input data, the boundaries in experimental conditions as well as the applicability domain and the use of different in vitro kinetic models need to be described in guidance documents.

Updated in silico prediction methods for fractions absorbed and absorption rate constants of 372 disparate chemicals for use in physiologically based pharmacokinetic models for estimating internal concentrations in rats.

Physiologically based pharmacokinetic (PBPK) modeling has the potential to estimate internal chemical exposures. Algorithms for predicting the input parameters for PBPK modeling, such as absorption rate constants (ka), were previously reported for 323 chemicals in rats. In this study, a currently updated system for estimating the fraction absorbed × intestinal availability of compounds, along with a modified estimation system that generates ka values, is reported, based on the previously analyzed 323 primary compounds, 10 secondary compounds, and 39 additional substances. The in silico estimation of input parameters for PBPK models (i.e., fraction absorbed × intestinal availability and ka) was adapted for an updated panel of 372 chemicals using machine learning algorithms based on between 16 and 18 in silico-calculated chemical properties. Simplified human PBPK models were then used to calculate virtual areas under the plasma concentration-time curve (AUC) based on two sets of input parameters, i.e., traditionally derived values from in vivo data and those calculated in silico using the current updated machine learning systems. The AUC data sets were well correlated; the current correlation coefficient increased from 0.61 to 0.82 (p < 0.01, n = 372). Therefore, the above-described computational methods constitute a new alternative approach that could contribute to chemical safety evaluations.

Impact of in vitro experimental variation in kinetic parameters on physiologically based kinetic (PBK) model simulations_suppl1

In vitro toxicokinetic data are critical in meeting an increased regulatory need to improve chemical safety evaluations towards a better understanding of internal human chemical exposure and toxicity. In vitro intrinsic hepatic clearance (CLint), the fraction unbound in plasma (Fup), and the intestinal apparent permeability (Papp) are important parameters as input in a physiologically based kinetic (PBK) model to make first estimates of internal exposure after oral dosing. In the present study we explored the experimental variation in the values for these parameters as reported in the literature. Furthermore, the impact that this experimental variation has on PBK model predictions of maximum plasma concentration (Cmax) and the area under the concentration time curve (AUC0-24h) was determined. As a result of the experimental variation in CLint, Papp, and Fup, the predicted variation in Cmax for individual compounds ranged between 1.4- to 28-fold and the predicted variation in AUC0-24h ranged between 1.4- and 23-fold. These results indicate that there are still some important steps to take to achieve robust data that can be used in regulatory applications. To gain regulatory acceptance of in vitro kinetic data and PBK models based on in vitro input data, the boundaries in experimental conditions as well as the applicability domain and the use of different in vitro kinetic models need to be described in guidance documents.