Qualitative and quantitative concentration-response modelling of gene co-expression networks to unlock hepatotoxic mechanisms for next generation chemical safety assessment.

Next generation risk assessment of chemicals revolves around the use of mechanistic information without animal experimentation. In this regard, toxicogenomics has proven to be a useful tool to elucidate the underlying mechanisms of adverse effects of xenobiotics. In the present study, two widely used human in vitro hepatocyte culture systems, namely primary human hepatocytes (PHH) and human hepatoma HepaRG cells, were exposed to liver toxicants known to induce liver cholestasis, steatosis or necrosis. Benchmark concentration-response modelling was applied to transcriptomics gene co-expression networks (modules) to derive benchmark concentrations (BMCs) and to gain mechanistic insight into the hepatotoxic effects. BMCs derived by concentration-response modelling of gene co-expression modules recapitulated concentration-response modelling of individual genes. Although PHH and HepaRG cells showed overlap in deregulated genes and modules by the liver toxicants, PHH demonstrated a higher responsiveness, based on the lower BMCs of co-regulated gene modules. Such BMCs can be used as transcriptomics point of departure (tPOD) for assessing module-associated cellular (stress) pathways/processes. This approach identified clear tPODs of around maximum systemic concentration (Cmax) levels for the tested drugs, while for cosmetics ingredients the BMCs were 10-100-fold higher than the estimated plasma concentrations. This approach could serve next generation risk assessment practice to identify early responsive modules at low BMCs, that could be linked to key events in liver adverse outcome pathways. In turn, this can assist in delineating potential hazards of new test chemicals using in vitro systems and used in a risk assessment when BMCs are paired with chemical exposure assessment.

Risk assessment of chemicals has traditionally been focused on animal experiments. In contrast, next generation risk assessment uses biological information obtained from experiments in cell culture models without animals to identify potential hazards. Since the liver is the main target organ of toxicity, many liver cell (hepatocyte) models have been developed and applied for hazard assessment. In this study, two widely used human hepatocyte cell models, PHH and HepaRG, were exposed to liver toxic chemicals. Biological changes in gene expression were measured in a concentration range to identify the concentration at which a biological response was perturbed using concentration response modelling. Genes belonging to the same biological process were joined based on co-expression to derive an average concentration of this process. This animal-free approach could be applied for risk assessment when biological response concentrations were related to the expected human exposure to identify potential hazard of the test chemicals.

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.

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

Green toxicology is marching chemistry into the 21st century. This emerging framework will transform how chemical safety is evaluated by incorporating evaluation of the hazards, exposures, and risks associated with chemicals into early product development in a way that minimizes adverse impacts on human and environmental health. The goal is to minimize toxic threats across entire supply chains through smarter designs and policies. Traditional animal testing methods are replaced by faster, cutting-edge innovations like organs-on-chips and artificial intelligence predictive models that are also more cost-effective. Core principles of green toxicology include utilizing alternative test methods, applying the precautionary principle, considering lifetime impacts, and emphasizing risk prevention over reaction. This paper provides an overview of these foundational concepts and describes current initiatives and future opportunities to advance the adoption of green toxicology approaches. Chal-lenges and limitations are also discussed. Green shoots are emerging with governments offering carrots like the European Green Deal to nudge industry. Noteworthy, animal rights and environ-mental groups have different ideas about the needs for testing and their consequences for animal use. Green toxicology represents the way forward to support both these societal needs with sufficient throughput and human relevance for hazard information and minimal animal suffering. Green toxi-cology thus sets the stage to synergize human health and ecological values. Overall, the integration of green chemistry and toxicology has potential to profoundly shift how chemical risks are evaluated and managed to achieve safety goals in a more ethical, ecologically-conscious manner.

Green toxicology aims to make chemicals safer by design. It focuses on preventing toxicity issues early during development instead of testing after products are developed. Green toxicology uses modern non-animal methods like computer models and lab tests with human cells to predict if chem­icals could be hazardous. Benefits are faster results, lower costs, and less animal testing. The principles of green toxicology include using alternative tests, applying caution even with uncertain data, con­sidering lifetime impacts across global supply chains, and emphasizing prevention over reaction. The article highlights European and US policy efforts to spur sustainable chemistry innovation which will necessitate greener approaches to assess new materials and drive adoption. Overall, green toxi­cology seeks to integrate safer design concepts so that human and environmental health are valued equally with functionality and profit. This alignment promises safer, ethical products but faces chal­lenges around validating new methods and overcoming institutional resistance to change.

EPA scraps plan to end all testing in mammals by 2035.

U.S. environmental agency's hard deadline had split scientific community.

Developing prototypes of a modernized approach to assess crop protection chemical safety.

In 2019, the US EPA Administrator issued a directive directing the agency away from reliance on vertebrate tests by 2035, whilst maintaining high-quality human health and environmental risk assessments. There is no accepted approach to achieve this. The decade-long duration of the crop protection (CP) chemical R&D process therefore requires both the invention and application of a modernized approach to those CP chemical projects entering corporate research portfolios by the mid-2020s. We conducted problem formulation discussions with regulatory agency scientists which created the problem statement: "Develop, demonstrate, and implement a modern scientifically sound and robust strategy that applies appropriate and flexible exposure and effects characterization without chemical specific vertebrate tests to reliably address risk, uncertainties, and deficiencies in data and its interpretation with equivalent confidence as do the currently accepted test guidelines and meet the regulatory needs of the agencies". The solution must provide the knowledge needed to confidently conclude human health and environmental protective risk assessments. Exploring this led to a conceptual model involving the creation and parallel submission of a new approach without reliance on chemical-specific vertebrate tests. Assessment in parallel to a traditional package will determine whether it supports some, or all, of the necessary risk management actions. Analysis of any deficiencies will provide valuable feedback to focus development of tools or approaches for subsequent iterations. When found to provide sufficient information, it will form the technical foun­dation of stakeholder engagement to explore acceptance of a new approach to CP chemical risk assessment.

The US EPA, and other regulatory agencies, aim to reduce the use of vertebrate animal tests for assessing risks of crop protection chemicals. There is currently no accepted way to do this. We outline a proposal to perform both the assessment using traditional vertebrate testing and a set of new non-animal methods. These data sets must each be combined with a calculated estimate of user exposure to the pesticide based on its intended use. Comparing the outcome of these two assess­ments will show whether the set of non-animal methods needs to be improved further. When the new approach appears to reliably predict the risks, the different stakeholders must be brought together to assess whether the non-animal methods package is acceptable and can replace the tests on vertebrate animals while maintaining the same level of protection of human health and the environment.

[Understanding the Underlying Mechanism of Xenobiotic-Sensing Nuclear Receptor Activation].

The nuclear receptor superfamily comprises 48 members in humans. In various organs, nuclear receptors regulate a variety of physiological functions through transcription of target genes. They are associated with the development and progression of endocrine and metabolic disorders, as well as with cancer development. Therefore, agonists and antagonists targeting nuclear receptors are currently being developed as therapeutic drugs for these diseases. Nuclear receptors can be activated through ligand binding or phosphorylation, which is mediated by various cellular signaling pathways. Activation of a nuclear receptor necessitates significant structural modifications in each of its domains. My research has been focused on unraveling the intricate mechanisms underlying the activation of nuclear receptors using constitutive androstane receptor (CAR) and pregnane X receptor (PXR) as model nuclear receptor proteins. CAR and PXR are highly expressed in the liver and are activated by a wide range of xenobiotics. Given their crucial roles in the metabolism and disposition of xenobiotics, as well as their potential in mediating drug-drug interactions, it is imperative to extensively study the mechanisms of xenobiotic-induced activation of these receptors. Such studies are essential for advancements in drug development, as well as for ensuring food and chemical safety. In this review, I elucidate the molecular basis underlying the activation of xenobiotic-responsive nuclear receptors.

Disclosing Environmental Ligands of L-FABP and PPARγ: Should We Re-evaluate the Chemical Safety of Hydrocarbon Surfactants?

Chemical contaminants can cause adverse effects by binding to the liver-fatty acid binding protein (L-FABP) and peroxisome proliferator-activated nuclear receptor γ (PPARγ), which are vital in lipid metabolism. However, the presence of numerous compounds in the environment has hindered the identification of their ligands, and thus only a small portion have been discovered to date. In this study, protein Affinity Purification with Nontargeted Analysis (APNA) was employed to identify the ligands of L-FABP and PPARγ in indoor dust and sewage sludge. A total of 83 nonredundant features were pulled-out by His-tagged L-FABP as putative ligands, among which 13 were assigned as fatty acids and hydrocarbon surfactants. In contrast, only six features were isolated when His-tagged PPARγ LBD was used as the protein bait. The binding of hydrocarbon surfactants to L-FABP and PPARγ was confirmed using both recombinant proteins and reporter cells. These hydrocarbon surfactants, along with >50 homologues and isomers, were detected in dust and sludge at high concentrations. Fatty acids and hydrocarbon surfactants explained the majority of L-FABP (57.7 ± 32.9%) and PPARγ (66.0 ± 27.1%) activities in the sludge. This study revealed hydrocarbon surfactants as the predominant synthetic ligands of L-FABP and PPARγ, highlighting the importance of re-evaluating their chemical safety.

Mode of action hypothesis testing in chemical safety assessments using metabolomics as supporting evidence: Phenobarbital and cyclobutrifluram metabolomics profile comparison.

In long term rodent studies administering Cyclobutrifluram (TYMIRIUM® Technology), a new agrochemical, there was a slight elevation of incidence of hepatocellular carcinomas in male CD-1 mice that was within the historical control range but appeared to be dose responsive. Cyclobutrifluram's ability to activate mouse constitutive androstane receptor (CAR) mediated gene transcription was confirmed in vitro, therefore a 28-day dietary toxicity study was conducted in vivo in male CD-1 mice to assess the CAR activation mode of action hypothesis of Cyclobutrifluram along with phenobarbital, a known CAR activator. In addition to other end points comprehensive (polar and lipidomic) hybrid metabolomics analyses were performed on terminal plasma and liver samples following 2-, 7- and 28-days dietary exposure to cyclobutrifluram and phenobarbital. The data generation and quality assessments were performed in line with the principles of the MEtabolomics standaRds Initiative in Toxicology (MERIT).First the full annotated feature set was used to compare the metabolomic changes induced by the administration of the two test substances using Shared and Unique Structures plots. This gave a comprehensive overview of the similarity of the two effect profiles showing good correlation and demonstrated that no other, alternative effect signatures were detected. Then the phenobarbital induced differentially abundant metabolites were selected, compared to the literature and their direction of change was assessed in cyclobutrifluram profiles, finding good agreement. Both approaches concluded that the metabolomics data supports the CAR activation hypothesis. Comparison of the metabolomic effect profiles can be a line of evidence in mode of action hypothesis testing in the chemical risk assessment process.

The 19th FRAME Annual Lecture, November 2022: Safer Chemicals and Sustainable Innovation Will Be Achieved by Regulatory Use of Modern Safety Science, Not by More Animal Testing.

The decisions we make on chemical safety, for consumers, workers and the environment, must be based on the best scientific data and knowledge available. Rapid advances in biology, in cell-based technologies and assays, and in analytical and computational approaches, have led to new types of highly relevant scientific data being generated. Such data enable us to improve the safety decisions we make, whilst also enabling us to avoid animal testing. Stimulated by the UK and EU bans on animal testing for cosmetics, Next Generation Risk Assessment (NGRA) approaches, which integrate various types of non-animal scientific data, have been established for assessing the safety of chemical ingredients used in cosmetics and other consumer products. In stark contrast, the chemicals regulations in Europe and other parts of the world have not kept pace with modern safety science and regulators are now mandating even more animal testing. Urgently closing this science-regulation gap is essential to upholding the EU's legislative requirement that any animal testing is a last resort. The ongoing revisions of UK and EU chemicals strategy and regulations provide an opportunity to fundamentally change the design and assessment paradigm needed to underpin safe and more sustainable innovation, through applying the best science and tools available rather than continuing to be anchored in animal tests dating back many decades. A range of initiatives have recently been launched in response to this urgent need, in the UK as well as in the EU.

Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations.

Explainable machine learning for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experimental cost and time while mitigating ethical concerns by significantly reducing animal and clinical testing. Herein, we use a deep learning framework for simultaneously modeling in vitro, in vivo, and clinical toxicity data. Two different molecular input representations are used; Morgan fingerprints and pre-trained SMILES embeddings. A multi-task deep learning model accurately predicts toxicity for all endpoints, including clinical, as indicated by the area under the Receiver Operator Characteristic curve and balanced accuracy. In particular, pre-trained molecular SMILES embeddings as input to the multi-task model improved clinical toxicity predictions compared to existing models in MoleculeNet benchmark. Additionally, our multitask approach is comprehensive in the sense that it is comparable to state-of-the-art approaches for specific endpoints in in vitro, in vivo and clinical platforms. Through both the multi-task model and transfer learning, we were able to indicate the minimal need of in vivo data for clinical toxicity predictions. To provide confidence and explain the model's predictions, we adapt a post-hoc contrastive explanation method that returns pertinent positive and negative features, which correspond well to known mutagenic and reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent feature analysis captures more of the in vitro (53%) and in vivo (56%), rather than of the clinical (8%), endpoints, and indeed uncovers a preference in known toxicophore data towards in vitro and in vivo experimental data. To our knowledge, this is the first contrastive explanation, using both present and absent substructures, for predictions of clinical and in vivo molecular toxicity.

Differences in perception of the importance of process safety indicators between experts in Iran and the West.

INTRODUCTION: The importance of safety in high-risk industries such as oil and gas facilities has been reported previously. Process safety performance indicators can provide insight into improving the safety of process industries. This paper aims to rank the process safety indicators (metrics) by Fuzzy Best-Worst Method (FBWM) using the data gathered through a survey. METHOD: The study uses a structured approach considering the UK Health and Safety Executive (HSE), the Center for Chemical Process Safety (CCPS), and the IOGP (International Association of Oil and Gas Producers) recommendations and guidelines to generate an aggregate set of indicators. It calculates the level of importance of each indicator based on the opinions of experts from Iran and some Western countries. RESULTS: The findings of the study demonstrate that some lagging indicators such as the number of times processes do not proceed as planned due to insufficient staff competence and the number of unexpected disruptions of the process due to failure in instrumentation and alarms are important in process industries in both Iran and Western countries. Western experts identified process safety incident severity rate as an important lagging indicator, whereas Iranian experts considered this as relatively unimportant. In addition, leading indicators such as sufficient process safety training and competency, the desired function of instrumentation and alarms, and proper management of fatigue risk play an important role in enhancing the safety performance of process industries. Experts in Iran viewed permit to work as an important leading indicator, while experts in the West focused on fatigue risk management. PRACTICAL APPLICATIONS: The methodology used in the current study gives a good view to managers and safety professionals in regard to the most important indicators of process safety and allows them to focus more on important process safety indicators.

Towards harmonisation of testing of nanomaterials for EU regulatory requirements on chemical safety - A proposal for further actions.

Over the recent years, EU chemicals legislation, guidance and test guidelines have been developed or adapted for nanomaterials to facilitate safe use of nanomaterials. This paper provides an overview of the information requirements across different EU regulatory areas. For each information requirement, a group of 22 experts identified potential needs for further action to accommodate guidance and test guidelines to nanomaterials. Eleven different needs for action were identified, capturing twenty-two information requirements that are specific to nanomaterials and relevant to multiple regulatory areas. These were further reduced to three overarching issues: 1) resolve issues around nanomaterial dispersion stability and dosing in toxicity testing, in particular for human health endpoints, 2) further develop tests or guidance on degradation and transformation of organic nanomaterials or nanomaterials with organic components, and 3) further develop tests and guidance to measure (a)cellular reactivity of nanomaterials. Efforts towards addressing these issues will result in better fit-for-purpose test methods for (EU) regulatory compliance. Moreover, it secures validity of hazard and risk assessments of nanomaterials. The results of the study accentuate the need for a structural process of identification of information needs and knowledge generation, preferably as part of risk governance and closely connected to technological innovation policy.

A continuous in silico learning strategy to identify safety liabilities in compounds used in the leather and textile industry.

There is a widely recognized need to reduce human activity's impact on the environment. Many industries of the leather and textile sector (LTI), being aware of producing a significant amount of residues (Keßler et al. 2021; Liu et al. 2021), are adopting measures to reduce the impact of their processes on the environment, starting with a more comprehensive characterization of the chemical risk associated with the substances commonly used in LTI. The present work contributes to these efforts by compiling and toxicologically annotating the substances used in LTI, supporting a continuous learning strategy for characterizing their chemical safety. This strategy combines data collection from public sources, experimental methods and in silico predictions for characterizing four different endpoints: CMR, ED, PBT, and vPvB. We present the results of a prospective validation exercise in which we confirm that in silico methods can produce reasonably good hazard estimations and fill knowledge gaps in the LTI chemical space. The proposed protocol can speed the process and optimize the use of resources including the lives of experimental animals, contributing to identifying potentially harmful substances and their possible replacement by safer alternatives, thus reducing the environmental footprint and impact on human health.

Enhancing global and local decision making for chemical safety assessments through increasing the availability of data.

Toxicity safety assessments are a fundamental part of the lifecycle of products and aim to protect human health and the environment from harmful exposures to chemical substances. To make decisions regarding the suitability of testing strategies, the applicability of individual tests or concluding an assessment for an individual chemical requires data. This review outlines how different forms of data sharing, from enhancing publicly-available data to extracting knowledge from commercially-sensitive data, leads to increased quantity and quality of evidence being available for safety assessors to review. This can result in more confident decisions for different use cases in the context of chemical safety assessments. Although a number of challenges remain with progressing the evolution of toxicity safety assessments, data sharing should be considered as a key approach to accelerating the development and uptake of new best practices.

A microfluidic thyroid-liver platform to assess chemical safety in humans.

Thyroid hormones (THs) are crucial regulators of human metabolism and early development. During the safety assessment of plant protection products, the human relevance of chemically induced TH perturbations observed in test animals remains uncertain. European regulatory authorities request follow-up in vitro studies to elucidate human-relevant interferences on thyroid gland function or TH catabolism through hepatic enzyme induction. However, human in vitro assays based on single molecular initiating events poorly reflect the complex TH biology and related liver-thyroid axis. To address this complexity, we present human three-dimensional thyroid and liver organoids with key functions of TH metabolism. The thyroid model resembles in vivo-like follicular architecture and a TSH-dependent triiodothyronine synthesis over 21 days, which is inhibited by methimazole. The HepaRG-based liver model, secreting the critical TH-binding proteins albumin and thyroxine-binding globulin, emulates an active TH catabolism via the formation of glucuronidated and sulfated thyroxine (gT4/sT4). Activation of the nuclear receptors PXR and AHR was demonstrated via the induction of specific CYP isoenzymes by rifampicin, pregnenolone-16α-carbonitrile, and ß-naphthoflavone. However, this nuclear receptor activation, assumed to regulate UDP-glucuronosyltransferases and sulfotransferases, appeared to have no effect on gT4 and sT4 formation in this human-derived hepatic cell line model. Finally, established single-tissue models were successfully co-cultured in a perfused two-organ chip for 21 days. In conclusion, this model presents a first step towards a complex multimodular human platform that will help to identify both direct and indirect thyroid disruptors that are relevant from a human safety perspective.

Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach.

Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.

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.

Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment.

Omic-based technologies are of particular interest and importance for hazard identification and health risk characterization of chemicals. Their application in the new approach methodologies (NAMs) anchored on cellular toxicity pathways is based on the premise that any apical health endpoint change must be underpinned by some alterations at the omic levels. In the present study we examined the cellular responses to two chemicals, caffeine and coumarin, by generating and integrating multi-omic data from multi-dose and multi-time point transcriptomic, proteomic and phosphoproteomic experiments. We showed that the methodology presented here was able to capture the complete chain of events from the first chemical-induced changes at the phosphoproteome level, to changes in gene expression, and lastly to changes in protein abundance, each with vastly different points of departure (PODs). In HepG2 cells we found that the metabolism of lipids and general cellular stress response to be the dominant biological processes in response to caffeine and coumarin exposure, respectively. The phosphoproteomic changes were detected early in time, at very low doses and provided a fast, adaptive cellular response to chemical exposure with 7-37-fold lower points of departure comparing to the transcriptomics. Changes in protein abundance were found much less frequently than transcriptomic changes. While challenges remain, our study provides strong and novel evidence supporting the notion that these three omic technologies can be used in an integrated manner to facilitate a more complete understanding of pathway perturbations and POD determinations for risk assessment of chemical exposures.