Perspectives on In Vitro to In Vivo Extrapolations

Wildlife ecological risk assessment in the 21st century: Promising technologies to assess toxicological effects

Despite advances in toxicity testing and the development of new approach methodologies (NAMs) for hazard assessment, the ecological risk assessment (ERA) framework for terrestrial wildlife (i.e., air-breathing amphibians, reptiles, birds, and mammals) has remained unchanged for decades. While survival, growth, and reproductive endpoints derived from whole-animal toxicity tests are central to hazard assessment, nonstandard measures of biological effects at multiple levels of biological organization (e.g., molecular, cellular, tissue, organ, organism, population, community, ecosystem) have the potential to enhance the relevance of prospective and retrospective wildlife ERAs. Other factors (e.g., indirect effects of contaminants on food supplies and infectious disease processes) are influenced by toxicants at individual, population, and community levels, and need to be factored into chemically based risk assessments to enhance the "eco" component of ERAs. Regulatory and logistical challenges often relegate such nonstandard endpoints and indirect effects to postregistration evaluations of pesticides and industrial chemicals and contaminated site evaluations. While NAMs are being developed, to date, their applications in ERAs focused on wildlife have been limited. No single magic tool or model will address all uncertainties in hazard assessment. Modernizing wildlife ERAs will likely entail combinations of laboratory- and field-derived data at multiple levels of biological organization, knowledge collection solutions (e.g., systematic review, adverse outcome pathway frameworks), and inferential methods that facilitate integrations and risk estimations focused on species, populations, interspecific extrapolations, and ecosystem services modeling, with less dependence on whole-animal data and simple hazard ratios. Integr Environ Assess Manag 2024;20:725-748. © 2023 His Majesty the King in Right of Canada and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

The Promise and Potential of Brain Organoids

Brain organoids are 3D in vitro culture systems derived from human pluripotent stem cells that self-organize to model features of the (developing) human brain. This review examines the techniques behind organoid generation, their current and potential applications, and future directions for the field. Brain organoids possess complex architecture containing various neural cell types, synapses, and myelination. They have been utilized for toxicology testing, disease modeling, infection studies, personalized medicine, and gene-environment interaction studies. An emerging concept termed Organoid Intelligence (OI) combines organoids with artificial intelligence systems to generate learning and memory, with the goals of modeling cognition and enabling biological computing applications. Brain organoids allow neuroscience studies not previously achievable with traditional techniques, and have the potential to transform disease modeling, drug development, and the understanding of human brain development and disorders. The aspirational vision of OI parallels the origins of artificial intelligence, and efforts are underway to map a roadmap toward its realization. In summary, brain organoids constitute a disruptive technology that is rapidly advancing and gaining traction across multiple disciplines.

Usage of model combination in computational toxicology

New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.

Development of a non-target strategy for evaluation of potential biological effects of inhalable aerosols generated during purposeful room conditioning using an in vitro inhalation model

OBJECTIVES

An integrated in vitro inhalation approach was outlined to estimate potential adverse acute inhalation effects of aerosols from commercial nebulizer applications used for purposeful room conditioning such as disinfection, scenting or others. Aerosol characterization, exposure estimation and evaluation of acute biological effects by in vitro inhalation were included to generate dose-response data, allowing for determination of in vitro lowest observable adverse effect levels (LOAELs). Correlation of these to estimates of human lung deposition was included for quantitative in vitro to in vivo extrapolation approach (QIVIVE) for acute effects during human exposure.

METHODS

To test the proposed approach, a case study was undertaken using two realistic test materials. An acute in vitro inhalation setup with air-liquid interface A549-cells in an optimized exposure situation (P.R.I.T.® ExpoCube®) was used to expose cells and analysis of relevant biological effects (viability, mitochondrial membrane potential, stress, IL-8 release) was carried out.

RESULTS

The observed dose-responsive effects in a sub-toxic dose-range could be attributed to the main component of one test material and its presence in the aerosol phase of the nebulized material. QIVIVE resulted in a factor of at least 256 between the in vitro LOAEL and the estimated acute human lung exposure for this test material.

CONCLUSIONS

The case-study shows the value of the non-target in vitro inhalation testing approach especially in case of a lack of knowledge on complex product composition. It is expected that approaches like this will be of high value for product safety and environmental health in the future.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.

Difficulties in translating in vitro hazards of local acute irritants to relevant human risk: Learnings from Captan and Folpet

In order to assess the regulatory value of New Approach Methodologies (NAMs), authors should provide their opinion on the physiological and exposure relevance of observed in vitro effects for correlation with predicted in vivo effects. Further, peer-reviewers should be encouraged to request such information during review. This is critical to scientifically transition to animal-free, reliable, robust and -- most importantly -- relevant regulatory toxicology and risk assessment approaches. Recently published studies using NAMs for the fungicides Captan and Folpet illustrate the difficulties and limitations of applying NAMs to adequately assess the toxicological relevance of these substances.

Evaluation of in vitro toxicity information for zebrafish as a promising alternative for chemical hazard and risk assessment

In vitro assays are widely proposed as a test alternative to traditional in vivo standard acute and chronic toxicity tests. However, whether toxicity information derived from in vitro assays instead of in vivo tests could provide sufficient protection (e.g., 95 % of protection) for chemical risks remain evaluated. To investigate the feasibility of zebrafish (Danio rerio) cell-based in vitro test method as a test alternative, we comprehensively compared sensitivity differences among endpoints, among test methods (in vitro, FET and in vivo), and between zebrafish and rat (Rattus norvegicus), respectively using chemical toxicity distribution (CTD) approach. For each test method involved, sublethal endpoints were more sensitive than lethal endpoints for both zebrafish and rat, respectively. Biochemistry (zebrafish in vitro), development (zebrafish in vivo and FET), physiology (rat in vitro) and development (rat in vivo) were the most sensitive endpoints for each test method. Nonetheless, zebrafish FET test was the least sensitive one compared to its in vivo and in vitro tests for either lethal or sublethal responses. Comparatively, rat in vitro tests considering cell viability and physiology endpoints were more sensitive than rat in vivo test. Zebrafish was found to be more sensitive than rat regardless of in vivo or in vitro tests for each pairwise endpoint of concern. Those findings indicate that zebrafish in vitro test is a feasible test alternative to zebrafish in vivo and FET test and traditional mammalian test. It is suggesting that zebrafish in vitro test can be optimized by choosing more sensitive endpoints, such as biochemistry to provide sufficient protection for zebrafish in vivo test and to establish applications of zebrafish in vitro test in future risk assessment. Our findings are vital for evaluating and further application of in vitro toxicity toxicity information as an alternative for chemical hazard and risk assessment.

Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis

BACKGROUND

Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks.

RESULTS

According to statistical analysis (negative binomial model), the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number of direct targets. With respect to the recovery of signalling pathways, CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 'landmark' genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets.

CONCLUSIONS

Overall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform.

In vitro to in vivo extrapolation and high-content imaging for simultaneous characterization of chemically induced liver steatosis and markers of hepatotoxicity

Chemically induced steatosis is characterized by lipid accumulation associated with mitochondrial dysfunction, oxidative stress and nucleus distortion. New approach methods integrating in vitro and in silico models are needed to identify chemicals that may induce these cellular events as potential risk factors for steatosis and associated hepatotoxicity. In this study we used high-content imaging for the simultaneous quantification of four cellular markers as sentinels for hepatotoxicity and steatosis in chemically exposed human liver cells in vitro. Furthermore, we evaluated the results with a computational model for the extrapolation of human oral equivalent doses (OED). First, we tested 16 reference chemicals with known capacities to induce cellular alterations in nuclear morphology, lipid accumulation, mitochondrial membrane potential and oxidative stress. Then, using physiologically based pharmacokinetic modeling and reverse dosimetry, OEDs were extrapolated from data of any stimulated individual sentinel response. The extrapolated OEDs were confirmed to be within biologically relevant exposure ranges for the reference chemicals. Next, we tested 14 chemicals found in food, selected from thousands of putative chemicals on the basis of structure-based prediction for nuclear receptor activation. Amongst these, orotic acid had an extrapolated OED overlapping with realistic exposure ranges. Thus, we were able to characterize known steatosis-inducing chemicals as well as data-scarce food-related chemicals, amongst which we confirmed orotic acid to induce hepatotoxicity. This strategy addresses needs of next generation risk assessment and can be used as a first chemical prioritization hazard screening step in a tiered approach to identify chemical risk factors for steatosis and hepatotoxicity-associated events.

Closing the gap between in vivo and in vitro omics: using QA/QC to strengthen ex vivo NMR metabolomics

Metabolomics aims to achieve a global quantitation of the pool of metabolites within a biological system. Importantly, metabolite concentrations serve as a sensitive marker of both genomic and phenotypic changes in response to both internal and external stimuli. NMR spectroscopy greatly aids in the understanding of both in vitro and in vivo physiological systems and in the identification of diagnostic and therapeutic biomarkers. Accordingly, NMR is widely utilized in metabolomics and fluxomics studies due to its limited requirements for sample preparation and chromatography, its non-destructive and quantitative nature, its utility in the structural elucidation of unknown compounds, and, importantly, its versatility in the analysis of in vitro, in vivo, and ex vivo samples. This review provides an overview of the strengths and limitations of in vitro and in vivo experiments for translational research and discusses how ex vivo studies may overcome these weaknesses to facilitate the extrapolation of in vitro insights to an in vivo system. The application of NMR-based metabolomics to ex vivo samples, tissues, and biofluids can provide essential information that is close to a living system (in vivo) with sensitivity and resolution comparable to those of in vitro studies. The success of this extrapolation process is critically dependent on high-quality and reproducible data. Thus, the incorporation of robust quality assurance and quality control checks into the experimental design and execution of NMR-based metabolomics experiments will ensure the successful extrapolation of ex vivo studies to benefit translational medicine.

Comparing species-different responses in pulmonary fibrosis research: Current understanding of in vitro lung cell models and nanomaterials

Pulmonary fibrosis (PF) is a chronic, irreversible lung disease that is typically fatal and characterized by an abnormal fibrotic response. As a result, vast areas of the lungs are gradually affected, and gas exchange is impaired, making it one of the world's leading causes of death. This can be attributed to a lack of understanding of the onset and progression of the disease, as well as a poor understanding of the mechanism of adverse responses to various factors, such as exposure to allergens, nanomaterials, environmental pollutants, etc. So far, the most frequently used preclinical evaluation paradigm for PF is still animal testing. Nonetheless, there is an urgent need to understand the factors that induce PF and find novel therapeutic targets for PF in humans. In this regard, robust and realistic in vitro fibrosis models are required to understand the mechanism of adverse responses. Over the years, several in vitro and ex vivo models have been developed with the goal of mimicking the biological barriers of the lung as closely as possible. This review summarizes recent progress towards the development of experimental models suitable for predicting fibrotic responses, with an emphasis on cell culture methods, nanomaterials, and a comparison of results from studies using cells from various species.

Combinatorial treatments of tamoxifen and SM6Met, an extract from Cyclopia subternata Vogel, are superior to either treatment alone in MCF-7 cells

Synergistic drug combinations are not only popular in antibiotic, anti-microbial, immune disease (i.e., AIDS) and viral infection studies, but has also gained traction in the field of cancer research as a multi-targeted approach. It has the potential to lower the doses needed of standard of care (SOC) therapeutic agents, whilst maintaining an effective therapeutic level. Lower dosages could ameliorate the fundamental problems such as drug resistance and metastasis associated with current SOC therapies. In the current study, we show that the combination of SM6Met with (2)-4-hydroxytamoxifen (4-OH-Tam, the active metabolite of tamoxifen) produces a strong synergistic effect in terms of inhibiting MCF7 ER-positive (ER⁺) breast cancer cell proliferation and that a 20 times lower dose of 4-OH-Tam in combination with SM6Met is required to produce the same inhibitory effect on cell proliferation as 4-OH-Tam on its own. Cell cycle analyses of the best combination ratios of SM6Met and 4-OH-Tam also suggests that the combination results in increased accumulation of cells in the S-phase and in the apoptotic phase. Moreover, the best combination ratio (20:1) of SM6Met with 4-OH-Tam displayed greater anti-metastatic potential in terms of inhibiting ER⁺ breast cancer cell migration, invasion, and colony formation than the SOC therapy alone, suggesting that SM6Met together with 4-OH-Tam could be a viable drug combination for not only delaying resistance and ameliorating the negative side-effects associated with current SOC therapies, like tamoxifen, but could also provide a novel, more affordable therapeutic alternative for treating or preventing ER⁺ breast cancer metastasis.

The path via pathway-based approaches towards safety assessment: A concise review

For decades, chemical safety assessment has been proposed to shift from animal testing to in vitro testing systems in response to the call for the 3R. In Europe, the answer was to combine various information sources in integrated testing strategies (ITS); In the US, it was in 2007 when the landmark report by the National Research Council put forward a vision of in vitro toxicity testing paradigm. Since then, efforts to develop pathway-based assessment framework have been on the track. In 2010, systems biology brought out a conceptual framework called adverse outcome pathway (AOP), which took one step further from toxicity pathway to regulatory toxicology. Computational modeling, high-throughput screening, high-content omics have all been approached to facilitate this progress. This paper briefly reviewed the achievement of pathway-based chemical assessment since 2007, discussed potential pitfalls and challenges that mechanism-driven chemical assessment may undergo, and presented future perspectives of safety assessment that is to be based on computational system biology.

Biomarkers of nanomaterials hazard from multi-layer data

There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.

Next Generation Risk Assessment of the Anti-Androgen Flutamide Including the Contribution of Its Active Metabolite Hydroxyflutamide

In next generation risk assessment (NGRA), non-animal approaches are used to quantify the chemical concentrations required to trigger bioactivity responses, in order to assure safe levels of human exposure. A limitation of many in vitro bioactivity assays, which are used in an NGRA context as new approach methodologies (NAMs), is that toxicokinetics, including biotransformation, are not adequately captured. The present study aimed to include, as a proof of principle, the bioactivity of the metabolite hydroxyflutamide (HF) in an NGRA approach to evaluate the safety of the anti-androgen flutamide (FLU), using the AR-CALUX assay to derive the NAM point of departure (PoD). The NGRA approach applied also included PBK modelling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE). The PBK model describing FLU and HF kinetics in humans was developed using GastroPlus™ and validated against human pharmacokinetic data. PBK model-facilitated QIVIVE was performed to translate the in vitro AR-CALUX derived concentration-response data to a corresponding in vivo dose-response curve for the anti-androgenicity of FLU, excluding and including the activity of HF (-HF and +HF, respectively). The in vivo benchmark dose 5% lower confidence limits (BMDL₀₅) derived from the predicted in vivo dose-response curves for FLU, revealed a 440-fold lower BMDL₀₅ when taking the bioactivity of HF into account. Subsequent comparison of the predicted BMDL₀₅ values to the human therapeutic doses and historical animal derived PoDs, revealed that PBK modelling-facilitated QIVIVE that includes the bioactivity of the active metabolite is protective and provides a more appropriate PoD to assure human safety via NGRA, whereas excluding this would potentially result in an underestimation of the risk of FLU exposure in humans.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Probabilistic risk assessment - the keystone for the future of toxicology

Safety sciences must cope with uncertainty of models and results as well as information gaps. Acknowledging this uncertainty necessitates embracing probabilities and accepting the remaining risk. Every toxicological tool delivers only probable results. Traditionally, this is taken into account by using uncertainty / assessment factors and worst-case / precautionary approaches and thresholds. Probabilistic methods and Bayesian approaches seek to characterize these uncertainties and promise to support better risk assessment and, thereby, improve risk management decisions. Actual assessments of uncertainty can be more realistic than worst-case scenarios and may allow less conservative safety margins. Most importantly, as soon as we agree on uncertainty, this defines room for improvement and allows a transition from traditional to new approach methods as an engineering exercise. The objective nature of these mathematical tools allows to assign each methodology its fair place in evidence integration, whether in the context of risk assessment, systematic reviews, or in the definition of an integrated testing strategy (ITS) / defined approach (DA) / integrated approach to testing and assessment (IATA). This article gives an overview of methods for probabilistic risk assessment and their application for exposure assessment, physiologically-based kinetic modelling, probability of hazard assessment (based on quantitative and read-across based structure-activity relationships, and mechanistic alerts from in vitro studies), individual susceptibility assessment, and evidence integration. Additional aspects are opportunities for uncertainty analysis of adverse outcome pathways and their relation to thresholds of toxicological concern. In conclusion, probabilistic risk assessment will be key for constructing a new toxicology paradigm – probably!

Integrating in vitro chemical transplacental passage into a generic PBK model: A QIVIVE approach

With the increasing application of cell culture models as primary tools for predicting chemical safety, the quantitative extrapolation of the effective dose from in vitro to in vivo (QIVIVE) is of increasing importance. For developmental toxicity this requires scaling the in vitro observed dose-response characteristics to in vivo fetal exposure, while integrating maternal in vivo kinetics during pregnancy, in particular transplacental transfer. Here the transfer of substances across the placental barrier, has been studied using the in vitro BeWo cell assay and six embryotoxic compounds of different kinetic complexity. The BeWo assay results were incorporated in an existing generic Physiologically Based Kinetic (PBK) model which for this purpose was extended with rat pregnancy. Finally, as a "proof of principle", the BeWo PBK model was used to perform a QIVIVE based on developmental toxicity as observed in various different in vitro toxicity assays. The BeWo results illustrated different transport profiles of the chemicals across the BeWo monolayer, allocating the substances into two distinct groups: the 'quickly-transported' and the 'slowly-transported'. BeWo PBK exposure simulations during gestation were compared to experimentally measured maternal blood and fetal concentrations and a reverse dosimetry approach was applied to translate in vitro observed embryotoxicity into equivalent in vivo dose-response curves. This approach allowed for a direct comparison of the in vitro dose-response characteristics as observed in the Whole Embryo Culture (WEC), and the Embryonic Stem Cell test (cardiac:ESTc and neural:ESTn) with in vivo rat developmental toxicity data. Overall, the in vitro to in vivo comparisons suggest a promising future for the application of such QIVIVE methodologies for screening and prioritization purposes of developmental toxicants. Nevertheless, the clear need for further improvements is acknowledged for a wider application of the approach in chemical safety assessment.

Cyclic AMP and calcium signaling are involved in antipsychotic-induced diabetogenic effects in isolated pancreatic β cells of CD1 mice

OBJECTIVES

Antipsychotics (APs) are medications used for different psychological disorders. They can introduce diabetogenic effects through different mechanisms, including cyclic adenosine monophosphate (cAMP) and calcium (Ca²⁺) signaling pathways. However, this effect is poorly understood. Therefore, this study aimed to evaluate the effect of three widely used APs (chlorpromazine, haloperidol, and clozapine) on cAMP and Ca²⁺ signaling.

METHODS

The local bioethics committee of Northern Border University approved the study. Pancreatic β-cells were isolated from male CD1 mice, and three drug stock solutions were made in different concentrations (0.1, 1, 10, and 100 μM). The levels of glucose-stimulated insulin secretion (GSIS) and cAMP as well as the activities of adenylyl cyclase (AC), cAMP-dependent protein kinase (PKA), guanine-nucleotide exchange protein activated by cAMP (Epac 1 and 2), Ca²⁺ mobilization, and Ca²⁺/calmodulin kinase II (CaMKII) were then determined using different methods.

RESULTS

APs were found to be cytotoxic to pancreatic β cells and caused a parallel and significant decrease in GSIS. APs significantly reduced the levels of cAMP in the treated cells, with an associated reduction in ATP production, CaMKII, PKA, and transmembrane AC activities as well as Ca²⁺ mobilization to variable extents. In addition, the gene expression results showed that APs significantly decreased the expression of both the active subunits AC1 and AC8, the PKA α and β subunits, Epac1 and Epac2 as well as the four main subunits of CaMKII to variable extents.

CONCLUSION

AP-induced alterations in the cAMP and Ca²⁺ signaling pathways can play a significant role in their diabetogenic potential.

Implementation of a Dynamic Co-Culture Model Abated Silver Nanoparticle Interactions and Nanotoxicological Outcomes In Vitro

The incorporation of engineered nanoparticles (NPs) into everyday consumer goods, products, and applications has given rise to the field of nanotoxicology, which evaluates the safety of NPs within biological environments. The unique physicochemical properties of NPs have made this an insurmountable challenge, as their reactivity and variable behavior have given rise to discrepancies between standard cell-based in vitro and animal in vivo models. In this study, enhanced in vitro models were generated that retained the advantages of traditional cell cultures, but incorporated the modifications of (1) inclusion of an activated immune element and (2) the presence of physiologically-relevant dynamic flow. Following verification that the human alveolar epithelial and macrophage (A549/U937) co-culture could be successfully sustained under both static and dynamic conditions, these cultures, in addition to a standard A549 static model, were challenged with 10 nm citrate coated silver NPs (AgNPs). This work identified a reshaping of the AgNP-cellular interface and differential biological responses following exposure. The presence of dynamic flow modified cellular morphology and reduced AgNP deposition by approximately 20% over the static exposure environments. Cellular toxicity and stress endpoints, including reactive oxygen species, heat shock protein 70, and secretion of pro-inflammatory cytokines, were found to vary as a function of both cellular composition and flow conditions; with activated macrophages and fluid flow both mitigating the severity of AgNP-dependent bioeffects. This work highlights the possibility of enhanced in vitro systems to assess the safety of engineered NPs and demonstrates their effectiveness in elucidating novel NP-cellular interactions and toxicological profiles.

Derivation of a Human In Vivo Benchmark Dose for Perfluorooctanoic Acid From ToxCast In Vitro Concentration-Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation

A computational workflow which integrates physiologically based kinetic (PBK) modeling, global sensitivity analysis (GSA), approximate Bayesian computation (ABC), and Markov Chain Monte Carlo (MCMC) simulation was developed to facilitate quantitative in vitro to in vivo extrapolation (QIVIVE). The workflow accounts for parameter and model uncertainty within a computationally efficient framework. The workflow was tested using a human PBK model for perfluorooctanoic acid (PFOA) and high throughput screening (HTS) in vitro concentration–response data, determined in a human liver cell line, from the ToxCast/Tox21 database. In vivo benchmark doses (BMDs) for PFOA intake (ng/kg BW/day) and drinking water exposure concentrations (µg/L) were calculated from the in vivo dose responses and compared to intake values derived by the European Food Safety Authority (EFSA). The intake benchmark dose lower confidence limit (BMDL₅) of 0.82 was similar to 0.86 ng/kg BW/day for altered serum cholesterol levels derived by EFSA, whereas the intake BMDL₅ of 6.88 was six-fold higher than the value of 1.14 ng/kg BW/day for altered antibody titer also derived by the EFSA. Application of a chemical-specific adjustment factor (CSAF) of 1.4, allowing for inter-individual variability in kinetics, based on biological half-life, gave an intake BMDL₅ of 0.59 for serum cholesterol and 4.91 (ng/kg BW/day), for decreased antibody titer, which were 0.69 and 4.31 the EFSA-derived values, respectively. The corresponding BMDL₅ for drinking water concentrations, for estrogen receptor binding activation associated with breast cancer, pregnane X receptor binding associated with altered serum cholesterol levels, thyroid hormone receptor α binding leading to thyroid disease, and decreased antibody titer (pro-inflammation from cytokines) were 0.883, 0.139, 0.086, and 0.295 ng/ml, respectively, with application of no uncertainty factors. These concentrations are 5.7-, 36-, 58.5-, and 16.9-fold lower than the median measured drinking water level for the general US population which is approximately, 5 ng/ml.

Plant-Derived Anticancer Compounds as New Perspectives in Drug Discovery and Alternative Therapy

Despite the recent advances in the field of chemically synthetized pharmaceutical agents, nature remains the main supplier of bioactive molecules. The research of natural products is a valuable approach for the discovery and development of novel biologically active compounds possessing unique structures and mechanisms of action. Although their use belongs to the traditional treatment regimes, plant-derived compounds still cover a large portion of the current-day pharmaceutical agents. Their medical importance is well recognized in the field of oncology, especially as an alternative to the limitations of conventional chemotherapy (severe side effects and inefficacy due to the occurrence of multi-drug resistance). This review offers a comprehensive perspective of the first blockbuster chemotherapeutic agents of natural origin’s (e.g. taxol, vincristine, doxorubicin) mechanism of action using 3D representation. In addition is portrayed the step-by-step evolution from preclinical to clinical evaluation of the most recently studied natural compounds with potent antitumor activity (e.g. resveratrol, curcumin, betulinic acid, etc.) in terms of anticancer mechanisms of action and the possible indications as chemotherapeutic or chemopreventive agents and sensitizers. Finally, this review describes several efficient platforms for the encapsulation and targeted delivery of natural compounds in cancer treatment

Nanomedicines: Redefining traditional medicine

Nanomedicines offer nanoscale drug delivery system. They offer ways of promising drug transportation, and address the issues of lack of targeting and permeability of traditional drugs. The physical and chemical properties in the domain of nanomedicine applications in vivo have not been sufficiently delivered. What's more, the metabolic of nanomedicines is not clear enough. Those factors which mentioned above determine that many nanomedicines have not yet realized clinical application due to their safety problems and in vivo efficacy. For example, they may cause immune response and cytotoxicity, as well as the ability to clear organs in vivo, the penetration ability of them and the lack of targeting ability may also cause poor efficacy of drugs in vivo. In this review, the new progresses of different kinds of nanomedicines (including gold nanoparticles, nanorobots, black phosphorus nanoparticles, brain diseases, gene editing and immunotherapy etc.) in anti-tumor, antibacterial, ocular diseases and arteriosclerosis in recent years were summarized. Their shortcomings were pointed out, and the new methods to improve the biosafety and efficacy were summarized.

Integrating biokinetics and in vitro studies to evaluate developmental neurotoxicity induced by chlorpyrifos in human iPSC-derived neural stem cells undergoing differentiation towards neuronal and glial cells

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Human iPSC-derived NSCs undergoing differentiation possess some metabolic competence.

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CPF entered the cells and was biotrasformed into its two main metabolites (CPFO and TCP).

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After repeated exposure, very limited bioaccumulation of CPF was observed.

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Treatment with CPF decreased neurite outgrowth, synapse number and electrical activity.

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Treatment with CPF increased BDNF levels and the percentage of astrocytes.

For some complex toxicological endpoints, chemical safety assessment has conventionally relied on animal testing. Apart from the ethical issues, also scientific considerations have been raised concerning the traditional approach, highlighting the importance for considering real life exposure scenario. Implementation of flexible testing strategies, integrating multiple sources of information, including in vitro reliable test methods and in vitro biokinetics, would enhance the relevance of the obtained results. Such an approach could be pivotal in the evaluation of developmental neurotoxicity (DNT), especially when applied to human cell-based models, mimicking key neurodevelopmental processes, relevant to human brain development.

Here, we integrated the kinetic behaviour with the toxicodynamic alterations of chlorpyrifos (CPF), such as in vitro endpoints specific for DNT evaluation, after repeated exposure during differentiation of human neural stem cells into a mixed culture of neurons and astrocytes. The upregulation of some cytochrome P450 and glutathione S-transferase genes during neuronal differentiation and the formation of the two major CPF metabolites (due to bioactivation and detoxification) supported the metabolic competence of the used in vitro model. The alterations in the number of synapses, neurite outgrowth, brain derived neurotrophic factor, the proportion of neurons and astrocytes, as well as spontaneous electrical activity correlated well with the CPF ability to enter the cells and be bioactivated to CPF-oxon. Overall, our results confirm that combining in vitro biokinetics and assays to evaluate effects on neurodevelopmental endpoints in human cells should be regarded as a key strategy for a quantitative characterization of DNT effects.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

A Systematic Approach to Review of in vitro Methods in Brain Tumour Research (SAToRI-BTR): Development of a Preliminary Checklist for Evaluating Quality and Human Relevance

Background

A wide range of human in vitro methods have been developed and there is considerable interest in the potential of these studies to address questions related to clinical (human) use of drugs, and the pathobiology of tumours. This requires agreement on how to assess the strength of evidence available (i.e., quality and quantity) and the human-relevance of such studies. The SAToRI-BTR (Systematic Approach To Review of in vitro methods in Brain Tumour Research) project seeks to identify relevant appraisal criteria to aid planning and/or evaluation of brain tumour studies using in vitro methods.

Objectives

To identify criteria for evaluation of quality and human relevance of in vitro brain tumour studies; to assess the general acceptability of such criteria to senior scientists working within the field.

Methods

Stage one involved identification of potential criteria for evaluation of in vitro studies through: (1) an international survey of brain tumour researchers; (2) interviews with scientists, clinicians, regulators, and journal editors; (3) analysis of relevant reports, documents, and published studies. Through content analysis of findings, an initial list of criteria for quality appraisal of in vitro studies of brain tumours was developed. Stage two involved review of the criteria by an expert panel (Delphi process).

Results

Results of stage one indicated that methods for and quality of review of in vitro studies are highly variable, and that improved reporting standards are needed. 129 preliminary criteria were identified; duplicate and highly context-specific items were removed, resulting in 48 criteria for review by the expert (Delphi) panel. 37 criteria reached agreement, resulting in a provisional checklist for appraisal of in vitro studies in brain tumour research.

Conclusion

Through a systematic process of collating assessment criteria and subjecting these to expert review, SAToRI-BTR has resulted in preliminary guidance for appraisal of in vitro brain tumour studies. Further development of this guidance, including investigating strategies for adaptation and dissemination across different sub-fields of brain tumour research, as well as the wider in vitro field, is planned.

Application of cell-based biological bioassays for health risk assessment of PM2.5 exposure in three megacities, China

The determination of PM_2.5-induced biological response is essential for understanding the adverse health risk associated with PM_2.5 exposure. In this study, we conducted cell-based bioassays to measure the toxic effects of PM_2.5 exposure, including cytotoxicity, oxidative stress, genotoxicity and inflammatory response. The concentration-response relationship was analyzed by benchmark dose (BMD) modeling and the BMDL₁₀ was used to estimate the biological potency of PM_2.5 exposure. PM_2.5 samples were collected from three typical megacities of China (Beijing, BJ; Wuhan, WH; Guangzhou, GZ) in typical seasons (winter and summer). The total PM, water-soluble fractions (WSF), and organic extracts (OE) were prepared and subjected to examination of toxic effects. The biological potencies for cytotoxicity, oxidative stress and genotoxicity were generally higher in winter samples, while the inflammatory potency of PM_2.5 was higher in summer samples. The relative health risk (RHR) was determined by integration of the biological potencies and the cumulative exposure level, and the ranks of RHR were BJ-W > WH-W > BJ-S > WH-S > GZ-W > GZ-S. Notably, we note that different PM_2.5 compositions were associated with distinct biological effects, and the health effects distribution of PM_2.5 varied in regions and seasons. These findings demonstrate that the approach of integrated cell-based bioassays could be used for the evaluation of health effects of PM_2.5 exposure.

Natural products and their derivatives against coronavirus: A review of the non-clinical and pre-clinical data

Several corona viral infections have created serious threats in the last couple of decades claiming the death of thousands of human beings. Recently, corona viral epidemic raised the issue of developing effective antiviral agents at the earliest to prevent further losses. Natural products have always played a crucial role in drug development process against various diseases, which resulted in screening of such agents to combat emergent mutants of corona virus. This review focuses on those natural compounds that showed promising results against corona viruses. Although inhibition of viral replication is often considered as a general mechanism for antiviral activity of most of the natural products, studies have shown that some natural products can interact with key viral proteins that are associated with virulence. In this context, some of the natural products have antiviral activity in the nanomolar concentration (e.g., lycorine, homoharringtonine, silvestrol, ouabain, tylophorine, and 7-methoxycryptopleurine) and could be leads for further drug development on their own or as a template for drug design. In addition, a good number of natural products with anti-corona virus activity are the major constituents of some common dietary supplements, which can be exploited to improve the immunity of the general population in certain epidemics.

A multiparametric organ toxicity predictor for drug discovery

The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.

A Computational Workflow for Probabilistic Quantitative in Vitro to in Vivo Extrapolation

A computational workflow was developed to facilitate the process of quantitative in vitro to in vivo extrapolation (QIVIVE), specifically the translation of in vitro concentration-response to in vivo dose-response relationships and subsequent derivation of a benchmark dose value (BMD). The workflow integrates physiologically based pharmacokinetic (PBPK) modeling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) simulation. For a given set of in vitro concentration and response data the algorithm returns the posterior distribution of the corresponding in vivo, population-based dose-response values, for a given route of exposure. The novel aspect of the workflow is a rigorous statistical framework for accommodating uncertainty in both the parameters of the PBPK model (both parameter uncertainty and population variability) and in the structure of the PBPK model itself recognizing that the model is an approximation to reality. Both these sources of uncertainty propagate through the workflow and are quantified within the posterior distribution of in vivo dose for a fixed representative in vitro concentration. To demonstrate this process and for comparative purposes a similar exercise to previously published work describing the kinetics of ethylene glycol monoethyl ether (EGME) and its embryotoxic metabolite methoxyacetic acid (MAA) in rats was undertaken. The computational algorithm can be used to extrapolate from in vitro data to any organism, including human. Ultimately, this process will be incorporated into a user-friendly, freely available modeling platform, currently under development, that will simplify the process of QIVIVE.

Culturing substrates influence the morphological, mechanical and biochemical features of lung adenocarcinoma cells cultured in 2D or 3D

Alternative models such as three-dimensional (3D) cell cultures represent a distinct milestone towards capturing the realities of cancer biology in vitro and reduce animal experimentation in the preclinical stage of drug discovery. Significant work remains to be done to understand how substrates used in in vitro alternatives influence cancer cells phenotype and drug efficacy responses, so that to accurately link such models to specific in vivo disease scenarios. Our study describes how the morphological, mechanical and biochemical properties of adenocarcinoma (A549) cells change in response to a 3D environment and varying substrates. Confocal Laser Scanning (LSCM), He-Ion (HIM) and Atomic Force (AFM) microscopies, supported by ELISA and Western blotting, were used. These techniques enabled us to evaluate the shape, cytoskeletal organization, roughness, stiffness and biochemical signatures of cells grown within soft 3D matrices (PuraMatrix™ and Matrigel™), and to compare them to those of cells cultured on two-dimensional glass substrates. Cell cultures are also characterized for their biological response to docetaxel, a taxane-type drug used in Non-Small-Cell Lung Cancer (NSCLC) treatment. Our results offer an advanced biophysical insight into the properties and potential application of 3D cultures of A549 cells as in vitro alternatives in lung cancer research.