Investigating open access new approach methods (NAM) to assess biological points of departure: A case study with 4 neurotoxic pesticides
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PROVIDER: S-EPMC10891343 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:New approach methods (NAMs) can reduce the need for chronic animal studies. Here, we apply benchmark dose (concentration) (BMD(C))-response modeling to transcriptomic changes in the liver of mice and in fathead minnow larvae after short-term exposures (7 days and 1 day, respectively) to several dose/concentrations of three organophosphate pesticides (OPPs): fenthion, methidathion, and parathion. The mouse liver transcriptional points of departure (TPODs) for fenthion, methidathion, and parathion were 0.009, 0.093, and 0.046 mg/Kg-bw/day, while the fathead minnow larva TPODs were 0.007, 0.115, and 0.046 mg/L, respectively. The TPODs were consistent across both species and reflected the relative potencies from traditional chronic toxicity studies with fenthion identified as the most potent. Moreover, the mouse liver TPODs were more sensitive than or within a 10-fold difference from the chronic apical points of departure (APODs) for mammals, while the fathead minnow larva TPODs were within an 18-fold difference from the chronic APODs for fish species. Short-term exposure to OPPs significantly impacted acetylcholinesterase mRNA abundance (FDR p-value <0.05, |fold change| ≥2) and canonical pathways (IPA, p-value <0.05) associated with organism death and neurological/immune dysfunctions, indicating the conservation of key events related to OPP toxicity. Together, these results build confidence in using short-term, molecular-based assays for the characterization of chemical toxicity and risk, thereby reducing reliance on chronic animal studies.
| S-EPMC10611195 | biostudies-literature
Project description:New approach methods (NAMs) can reduce the need for chronic, animal-based toxicity and carcinogenicity bioassays. Here, we apply benchmark dose (BMD) response modeling (BMDExpress 2.3) to transcriptomic data after short-term exposures to three organophosphate pesticides (OPPs) to estimate chronic adverse effect levels in mouse. Specifically, mouse-liver were collected for RNA sequencing after 7-days of exposure, respectively, to several dose-levels of well-known OPPs: fenthion, methidathion and parathion. Transcriptional points-of-departure (TPODs) were compared to chronic, apical points-of-departures (APOD) for each OPP to inform more efficient means of estimating chemical potency and risk. The mouse-liver TPODs for fenthion, methidathion, and parathion were 0.009, 0.093, and 0.046 mg/Kg-day, respectively. These short-term TPODs reflected the relative potencies of the most sensitive, apical effects from the chronic studies with fenthion identified as the most potent OPP. Moreover, the TPODs were 2.3 to 17-fold more sensitive than the chronic APODs for mouse, suggesting utility in using short-term gene response for estimating long-term chemical risk. Further investigation into OPPs’ modes of action identified significant impacts on acetylcholinesterase mRNA abundance (FDR p-value <0.05, |FC|>2) as well as enrichment of canonical pathways (IPA, z-score>|2|) associated with organism death and neurological and immune dysfunctions, which have been known to be affected by OPPs. Despite bioassay-related differences, our results indicate the conservation of key events potentially important to OPPs biological activity and potency across species. While additional research is needed, these results build confidence in using short-term, molecular-based assays for the characterization of toxicity and risk, thereby reducing reliance on chronic, rodent-based studies.
2023-11-13 | GSE240853 | GEO
Project description:Short-term transcriptomic points-of-departure are consistent with chronic points-of departure for three organophosphate pesticides across mouse and fathead minnow.
| PRJNA1005574 | ENA
Project description:Human health risk assessment for environmental chemical exposure is limited by a vast majority of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models based on chemical structure information, can predict hazard in the absence of experimental data. Risk assessment requires identification of a quantitative point-of-departure (POD) value, the point on the dose-response curve that marks the beginning of a low-dose extrapolation. This study presents two sets of QSAR models to predict POD values (PODQSAR) for repeat dose toxicity. For training and validation, a publicly available in vivo toxicity dataset for 3592 chemicals was compiled using the U.S. Environmental Protection Agency's Toxicity Value database (ToxValDB). The first set of QSAR models predict point-estimates of POD values (PODQSAR) using structural and physicochemical descriptors for repeat dose study types and species combinations. A random forest QSAR model using study type and species as descriptors showed the best performance, with an external test set root mean square error (RMSE) of 0.71 log10-mg/kg/day and coefficient of determination (R2) of 0.53. The second set of QSAR models predict the 95% confidence intervals for PODQSAR using a constructed POD distribution with a mean equal to the median POD value and a standard deviation of 0.5 log10-mg/kg/day, based on previously published typical study-to-study variability that may lead to uncertainty in model predictions. Bootstrap resampling of the pre-generated POD distribution was used to derive point-estimates and 95% confidence intervals for each POD prediction. Enrichment analysis to evaluate the accuracy of PODQSAR showed that 80% of the 5% most potent chemicals were found in the top 20% of the most potent chemical predictions, suggesting that the repeat dose POD QSAR models presented here may help inform screening level human health risk assessments in the absence of other data.
| S-EPMC8128699 | biostudies-literature
Project description:This review discusses the sex-specific effects of exposure to various organophosphate (OP) pesticides throughout the life course and potential reasons for the differential vulnerabilities observed across sexes.Sex is a crucial factor in the response to toxicants, yet the sex-specific effects of OP exposure, particularly in juveniles and adults, remain unresolved. This is largely due to study design and inconsistencies in exposure and outcome assessments. Exposure to OPs results in multiple adverse outcomes influenced by many factors including sex. Reported sex-specific effects suggest that males are more susceptible to OPs, which reflects the sex-dependent prevalence of various neurodevelopmental and neurodegenerative disorders such as autism and amyotrophic lateral sclerosis (ALS), in which males are at greater risk. Thus, this review proposes that the biological sex-specific effects elicited by OP exposure may in part underlie the dimorphic susceptibilities observed in neurological disorders. Understanding the immediate and long-term effects of OP exposure across sexes will be critical in advancing our understanding of OP-induced neurotoxicity and disease.
| S-EPMC5677564 | biostudies-literature
Project description:Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently available in the knowledge base and make interpretation challenging as scientists need to interpret PoDs for thousands of genes or hundreds of pathways. In this work, we aimed to address these issues by developing a computational workflow to investigate the pathway concentration-response relationships in a way that is not fully constrained by known biology and also facilitates interpretation. We employed the Pathway-Level Information ExtractoR (PLIER) to identify latent variables (LVs) describing biological activity and then investigated in vitro LVs' concentration-response relationships using the ToxCast pipeline. We applied this methodology to a published transcriptomic concentration-response data set for 44 chemicals in MCF-7 cells and showed that our workflow can capture known biological activity and discriminate between estrogenic and antiestrogenic compounds as well as activity not aligning with the existing knowledge base, which may be relevant in a risk assessment scenario. Moreover, we were able to identify the known estrogen activity in compounds that are not well-established ER agonists/antagonists supporting the use of the workflow in read-across. Next, we transferred its application to chemical compounds tested in HepG2, HepaRG, and MCF-7 cells and showed that PoD estimates are in strong agreement with those estimated using a recently developed Bayesian approach (cor = 0.89) and in weak agreement with those estimated using a well-established approach such as BMDExpress2 (cor = 0.57). These results demonstrate the effectiveness of using PLIER in a concentration-response scenario to investigate pathway activity in a way that is not fully constrained by the knowledge base and to ease the biological interpretation and support the development of an NGRA framework with the ability to improve current risk assessment strategies for chemicals using new approach methodologies.
| S-EPMC9019810 | biostudies-literature
Project description:There is growing scientific and regulatory interest in transcriptomic points of departure (tPOD) values from in vitro experiments as an alternative to animal test method. The objective of this study was to calculate tPOD values in rainbow trout gill cells (RTgill-W1 following OECD 249) exposed to 20 pesticides, and to evaluate how these values compare to fish acute and chronic toxicity data from the ECOTOX database. Cells were exposed to one fungicide (chlorothalonil), ten herbicides (atrazine, glyphosate, imazethapyr, metolachlor, diquat, s-metolachlor, AMPA, dicamba, dimethenamid-P, metribuzin), eight insecticides (chlorpyrifos, diazinon, permethrin, carbaryl, clothianidin, imidacloprid, thiamethoxam, chlorantraniliprole), and OECD 249 positive control 3,4-dichloroaniline. Sequencing libraries were prepared with UPXome, and tPODs calculated with ExpressAnalyst. The method identified 44,262 unique genes, with 1,115 genes having >5 counts in the 576 samples sequenced. For all chemicals, tPODs were derived and tPOD mode values ranged from 0.003 to 141µM with an average of 37µM. There were significant correlations between these tPOD mode values (x-value) and in vitro cytotoxicity EC50s from RTgill-W1 cells (y=1.1x+0.89, R2=0.85, p<0.001; n=11), rainbow trout acute toxicity LC50s (y=0.9x+0.59, R2=0.56, p<0.001; n=20), fish chronic sub-lethal effect concentrations (y=0.62x+0.38, R2=0.36, p=0.01; n=16) and fish chronic lethal effect concentrations (y=0.77x-0.28, R2=0.57, p=0.002; n=14). Bland–Altman plot statistical analyses of these comparisons also showed good agreement. Overall, these data demonstrate that tPOD values can be derived from an experimental platform that links together elements of OECD 249 with a transcriptomic method with high throughput potential. The findings support the notion that tPODs from short-term in vitro studies may be comparable to effect concentration data from in vivo studies of fish exposed for chronic durations.
2025-01-01 | GSE283712 | GEO
Project description:QSAR modeling can be used to aid testing prioritization of the thousands of chemical substances for which no ecological toxicity data are available. We drew on the U.S. Environmental Protection Agency's ECOTOX database with additional data from ECHA to build a large data set containing in vivo test data on fish for thousands of chemical substances. This was used to create QSAR models to predict two types of end points: acute LC50 (median lethal concentration) and points of departure similar to the NOEC (no observed effect concentration) for any duration (named the "LC50" and "NOEC" models, respectively). These models used study covariates, such as species and exposure route, as features to facilitate the simultaneous use of varied data types. A novel method of substituting taxonomy groups for species dummy variables was introduced to maximize generalizability to different species. A stacked ensemble of three machine learning methods-random forest, gradient boosted trees, and support vector regression-was implemented to best make use of a large data set with many descriptors. The LC50 and NOEC models predicted end points within 1 order of magnitude 81% and 76% of the time, respectively, and had RMSEs of roughly 0.83 and 0.98 log10(mg/L), respectively. Benchmarks against the existing TEST and ECOSAR tools suggest improved prediction accuracy.
| S-EPMC7047609 | biostudies-literature
Project description:Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012-2013) participants aged 60-83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.32, 95%CI:0.29,0.34) and total sleep duration (kappa = 0.39, 0.36,0.42). This estimate was lower for time in bed for women, depressed participants, those reporting more insomnia symptoms, and on weekend days. No such group differences were found for total sleep duration. Our algorithm was validated against data from a polysomnography study on 28 persons which found a longer time window and lower angle threshold to have better sensitivity to wakefulness, while the reverse was true for sensitivity to sleep. The novelty of our method is the use of a generic algorithm that will allow comparison between studies rather than a "count" based, device specific method.
| S-EPMC4646630 | biostudies-literature
Project description:Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B1, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B1, benzo[a]pyrene, cyclosporin A: from 0.2048 μM to 50 μM; rotenone: from 0.04096 to 10 μM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.
| S-EPMC9968698 | biostudies-literature