Altex-Alternatives To Animal Experimentation最新文献

A structurally diverse set of 147 per- and polyfluoroalkyl substances (PFAS) was screened in a panel of 12 human primary cell systems by measuring 148 biomarkers relevant to (patho)physiological pathways to inform hypotheses about potential mechanistic effects of data-poor PFAS in human model systems. This analysis focused on immunosuppressive activity, which was previously reported as an in vivo effect of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), by comparing PFAS responses to four pharmacological immunosuppressants. The PFOS response profile had little correlation with reference immunosuppressants, suggesting in vivo activity does not occur by similar mechanisms. The PFOA response profile did share features with the profile of dexamethasone, although some distinct features were lacking. Other PFAS, including 2,2,3,3-tetrafluoropropyl acrylate, demonstrated more similarity to the reference immunosuppressants but with additional activities not found in the reference immunosuppressive drugs. Correlation of PFAS profiles with a database of environmental chemical responses and pharmacological probes identified potential mechanisms of bioactivity for some PFAS, including responses similar to ubiquitin ligase inhibitors, deubiquitylating enzyme (DUB) inhibitors, and thioredoxin reductase inhibitors. Approximately 21% of the 147 PFAS with confirmed sample quality were bioactive at nominal testing concentrations in the 1-60 micromolar range in these human primary cell systems. These data provide new hypotheses for mechanisms of action for a subset of PFAS and may further aid in development of a PFAS categorization strategy useful in safety assessment.

Quantitative adverse outcome pathway network (qAOPN) is gaining momentum due to its predictive nature, alignment with quantitative risk assessment, and great potential as a computational new approach methodology (NAM) to reduce laboratory animal tests. The present work aimed to demonstrate two advanced modeling approaches, piecewise structural equation modeling (PSEM) and Bayesian network (BN), for de novo qAOPN model construction based on routine ecotoxicological data. A previously published AOP network comprised of four linear AOPs linking excessive reactive oxygen species production to mortality in aquatic organisms was employed as a case study. The demonstrative case study intended to answer: Which linear AOP in the network contributed the most to the AO? Can any of the upstream KEs accurately predict the AO? What are the advantages and limitations of PSEM or BN in qAOPN development? The outcomes from the two approaches showed that both PSEM and BN are suitable for constructing a complex qAOPN based on limited experimental data. Besides quantification of response-response relationships, both approaches could identify the most influencing linear AOP in a complex network and evaluate the predictive ability of the AOP, albeit some discrepancies in predictive ability were identified for the two approaches using this specific dataset. The advantages and limitations of the two approaches for qAOPN construction are discussed in detail, and suggestions on optimal workflows of PSEM and BN are provided to guide future qAOPN development.

Many interventions that show promising results in preclinical development do not pass clinical tests. Part of this may be explained by poor animal-to-human translation. Using animal models with low predictability for humans is neither ethical nor efficient. If translational success shows variation between medical research fields, analyses of common practices in these fields could identify factors contributing to successful translation. We assessed translational success rates in medical research fields using two approaches: through literature and clinical trial registers.Literature: We comprehensively searched PubMed for pharmacology, neuroscience, cancer research, animal models, clinical trials, and translation. After screening, 117 review papers were included in this scoping review. Translational success rates were not different within pharmacology (72%), neuroscience (62%), and cancer research (69%).Clinical trials: The fraction of phase-2 clinical trials with a positive outcome was used as a proxy (i.e., an indirect resemblance measure) for translational success. Trials were retrieved from the WHO trial register and categorized into medical research fields following the international classification of disease (ICD-10). Of the phase-2 trials analyzed, 65.2% were successful. Fields with the highest success rates were disorders of lipoprotein metabolism (86.0%) and epilepsy (85.0%). Fields with the lowest success rates were schizophrenia (45.4%) and pancreatic cancer (46.0%).Our combined analyses suggest relevant differences in success rates between medical research fields. Based on the clinical trials, comparisons of practice, e.g., between epilepsy and schizophrenia, might identify factors that influence translational success.

Hazard assessments of skin sensitizers are increasingly performed using new approach methodologies (NAMs), with several in chemico, in vitro, and most recently, also defined approaches accepted for regulatory use. However, keeping track of potential limitations of each method to define applicability domains remains a crucial component to ensure adequate predictivity and to facilitate the appropriate selection of method(s) for each hazard assessment task. The objective of this report is to share test results generated with the GARD™skin assay on chemicals that have traditionally been considered difficult to test in some of the conventional in vitro and in chemico OECD Test Guidelines for skin sensitization. Such compounds may include, for example, indirectly acting haptens, hydrophobic substances, and substances of unknown or variable composition, complex reaction products or biological substances (UVCBs). Based on the results of this study, the sensitivity for prediction of skin sensitizing hazard of indirectly acting haptens was 92.4% and 87.5% when compared with local lymph node assay (LLNA) (n = 25) and human data (n = 8), respectively. Similarly, the sensitivity for prediction of skin sensitizing hazard of hydrophobic substances was 85.1% and 100% when compared with LLNA (n = 24) and human data (n = 9), respectively. Lastly, a case study involving assessment of a set of hydrophobic UVCBs (n = 7) resulted in a sensitivity of 100% compared to available reference data. These data provide support for the inclusion of such chemistries in the GARD™skin applicability domain without an increased risk of false negative classifications.

Absorption in the gastrointestinal tract is a key factor determining the bioavailability of chemicals after oral exposure but is frequently assumed to have a conservative value of 100% for environmental chemicals, particularly in the context of high-throughput toxicokinetics for in vitro-to-in vivo extrapolation (IVIVE). For pharmaceutical compounds, the physiologically based advanced compartmental absorption and transit (ACAT) model has been used extensively to predict gut absorption but has not generally been applied to environmental chemicals. Here we develop a probabilistic environmental compart­mental absorption and transit (PECAT) model, adapting the ACAT model to environmental chemicals. We calibrated the model parameters to human in vivo, ex vivo, and in vitro datasets of drug permeability and fractional absorption by con­sidering two key factors: (1) differences between permeability in Caco-2 cells and in vivo permeability in the jejunum, and (2) differences in in vivo permeability across different gut segments. Incorporating these factors probabilistically, we found that given Caco-2 permeability measurements, predictions of the PECAT model are consistent with the (limited) available gut absorption data for environmental chemicals. However, the substantial chemical-to-chemical variability observed in the cal­ibration data often led to wide probabilistic confidence bounds in the predicted fraction absorbed and resulting steady state blood concentration. Thus, while the PECAT model provides a statistically rigorous, physiologically based approach for incor­porating in vitro data on gut absorption into toxicokinetic modeling and IVIVE, it also highlights the need for more accurate in vitro models and data for measuring gut segment-specific in vivo permeability of environmental chemicals.