Dose-response Data Research Articles

Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays.

The application of in vitro biological assays as new approach methodologies (NAMs) to support grouping of UVCB (unknown or variable composition, complex reaction products, and biological materials) substances has recently been demonstrated. In addition to cell-based phenotyping as NAMs, in vitro transcriptomic profiling is used to gain deeper mechanistic understanding of biological responses to chemicals and to support grouping and read-across. However, the value of gene expression profiling for characterizing complex substances like UVCBs has not been explored. Using 141 petroleum substance extracts, we performed dose-response transcriptomic profiling in human induced pluripotent stem cell (iPSC)-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, as well as cell lines MCF7 and A375. The goal was to determine whether transcriptomic data can be used to group these UVCBs and to further characterize the molecular basis for in vitro biological responses. We found distinct transcriptional responses for petroleum substances by manufacturing class. Pathway enrichment informed interpretation of effects of substances and UVCB petroleum-class. Transcriptional activity was strongly correlated with concentration of polycyclic aromatic compounds (PAC), especially in iPSC-derived hepatocytes. Supervised analysis using transcriptomics, alone or in combination with bioactivity data collected on these same substances/cells, suggest that transcriptomics data provide useful mechanistic information, but only modest additional value for grouping. Overall, these results further demonstrate the value of NAMs for grouping of UVCBs, identify informative cell lines, and provide data that could be used for justifying selection of substances for further testing that may be required for registration.

Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays_suppl

Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays_suppl

CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints.

The nature of high-throughput screening (HTS) puts certain limits on optimal test conditions for each particular sample; therefore, on top of usual data normalization, additional parsing is often needed to account for incomplete read outs or various artifacts that arise from signal interferences.CurveP is a heuristic, user-tunable curve-cleaning algorithm that attempts to find a minimum set of corrections, which would give a monotonic dose-response curve. After applying the corrections, the algorithm proceeds to calculate a set of numeric features, which can be used as a fingerprint characterizing the sample, or as a vector of independent variables (e.g., molecular descriptors in case of chemical substances testing). The resulting output can be a part of HTS data analysis or can be used as input for a broad spectrum of computational applications, such as quantitative structure-activity relationship (QSAR ) modeling, computational toxicology, bioinformatics, and cheminformatics.

Protein Tyrosine Phosphatase Biochemical Inhibition Assays.

Disturbance of the dynamic balance between protein tyrosine phosphorylation and dephosphorylation, modulated by protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs), is known to be crucial for the development of many human diseases. The discovery of agents that restore this balance has been the subject of many drug research efforts, most of which have focused on tyrosine kinase inhibitors (TKIs), resulting in the development of more than 50 FDA-approved TKIs during the past two decades. More recently, accumulating evidence has suggested that members of the PTP superfamily are also promising drug targets, and efforts to discover tyrosine phosphatase inhibitors (TPIs) have increased dramatically. Here, we provide protocols for determining the potency of TPIs in vitro. We focus on the use of fluorescence-based substrates, which exhibit a dramatic increase in fluorescence emission when dephosphorylated by the PTP, and thus allow setting up highly sensitive and miniaturized phosphatase activity assays using 384-well or 1536-well microplates and a continuous (kinetic) assay format. The protocols cover PTP specific activity assays, Michaelis-Menten kinetics, dose-response inhibition assays, and dose-response data analysis for determining IC 50 values. Potential pitfalls are also discussed. While advanced instrumentation is utilized for compound spotting and liquid dispensing, all the assays can be adapted to existing equipment in most laboratories. Assays are described for selected PTP drug targets, including SHP2 ( PTPN11 ), PTP1B ( PTPN1 ), STEP ( PTPN5 ), and VHR ( DUSP3 ). However, all protocols are applicable to members of the PTP enzyme family in general. Graphical abstract.

Evaluation of an in vitro three-dimensional HepaRG spheroid model for genotoxicity testing using the high-throughput CometChip platform.

Three-dimensional (3D) culture systems are increasingly being used for genotoxicity studies due to improved cell-to-cell interactions and tissue-like structures that are limited or lacking in 2D cultures. The present study optimized a 3D culture system using metabolically competent HepaRG cells for in vitro genotoxicity testing. 3D HepaRG spheroids, formed in 96- or 384-well ultra-low attachment plates, were exposed to various concentrations of 34 test articles, including 8 direct-acting and 11 indirect-acting genotoxicants/carcinogens as well as 15 compounds that show different genotoxic responses in vitro and in vivo. DNA damage was evaluated using the high-throughput CometChip assay with con-current cytotoxicity assessment by the ATP assay in both 2D and 3D cultures. 3D HepaRG spheroids maintained a stable phenotype for up to 30 days with higher levels of albumin secretion, cytochrome P450 gene expression, and enzyme activities compared to 2D cultures. 3D spheroids also demonstrated a higher sensitivity than 2D cultures for detecting both direct- and indirect-acting genotoxicants/carcinogens, indicating a better prediction of in vivo genotoxicity responses. When DNA damage dose-response data were quantified using PROAST software, 3D spheroids generally had lower or similar benchmark dose values compared to 2D HepaRG cells and were more comparable with primary human hepatocytes. These results demonstrate that 3D models can be adapted to the CometChip technology for high-throughput genotoxicity testing and that 3D HepaRG spheroids may be used as a reliable and pragmatic in vitro approach to better support the hazard identification and risk assessment of potential human genotoxic carcinogens.

Radiation cancer risk at different dose rates: new dose-rate effectiveness factors derived from revised A-bomb radiation dosimetry data and non-tumor doses.

The dose rate of atomic bomb (A-bomb) radiation to the survivors has still remained unclear, although the dose–response data of A-bomb cancers has been taken as a standard in estimating the cancer risk of radiation and the dose and dose-rate effectiveness factor (DDREF). Since the applicability of the currently used DDREF of 2 derived from A-bomb data is limited in a narrow dose-rate range, 0.25-75 Gy/min as estimated from analysis of DS86 dosimetry data in the present study, a non-tumor dose (Dnt) was applied in an attempt to gain a more universal dose-rate effectiveness factor (DREF), where Dnt is an empirical parameter defined as the highest dose at which no statistically significant tumor increase is observed above the control level and its magnitude depends on the dose rate. The new DREF values were expressed as a function of the dose rate at four exposure categories, i.e. partial body low LET, whole body low linear energy transfer (LET), partial body high LET and whole body high LET and provided a value of 14 for environmental level radiation at a dose rate of 10−9 Gy/min for whole body low LET.

PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis.

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose–response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug–response analysis such as tissue distribution of dose–response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug–response phenotypes of cancer models.

In vitro effect of biological and conventional disease-modifying antirheumatic drugs on fibrocyte differentiation in patients with rheumatoid arthritis and healthy controls.

Fibrocytes are circulating bone-marrow-derived cells that migrate to organs with ongoing repair or inflammation. In the target organ, the cells differentiate, become long and spindle-shaped, and are able to produce extracellular matrix components. In fibrotic diseases, the levels of fibrocytes are increased, both in circulation and the diseased tissue. In rheumatoid arthritis (RA), fibrocytes have been proposed to be involved in the spread of the disease and possibly in RA fibrotic manifestations, as can be seen in RA interstitial lung disease (RA-ILD). Therefore, we aimed to investigate a range of current RA treatment modalities (corticosteroids and conventional and biological disease-modifying antirheumatic drugs (DMARDs)) regarding their effect on in vitro fibrocyte differentiation. A total of 10 participants were included (5 patients with RA and 5 healthy controls). Peripheral blood mononuclear cells (PBMCs) were isolated and cultured for 5 days with prednisolone, conventional DMARDs (methotrexate, sulfasalazine, and hydroxychloroquine), and biological DMARDs (etanercept, tocilizumab, adalimumab, abatacept, and rituximab). The numbers of fibrocytes were counted. Dose-response data for abatacept and tocilizumab were collected. Abatacept and prednisolone significantly suppressed differentiation of PBMC into fibrocytes compared with control (p=0.02 and p<0.01, respectively) (n=10). In overall analysis (n=10), abatacept reduced fibrocyte levels with an average of 44% overall and 71% in the RA group compared with the control wells. Tocilizumab reduced the fibrocyte count by 63% overall and 45% in the RA group, although it was not significant (p=0.07 and p=0.06, respectively). Both tocilizumab and abatacept display a dose-response relationship. Abatacept and prednisolone suppress the differentiation of mononuclear cells to mature fibrocytes in vitro in patients with RA, and data indicate a similar effect of tocilizumab; this was further supported by the observed dose-response relationship. Clinical trials are needed to compare the effect of these drugs on fibrotic RA manifestations, for example, RA-ILD.

Dose selection and tolerability of anticancer agents evaluated by the European Medicines Agency in the period 2015-2020

BackgroundNovel anticancer agents are initially evaluated in a palliative setting in phase I studies. The benefit–risk applying the selected dose from these phase I studies can be considered acceptable at time of registration, however, it is unknown if the optimal dose has been selected during drug development.MethodsThe European Medicines Agency (EMA) European Public Assessment Reports (EPARs) overview was used to select anticancer agents evaluated between 2015 and 2020. The dose selection and tolerability data of EMA assessed anticancer agents was analysed to evaluate dose selection.ResultsSixty EPARs were included for analysis. A dose–response relation was identified in five dossiers (8%). The maximum tolerated dose (MTD) was the selected dose for 15 anticancer agents (25%). The MTD was not determined in 27 out of 60 cases (59%). When the MTD was determined but not applied as final dose, the most frequently used dose selection criteria were the combination of toxicity, exposure response, pharmacokinetic data and pharmacodynamic data (in 7 out of 18 cases). Data on tolerability were analysed separately for protein kinase inhibitors and monoclonal antibodies as the dosing interval and mitigation of adverse events (AEs) differs. The median discontinuation, dose reduction and dose interruption rates due to AEs of protein kinase inhibitors were 10%, 26% and 45% for monotherapy and 13%, 47% and 55% for combination therapy, respectively. The median discontinuation rates due to AEs for monoclonal antibodies were 8% for monotherapy and 26% for combination therapy.ConclusionThe dose–response relationship has not been established for the majority of the registered anticancer agents. The selected posology is often poorly tolerable as reflected by the high discontinuation and dose reduction rates. Due to the absence of dose–response data, it is often unknown if the optimal dose has been selected for anticancer agents.

Utility of a next-generation framework for assessment of genomic damage: A case study using the pharmaceutical drug candidate etoposide.

We present a hypothetical case study to examine the use of a next‐generation framework developed by the Genetic Toxicology Technical Committee of the Health and Environmental Sciences Institute for assessing the potential risk of genetic damage from a pharmaceutical perspective. We used etoposide, a genotoxic carcinogen, as a representative pharmaceutical for the purposes of this case study. Using the framework as guidance, we formulated a hypothetical scenario for the use of etoposide to illustrate the application of the framework to pharmaceuticals. We collected available data on etoposide considered relevant for assessment of genetic toxicity risk. From the data collected, we conducted a quantitative analysis to estimate margins of exposure (MOEs) to characterize the risk of genetic damage that could be used for decision‐making regarding the predefined hypothetical use. We found the framework useful for guiding the selection of appropriate tests and selecting relevant endpoints that reflected the potential for genetic damage in patients. The risk characterization, presented as MOEs, allows decision makers to discern how much benefit is critical to balance any adverse effect(s) that may be induced by the pharmaceutical. Interestingly, pharmaceutical development already incorporates several aspects of the framework per regulations and health authority expectations. Moreover, we observed that quality dose response data can be obtained with carefully planned but routinely conducted genetic toxicity testing. This case study demonstrates the utility of the next‐generation framework to quantitatively model human risk based on genetic damage, as applicable to pharmaceuticals.

Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies

Human health risks from chronic exposures to environmental chemicals are typically estimated from potential human exposure estimates and dose-response data obtained from repeated-dose animal toxicity studies. Various criteria are available for selecting the top (highest) dose used in these animal studies. For example, toxicokinetic (TK) and toxicological data provided by shorter-term or dose range finding studies can be evaluated in a weight of evidence approach to provide insight into the dose range that would provide dose-response data that are relevant to human exposures. However, there are concerns that a top dose resulting from the consideration of TK data may be too low compared to other criteria, such as the limit dose or the maximum tolerated dose. In this paper, we address several concerns related to human exposures by discussing 1) the resources and methods available to predict human exposure levels and the associated uncertainty and variability, and 2) the margin between predicted human exposure levels and the dose levels used in repeated-dose animal studies. A series of case studies, ranging from data-rich to data-poor chemicals, are presented to demonstrate that expected human exposures to environmental chemicals are typically orders of magnitude lower than no-observed-adverse-effect levels/lowest-observed-adverse-effect levels (NOAELs/LOAELs) when available (used as conservative surrogates for top doses). The results of these case studies support that a top dose based, in part, on TK data is typically orders of magnitude higher than expected human exposure levels.

Biomarkers of DNA damage response improve in vitro micronucleus assays by revealing genotoxic mode of action and reducing the occurrence of irrelevant positive results.

We have previously described two flow cytometry-based in vitro genotoxicity tests: micronucleus (MN) scoring (MicroFlow®) and a multiplexed DNA damage response biomarker assay (MultiFlow®). Here, we describe a strategy for combining the assays in order to efficiently supplement MN analyses with a panel of biomarkers that comment on cytotoxicity (i.e. relative nuclei count, relative increased nuclei count, cleaved PARP-positive chromatin and ethidium monoazide-positive chromatin) and genotoxic mode of action (MoA; i.e. γH2AX, phospho-histone H3, p53 activation and polyploidy). For these experiments, human TK6 cells were exposed to each of 32 well-studied reference chemicals in 96-well plates for 24 continuous hours. The test chemicals were evaluated over a range of concentrations in the presence and absence of a rat liver S9-based metabolic activation system. MultiFlow assay data were acquired at 4 and 24 h, and micronuclei were scored at 24 h. Testing 32 chemicals in two metabolic activation arms translated into 64 a priori calls: 42 genotoxicants and 22 non-genotoxicants. The MN assay showed high sensitivity and moderate specificity (90% and 68%, respectively). When a genotoxic call required significant MN and MultiFlow responses, specificity increased to 95% without adversely affecting sensitivity. The dose-response data were analysed with PROAST Benchmark Dose (BMD) software in order to calculate potency metrics for each endpoint, and ToxPi software was used to synthesise the resulting lower and upper bound 90% confidence intervals into visual profiles. The BMD/ToxPi combination was found to represent a powerful strategy for synthesising multiple BMD confidence intervals, as the software output provided MoA information as well as insights into genotoxic potency.

A Universal Delayed Difference Model Fitting Dose-response Curves.

PurposeDose-response curves, which fit a multitude of experimental data derived from toxicology, are widely used in physics, chemistry, biology, and other fields. Although there are many dose-response models for fitting dose-response curves, the application of these models is limited by many restrictions and lacks universality, so there is a need for a novel, universal dynamical model that can improve fits to various types of dose-response curves.MethodsWe expand the hormetic Ricker model, taking the delay inherent in the dose-response into account, and develop a novel and dynamic delayed Ricker difference model (DRDM) to fit various types of dose-response curves. Furthermore, we compare the DRDM with other dose-response models to confirm that it can mimic different types of dose-response curves.Data analysisBy fitting various types of dose-response data sets derived from drug applications, disease treatment, pest control, and plant management, and comparing the imitative effect of the DRDM with other models, we find that the DRDM fits monotonic dose-response data well and, in most circumstances, the DRDM has a better imitative effect to non-monotonic dose-response data with hormesis than other models do.ResultsThe MSE of fits of the DRDM to S-shaped dose-response data (DS2-G) is not lower than those for four other models, but the MSE of fits to U-shaped (DS7) and inverted U-shaped dose-response data (DS10) were lower than for two other models. This means that the imitative effect of the DRDM is comparable to other models of monotonic dose-response data, but is a significant improvement compared to traditional models of non-monotonic dose-response data with hormesis.ConclusionWe propose a novel dynamic model (DRDM) for fitting to various types of dose-response curves, which can reflect the dynamic trend of the population growth compared with traditional static dose-response models. By analyzing data, we have confirmed that the DRDM provides an ideal description of various dose-response observations and it can be used to fit a wide range of dose-response data sets, especially for hormetic data sets. Therefore, we conclude that the DRDM has a good universality for dose-response curve fitting.

Dose-dependent toxicological effects in rats following a 90-day dietary exposure to PCB-156 include retinoid disruption

The toxicity of PCB-156 (2,3,3′,4,4′,5-hexachlorobiphenyl) was investigated in rats following subchronic dietary exposure. Groups of 10 male and female Sprague-Dawley rats were administered PCB-156 in the diet at 0, 0.01, 0.1, 1 or 10 ppm for 90 days. Dose-dependent increases were detected for the liver, lung and kidney weights, as well as for the liver EROD, PROD and UDPGT enzyme activities and liver uroporphyrin concentration. Dose-dependent decreases were observed in final body weight, body weight gain, and thymus weight. Apolar retinoid concentrations were decreased in the liver and lungs and increased in the kidneys. Histopathological examination of the liver, thyroid, and thymus showed mild to moderate dose-related changes.A LOAEL of 0.01 ppm was established, based on reduced apolar liver retinoid concentration. Benchmark dose-modelling corroborated the sensitivity of liver retinoid endpoints. The lower confidence limits (BMDL) for a 5% decrease in apolar liver retinoid concentrations were 0.0009 and 0.0007 ppm, respectively, in males and females, corresponding to a daily dose of 0.06 μg PCB-156 per kg body weight. Organizing dose-response data for the individual hepatic endpoints along the PCB-156 dosing scale revealed a sequence of events compatible with a causal link between depletion of apolar retinoids and the other liver biochemistry and pathology findings. Taken together, data suggest that the retinoid endpoints should be further evaluated for a causal relationship to PCB-induced liver toxicity and that retinoid system endpoints are identified and characterized to support health risk assessment in the emerging research fields of endocrine disruption and mixture toxicology.

Bayesian EMAX model with a mixture of normal distributions for dose–response in clinical trials

When a dose–response relationship is monotonic, the EMAX model has been shown to provide a good empirical fit for designing and analyzing dose–response data across a wide range of pharmaceutical studies. However, the EMAX model has never been applied to a finite mixture distribution. Motivated by a proposal investigating DHA dose effect on preterm birth (PTB, <37 weeks gestation) rate, we developed a Bayesian EMAX mixture model incorporating the three normal components finite mixture model into the EMAX framework. The proposed Bayesian EMAX mixture model analyzes gestational age as a continuous variable, which allows for statistically efficient estimates of PTB rate using various cut point with the same parsimonious model. For example, we can estimate the rate of early PTB (ePTB, <34 weeks gestation), PTB (<37 weeks gestation), and late-term birth (>41 weeks gestation) using the same model. We compared our proposed EMAX mixture model with an EMAX logistic model and an independent doses logistic model for a dichotomized endpoint using extensive simulations. Across the scenarios under consideration, the EMAX mixture model achieved higher power than the EMAX logistic model and the independent doses logistic model in detecting the effect of DHA supplementation on the PTB rate. The EMAX mixture model also resulted in smaller mean squared errors (MSE) in PTB rate estimates.

Comparative susceptibility of Rhipicephalus microplus collected from the northern state of India to coumaphos, malathion, deltamethrin, ivermectin, and fipronil.

The chemical-based tick management method is gradually losing its clutch due to the establishment of resistant ticks. For development of region-specific tick management strategies, the present study was aimed to evaluate the comparative resistance profile ofRhipicephalus microplusisolates collected from seven districts of Uttar Pradesh, a northern state of India. Comparative analysis of the dose-response data using adult immersion test (AIT) against coumaphos, malathion, deltamethrin, ivermectin, and fipronil revealed that all the isolates were resistant to discriminating concentration of deltamethrin having LC50of 295.12-436.52ppm with a resistance ratio of 22.02-32.58. An emerging low level of ivermectin resistance (resistance ratio, RR50 = 1.03-2.26) with LC50 in the range of 22.39-48.98ppm was found across the isolates. The coumaphos was highly effective against all except Amethi (AMT) isolate. Similarly, malathion was efficacious against most of the isolates except Pratapgarh (PRT) and Sultanpur (SUL) isolates showing LC50of 5128.61 and 5623.41ppm, respectively. All the isolates were responsive to fipronil. Comparative detoxifying enzymes profiles revealed a significant correlation between the increased activity of esterase and deltamethrin resistance. The GST activity was 51.2% correlated with RR50of malathion while esterase activity was significantly correlated (68.9%) with RR50 of coumaphos. No correlation between the ivermectin resistance and enzyme activity was established. Multiple sequence analysis of S4-5 linker region of the sodium channel gene of all the isolates revealed a point mutation at 190th position (C190A) which is associated with deltamethrin resistance. The possible tick management strategies in this part of the country are discussed.

Predicting the in vivo developmental toxicity of benzo[a]pyrene (BaP) in rats by an in vitro\u2013in silico approach

Developmental toxicity testing is an animal-intensive endpoints in toxicity testing and calls for animal-free alternatives. Previous studies showed the applicability of an in vitro–in silico approach for predicting developmental toxicity of a range of compounds, based on data from the mouse embryonic stem cell test (EST) combined with physiologically based kinetic (PBK) modelling facilitated reverse dosimetry. In the current study, the use of this approach for predicting developmental toxicity of polycyclic aromatic hydrocarbons (PAHs) was evaluated, using benzo[a]pyrene (BaP) as a model compound. A rat PBK model of BaP was developed to simulate the kinetics of its main metabolite 3-hydroxybenzo[a]pyrene (3-OHBaP), shown previously to be responsible for the developmental toxicity of BaP. Comparison to in vivo kinetic data showed that the model adequately predicted BaP and 3-OHBaP blood concentrations in the rat. Using this PBK model and reverse dosimetry, a concentration–response curve for 3-OHBaP obtained in the EST was translated into an in vivo dose–response curve for developmental toxicity of BaP in rats upon single or repeated dose exposure. The predicted half maximal effect doses (ED50) amounted to 67 and 45 mg/kg bw being comparable to the ED50 derived from the in vivo dose–response data reported for BaP in the literature, of 29 mg/kg bw. The present study provides a proof of principle of applying this in vitro–in silico approach for evaluating developmental toxicity of BaP and may provide a promising strategy for predicting the developmental toxicity of related PAHs, without the need for extensive animal testing.

A Stochastic Intracellular Model of Anthrax Infection With Spore Germination Heterogeneity.

We present a stochastic mathematical model of the intracellular infection dynamics of Bacillus anthracis in macrophages. Following inhalation of B. anthracis spores, these are ingested by alveolar phagocytes. Ingested spores then begin to germinate and divide intracellularly. This can lead to the eventual death of the host cell and the extracellular release of bacterial progeny. Some macrophages successfully eliminate the intracellular bacteria and will recover. Here, a stochastic birth-and-death process with catastrophe is proposed, which includes the mechanism of spore germination and maturation of B. anthracis. The resulting model is used to explore the potential for heterogeneity in the spore germination rate, with the consideration of two extreme cases for the rate distribution: continuous Gaussian and discrete Bernoulli. We make use of approximate Bayesian computation to calibrate our model using experimental measurements from in vitro infection of murine peritoneal macrophages with spores of the Sterne 34F2 strain of B. anthracis. The calibrated stochastic model allows us to compute the probability of rupture, mean time to rupture, and rupture size distribution, of a macrophage that has been infected with one spore. We also obtain the mean spore and bacterial loads over time for a population of cells, each assumed to be initially infected with a single spore. Our results support the existence of significant heterogeneity in the germination rate, with a subset of spores expected to germinate much later than the majority. Furthermore, in agreement with experimental evidence, our results suggest that most of the spores taken up by macrophages are likely to be eliminated by the host cell, but a few germinated spores may survive phagocytosis and lead to the death of the infected cell. Finally, we discuss how this stochastic modelling approach, together with dose-response data, allows us to quantify and predict individual infection risk following exposure.

Seroconversion and fever are dose-dependent in a nonhuman primate model of inhalational COVID-19.

While evidence exists supporting the potential for aerosol transmission of SARS-CoV-2, the infectious dose by inhalation remains unknown. In the present study, the probability of infection following inhalation of SARS-CoV-2 was dose-dependent in a nonhuman primate model of inhalational COVID-19. The median infectious dose, assessed by seroconversion, was 52 TCID50 (95% CI: 23–363 TCID50), and was significantly lower than the median dose for fever (256 TCID50, 95% CI: 102–603 TCID50), resulting in a group of animals that developed an immune response post-exposure but did not develop fever or other clinical signs of infection. In a subset of these animals, virus was detected in nasopharyngeal and/or oropharyngeal swabs, suggesting that infected animals without signs of disease are able to shed virus and may be infectious, which is consistent with reports of asymptomatic spread in human cases of COVID-19. These results suggest that differences in exposure dose may be a factor influencing disease presentation in humans, and reinforce the importance of public health measures that limit exposure dose, such as social distancing, masking, and increased ventilation. The dose-response data provided by this study are important to inform disease transmission and hazard modeling, and, ultimately, mitigation strategies. Additionally, these data will be useful to inform dose selection in future studies examining the efficacy of therapeutics and vaccines against inhalational COVID-19, and as a baseline in healthy, young adult animals for assessment of the importance of other factors, such as age, comorbidities, and viral variant, on the infectious dose and disease presentation.

Creation of a prototype biomimetic fish to better understand impact trauma caused by hydropower turbine blade strikes

Biomimetic model organisms could be useful surrogates for live animals in many applications if the models have sufficient biofidelity. One such application is for use in field and laboratory tests of fish mortality associated with passage through hydropower turbines. Laboratory trials suggest that blade strikes are especially injurious and often causes mortality when fish are struck by thinner blades moving at higher velocities. Dose-response relationships have been created from these data, but the exact relationship between fish mortality and the actual forces enacted on fish during simulated blade strike testing remains unknown. Here, we describe the methods used to create a prototype biomimetic model fish composed of ballistic gelatin and covered with a surrogate skin to better approximate the biomechanical properties of a fish body. Frozen fish were scanned with high-fidelity laser scanners, and a 3D-printed, reusable mold was created from which to cast our gelatin model. Computed tomography scan data, imaged directly or taken from online data repositories, were also successfully used to create CAD models for use in additive manufacturing of molds. One 3-axis accelerometer was embedded into the gelatin to compare accelerometer data to dose-response data from previous laboratory research on live fish. The resulting model (i.e., Gelfish) had a statistically indistinguishable tissue durometer to that of real fish tissue and preliminary blade strike impact testing suggested its overall flexibility was similar to that of live fish. Gelfish was designed with biofidelity as its guiding principle and our results suggest initial experimentation was successful. Future research will include replication of initial Gelfish test results, quantitative measurement of model flexibility relative to real fish, and inclusion of surrogate skeletal structures to enhance biofidelity. Use of more sophisticated sensors would also better quantify the physical forces of blade strike impact and help determine how said forces correlate with rates of mortality observed during tests on live fish.