scholarly journals A ready-to-use dose-response model of Campylobacter jejuni implemented in the FSKX-standard

2021 ◽  
Vol 2 ◽  
Author(s):  
Esther M. Sundermann ◽  
Maarten Nauta ◽  
Arno Swart
Keyword(s):  
Campylobacter Jejuni ◽  
Dose Response ◽  
Risk Assessments ◽  
Response Model ◽  
Response Models ◽  
Model Parameter ◽  
Parameter Values ◽  
Executable Model

Dose-response models are an important part of quantitative microbiological risk assessments. In this paper, we present a transparent and ready-to-use version of a published dose-response model that estimates the probability of infection and illness after the consumption of a meal that is contaminated with the pathogen Campylobacter jejuni. To this end, model and metadata are implemented in the fskx-standard. The model parameter values are based on data from a set of different studies on the infectivity and pathogenicity of Campylobacter jejuni. Both, challenge studies and outbreaks are considered, users can decide which of these is most suitable for their purpose. We present examples of results for typical ingested doses and demonstrate the utility of our ready-to-use model re-implementation by supplying an executable model embedded in this manuscript.

Risk Assessment for Fetal Toxicity

1985 ◽  
Vol 1 (4) ◽  
pp. 299-310 ◽  
Author(s):  
John Van Ryzin
Keyword(s):  
Risk Assessment ◽  
Toxic Effect ◽  
Dose Response ◽  
Risk Assessments ◽  
Control Group ◽  
Response Model ◽  
Statistical Research ◽  
Response Models ◽  
Fetal Toxicity ◽  
Using Data

This paper reviews the problem of performing risk assessments using data on fetal toxic effects. It briefly discusses the usual dose-response models and their inappropriateness for application to such data. The paper then considers tests for determining whether the fetal toxic effect is increased over that of the control group. Assuming an increase has been shown, the use of a fetal toxicity, dose-response model for risk assessment is discussed. The paper then applies these methods to data from an experiment using female mice mated with irradiated males. Finally, the paper discusses the need for further statistical research in this important area.

scholarly journals Modeling Dose-Response Relationships in Biological Control: Partitioning Host Responses to the Pathogen and Biocontrol Agent

1997 ◽  
Vol 87 (7) ◽  
pp. 720-729 ◽  
Author(s):  
Kevin P. Smith ◽  
Jo Handelsman ◽  
Robert M. Goodman
Keyword(s):  
Biological Control ◽  
Dose Response ◽  
Host Response ◽  
Biocontrol Agent ◽  
Inbred Lines ◽  
Response Models ◽  
Model Parameter ◽  
Biocontrol Efficacy ◽  
Parameter Values ◽  
Best Fit

Breeding plants to improve the effectiveness of biocontrol agents is a promising approach to enhance disease suppression by microorganisms. Differences in biocontrol efficacy among cultivars suggest there is genetic variation for this trait within crop germplasm. The ability to quantify host differences in support of biological control is influenced by variation in host response to the pathogen and the dose of pathogen and biocontrol agent applied to the host. To assess the contribution of each of these factors to successful biocontrol interactions, we measured disease over a range of pathogen (Pythium) and biocontrol agent (Bacillus cereus UW85) inoculum doses. We fit dose-response models to these data and used model parameter estimates to quantify host differences in response to the pathogen and biocontrol agent. We first inoculated eight plant species separately with three species of Pythium and evaluated three dose-response models for their ability to describe the disease response to pathogen inoculum level. All three models fit well to at least some of the host-pathogen combinations; the hyperbolic saturation model provided the best overall fit. To quantify the host contribution to biological control, we next evaluated these models with data from a tomato assay, using six inbred tomato lines, P. torulosum, and UW85. The lowest dose of pathogen applied revealed the greatest differences in seedling mortality among the inbred lines, ranging from 40 to 80%. The negative exponential (NE) pathogen model gave the best fit to these pathogen data, and these differences corresponded to model parameter values, which quantify pathogen efficiency, of 0.023 and 0.091. At a high pathogen dose, we detected the greatest differences in biocontrol efficacy among the inbred lines, ranging from no effect to a 68% reduction in mortality. The NE pathogen model with a NE biocontrol component, the NE/NE biocontrol model, gave the best fit to these biocontrol data, and these reductions corresponded to model parameter values, which quantify biocontrol efficiency, of 0.00 and 0.038, respectively. There was no correlation between the host response to the pathogen and biocontrol agent for these inbred lines. This work demonstrates the utility of epidemiological modeling approaches for the study of biological control and lays the groundwork to employ manipulation of host genetics to improve biocontrol efficacy.

scholarly journals Pharmacodynamic effect of bempedoic acid and statin combinations: predictions from a dose–response model

2021 ◽  
Author(s):  
Satyawan B Jadhav ◽  
Ryan L Crass ◽  
Sunny Chapel ◽  
Michael Kerschnitzki ◽  
William J Sasiela ◽  
...  
Keyword(s):  
Dose Response ◽  
Phase 1 ◽  
Lipid Lowering ◽  
Similar Degree ◽  
Lipid Lowering Therapy ◽  
Response Model ◽  
Atp Citrate Lyase ◽  
Pharmacodynamic Interaction ◽  
Response Models ◽  
Statin Dose

Abstract Aims Many patients are unable to achieve guideline-recommended LDL cholesterol (LDL-C) targets, despite taking maximally tolerated lipid-lowering therapy. Bempedoic acid, a competitive inhibitor of ATP citrate lyase, significantly lowers LDL-C with or without background statin therapy in diverse populations. Because pharmacodynamic interaction between statins and bempedoic acid is complex, a dose–response model was developed to predict LDL-C pharmacodynamics following administration of statins combined with bempedoic acid. Methods and results Bempedoic acid and statin dosing and LDL-C data were pooled from 14 phase 1–3 clinical studies. Dose–response models were developed for bempedoic acid monotherapy and bempedoic acid–statin combinations using previously published statin parameters. Simulations were performed using these models to predict change in LDL-C levels following treatment with bempedoic acid combined with clinically relevant doses of atorvastatin, rosuvastatin, simvastatin, and pravastatin. Dose–response models predicted that combining bempedoic acid with the lowest statin dose of commonly used statins would achieve a similar degree of LDL-C lowering as quadrupling that statin dose; for example, the predicted LDL-C lowering was 54% with atorvastatin 80 mg compared with 54% with atorvastatin 20 mg + bempedoic acid 180 mg, and 42% with simvastatin 40 mg compared with 46% with simvastatin 10 mg + bempedoic acid 180 mg. Conclusion These findings suggest bempedoic acid combined with lower statin doses offers similar LDL-C lowering compared with statin monotherapy at higher doses, potentially sparing patients requiring additional lipid-lowering therapies from the adverse events associated with higher statin doses.

scholarly journals Implementing a New Dose-response Model for Estimating Infection Probability of Campylobacter Jejuni based on the Key Events Dose-response Framework

2021 ◽  
Author(s):  
Hiroki Abe ◽  
Kohei Takeoka ◽  
Yuto Fuchisawa ◽  
Kento Koyama ◽  
Shigenobu Koseki
Keyword(s):  
Campylobacter Jejuni ◽  
Dose Response ◽  
Cell Invasion ◽  
Foodborne Pathogens ◽  
Pathogenic Bacteria ◽  
Underlying Mechanism ◽  
Response Model ◽  
Response Relationship ◽  
Dose Response Relationship ◽  
Key Factor

Abstract Understanding the dose-response relationship between ingested pathogenic bacteria and infection probability is a key factor for appropriate risk assessment of foodborne pathogens. The objectives of this study were to develop and validate a novel mechanistic dose-response model for Campylobacter jejuni and simulate the underlying mechanism of foodborne illness during digestion. Bacterial behavior in the human gastrointestinal environment, including gastric reductions, transition to intestines, and invasion to intestinal tissues, was described using a Bayesian statistical model based on the reported experimental results of each process while considering physical food types (liquid or solid) and host age (young or elderly). Combining the models in each process, the relationship between pathogen intake and the cell invasion probability of C. jejuni was estimated and compared with reported epidemiological dose-response relationships. Taking food types into account, estimations of the cell invasion probability of C. jejuni successfully described the reported dose-response relationships from substantial accidents. The developed calculation framework is thus potentially applicable to other pathogens to quantify the dose-response relationship from experimental data obtained from digestion.

scholarly journals Modeling Invasion of Campylobacter jejuni into Human Small Intestinal Epithelial-Like Cells by Bayesian Inference

2020 ◽  
Vol 87 (1) ◽  
Author(s):  
Hiroki Abe ◽  
Kento Koyama ◽  
Shigenobu Koseki
Keyword(s):  
Mathematical Model ◽  
Campylobacter Jejuni ◽  
Dose Response ◽  
Intestinal Cells ◽  
Intestinal Epithelial ◽  
Response Model ◽  
Content Type ◽  
Alternative Approach ◽  
Response Modeling ◽  
Invasion Behavior

ABSTRACT Current approaches used for dose-response modeling of low-dose exposures of pathogens rely on assumptions and extrapolations. These models are important for quantitative microbial risk assessment of food. A mechanistic framework has been advocated as an alternative approach for evaluating dose-response relationships. The objectives of this study were to investigate the invasion behavior of Campylobacter jejuni, which could arise as a foodborne illness even if there are low counts of pathogens, into Caco-2 cells as a model of intestinal cells and to develop a mathematical model for invading cell counts to reveal a part of the infection dose-response mechanism. Monolayer-cultured Caco-2 cells and various concentrations of C. jejuni in culture were cocultured for up to 12 h. The numbers of C. jejuni bacteria invading Caco-2 cells were determined after coculture for different time periods. There appeared to be a maximum limit to the invading bacterial counts, which showed an asymptotic exponential increase. The invading bacterial counts were higher with higher exposure concentrations (maximum, 5.0 log CFU/cm2) than with lower exposure concentrations (minimum, 0.6 log CFU/cm2). In contrast, the ratio of invading bacteria (number of invading bacteria divided by the total number of bacteria exposed) showed a similar trend regardless of the exposure concentration. Invasion of C. jejuni into intestinal cells was successfully demonstrated and described by the developed differential equation model with Bayesian inference. The model accuracy showed that the 99% prediction band covered more than 97% of the observed values. These findings provide important information on mechanistic pathogen dose-response relationships and an alternative approach for dose-response modeling. IMPORTANCE One of the infection processes of C. jejuni, the invasion behavior of the bacteria in intestinal epithelial cells, was revealed, and a mathematical model for prediction of the cell-invading pathogen counts was developed for the purpose of providing part of a dose-response model for C. jejuni based on the infection mechanism. The developed predictive model showed a high accuracy of more than 97% and successfully described the C. jejuni invading counts. The bacterial invasion predictive model of this study will be essential for the development of a dose-response model for C. jejuni based on the infection mechanism.

scholarly journals Implementing a new dose-response model for estimating infection probability of Campylobacter jejuni based on the key events dose-response framework

2021 ◽  
Author(s):  
Hiroki Abe ◽  
Kohei Takeoka ◽  
Yuto Fuchisawa ◽  
Kento Koyama ◽  
Shigenobu Koseki
Keyword(s):  
Risk Assessment ◽  
Campylobacter Jejuni ◽  
Dose Response ◽  
Food Type ◽  
Response Model ◽  
Host Age ◽  
Response Relationship ◽  
Dose Response Relationship ◽  
Mechanistic Approach ◽  
Key Events

Understanding the dose-response relationship between ingested pathogenic bacteria and infection probability is a key factor for appropriate risk assessment of foodborne pathogens. The objectives of this study were to develop and validate a novel mechanistic dose-response model for Campylobacter jejuni and simulate the underlying mechanism of foodborne illness during digestion. Bacterial behavior in the human gastrointestinal environment, including survival at low pH in the gastric environment after meals, transition to intestines, and invasion to intestinal tissues, was described using a Bayesian statistical model based on the reported experimental results of each process while considering physical food types (liquid or solid) and host age (young adult or elderly). Combining the models in each process, the relationship between pathogen intake and the infection probability of C. jejuni was estimated and compared with reported epidemiological dose-response relationships. Taking food types and host age into account, the prediction range of the infection probability of C. jejuni successfully covered the reported dose-response relationships from actual C. jejuni outbreaks. According to sensitivity analysis of predicted infection probabilities, the host age factor and the food type factor have relatively higher relevance than other factors. Thus, the developed Key Events Dose Response Framework can derive novel information for quantitative microbiological risk assessment in addition of dose-response relationship. The developed framework is potentially applicable to other pathogens to quantify the dose-response relationship from experimental data obtained from digestion. Importance Based on the mechanistic approach called Key Events Dose Response Framework alternative to previous non-mechanistic approach, the dose-response models for infection probability of C. jejuni were developed considering with age of people who take pathogen and food type. The developed predictive framework illustrated highly accurate prediction of dose (minimum difference 0.21 log CFU) for a certain infection probability compared with the previously reported dose-response relationship. In addition, the developed prediction procedure revealed that the dose-response relationship strongly depends on food type as well as host age. The implementation of Key Event Dose Response Framework will mechanistically and logically reveal the dose-response relationship and provide useful information with quantitative microbiological risk assessment of C. jejuni on foods.

scholarly journals Development of a dose–response model for Naegleria fowleri

2018 ◽  
Vol 17 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Kara Dean ◽  
Mark H. Weir ◽  
Jade Mitchell
Keyword(s):  
Dose Response ◽  
Response Model ◽  
Naegleria Fowleri ◽  
Data Set ◽  
Response Models ◽  
Exposure Pathway ◽  
Accurate Dose ◽  
Plumbing Systems ◽  
Nægleria Fowleri ◽  
Measured Response

Abstract This study develops novel dose–response models for Naegleria fowleri from selected peer-reviewed experiments on the virulence based on the intranasal exposure pathway. One data set measured the response of mice intranasally inoculated with the amebae and the other study addressed the response of mice swimming in N. fowleri infected water. The measured response for both studies was death. All experimental data were best fit by the beta-Poisson dose–response model. The three swimming experiments could be pooled, and this is the final recommended model with an LD50 of 13,257 amebae. The results of this study provide a better estimate of the probability of the risk to N. fowleri exposure than the previous models developed based on an intravenous exposure. An accurate dose–response model is the first step in quantifying the risk of free-living amebae like N. fowleri, which pose risks in recreational environments and have been detected in drinking water and premise plumbing systems. A better understanding of this risk will allow for risk management that limits the ability for pathogen growth, proliferation, and exposure.

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