Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models.

Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models: Extension to Volatile Organic Compounds.

Chemical risk assessors use physiologically based pharmacokinetic (PBPK) models to perform dosimetric calculations, including extrapolations between exposure scenarios, species, and populations of interest. Assessors should complete a thorough quality assurance (QA) review to ensure biological accuracy and correct implementation prior to using these models. This process can be time-consuming, and we developed a PBPK model template that allows for faster, more efficient QA review. The model template consists of a single model "superstructure" with equations and logic commonly found in PBPK models, allowing users to implement a wide variety of chemical-specific PBPK models. QA review can be completed more quickly than for conventional PBPK model implementations because the general model equations have already been reviewed and only parameters describing chemical-specific model and exposure scenarios need review for any given model implementation. We have expanded a previous version of the PBPK model template by adding features commonly included in PBPK models for volatile organic compounds (VOCs). We included multiple options for representing concentrations in blood, describing metabolism, and modeling gas exchange processes to allow for inhalation exposures. We created PBPK model template implementations of published models for seven VOCs: dichloromethane, methanol, chloroform, styrene, vinyl chloride, trichloroethylene, and carbon tetrachloride. Simulations performed using our template implementations matched published simulation results to a high degree of accuracy (maximum observed percent error: 1%). Thus, the model template approach can now be applied to a broader class of chemical-specific PBPK models while continuing to bolster efficiency of QA processes that should be conducted prior to using models for risk assessment applications.

A method for incorporating a time-dose-response model into a Giardia lamblia outbreak.

Experimental time-to-infection data is a useful, but often underutilized, material for examining the mechanics of in vivo pathogen growth. In this paper, the authors attempt to incorporate a time-dose-response (TDR) equation into a model which predicts the number of ill persons per day in a Giardia lamblia epidemic using data collected from a Pittsfield, Massachusetts outbreak. To this end, dose-response and TDR models were generated for Giardia exposure to beaver and human volunteers, and a maximum likelihood estimation approach was used to ensure that the models provided acceptable fits. The TDR equation that best-fit the human data was the beta-Poisson with exponential-reciprocal dependency model, and this was chosen to be incorporated into the outbreak model. The outbreak model is an expanded probability model that convolutes an assumed incubation distribution of the infectious agent with an exposure distribution. Since the beta-Poisson with exponential-reciprocal dependency models the time-to-infection density distribution, it is input as the incubation distribution. Several density functions, including the Weibull, lognormal, gamma, and uniform functions served as exposure distributions. The convolution of the time-dependent probability distribution with the lognormal distribution yielded the best-fit for the outbreak model.

Incorporating Time-Dose-Response into Legionella Outbreak Models.

A novel method was used to incorporate in vivo host-pathogen dynamics into a new robust outbreak model for legionellosis. Dose-response and time-dose-response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best-fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best-fit TDR from the murine dosing study was the beta-Poisson with exponential-reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non-time-dependent distributions to explore the performance of the time-dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host-pathogen system.

Agreement between novice and experienced trachoma graders improves after a single day of didactic training.

BACKGROUND/AIMS: Prevalence estimates and treatment decisions for trachoma are based entirely on ocular clinical examination. The aim of the current study is to demonstrate that ophthalmic assistants can be trained and certified to provide trachoma grading within a single day. METHODS: Conjunctival photographs from an area with endemic trachoma were randomised into two sets of 60 cases. Photographs were graded for trachomatous inflammation-follicular (TF) and trachomatous inflammation-intense (TI) by three experienced graders. Inter-rater reliability of eight ophthalmic assistants and three experienced graders were compared before and after training. RESULTS: The mean κ agreement between the ophthalmic assistants and the consensus grades of the experienced graders for TF was 0.38 (95% CI 0.18 to 0.58) before training, and increased to 0.60 (95% CI 0.42 to 0.78) after training (p=0.07). The mean κ agreement for TI was 0.16 (95% CI 0.02 to 0.30) before training, and increased to 0.39 (95% CI 0.20 to 0.58) after training (p=0.02). CONCLUSION: A single day of training improves agreement between prospective and experienced trachoma graders, and provides the basis for certification of workers who are able to accurately grade trachoma and generate reliable prevalence estimates.