A physiologically based pharmacokinetic model for the oxime TMB-4: simulation of rodent and human data

Abstract

Multiple oximes have been synthesized and evaluated for use as countermeasures against chemical warfare nerve agents. The current U.S. military and civilian oxime countermeasure, 2-[(hydroxyimino)methyl]-1-methylpyridin-1-ium chloride (2-PAM), is under consideration for replacement with a more effective acetylcholinesterase reactivator, 1,1'-methylenebis{4-hydroxyiminomethyl}pyridinium dimethanesulfonate (MMB-4). Kinetic data in the scientific literature for MMB-4 are limited; therefore, a physiologically based pharmacokinetic (PBPK) model was developed for a structurally related oxime, 1,1'-trimethylenebis{4-hydroximinomethyl}pyridinium dibromide. Based on a previous model structure for the organophosphate diisopropylfluorophosphate, the model includes key sites of acetylcholinesterase inhibition (brain and diaphragm), as well as fat, kidney, liver, rapidly perfused tissues and slowly perfused tissues. All tissue compartments are diffusion limited. Model parameters were collected from the literature, predicted using quantitative structure-property relationships or, when necessary, fit to available pharmacokinetic data from the literature. The model was parameterized using rat plasma, tissue and urine time course data from intramuscular administration, as well as human blood and urine data from intravenous and intramuscular administration; sensitivity analyses were performed. The PBPK model successfully simulates rat and human data sets and has been evaluated by predicting intravenous mouse and intramuscular human data not used in the development of the model. Monte Carlo analyses were performed to quantify human population kinetic variability in the human evaluation data set. The model identifies potential pharmacokinetic differences between rodents and humans, indicated by differences in model parameters between species. The PBPK model can be used to optimize the dosing regimen to improve oxime therapeutic efficacy in a human population.

Physiologically based pharmacokinetic modeling of human exposure to perfluorooctanoic acid suggests historical non drinking-water exposures are important for predicting current serum concentrations

“…For simulation of the exposure occurring in the Mid-Ohio Valley, body weights for adults aged 20 years and older as reported in the National Health and Nutrition Examination Survey (NHANES) (Ogden et al, 2004) were truncated at the 2.5th and 97.5th percentiles and used to determine the 95% distribution for this parameter (Tan et al, 2006; Shankaran et al, 2013; Sterner et al, 2013). For simulation of the exposure occurring in North Alabama, measured body weights (ATSDR, 2016a) were used to determine the probabilistic distribution.…”

Inhibitors of Acetylcholinesterase Derived from 7-Methoxytacrine and Their Effects on the Choline Transporter CHT1

“…1). Although our previous experiments revealed that C 6 -C 8 derivatives were very effective inhibitors of human recombinant AChE (and of human plasmatic butyrylcholinesterase), similarly as tacrine, and that C 4 -C 5 derivatives were only less effective inhibitors of AChE, similarly as 7-MEOTA [4], the observed differences seem to be in an accord with studies reporting marked alterations in the pharmacokinetic parameters of AChE between rodent and human species, depending also on the type of inhibitor [55,56,57]. The results of our study also revealed that all new derivatives, but not HC-3, are inhibitors of a mixed type, similarly as 7-MEOTA or tacrine (Table 1), suggesting involvements of both competitive and noncompetitive mechanisms, in accordance with data in the literature [58].…”

Investigation of an alternative generic model for predicting pharmacokinetic changes during physiological stress

“…One can also assess the sensitivity of the outcome to a variability that is expected in the measurement of the input parameter, but this may be impractical as one must know a priori what the measurement error actually is. Given the objective of this work to verify the flexibility of development of a PBPK model by using MATLAB Simulink® to predict pharmacokinetic changes during physiological stress, local sensitivity analysis with a 1% change for each parameter of interested was adopted as reported in the literature …”

“…Given the objective of this work to verify the flexibility of development of a PBPK model by using MATLAB Simulink ® to predict pharmacokinetic changes during physiological stress, local sensitivity analysis with a 1% change for each parameter of interested was adopted as reported in the literature. 36 It is apparent that the simulation time affects the PK parameter outcome. For example with the PK-Sim ® model, while the model parameters were identical for the first two studies in Table 3, the simulated V ss was slightly different (215 L vs. 209 L).…”