Modeling the effect of succimer (DMSA; dimercaptosuccinic acid) chelation therapy in patients poisoned by lead.

CONTEXT: Kinetic models could assist clinicians potentially in managing cases of lead poisoning. Several models exist that can simulate lead kinetics but none of them can predict the effect of chelation in lead poisoning. Our aim was to devise a model to predict the effect of succimer (dimercaptosuccinic acid; DMSA) chelation therapy on blood lead concentrations. MATERIALS AND METHODS: We integrated a two-compartment kinetic succimer model into an existing PBPK lead model and produced a Chelation Lead Therapy (CLT) model. The accuracy of the model's predictions was assessed by simulating clinical observations in patients poisoned by lead and treated with succimer. The CLT model calculates blood lead concentrations as the sum of the background exposure and the acute or chronic lead poisoning. The latter was due either to ingestion of traditional remedies or occupational exposure to lead-polluted ambient air. The exposure duration was known. The blood lead concentrations predicted by the CLT model were compared to the measured blood lead concentrations. RESULTS: Pre-chelation blood lead concentrations ranged between 99 and 150 µg/dL. The model was able to simulate accurately the blood lead concentrations during and after succimer treatment. The pattern of urine lead excretion was successfully predicted in some patients, while poorly predicted in others. CONCLUSIONS: Our model is able to predict blood lead concentrations after succimer therapy, at least, in situations where the duration of lead exposure is known.

Modelling dimercaptosuccinic acid (DMSA) plasma kinetics in humans.

CONTEXT: No kinetic models presently exist which simulate the effect of chelation therapy on lead blood concentrations in lead poisoning. OBJECTIVE: Our aim was to develop a kinetic model that describes the kinetics of dimercaptosuccinic acid (DMSA; succimer), a commonly used chelating agent, that could be used in developing a lead chelating model. MATERIAL AND METHODS: This was a kinetic modelling study. We used a two-compartment model, with a non-systemic gastrointestinal compartment (gut lumen) and the whole body as one systemic compartment. The only data available from the literature were used to calibrate the unknown model parameters. The calibrated model was then validated by comparing its predictions with measured data from three different experimental human studies. RESULTS: The model predicted total DMSA plasma and urine concentrations measured in three healthy volunteers after ingestion of DMSA 10 mg/kg. The model was then validated by using data from three other published studies; it predicted concentrations within a factor of two, representing inter-human variability. CONCLUSIONS: A simple kinetic model simulating the kinetics of DMSA in humans has been developed and validated. The interest of this model lies in the future potential to use it to predict blood lead concentrations in lead-poisoned patients treated with DMSA.

Evaluation of semi-generic PBTK modeling for emergency risk assessment after acute inhalation exposure to volatile hazardous chemicals.

BACKGROUND: Physiologically Based Toxicokinetic Models (PBTK) may facilitate emergency risk assessment after chemical incidents with inhalation exposure, but they are rarely used due to their relative complexity and skill requirements. We aimed to tackle this problem by evaluating a semi-generic PBTK model built in MS Excel for nine chemicals that are widely-used and often released in a chemical incident. MATERIAL & METHODS: The semi-generic PBTK model was used to predict blood concentration-time curves using inhalation exposure scenarios from human volunteer studies, case reports and hypothetical exposures at Emergency Response Planning Guideline, Level 3 (ERPG-3) levels.(2) Predictions using this model were compared with measured blood concentrations from volunteer studies or case reports, as well as blood concentrations predicted by chemical-specific models. The performances of the semi-generic model were evaluated on biological rationale, accuracy, and ease of use and range of application. RESULTS: Our results indicate that the semi-generic model can be easily used to predict blood levels for eight out of nine parent chemicals (dichloromethane, benzene, xylene, styrene, toluene, isopropanol trichloroethylene and tetrachloroethylene). However, for methanol, 2-propanol and dichloromethane the semi-generic model could not cope with the endogenous production of methanol and of acetone (being a metabolite of 2-propanol) nor could it simulate the formation of HbCO, which is one of the toxic end-points of dichloromethane. The model is easy and intuitive to use by people who are not so familiar with toxicokinetic models. CONCLUSION: A semi-generic PBTK modeling approach can be used as a 'quick-and-dirty' method to get a crude estimate of the exposure dose.