RESUMEN
The pharmacokinetics of nicotine, the pharmacologically active alkaloid in tobacco responsible for addiction, are well characterized in humans. We developed a physiologically based pharmacokinetic/pharmacodynamic model of nicotine pharmacokinetics, brain dosimetry and brain nicotinic acetylcholine receptor (nAChRs) occupancy. A Bayesian framework was applied to optimize model parameters against multiple human data sets. The resulting model was consistent with both calibration and test data sets, but in general underestimated variability. A pharmacodynamic model relating nicotine levels to increases in heart rate as a proxy for the pharmacological effects of nicotine accurately described the nicotine related changes in heart rate and the development and decay of tolerance to nicotine. The PBPK model was utilized to quantitatively capture the combined impact of variation in physiological and metabolic parameters, nicotine availability and smoking compensation on the change in number of cigarettes smoked and toxicant exposure in a population of 10,000 people presented with a reduced toxicant (50%), reduced nicotine (50%) cigarette Across the population, toxicant exposure is reduced in some but not all smokers. Reductions are not in proportion to reductions in toxicant yields, largely due to partial compensation in response to reduced nicotine yields. This framework can be used as a key element of a dosimetry-driven risk assessment strategy for cigarette smoke constituents.
Asunto(s)
Cotinina/farmacocinética , Modelos Biológicos , Nicotina/farmacocinética , Receptores Nicotínicos/metabolismo , Animales , Teorema de Bayes , Encéfalo/metabolismo , Frecuencia Cardíaca/efectos de los fármacos , Humanos , Nicotina/administración & dosificación , Nicotina/farmacología , Agonistas Nicotínicos/farmacocinética , Agonistas Nicotínicos/farmacología , Medición de Riesgo/métodos , Fumar/efectos adversos , Fumar/metabolismo , Distribución TisularRESUMEN
The coupling of dosimetry measurements and modeling represents a promising strategy for deciphering the relationship between chemical exposure and disease outcome. To support the development and implementation of biological monitoring programs, quantitative technologies for measuring xenobiotic exposure are needed. The development of portable nanotechnology-based electrochemical (EC) sensors has the potential to meet the needs for low cost, rapid, high-throughput, and ultrasensitive detectors for biomonitoring an array of chemical markers. Highly selective EC sensors capable of pM sensitivity, high-throughput and low sample requirements (<50 microl) are discussed. These portable analytical systems have many advantages over currently available technologies, thus potentially representing the next generation of biomonitoring analyzers. This paper highlights research focused on the development of field-deployable analytical instruments based on EC detection. Background information and a general overview of EC detection methods and integrated use of nanomaterials in the development of these sensors are provided. New developments in EC sensors using various types of screen-printed electrodes, integrated nanomaterials, and immunoassays are presented. Recent applications of EC sensors for assessing exposure to pesticides or detecting biomarkers of disease are highlighted to demonstrate the ability to monitor chemical metabolites, enzyme activity, or protein biomarkers of disease. In addition, future considerations and opportunities for advancing the use of EC platforms for dosimetric studies are discussed.
Asunto(s)
Exposición a Riesgos Ambientales/análisis , Monitoreo del Ambiente/instrumentación , Monitoreo del Ambiente/métodos , Sustancias Peligrosas/análisis , Nanotecnología/instrumentación , Nanotecnología/métodos , Electroquímica , Medición de RiesgoRESUMEN
Computational fluid dynamic (CFD) models of the respiratory system provide a quantitative basis for extrapolating the localized dose of inhaled materials and improving human health risk assessments based upon inhalation studies conducted in animals. Nevertheless, model development and validation have historically been tedious and time-consuming tasks. In recognition of this, we previously reported on the use of proton (1H) magnetic resonance (MR) imaging for visualizing nasal-sinus passages in the rat, and for speeding computational mesh generation. Here, the generation and refinement of meshes for rat nasal airways are described in more detail and simulated airflows are presented. To extend the CFD models to the complete respiratory tract, three-dimensional (3D) 1H MR imaging of rat pulmonary casts was also utilized to construct pulmonary airway meshes using procedures developed for the nasal airways. Furthermore, the feasibility of validating CFD predictions with MR was tested by imaging hyperpolarized 3He gas at physiological flow rates in a straight pipe with a diameter comparable to the rat trachea. Results from these diverse studies highlight the potential utility of MR imaging not only for speeding CFD development but also possibly for model validation.