Physiologically based pharmacokinetic modeling of hydrogen cyanide levels in human breath

Human continuous hydrogen cyanide inhalation predictor with a physiologically based pharmacokinetic (PBPK) model

Hydrogen cyanide (HCN) is volatile and highly toxic with acute and chronic effects on humans. Gaseous HCN enters the atmosphere from natural processes or industrial activities, which lead to human exposure. Effective intervention in cases of HCN inhalation requires an efficient diagnostic tool. The existing physiologically based pharmacokinetic (PBPK) model for HCN cannot clearly simulate continuous HCN inhalation or predict HCN levels in inhaled air. The current study presents a PBPK model for continuous inhalation of HCN, called Human Continuous Cyanide Inhalation Predictor (HCCIP). Since existing data on pharmacokinetics of HCN inhalation are limited, HCCIP utilizes extensive data from the current authors' PBPK model on cyanide ingestion. The structure of HCCIP comprises the lungs, kidneys, liver, and slowly perfused tissue. In both the human body and in exhaled air, HCCIP features the ability to predict concentration-time courses of cyanide. Moreover, HCCIP can predict HCN concentration in inhaled air from known blood cyanide levels. After completion, the results of HCCIP were validated against preexisting published datasets. The simulation results agreed with these datasets, validating the model. The HCCIP model is an effective tool for assessing risk from continuous HCN inhalation, and HCCIP extends the capabilities of air dispersion modeling, such as AERMOD or CALPUFF, to assess HCN risk from specific release sources.

Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations

Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.