A spreadsheet program for modeling quantitative structure-pharmacokinetic relationships for inhaled volatile organics in humans

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.

Cancer risk of petrochemical workers exposed to airborne PAHs in industrial Lanzhou City, China

This paper reports the connections between red blood cells abnormality risk of petrochemical workers and their exposure to airborne polycyclic aromatic hydrocarbons (PAHs). Urinary 1-hydroxypyrene (1-OHP), as the biomarker of PAHs exposure, was adopted to assess the exposure risk of the petrochemical workers to PAHs in Xigu, the west suburb of Lanzhou where petrochemical industries are located. Fifty-three workers, sub-grouped to 36 petrochemical workers and 17 office workers, participated in this investigation. Logistic regression model and spearman correlation analysis were performed to estimate the associations between PAHs exposure levels and red blood cells abnormality risk of petrochemical workers. Strong associations between some red cell indices (MCH, MCHC, RDW) and 1-OHP concentration were found. Results also show that the red blood cells abnormality risk increased with increasing PAHs exposure level. Compared with office workers, risk level of red blood cells abnormality in petrochemical workers was higher by 41.7 % (OR, 1.417; 95 % CI: 0.368-5.456) than that in office workers. This result was verified by the tissue-to-human blood partition coefficient for pyrene and 1-OHP. The quantitative assessments of the potential health risk through inhalation exposure to PAHs were conducted using the Incremental Lifetime Cancer Risk (ILCR) model. It was found the ILCR from inhalation exposure to PAHs for the petrochemical workers ranged from 10(-5) to 10(-4) with 95 % probability, indicating that petrochemical plant workers were under a high potential cancer risk level.

An in silico approach for evaluating a fraction-based, risk assessment method for total petroleum hydrocarbon mixtures

Both the Massachusetts Department of Environmental Protection (MADEP) and the Total Petroleum Hydrocarbon Criteria Working Group (TPHCWG) developed fraction-based approaches for assessing human health risks posed by total petroleum hydrocarbon (TPH) mixtures in the environment. Both organizations defined TPH fractions based on their expected environmental fate and by analytical chemical methods. They derived toxicity values for selected compounds within each fraction and used these as surrogates to assess hazard or risk of exposure to the whole fractions. Membership in a TPH fraction is generally defined by the number of carbon atoms in a compound and by a compound's equivalent carbon (EC) number index, which can predict its environmental fate. Here, we systematically and objectively re-evaluate the assignment of TPH to specific fractions using comparative molecular field analysis and hierarchical clustering. The approach is transparent and reproducible, reducing inherent reliance on judgment when toxicity information is limited. Our evaluation of membership in these fractions is highly consistent (˜80% on average across various fractions) with the empirical approach of MADEP and TPHCWG. Furthermore, the results support the general methodology of mixture risk assessment to assess both cancer and noncancer risk values after the application of fractionation.

A generic, cross-chemical predictive PBTK model with multiple entry routes running as application in MS Excel; design of the model and comparison of predictions with experimental results

AIM

Physiologically based toxicokinetic (PBTK) models are computational tools, which simulate the absorption, distribution, metabolism, and excretion of chemicals. The purpose of this study was to develop a physiologically based pharmacokinetic (PBPK) model with a high level of transparency. The model should be able to predict blood and urine concentrations of environmental chemicals and metabolites, given a certain environmental or occupational exposure scenario.

MODEL

The model refers to a reference human of 70 kg. The partition coefficients of the parent compound and its metabolites (blood:air and tissue:blood partition coefficients of 11 organs) are estimated by means of quantitative structure-property relationship, in which five easily available physicochemical properties of the compound are the independent parameters. The model gives a prediction of the fate of the compound, based on easily available chemical properties; therefore, it can be applied as a generic model applicable to multiple compounds. Three routes of uptake are considered (inhalation, dermal, and/or oral) as well as two built-in exercise levels (at rest and at light work). Dermal uptake is estimated by the use of a dermal diffusion-based module that considers dermal deposition rate and duration of deposition. Moreover, evaporation during skin contact is fully accounted for and related to the volatility of the substance. Saturable metabolism according to Michaelis-Menten kinetics can be modelled in any of 11 organs/tissues or in liver only. Renal tubular resorption is based on a built-in algorithm, dependent on the (log) octanol:water partition coefficient. Enterohepatic circulation is optional at a user-defined rate. The generic PBTK model is available as a spreadsheet application in MS Excel. The differential equations of the model are programmed in Visual Basic. Output is presented as numerical listing over time in tabular form and in graphs. The MS Excel application of the PBTK model is available as freeware.

EXPERIMENTAL

The accuracy of the model prediction is illustrated by simulating experimental observations. Published experimental inhalation and dermal exposure studies on a series of different chemicals (pyrene, N-methyl-pyrrolidone, methyl-tert-butylether, heptane, 2-butoxyethanol, and ethanol) were selected to compare the observed data with the model-simulated data. The examples show that the model-predicted concentrations in blood and/or urine after inhalation and/or transdermal uptake have an accuracy of within an order of magnitude.

CONCLUSIONS

It is advocated that this PBTK model, called IndusChemFate, is suitable for 'first tier assessments' and for early explorations of the fate of chemicals and/or metabolites in the human body. The availability of a simple model with a minimum burden of input information on the parent compound and its metabolites might be a stimulation to apply PBTK modelling more often in the field of biomonitoring and exposure science.

QSARs for PBPK modelling of environmental contaminants

Physiologically-based pharmacokinetic (PBPK) models are increasingly finding use in risk assessment applications of data-rich compounds. However, it is a challenge to determine the chemical-specific parameters for these models, particularly in time- and resource-limiting situations. In this regard, SARs, QSARs and QPPRs are potentially useful for computing the chemical-specific input parameters of PBPK models. Based on the frequency of occurrence of molecular fragments (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure) and exposure conditions, the available QSAR-PBPK models facilitate the simulation of tissue and blood concentrations for some inhaled volatile organic chemicals. The application domain of existing QSARs for developing PBPK models is limited, due to lack of relevant data for diverse chemicals and mechanisms. Even though this approach is conceptually applicable to non-volatile and high molecular weight organics as well, it is more challenging to predict the other PBPK model parameters required for modelling the kinetics of these chemicals (particularly tissue diffusion coefficients, association constants for binding and oral absorption rates). As the level of our understanding of the mechanistic basis of toxicokinetic processes improves, QSARs to provide a priori predictions of key chemical-specific PBPK parameters can be developed to expedite the internal dose-based health risk assessments in data-poor situations.

PKSolver: An add-in program for pharmacokinetic and pharmacodynamic data analysis in Microsoft Excel

This study presents PKSolver, a freely available menu-driven add-in program for Microsoft Excel written in Visual Basic for Applications (VBA), for solving basic problems in pharmacokinetic (PK) and pharmacodynamic (PD) data analysis. The program provides a range of modules for PK and PD analysis including noncompartmental analysis (NCA), compartmental analysis (CA), and pharmacodynamic modeling. Two special built-in modules, multiple absorption sites (MAS) and enterohepatic circulation (EHC), were developed for fitting the double-peak concentration-time profile based on the classical one-compartment model. In addition, twenty frequently used pharmacokinetic functions were encoded as a macro and can be directly accessed in an Excel spreadsheet. To evaluate the program, a detailed comparison of modeling PK data using PKSolver and professional PK/PD software package WinNonlin and Scientist was performed. The results showed that the parameters estimated with PKSolver were satisfactory. In conclusion, the PKSolver simplified the PK and PD data analysis process and its output could be generated in Microsoft Word in the form of an integrated report. The program provides pharmacokinetic researchers with a fast and easy-to-use tool for routine and basic PK and PD data analysis with a more user-friendly interface.

An integrated QSPR-PBPK modelling approach for in vitro-in vivo extrapolation of pharmacokinetics in rats

In vitro data on metabolism and partitioning may be integrated within physiologically-based pharmacokinetic (PBPK) models to provide simulations of the kinetics and bioaccumulation of chemicals in intact organisms. Quantitative structure-property relationship (QSPR) modelling of available in vitro data may be performed to predict metabolism rates and partition coefficients (PCs) for developing in vivo PBPK models. The objective of the present study was to develop an integrated QSPR-PBPK modelling approach for the conduct of in vitro to in vivo extrapolation. For this purpose, data on rat blood:air (P(b)) and fat:air (P(f)) PCs, as well as intrinsic metabolic clearance (CL(int)) obtained using rat liver slices for some C(5)-C(10) volatile organic compounds (VOCs) were compiled from the literature. Multilinear additive QSPR models for P(f), P(b) and CL(int) were developed based on the number and nature of molecular fragments in these VOCs (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure). The mean estimated/experimental (est/exp) ratios (+/-SD; range) were 1.0 (+/-0.04; 0.93 - 1.06) for log P(f), 1.08 (+/-0.26; 0.70 - 1.62) for log P(b), and 1.07 (+/- 0.21; 0.80 - 1.44) for CL(int). By accounting for the difference in the content of neutral lipids in fat and other tissues, the liver : air and muscle : air PCs of the compounds investigated in this study, with the excerption of n-decane, were adequately predicted from P(f). Integrating the QSPRs for P(f), P(b) and CL(int) within a rat PBPK model, simulations of inhalation pharmacokinetics of several VOCs were generated on the basis of molecular structure, for a given exposure scenario. The integrated QSPR-PBPK model developed in this study is a potentially useful tool for predicting in vivo kinetics and bioaccumulation of chemicals in rats under poor data situations.

Whole-body physiologically based pharmacokinetic models

This review summarizes the most recent developments in and applications of physiologically based pharmacokinetic (PBPK) modeling methodology originating from both the pharmaceutical and environmental toxicology areas. It focuses on works published in the last 5 years, although older seminal papers have also been referenced. After a brief introduction to the field and several essential definitions, the main body of the text is structured to follow the major steps of a typical PBPK modeling exercise. Various applications of the methodology are briefly described. The major future trends and perspectives are outlined. The main conclusion from the review of the available literature is that PBPK modeling, despite its obvious potential and recent incremental developments, has not taken the place it deserves, especially in pharmaceutical and drug development sciences.