Decamethylcyclopentasiloxane (D(5)), a volatile cyclic methyl siloxane (VCMS), is used in industrial and consumer products. Inhalation pharmacokinetics of another VCMS, octamethylcyclotetrasiloxane (D(4)), have been extensively investigated and successfully modeled with a multispecies physiologically based pharmacokinetic (PBPK) model. Here, we develop an inhalation PBPK description for D(5), using the D(4) model structure as a starting point, with the objective of understanding factors that regulate free blood and tissue concentrations of this highly lipophilic vapor after inhalation in rats and humans. Compared with D(4), the more lipophilic D(5) required deep compartments in lung, liver, and plasma to account for slow release from tissues after cessation of exposures. Simulations of the kinetics of a stable D(5) metabolite, HO-D(5), required diffusion-limited uptake in fat, a deep tissue store in lung, and its elimination by fecal excretion and metabolism to linear silanols. The combined D(5)/HO-D(5) model described blood and tissue concentrations of parent D(5) and elimination of total radioactivity in single and repeat exposures in male and female rats at 7 and 160 ppm. In humans, D(5) kinetic data are more sparse and the model structure though much simplified, still required free and bound blood D(5) to simulate exhaled air and blood time courses from 1 h inhalation exposures at 10 ppm in five human volunteers. This multispecies PBPK model for D(5) highlights complications in interpreting kinetic studies where chemical in blood and tissues represents various pools with only a portion free. The ability to simulate free concentrations is essential for dosimetry based risk assessments for these VCMS.

The pharmacokinetics of octamethylcyclotetrasiloxane (D4), a highly lipophilic and well-metabolized volatile cyclic siloxane, are more complex than those of other volatile hydrocarbons. The purpose of the present study was to evaluate rate constants for saturable metabolism in the body, to estimate possible presystemic D4 clearance by respiratory-tract tissues, and to assess rate constants for uptake of D4 after oral dosing. These experiments provided the opportunity to refine current physiologically based pharmacokinetic (PBPK) models for D4 and to independently estimate key model parameters by sensitive inhalation methods. The PBPK model could only be fitted to gas uptake results when metabolic capacity was included in the respiratory-tract epithelium. The model simulations were highly sensitive to the parameter for total percent of whole-body metabolism allocated to the respiratory tract, with optimal fits observed with this value equal to 5%. Oral uptake of D4 was evaluated using both closed and open chamber concentration time-course studies after intubation of D4 in corn oil. Conclusions from the oral uptake studies were also verified by comparison with independent data sets for blood concentrations of D4 after oral dosing. The pharmacokinetic (PK) analysis of uptake from the gut and release from blood into chamber air results for oral doses from 10 to 300 mg D4/kg body weight were consistent with a combination of prolonged and slow uptake of D4 from the gastrointestinal tract and of reduced absorption at higher doses, as well as the extrahepatic clearance of D4 in pulmonary tissues. These closed chamber gas uptake studies provide a valuable confirmation of the conclusions reached in other pharmacokinetic studies and have uncovered a situation where closed chamber loss is highly sensitive to respiratory-tract clearance. This sensitivity largely arises from the unusual characteristics of D4: high-affinity metabolic clearance and low blood:air partitioning.

Under OSHA and American Conference of Governmental Industrial Hygienists (ACGIH((R))) guidelines, the mixture formula (unity calculation) provides a method for evaluating exposures to mixtures of chemicals that cause similar toxicities. According to the formula, if exposures are reduced in proportion to the number of chemicals and their respective exposure limits. the overall exposure is acceptable. This approach assumes that responses are additive, which is not the case when pharmacokinetic interactions occur. To determine the validity of the additivity assumption, we performed unity calculations for a variety of exposures to toluene, ethylbenzene, and/or xylene using the concentration of each chemical in blood in the calculation instead of the inhaled concentration. The blood concentrations were predicted using a validated physiologically based pharmacokinetic (PBPK) model to allow exploration of a variety of exposure scenarios. In addition. the Occupational Safety and Health Administration and ACGIH((R)) occupational exposure limits were largely based on studies of humans or animals that were resting during exposure. The PBPK model was also used to determine the increased concentration of chemicals in the blood when employees were exercising or performing manual work. At rest, a modest overexposure occurs due to pharmacokinetic interactions when exposure is equal to levels where a unity calculation is 1.0 based on threshold limit values (TLVs((R))). Under work load, however, internal exposure was 87% higher than provided by the TLVs. When exposures were controlled by a unity calculation based on permissible exposure limits (PELs), internal exposure was 2.9 and 4.6 times the exposures at the TLVs at rest and workload, respectively. If exposure was equal to PELs outright, internal exposure was 12.5 and 16 times the exposure at the TLVs at rest and workload, respectively. These analyses indicate the importance of (1) selecting appropriate exposure limits, (2) performing unity calculations, and (3) considering the effect of work load on internal doses, and they illustrate the utility of PBPK modeling in occupational health risk assessment.

The rapid expansion of PBPK modeling in toxicology arose due to a focus on risk assessment applications with a variety of chemicals used in industry and commerce. Advances were made as methods were developed to assess the pharmacokinetic determinants of disposition (e.g., partition coefficients and metabolic constants) of volatile compounds. This first chapter on PBPK models for volatile organic compounds reviews principles guiding development of PBPK models for volatile organics. Additionally, models for volatile anesthetics, refrigerant gases and other halogenated alkanes provide a basic understanding of PBPK model techniques and applications. PBPK models have been used to understand the pharmacokinetic factors affecting anesthesia uptake and the risks posed by potential replacements for chlorofluorocarbons. Initial studies on methylene chloride focused on reactive metabolites in metabolizing organs and on formation of carbon monoxide. PBPK models have been used to understand the risks posed by chloroform from many types of household exposures.

Because of the pioneering vision of certain leaders in the biomedical field, the last two decades witnessed rapid advances in the area of chemical mixture toxicology. Earlier studies utilized conventional toxicology protocol and methods, and they were mainly descriptive in nature. Two good examples might be the parallel series of studies conducted by the U.S. National Toxicology Program and TNO in The Netherlands, respectively. As a natural course of progression, more and more sophistication was incorporated into the toxicology studies of chemical mixtures. Thus, at least the following seven areas of scientific achievements in chemical mixture toxicology are evident in the literature: (a) the application of better and more robust statistical methods; (b) the exploration and incorporation of mechanistic bases for toxicological interactions; (c) the application of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling; (d) the studies on more complex chemical mixtures; (e) the use of science-based risk assessment approaches; (f) the utilization of functional genomics; and (g) the application of technology. Examples are given for the discussion of each of these areas. Two important concepts emerged from these studies and they are: (1) dose-dependent toxicologic interactions; and (2) “interaction thresholds”. Looking into the future, one of the most challenging areas in chemical mixture research is finding the answer to the question “when one tries to characterize the health effects of chemical mixtures, how does one deal with the infinite number of combination of chemicals, and other possible stressors?” Undoubtedly, there will be many answers from different groups of researchers. Our answer, however, is first to focus on the finite (biological processes) rather than the infinite (combinations of chemical mixtures and multiple stressors). The idea is that once we know a normal biological process(es), all stimuli and insults from external stressors are merely perturbations of the normal biological process(es). The next step is to “capture” the biological process(es) by integrating the recent advances in computational technology and modern biology. Here, the computer-assisted Reaction Network Modeling, linked with PBPK modeling, offers a ray of hope to dealing with the complex biological systems.

Petroleum hydrocarbon mixtures such as gasoline, diesel fuel, aviation fuel, and asphalt liquids typically contain hundreds of compounds. These compounds include aliphatic and aromatic hydrocarbons within a specific molecular weight range and sometimes lesser amounts of additives, and often exhibit qualitatively similar pharmacokinetic (PK) and pharmacodynamic properties. However, there are some components that exhibit specific biological effects, such as methyl t-butyl ether and benzene in gasoline. One of the potential pharmacokinetic interactions of many components in such mixtures is inhibition of the metabolism of other components. Due to the complexity of the mixtures, a quantitative description of the pharmacokinetics of each component, particularly in the context of differing blends of these mixtures, has not been available. We describe here a physiologically-based pharmacokinetic (PBPK) modeling approach to describe the PKs of whole gasoline. The approach simplifies the problem by isolating specific components for which a description is desired and treating the remaining components as a single lumped chemical. In this manner, the effect of the non-isolated components (i.e. inhibition) can be taken into account. The gasoline model was based on PK data for the single chemicals, for simple mixtures of the isolated chemicals, and for the isolated and lumped chemicals during gas uptake PK experiments in rats exposed to whole gasoline. While some sacrifice in model accuracy must be made when a chemical lumping approach is used, our lumped PK model still permitted a good representation of the PKs of five isolated chemicals (n-hexane, benzene, toluene, ethylbenzene, and o-xylene) during exposure to various levels of two different blends of gasoline. The approach may be applicable to other hydrocarbon mixtures when appropriate PK data are available for model development.

A combination of experimental and simulation approaches was used to analyze clonal growth of glutathione-S-transferase pi (GST-P) enzyme-altered foci during liver carcinogenesis in an initiation-promotion regimen for 1,4-dichlorobenzene (DCB), 1,2,4,5-tetrachlorobenzene (TECB), pentachlorobenzene (PECB), and hexachlorobenzene (HCB). Male Fisher 344 rats, eight weeks of age, were initiated with a single dose (200 mg/kg, ip) of diethylnitrosamine (DEN). Two weeks later, daily dosing of 0.1 mol/kg chlorobenzene was maintained for six weeks. Partial hepatectomy was performed three weeks after initiation. Liver weight, normal hepatocyte division rates, and the number and volume of GST-P positive foci were obtained at 23, 26, 28, 47, and 56 days after initiation. A clonal growth stochastic model separating the initiated cell population into two distinct subtypes (referred to as A and B cells) was successfully used to describe the foci development data for the four chlorobenzenes. The B cells are initiated cells that display a selective growth advantage under conditions that inhibit the growth of initiated A cells or normal hepatocytes. The simulation exercise for the four chlorobenzenes indicates a positive correlation between the estimated net growth rate of B cells during the 2-week regeneration period following partial hepatectomy and final foci volume at the end of the bioassay. This observation is consistent with the sensitivity analysis of model parameters. While TECB, PECB, and HCB all significantly increased foci volume, only HCB increased normal hepatocyte proliferation. Together, these results indicate that examining effects of chemicals on regenerative responses following partial hepatectomy may be a means for understanding the carcinogenicity potential of chlorobenzene compounds.

Gas uptake methods together with physiologically based pharmacokinetic (PBPK) modeling have been used to assess metabolic parameters and oral absorption rates for a wide variety of volatile organic compounds. We applied these techniques to study the in vivo metabolism of hexamethyldisiloxane (HMDS), a volatile siloxane with low blood/air (partition coefficient PB ≈ 1.00) and high fat/blood partitioning (partition coefficient PF ≈ 300). In contrast to other classes of metabolized volatiles, metabolic parameters could only be estimated from closed-chamber results with confidence by evaluating both closed-chamber disappearance curves and constant concentration inhalation studies. The constant-concentration inhalation results refine the estimate of the blood/air partition coefficient and constrain model structure for storage of the lipophilic compound in blood and tissues. The gas uptake results, from Fischer 344 rats (male, 8-9 wk old) exposed to initial HMDS air concentrations from 500 to 5000 ppm, were modeled with a 5-tissue PBPK model. Excellent fits were obtained with diffusion-limited uptake of HMDS in fat and a lipid storage pool in the blood. Metabolism, restricted to the liver, was described as a single saturable process (V max = 113.6 µmol/h/kg; K m = 42.6 µmol/L) and was affected by inhibitors (diethyldithiocarbamate) or inducers (phenobarbital) of cytochrome P-450s. Exhalation kinetics of HMDS after oral/intraperitoneal administration showed low bioavailability and significant lag times, also quite different from results of other classes of volatile hydrocarbons. In general, estimates of metabolic clearance by gas uptake studies were improved by simultaneous examination of time-course results from constant concentration inhalation studies. This conclusion is likely to hold for any volatile lipophilic compound with low blood/air partitioning.

In a recent pharmacokinetic study, six human volunteers were exposed by inhalation to 10 ppm 14C-D4 for 1 h during alternating periods of rest and exercise. Octamethylcyclotetrasiloxane (D4) concentrations were determined in exhaled breath and blood. Total metabolite concentrations were estimated in blood, while the amounts of individual metabolites were measured in urine. Here, we use these data to develop a physiologically based pharmacokinetic (PBPK) model for D4 in humans. Consistent with PBPK modeling efforts for D4 in the rat, a conventional inhalation PBPK model assuming flow-limited tissue uptake failed to adequately describe these data. A refined model with sequestered D4 in blood, diffusion-limited tissue uptake, and an explicit pathway for D4 metabolism to short-chain linear siloxanes successfully described all data. Hepatic extraction in these volunteers, calculated from model parameters, was 0.65 to 0.8, i.e., hepatic clearance was nearly flow-limited. The decreased retention of inhaled D4 seen in humans during periods of exercise was explained by altered ventilation/perfusion characteristics during exercise and a rapid approach to steady-state conditions. The urinary time course excretion of metabolites was consistent with a metabolic scheme in which sequential hydrolysis of linear siloxanes followed oxidative demethylation and ring opening. The unusual properties of D4 (high lipophilicity coupled with high hepatic and exhalation clearance) lead to rapid decreases in free D4 in blood. The success of D4 PBPK models with a similar physiological structure in both humans and rats increases confidence in the utility of the model for predicting human tissue concentrations of D4 and metabolites during inhalation exposures.

CorrespondenceOpen AccessFurther Comments on the Bioavailability of D4 ME Andersen ME Andersen Search for more papers by this author Published:1 August 2002https://doi.org/10.1289/ehp.110-a444Cited by:5AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit FiguresReferencesRelatedDetailsCited By Dekant W, Scialli A, Plotzke K and Klaunig J (2017) Biological relevance of effects following chronic administration of octamethylcyclotetrasiloxane (D4) in Fischer 344 rats, Toxicology Letters, 10.1016/j.toxlet.2017.01.010, 279, (42-53), Online publication date: 1-Oct-2017. Johnson W, Bergfeld W, Belsito D, Hill R, Klaassen C, Liebler D, Marks J, Shank R, Slaga T, Snyder P and Andersen F (2012) Safety Assessment of Cyclomethicone, Cyclotetrasiloxane, Cyclopentasiloxane, Cyclohexasiloxane, and Cycloheptasiloxane, International Journal of Toxicology, 10.1177/1091581811428184, 30:6_suppl, (149S-227S), Online publication date: 1-Dec-2011. Krewski D, Acosta D, Andersen M, Anderson H, Bailar J, Boekelheide K, Brent R, Charnley G, Cheung V, Green S, Kelsey K, Kerkvliet N, Li A, McCray L, Meyer O, Patterson R, Pennie W, Scala R, Solomon G, Stephens M, Yager J, Zeise L and Staff of Committee on Toxicity Test (2010) Toxicity Testing in the 21st Century: A Vision and a Strategy, Journal of Toxicology and Environmental Health, Part B, 10.1080/10937404.2010.483176, 13:2-4, (51-138), Online publication date: 17-Jun-2010. Félix Camiña M, Casas J, Victoria Castaño M, Couce M, Gato A, Herbello-Hermelo P, Sánchez A, Sordo J and Dolores Torres M (2010) Interaction of Pb2+, PbMe22+ and PbPh22+ with 3-(phenyl)-2-sulfanylpropenoic acid: A coordinative and toxicological approach, Journal of Inorganic Biochemistry, 10.1016/j.jinorgbio.2010.02.001, 104:5, (599-610), Online publication date: 1-May-2010. Phillips K, Foster W, Leiss W, Sahni V, Karyakina N, Turner M, Kacew S and Krewski D (2008) Assessing and Managing Risks Arising from Exposure to Endocrine-Active Chemicals, Journal of Toxicology and Environmental Health, Part B, 10.1080/10937400701876657, 11:3-4, (351-372), Online publication date: 20-Mar-2008. Vol. 110, No. 8 August 2002Metrics About Article Metrics Publication History Originally published1 August 2002Published in print1 August 2002 Financial disclosuresPDF download License information EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Note to readers with disabilities EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days.

Linear dextran and helix amylose were covalently bonded with nonlinear optical (NLO) chromophores. The influence of the conformation of the polymer matrix on the NLO behavior of the supramolecular structure has been studied. The second order hyperpolarizability depends not only on the secondary structure of the biopolymer, but also on the position of the chromophore towards the polymeric backbone. Functionalization of NLO-phores with biopolymers led to increased thermo-and photostability.

The complexity and the astronomic number of possible chemical mixtures preclude any systematic experimental assessment of toxicology of all potentially troublesome chemical mixtures. Thus, the use of computer modeling and mechanistic toxicology for the development of a predictive tool is a promising approach to deal with chemical mixtures. In the past 15 years or so, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been applied to the toxicologic interactions of chemical mixtures. This approach is promising for relatively simple chemical mixtures; the most complicated chemical mixtures studied so far using this approach contained five or fewer component chemicals. In this presentation we provide some examples of the utility of PBPK/PD modeling for toxicologic interactions in chemical mixtures. The probability of developing predictive tools for simple mixtures using PBPK/PD modeling is high. Unfortunately, relatively few attempts have been made to develop paradigms to consider the risks posed by very complex chemical mixtures such as gasoline, diesel, tobacco smoke, etc. However, recent collaboration between scientists at Colorado State University and engineers at Rutgers University attempting to use reaction network modeling has created hope for the possible development of a modeling approach with the potential of predicting the outcome of toxicology of complex chemical mixtures. We discuss the applications of reaction network modeling in the context of petroleum refining and its potential for elucidating toxic interactions with mixtures.

In this study, we integrated our understanding of biochemistry, physiology, and metabolism of three commonly used organic solvents with computer simulation to present a new approach that we call "in silico" toxicology. Thus, we developed an interactive physiologically based pharmacokinetic (PBPK) model to predict the individual kinetics of trichloroethylene (TCE), perchloroethylene (PERC), and methylchloroform (MC) in humans exposed to differently constituted chemical mixtures of the three solvents. Model structure and parameterization originate from the literature. We calibrated the single-compound PBPK models using published data and described metabolic interactions within the chemical mixture using kinetic constants estimated in rats. The mixture model was used to explore the general pharmacokinetic profile of two common biomarkers of exposure, peak TCE blood levels and total amount of TCE metabolites generated, in rats and humans. Assuming that a 10% change in the biomarkers corresponds to a significant health effect, we calculated interaction thresholds for binary and ternary mixtures of TCE, PERC, and MC. Increases in the TCE blood levels led to higher availability of the parent compound for glutathione conjugation, a metabolic pathway associated with kidney toxicity/carcinogenicity. The simulated change in production rates of toxic conjugative metabolites exceeded 17% for a corresponding 10% increase in TCE blood concentration, indicating a nonlinear risk increase due to combined exposures to TCE. Evaluation of metabolic interactions and their thresholds illustrates a unique application of PBPK modeling in risk assessment of occupational exposures to chemical mixtures.

Octamethylcyclotetrasiloxane (D4) is an ingredient in selected consumer and precision cleaning products. Workplace inhalation exposures may occur in some D4 production operations. In this study, we analyzed tissue, plasma, and excreta time-course data following D4 inhalation in Fischer 344 rats (K. Plotzke et al., 2000, Drug Metab. Dispos. 28, 192-204) to assess the degree to which the disposition of D4 is similar to or different from that of volatile hydrocarbons that lack silicone substitution. We first applied a basic physiologically based pharmacokinetic (PBPK) model (J. C. Ramsey and M. E. Andersen, 1984, Toxicol. Appl. Pharmacol. 73, 159-175) to characterize the biological determinants of D4 kinetics. Parameter estimation techniques indicated an unusual set of characteristics, i.e., a low blood:air (Pb:a congruent to 0.9) and a high fat:blood partition coefficient (P, congruent to 550). These parameters were then determined experimentally by equilibrating tissue or liquid samples with saturated atmospheres of D4. Consistent with the estimates from the time-course data, blood:air partition coefficients were small, ranging from 1.9 to 6.9 in six samples. Perirenal fat:air partition coefficients were large, from 1400 to 2500. The average P,, was determined to be 485. This combination of partitioning characteristics leads to rapid exhalation of free D4 at the cessation of the inhalation exposure followed by a much slower redistribution of D4 from fat and tissue storage compartments. The basic PK model failed to describe D4 tissue kinetics in the postexposure period and had to be expanded by adding deep-tissue compartments in liver and lung, a mobile chylomicron-like lipid transport pool in blood, and a second fat compartment. Model parameters for the refined model were optimized using single-exposure data in male and female rats exposed at three concentrations: 7, 70, and 700 ppm. With inclusion of induction of D4 metabolism at 700 ppm (3-fold in males, 1-fold in females), the parameter set from the single exposures successfully predicted PK results from 14-day multiple exposures at 7 and 700 ppm. A common parameter set worked for both genders. Despite its very high lipophilicity, D4 does not show prolonged retention because of high hepatic and exhalation clearance. The high lipid solubility, low blood:air partition coefficient, and plasma lipid storage with D4 led to novel distributional characteristics not previously noted for inhaled organic hydrocarbons. These novel characteristics were only made apparent by analysis of the time-course data with PBPK modeling techniques.

The volatile organic solvents trichloroethylene (TCE), tetrachloroethylene (perchloroethylene, PERC), and 1,1,1-trichloroethane (methylchloroform, MC) are widely distributed environmental pollutants and common contaminants of many chemical waste sites. To investigate the mode of pharmacokinetic interactions among TCE, PERC, and MC and to calculate defined "interaction thresholds", gas-uptake experiments were performed using a closed-chamber exposure system. In each experiment, two rats (Fischer 344, male, 8-9 weeks old) were exposed to different initial concentrations of TCE, PERC, and MC, applied singly or as a mixture, and their concentration in the gas phase of the chamber was monitored over a period of 6 h. A physiologically based pharmacokinetic (PBPK) model was developed to test multiple mechanisms of inhibitory interactions, i.e., competitive, non-competitive, or uncompetitive. All mixture exposure data were accurately described by a system of equations in which a PBPK model was provided for each chemical and each was regarded as an inhibitor of the others' metabolism. Sensitivity-analysis techniques were used to investigate the impact of key parameters on model output and optimize experimental design. Model simulations indicated that, among these three chemicals, the inhibition was competitive. The PBPK model was extended to assess occupationally relevant exposures at or below the current threshold-limit values (TLVs). Based on 10% elevation in TCE blood levels as a criterion for significant interaction and assuming TCE exposure is set at TLV of 50 ppm, the calculated interaction thresholds for PERC and MC were 25 and 135 ppm, respectively. TLV exposures to binary TCE/PERC mixture were below the 10% significance level. The interaction threshold for TCE and MC co-exposure would be reached at 50 and 175 ppm, respectively. Such interactive PBPK models should be of value in risk assessment of occupational and environmental exposure to solvent mixtures.

Nonlinear optical chromophores can be organized as orientationally correlated side groups of polypeptides with a rigid backbone. In such an organization, each chromophore contributes coherently to the second-order nonlinear response of the polymer structure. The synthesis of poly-l-tyrosine, functionalized with the NLO-phores 4-nitroaniline, 2,4-dinitroaniline, 2-cyano-4-nitroaniline and 4-nitro-4'-nitroazobenzene, which is organized in alpha-helix conformation are presented. Solvatochromic measurements show that the hyperpolarizability of the supramolecular structure is a module factor of 18.7 higher than that of the model chromophore and that conformational organization leads to a strong enhancement of the molecular hyperpolarizability beta.