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We propose a Bayesian population modeling and virtual bioequivalence assessment approach to establishing dissolution specifications for oral dosage forms. A generalizable semi-physiologically based pharmacokinetic absorption model with six gut segments and liver, connected to a two-compartment model of systemic disposition for bupropion hydrochloride oral dosage forms was developed. Prior information on model parameters for gut physiology, bupropion physicochemical properties, and drug product properties were obtained from the literature. The release of bupropion hydrochloride from immediate-, sustained- and extended-release oral dosage forms was described by a Weibull function. In vitro dissolution data were used to assign priors to the in vivo release properties of the three bupropion formulations. We applied global sensitivity analysis to identify the influential parameters for plasma bupropion concentrations and calibrated them. To quantify inter- and intra-individual variability, plasma concentration profiles in healthy volunteers that received the three dosage forms, each at two doses, were used. The calibrated model was in good agreement with both in vitro dissolution and in vivo exposure data. Markov Chain Monte Carlo samples from the joint posterior parameter distribution were used to simulate virtual crossover clinical trials for each formulation with distinct drug dissolution profiles. For each trial, an allowable range of dissolution parameters ("safe space") in which bioequivalence can be anticipated was established. These findings can be used to assure consistent product performance throughout the drug product life-cycle and to support manufacturing changes. Our framework provides a comprehensive approach to support decision-making in drug product development.

A full Bayesian statistical treatment of complex pharmacokinetic or pharmacodynamic models, in particular in a population context, gives access to powerful inference, including on model structure. Markov Chain Monte Carlo (MCMC) samplers are typically used to estimate the joint posterior parameter distribution of interest. Among MCMC samplers, the simulated tempering algorithm (TMCMC) has a number of advantages : it can sample from sharp multi-modal posteriors; it provides insight into identifiability issues useful for model simplification; it can be used to compute accurate Bayes factors for model choice; the simulated Markov chains mix quickly and have assured convergence in certain conditions. The main challenge when implementing this approach is to find an adequate scale of auxiliary inverse temperatures (perks) and associated scaling constants. We solved that problem by adaptive stochastic optimization and describe our implementation of TMCMC sampling in the GNU MCSim software. Once a grid of perks is obtained, it is easy to perform posterior-tempered MCMC sampling or likelihood-tempered MCMC (thermodynamic integration, which bridges the joint prior and the posterior parameter distributions, with assured convergence of a single sampling chain). We compare TMCMC to other samplers and demonstrate its efficient sampling of multi-modal posteriors and calculation of Bayes factors in two stylized case-studies and two realistic population pharmacokinetic inference problems, one of them involving a large PBPK model.

Motivation The evaluation of chemicals for their carcinogenic hazard requires the analysis of a wide range of data and the characterization of these results relative to the key characteristics of carcinogens. The workflow used historically requires many manual steps that are labor-intensive and can introduce errors, bias and inconsistencies. Results The automation of parts of the evaluation workflow using the kc-hits software has led to significant improvements in process efficiency, as well as more consistent and comprehensive results. Availability and implementation https://gitlab.com/i1650/kc-hits.git. Supplementary information Supplementary data are available at Bioinformatics online.

Artemisinin-based combination therapies (ACTs) have proven to be effective in helping to combat the global malaria epidemic. To optimally apply these drugs, information about their tissue-specific disposition is required, and one approach to predict these pharmacokinetic characteristics is physiologically based pharmacokinetic (PBPK) modeling. In this study, a whole-body PBPK model was developed to simulate the time-dependent tissue concentrations of artesunate (AS) and its active metabolite, dihydroartemisinin (DHA). The model was developed for both rats and humans and incorporated drug metabolism of the parent compound and major metabolite. Model calibration was conducted using data from the literature in a Bayesian framework, and model verification was assessed using separate sets of data. Results showed good agreement between model predictions and the validation data, demonstrating the capability of the model in predicting the blood, plasma, and tissue pharmacokinetics of AS and DHA. It is expected that such a tool will be useful in characterizing the disposition of these chemicals and ultimately improve dosing regimens by enabling a quantitative assessment of the tissue-specific drug levels critical in the evaluation of efficacy and toxicity.

Artemisinin-based combination therapies (ACTs) have proven to be effective in helping to combat the global malaria epidemic. To optimally apply these drugs, information about their tissue-specific disposition is required, and one approach to predict these pharmacokinetic characteristics is physiologically-based pharmacokinetic (PBPK) modeling. In this study, a whole-body PBPK model was developed to simulate the time-dependent tissue concentrations of artesunate (AS) and its active metabolite, dihydroartemisinin (DHA). The model was developed for both rats and humans and incorporated drug metabolism of the parent compound and major metabolite. Model calibration was conducted using data from the literature in a Bayesian framework, and model verification was assessed using separate sets of data. Results showed good agreement between model predictions and the validation data, demonstrating the capability of the model in predicting the blood, plasma, and tissue pharmacokinetics of AS and DHA. It is expected that such a tool will be useful in characterizing the disposition of these chemicals and ultimately improve dosing regimens by enabling a quantitative assessment of the tissue-specific drug levels critical in the evaluation of efficacy and toxicity.

To reconcile and unify available results regarding paraquat exposure and Parkinson's disease (PD), we conducted a systematic review and meta-analysis to provide a quantitative estimate of the risk of PD associated with paraquat exposure. Six scientific databases including PubMed, Cochrane libraries, EMBASE, Scopus, ISI Web of Knowledge, and TOXLINE were systematically searched. The overall odds ratios (ORs) with corresponding 95% CIs were calculated using a random-effects model. Of 7,309 articles identified, 13 case control studies with 3,231 patients and 4,901 controls were included into our analysis. Whereas, one prospective cohort studies was included into our systematic review. A subsequent meta-analysis showed an association between PD and paraquat exposure (odds ratio = 1.64 (95% CI: 1.27-2.13

Rifapentine (RPT) is a rifamycin antimycobacterial and, as part of a combination therapy, is indicated for the treatment of pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis . Although the results from a number of studies indicate that rifapentine has the potential to shorten treatment duration and enhance completion rates compared to other rifamycin agents utilized in antituberculosis drug regimens (i.e., regimens 1 to 4), its optimal dose and exposure in humans are unknown. To help inform such an optimization, a physiologically based pharmacokinetic (PBPK) model was developed to predict time course, tissue-specific concentrations of RPT and its active metabolite, 25-desacetyl rifapentine (dRPT), in humans after specified administration schedules for RPT. Starting with the development and verification of a PBPK model for rats, the model was extrapolated and then tested using human pharmacokinetic data. Testing and verification of the models included comparisons of predictions to experimental data in several rat tissues and time course RPT and dRPT plasma concentrations in humans from several single- and repeated-dosing studies. Finally, the model was used to predict RPT concentrations in the lung during the intensive and continuation phases of a current recommended TB treatment regimen. Based on these results, it is anticipated that the PBPK model developed in this study will be useful in evaluating dosing regimens for RPT and for characterizing tissue-level doses that could be predictors of problems related to efficacy or safety.

Aim In cases of paracetamol (acetaminophen, APAP) overdose, an accurate estimate of tissue-specific paracetamol pharmacokinetics (PK) and ingested dose can offer health care providers important information for the individualized treatment and follow-up of affected patients. Here a novel methodology is presented to make such estimates using a standard serum paracetamol measurement and a computational framework. Methods The core component of the computational framework was a physiologically-based pharmacokinetic (PBPK) model developed and evaluated using an extensive set of human PK data. Bayesian inference was used for parameter and dose estimation, allowing the incorporation of inter-study variability, and facilitating the calculation of uncertainty in model outputs. Results Simulations of paracetamol time course concentrations in the blood were in close agreement with experimental data under a wide range of dosing conditions. Also, predictions of administered dose showed good agreement with a large collection of clinical and emergency setting PK data over a broad dose range. In addition to dose estimation, the platform was applied for the determination of optimal blood sampling times for dose reconstruction and quantitation of the potential role of paracetamol conjugate measurement on dose estimation. Conclusions Current therapies for paracetamol overdose rely on a generic methodology involving the use of a clinical nomogram. By using the computational framework developed in this study, serum sample data, and the individual patient's anthropometric and physiological information, personalized serum and liver pharmacokinetic profiles and dose estimate could be generated to help inform an individualized overdose treatment and follow-up plan.

Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

The principal aim of this study was to develop, validate, and demonstrate a physiologically based pharmacokinetic (PBPK) model to predict and characterize the absorption, distribution, metabolism, and excretion of acetaminophen (APAP) in humans. A PBPK model was created that included pharmacologically and toxicologically relevant tissue compartments and incorporated mechanistic descriptions of the absorption and metabolism of APAP, such as gastric emptying time, cofactor kinetics, and transporter-mediated movement of conjugated metabolites in the liver. Through the use of a hierarchical Bayesian framework, unknown model parameters were estimated using a large training set of data from human pharmacokinetic studies, resulting in parameter distributions that account for data uncertainty and inter-study variability. Predictions from the model showed good agreement to a diverse test set of data across several measures, including plasma concentrations over time, renal clearance, APAP absorption, and pharmacokinetic and exposure metrics. The utility of the model was then demonstrated through predictions of cofactor depletion, dose response of several pharmacokinetic endpoints, and the relationship between APAP biomarker levels in the plasma and those in the liver. The model addressed several limitations in previous PBPK models for APAP, and it is anticipated that it will be useful in predicting the pharmacokinetics of APAP in a number of contexts, such as extrapolating across doses, estimating internal concentrations, quantifying population variability, assessing possible impacts of drug coadministration, and, when coupled with a suitable pharmacodynamic model, predicting toxicity.

Paraquat (N, N'-dimethyl-4,4'-bipyridium dichloride) is a potent and widely used herbicide in agricultural countries, including Thailand. The presence of this chemical in the body can lead to toxic effects in the liver, kidney, and lung. Pulmonary toxicity has been identified as the main cause of acute toxicity in animals and humans. Chronic exposure to paraquat is associated with Parkinson's disease in humans. Paraquat is transported into the lungs by neutral amino acid transporter. Therefore, a physiologically based pharmacokinetic (PBPK) model of paraquat was developed with a description of the protein transporter mechanism. To develop a PBPK model of paraquat, a pharmacokinetic study of paraquat in rats was selected from the ThaiLIS and Pubmed database. The selected study contained tissue-specific concentration-time course information such as paraquat concentration levels in liver, kidney and lung. Physiologic parameters were acquired from the literature or determined using a Markov-Chain Monte Carlo (MCMC) technique. The developed PBPK model consisted of 5 organ compartments (i.e. kidney, liver, slowly perfused organs, richly perfuse organs and lung), featuring an incorporation of neutral amino acid transporter in the lung. Our model simulations could explain the data from the literature and adequately describe pharmacokinetics of paraquat in the rats. This developed PBPK model may be able help in understanding of paraquat-induced Parkinson's disease as well as in risk assessment of paraquat.

Exposure to industrial solvent and water pollutant trichloroethylene (TCE) can promote autoimmunity, and expand effector/memory (CD62L) CD4+ T cells. In order to better understand etiology reduced representation bisulfite sequencing was used to study how a 40-week exposure to TCE in drinking water altered methylation of ∼337 770 CpG sites across the entire genome of effector/memory CD4+ T cells from MRL+/+ mice. Regardless of TCE exposure, 62% of CpG sites in autosomal chromosomes were hypomethylated (0–15% methylation), and 25% were hypermethylated (85–100% methylation). In contrast, only 6% of the CpGs on the X chromosome were hypomethylated, and 51% had mid-range methylation levels. In terms of TCE impact, TCE altered (≥ 10%) the methylation of 233 CpG sites in effector/memory CD4+ T cells. Approximately 31.7% of these differentially methylated sites occurred in regions known to bind one or more Polycomb group (PcG) proteins, namely Ezh2, Suz12, Mtf2 or Jarid2. In comparison, only 23.3% of CpG sites not differentially methylated by TCE were found in PcG protein binding regions. Transcriptomics revealed that TCE altered the expression of ∼560 genes in the same effector/memory CD4+ T cells. At least 80% of the immune genes altered by TCE had binding sites for PcG proteins flanking their transcription start site, or were regulated by other transcription factors that were in turn ordered by PcG proteins at their own transcription start site. Thus, PcG proteins, and the differential methylation of their binding sites, may represent a new mechanism by which TCE could alter the function of effector/memory CD4+ T cells.

Background: Organophosphorus (OP) compounds are the most widely used group of insecticides in the world. Risk assessments for these chemicals have focused primarily on 10% inhibition of acetylcholinesterase in the brain as the critical metric of effect. Aside from cholinergic effects resulting from acute exposure, many studies suggest a linkage between cognitive deficits and long-term OP exposure. Objective: In this proof-of-concept study, we focused on one of the most widely used OP insecticides in the world, chlorpyrifos (CPF), and utilized an existing physiologically based pharmacokinetic (PBPK) model and a novel pharmacodynamic (PD) dose–response model to develop a point of departure benchmark dose estimate for cognitive deficits following long-term, low-dose exposure to this chemical in rodents. Methods: Utilizing a validated PBPK/PD model for CPF, we generated a database of predicted biomarkers of exposure and internal dose metrics in both rat and human. Using simulated peak brain CPF concentrations, we developed a dose–response model to predict CPF-induced spatial memory deficits and correlated these changes to relevant biomarkers of exposure to derive a benchmark dose specific to neurobehavioral changes. We extended these cognitive deficit predictions to humans and simulated corresponding exposures using a model parameterized for humans. Results: Results from this study indicate that the human-equivalent benchmark dose (BMD) based on a 15% cognitive deficit as an end point is lower than that using the present threshold for 10% brain AChE inhibition. This predicted human-equivalent subchronic BMD threshold compares to occupational exposure levels determined from biomarkers of exposure and corresponds to similar exposure conditions where deficits in cognition are observed. Conclusions: Quantitative PD models based on neurobehavioral testing in animals offer an important addition to the methodologies used for establishing useful environmental public health indicators and BMDs, and predictions from such models could help inform the human health risk assessment for chlorpyrifos. https://doi.org/10.1289/EHP1743

Aim: Autoimmune disease and CD4+ T-cell alterations are induced in mice exposed to the water pollutant trichloroethylene (TCE). We examined here whether TCE altered gene-specific DNA methylation in CD4+ T cells as a possible mechanism of immunotoxicity. Materials & methods: Naive and effector/memory CD4+ T cells from mice exposed to TCE (0.5 mg/ml in drinking water) for 40 weeks were examined by bisulfite next-generation DNA sequencing. Results: A probabilistic model calculated from multiple genes showed that TCE decreased methylation control in CD4+ T cells. Data from individual genes fitted to a quadratic regression model showed that TCE increased gene-specific methylation variance in both CD4 subsets. Conclusion: TCE increased epigenetic drift of specific CpG sites in CD4+ T cells.

The emergence of multidrug-resistant tuberculosis (MDR-TB) has led to a renewed interest in the use of second-line antibiotic agents. Unfortunately, there are currently dearths of information, data, and computational models that can be used to help design rational regimens for administration of these drugs. To help fill this knowledge gap, an exploratory physiologically based pharmacokinetic (PBPK) model, supported by targeted experimental data, was developed to predict the absorption, distribution, metabolism, and excretion (ADME) of the second-line agent capreomycin, a cyclic peptide antibiotic often grouped with the aminoglycoside antibiotics. To account for interindividual variability, Bayesian inference and Monte Carlo methods were used for model calibration, validation, and testing. Along with the predictive PBPK model, the first for an antituberculosis agent, this study provides estimates of various key pharmacokinetic parameter distributions and supports a hypothesized mechanism for capreomycin transport into the kidney.

Cytochrome P450 (CYP) enzymes play a critical role in detoxication and bioactivation of xenobiotics; thus, the ability to predict the biotransformation rates and regioselectivity of CYP enzymes toward substrates is an important goal in toxicology and pharmacology. Here, we present the use of the semiempirical quantum chemistry method SAM1 to rapidly estimate relative activation enthalpies (ΔH(‡)) for the hydroxylation of aliphatic carbon centers of various substrates. The ΔH(‡) were determined via a reaction path calculation, in the reverse direction (RRP), using the iron-hydroxo-porphine intermediate and the substrate radical. The SAM1 ΔH(‡) were calculated via unrestricted Hartree-Fock (UHF) and configuration interaction (CI) formalisms for both the doublet and quartet spin states. The SAM1 RRP ΔH(‡), after subtracting a correction factor, were compared with density functional theory (DFT) B3LYP activation energies for two sets of substrates and showed R(2) ranging from 0.69 to 0.89, and mean absolute differences ranging from 1.2 ± 1.0 to 1.7 ± 1.5 kcal/mol. SAM1 UHF and CI RRP calculation times were, on average, more than 200 times faster than those for the corresponding forward reaction path DFT calculations. Certain key transition-state (TS) geometry measurements, such as the forming O···H bond length, showed good correlation with the DFT values. These results suggest that the SAM1 RRP approach can be used to rapidly estimate the DFT activation energy and some key TS geometry measurements and can potentially be applied to estimate substrate hydroxylation rates and regioselectivity by CYP enzymes.

Background One problem of interpreting population-based biomonitoring data is the reconstruction of corresponding external exposure in cases where no such data are available. Objectives We demonstrate the use of a computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of environmental chloroform source concentrations consistent with human biomonitoring data. The biomonitoring data consist of chloroform blood concentrations measured as part of the Third National Health and Nutrition Examination Survey (NHANES III), and for which no corresponding exposure data were collected. Methods We used a combined PBPK and shower exposure model to consider several routes and sources of exposure: ingestion of tap water, inhalation of ambient household air, and inhalation and dermal absorption while showering. We determined posterior distributions for chloroform concentration in tap water and ambient household air using U.S. Environmental Protection Agency Total Exposure Assessment Methodology (TEAM) data as prior distributions for the Bayesian analysis. Results Posterior distributions for exposure indicate that 95% of the population represented by the NHANES III data had likely chloroform exposures ≤ 67 μg/L in tap water and ≤ 0.02 μg/L in ambient household air. Conclusions Our results demonstrate the application of computer simulation to aid in the interpretation of human biomonitoring data in the context of the exposure–health evaluation–risk assessment continuum. These results should be considered as a demonstration of the method and can be improved with the addition of more detailed data.

Cytochromes P450 (CYPs) are a superfamily of enzymes that metabolize the majority of xenobiotics in humans. This review presents a structure-based outline of CYP-catalyzed biotransformations of selected substrates, representing diverse structural classes of chemicals, ranging from drugs to toxicants. Data are presented in a tabular format for easy reference, with visual representations of all substrates and sites-of-attack. The major metabolites, isozymes responsible, chemical classification, and other information related to the biotransformation are provided. Pharmacophores proposed for the major CYP isozymes are discussed. This visual combination of substrates and biotransformation sites can serve as a useful reference for researchers.

In cases of paracetamol (acetaminophen, APAP) overdose, an accurate estimate of tissue-specific paracetamol pharmacokinetics (PK) and ingested dose can offer health care providers important information for the individualized treatment and follow-up of affected patients. Here a novel methodology is presented to make such estimates using a standard serum paracetamol measurement and a computational framework.The core component of the computational framework was a physiologically-based pharmacokinetic (PBPK) model developed and evaluated using an extensive set of human PK data. Bayesian inference was used for parameter and dose estimation, allowing the incorporation of inter-study variability, and facilitating the calculation of uncertainty in model outputs.Simulations of paracetamol time course concentrations in the blood were in close agreement with experimental data under a wide range of dosing conditions. Also, predictions of administered dose showed good agreement with a large collection of clinical and emergency setting PK data over a broad dose range. In addition to dose estimation, the platform was applied for the determination of optimal blood sampling times for dose reconstruction and quantitation of the potential role of paracetamol conjugate measurement on dose estimation.Current therapies for paracetamol overdose rely on a generic methodology involving the use of a clinical nomogram. By using the computational framework developed in this study, serum sample data, and the individual patient's anthropometric and physiological information, personalized serum and liver pharmacokinetic profiles and dose estimate could be generated to help inform an individualized overdose treatment and follow-up plan.

Ritonavir-boosted lopinavir (LPV/r) has been recommended as an alternative regimen for HIV-naive patients who cannot tolerate nevirapine (NVP) and/or efavirenz (EFV). Although combinations of ritonavir and lopinavir have shown higher plasma concentration level of LPV in clinical settings, dosage adjustment is still required to maintain an adequate therapeutic efficacy and reduce side effects. A compartmental pharmacokinetic (PK) model of LPV/r was developed, including a mechanistic description of competitive inhibition. Systematic simulations were performed and predicted plasma drug concentration levels were compared with those from the literature. In particular, the simulated and experimental area under the curve (AUC) based on oral dosing were 76.10 μMol/L, and 76.25 μMol/L, respectively Results from the mathematical model support the hypothesis that the mechanism of LPV/r interaction is due to the competitive inhibition of CYP3A4 in the liver by ritonavir, resulting in an increasing LPV plasma concentration levels. The simulated plasma concentration-time courses were consistent with those from the literature with the goodness of fit (R(2)) of 0.9025 (0.8269-0.9862 95%CI).

All organisms are exposed to multiple xenobiotics, through food, environmental contamination, and drugs. These xenobiotics often undergo biotransformation, a complex process that plays a critical role in xenobiotic elimination or bioactivation to toxic metabolites. Here we describe the results of a new computer-based simulation tool that predicts metabolites from exposure to multiple chemicals and interconnects their metabolic pathways, using four common drinking water pollutants (trichloroethylene, perchloroethylene, methylchloroform, and chloroform) as a test case. The simulation tool interconnected the metabolic pathways for these compounds, predicted reactive intermediates, such as epoxides and acid chlorides, and uncovered points in the metabolic pathways where typical endogenous compounds, such as glutathione or carbon dioxide, are consumed or generated. Moreover, novel metabolites, not previously reported, were predicted via this methodology. Metabolite prediction is based on a reaction-mechanism-based methodology, which applies fundamental organic and enzyme chemistry. The tool can be used to (a) complement experimental studies of chemical mixtures, (b) aid in risk assessment, and (c) help understand the effects of complex chemical mixtures. Our results indicate that this tool is useful for predictive xenobiotic metabolomics, providing new and important insights into metabolites and the interrelationship between diverse chemicals that hitherto may have remained unnoticed.

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.

Chronic exposure to industrial solvent and water pollutant trichloroethylene (TCE) in female MRL+/+ mice generates disease similar to human autoimmune hepatitis. The current study was initiated to investigate why TCE-induced autoimmunity targeted the liver. Compared to other tissues the liver has an unusually robust capacity for repair and regeneration. This investigation examined both time-dependent and dose-dependent effects of TCE on hepatoprotective and pro-inflammatory events in liver and macrophages from female MRL+/+ mice. After a 12-week exposure to TCE in drinking water a dose-dependent decrease in macrophage production of IL-6 at both the transcriptional and protein level was observed. A longitudinal study similarly showed that TCE inhibited macrophage IL-6 production. In terms of the liver, TCE had little effect on expression of pro-inflammatory genes (Tnfa, Saa2 or Cscl1) until the end of the 40-week exposure. Instead, TCE suppressed hepatic expression of genes involved in IL-6 signaling (Il6r, gp130, and Egr1). Linear regression analysis confirmed liver histopathology in the TCE-treated mice correlated with decreased expression of Il6r. A toxicodynamic model was developed to estimate the effects of TCE on IL-6 signaling and liver pathology under different levels of exposure and rates of repair. This study underlined the importance of longitudinal studies in mechanistic evaluations of immuntoxicants. It showed that later-occurring liver pathology caused by TCE was associated with early suppression of hepatoprotection rather than an increase in conventional pro-inflammatory events. This information was used to create a novel toxicodynamic model of IL-6-mediated TCE-induced liver inflammation.

Mathematical modeling has been used extensively to quantify and characterize the disposition, fate, and risk associated with the volatile organic chemical trichloroethylene (TCE). Here, we summarize many of these models that have been developed and applied across the exposure-dose-effect continuum, ranging from pharmacokinetic and pharmacodynamic models to quantitative structure-activity relationships. We conclude by reviewing some future directions in computational modeling that are increasingly used to inform an understanding of the adverse health effects associated with exposure to TCE, and introduce elements of a first-generation systems biology model of TCE-induced autoimmune disease.

One problem associated with regimen-based development of antituberculosis (anti-TB) drugs is the difficulty of a systematic and thorough in vivo evaluation of the large number of possible regimens that arise from consideration of multiple drugs tested together. A mathematical model capable of simulating the pharmacokinetics and pharmacodynamics of experimental combination chemotherapy of TB offers a way to mitigate this problem by extending the use of available data to investigate regimens that are not initially tested. In order to increase the available mathematical tools needed to support such a model for preclinical anti-TB drug development, we constructed a preliminary whole-body physiologically based pharmacokinetic (PBPK) model of rifampin in mice, using data from the literature. Interindividual variability was approximated using Monte Carlo (MC) simulation with assigned probability distributions for the model parameters. An MC sensitivity analysis was also performed to determine correlations between model parameters and plasma concentration to inform future model development. Model predictions for rifampin concentrations in plasma, liver, kidneys, and lungs, following oral administration, were generally in agreement with published experimental data from multiple studies. Sensitive model parameters included those descriptive of oral absorption, total clearance, and partitioning of rifampin between blood and muscle. This PBPK model can serve as a starting point for the integration of rifampin pharmacokinetics in mice into a larger mathematical framework, including the immune response to Mycobacterium tuberculosis infection, and pharmacokinetic models for other anti-TB drugs.

A two-dimensional finite-element model was developed to simulate the transport and clearance of drugs released from polymers into the brain. This computer model includes the effects of diffusion and bulk flow of interstitial fluid, metabolism and capillary uptake, and is responsive to various controlled release scenarios and anatomical variation. The capability of this model is illustrated by examining the transport of three compounds of interest for neurological applications.

This chapter lists some of the software and tools that are used in computational toxicology, as presented in this volume.

Computational toxicology is a vibrant and rapidly developing discipline that integrates information and data from a variety of sources to develop mathematical and computer-based models to better understand and predict adverse health effects caused by chemicals, such as environmental pollutants and pharmaceuticals. Encompassing medicine, biology, biochemistry, chemistry, mathematics, computer science, engineering, and other fields, computational toxicology investigates the interactions of chemical agents and biological organisms across many scales (e.g., population, individual, cellular, and molecular). This multidisciplinary field has applications ranging from hazard and risk prioritization of chemicals to safety screening of drug metabolites, and has active participation and growth from many organizations, including government agencies, not-for-profit organizations, private industry, and universities.

Previous studies have shown that short-term (4 weeks) or chronic (32 weeks) exposure to trichloroethylene (TCE) in drinking water of female MRL+/+ mice generated CD4(+) T cells that secreted increased levels of interferon (IFN)-γ and expressed an activated (CD44(hi)CD62L(lo)) phenotype. In contrast, the current study of subchronic TCE exposure showed that midway in the disease process both of these parameters of CD4(+) T cell activation were reversed. This phase of the disease process may represent an attempt by the body to counteract the inflammatory effects of TCE. The decrease in CD4(+) T cell production of IFN-γ following subchronic TCE exposure could not be attributed to skewing toward a Th2 or Th17 phenotype or to an increase in Treg cells. Instead, the suppression corresponded to alterations in markers used to assess DNA methylation, namely increased expression of retrotransposons Iap (intracisternal A particle) and Muerv (murine endogenous retrovirus). Also observed was an increase in the expression of Dnmt1 (DNA methyltransferase-1) and decreased expression of several genes known to be downregulated by DNA methylation, namely Ifng, Il2, and Cdkn1a. CD4(+) T cells from a second study in which MRL+/+ mice were treated for 17 weeks with TCE showed a similar increase in Iap and decrease in Cdkn1a. In addition, DNA collected from the CD4(+) T cells in the second study showed TCE-decreased global DNA methylation. Thus, these results described the biphasic nature of TCE-induced alterations in CD4(+) T cell function and suggested that these changes represented potentially reversible alterations in epigenetic processes.

This chapter lists some of the software and tools that are used in computational toxicology, as presented in this volume.

We consider the flow of a viscous liquid film on the surface of a cylinder that is heated or cooled. Lubrication theory is used to study a thin film under the influence of gravity, capillary, thermocapillary, and intermolecular forces. We derive evolution equations for the interface shapes as a function of the azimuthal angle about the cylinder that govern the behaviour of the film subject to the above coupled forces. We use both analytical and numerical techniques to elucidate the dynamics and steady states of the thin layer over a wide range of thermal conditions and material properties. Finally, we extend our derivation to the case of three-dimensional dynamics and explore the stability of the film to small axial disturbances.

Smoking and asbestos exposure are important risks for lung cancer. Several epidemiological studies have linked asbestos exposure and smoking to lung cancer. To reconcile and unify these results, we conducted a systematic review and meta-analysis to provide a quantitative estimate of the increased risk of lung cancer associated with asbestos exposure and cigarette smoking and to classify their interaction. Five electronic databases were searched from inception to May, 2015 for observational studies on lung cancer. All case-control (N = 10) and cohort (N = 7) studies were included in the analysis. We calculated pooled odds ratios (ORs), relative risks (RRs) and 95% confidence intervals (CIs) using a random-effects model for the association of asbestos exposure and smoking with lung cancer. Lung cancer patients who were not exposed to asbestos and non-smoking (A-S-) were compared with; (i) asbestos-exposed and non-smoking (A+S-), (ii) non-exposure to asbestos and smoking (A-S+), and (iii) asbestos-exposed and smoking (A+S+). Our meta-analysis showed a significant difference in risk of developing lung cancer among asbestos exposed and/or smoking workers compared to controls (A-S-), odds ratios for the disease (95% CI) were (i) 1.70 (A+S-, 1.31-2.21), (ii) 5.65; (A-S+, 3.38-9.42), (iii) 8.70 (A+S+, 5.8-13.10). The additive interaction index of synergy was 1.44 (95% CI = 1.26-1.77) and the multiplicative index = 0.91 (95% CI = 0.63-1.30). Corresponding values for cohort studies were 1.11 (95% CI = 1.00-1.28) and 0.51 (95% CI = 0.31-0.85). Our results point to an additive synergism for lung cancer with co-exposure of asbestos and cigarette smoking. Assessments of industrial health risks should take smoking and other airborne health risks when setting occupational asbestos exposure limits.

We introduce the general concept and background knowledge of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling. Examples of application of PBPK/PD modeling are provided. We illustrate the utility of PBPK/PD modeling, particularly in the pharmaceutical drug development process. We project future development on “second-generation” PBPK/PD modeling and In silico toxicology. An emphasis is given to introducing the concepts of PBPK/PD modeling rather than the details of its techniques and processes. Keywords: pharmacokinetic/pharmacodynamic (PBPK/PD) modeling; classical pharmacokinetics; differences; myths; data requirements; chemical interactions; application; validation; second-generation PBPK/PD model; reaction network modeling

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.

In this paper, we describe a modeling approach to predict the interlinked pathways and kinetics resulting from CYP2E1-mediated metabolism of both pure species and chemical mixtures. This approach is based on the concept of chemical reaction networks, an idea that has formed the basis for simulation tools that have shown good predictive capabilities in the petroleum industry, but also an idea that has heretofore seen minimal application in the biomedical research arena. Although the initial target for developing this reaction network approach was cytochrome P450 2E1 (CYP2E1) and its over 200 substrates, this technology has been used for other families of CYP enzymes and their substrates in our laboratory. Utilizing this approach, we have produced a modular 'predictive metabolomics' simulation framework comprising interdependent software components that perform such tasks as testing of substrate binding feasibility, performing virtual chemistry, formulating reaction-rate equations, computing reaction kinetics and predicting time-dependent species concentrations. As an illustrative example, we outline the application of this framework to the prediction of the reaction networks resulting from the Phase I metabolism of two compounds of important toxicological interest. The potential of this modeling technology is immense in providing a computer simulation platform for complex-chemical mixtures and complex-biological systems. It is possible that this technology will play an important role in formulating a 'Virtual Human'.

Summary: Assessing and improving the safety of chemicals and the efficacy of drugs depends on an understanding of the biodistribution, clearance and biological effects of the chemical(s) of interest. A promising methodology for the prediction of these phenomena is physiologically based pharmacokinetic/pharmacodynamic modeling, which centers on the prediction of chemical absorption, distribution, metabolism and excretion (pharmacokinetics) and the biological effects (pharmacodynamics) of the chemical on the organism. Strengths of this methodology include modeling across multiple scales of biological organization and facilitate the extrapolation of results across routes of exposure, dosing levels and species. It is also useful as the foundation for tools to (i) predict biomarker levels (concentrations of chemical species found in the body that indicate exposure to a foreign chemical), given a chemical dose or exposure; (ii) reconstruct a dose, given the levels of relevant biomarkers; and (iii) estimate population variability. Despite the importance and promise of physiologically based pharmacokinetic /pharmacodynamics-based approaches to forward and reverse dosimetry, there is currently a lack of user-friendly, freely available implementations that are accessible and useful to a broad range of users. DoseSim was developed to begin to fill this gap. Availability: The application is available under the GNU General Public License from http://scb.colostate.edu/dosesim.html. Contact: brad.reisfeld@colostate.edu

The stability of a heated liquid film subjected to surface tension and van der Waals forces is explored in the case where the fluid viscosity is temperature dependent. The limits of very large and very small Biot numbers are examined, and in these cases it is found that the evolution equation can be rescaled to return the model equation developed by Williams and Davis [J. Colloid Interface Sci. 90, 220 (1982)] for an isothermal film. The effect of variable viscosity is deduced and is found to reduce the rupture time of the film relative to the constant‐viscosity result.