A deep-learning approach for identifying prospective chemical hazards

With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identification of completely new chemicals that are likely hazardous and elicit an adverse biological effect. Here, we detail a novel in silico, deep-learning framework that is designed to systematically generate structures for new chemical compounds that are predicted to be chemical hazards. To assess the utility of the framework, we applied the tool to four endpoints related to environmental toxicants and their impacts on human and animal health: (i) toxicity to honeybees, (ii) immunotoxicity, (iii) endocrine disruption via ER-α antagonism, and (iv) mutagenicity. In addition, we characterized the predicted potency of these compounds and examined their structural relationship to existing chemicals of concern. As part of the array of emerging new approach methodologies (NAMs), we anticipate that such a framework will be a significant asset to risk assessors and other environmental scientists when planning and forecasting. Though not in the scope of the present study, we expect that the methodology detailed here could also be useful in the de novo design of more environmentally-friendly industrial chemicals.

The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases

Owing to their potential to cause serious adverse health effects, significant efforts have been made to develop antidotes for organophosphate (OP) anticholinesterases, such as nerve agents. To be optimally effective, antidotes must not only reactivate inhibited target enzymes, but also have the ability to cross the blood-brain barrier (BBB). Progress has been made toward brain-penetrating acetylcholinesterase reactivators through the development of a new group of substituted phenoxyalkyl pyridinium oximes. To help in the selection and prioritization of compounds for future synthesis and testing within this class of chemicals, and to identify candidate broad-spectrum molecules, an in silico framework was developed to systematically generate structures and screen them for reactivation efficacy and BBB penetration potential.

Adapting physiologically-based pharmacokinetic models for machine learning applications

Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine learning (ML) pipelines could offer significant benefits in improving the accuracy and scope of drug screening and evaluation procedures. Here, we describe the development and testing of a self-contained machine learning module capable of faithfully recapitulating summary pharmacokinetic (PK) parameters produced by a full PBPK model, given a set of input drug-specific and regimen-specific information. Because of its widespread use in characterizing the disposition of orally administered drugs, the PBPK model chosen to demonstrate the methodology was an open-source implementation of a state-of-the-art compartmental and transit model called OpenCAT. The model was tested for drug formulations spanning a large range of solubility and absorption characteristics, and was evaluated for concordance against predictions of OpenCAT and relevant experimental data. In general, the values predicted by the ML models were within 20% of those of the PBPK model across the range of drug and formulation properties. However, summary PK parameter predictions from both the ML model and full PBPK model were occasionally poor with respect to those derived from experiments, suggesting deficiencies in the underlying PBPK model.

The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases

Because of their potential to cause serious adverse health effects, significant efforts have been made to develop antidotes for organophosphate (OP) anticholinesterases, such as nerve agents. To be optimally effective, antidotes must not only reactivate inhibited target enzymes, but also have the ability to cross the blood brain barrier (BBB). Progress has been made toward brain-penetrating acetylcholinesterase reactivators through the development of a new group of substituted phenoxyalkyl pyridinium oximes. To help in the selection and prioritization of compounds for future synthesis and testing within this class of chemicals, and to identify candidate broad-spectrum molecules, an in silico framework was developed to systematically generate structures and screen them for reactivation efficacy and BBB penetration potential.

kc-hits: a tool to aid in the evaluation and classification of chemical carcinogens

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.

Well-tempered MCMC simulations for population pharmacokinetic models

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.

pksensi: An R package to apply global sensitivity analysis in physiologically based kinetic modeling

Sensitivity analysis (SA) is an essential tool for modelers to understand the influence of model parameters on model outputs. It is also increasingly used in developing and assessing physiologically based kinetic (PBK) models. For instance, several studies have applied global SA to reduce the computational burden in the Bayesian Markov chain Monte Carlo-based calibration process PBK models. Although several SA algorithms and software packages are available, no comprehensive software package exists that allows users to seamlessly solve differential equations in a PBK model, conduct and visualize SA results, and discriminate between the non-influential model parameters that can be fixed and those that need calibration. Therefore, we developed an R package, named pksensi, to make global SA more accessible in PBK modeling. This package can investigate both uncertainty and sensitivity in PBK models, including those with multivariate model outputs. It also includes functions to check the convergence of the global SA results. Overall, pksensi improves the user experience of performing global SA and can create robust and reproducible results for decision making in PBK model calibration.

Paraquat exposure and Parkinson's disease: A systematic review and meta-analysis

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; I² = 24.8%). There is a statistically significant association between paraquat exposure and PD. Thus, future studies regarding paraquat and Parkinson's disease are warranted.

Development of a physiologically based pharmacokinetic model of paraquat

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.

Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling

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.

A Novel Method for the Development of Environmental Public Health Indicators and Benchmark Dose Estimation Using a Health-Based End Point for Chlorpyrifos

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.

Trichloroethylene-induced alterations in DNA methylation were enriched in polycomb protein binding sites in effector/memory CD4+ T cells

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.

Chronic exposure to water pollutant trichloroethylene increased epigenetic drift in CD4(+) T cells

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.

Association of HLA-B*5701 genotypes and abacavir-induced hypersensitivity reaction: a systematic review and meta-analysis

OBJECTIVES

This study aimed to systematically review and quantitatively synthesize the association between HLA-B*5701 and abacavir-induced hypersensitivity reaction (ABC-HSR).

METHODS

We searched for studies that investigated the association between HLA-B genotype and ABC-HSR and provided information about the frequency of carriers of HLA-B genotypes among cases and controls. We then performed a meta-analysis with a random-effects model to pool the data and to investigate the sources of heterogeneity.

RESULTS

From 1,026 articles identified, ten studies were included. Five using clinical manifestation as their diagnostic criteria, 409 and 1,883 subjects were included as cases and controls. Overall OR was 23.6 (95% CI = 15.4 - 36.3). Whereas, the another five studies using confirmed immunologic test as their diagnostic criteria, 110 and 1,968 subjects were included as cases and controls, respectively. The association of ABC-HSR was strong in this populations with HLA-B*5701. Overall OR was 1,056.2 (95% CI = 345.0 - 3,233.3).

CONCLUSIONS

Using meta-analysis technique, the association between HLA-B*5701 and ABC-HSR is strong in the studies using immunologic confirmation to identify ABC-HSR. These results support the US FDA recommendations for screening HLA-B*5701 allele before initiating abacavir therapy.

A novel approach for estimating ingested dose associated with paracetamol overdose

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.

A pharmacokinetic model of lopinavir in combination with ritonavir in human

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).

Additive Synergism between Asbestos and Smoking in Lung Cancer Risk: A Systematic Review and Meta-Analysis

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.

Modeling toxicodynamic effects of trichloroethylene on liver in mouse model of autoimmune hepatitis

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.

DoseSim: a tool for pharmacokinetic/pharmacodynamic analysis and dose reconstruction

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.

Epigenetic alterations may regulate temporary reversal of CD4(+) T cell activation caused by trichloroethylene exposure

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.

What is computational toxicology?

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.

Tools and techniques

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

Predicting activation enthalpies of cytochrome-P450-mediated hydrogen abstractions. 2. Comparison of semiempirical PM3, SAM1, and AM1 with a density functional theory method

Predicting the biotransformation of xenobiotics is important in the chemical and pharmaceutical industries, as well as in toxicology. Here, we extend and evaluate the rapid methodology of Korzekwa, Jones, and Gillette (J. Am. Chem. Soc. 1990, 112, 7042-7046 ) to estimate the activation enthalpy (DeltaH) of hydrogen-abstraction by cytochrome P450 (CYP) enzymes, using the p-nitrosophenoxy radical (PNPO) as a simple surrogate for the CYP active oxygen species. The DeltaH is estimated with a linear regression model using the reaction enthalpy and ionization energy (of the substrate radical) as predictor variables, calculated by semiempirical (SE) methods. While Korzekwa et al. used the SE method AM1, we applied PM3 and SAM1 and compared the results of the three methods. For 24 substrates, the AM1-, PM3-, and SAM1-derived regression models showed R(2) values of 0.89, 0.90, and 0.93, respectively, for the correlation between calculated and predicted DeltaH. Furthermore, we compared the DeltaH() calculated semiempirically using PNPO radical with density functional theory (DFT) B3LYP activation energies calculated by Olsen et al. (J. Med. Chem. 2006, 49, 6489-6499 ) using a more realistic iron-oxo-porphine model, and the results revealed limitations of the PNPO radical model. Thus, predictive models developed using SE predictors provide rapid and generally internally consistent results, but they should be interpreted and used cautiously.

Computational toxicology of chloroform: reverse dosimetry using Bayesian inference, Markov chain Monte Carlo simulation, and human biomonitoring data

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 < or = 67 microg/L [corrected] in tap water and < or = 0.02 microg/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: a structure-based summary of biotransformations using representative substrates

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.

Biochemical reaction network modeling: predicting metabolism of organic chemical mixtures

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.

Chemical mixture toxicology: from descriptive to mechanistic, and going on to in silico toxicology

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.

A reaction network model for CYP2E1-mediated metabolism of toxicant mixtures

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'.

Application of biologically based computer modeling to simple or complex mixtures

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.

The use of magnetic resonance imaging to track controlled drug release and transport in the brain

A method has been developed to track controlled drug release and transport in the brain. This method entails the use of a polymeric implant to release, over time, a paramagnetically labelled compound into the brain. Magnetic resonance imaging is used to determine the evolving concentration distribution. This method is well suited to other types of intracranial drug delivery systems as well as to track transport in other organs of the body.

Longitudinal mixing in dog lungs during high-frequency forced flow oscillation

Longitudinal mixing in the conducting airways of eight intubated anesthetized beagles (10.8 +/- 0.9 kg) was studied at functional residual capacity in the presence of forced sinusoidal flow oscillations and in the absence of fresh air bias flow. The ranges of oscillation conditions were: frequencies, f, from 3 to 18 Hz and minute volumes, Vosc, from 50 to 150 ml/sec, corresponding to tidal volumes, Vosc/f, from 0.3 to 4.5 ml/kg body mass. Oscillations were imposed during a breath holding interval incorporated into a modified single-breath nitrogen (N2) washout maneuver. The expired N2 fraction curves were analyzed with a Fickian diffusion model by adjusting the value of a global mixing parameter, (DA2), to achieve an optimal fit of the model to the data. The mixing parameter was an increasing function of minute volume and a decreasing function of frequency, which is well represented by the equation: (DA2) = 2.72 Vosc 1.74 f-1.57 By comparison to available theory and previous measurements in physical systems, this formula implies that Taylor-type dispersion is the dominant mixing mechanism in the conducting airways. Also, the diffusion model predicted, and the data verified, the existence of a mouth-ward 'diffusion flow' during breath holding. This effect, caused by the non-uniform nature of the summed airway cross-section, is directly correlated with the value of (DA2).