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.

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.

Predicting the Disposition of the Antimalarial Drug Artesunate and Its Active Metabolite Dihydroartemisinin Using Physiologically Based Pharmacokinetic Modeling.

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.

A Bayesian population physiologically based pharmacokinetic absorption modeling approach to support generic drug development: application to bupropion hydrochloride oral dosage forms.

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.

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.

Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans.

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.

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.

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.

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

UNLABELLED: 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.

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.

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.

A physiologically based pharmacokinetic model of rifampin in mice.

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.

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.

Rapid estimation of activation enthalpies for cytochrome-P450-mediated hydroxylations.

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.

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.

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.

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.

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.