A generic Bayesian hierarchical model for the meta-analysis of human population variability in kinetics and its applications in chemical risk assessment

Quantifying the effect of human interindividual kinetic differences on the relative potency value for riddelliine N-oxide at low dose levels by a new approach methodology

Pyrrolizidine alkaloid N-oxides (PA-N-oxides) are predominant in plants and herbal foods, and are converted to pyrrolizidine alkaloids (PAs) upon consumption, leading to toxicity. The effect of interindividual kinetic differences on the relative potency values of PA-N-oxides compared to their PAs (REPPANO to PA) was studied, with riddelliine N-oxide (RIDO) and riddelliine (RID) as model compounds. In vitro kinetic data measured in incubations with 30 fecal and 25 liver S9 donor samples showed high variation across individuals, where the interindividual variability was captured with Bayesian multilevel regression. The distributions of influential PBK model parameters were used as input for physiologically based kinetic (PBK) modeling combined with Monte Carlo (MC) simulations to calculate the probability distribution of REPRIDO to RID values. At low dose levels, interindividual differences were shown to be a factor that influences the REPRIDO to RID value while neither dose nor endpoint used plays a role. The distribution of the REPRIDO to RID value ranged from 0.71 to 0.97 (95th percentile) with a mean value of 0.87. The approach described enables determination of interindividual REPPANO to PA values at low dose levels, which are not accessible in in vivo experiments quantifying the REPPANO to PAvalue.

Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically.

Human health risk assessment for contaminated sites: A retrospective review

Soil contamination is a serious global hazard as contaminants can migrate to the human body through the soil, water, air, and food, threatening human health. Human Health Risk Assessment (HHRA) is a commonly used method for estimating the magnitude and probability of adverse health effects in humans that may be exposed to contaminants in contaminated environmental media in the present or future. Such estimations have improved for decades with various risk assessment frameworks and well-established models. However, the existing literature does not provide a comprehensive overview of the methods and models of HHRA that are needed to grasp the current status of HHRA and future research directions. Thus, this paper aims to systematically review the HHRA approaches and models, particularly those related to contaminated sites from peer-reviewed literature and guidelines. The approaches and models focus on methods used in hazard identification, toxicity databases in dose-response assessment, approaches and fate and transport models in exposure assessment, risk characterization, and uncertainty characterization. The features and applicability of the most commonly used HHRA tools are also described. The future research trend for HHRA for contaminated sites is also forecasted. The transition from animal experiments to new methods in risk identification, the integration and update and sharing of existing toxicity databases, the integration of human biomonitoring into the risk assessment process, and the integration of migration and transformation models and risk assessment are the way forward for risk assessment in the future. This review provides readers with an overall understanding of HHRA and a grasp of its developmental direction.

Human variability in polymorphic CYP2D6 metabolism: Implications for the risk assessment of chemicals in food and emerging designer drugs

The major human cytochrome P450 CYP2D6 isoform enzyme plays important roles in the liver and in the brain with regards to xenobiotic metabolism. Xenobiotics as CYP2D6 substrates include a whole range of pharmaceuticals, pesticides and plant alkaloids to cite but a few. In addition, a number of endogenous compounds have been shown to be substrates of CYP2D6 including trace amines in the brain such as tyramine and 5-methoxytryptamine as well as anandamide and progesterone. Because of the polymorphic nature of CYP2D6, considerable inter-phenotypic and inter-ethnic differences in the pharmaco/toxicokinetics (PK/TK) and metabolism of CYP2D6 substrates exist with potential consequences on the pharmacology and toxicity of chemicals. Here, large extensive literature searches have been performed to collect PK data from published human studies for a wide range of pharmaceutical probe substrates and investigate human variability in CYP2D6 metabolism. The computed kinetic parameters resulted in the largest open source database, quantifying inter-phenotypic differences for the kinetics of CYP2D6 probe substrates in Caucasian and Asian populations, to date. The database is available in supplementary material (CYPD6 DB) and EFSA knowledge junction (DOI to added). Subsequently, meta-analyses using a hierarchical Bayesian model for markers of chronic oral exposure (oral clearance, area under the plasma concentration time curve) and acute oral exposure (maximum plasma concentration (Cmax) provided estimates of inter-phenotypic differences and CYP2D6-related uncertainty factors (UFs) for chemical risk assessment in Caucasian and Asian populations classified as ultra-rapid (UM), extensive (EMs), intermediate (IMs) and poor metabolisers (PMs). The model allowed the integration of inter-individual (i.e. inter-phenotypic and inter-ethnic), inter-compound and inter-study variability together with uncertainty in each PK parameter. Key findings include 1. Higher frequencies of PMs in Caucasian populations compared to Asian populations (>8% vs 1-2%) for which EM and IM were the most frequent phenotype. 2. Large inter-phenotypic differences in PK parameters for Caucasian EMs (coefficients of variation (CV) > 50%) compared with Caucasian PMs and Asian EMs and IMs (i.e CV < 40%). 3. Inter-phenotypic PK differences between EMs and PMs in Caucasian populations increase with the quantitative contribution of CYP2D6 for the metabolism (fm) for a range of substrates (fm_CYP2D6 range: 20-95% of dose) (range: 1-54) to a much larger extent than those for Asian populations (range: 1-4). 4. Exponential meta-regressions between Fm_CYP2D6 in EMs and inter-phenotypic differences were also shown to differ between Caucasian and Asian populations as well as CYP2D6-related UFs. Finally, implications of these results for the risk assessment of food chemicals and emerging designer drugs of public health concern, as CYP2D6 substrates, are highlighted and include the integration of in vitro metabolism data and CYP2D6-variability distributions for the development of quantitative in vitro in vivo extrapolation models.

Human Variability in Carboxylesterases and carboxylesterase-related Uncertainty Factors for Chemical Risk Assessment

Carboxylesterases (CES) are an important class of enzymes involved in the hydrolysis of a range of chemicals and show large inter-individual variability in vitro. An extensive literature search was performed to identify in vivo probe substrates for CES1 and CES2 together with their protein content and enzymatic activity. Human pharmacokinetic (PK) data on Cmax, clearance, and AUC were extracted from 89 publications and Bayesian meta-analysis was performed using a hierarchical model to derive CES-related variability distributions and related uncertainty factors (UF). The CES-related variability indicated that 97.5% of healthy adults are covered by the kinetic default UF (3.16), except for clopidogrel and dabigatran etexilate. Clopidogrel is metabolised for a small amount by the polymorphic CYP2C19, which can have an impact on the overall pharmacokinetics, while the variability seen for dabigatran etexilate might be due to differences in the absorption, since this can be influenced by food intake. The overall CES-related variability was moderate to high in vivo (<CV 50%), which might be due to possible polymorphism in the enzyme but also to the small sample size available per chemical. The presented CES-related variability can be used in combination with in vitro data to derive pathway-specific distributions.

Inter-phenotypic differences in CYP2C9 and CYP2C19 metabolism: Bayesian meta-regression of human population variability in kinetics and application in chemical risk assessment

Quantifying variability in pharmacokinetics (PK) and toxicokinetics (TK) provides a science-based approach to refine uncertainty factors (UFs) for chemical risk assessment. In this context, genetic polymorphisms in cytochromes P450 (CYPs) drive inter-phenotypic differences and may result in reduction or increase in metabolism of drugs or other xenobiotics. Here, an extensive literature search was performed to identify PK data for probe substrates of the human polymorphic isoforms CYP2C9 and CYP2C19. Relevant data from 158 publications were extracted for markers of chronic exposure (clearance and area under the plasma concentration-time curve) and analysed using a Bayesian meta-regression model. Enzyme function (EF), driven by inter-phenotypic differences across a range of allozymes present in extensive and poor metabolisers (EMs and PMs), and fraction metabolised (Fm), were identified as exhibiting the highest impact on the metabolism. The Bayesian meta-regression model provided good predictions for such inter-phenotypic differences. Integration of population distributions for inter-phenotypic differences and estimates for EF and Fm allowed the derivation of CYP2C9- and CYP2C19-related UFs which ranged from 2.7 to 12.7, and were above the default factor for human variability in TK (3.16) for PMs and major substrates (Fm >60%). These results provide population distributions and pathway-related UFs as conservative in silico options to integrate variability in CYP2C9 and CYP2C19 metabolism using in vitro kinetic evidence and in the absence of human data. The future development of quantitative extrapolation models is discussed with particular attention to integrating human in vitro and in vivo PK or TK data with pathway-related variability for chemical risk assessment.

Human variability in isoform-specific UDP-glucuronosyltransferases: markers of acute and chronic exposure, polymorphisms and uncertainty factors

UDP-glucuronosyltransferases (UGTs) are involved in phase II conjugation reactions of xenobiotics and differences in their isoform activities result in interindividual kinetic differences of UGT probe substrates. Here, extensive literature searches were performed to identify probe substrates (14) for various UGT isoforms (UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9, UGT2B7 and UGT2B15) and frequencies of human polymorphisms. Chemical-specific pharmacokinetic data were collected in a database to quantify interindividual differences in markers of acute (Cmax) and chronic (area under the curve, clearance) exposure. Using this database, UGT-related uncertainty factors were derived and compared to the default factor (i.e. 3.16) allowing for interindividual differences in kinetics. Overall, results show that pharmacokinetic data are predominantly available for Caucasian populations and scarce for other populations of different geographical ancestry. Furthermore, the relationships between UGT polymorphisms and pharmacokinetic parameters are rarely addressed in the included studies. The data show that UGT-related uncertainty factors were mostly below the default toxicokinetic uncertainty factor of 3.16, with the exception of five probe substrates (1-OH-midazolam, ezetimibe, raltegravir, SN38 and trifluoperazine), with three of these substrates being metabolised by the polymorphic isoform 1A1. Data gaps and future work to integrate UGT-related variability distributions with in vitro data to develop quantitative in vitro-in vivo extrapolations in chemical risk assessment are discussed.

Bayesian meta-analysis of inter-phenotypic differences in human serum paraoxonase-1 activity for chemical risk assessment

Human variability in paraoxonase-1 (PON1) activities is driven by genetic polymorphisms that affect the internal dose of active oxons of organophosphorus (OP) insecticides. Here, an extensive literature search has been performed to collect human genotypic frequencies (i.e. L55M, Q192R, and C-108T) in subgroups from a range of geographical ancestry and PON1 activities in three probe substrates (paraoxon, diazoxon and phenyl acetate). Bayesian meta-analyses were performed to estimate variability distributions for PON1 activities and PON1-related uncertainty factors (UFs), while integrating quantifiable sources of inter-study, inter-phenotypic and inter-individual differences. Inter-phenotypic differences were quantified using the population with high PON1 activity as the reference group. Results from the meta-analyses provided PON1 variability distributions and these can be implemented in generic physiologically based kinetic models to develop quantitative in vitro in vivo extrapolation models. PON1-related UFs in the Caucasian population were above the default toxicokinetic UF of 3.16 for two specific genotypes namely -108CC using diazoxon as probe substrate and, -108CT, -108TT, 55MM and 192QQ using paraoxon as probe substrate. However, integration of PON1 genotypic frequencies and activity distributions showed that all UFs were within the default toxicokinetic UF. Quantitative inter-individual differences in PON1 activity are important for chemical risk assessment particularly with regards to the potential sensitivity to organophosphates' toxicity.