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.

Investigating combined toxicity of binary mixtures in bees: Meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment.

Bees are exposed to a wide range of multiple chemicals "chemical mixtures" from anthropogenic (e.g. plant protection products or veterinary products) or natural origin (e.g. mycotoxins, plant toxins). Quantifying the relative impact of multiple chemicals on bee health compared with other environmental stressors (e.g. varroa, viruses, and nutrition) has been identified as a priority to support the development of holistic risk assessment methods. Here, extensive literature searches and data collection of available laboratory studies on combined toxicity data for binary mixtures of pesticides and non-chemical stressors has been performed for honey bees (Apis mellifera), wild bees (Bombus spp.) and solitary bee species (Osmia spp.). From 957 screened publications, 14 publications provided 218 binary mixture toxicity data mostly for acute mortality (lethal dose: LD50) after contact exposure (61%), with fewer studies reporting chronic oral toxicity (20%) and acute oral LC50 values (19%). From the data collection, available dose response data for 92 binary mixtures were modelled using a Toxic Unit (TU) approach and the MIXTOX modelling tool to test assumptions of combined toxicity i.e. concentration addition (CA), and interactions (i.e. synergism, antagonism). The magnitude of interactions was quantified as the Model Deviation Ratio (MDR). The CA model applied to 17% of cases while synergism and antagonism were observed for 72% (MDR > 1.25) and 11% (MDR < 0.83) respectively. Most synergistic effects (55%) were observed as interactions between sterol-biosynthesis-inhibiting (SBI) fungicides and insecticide/acaricide. The mechanisms behind such synergistic effects of binary mixtures in bees are known to involve direct cytochrome P450 (CYP) inhibition, resulting in an increase in internal dose and toxicity of the binary mixture. Moreover, bees are known to have the lowest number of CYP copies and other detoxification enzymes in the insect kingdom. In the light of these findings, occurrence of these binary mixtures in relevant crops (frequency and concentrations) would need to be investigated. Addressing this exposure dimension remains critical to characterise the likelihood and plausibility of such interactions to occur under field realistic conditions. Finally, data gaps and further work for the development of risk assessment methods to assess multiple stressors in bees including chemicals and non-chemical stressors in bees are discussed.

The Yin-Yang of CYP3A4: a Bayesian meta-analysis to quantify inhibition and induction of CYP3A4 metabolism in humans and refine uncertainty factors for mixture risk assessment.

Quantifying differences in pharmacokinetics (PK) and toxicokinetics (TK) provides a science-based approach to refine uncertainty factors (UFs) for chemical risk assessment. Cytochrome P450 (CYP) 3A4-the major hepatic and intestinal human CYP-and the P-glycoprotein (Pgp) transporter share a vast range of common substrates for which PK may be modulated through inhibition or induction in the presence of grapefruit juice (GFJ) or St. John's wort (SJW), respectively. Here, an extensive literature search was performed on PK interactions for CYP3A4 and Pgp substrates after oral co-exposure to GFJ and SJW. Relevant data from 109 publications, extracted for both markers of acute (Cmax) and chronic [clearance and area under the plasma concentration-time curve (AUC)] exposure, were computed into a Bayesian hierarchical meta-analysis model. Bioavailability (F) and substrate fraction metabolised by CYP3A4 (Fm) were identified as the variables exhibiting the highest impact on the magnitude of interaction. The Bayesian meta-regression model developed provided good predictions for magnitudes of inhibition (maximum 5.3-fold with GFJ) and induction (maximum 2.3-fold with SJW). Integration of CYP3A4 variability, F, Fm and magnitude of interaction provided the basis to derive a range of CYP3A4 and Pgp-related UFs. Such CYP3A4 and Pgp-related UFs can be derived in the absence of human data using in vitro TK evidence for CYP3A4/Pgp inhibition or induction as conservative in silico options. The future development of quantitative in vitro-in vivo extrapolation models for mixture risk assessment is discussed with particular attention to integrating human in vitro and in vivo P/TK data on interactions with pathway-related variability.

Challenges for Optimizing Real-World Evidence in Alzheimer's Disease: The ROADMAP Project.

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer's disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit.

Clinical and economic burden of severe asthma: A French cohort study.

OBJECTIVE: To describe the clinical and economic burden of severe asthma in France over 12 months. METHODS: Data were retrieved from the observational, prospective "Cohorte Obstruction Bronchique et Asthme" (COBRA) cohort, which has enrolled nearly 1000 asthma patients since 2007 from throughout France. Patients undergoing treatment with GINA step-4 or 5 medications uninterruptedly for 12 months (thus defining "severe asthma") were identified and their clinical data used to describe the clinical burden of asthma (exacerbations, symptoms outside exacerbations, and level of asthma control). Patients' utilization of healthcare resources was described and used to estimate the direct medical costs incurred to treat severe asthma. RESULTS: 155 patients were included in the present study. Over the 12-month period of interest, 128 (83%) patients experienced at least one asthma exacerbation, 22 (14%) patients were hospitalized for asthma, 133 (86%) patients experienced continuous symptoms outside exacerbations, and 77 (50%) patients experienced important limitations in daily life activities. The median number of asthma-related drugs used was 4. The mean estimated annual asthma-related cost was 8,222 euros (standard deviation, SD = 11,886), including 7,229 euros (SD = 11,703) for controller medications. CONCLUSION: Symptoms outside exacerbation periods are highly prevalent in severe asthma patients, for whom the main driver of medical costs is controller medication.

Impact of increased influenza vaccination in 2-3-year-old children on disease burden within the general population: A Bayesian model-based approach.

INTRODUCTION: During the 2013-2014 influenza season, Public Health England extended routine influenza vaccination to all 2- and 3-year-old children in England. To estimate the impact of this change in policy on influenza-related morbidity and mortality, we developed a disease transmission and surveillance model informed by real-world data. METHODS: We combined real-world and literature data sources to construct a model of influenza transmission and surveillance in England. Data were obtained for four influenza seasons, starting with the 2010-2011 season. Bayesian inference was used to estimate model parameters on a season-by-season basis to assess the impact of targeting 2- and 3-year-old children for influenza vaccination. This provided the basis for the construction of counterfactual scenarios comparing vaccination rates of ~2% and ~35% in the 2- and 3- year-old population to estimate reductions in general practitioner (GP) influenza-like-illness (ILI) consultations, respiratory hospitalizations and deaths in the overall population. RESULTS: Our model was able to replicate the main patterns of influenza across the four seasons as observed through laboratory surveillance data. Targeting 2- and 3-year-old children for influenza vaccination resulted in reductions in the general population of between 6.2-9.9% in influenza-attributable GP ILI consultations, 6.1-10.7% in influenza-attributable respiratory hospitalizations, and 5.7-9.4% in influenza-attributable deaths. The decrease in influenza-attributable ILI consultations represents a reduction of between 4.5% and 7.3% across all ILI consultations. The reduction in influenza-attributable respiratory hospitalizations represents a reduction of between 1.2% and 2.3% across all respiratory hospitalizations. Reductions in influenza-attributable respiratory deaths represent a reduction of between 0.9% and 2.4% in overall respiratory deaths. CONCLUSION: This study has provided evidence that extending routine influenza vaccination to all healthy children aged 2 and 3 years old leads to benefits in terms of reduced utilization of healthcare resources and fewer respiratory health outcomes and deaths.

Pragmatic Multicriteria Decision Analysis (MCDA) Combined With Advanced Pharmacoepidemiology for Benefit-Risk Assessments of Medicines Adapted to the Real-Life Constraints of Regulators: Development and Case Study.

BACKGROUND: Multicriteria decision analysis (MCDA) represents a promising method for benefit-risk assessment. Our goal was to develop features of pragmatic MCDA (EVIDEM [Evidence and Value: Impact on DEcisionMaking]) addressing real-life regulatory decision-making needs, incorporate advanced pharmacoepidemiology, and test the resulting benefit-risk framework using a case study. METHODS: The Intervention Outcomes domain of EVIDEM was transformed into a generic benefit-risk framework including clinical efficacy, patient-reported outcomes, and adverse event (AE) criteria. The concept of relative benefit-risk balance (RBRB) was developed for comparability across products and therapeutic areas and over time. Evidence matrix was designed to include most relevant data from trials, observational studies, and models, including Bayesian and longitudinal modeling. The framework was tested with a panel of stakeholders using efalizumab for psoriasis as retrospective case study. Uncertainty was explored. RESULTS: The MCDA benefit-risk tree was adapted with psoriasis-specific subcriteria. Panelists assigned similar weights to benefits (0.48; SD, 0.20-0.70) and risks (0.52; SD, 0.10-0.60), with large variations reflecting diverse perspectives. Panelist scores reflected higher efficacy versus placebo, lower efficacy versus active comparators, and serious and fatal AEs identified postlicensing. Efalizumab's RBRB was positive at licensing in 2004 (0.29, scale -1 to +1) and ranged from -0.41 (vs active comparators) to 0.01 (vs placebo) in 2009, when its market authorization was withdrawn. Retesting indicated good reproducibility. Panelists acknowledged good face validity and the importance of criteria beyond benefit-risk in real-life assessments. CONCLUSIONS: The approach allows quantification and visualization of benefit-risk over time and across comparators. Combination of pragmatic MCDA designed to integrate criteria beyond benefit-risk and advanced statistics supports application of MCDA to further accountable benefit-risk assessments for real-life decision making.

Human risk assessment of heavy metals: principles and applications.

Humans are exposed to a number of "heavy metals" such as cadmium, mercury and its organic form methylmercury, uranium, lead, and other metals as wel as metalloids, such as arsenic, in the environment, workplace, food, and water supply. Exposure to these metals may result in adverse health effects, and national and international health agencies have methodologies to set health-based guidance values with the aim to protect the human population. This chapter introduces the general principles of chemical risk assessment, the common four steps of chemical risk assessment: hazard identification, hazard characterization, exposure assessment, risk characterization, and toxicokinetic and toxicity aspects. Finally, the risk assessments performed by international health agencies such as the World Health Organisation, the Environmental Protection Agency of the United States, and the European Food Safety Authority are reviewed for cadmium, lead, mercury, uranium, and arsenic.

A statistical assessment of differences and equivalences between genetically modified and reference plant varieties.

BACKGROUND: Safety assessment of genetically modified organisms is currently often performed by comparative evaluation. However, natural variation of plant characteristics between commercial varieties is usually not considered explicitly in the statistical computations underlying the assessment. RESULTS: Statistical methods are described for the assessment of the difference between a genetically modified (GM) plant variety and a conventional non-GM counterpart, and for the assessment of the equivalence between the GM variety and a group of reference plant varieties which have a history of safe use. It is proposed to present the results of both difference and equivalence testing for all relevant plant characteristics simultaneously in one or a few graphs, as an aid for further interpretation in safety assessment. A procedure is suggested to derive equivalence limits from the observed results for the reference plant varieties using a specific implementation of the linear mixed model. Three different equivalence tests are defined to classify any result in one of four equivalence classes. The performance of the proposed methods is investigated by a simulation study, and the methods are illustrated on compositional data from a field study on maize grain. CONCLUSIONS: A clear distinction of practical relevance is shown between difference and equivalence testing. The proposed tests are shown to have appropriate performance characteristics by simulation, and the proposed simultaneous graphical representation of results was found to be helpful for the interpretation of results from a practical field trial data set.

Population toxicokinetic modeling of cadmium for health risk assessment.

BACKGROUND: Cadmium is a widespread environmental pollutant that has been shown to exert toxic effects on kidney and bones in humans after long-term exposure. Urinary cadmium concentration is considered a good biomarker of accumulated cadmium in kidney, and diet is the main source of cadmium among nonsmokers. OBJECTIVE: Modeling the link between urinary cadmium and dietary cadmium intake is a key step in the risk assessment of long-term cadmium exposure. There is, however, little knowledge on how this link may vary, especially for susceptible population strata. METHODS: We used a large population-based study (the Swedish Mammography Cohort), with repeated dietary intake data covering a period of 20 years, to compare estimated dietary cadmium intake with urinary cadmium concentrations on an individual basis. A modified version of the Nordberg-Kjellström model and a one-compartment model were evaluated in terms of their predictions of urinary cadmium. We integrated the models and quantified the between-person variability of cadmium half-life in the population. Finally, sensitivity analyses and Monte Carlo simulations were performed to illustrate how the latter model could serve as a robust tool supporting the risk assessment of cadmium in humans. RESULTS: The one-compartment population model appeared to be an adequate modeling option to link cadmium intake to urinary cadmium and to describe the population variability. We estimated the cadmium half-life to be about 11.6 years, with about 25% population variability. CONCLUSIONS: Population toxicokinetic models can be robust and useful tools for risk assessment of chemicals, because they allow quantification and integration of population variability in toxicokinetics.