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We provide a short overview of clinical trials of vaccines, with particular focus on (i) adaptive and platform trials, (ii) human challenge trials, and (iii) vaccine use optimization, especially fractional dosing. We describe their relationship with regulatory approval and review main developments during Covid-19. We review the literature on risk–benefit analyses of alternative testing approaches but find few results, suggesting need for further quantitative research. We conclude by discussing some lessons for the next pandemic, such as the need for pre-pandemic R&D and clear guidelines; improving capability to use new trial approaches; arguments for diversifying research methods; research incentives and disincentives; and the need to use risk–benefit in approving new vaccines and targeting.

To assess the combined role of anti-viral monoclonal antibodies (mAbs) and vaccines in reducing severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) transmission and mortality in the United States, an agent-based model was developed that accounted for social contacts, movement/travel, disease progression, and viral shedding. The model was calibrated to coronavirus disease 2019 (COVID-19) mortality between October 2020 and April 2021 (aggressive pandemic phase), and projected an extended outlook to estimate mortality during a less aggressive phase (April-August 2021). Simulated scenarios evaluated mAbs for averting infections and deaths in addition to vaccines and aggregated non-pharmaceutical interventions. Scenarios included mAbs as a treatment of COVID-19 and for passive immunity for postexposure prophylaxis (PEP) during a period when variants were susceptible to the mAbs. Rapid diagnostic testing paired with mAbs was evaluated as an early treatment-as-prevention strategy. Sensitivity analyses included increasing mAb supply and vaccine rollout. Allocation of mAbs for use only as PEP averted up to 14% more infections than vaccine alone, and targeting individuals ≥ 65 years averted up to 37% more deaths. Rapid testing for earlier diagnosis and mAb use amplified these benefits. Doubling the mAb supply further reduced infections and mortality. mAbs provided benefits even as proportion of the immunized population increased. Model projections estimated that ~ 42% of expected deaths between April and August 2021 could be averted. Assuming sensitivity to mAbs, their use as early treatment and PEP in addition to vaccines would substantially reduce SARS-CoV-2 transmission and mortality even as vaccination increases and mortality decreases. These results provide a template for informing public health policy for future pandemic preparedness.

BackgroundFew studies have assessed participant safety in human challenge trials (HCTs). Key questions regarding HCTs include how risky such trials have been, how often adverse events (AEs) and serious adverse events (SAEs) occur, and whether risk mitigation measures have been effective.MethodsA systematic search of PubMed and PubMed Central for articles reporting on results of HCTs published between 1980 and 2021 was performed and completed by 7 October 2021.ResultsOf 2838 articles screened, 276 were reviewed in full. A total of 15 046 challenged participants were described in 308 studies that met inclusion criteria; 286 (92.9%) of these studies reported mitigation measures used to minimize risk to the challenge population. Among 187 studies that reported on SAEs, 0.2% of participants experienced at least 1 challenge-related SAE. Among 94 studies that graded AEs by severity, challenge-related AEs graded “severe” were reported by between 5.6% and 15.8% of participants. AE data were provided as a range to account for unclear reporting. Eighty percent of studies published after 2010 were registered in a trials database.ConclusionsHCTs are increasingly common and used for an expanding list of diseases. Although AEs occur, severe AEs and SAEs are rare. Reporting has improved over time, though not all papers provide a comprehensive report of relevant health impacts. We found very few severe symptoms or SAEs in studies that reported them, but many HCTs did not report relevant safety data. This study was preregistered on PROSPERO as CRD42021247218.

Due to the enormous economic, health, and social costs of the COVID-19 pandemic, there are high expected social returns to investing in parallel in multiple approaches to accelerating vaccination. We argue there are high expected social returns to investigating the scope for lowering the dosage of some COVID-19 vaccines. While existing evidence is not dispositive, available clinical data on the immunogenicity of lower doses combined with evidence of a high correlation between neutralizing antibody response and vaccine efficacy suggests that half or even quarter doses of some vaccines could generate high levels of protection, particularly against severe disease and death, while potentially expanding supply by 450 million to 1.55 billion doses per month, based on supply projections for 2021. An epidemiological model suggests that, even if fractional doses are less effective than standard doses, vaccinating more people faster could substantially reduce total infections and deaths. The costs of further testing alternative doses are much lower than the expected public health and economic benefits. However, commercial incentives to generate evidence on fractional dosing are weak, suggesting that testing may not occur without public investment. Governments could support either experimental or observational evaluations of fractional dosing, for either primary or booster shots. Discussions with researchers and government officials in multiple countries where vaccines are scarce suggests strong interest in these approaches.

Build more capacity, and stretch what we already have

Background & AimsOne of the early key events of drug-induced liver injury (DILI) is the activation of adaptive stress responses, a cellular mechanism to overcome stress. Given the diversity of DILI outcomes and lack in understanding of population variability, we mapped the inter-individual variability in stress response activation to improve DILI prediction.Approach & ResultsHigh-throughput transcriptome analysis of over 8,000 samples was performed in primary human hepatocytes of 50 individuals upon 8 to 24 h exposure to broad concentration ranges of stress inducers: tunicamycin to induce the unfolded protein response (UPR), diethyl maleate for the oxidative stress response, cisplatin for the DNA damage response and TNFα for NF-κB signalling. This allowed investigation of the inter-individual variability in concentration-dependent stress response activation, where the average of benchmark concentrations (BMCs) had a maximum difference of 864, 13, 13 and 259-fold between different hepatocytes for UPR, oxidative stress, DNA damage and NF-κB signalling-related genes, respectively. Hepatocytes from patients with liver disease resulted in less stress response activation. Using a population mixed-effect framework, the distribution of BMCs and maximum fold change were modelled, allowing simulation of smaller or larger PHH panel sizes. Small panel sizes systematically under-estimated the variance and resulted in low probabilities in estimating the correct variance for the human population. Moreover, estimated toxicodynamic variability factors were up to 2-fold higher than the standard uncertainty factor of 101/2 to account for population variability during risk assessment, exemplifying the need of data-driven variability factors.ConclusionsOverall, by combining high-throughput transcriptome analysis and population modelling, improved understanding of variability in stress response activation across the human population could be established, thereby contributing towards improved prediction of DILI.

Millions of people are being vaccinated against SARS-CoV-2 every day, but the virus is also mutating and spreading fast. Vaccine production is increasing, but supply still constrains vaccinations worldwide. Using lower doses of vaccines could dramatically accelerate vaccination. Available evidence on efficacy is not dispositive but suggests half- or even quarter-doses of some vaccines could be almost as effective as currently-used doses. Even if fractional doses are less effective than standard doses, an epidemiological model suggests they could significantly reduce total infections and deaths. The social value of testing dwarfs the costs. However, firms do not internalize the full social value, a market failure that could be addressed with public funding. Governments could support either experimental or observational evaluations of fractional dosing.

We argue that alternative COVID-19 vaccine dosing regimens could potentially dramatically accelerate global COVID-19 vaccination and reduce mortality, and that the costs of testing these regimens are dwarfed by their potential benefits. We first use the high correlation between neutralizing antibody response and efficacy against disease (Khoury et. al. 2021) to show that half or even quarter doses of some vaccines generate immune responses associated with high vaccine efficacy. We then use an SEIR model to estimate that under these efficacy levels, doubling or quadrupling the rate of vaccination by using fractional doses would dramatically reduce infections and mortality. Since the correlation between immune response and efficacy may not be fully predictive of efficacy with fractional doses, we then use the SEIR model to show that fractional dosing would substantially reduce infections and mortality over a wide range of plausible efficacy levels. Further immunogenicity studies for a range of vaccine and dose combinations could deliver outcomes in weeks and could be conducted with a few hundred healthy volunteers. National regulatory authorities could also decide to test efficacy of fractional dosing in the context of vaccination campaigns based on existing immune response data, as some did for delayed second doses. If efficacy turned out to be high, the approach could be implemented broadly, while if it turned out to be low, downside risk could be limited by administering full doses to those who had received fractional doses. The SEIR model also suggests that delaying second vaccine doses will likely have substantial mortality benefits for multiple, but not all, vaccine-variant combinations, underscoring the importance of ongoing surveillance. Finally, we find that for countries choosing between approved but lower efficacy vaccines available immediately and waiting for mRNA vaccines, using immediately available vaccines typically reduces mortality.

SummaryBackgroundAntiviral monoclonal antibodies (mAbs) developed for treatment of COVID-19 reduce the magnitude and duration of viral shedding and can thus potentially contribute to reducing transmission of the causative virus, severe acute respiratory coronavirus 2 (SARS-CoV-2). However, use of these mAbs in combination with a vaccine program has not been considered in public health strategic planning.MethodsWe developed an agent-based model to characterize SARS-CoV-2 transmission in the US population during an aggressive phase of the pandemic (October 2020 to April 2021), and simulated the effects on infections and mortality of combining mAbs as treatment and post-exposure prophylaxis (PEP) with a vaccine program plus non-pharmaceutical interventions. We also interrogated the impact of rapid diagnostic testing, increased mAb supply, and vaccine rollout.FindingsAllocation of mAbs as PEP or targeting those ≥65 years provided the greatest incremental benefits relative to vaccine in averting infections and deaths, by up to 17% and 41%, respectively. Rapid testing, facilitating earlier diagnosis and mAb use, amplified these benefits. The model was sensitive to mAb supply; doubling supply further reduced infections and mortality, by up to two-fold, relative to vaccine. mAbs continued to provide incremental benefits even as proportion of the vaccinated population increased.InterpretationUse of anti-viral mAbs as treatment and PEP in combination with a vaccination program would substantially reduce SARS-CoV-2 transmission and pandemic burden. These results may help guide resource allocation and patient management decisions for COVID-19 and can also be used to inform public health policy for current and future pandemic preparedness.FundingRegeneron Pharmaceuticals.

Vaccinating the world’s population quickly in a pandemic has enormous health and economic benefits. We analyze the problem faced by governments in determining the scale and structure of procurement for vaccines. We analyze alternative approaches to procurement. We find that if the goal is to accelerate the vaccine delivery timetable, buyers should directly fund manufacturing capacity and shoulder most of the risk of failure, while maintaining some direct incentives for speed. We analyzed the optimal portfolio of vaccine investments for countries with different characteristics as well as the implications for international cooperation. Our analysis, considered in light of the experience of 2020, suggests lessons for future pandemics.

International audience; 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.

Background Cabozantinib has recently been evaluated as a first-line treatment in advanced renal cell carcinoma (aRCC). Objective To indirectly assess efficacy of cabozantinib versus standard-of-care (SoC) comparators in the first-line treatment of aRCC. Methods We conducted a systematic literature review (SLR) to identify randomized controlled studies in the first-line setting for aRCC. The outcomes analyzed were overall survival (OS) and progression-free survival (PFS). A network meta-analysis (NMA) was conducted comparing OS and PFS hazard ratios (HRs). Results Thirteen studies were identified in the SLR to be eligible for inclusion in the NMA. The overall study populations were heterogeneous in terms of risk groups; some studies included favorable risk patients. In intermediate-risk patients, HRs (95% confidence interval) for PFS were 0.52 (0.33, 0.82), 0.46 (0.26, 0.80), 0.20 (0.12, 0.36), and 0.37 (0.20, 0.68) when cabozantinib was compared with sunitinib, sorafenib, interferon (IFN), or bevacizumab plus IFN, respectively. In poor-risk patients, the NMA also demonstrated significant superiority in terms of PFS for cabozantinib; HRs were 0.31 (0.11, 0.90), 0.22 (0.06, 0.87), 0.16 (0.04, 0.64), and 0.20 (0.05, 0.88), when cabozantinib was compared with sunitinib, temsirolimus, IFN, or bevacizumab plus IFN, respectively. When the overall study populations were compared, the results were similar to the subgroup analyses. OS HRs in all analyses favored cabozantinib, but were not statistically significant. Conclusions The results suggest that cabozantinib significantly increases PFS in intermediate-, and poor-risk subgroups when compared to standard-of-care comparators. Although overall populations included favorable risk patients in some studies, the results seen were consistent with the subgroup analyses. Electronic supplementary material The online version of this article (10.1007/s11523-018-0559-0) contains supplementary material, which is available to authorized users.

International audience; 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.

This study set out to evaluate influenza- and respiratory-related illnesses recorded during primary care physician consultations in England following the H1N1 pandemic in 2009 and to enable the development of a dynamic disease model. Data were obtained from the Clinical Practice Research Datalink of primary care records over four influenza seasons (2010–2014). The primary outcome of the study was incidence of influenza- and respiratory-related diagnoses, calculated per practice and by season and age group. Upper respiratory tract infection diagnoses were most frequently recorded (mean seasonal practice level incidence; 3,762 consultations per 100,000 [SD = 1,989]), and influenza-related diagnoses were least frequently recorded across all seasons, except one. Incidence rates for the under 18 population were higher than those for the general population, in particular for upper respiratory tract infection (range of 8,024–9,950 versus 3,228–4,120, respectively) and otitis media diagnoses (2,668–3,652 versus 782–1,057, respectively). For influenza-related diagnoses, the 65+ age group, the 0 to

Background: Ankylosing spondylitis (AS) is an inflammatory disease resulting in progressive disability due to structural damage in the spine. The identification of predictors of progression would allow treating physicians to personalize treatments but requires an approach accounting for a relatively short follow-up of clinical studies, large between-patient variability, and low sensitivity of X-ray images which generate intra-patient variability when repeated measurements are taken. Objectives: 1) To identify patient characteristics predicting faster structural damage progression. 2) To quantify the progression over four years of secukinumab treatment, depending on dose/exposure. Methods: Data came from the phase 3 randomized placebo-controlled trial MEASURE 1 (NCT01358175)1 in which patients were treated with secukinumab (intravenous loading of 10 mg/kg at weeks 0, 2, and 4, followed by secukinumab subcutaneously at a dose of either 75 mg or 150 mg every 4 weeks) over 208 weeks. Only patients treated with secukinumab with at least two assessments of structural damage were included. We explored the effect of multiple baseline demographic traits, disease stage, severity, bone cartilage biomarkers as well as prior and current treatment on change in modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) using a longitudinal Bayesian mixture model with random effects, which accounted simultaneously for the probability of progression, magnitude of progression and inter-patient variability. Posterior predictive check was performed. Results: Out of 249 patients randomized to secukinumab, 167 had their structural damage assessed at least twice. These patients contributed in total 409 assessments of change in structural damage between weeks 0, 52, 104 and 208. Of the factors tested, a higher baseline BASMI score was associated with faster progression rate in a statistically significant way (+0.60 in mSASSS/year for each additional standard deviation of baseline BASMI (SD=1.74), 95% interval 0.03 to 1.14). Trends were also detected for association between faster mSASSS progression and younger age, prior exposure to TNFα inhibitor, HLA-B27 positivity, and higher osteocalcin levels. Increased exposure to secukinumab was associated with slower structural damage progression (-0.23 in mSASSS/year for each additional standard deviation in exposure, 95% interval -0.58 to 0.10). Model estimation suggested that secukinumab 150 mg was associated with a yearly progression of -0.2 (95% interval -1.2 to 0.8) in the first two years of treatment and 0.1 mSASSS (95% interval -0.8 to 1.0) in the third and fourth year of treatment. Analogously, secukinumab 75mg (currently not approved for clinical use) was associated with a progression of -0.2 mSASSS/year (95% interval -1.3 to 0.9) in the first two years of treatment and 0.5 mSASSS/year (95% interval -0.5 to 1.6) in the third and fourth year of treatment. Conclusion: Potential predictors of structural progression suggested by the model were higher baseline BASMI, younger age, prior exposure to TNFα inhibitor, HLA-B27 positivity, and higher osteocalcin at baseline, although only baseline BASMI showed statistical significance. Further analyses using larger, deeper and real-world data are needed to confirm these findings and to estimate progression rates in AS patients in daily practice. Reference [1] Baeten D, Sieper J, Braun J, et al. Secukinumab, an Interleukin-17A Inhibitor, in Ankylosing Spondylitis. N Engl J Med. 2015;373(26):2534-2548. Disclosure of Interests: Olivier Luttringer Shareholder of: Novartis, Employee of: Employee of Novartis Pharma AG, Todd Fox Employee of: Novartis, Brian Porter Shareholder of: Novartis, Employee of: Novartis, Abhijit Shete Shareholder of: Novartis Pharma AG, Employee of: Novartis Pharma AG, Hanno Richards Shareholder of: Novartis Pharma AG, Employee of: Novartis Pharma AG, Witold Wiecek Consultant for: Employee of CERTARA Strategic Consulting., Zuzanna Angehrn Shareholder of: Owns funds which might occassionaly invest in pharmaceutical companies’ stocks., Consultant for: Employee of CERTARA strategic consulting., Helene Karcher Consultant for: Former employee of Analytica Laser, a Certara company. Former independent consultant. Current employee of PAREXEL., Employee of: Former employee of Novartis., Billy Amzal Consultant for: Employee of CERTARA Strategic Consulting., Employee of: Former employee of Novartis.

International audience; requirements for pre-market authorisation of pesticides (regulation 283-284/2013) for which comparative in vitro metabolism (rat) human and in vivo rat TK data are required. As a consequence, EFSA has been involved in collaborative research to develop TK tools and databases for food safety in the human health, animal health and ecological area. After problem formulation, risk assessors may start at low tier (e.g. basic information and default values) under time and resource constraints, or use all data available for a particular chemical. In this situation, ADME and toxicity information may allow sound dose response modelling and a full quantitative assessment. In a broad context, developing generic tools and databases allow replacing default values with quantitative metrics for physiological, TK and toxicity parameters in any data situation. First, physiological parameters for species of interest are collected and integrated, within a set of differential equations, into generic models. These range from allometric scaling, one compartment model to full physiologically-based TK models (PB-TK). Third, databases reporting physico-chemical and ADME properties, TK parameters and bioaccumulation provide input data to run the models for predicting blood concentrations or elimination patterns. Applications are illustrated including modelling interspecies differences in renal elimination using allometric scaling, integrating variability in human metabolism into generic PB-TK models and development of a modelling platform: TKplate (Wiecek et al., 2018, Darney et al., 2018). Future perspectives include the development of guidance and case studies to use TK tools in routine risk assessment and training programmes.

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.

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.

Traditionally, chemical risk assessment in humans aims to derive safe levels of exposure to chemicals using toxicological data in test species while applying a default 100-fold uncertainty factor (UF) to allow for both interspecies differences (10) and human variability (10) in toxicokinetic (TK) and toxicodynamic (TD) processes. Over the last two decades, meta-analysis methods have allowed to perform quantitative analysis of population variability in kinetics and dynamics to replace these default UFs with data-based UFs. Pathway-related UFs can now be derived using population variability in kinetics for known metabolic pathways. This study presents a new hierarchical Bayesian model for the meta-analysis of human population variability in TK parameters, along with its application to the derivation of UFs. Data standardisation and data gaps are also discussed, as well as model implementation, selection and validation. Applications of the generic model in human risk assessment are illustrated through a case study quantifying inter-phenotypic differences in TK for substances metabolised via the CYP2D6 polymorphic enzyme. Model implementations in open-source software are provided and future refinements and applications of the model are proposed.

Renal-cell carcinoma (RCC) affects over 330,000 new patients every year, of whom 1/3 present with metastatic RCC (mRCC) at diagnosis. Most mRCC patients treated with a first-line agent relapse within 1 year and need second-line therapy. The present study aims to compare overall survival (OS) between nivolumab and cabozantinib from two recent pivotal studies comparing, respectively, each one of the two emerging treatments against everolimus in patients who relapse following first-line treatment. Comparison is traditionally carried out using the Bucher method, which assumes proportional hazard. Since OS curves intersected in one of the pivotal studies, models not assuming proportional hazards were also considered to refine the comparison. Four Bayesian parametric survival network meta-analysis models were implemented on overall survival (OS) data digitized from the Kaplan-Meier curves reported in the studies. Three models allowing hazard ratios (HR) to vary over time were assessed against a fixed-HR model. The Bucher method favored cabozantinib, with a fixed HR for OS vs. nivolumab of 1.09 (95% confidence interval: [0.77, 1.54]). However, all models with time-varying HR showed better fits than the fixed-HR model. The log-logistic model fitted the data best, exhibiting a HR for OS initially favoring cabozantinib, the trend inverting to favor nivolumab after month 5 (95% credible interval

International audience; In food safety, sound risk characterisation requires the integration of external dose (oral exposure), internal dose (toxicokinetics (TK) and toxicodynamic (TD) data. Here, a workflow allowing such integration is presented and illustrated with examples from the human health, animal health and ecological area. Since its creation in 2002, the European Food Safety Authority (EFSA) has performed risk assessments for more than 5000 chemicals. An open source database: OpenFoodTox, designed using OECD harmonised templates, and providing summary toxicological data used by EFSA for setting safe levels (reference points and reference values) of food and feed chemicals in humans, animals and the environment has been recently published. OpenFoodTox is available in a. EFSA's Data Warehouse as a microstrategy tool, b. EFSA's knowledge junction for full download, c. The OECD e-chem portal. Since 2014, EFSA has funded collaborative research to develop generic TK and PB-TK models in the R freeware available on an open source modelling platform: TK Plate, to support integration of exposure, TK and TD parameters under different situations (data poor, data rich). Several models have been developed including TK and PB-TK models in humans, farm and companion animals (pig, chicken, cat, sheep), fish (zebra fish, rainbow trout, fathead minnow) and Dynamic Energy Budget (DEB) models for terrestrial and aquatic organisms. The first prototype of TKPlate has been designed for human TK and PB-PK models using the US-EPA ‘httk’ package and includes variability distributions for human metabolism. Future developments of OpenFoodTox 2.0 and TK Plate include: 1. Data collection for physico-chemical properties, TK, biodegradation and bioaccumulation data, exposure data, 2. Integration of QSAR tools to predict TK and TD properties, 3. Integration of all models in TKplate for all species, 4. Development of a guidance document to illustrate the global use of TK and metabolism data in chemical risk assessment with case studies (e.g. regulated products and contaminants). In conclusion, the importance of international cooperation is emphasised, in particular between national, international scientific advisory bodies and academic institutions as the corner stone for the translation of 21st century toxicology into harmonised methodologies and tools and for the training of the next generation of risk assessors.