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BackgroundPersonas, based on customer or population data, are widely used to inform design decisions in the commercial sector. The variety of methods available means that personas can be produced from projects of different types and scale. ObjectiveThis study aims to experiment with the use of personas that bring together data from a survey, household air measurements and electricity usage sensors, and an interview within a research and innovation project, with the aim of supporting eHealth and eWell-being product, process, and service development through broadening the engagement with and understanding of the data about the local community. MethodsThe project participants were social housing residents (adults only) living in central Cornwall, a rural unitary authority in the United Kingdom. A total of 329 households were recruited between September 2017 and November 2018, with 235 (71.4%) providing complete baseline survey data on demographics, socioeconomic position, household composition, home environment, technology ownership, pet ownership, smoking, social cohesion, volunteering, caring, mental well-being, physical and mental health–related quality of life, and activity. K-prototype cluster analysis was used to identify 8 clusters among the baseline survey responses. The sensor and interview data were subsequently analyzed by cluster and the insights from all 3 data sources were brought together to produce the personas, known as the Smartline Archetypes. ResultsThe Smartline Archetypes proved to be an engaging way of presenting data, accessible to a broader group of stakeholders than those who accessed the raw anonymized data, thereby providing a vehicle for greater research engagement, innovation, and impact. ConclusionsThrough the adoption of a tool widely used in practice, research projects could generate greater policy and practical impact, while also becoming more transparent and open to the public.

BackgroundModeling suggests that climate change mitigation actions can have substantial human health benefits that accrue quickly and locally. Documenting the benefits can help drive more ambitious and health-protective climate change mitigation actions; however, documenting the adverse health effects can help to avoid them. Estimating the health effects of mitigation (HEM) actions can help policy makers prioritize investments based not only on mitigation potential but also on expected health benefits. To date, however, the wide range of incompatible approaches taken to developing and reporting HEM estimates has limited their comparability and usefulness to policymakers.ObjectiveThe objective of this effort was to generate guidance for modeling studies on scoping, estimating, and reporting population health effects from climate change mitigation actions.MethodsAn expert panel of HEM researchers was recruited to participate in developing guidance for conducting HEM studies. The primary literature and a synthesis of HEM studies were provided to the panel. Panel members then participated in a modified Delphi exercise to identify areas of consensus regarding HEM estimation. Finally, the panel met to review and discuss consensus findings, resolve remaining differences, and generate guidance regarding conducting HEM studies.ResultsThe panel generated a checklist of recommendations regarding stakeholder engagement: HEM modeling, including model structure, scope and scale, demographics, time horizons, counterfactuals, health response functions, and metrics; parameterization and reporting; approaches to uncertainty and sensitivity analysis; accounting for policy uptake; and discounting.DiscussionThis checklist provides guidance for conducting and reporting HEM estimates to make them more comparable and useful for policymakers. Harmonization of HEM estimates has the potential to lead to advances in and improved synthesis of policy-relevant research that can inform evidence-based decision making and practice. https://doi.org/10.1289/EHP6745.

Abstract: High throughput screening (HTS) of our compound file provided an attractive lead compound with modest P2X7 receptor antagonist potency and high selectivity against a panel of receptors and channels, but also with high human plasma protein binding and a predicted short half-life in humans. Multi-parameter optimization was used to address the potency, physicochemical and pharmacokinetic properties which led to potent P2X7R antagonists with good disposition properties. Compound 33 (CE-224,535) was advanced to clinical studies for the treatment of rheumatoid arthritis. [Copyright &y& Elsevier]

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker . This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

The disposition of 6-(4-(2,5-difluorophenyl)oxazol-5-yl)-3-isopropyl-[1,2,4]-triazolo[4,3-a]pyridine (1), a potent and selective inhibitor of mitogen activated protein (MAP) kinase p38alpha, was characterized in several animal species in support of its selection for preclinical safety studies and potential clinical development. 1 demonstrated generally favorable pharmacokinetic properties in all species examined. Following intravenous (i.v.) administration, 1 exhibited low volumes of distribution at steady state (Vd(ss)) ranging from 0.4-1.3 l/kg (2.4-26 l/m(2)) in the rat, dog and monkey. Systemic plasma clearance was low in cynomolgus monkeys (6.00 ml/min/kg, 72.0 ml/min/m(2)) and Sprague-Dawley rats (7.65+/-1.08 ml/min/kg, 45.9+/-6.48 ml/min/m(2) in male rats and 3.15+/-0.27 ml/min/kg, 18.9+/-1.62 ml/min/m(2) in female rats) and moderate in beagle dogs (12.3+/-5.1 ml/min/kg, 246+/-102 ml/min/m(2)) resulting in plasma half-lives ranging from 1 to 5 h in preclinical species. Moderate to high bioavailability of 1 was observed in rats (30-65%), dogs (87%) and monkeys (40%) after oral (p.o.) dosing consistent with the in vitro absorption profile of 1 in the Caco-2 permeability assay. In rats, the oral pharmacokinetics were dose dependent over the dose range studied (5, 50 and 100 mg/kg). The principal route of clearance of 1 in rat, dog, monkey and human liver microsomes and in vivo in preclinical species involved oxidative metabolism mediated by cytochrome P450 enzymes. The major metabolic fate of 1 in preclinical species and humans involved hydroxylation on the isopropyl group to yield the tertiary alcohol metabolite 2. In human liver microsomes, this transformation was catalysed by CYP3A4 as judged from reaction phenotyping analysis using isozyme-specific inhibitors and recombinant CYP enzymes. Metabolite 2 was also shown to possess inhibitory potency against p38alpha in a variety of in vitro assays. 1 as well as the active metabolite 2 were moderately to highly bound to plasma proteins (f(u) approximately 0.1-0.33) in rat, mouse, dog, monkey and human. 1 as well as the active metabolite 2 did not exhibit competitive inhibition of the five major cytochrome P450 enzymes namely CYP1A2, 2C9, 2C19, 2D6 and 3A4 (IC(50)>50 microM). Overall, these results indicate that the absorption, distribution, metabolism and excretion (ADME) profile of 1 is relatively consistent across preclinical species and predict potentially favorable pharmacokinetic properties in humans, supporting its selection for toxicity/safety assessment studies and possible investigations in humans as an anti-inflammatory agent.

The relationship between lipophilicity and CYP2D6 affinity of cyclic tertiary (N-alkyl-4-phenyl-1,2,3,6-tetrahydropyridines) and quaternary (N-alkyl-4-phenylpyridinium) amines was examined. The 1,2,3,6-tetrahydropyridine scaffold was chosen due to its common occurrence in the structures of CYP2D6 ligands such as the Parkinsonian neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and the dehydrated haloperidol metabolite N-[4-(4-fluorophenyl)-4-oxobutyl]-4-(4-chlorophenyl)-1,2,3,6-tetrahydropyridine (HPTP). Likewise, the pyridinium framework is found in and 4-(4-chlorophenyl)-1-[4-(4-fluorophenyl)-4-oxobutyl]pyridinium and N-methyl-4-phenylpyridinium (MPP(+)), the positively charged metabolites of MPTP and haloperidol. The lack of CYP2D6 inhibition by MPTP and its pyridinium metabolite MPP(+) was due to their hydrophilic nature since higher N-alkyl homologs revealed substantial increases in inhibitory potency against recombinant CYP2D6-mediated bufuralol-1'-hydroxylation. The reasonable correlation between lipophilicity and CYP2D6 inhibition by pyridiniums and 1,2,3,6-tetrahydropyridines was only limited to straight chain N-alkyl analogs, since certain N-alkylaryl analogs of lower lipophilicity were better CYP2D6 inhibitors. CYP2D6 substrate properties of straight chain N-alkyltetrahydropyridines were also governed by lipophilicity, and N-heptyl-4-phenyl-1,2,3,6-tetrahydropyridine was the optimal substrate (K(mapp) = 0.63 microM). Metabolism studies indicated that the N-heptyl analog underwent monohydroxylation on the aromatic ring and on the N-heptyl group suggesting that 1,2,3,6-tetrahydropyridines can bind in more than one conformation in the CYP2D6 active site. Increased lipophilicity of haloperidol metabolites did not correlate with inhibitory potency since the more lipophilic HPTP metabolite was less potent as an inhibitor than reduced-haloperidol and reduced-HPTP. Furthermore, HPTP and reduced-HPTP, of comparable lipophilicity to the N-heptyltetrahydropyridine analog were inactive as CYP2D6 substrates. This observation suggests that steric constraints rather than lipophilicity are responsible for the lack of CYP2D6 substrate properties of cyclic tertiary amines tethered to bulky N-substituents. This phenomenon appears to be a common theme among several cyclic tertiary amine-containing anti-depressants and should be taken into consideration when designing central nervous system agents devoid of CYP2D6 substrate properties.