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This article reports the outcome of an in silico analysis of more than 12,000 small molecule drugs and drug impurities, identifying the nitrosatable structures, assessing their potential to form nitrosamines under relevant conditions and the challenges to determine compound-specific AIs based on data available or read-across approaches for these nitrosamines and their acceptance by health authorities. Our data indicate that the presence of nitrosamines in pharmaceuticals is likely more prevalent than originally expected. In total, 40.4 % of the analyzed APIs and 29.6 % of the API impurities are potential nitrosamine precursors. Most structures identified through our workflow could form complex API-related nitrosamines, so-called nitrosamine drug substance related impurities (NDSRIs), although we also found structures that could release the well-known small and potent nitrosamines NDMA, NDEA, and others. Due to common structural motifs including secondary or tertiary amine moieties, whole essential drug classes such as beta blockers and ACE inhibitors are at risk. To avoid the risk of drug shortages or even the complete loss of therapeutic options, it will be essential that the well-established ICH M7 principles remain applicable for nitrosamines and that that the industry and regulatory authorities keep an open communication not only about the science but also to make sure there is a good balance between risk and benefit to patients.

Background: In response to the COVID-19 pandemic, the UK first adopted physical distancing measures in March, 2020. Vaccines against SARS-CoV-2 became available in December, 2020. We explored the health and economic value of introducing SARS-CoV-2 immunisation alongside physical distancing in the UK to gain insights about possible future scenarios in a post-vaccination era. Methods: We used an age-structured dynamic transmission and economic model to explore different scenarios of UK mass immunisation programmes over 10 years. We compared vaccinating 75% of individuals aged 15 years or older (and annually revaccinating 50% of individuals aged 15–64 years and 75% of individuals aged 65 years or older) to no vaccination. We assumed either 50% vaccine efficacy against disease and 45-week protection (worst-case scenario) or 95% vaccine efficacy against infection and 3-year protection (best-case scenario). Natural immunity was assumed to wane within 45 weeks. We also explored the additional impact of physical distancing on vaccination by assuming either an initial lockdown followed by voluntary physical distancing, or an initial lockdown followed by increased physical distancing mandated above a certain threshold of incident daily infections. We considered benefits in terms of quality-adjusted life-years (QALYs) and costs, both to the health-care payer and the national economy. We discounted future costs and QALYs at 3·5% annually and assumed a monetary value per QALY of £20 000 and a conservative long-run cost per vaccine dose of £15. We explored and varied these parameters in sensitivity analyses. We expressed the health and economic benefits of each scenario with the net monetary value: QALYs × (monetary value per QALY) – costs. Findings: Without the initial lockdown, vaccination, and increased physical distancing, we estimated 148·0 million (95% uncertainty interval 48·5–198·8) COVID-19 cases and 3·1 million (0·84–4·5) deaths would occur in the UK over 10 years. In the best-case scenario, vaccination minimises community transmission without future periods of increased physical distancing, whereas SARS-CoV-2 becomes endemic with biannual epidemics in the worst-case scenario. Ongoing transmission is also expected in intermediate scenarios with vaccine efficacy similar to published clinical trial data. From a health-care perspective, introducing vaccination leads to incremental net monetary values ranging from £12·0 billion to £334·7 billion in the best-case scenario and from –£1·1 billion to £56·9 billion in the worst-case scenario. Incremental net monetary values of increased physical distancing might be negative from a societal perspective if national economy losses are persistent and large. Interpretation: Our model findings highlight the substantial health and economic value of introducing SARS-CoV-2 vaccination. Smaller outbreaks could continue even with vaccines, but population-wide implementation of increased physical distancing might no longer be justifiable. Our study provides early insights about possible future post-vaccination scenarios from an economic and epidemiological perspective. Funding: National Institute for Health Research, European Commission, Bill & Melinda Gates Foundation.

A promising method for the production and extraction of 52Fe utilizing near-symmetric, heavy-ion fusion-evaporation reactions is presented. A 28Si beam was used to bombard a 27Al target, producing 52Fe through the reaction: 27Al (28Si, p2n)52Fe. After irradiation, the aluminum target was dissolved, and the 52Fe was separated from the aluminum matrix and other reaction products through the use of a cationic-exchange column. It was possible to recover the 52Fe (t1/2 = 8.3 h) with high specific activity, higher than can be achieved with the high energy proton spallation reactions currently used to produce this isotope, which has significance in medical research. The production yields for this reaction were measured and compared to PACE4 calculations. These results demonstrate the potential for dedicated accelerator production of 52Fe using heavy-ion beams, which will result in higher specific activities with robust radiochemical purification techniques.

Particle-induced gamma-ray emission spectroscopy is a quantitative, sensitive technique that measures light element abundance in materials using external reference standards with known beam energy and intensity. When the ion beam is used ex vacuo, gamma-ray spectroscopy on targets in air greatly increases sample throughput. However, for most targets the accuracy of these measurements is limited by the uncertainty of the collected charge. An indirect beam current measurement technique has been developed using an ion beam reaction on atmospheric argon, the only atmospheric component with sufficient abundance (~1%) that produces abundant gamma rays with low-energy proton beams. The 40Ar(p,nγ)40K reaction has been studied here, and the characteristic 770 keV gamma ray is observed to serve as a reliable monitor for proton flux. This method allows a real-time calibration of beam intensity on target to measured upstream currents for ion beam analysis at beam energies above 3.5 MeV.

Background: National immunisation programmes globally are at risk of suspension due to the severe health system constraints and physical distancing measures in place to mitigate the ongoing COVID-19 pandemic. We aimed to compare the health benefits of sustaining routine childhood immunisation in Africa with the risk of acquiring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through visiting routine vaccination service delivery points. Methods: We considered a high-impact scenario and a low-impact scenario to approximate the child deaths that could be caused by immunisation coverage reductions during COVID-19 outbreaks. In the high-impact scenario, we used previously reported country-specific child mortality impact estimates of childhood immunisation for diphtheria, tetanus, pertussis, hepatitis B, Haemophilus influenzae type b, Streptococcus pneumoniae, rotavirus, measles, meningitis A, rubella, and yellow fever to approximate the future deaths averted before 5 years of age by routine childhood vaccination during a 6-month COVID-19 risk period without catch-up campaigns. In the low-impact scenario, we approximated the health benefits of sustaining routine childhood immunisation on only the child deaths averted from measles outbreaks during the COVID-19 risk period. We assumed that contact-reducing interventions flattened the outbreak curve during the COVID-19 risk period, that 60% of the population will have been infected by the end of that period, that children can be infected by either vaccinators or during transport, and that upon child infection the whole household will be infected. Country-specific household age structure estimates and age-dependent infection-fatality rates were applied to calculate the number of deaths attributable to the vaccination clinic visits. We present benefit–risk ratios for routine childhood immunisation, with 95% uncertainty intervals (UIs) from a probabilistic sensitivity analysis. Findings: In the high-impact scenario, for every one excess COVID-19 death attributable to SARS-CoV-2 infections acquired during routine vaccination clinic visits, 84 (95% UI 14–267) deaths in children could be prevented by sustaining routine childhood immunisation in Africa. The benefit–risk ratio for the vaccinated children is 85 000 (4900–546 000), for their siblings (60 years) is 96 (14–307). In the low-impact scenario that approximates the health benefits to only the child deaths averted from measles outbreaks, the benefit–risk ratio to the households of vaccinated children is 3 (0–10); if the risk to only the vaccinated children is considered, the benefit–risk ratio is 3000 (182–21 000). Interpretation: The deaths prevented by sustaining routine childhood immunisation in Africa outweigh the excess risk of COVID-19 deaths associated with vaccination clinic visits, especially for the vaccinated children. Routine childhood immunisation should be sustained in Africa as much as possible, while considering other factors such as logistical constraints, staff shortages, and reallocation of resources during the COVID-19 pandemic. Funding: Gavi, the Vaccine Alliance; Bill & Melinda Gates Foundation.

Virtual Control Groups (VCGs) based on Historical Control Data (HCD) in preclinical toxicity testing have the potential to reduce animal usage. As a case study we retrospectively analyzed the impact of replacing Concurrent Control Groups (CCGs) with VCGs on the treatment-relatedness of 28 selected histopathological findings reported in either rat or dog in the eTOX database. We developed a novel methodology whereby statistical predictions of treatment-relatedness using either CCGs or VCGs of varying covariate similarity to CCGs were compared to designations from original toxicologist reports; and changes in agreement were used to quantify changes in study outcomes. Generally, the best agreement was achieved when CCGs were replaced with VCGs with the highest level of similarity; the same species, strain, sex, administration route, and vehicle. For example, balanced accuracies for rat findings were 0.704 (predictions based on CCGs) vs. 0.702 (predictions based on VCGs). Moreover, we identified covariates which resulted in poorer identification of treatment-relatedness. This was related to an increasing incidence rate divergence in HCD relative to CCGs. Future databases which collect data at the individual animal level including study details such as animal age and testing facility are required to build adequate VCGs to accurately identify treatment-related effects.

Preclinical inter-species concordance can increase the predictivity of observations to the clinic, potentially reducing drug attrition caused by unforeseen adverse events. We quantified inter-species concordance of histopathological findings and target organ toxicities across four preclinical species in the eTOX database using likelihood ratios (LRs). This was done whilst only comparing findings between studies with similar compound exposure (Δ|C

This work presents results of macro, micro, and nanoscale surface characterization for a set of Roman denarii, ranging from 136 BCE to 240 CE, using large-scale X-ray fluorescence (XRF) mapping, particle-induced X-ray emission (PIXE) spectroscopy, focused-ion-beam-assisted scanning electron microscopy (FIB-SEM) with energy dispersive spectroscopy (EDS). The combination of XRF and PIXE, with varying beam energies, allowed for visualization and quantification of individual elements as a function of the surface distribution. These results helped to select suitable areas for FIB-SEM-EDS analysis. The edges of selected coins were polished to image the inner composition and surface morphology of the coins using a backscattering electron (BSE) imaging method and EDS mapping. Nanosized lamellas extracted from the coins were investigated by transmission electron microscopy (TEM) as well. The combination of these methods enabled the surface, the subsurface, and volume composition of these coins to be probed to better understand their production methods, their surface treatment methods, and their corrosion. The results also provide evidence that a particular surface treatment method, amalgam silvering, had been used to make authentic Roman coins as early as the third century CE.

In a broad environmental study in St. Joseph County, Indiana, elemental data from ∼2000 soil samples and ∼800 paint samples were collected with X-ray Fluorescence (XRF) spectroscopy. The observed lead concentrations were compared to other elemental concentrations in these data. A strong correlation between lead and bismuth concentrations was observed in a subset of the soil samples and in nearly all of the paint samples, with lead levels approximately 150 times higher than bismuth. However, some soil samples contained lead with no bismuth present. Since most lead sources likely contain bismuth as an impurity from refining of native lead ore, but leaded gasoline does not contain any bismuth impurities due to the manufacturing process of tetraethyl lead, it may be possible to distinguish environmental lead sources by XRF. To test if leaded gasoline could be the source of lead in the subset of soil samples containing no bismuth, leaded paint samples were analyzed with Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES), which confirmed the presence of bismuth in leaded paint. Aviation gasoline, which contains tetraethyl lead, was also analyzed by ICP-OES to confirm the absence of bismuth in leaded gasoline. This discovery suggests that XRF can be used to rapidly distinguish different legacy lead contamination sources from one another. For low lead concentrations, elemental measurements of bismuth by ICP-OES can be used in environmental forensics to distinguish leaded gasoline contamination from other sources of lead.

Background: The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need (“pillar 1”) before expanding to community-wide symptomatics (“pillar 2”). This study aimed to ascertain whether biases in case data resulting from varying testing coverage could be addressed by drawing inference from COVID-19-related deaths. Methods: We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January - 30 June 2020), with respect to the local epidemic timing and the age, deprivation and ethnic composition of its population. We drew predictions averaging these sources of case-fatality variation, and back-translated according to a population case fatality ratio estimated under community-wide testing. Results: A model including temporally- and spatially-correlated random effects best accommodated the observed variation in mortality, after accounting for local population characteristics. Final predictions suggest a total of 276,219-420,491 cases over the first wave - an increase of 19-81% from the reported. Conclusions: Limitations in testing capacity biased the observed trajectory of COVID-19 cases throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of symptomatic infections nationally, to which different parts of the country contribute unequally. Funding Information: The following funding sources are acknowledged as providing funding for the named authors: This research was partly funded by the Bill & Melinda Gates Foundation (NTD Modelling Consortium OPP1184344: GFM; OPP1183986: ESN). Royal Society (Dorothy Hodgkin Fellowship: RL). Wellcome Trust (206250/Z/17/Z: TWR; 206471/Z/17/Z: OJB; 210758/Z/18/Z: SA). The following funding sources are acknowledged as providing funding for the working group authors: This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: MQ; INV-003174: JYL, KP, MJ, YL; INV-016832: SRP; NTD Modelling Consortium OPP1184344: CABP; OPP1139859: BJQ; OPP1191821: KO'R). BMGF (INV-016832; OPP1157270: KA). CADDE MR/S0195/1 & FAPESP 18/14389-0 (PM). EDCTP2 (RIA2020EF-2983-CSIGN: HPG). Elrha R2HC/UK FCDO/Wellcome Trust/This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care (KvZ). ERC Starting Grant (#757699: MQ). ERC (SG 757688: CJVA, KEA). This project has received funding from the European Union's Horizon 2020 research and innovation programme - project EpiPose (101003688: AG, KP, MJ, RCB, WJE, YL). FCDO/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: CABP, KvZ). This research was partly funded by the Global Challenges Research Fund (GCRF) project 'RECAP' managed through RCUK and ESRC (ES/P010873/1: CIJ, TJ). HDR UK (MR/S003975/1: RME). HPRU (NIHR200908: NIB). Innovation Fund (01VSF18015: FK). MRC (MR/N013638/1: EF, NRW; MR/V027956/1: WW). Nakajima Foundation (AE). NIHR (16/136/46: BJQ; 16/137/109: BJQ, FYS, MJ, YL; 1R01AI141534-01A1: DH; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: TJ; NIHR200908: AJK, RME; NIHR200929: CVM, FGS, MJ, NGD; PR-OD-1017-20002: AR, WJE). Singapore Ministry of Health (RP). UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: HPG). UK MRC (MC_PC_19065 - Covid 19: Understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: NGD, RME, SC, TJ, WJE, YL; MR/P014658/1: GMK). Authors of this research receive funding from UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care (TJ). UKRI Research England (NGD). UKRI (MR/V028456/1: YJ). Wellcome Trust (206250/Z/17/Z: AJK; 208812/Z/17/Z: SC, SFlasche; 210758/Z/18/Z: JDM, JH, SFunk, SRM; 221303/Z/20/Z: MK; UNS110424: FK). No funding (AMF, DCT, YWDC). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Approval for the use of anonymised linelist data was granted by Public Health England and the Department for Health and Social Care. Consent of individuals was not required as no patient identifiable information was used.

Background: The isolation of symptomatic cases and tracing of contacts has been used as an early COVID-19 containment measure in many countries, with additional physical distancing measures also introduced as outbreaks have grown. To maintain control of infection while also reducing disruption to populations, there is a need to understand what combination of measures—including novel digital tracing approaches and less intensive physical distancing—might be required to reduce transmission. We aimed to estimate the reduction in transmission under different control measures across settings and how many contacts would be quarantined per day in different strategies for a given level of symptomatic case incidence. Methods: For this mathematical modelling study, we used a model of individual-level transmission stratified by setting (household, work, school, or other) based on BBC Pandemic data from 40 162 UK participants. We simulated the effect of a range of different testing, isolation, tracing, and physical distancing scenarios. Under optimistic but plausible assumptions, we estimated reduction in the effective reproduction number and the number of contacts that would be newly quarantined each day under different strategies. Results: We estimated that combined isolation and tracing strategies would reduce transmission more than mass testing or self-isolation alone: mean transmission reduction of 2% for mass random testing of 5% of the population each week, 29% for self-isolation alone of symptomatic cases within the household, 35% for self-isolation alone outside the household, 37% for self-isolation plus household quarantine, 64% for self-isolation and household quarantine with the addition of manual contact tracing of all contacts, 57% with the addition of manual tracing of acquaintances only, and 47% with the addition of app-based tracing only. If limits were placed on gatherings outside of home, school, or work, then manual contact tracing of acquaintances alone could have an effect on transmission reduction similar to that of detailed contact tracing. In a scenario where 1000 new symptomatic cases that met the definition to trigger contact tracing occurred per day, we estimated that, in most contact tracing strategies, 15 000–41 000 contacts would be newly quarantined each day. Interpretation: Consistent with previous modelling studies and country-specific COVID-19 responses to date, our analysis estimated that a high proportion of cases would need to self-isolate and a high proportion of their contacts to be successfully traced to ensure an effective reproduction number lower than 1 in the absence of other measures. If combined with moderate physical distancing measures, self-isolation and contact tracing would be more likely to achieve control of severe acute respiratory syndrome coronavirus 2 transmission. Funding: Wellcome Trust, UK Engineering and Physical Sciences Research Council, European Commission, Royal Society, Medical Research Council.

Background: The COVID-19 pandemic in the UK has been characterised by periods of exponential growth and decline, as different non-pharmaceutical interventions (NPIs) are brought into play. During the early uncontrolled phase of the outbreak (early March 2020) there was a period of prolonged exponential growth with epidemiological observations such as hospitalisation doubling every 3-4 days. The enforcement of strict lockdown measures led to a noticeable decline in all epidemic quantities, that slowed during the summer as control measures were relaxed. Since August, infections, hospitalisations and deaths have been rising (precise estimation of the current growth rate is difficult due to extreme regional heterogeneity and temporal lags between the different epidemiological observations) and various NPIs have been applied locally throughout the UK in response. Methods: We investigate the impact of planned, limited duration periods of strict NPIs using simple models of exponential growth or decline and more complex age-structured models matched to a variety of data sources. We simulate the impact of two weeks of sustained control against a variety of intrinsic growth rates. We consider the impact of these breaks on the prevalence of infection, as well as the total number of predicted hospitalisations and deaths. Findings: Precautionary breaks are characterised by both a relative decline in infection and the temporal reset - how far back in time since this reduced level of cases was previously observed. Precautionary breaks provide the biggest temporal gains when the growth rate is low, but offer a much needed reduction to increasing infection when the growth rate is higher. Although not a long-term solution, an intense two-week break can reduce the number of deaths or hospitalisations by around 50% in the short-term. Interpretation The planned nature, and finite duration, of precautionary breaks, may lessen their social and economic impact, while by driving infection to lower levels they may allow resource limited controls (such as test, trace and isolated) to be more effective. Funding Statement: This work has been supported by the Engineering and Physical Sciences Research Council through the MathSys CDT [grant number EP/S022244/1], the Medical Research Council through the COVID-19 Rapid Response Rolling Call [grant number MR/V009761/1], and the Biotechnology and Biological sciences Research Council through the Midlands Integrative Biosciences Training Partnership (MIBTP) [grant number BB/M01116X/1]. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Data from the CHESS database were supplied after anonymisation under strict data protection protocols agreed between the University of Warwick and Public Health England. The ethics of the use of these data for these purposes was agreed by Public Health England with the Government’s SPI-M(O) / SAGE committees.

The radionuclide 149Tb (t1/2 = 4.1 h) is a potential theranostic isotope which can simultaneously be used for targeted-alpha-particle therapy and positron-emission tomography. Feasibility experiments were performed to test a near-symmetric heavy-ion reaction of 63Cu bombardment on monoisotopic 89Y. The indirect reaction was studied to avoid isomer production. Offline gamma spectroscopy was used to quantify thick-target physical yields and experimental results show modest agreement to the fusion-evaporation code PACE4. A near-symmetric fission yield was also observed.

Designing strategies to introduce new HIV prevention technologies requires balancing equitable access with sustainable distribution, particularly in resource constrained settings with high HIV prevalence. This paper explores how knowledge of preference heterogeneity can guide the equitable targeting of HIV prevention products using differentiated advertising and product placement to balance increased access with sustainability. A discrete choice experiment elicited 1016 women's preferences for distribution of HIV prevention products in South Africa. Qualitative research guided the experimental design which considered distribution outlet, collection method, advertising message, and price. A range of choice models, including random parameters logit, latent class and latent class random parameters logit models, were compared for fit. A latent class model showed the best fit and distinguished two classes of women: Class 1 were significantly more likely to be cohabiting and unemployed, who preferred products advertised for HIV prevention distributed through clinics and were highly price sensitive. Class 2 significantly preferred distribution through pharmacies and advertising around women's empowerment, while price was not a key factor. This analysis suggests that equity in access to new products could be advanced through exploiting preference heterogeneity between groups. The identified groups can be then used to design social marketing differentiated distribution strategies. Distributing free products promoted for HIV prevention could discourage ‘leakage’ of highly subsidised products to women with some capacity to pay, while priced products marketed for women's empowerment through pharmacies could encourage cost recovery with minimal reductions in coverage among employed women.