Dynamic modelling of blood glucose concentration in people with type 1 diabetes

The behaviour of blood glucose concentration (BGC) in free living conditions is not well understood in people with type 1 diabetes; in particular, the effect of different types of activity experienced in everyday life has not been fully investigated. Better understanding of the effect of major disturbances to BGC can improve treatment regimes and delay or prevent complications associated with diabetes. The current research investigates approaches to modelling BGC, based on blood glucose, physical activity, food and insulin data collected from a Diabetes UK study. Exploratory analysis of the study data found that BGC is non-stationary and exhibits strong autocorrelation, which varies among and within individuals. Analysis of BGC in the frequency domain also highlights indistinct low-frequency periodicities. However, BGC measurements alone are not enough to predict BGC over several hours using autoregressive models. Dynamic linear models are used to model BGC empirically using inputs from measured physical activity, and estimates of glucose and insulin absorption after food intake and injections, respectively, derived from physiological models in the literature. Dynamic linear models are used for parameter learning and predicting BGC over several hours: the models show some capability for predicting BGC for up to one hour, in particular highlighting periods of low and high BGC, but parameter estimates do not comply with established physiological knowledge. A new semi-empirical compartmental model is developed to impose a structure that incorporates well established physiology. A set of differential equations are converted into a probabilistic Bayesian framework, suitable for simultaneous, model-wide parameter estimation and prediction. A simulation study is conducted to determine the feasibility of using Markov chain Monte Carlo methods as a means for parameter estimation, and test performance in the predictive space. The methods show an ability to estimate a subset of the parameters simultaneously with good coverage, robustness to parameter misspecification, and insensitivity to specification of prior distributions. The current research represents a new paradigm for analysing mathematical models of BGC, and highlights important practical and theoretical issues not previously addressed in the quest for an artificial pancreas as treatment for type 1 diabetes

Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic.

BACKGROUND: Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge. METHODS: We present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments. RESULTS: We present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs. CONCLUSIONS: This example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods

Viral load is strongly associated with length of stay in adults hospitalised with viral acute respiratory illness

Background: respiratory viruses are detectable in a large proportion of adults hospitalised with acute respiratory illness. For influenza and other viruses there is evidence that viral load and persistence are associated with certain clinical outcomes but it is not known if there is an association between viral load and hospital length of stay. Methods: 306 adults hospitalised with viral acute respiratory illness were studied. Associations between viral load and length of stay were examined. Multiple linear regression analysis was performed to control for age, comorbidity, influenza vaccine status, duration of illness prior to hospitalisation, bacterial co-infection, clinical group and virus subtype.Results: high viral load was associated with a longer duration of hospitalisation for all patients (p &lt;0.0001). This remained significant across all virus types and clinical groups and when adjusted for age, comorbidity, duration of illness prior to hospitalisation, bacterial co-infection and other factors. Conclusions: high viral loads are associated with prolonged hospital length of stay in adults with viral acute respiratory illness. This further supports existing evidence demonstrating that viral acute respiratory illness is a viral load driven process and suggests that viral load could be used in clinical practise to predict prolonged hospitalisation and prioritise antivirals. International Standard Randomised Controlled Trial Number (ISRCTN): 21521552<br/

Electronic prescribing system design priorities for antimicrobial stewardship: a cross-sectional survey of 142 UK infection specialists.

The implementation of electronic prescribing and medication administration (EPMA) systems is a priority for hospitals and a potential component of antimicrobial stewardship (AMS).Accepted manuscript, 12 month embarg

An Open Label, Adaptive, Phase 1 Trial of High-Dose Oral Nitazoxanide in Healthy Volunteers: An Antiviral Candidate for SARS-CoV-2.

Funder: UnitaidRepurposing approved drugs may rapidly establish effective interventions during a public health crisis. This has yielded immunomodulatory treatments for severe coronavirus disease 2019 (COVID-19), but repurposed antivirals have not been successful to date because of redundancy of the target in vivo or suboptimal exposures at studied doses. Nitazoxanide is a US Food and Drug Administration (FDA) approved antiparasitic medicine, that physiologically-based pharmacokinetic (PBPK) modeling has indicated may provide antiviral concentrations across the dosing interval, when repurposed at higher than approved doses. Within the AGILE trial platform (NCT04746183) an open label, adaptive, phase I trial in healthy adult participants was undertaken with high-dose nitazoxanide. Participants received 1,500 mg nitazoxanide orally twice-daily with food for 7 days. Primary outcomes were safety, tolerability, optimum dose, and schedule. Intensive pharmacokinetic (PK) sampling was undertaken day 1 and 5 with minimum concentration (Cmin ) sampling on days 3 and 7. Fourteen healthy participants were enrolled between February 18 and May 11, 2021. All 14 doses were completed by 10 of 14 participants. Nitazoxanide was safe and with no significant adverse events. Moderate gastrointestinal disturbance (loose stools or diarrhea) occurred in 8 participants (57.1%), with urine and sclera discoloration in 12 (85.7%) and 9 (64.3%) participants, respectively, without clinically significant bilirubin elevation. This was self-limiting and resolved upon drug discontinuation. PBPK predictions were confirmed on day 1 but with underprediction at day 5. Median Cmin was above the in vitro target concentration on the first dose and maintained throughout. Nitazoxanide administered at 1,500 mg b.i.d. with food was safe with acceptable tolerability a phase Ib/IIa study is now being initiated in patients with COVID-19

AGILE-ACCORD: A Randomized, Multicentre, Seamless, Adaptive Phase I/II Platform Study to Determine the Optimal Dose, Safety and Efficacy of Multiple Candidate Agents for the Treatment of COVID-19: A structured summary of a study protocol for a randomised platform trial.

OBJECTIVES: Phase I - To determine the optimal dose of each candidate (or combination of candidates) entered into the platform. Phase II - To determine the efficacy and safety of each candidate entered into the platform, compared to the current Standard of Care (SoC), and recommend whether it should be evaluated further in a later phase II & III platforms. TRIAL DESIGN: AGILE-ACCORD is a Bayesian multicentre, multi-arm, multi-dose, multi-stage open-label, adaptive, seamless phase I/II randomised platform trial to determine the optimal dose, activity and safety of multiple candidate agents for the treatment of COVID-19. Designed as a master protocol with each candidate being evaluated within its own sub-protocol (Candidate Specific Trial (CST) protocol), randomising between candidate and SoC with 2:1 allocation in favour of the candidate (N.B the first candidate has gone through regulatory approval and is expected to open to recruitment early summer 2020). Each dose will be assessed for safety sequentially in cohorts of 6 patients. Once a phase II dose has been identified we will assess efficacy by seamlessly expanding into a larger cohort. PARTICIPANTS: Patient populations can vary between CSTs, but the main eligibility criteria include adult patients (≥18 years) who have laboratory-confirmed infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We will include both severe and mild-moderate patients defined as follows: Group A (severe disease) - patients with WHO Working Group on the Clinical Characteristics of COVID-19 infection 9-point ordinal scale of Grades 4 (hospitalised, oxygen by mask or nasal prongs), 5 (hospitalised, non-invasive ventilation or high flow oxygen), 6 (hospitalised, intubation and mechanical ventilation) or 7 (hospitalised, ventilation and additional organ support); Group B (mild-moderate disease) - ambulant or hospitalised patients with peripheral capillary oxygen saturation (SpO2) >94% RA. If any CSTs are included in the community setting, the CST protocol will clarify whether patients with suspected SARS-CoV-2 infection are also eligible. Participants will be recruited from England, North Ireland, Wales and Scotland. INTERVENTION AND COMPARATOR: Comparator is the current standard of care (SoC), in some CSTs plus placebo. Candidates that prevent uncontrolled cytokine release, prevention of viral replication, and other anti-viral treatment strategies are at various stages of development for inclusion into AGILE-ACCORD. Other CSTs will be added over time. There is not a set limit on the number of CSTs we can include within the AGILE-ACCORD Master protocol and we will upload each CST into this publication as each opens to recruitment. MAIN OUTCOMES: Phase I: Dose limiting toxicities using Common Terminology Criteria for Adverse Events v5 Grade ≥3 adverse events. Phase II: Agreed on a CST basis depending on mechanism of action of the candidate and patient population. But may include; time to clinical improvement of at least 2 points on the WHO 9-point category ordinal scale [measured up to 29 days from randomisation], progression of disease (oxygen saturation (SaO2) <92%) or hospitalization or death, or change in time-weighted viral load [measured up to 29 days from randomisation]. RANDOMISATION: Varies with CST, but default is 2:1 allocation in favour of the candidate to maximise early safety data. BLINDING (MASKING): For the safety phase open-label although for some CSTs may include placebo or SoC for the efficacy phase. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): Varies between CSTs. However simulations have shown that around 16 participants are necessary to determine futility or promise of a candidate at a given dose (in efficacy evaluation alone) and between 32 and 40 participants are required across the dose-finding and efficacy evaluation when capping the maximum number of participants contributing to the evaluation of a treatment at 40. TRIAL STATUS: Master protocol version number v5 07 May 2020, trial is in setup with full regulatory approval and utilises several digital technology solutions, including Medidata's Rave EDC [electronic data capture], RTSM for randomisation and patient eConsent on iPads via Rave Patient Cloud. The recruitment dates will vary between CSTs but at the time of writing no CSTs are yet open for recruitment. TRIAL REGISTRATION: EudraCT 2020-001860-27 14th March 2020 FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol

Pharmacokinetics of ss-d-N4-Hydroxycytidine, the Parent Nucleoside of Prodrug Molnupiravir, in Nonplasma Compartments of Patients With Severe Acute Respiratory Syndrome Coronavirus 2 Infection

ß-d-N4-hydroxycytidine (NHC), the parent nucleoside of molnupiravir, a COVID-19 antiviral, was quantified at sites of SARS-CoV-2 transmission in twelve patients enrolled in AGILE CST-2 (NCT04746183). Saliva, nasal and tear concentrations were 3, 21 and 22% that of plasma. Saliva and nasal NHC concentrations were significantly correlated with plasma (p&amp;lt;0.0001)

Promoting help-seeking in response to symptoms amongst primary care patients at high risk of lung cancer: a mixed method study

Background: Lung cancer symptoms are vague and difficult to detect. Interventions are needed to promote early diagnosis, however health services are already pressurised. This study explored symptomology and help-seeking behaviours of primary care patients at ‘high-risk’ of lung cancer (≥50 years old, recent smoking history), to inform targeted interventions. Methods: Mixed method study with patients at eight general practitioner (GP) practices across south England. Study incorporated: postal symptom questionnaire; clinical records review of participant consultation behaviour 12 months pre- and post-questionnaire; qualitative participant interviews (n = 38) with a purposive sample. Results: A small, clinically relevant group (n = 61/908, 6.7%) of primary care patients was identified who, despite reporting potential symptoms of lung cancer in questionnaires, had not consulted a GP ≥12 months. Of nine symptoms associated with lung cancer, 53.4% (629/1172) of total respondents reported ≥1, and 35% (411/1172) reported ≥2. Most participants (77.3%, n = 686/908) had comorbid conditions; 47.8%, (n = 414/908) associated with chest and respiratory symptoms. Participant consulting behaviour significantly increased in the 3-month period following questionnaire completion compared with the previous 3-month period (p = .002), indicating questionnaires impacted upon consulting behaviour. Symptomatic non-consulters were predominantly younger, employed, with higher multiple deprivation scores than their GP practice mean. Of symptomatic non-consulters, 30% (18/61) consulted ≤1 month post-questionnaire, with comorbidities subsequently diagnosed for five participants. Interviews (n = 39) indicated three overarching differences between the views of consulting and non-consulting participants: concern over wasting their own as well as GP time; high tolerance threshold for symptoms; a greater tendency to self-manage symptoms. Conclusions: This first study to examine symptoms and consulting behaviour amongst a primary care population at ‘high- risk’ of lung cancer, found symptomatic patients who rarely consult GPs, might respond to a targeted symptom elicitation intervention. Such GP-based interventions may promote early diagnosis of lung cancer or other comorbidities, without burdening already pressurised services

A web-based intervention (RESTORE) to support self-management of cancer-related fatigue following primary cancer treatment: a multi-centre proof of concept randomised controlled trial

. A web-based intervention (RESTORE) to support self-management of cancer-related fatigue following primary cancer treatment: a multi-centre proof of concept randomised controlled trial. Supportive Care in Cancer, Results One hundred and sixty-three people participated in the trial and 19 in the process evaluation. The intervention was feasible (39 % of eligible patients consented) and acceptable (attrition rate 36 %). There was evidence of higher fatigue self-efficacy at T1 in the intervention group vs comparator (mean difference 0.51 [−0.08 to 1.11]), though the difference in groups decreased by 12 weeks. Time since diagnosis influenced perceived usefulness of the intervention. Modifications were suggested