How Can Viral Dynamics Models Inform Endpoint Measures in Clinical Trials of Therapies for Acute Viral Infections?

Acute viral infections pose many practical challenges for the accurate assessment of the impact of novel therapies on viral growth and decay. Using the example of influenza A, we illustrate how the measurement of infection-related quantities that determine the dynamics of viral load within the human host, can inform investigators on the course and severity of infection and the efficacy of a novel treatment. We estimated the values of key infection-related quantities that determine the course of natural infection from viral load data, using Markov Chain Monte Carlo methods. The data were placebo group viral load measurements collected during volunteer challenge studies, conducted by Roche, as part of the oseltamivir trials. We calculated the values of the quantities for each patient and the correlations between the quantities, symptom severity and body temperature. The greatest variation among individuals occurred in the viral load peak and area under the viral load curve. Total symptom severity correlated positively with the basic reproductive number. The most sensitive endpoint for therapeutic trials with the goal to cure patients is the duration of infection. We suggest laboratory experiments to obtain more precise estimates of virological quantities that can supplement clinical endpoint measurements

Using Clinical Trial Simulators to Analyse the Sources of Variance in Clinical Trials of Novel Therapies for Acute Viral Infections.

BACKGROUND:About 90% of drugs fail in clinical development. The question is whether trials fail because of insufficient efficacy of the new treatment, or rather because of poor trial design that is unable to detect the true efficacy. The variance of the measured endpoints is a major, largely underestimated source of uncertainty in clinical trial design, particularly in acute viral infections. We use a clinical trial simulator to demonstrate how a thorough consideration of the variability inherent in clinical trials of novel therapies for acute viral infections can improve trial design. METHODS AND FINDINGS:We developed a clinical trial simulator to analyse the impact of three different types of variation on the outcome of a challenge study of influenza treatments for infected patients, including individual patient variability in the response to the drug, the variance of the measurement procedure, and the variance of the lower limit of quantification of endpoint measurements. In addition, we investigated the impact of protocol variation on clinical trial outcome. We found that the greatest source of variance was inter-individual variability in the natural course of infection. Running a larger phase II study can save up to $38 million, if an unlikely to succeed phase III trial is avoided. In addition, low-sensitivity viral load assays can lead to falsely negative trial outcomes. CONCLUSIONS:Due to high inter-individual variability in natural infection, the most important variable in clinical trial design for challenge studies of potential novel influenza treatments is the number of participants. 100 participants are preferable over 50. Using more sensitive viral load assays increases the probability of a positive trial outcome, but may in some circumstances lead to false positive outcomes. Clinical trial simulations are powerful tools to identify the most important sources of variance in clinical trials and thereby help improve trial design

Health economic analyses of latent tuberculosis infection screening and preventive treatment among people living with HIV in lower tuberculosis incidence settings: a systematic review [version 1; peer review: awaiting peer review]

INTRODUCTION: In lower tuberculosis (TB) incidence countries (<100 cases/100,000/year), screening and preventive treatment (PT) for latent TB infection (LTBI) among people living with HIV (PLWH) is often recommended, yet guidelines advising which groups to prioritise for screening can be contradictory and implementation patchy. Evidence of LTBI screening cost-effectiveness may improve uptake and health outcomes at reasonable cost. METHODS: Our systematic review assessed cost-effectiveness estimates of LTBI screening/PT strategies among PLWH in lower TB incidence countries to identify model-driving inputs and methodological differences. Databases were searched 1980-2020. Studies including health economic evaluation of LTBI screening of PLWH in lower TB incidence countries (<100 cases/100,000/year) were included. Study quality was assessed using the CHEERS checklist. RESULTS: Of 2,644 articles screened, nine studies were included. Cost-effectiveness estimates of LTBI screening/PT for PLWH varied widely, with universal screening/PT found highly cost-effective by some studies, while only targeting to high-risk groups (such as those from mid/high TB incidence countries) deemed cost-effective by others. Cost-effectiveness of strategies screening all PLWH from studies published in the past five years varied from US$2828 to US$144,929/quality-adjusted life-year gained (2018 prices). Study quality varied, with inconsistent reporting of methods and results limiting comparability of studies. Cost-effectiveness varied markedly by screening guideline, with British HIV Association guidelines more cost-effective than NICE guidelines in the UK. DISCUSSION: Cost-effectiveness studies of LTBI screening/PT for PLWH in lower TB incidence settings are scarce, with large variations in methods and assumptions used, target populations and screening/PT strategies evaluated. The limited evidence suggests LTBI screening/PT may be cost-effective for some PLWH groups but further research is required, particularly on strategies targeting screening/PT to PLWH at higher risk. Standardisation of model descriptions and results reporting could facilitate reliable comparisons between studies, particularly to identify those factors driving the wide disparity between cost-effectiveness estimates. REGISTRATION: PROSPERO CRD42020166338 (18/03/2020)

Plasma proteomic profiling in postural orthostatic tachycardia syndrome (POTS) reveals new disease pathways

Postural orthostatic tachycardia syndrome (POTS) is a cardiovascular autonomic disorder characterized by excessive heart rate increase on standing, leading to debilitating symptoms with limited therapeutic possibilities. Proteomics is a large-scale study of proteins that enables a systematic unbiased view on disease and health, allowing stratification of patients based on their protein background. The aim of the present study was to determine plasma protein biomarkers of POTS and to reveal proteomic pathways differentially regulated in POTS. We performed an age- and sex-matched, case–control study in 130 individuals (case–control ratio 1:1) including POTS and healthy controls. Mean age in POTS was 30 ± 9.8 years (84.6% women) versus controls 31 ± 9.8 years (80.0% women). We analyzed plasma proteins using data-independent acquisition (DIA) mass spectrometry. Pathway analysis of significantly differently expressed proteins was executed using a cutoff log2 fold change set to 1.2 and false discovery rate (p-value) of < 0.05. A total of 393 differential plasma proteins were identified. Label-free quantification of DIA-data identified 30 differentially expressed proteins in POTS compared with healthy controls. Pathway analysis identified the strongest network interactions particularly for proteins involved in thrombogenicity and enhanced platelet activity, but also inflammation, cardiac contractility and hypertrophy, and increased adrenergic activity. Our observations generated by the first use a label-free unbiased quantification reveal the proteomic footprint of POTS in terms of a hypercoagulable state, proinflammatory state, enhanced cardiac contractility and hypertrophy, skeletal muscle expression, and adrenergic activity. These findings support the hypothesis that POTS may be an autoimmune, inflammatory and hyperadrenergic disorder

Sampling strategies for monitoring and evaluation of morbidity targets for soil-transmitted helminths

Background The current World Health Organization (WHO) target for the three major soil-transmitted helminth (STH) infections is to reduce prevalence of moderate-to-heavy infections to below 1% by 2020. In terms of monitoring and evaluation (M&E), the current WHO guidelines for control of STHs recommend evaluation of infection levels in school-age children (SAC) after five to six years of preventive chemotherapy (PC), using the standard Kato-Katz faecal smear. Here, we assess the predictive performance of various sampling designs for the evaluation of the morbidity target. Methodology/Principal findings Using two mathematical models for STH transmission and control, we simulate how the number of villages and SAC sampled affect the ability of survey results in sentinel villages to predict the achievement of the morbidity target in PC implementation units (e.g. districts). As PC is stopped when the prevalence of infection in SAC in sentinel villages is less than 1%, we estimate the positive predictive value (PPV) of this indicator for meeting the morbidity target in the whole district. The PPV varies by species and PC strategy, and it is generally higher in areas with lower pre-control prevalence. Sampling a fixed number of SAC spread out over 10 instead of 5 sentinel villages may increase the PPV by up to 20 percentage points. If every SAC in a village is tested, a higher number of villages may increase the PPV by up to 80 percentage points. Increasing the proportion of SAC tested per village does not result in a relevant increase of PPV. Conclusions/Significance Although the WHO guidelines provide a combined strategy to control the three STH species, the efficacy of PC strategies clearly differs by species. There is added value in considering more villages within implementation units for M&E of morbidity targets, the extent varying by STH species. A better understanding of pre- and post-control local STH prevalence levels is essential for an adequate M&E strategy including the definition of morbidity targets at the appropriate geographical scale

Defining stopping criteria for ending randomized clinical trials that investigate the interruption of transmission of soil-transmitted helminths employing mass drug administration

The current World Health Organization strategy to address soil-transmitted helminth (STH) infections in children is based on morbidity control through routine deworming of school and pre-school aged children. However, given that transmission continues to occur as a result of persistent reservoirs of infection in untreated individuals (including adults) and in the environment, in many settings such a strategy will need to be continued for very extended periods of time, or until social, economic and environmental conditions result in interruption of transmission. As a result, there is currently much discussion surrounding the possibility of accelerating the interruption of transmission using alternative strategies of mass drug administration (MDA). However, the feasibility of achieving transmission interruption using MDA remains uncertain due to challenges in sustaining high MDA coverage levels across entire communities. The DeWorm3 trial, designed to test the feasibility of interrupting STH transmission, is currently ongoing. In DeWorm3, three years of high treatment coverage—indicated by mathematical models as necessary for breaking transmission—will be followed by two years of surveillance. Given the fast reinfection (bounce-back) rates of STH, a two year no treatment period is regarded as adequate to assess whether bounce-back or transmission interruption have occurred in a given location. In this study, we investigate if criteria to determine whether transmission interruption is unlikely can be defined at earlier timepoints. A stochastic, individual-based simulation model is employed to simulate core aspects of the DeWorm3 community-based cluster-randomized trial. This trial compares a control arm (annual treatment of children alone with MDA) with an intervention arm (community-wide biannual treatment with MDA). Simulations were run for each scenario for both Ascaris lumbricoides and hookworm (Necator americanus). A range of threshold prevalences measured at six months after the last round of MDA and the impact of MDA coverage levels were evaluated to see if the likelihood of bounce-back or elimination could reliably be assessed at that point, rather than after two years of subsequent surveillance. The analyses suggest that all clusters should be assessed for transmission interruption after two years of surveillance, unless transmission interruption can be effectively ruled out through evidence of low treatment coverage. Models suggest a tight range of homogenous prevalence estimates following high coverage MDA across clusters which do not allow for discrimination between bounce back or transmission interruption within 24 months following cessation of MDA

Commentary on the use of the reproduction number R during the COVID-19 pandemic

Since the beginning of the COVID-19 pandemic, the reproduction number R has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, R is defined as the average number of secondary infections caused by one primary infected individual. R seems convenient, because the epidemic is expanding if R>1 and contracting if R<1. The magnitude of R indicates by how much transmission needs to be reduced to control the epidemic. Using R in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of R but many, and the precise definition of R affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined R, there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate R vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when R is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of R, and the data and methods used to estimate it, can make R a more useful metric for future management of the epidemic

Insights from quantitative analysis and mathematical modelling on the proposed who 2030 goals for soil-transmitted helminths [version 1; peer review: 2 approved]

Soil-transmitted helminths (STHs) are a group of parasitic worms that infect humans, causing a wide spectrum of disease, notably anaemia, growth retardation, and delayed cognitive development. The three main STHs are Ascaris lumbricoides, Trichuris trichiura and hookworm (Necator americanus and Ancylostoma duodenale). Approximately 1.5 billion people are infected with STHs worldwide. The World Health Organization goal for 2030 is morbidity control, defined as reaching <2% prevalence of medium-to-high intensity infections in preschool-age children and school-age children (SAC). Treatment guidelines for achieving this goal have been recommended. The Neglected Tropical Diseases Modelling Consortium has developed mathematical and statistical models to quantify, predict, and evaluate the impact of control measures on STHs. These models show that the morbidity target can be achieved following current guidelines in moderate prevalence settings (20-50% in SAC). In high prevalence settings, semi-annual preventive chemotherapy (PC) ideally including adults, or at least women of reproductive age, is required. For T. trichiura, dual therapy with albendazole and ivermectin is required. In general, stopping PC is not possible without infection resurgence, unless effective measures for improved access to water, hygiene, and sanitation have been implemented, or elimination of transmission has been achieved. Current diagnostic methods are based on egg counts in stool samples, but these are known to have poor sensitivity at low prevalence levels. A target threshold for novel, more sensitive diagnostics should be defined relative to currently preferred diagnostics (Kato-Katz). Our analyses identify the extent of systematic non-access to treatment and the individual patterns of compliance over multiple rounds of treatment as the biggest unknowns and the main impediment to reaching the target. Moreover, the link between morbidity and infection intensity has not been fully elucidated. By providing more insights on all the above, we aim to inform discussions on the goals and treatment guidelines for STHs

Predicted Impact of COVID-19 on Neglected Tropical Disease Programs and the Opportunity for Innovation

Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases (STH), trachoma, and visceral leishmaniasis, shows that the impact of this disruption will vary across the diseases. Programs face a risk of resurgence, which will be fastest in high-transmission areas. Furthermore, of the mass drug administration diseases, schistosomiasis, STH, and trachoma are likely to encounter faster resurgence. The case-finding diseases (gambiense sleeping sickness and visceral leishmaniasis) are likely to have fewer cases being detected but may face an increasing underlying rate of new infections. However, once programs are able to resume, there are ways to mitigate the impact and accelerate progress towards the 2030 goals.</p

Designing antifilarial drug trials using clinical trial simulators

Lymphatic filariasis and onchocerciasis are neglected tropical diseases (NTDs) targeted for elimination by mass (antifilarial) drug administration. These drugs are predominantly active against the microfilarial progeny of adult worms. New drugs or combinations are needed to improve patient therapy and to enhance the effectiveness of interventions in persistent hotspots of transmission. Several therapies and regimens are currently in (pre-)clinical testing. Clinical trial simulators (CTSs) project patient outcomes to inform the design of clinical trials but have not been widely applied to NTDs, where their resource-saving payoffs could be highly beneficial. We demonstrate the utility of CTSs using our individual-based onchocerciasis transmission model (EPIONCHO-IBM) that projects trial outcomes of a hypothetical macrofilaricidal drug. We identify key design decisions that influence the power of clinical trials, including participant eligibility criteria and post-treatment follow-up times for measuring infection indicators. We discuss how CTSs help to inform target product profiles