Investigational and emerging gastric inhibitory polypeptide (GIP) receptor-based therapies for the treatment of obesity.

INTRODUCTION: One billion people live with obesity. The most promising medications for its treatment are incretin-based therapies, based on enteroendocrine peptides released in response to oral nutrients, specifically glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP). The mechanisms by which GLP-1 receptor agonism cause weight reduction are becoming increasingly understood. However, the mechanisms by which GIP receptor-modulating medications cause weight loss remain to be clarified. AREAS COVERED: This review describes GLP-1 and GIP physiology and explores the conflicting data regarding GIP and weight management. It details examples of how to reconcile the contradictory findings that both GIP receptor agonism and antagonism cause weight reduction. Specifically, it discusses the concept of 'biased agonism' wherein exogenous peptides cause different post-receptor signaling patterns than native ligands. It discusses how GIP effects in adipose tissue and the central nervous system may cause weight reduction. It describes GIP receptor-modulating compounds and their most current trials regarding weight reduction. EXPERT OPINION: Effects of GIP receptor-modulating compounds on different tissues have implications for both weight reduction and other cardiometabolic diseases. Further study is needed to understand the implications of GIP agonism on not just weight reduction, but also cardiovascular disease, liver disease, bone health and fat storage.

A review of the 1918 herald pandemic wave: importance for contemporary pandemic response strategies.

Mounting epidemiological evidence supports the occurrence of a mild herald pandemic wave in the spring and summer of 1918 in North America and Europe, several months before the devastating autumn outbreak that killed an estimated 2% of the global population. These epidemiological findings corroborate the anecdotal observations of contemporary clinicians who reported widespread influenza outbreaks in spring and summer 1918, with sporadic occurrence of unusually severe clinical manifestations in young adults. Initially seen as controversial, these findings were eventually confirmed by retrospective identification of influenza specimens collected from U.S. soldiers who died from acute respiratory infections in May-August 1918. Other studies found that having an episode of influenza illness during the spring herald wave was highly protective in the severe autumn wave. Here, we conduct a systematic review of the clinical, epidemiological, and virological evidence supporting the global occurrence of mild herald waves of the 1918 pandemic and place these historic observations in the context of pandemic preparedness. Taken together, historic experience with the 1918 and subsequent pandemics shows that increased severity in second and later pandemic waves may be the rule rather than the exception. Thus, a sustained pandemic response in the first years following a future pandemic is critical; conversely, multiwave pandemic patterns allow for more time to rollout vaccines and antivirals.

The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.

Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens.

Application of the CDC EbolaResponse Modeling tool to disease predictions.

Model-based predictions were critical in eliciting a vigorous international public health response to the 2014 Ebola Virus Disease outbreak in West Africa. Here, we describe the performances of an extension of the CDC-initiated EbolaResponse Modeling tool to the Ebola Forecasting Challenge, which offered a controlled environment for epidemiological predictions. In the EbolaResponse tool, transmission risks and proportions of population affected by interventions were fitted to data via least square fitting. Prediction performances were evaluated for 5 prediction time points of 4 synthetic outbreaks. One-to-four week-ahead incidence predictions were well correlated with synthetic observations (rho ∼0.8), and overall ranking averaged over various error metrics was 4th of 8 teams participating in the context. EbolaResponse yielded moderately accurate predictions for final size, peak size and timing. The relative success of this easily adaptable mechanistic model, with reassessment of model parameters at fixed intervals, indicates that it can generate relatively accurate short-term forecasts, especially when interventions are staggered. An important downside of the model includes a lack of uncertainty estimates in its current framework. Overall, our results align with the conclusion that simple models with few parameters perform well for short-term prediction of epidemic trajectories.