The epidemiology of invasive fungal infections is changing, with new populations at risk and the emergence of resistance caused by the selective pressure from increased usage of antifungal agents in prophylaxis, empiric therapy, and agriculture. Limited antifungal therapeutic options are further challenged by drug-drug interactions, toxicity, and constraints in administration routes. Despite the need for more antifungal drug options, no new classes of antifungal drugs have become available over the last 2 decades, and only one single new agent from a known antifungal class has been approved in the last decade. Nevertheless, there is hope on the horizon, with a number of new antifungal classes in late-stage clinical development. In this review, we describe the mechanisms of drug resistance employed by fungi and extensively discuss the most promising drugs in development, including fosmanogepix (a novel Gwt1 enzyme inhibitor), ibrexafungerp (a first-in-class triterpenoid), olorofim (a novel dihyroorotate dehydrogenase enzyme inhibitor), opelconazole (a novel triazole optimized for inhalation), and rezafungin (an echinocandin designed to be dosed once weekly). We focus on the mechanism of action and pharmacokinetics, as well as the spectrum of activity and stages of clinical development. We also highlight the potential future role of these drugs and unmet needs.

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The Antifungal Pipeline: Fosmanogepix, Ibrexafungerp, Olorofim, Opelconazole, and Rezafungin.

Risk factors and outcome of pulmonary aspergillosis in critically ill coronavirus disease 2019 patients-a multinational observational study by the European Confederation of Medical Mycology.

Risk factors and outcome of pulmonary aspergillosis in critically ill coronavirus disease 2019 patients—a multinational observational study by the European Confederation of Medical Mycology

Coronavirus disease 2019 (COVID-19)-associated invasive fungal infections are an important complication in a substantial number of critically ill, hospitalized patients with COVID-19. Three groups of fungal pathogens cause co-infections in COVID-19: Aspergillus, Mucorales and Candida species, including Candida auris. Here we review the incidence of COVID-19-associated invasive fungal infections caused by these fungi in low-, middle- and high-income countries. By evaluating the epidemiology, clinical risk factors, predisposing features of the host environment and immunological mechanisms that underlie the pathogenesis of these co-infections, we set the scene for future research and development of clinical guidance. 

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COVID-19-associated fungal infections

Author(s): Hatzl, Stefan; Reisinger, Alexander C; Posch, Florian; Prattes, Juergen; Stradner, Martin; Pilz, Stefan; Eller, Philipp; Schoerghuber, Michael; Toller, Wolfgang; Gorkiewicz, Gregor; Metnitz, Philipp; Rief, Martin; Pruller, Florian; Rosenkranz, Alexander R; Valentin, Thomas; Krause, Robert; Hoenigl, Martin; Schilcher, Gernot | Abstract: BackgroundCoronavirus disease 19 (COVID-19)-associated pulmonary aspergillosis (CAPA) emerged as important fungal complications in patients with COVID-19-associated severe acute respiratory failure (ARF). Whether mould active antifungal prophylaxis (MAFP) can prevent CAPA remains elusive so far.MethodsIn this observational study, we included all consecutive patients admitted to intensive care units with COVID-19-associated ARF between September 1, 2020, and May 1, 2021. We compared patients with versus without antifungal prophylaxis with respect to CAPA incidence (primary outcome) and mortality (secondary outcome). Propensity score adjustment was performed to account for any imbalances in baseline characteristics. CAPA cases were classified according to European Confederation of Medical Mycology (ECMM)/International Society of Human and Animal Mycoses (ISHAM) consensus criteria.ResultsWe included 132 patients, of whom 75 (57%) received antifungal prophylaxis (98% posaconazole). Ten CAPA cases were diagnosed, after a median of 6ndays following ICU admission. Of those, 9 CAPA cases were recorded in the non-prophylaxis group and one in the prophylaxis group, respectively. However, no difference in 30-day ICU mortality could be observed. Thirty-day CAPA incidence estimates were 1.4% (95% CI 0.2-9.7) in the MAFP group and 17.5% (95% CI 9.6-31.4) in the group without MAFP (p = 0.002). The respective subdistributional hazard ratio (sHR) for CAPA incidence comparing the MAFP versus no MAFP group was of 0.08 (95% CI 0.01-0.63; p = 0.017).ConclusionIn ICU patients with COVID-19 ARF, antifungal prophylaxis was associated with significantly reduced CAPA incidence, but this did not translate into improved survival. Randomized controlled trials are warranted to evaluate the efficacy and safety of MAFP with respect to CAPA incidence and clinical outcomes.

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Antifungal prophylaxis for prevention of COVID-19-associated pulmonary aspergillosis in critically ill patients: an observational study.

Aspergillus fumigatus and aspergillosis: From basics to clinics.

Accurate predictive modeling of pandemics is essential for optimally distributing resources and setting policy. Dozens of case predictions models have been proposed but their accuracy over time and by model type remains unclear. In this study, we analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that more than one-third of models fail to outperform a simple static case baseline and two-thirds fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models, and error in modeling has increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US-CDC which risks giving them an official imprimatur and further raising concerns if utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this work to serve as the starting point towards the development of more accurate models.

J. Berman

Papers

Aspergillus fumigatus and aspergillosis: From basics to clinics.

Accuracy of US CDC COVID-19 Forecasting Models