Background Antivirals are needed to combat the COVID-19 pandemic, which is caused by SARS-CoV-2. The clinically-proven protease inhibitor Camostat mesylate inhibits SARS-CoV-2 infection by blocking the virus-activating host cell protease TMPRSS2. However, antiviral activity of Camostat mesylate metabolites and potential viral resistance have not been analyzed. Moreover, antiviral activity of Camostat mesylate in human lung tissue remains to be demonstrated. Methods We used recombinant TMPRSS2, reporter particles bearing the spike protein of SARS-CoV-2 or authentic SARS-CoV-2 to assess inhibition of TMPRSS2 and viral entry, respectively, by Camostat mesylate and its metabolite GBPA. Findings We show that several TMPRSS2-related proteases activate SARS-CoV-2 and that two, TMPRSS11D and TMPRSS13, are robustly expressed in the upper respiratory tract. However, entry mediated by these proteases was blocked by Camostat mesylate. The Camostat metabolite GBPA inhibited recombinant TMPRSS2 with reduced efficiency as compared to Camostat mesylate. In contrast, both inhibitors exhibited similar antiviral activity and this correlated with the rapid conversion of Camostat mesylate into GBPA in the presence of serum. Finally, Camostat mesylate and GBPA blocked SARS-CoV-2 spread in human lung tissue ex vivo and the related protease inhibitor Nafamostat mesylate exerted augmented antiviral activity. Interpretation Our results suggest that SARS-CoV-2 can use TMPRSS2 and closely related proteases for spread in the upper respiratory tract and that spread in the human lung can be blocked by Camostat mesylate and its metabolite GBPA. Funding NIH, Damon Runyon Foundation, ACS, NYCT, DFG, EU, Berlin Mathematics center MATH+, BMBF, Lower Saxony, Lundbeck Foundation, Novo Nordisk Foundation.

The authors unravel the molecular action principle of nafamostat and camostat, two potential COVID-19 drugs targeting the human protein TMPRSS2.

This tutorial provides an introduction to the construction of Markov models of molecular kinetics from molecular dynamics trajectory data with the PyEMMA software. Using tutorial notebooks, we will guide the user through the basic functionality as well as the more common advanced mechanisms. Short exercises to self check the learning progress and a notebook on troubleshooting complete this basic introduction.

Antiviral therapy is urgently needed to combat the coronavirus disease 2019 (COVID-19) pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The protease inhibitor camostat mesylate inhibits SARS-CoV-2 infection of lung cells by blocking the virus-activating host cell protease TMPRSS2. Camostat mesylate has been approved for treatment of pancreatitis in Japan and is currently being repurposed for COVID-19 treatment. However, potential mechanisms of viral resistance as well as camostat mesylate metabolization and antiviral activity of metabolites are unclear. Here, we show that SARS-CoV-2 can employ TMPRSS2-related host cell proteases for activation and that several of them are expressed in viral target cells. However, entry mediated by these proteases was blocked by camostat mesylate. The camostat metabolite GBPA inhibited the activity of recombinant TMPRSS2 with reduced efficiency as compared to camostat mesylate and was rapidly generated in the presence of serum. Importantly, the infection experiments in which camostat mesylate was identified as a SARS-CoV-2 inhibitor involved preincubation of target cells with camostat mesylate in the presence of serum for 2 h and thus allowed conversion of camostat mesylate into GBPA. Indeed, when the antiviral activities of GBPA and camostat mesylate were compared in this setting, no major differences were identified. Our results indicate that use of TMPRSS2-related proteases for entry into target cells will not render SARS-CoV-2 camostat mesylate resistant. Moreover, the present and previous findings suggest that the peak concentrations of GBPA established after the clinically approved camostat mesylate dose (600 mg/day) will result in antiviral activity.

Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastable and coherent sets, slow relaxation processes, collective variables, dominant transition pathways or manifolds and channels of probability flow can be of great importance for understanding and characterizing the kinetic, thermodynamic and mechanistic properties of the system. Deeptime is a general purpose Python library offering various tools to estimate dynamical models based on time-series data including conventional linear learning methods, such as Markov state models (MSMs), Hidden Markov Models and Koopman models, as well as kernel and deep learning approaches such as VAMPnets and deep MSMs. The library is largely compatible with scikit-learn, having a range of Estimator classes for these different models, but in contrast to scikit-learn also provides deep Model classes, e.g. in the case of an MSM, which provide a multitude of analysis methods to compute interesting thermodynamic, kinetic and dynamical quantities, such as free energies, relaxation times and transition paths. The library is designed for ease of use but also easily maintainable and extensible code. In this paper we introduce the main features and structure of the deeptime software. Deeptime can be found under https://deeptime-ml.github.io/.