Los Alamos National Laboratory

Manzo E., Touza W.A., Bednarz G., Guvvala N., Das A.K., Cheetham P., Pamidi S.V.

Schoenemann R.U., McNeel D.G., Mocko V., Schmidt D.R., Nobles J., Becker D.T., Magnelind P.E., Dede S., Fink C.W., Kossmann S.E., Schreiber K.A., Croce M.P., Winkelbauer J., Carpenter M.H.

Phan T., Ribeiro R.M., Edelstein G.E., Boucau J., Uddin R., Marino C., Liew M.Y., Barry M., Choudhary M.C., Tien D., Su K., Reynolds Z., Li Y., Sagar S., Vyas T.D., et. al.

ABSTRACT In a subset of SARS-CoV-2-infected individuals treated with the antiviral nirmatrelvir-ritonavir, the virus rebounds following treatment. The mechanisms driving this rebound are not well understood. We used a mathematical model to describe the longitudinal viral load dynamics of 51 individuals treated with nirmatrelvir-ritonavir, 20 of whom rebounded. Target cell preservation, either by a robust innate immune response or initiation of N-R near the time of symptom onset, coupled with incomplete viral clearance, appears to be the main factor leading to viral rebound. Moreover, the occurrence of viral rebound is likely influenced by the time of treatment initiation relative to the progression of the infection, with earlier treatments leading to a higher chance of rebound. A comparison with an untreated cohort suggests that early treatments with nirmatrelvir-ritonavir may be associated with a delay in the onset of an adaptive immune response. Nevertheless, our model demonstrates that extending the course of nirmatrelvir-ritonavir treatment to a 10-day regimen may greatly diminish the chance of rebound in people with mild-to-moderate COVID-19 and who are at high risk of progression to severe disease. Altogether, our results suggest that in some individuals, a standard 5-day course of nirmatrelvir-ritonavir starting around the time of symptom onset may not completely eliminate the virus. Thus, after treatment ends, the virus can rebound if an effective adaptive immune response has not fully developed. These findings on the role of target cell preservation and incomplete viral clearance also offer a possible explanation for viral rebounds following other antiviral treatments for SARS-CoV-2. IMPORTANCE Nirmatrelvir-ritonavir is an effective treatment for SARS-CoV-2. In a subset of individuals treated with nirmatrelvir-ritonavir, the initial reduction in viral load is followed by viral rebound once treatment is stopped. We show that the timing of treatment initiation with nirmatrelvir-ritonavir may influence the risk of viral rebound. Nirmatrelvir-ritonavir stops viral growth and preserves target cells but may not lead to full clearance of the virus. Thus, once treatment ends, if an effective adaptive immune response has not adequately developed, the remaining virus can lead to rebound. Our results provide insights into the mechanisms of rebound and can help develop better treatment strategies to minimize this possibility.

Wang Z., Yu N., Reichhardt C., Reichhardt C.J., Xu A., Chen X., Feng Y.

Schimming C.D., Reichhardt C.J., Reichhardt C.

Ekdahl C.

Sharma V., White A.J.

Quinton J., Fadel M., Xu J., Habib A., Chandra M., Ping Y., Sundararaman R.

Tyagi B., Suzuki F., Chernyak V.A., Sinitsyn N.A.

Zingale A., Waczynski S., Pogorelsky I., Polyanskiy M., Sears J., Lakis R.E., Milchberg H.M.

Vijayvargia A., Zhang H., Barros K., Lin S., Erten O.

Finney T.J., Wilson A.W., Poveda M.L., Davis B.L.

Thurin J., Modrak R., Tape C., McPherson A., Cardozo F.R., Kintner J., Ding L., Liu Q., Braunmiller J.

Summary We introduce MTUQ, an open-source Python package for seismic source estimation and uncertainty quantification, emphasizing flexibility and operational scalability. MTUQ provides MPI-parallelized grid search and global optimization capabilities, compatibility with 1D and 3D Green’s function database formats, customizable data processing, C-accelerated waveform and first-motion polarity misfit functions, and utilities for plotting seismic waveforms and visualizing misfit and likelihood surfaces. Applicability to a range of full- and constrained-moment tensor, point force, and centroid inversion problems is possible via a documented application programming interface (API), accompanied by example scripts and integration tests. We demonstrate the software using three different types of seismic events: 1) a 2009 intra-slab earthquake near Anchorage, Alaska; 2) an episode of the 2021 Barry Arm landslide in Alaska; and 3) the 2017 Democratic People’s Republic of Korea (DPRK) underground nuclear test. With these events, we illustrate the well-known complementary character of body waves, surface waves, and polarities for constraining source parameters. We also convey the distinct misfit patterns that arise from each individual data type, the importance of uncertainty quantification for detecting multi-modal or otherwise poorly constrained solutions, and the software’s flexible, modular design.

Zeng Z.C., Peter A.H., Du X., Yang S., Benson A., Cyr-Racine F., Jiang F., Mace C., Metcalf R.B.

Kumar R., Desilets H., Johnstone J.E., Hudan S., Chattopadhyay D., deSouza R.T., Ackermann D., Basson M., Brown K.W., Chbihi A., Cook K.J., Famiano M., Genard T., Harca I.M., Paneru S.N.