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Validation and invalidation of SARS-CoV-2 main protease inhibitors using the Flip-GFP and Protease-Glo luciferase assays.

ABSTRACT: SARS-CoV-2 main protease (M^pro) is one of the most extensively exploited drug targets for COVID-19. Structurally disparate compounds have been reported as M^pro inhibitors, raising the question of their target specificity. To elucidate the target specificity and the cellular target engagement of the claimed M^pro inhibitors, we systematically characterize their mechanism of action using the cell-free FRET assay, the thermal shift-binding assay, the cell lysate Protease-Glo luciferase assay, and the cell-based FlipGFP assay. Collectively, our results have shown that majority of the M^pro inhibitors identified from drug repurposing including ebselen, carmofur, disulfiram, and shikonin are promiscuous cysteine inhibitors that are not specific to M^pro, while chloroquine, oxytetracycline, montelukast, candesartan, and dipyridamole do not inhibit M^pro in any of the assays tested. Overall, our study highlights the need of stringent hit validation at the early stage of drug discovery.

SUBMITTER: Ma C

PROVIDER: S-EPMC8558150 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundVery frequently the same biological system is described by several, sometimes competing mathematical models. This usually creates confusion around their validity, ie, which one is correct. However, this is unnecessary since validity of a model cannot be established; model validation is actually a misnomer. In principle the only statement that one can make about a system model is that it is incorrect, ie, invalid, a fact which can be established given appropriate experimental data. Nonlinear models of high dimension and with many parameters are impossible to invalidate through simulation and as such the invalidation process is often overlooked or ignored.ResultsWe develop different approaches for showing how competing ordinary differential equation (ODE) based models of the same biological phenomenon containing nonlinearities and parametric uncertainty can be invalidated using experimental data. We first emphasize the strong interplay between system identification and model invalidation and we describe a method for obtaining a lower bound on the error between candidate model predictions and data. We then turn to model invalidation and formulate a methodology for discrete-time and continuous-time model invalidation. The methodology is algorithmic and uses Semidefinite Programming as the computational tool. It is emphasized that trying to invalidate complex nonlinear models through exhaustive simulation is not only computationally intractable but also inconclusive.ConclusionBiological models derived from experimental data can never be validated. In fact, in order to understand biological function one should try to invalidate models that are incompatible with available data. This work describes a framework for invalidating both continuous and discrete-time ODE models based on convex optimization techniques. The methodology does not require any simulation of the candidate models; the algorithms presented in this paper have a worst case polynomial time complexity and can provide an exact answer to the invalidation problem.

Project description:The rapid global emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused serious health problems, highlighting the urgent need for antiviral drugs. The viral main protease (Mpro) plays an important role in viral replication and thus remains the target of choice for the prevention or treatment of several viral diseases due to high sequence and structural conservation. Prolonged use of viral protease inhibitors can lead to the development of mutants resistant to those inhibitors and to many of the available antiviral drugs. Here, we used feline infectious peritonitis virus (FIPV) as a model to investigate its development of resistance under pressure from the Mpro inhibitor GC376. Passage of wild-type (WT) FIPV in the presence of GC376 selected for a mutation in the nsp12 region where Mpro cleaves the substrate between nsp12 and nsp13. This mutation confers up to 3-fold resistance to GC376 and nirmatrelvir, as determined by EC50 assay. In vitro biochemical and cellular experiments confirmed that FIPV adapts to the stress of GC376 by mutating the nsp12 and nsp13 hydrolysis site to facilitate cleavage by Mpro and release to mediate replication and transcription. Finally, we demonstrate that GC376 cannot treat FIP-resistant mutants that cause FIP in animals. Taken together, these results suggest that Mpro affects the replication of coronaviruses (CoVs) and the drug resistance to GC376 by regulating the amount of RdRp from a distant site. These findings provide further support for the use of an antiviral drug combination as a broad-spectrum therapy to protect against contemporary and emerging CoVs. IMPORTANCE CoVs cause serious human infections, and antiviral drugs are currently approved to treat these infections. The development of protease-targeting therapeutics for CoV infection is hindered by resistance mutations. Therefore, we should pay attention to its resistance to antiviral drugs. Here, we identified possible mutations that lead to relapse after clinical treatment of FIP. One amino acid substitution in the nsp12 polymerase at the Mpro cleavage site provided low-level resistance to GC376 after selection exposure to the GC376 parental nucleoside. Resistance mutations enhanced FIPV viral fitness in vitro and attenuated the therapeutic effect of GC376 in an animal model of FIPV infection. Our research explains the evolutionary characteristics of coronaviruses under antiviral drugs, which is helpful for a more comprehensive understanding of the molecular basis of virus resistance and provides important basic data for the effective prevention and control of CoVs.

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Validation and invalidation of SARS-CoV-2 main protease inhibitors using the Flip-GFP and Protease-Glo luciferase assays.

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