Abstract
A binary model for the classification of chronic diseases has formerly been proposed. The model classifies chronic diseases as “high Treg” or “low Treg” diseases according to the extent of regulatory T cells (Treg) activity (frequency or function) observed. The present paper applies this model to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The model correctly predicts the efficacy or inefficacy of several immune-modulating drugs in the treatment of severe coronavirus disease 2019 (COVID-19) disease. It also correctly predicts the class of pathogens mostly associated with SARS-CoV-2 infection. The clinical implications are the following: (a) any search for new immune-modulating drugs for the treatment of COVID-19 should exclude candidates that do not induce “high Treg” immune reaction or those that do not spare CD8+ T cells; (b) immune-modulating drugs, which are effective against SARS-CoV-2, may not be effective against any variant of the virus that does not induce “low Treg” reaction; (c) any immune-modulating drug, which is effective in treating COVID-19, will also alleviate most coinfections; and (d) severe COVID-19 patients should avoid contact with carriers of “low Treg” pathogens.
Highlights
In an earlier paper, a binary classification of chronic diseases was proposed [1]
Chronic diseases were classified according to the extent of regulatory T cell (Treg) activity, estimated in peripheral blood or within tissues implicated in the disease
A review of coinfections observed in hospitalized COVID-19 patients indicate the following bacterial infections [45]: Mycoplasma pneumoniae, Pseudomonas aeruginosa, Haemophilus influenza, Klebsiella pneumoniae, Enterobacter spp., Chlamydia spp., Acinetobacter baumannii, Serratia marcescens, methicillinresistant Staphylococcus aureus, and Enterococcus faecium
Summary
A binary classification of chronic diseases was proposed [1]. Chronic diseases were classified according to the extent of regulatory T cell (Treg) activity, estimated in peripheral blood or within tissues implicated in the disease. The effectiveness or ineffectiveness of certain immunemodulating drugs in the treatment of autoimmunity and cancer is elucidated by this binary model [1] It explains why “high Treg” inflammation promotes many solid cancers, while “low Treg” inflammation promotes lymphomas [2]. According to the binary model, drugs that induce Tregs activity (either frequency or function) are expected to have a beneficial effect on severe COVID-19 if they do not hamper the specific antiviral immune reaction. It is reasonable to assume that Tregs inducers, which suppress the inflammatory response but spare specific CD8+ T-cell anti-CoV-2 activity, will be effective in treating severe COVID-19. Ruxolitinib suppressed CTL in mice immunized with ovalbumin (an allergic reaction model) [22] Considering their effect on TCL, tofacitinib and baricitinib are expected to be effective in treating severe COVID-19 while ruxolitinib may not. The overall effect of rapamycin in COVID-19 is hard to forecast by the binary model alone
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