Immunomodulation as a Potent COVID-19 Pharmacotherapy: Past, Present and Future

Hertanto, Decsa Medika; Wiratama, Bayu Satria; Sutanto, Henry; Wungu, Citrawati Dyah Kencono

doi:10.2147/jir.s322831

Cited by 24 publications

(20 citation statements)

References 72 publications

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“…The primary cause of mortality due to COVID-19 is severe acute respiratory distress caused by epithelial infection and alveolar macrophage activation in the lungs [4]. Therefore, immune modulation and suppression therapy may prevent the deterioration of the condition of COVID-19 patients [5]. Some COVID-19 patients develop severe disease or die [6], while many more develop mild or moderate disease [7].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Biomarkers of Severity in Patients with COVID-19 Infection

Yamamoto

Wada

Ichikawa

et al. 2021

JCM

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Object: Although many Japanese patients infected with coronavirus disease 2019 (COVID-19) only experience mild symptoms, in some cases a patient’s condition deteriorates, resulting in a poor outcome. This study examines the behavior of biomarkers in patients with mild to severe COVID-19. Methods: The disease severity of 152 COVID-19 patients was classified into mild, moderate I, moderate II, and severe, and the behavior of laboratory biomarkers was examined across these four disease stages. Results: The median age and male/female ratio increased with severity. The mortality rate was 12.5% in both moderate II and severe stages. Underlying diseases, which were not observed in 45% of mild stage patients, increased with severity. An ROC analysis showed that C-reactive protein (CRP), ferritin, procalcitonin (PCT), hemoglobin (Hb) A1c, albumin, and lactate dehydrogenase (LDH) levels were significantly useful for the differential diagnosis of mild/moderate I stage and moderate II/severe stage. In the severe stage, Hb levels, coagulation time, total protein, and albumin were significantly different on the day of worsening from those observed on the day of admission. The frequency of hemostatic biomarker abnormalities was high in the severe disease stage. Conclusion: The evaluation of severity is valuable, as the mortality rate was high in the moderate II and severe stages. The levels of CRP, ferritin, PCT, albumin, and LDH were useful markers of severity, and hemostatic abnormalities were frequently observed in patients in the severe disease stage.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Biomarkers of Severity in Patients with COVID-19 Infection

Yamamoto

Wada

Ichikawa

et al. 2021

JCM

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“…Other papers have shown the greater effectiveness of methylprednisolone with respect to dexamethasone. In a recent review, an extensive description of clinical trials was discussed, and glucocorticoid action was suggested regarding immunomodulation as a potent COVID-19 pharmacotherapy [37]. As to hydrocortisone, the data reported so far are not clear [38] and will not be further evaluated in the present paper.…”

Section: Introductionmentioning

confidence: 97%

Mechanism of Caspase-1 Inhibition by Four Anti-inflammatory Drugs Used in COVID-19 Treatment

Caruso

Pedersen

Incerpi

et al. 2022

IJMS

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The inflammatory protease caspase-1 is associated with the release of cytokines. An excessive number of cytokines (a “cytokine storm”) is a dangerous consequence of COVID-19 infection and has been indicated as being among the causes of death by COVID-19. The anti-inflammatory drug colchicine (which is reported in the literature to be a caspase-1 inhibitor) and the corticosteroid drugs, dexamethasone and methylprednisolone, are among the most effective active compounds for COVID-19 treatment. The SERM raloxifene has also been used as a repurposed drug in COVID-19 therapy. In this study, inhibition of caspase-1 by these four compounds was analyzed using computational methods. Our aim was to see if the inhibition of caspase-1, an important biomolecule in the inflammatory response that triggers cytokine release, could shed light on how these drugs help to alleviate excessive cytokine production. We also measured the antioxidant activities of dexamethasone and colchicine when scavenging the superoxide radical using cyclic voltammetry methods. The experimental findings are associated with caspase-1 active site affinity towards these compounds. In evaluating our computational and experimental results, we here formulate a mechanism for caspase-1 inhibition by these drugs, which involves the active site amino acid Cys285 residue and is mediated by a transfer of protons, involving His237 and Ser339. It is proposed that the molecular moiety targeted by all of these drugs is a carbonyl group which establishes a S(Cys285)–C(carbonyl) covalent bond.

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“…Specifically, there was a relatively early recognition that severe disease was associated with "cytokine storm" (1)(2)(3)(4)(5)(6), namely that the body's inflammatory/immune response was producing unintended and detrimental collateral damage in response to the viral infection. As a result, there was a great deal of interest in repurposing immunomodulatory agents to attempt to mitigate disease severity (7)(8)(9), but to date, with the exception of the use of steroids for severe disease (10), none of these approaches have been proven to be effective. This should not come as a surprise.…”

Section: Introductionmentioning

confidence: 99%

Preparing for the next COVID: Deep Reinforcement Learning trained Artificial Intelligence discovery of multi-modal immunomodulatory control of systemic inflammation in the absence of effective anti-microbials

Larie

Cockrell

2022

Preprint

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Background: Despite a great deal of interest in the application of artificial intelligence (AI) to sepsis/critical illness, most current approaches are limited in their potential impact: prediction models do not (and cannot) address the lack of effective therapeutics and current approaches to enhancing the treatment of sepsis focus on optimizing the application of existing interventions, and thus cannot address the development of new treatment options/modalities. The inability to test new therapeutic applications was highlighted by the generally unsatisfactory results from drug repurposing efforts in COVID-19. Hypothesis: Addressing this challenge requires the application of simulation-based, model-free deep reinforcement learning (DRL) in a fashion akin to training the game-playing AIs. We have previously demonstrated the potential of this method in the context of bacterial sepsis in which the microbial infection is responsive to antibiotic therapy. The current work addresses the control problem of multi-modal, adaptive immunomodulation in the circumstance where there is no effective anti-pathogen therapy (e.g., in a novel viral pandemic or in the face of resistant microbes). Methods: This is a proof-of-concept study that determines the controllability of sepsis without the ability to pharmacologically suppress the pathogen. We use as a surrogate system a previously validated agent-based model, the Innate Immune Response Agent-based Model (IIRABM), for control discovery using DRL. The DRL algorithm trains an AI on simulations of infection where both the control and observation spaces are limited to operating upon the defined immune mediators included in the IIRABM (a total of 11). Policies were learned using the Deep Deterministic Policy Gradient approach, with the objective function being a return to baseline system health. Results: DRL trained an AI policy that improved system mortality from 85% to 10.4%. Control actions affected every one of the 11 targetable cytokines and could be divided into those with static/unchanging controls and those with variable/adaptive controls. Adaptive controls primarily targeted 3 different aspects of the immune response: 2nd order pro-inflammation governing TH1/TH2 balance, primary anti-inflammation, and inflammatory cell proliferation. Discussion: The current treatment of sepsis is hampered by limitations in therapeutic options able to affect the biology of sepsis. This is heightened in circumstances where no effective antimicrobials exist, as was the case for COVID-19. Current AI methods are intrinsically unable to address this problem; doing so requires training AIs in contexts that fully represent the counterfactual space of potential treatments. The synthetic data needed for this task is only possible through the use of high-resolution, mechanism-based simulations. Finally, being able to treat sepsis will require a reorientation as to the sensing and actuating requirements needed to develop these simulations and bring them to the bedside

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Immunomodulation as a Potent COVID-19 Pharmacotherapy: Past, Present and Future

Cited by 24 publications

References 72 publications

Evaluation of Biomarkers of Severity in Patients with COVID-19 Infection

Evaluation of Biomarkers of Severity in Patients with COVID-19 Infection

Mechanism of Caspase-1 Inhibition by Four Anti-inflammatory Drugs Used in COVID-19 Treatment

Preparing for the next COVID: Deep Reinforcement Learning trained Artificial Intelligence discovery of multi-modal immunomodulatory control of systemic inflammation in the absence of effective anti-microbials

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