Comparing the efficacy of tocilizumab with corticosteroid therapy in treating COVID-19 patients: a systematic review and meta-analysis

Effect of anti-inflammatory drugs on the storm of inflammatory factors in respiratory tract infection caused by SARS-CoV-2: an updated meta-analysis

Background

New reports suggest that anti-inflammatory drugs are widely used to treat respiratory tract infections caused by SARS-CoV-2. Anti-inflammatory drugs were the most frequently used treatment for the COVID-19-related cytokine storm in China. However, the efficacy of anti-inflammatory drugs has yet to be systematically analyzed, and clinicians are often uncertain which class of anti-inflammatory drug is the most effective in treating patients with respiratory tract infections caused by SARS-CoV-2, especially those with severe disease.

Methods

From 1 October 2022, relevant studies were searched in the PubMed, Embase, Medline, Cochrane Library, and Web of Science databases. A total of 16,268 publications were retrieved and collated according to inclusion and exclusion criteria, and sensitivity analyses were performed using STATA 14 software. Publication bias was assessed using funnel plots and Egger's test. Study quality was assessed using the PEDro scale, and the combined advantage ratio was expressed as a 95% confidence interval (CI). In total, 19 randomized controlled trials were included in the study. STATA 14 software was used for all random effects model analyses, and the results are expressed as relative risk ratios (RR) with 95% CI.

Results

Quantitative analyses were performed on 14,514 patients from 19 relevant randomized controlled clinical trials. Pooled estimates (RR = 0.59, 95% CI 0.44-0.80) revealed that the use of anti-inflammatory drugs resulted in a significant reduction in mortality in patients with respiratory tract infection caused by SARS-CoV-2 compared with controls, and methylprednisolone (RR = 0.14, 95% CI 0.03-0.56) was more effective than other anti-inflammatory drugs. Anti-inflammatory drugs were effective in reducing mortality in critically ill patients (RR = 0.67, 95% CI 0.45-0.98) compared with non-critically ill patients (RR = 0.50, 95% CI 0.34-0.76); however, more clinical evidence is needed to confirm these findings.

Conclusion

The use of anti-inflammatory drugs in patients with respiratory infections caused by SARS-CoV-2 reduces patient mortality, especially in severe cases. In individual studies, methylprednisolone was more effective than other drugs.

Tocilizumab for the treatment of COVID-19

INTRODUCTION

Since the beginning of the COVID-19 pandemic, the repurposing of medicines has been pursued to find interventions effective in preventing fatal outcome of the disease. One of these drugs was tocilizumab, an interleukin-6 inhibiting monoclonal antibody, previously used to treat several immune-related disorders.

AREAS COVERED

In this article, we present the results of the initial observational studies and subsequent randomized clinical trials on the efficacy and safety of tocilizumab in the treatment of COVID-19. Despite conflicting results, possibly due to the heterogeneity of the studied populations, large studies have ultimately proven that preventing IL-6 from attaching to its receptors can effectively reverse the fatal course of the disease. We also discuss the meta-analyses, which mostly supported the validity of tocilizumab therapy. We show how tocilizumab found its place in the most important recommendations on COVID-19 treatment and obtained authorization from the major regulatory authorities.

EXPERT OPINION

The criteria for optimizing tocilizumab therapy in COVID-19 still need to be established. They are also important considering the existing risks of future zoonotic spillovers and epidemics that may trigger hyperinflammation that could be efficiently blocked. The experience gained with tocilizumab shall be perceived as preparedness for future challenges.

Association of Polymorphisms of IL-6 Pathway Genes (IL6, IL6R and IL6ST) with COVID-19 Severity in an Amazonian Population

Interleukin-6 has been recognized as a major role player in COVID-19 severity, being an important regulator of the cytokine storm. Hence, the evaluation of the influence of polymorphisms in key genes of the IL-6 pathway, namely IL6, IL6R, and IL6ST, may provide valuable prognostic/predictive markers for COVID-19. The present cross-sectional study genotyped three SNPs (rs1800795, rs2228145, and rs7730934) at IL6. IL6R and IL6ST genes, respectively, in 227 COVID-19 patients (132 hospitalized and 95 non-hospitalized). Genotype frequencies were compared between these groups. As a control group, published data on gene and genotype frequencies were gathered from published studies before the pandemic started. Our major results point to an association of the IL6 C allele with COVID-19 severity. Moreover, IL-6 plasmatic levels were higher among IL6 CC genotype carriers. Additionally, the frequency of symptoms was higher at IL6 CC and IL6R CC genotypes. In conclusion, the data suggest an important role of IL6 C allele and IL6R CC genotype on COVID-19 severity, in agreement with indirect evidence from the literature about the association of these genotypes with mortality rates, pneumonia, and heightening of protein plasmatic levels pro-inflammatory driven effects.

Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19

Pulmonary fibrosis is one of the most severe long-term consequences of COVID-19. Corticosteroid treatment increases the chances of recovery; unfortunately, it can also have side effects. Therefore, we aimed to develop prediction models for a personalized selection of patients benefiting from corticotherapy. The experiment utilized various algorithms, including Logistic Regression, k-NN, Decision Tree, XGBoost, Random Forest, SVM, MLP, AdaBoost, and LGBM. In addition easily human-interpretable model is presented. All algorithms were trained on a dataset consisting of a total of 281 patients. Every patient conducted an examination at the start and three months after the post-COVID treatment. The examination comprised a physical examination, blood tests, functional lung tests, and an assessment of health state based on X-ray and HRCT. The Decision tree algorithm achieved balanced accuracy (BA) of 73.52%, ROC-AUC of 74.69%, and 71.70% F1 score. Other algorithms achieving high accuracy included Random Forest (BA 70.00%, ROC-AUC 70.62%, 67.92% F1 score) and AdaBoost (BA 70.37%, ROC-AUC 63.58%, 70.18% F1 score). The experiments prove that information obtained during the initiation of the post-COVID-19 treatment can be used to predict whether the patient will benefit from corticotherapy. The presented predictive models can be used by clinicians to make personalized treatment decisions.

Perspective for Discovery of Small Molecule IL-6 Inhibitors through Study of Structure–Activity Relationships and Molecular Docking

Interleukin-6 (IL-6) is a proinflammatory cytokine that plays a key role in the pathogenesis and physiology of inflammatory and autoimmune diseases, such as coronary heart disease, cancer, Alzheimer's disease, asthma, rheumatoid arthritis, and most recently COVID-19. IL-6 and its signaling pathway are promising targets in the treatment of inflammatory and autoimmune diseases. Although, anti-IL-6 monoclonal antibodies are currently being used in clinics, huge unmet medical needs remain because of the high cost, administration-related toxicity, lack of opportunity for oral dosing, and potential immunogenicity of monoclonal antibody therapy. Furthermore, nonresponse or loss of response to monoclonal antibody therapy has been reported, which increases the importance of optimizing drug therapy with small molecule drugs. This work aims to provide a perspective for the discovery of novel small molecule IL-6 inhibitors by the analysis of the structure-activity relationships and computational studies for protein-protein inhibitors targeting the IL-6/IL-6 receptor/gp130 complex.

Tocilizumab Outcomes in Critically Ill COVID-19 Patients Admitted to the ICU and the Role of Non-Tocilizumab COVID-19-Specific Medical Therapeutics

Background: Tocilizumab is a monoclonal antibody proposed to manage cytokine release syndrome (CRS) associated with severe COVID-19. Previously published reports have shown that tocilizumab may improve the clinical outcomes of critically ill patients admitted to the ICU. However, no precise data about the role of other medical therapeutics concurrently used for COVID-19 on this outcome have been published. Objectives: We aimed to compare the overall outcome of critically ill COVID-19 patients admitted to the ICU who received tocilizumab with the outcome of matched patients who did not receive tocilizumab while controlling for other confounders, including medical therapeutics for critically ill patients admitted to ICUs. Methods: A prospective, observational, multicenter cohort study was conducted among critically ill COVID-19 patients admitted to the ICU of 14 hospitals in Saudi Arabia between 1 March 2020, and October 31, 2020. Propensity-score matching was utilized to compare patients who received tocilizumab to patients who did not. In addition, the log-rank test was used to compare the 28 day hospital survival of patients who received tocilizumab with those who did not. Then, a multivariate logistic regression analysis of the matched groups was performed to evaluate the impact of the remaining concurrent medical therapeutics that could not be excluded via matching 28 day hospital survival rates. The primary outcome measure was patients’ overall 28 day hospital survival, and the secondary outcomes were ICU length of stay and ICU survival to hospital discharge. Results: A total of 1470 unmatched patients were included, of whom 426 received tocilizumab. The total number of propensity-matched patients was 1278. Overall, 28 day hospital survival revealed a significant difference between the unmatched non-tocilizumab group (586; 56.1%) and the tocilizumab group (269; 63.1%) (p-value = 0.016), and this difference increased even more in the propensity-matched analysis between the non-tocilizumab group (466.7; 54.6%) and the tocilizumab group (269; 63.1%) (p-value = 0.005). The matching model successfully matched the two groups’ common medical therapeutics used to treat COVID-19. Two medical therapeutics remained significantly different, favoring the tocilizumab group. A multivariate logistic regression was performed for the 28 day hospital survival in the propensity-matched patients. It showed that neither steroids (OR: 1.07 (95% CI: 0.75–1.53)) (p = 0.697) nor favipiravir (OR: 1.08 (95% CI: 0.61–1.9)) (p = 0.799) remained as a predictor for an increase in 28 day survival. Conclusion: The tocilizumab treatment in critically ill COVID-19 patients admitted to the ICU improved the overall 28 day hospital survival, which might not be influenced by the concurrent use of other COVID-19 medical therapeutics, although further research is needed to confirm this.

Long-Term Vaccination and Treatment Strategies for COVID-19 Disease and Future Coronavirus Pandemics

The appearance of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with increased infectivity and immune escape capabilities has allowed continuation of the COVID-19 pandemic for the foreseeable future. This review describes the worldwide efforts aimed at developing new vaccination and treatment strategies to keep pace with these variants as they emerge. In the case of vaccines and monoclonal antibody-based therapeutics, we describe the development of variant-specific, multivalent, and universal coronavirus directed approaches. Existing treatment approaches consist of repurposed medicines, such as antiviral compounds and anti-inflammatory agents, although efforts are underway to develop new ways of preventing or minimizing the effects of infection with the use of small molecules that disrupt binding the SARS-CoV-2 virus to host cells. Finally, we discuss the preclinical and clinical testing of natural products from medicinal herbs and spices, which have demonstrated anti-inflammatory and antiviral properties and therefore show potential as novel and safe COVID-19 treatment approaches.

Therapeutic trials in difficult to treat steroid sensitive nephrotic syndrome: challenges and future directions

Steroid sensitive nephrotic syndrome is a common condition in pediatric nephrology, and most children have excellent outcomes. Yet, 50% of children will require steroid-sparing agents due to frequently relapsing disease and may suffer consequences from steroid dependence or use of steroid-sparing agents. Several steroid-sparing therapeutic agents are available with few high quality randomized controlled trials to compare efficacy leading to reliance on observational data for clinical guidance. Reported trials focus on short-term outcomes such as time to first relapse, relapse rates up to 1-2 years of follow-up, and few have studied long-term remission. Trial designs often do not consider inter-individual variability, and differing response to treatments may occur due to heterogeneity in pathogenic mechanisms, and genetic and environmental influences. Strategies are proposed to improve the quantity and quality of trials in steroid sensitive nephrotic syndrome with integration of biomarkers, novel trial designs, and standardized outcomes, especially for long-term remission. Collaborative efforts among international trial networks will help move us toward a shared goal of finding a cure for children with nephrotic syndrome.

COVID-19 Case Management Outcomes Amongst Diabetes and Hypertensive Patients in the United Arab Emirates: A Prospective Study

The global pandemic of the novel Coronavirus infection 2019 (COVID-19) challenged the care of comorbid patients. The risk imposed by COVID-19 on diabetes patients is multisystemic, exponential, and involves glucose dysregulation. The increased burden for diabetes patients infected with COVID-19 is substantial in countries with a high prevalence of diabetics, such as the United Arab Emirates (UAE). This study aims to explore the prevalence of diabetes, clinical characteristic, and outcomes of patients admitted for COVID-19 treatment with or without a concurrent preadmission diagnosis of diabetes. A prospective study was performed on 1199 adults admitted with confirmed COVID-19 from December 2020 to April 2021 to a single hospital in the UAE. The study compared the demographics, clinical characteristics, and outcomes in COVID-19-infected patients with diabetes to patients without diabetes. The study endpoints include the development of new-onset diabetes, admission to ICU, trends in the blood glucose levels, and death. A total of 1199 patients (390 with diabetes) were included in the study. A diabetes prevalence was detected among 9.8% of the study population. Among the diabetes group, 10.8% were morbidly obese, 65.4% had associated hypertension, and 18.9% had coronary artery disease. Diabetes patients showed higher rates of ICU admission (11.1% vs. 7.1%), NIV requirement (9.6% vs. 6.4%), and intubation (5.45% vs. 2%) compared to the non-diabetes group. Advanced age was a predictor of a worsening COVID-19 course, while diabetes (p < 0.050) and hypertension (p < 0.025) were significant predictors of death from COVID-19. Nearly three-fourths (284 (73.4%)) of the diabetic patients developed worsened hyperglycemia as compared to one-fifth (171 (20.9%)) of the nondiabetic patients. New-onset diabetes was detected in 9.8% of COVID-19 patients. COVID-19 severity is higher in the presence of diabetes and is associated with worsening hyperglycemia and poor clinical outcomes. Preexisting hypertension is a predictor of COVID-19 severity and death.