Elevated inflammatory markers are associated with poor outcomes in COVID-19 patients treated with remdesivir

Efficacy of Remdesivir on Clinical Outcomes in COVID-19 Patients: A Study in a Tertiary Care Hospital in Pakistan

Background

As of October 3, 2023, the global COVID-19 case tally exceeded 696 million, with almost 7 million fatalities. Remdesivir, approved for treatment of COVID-19 by regulatory bodies, has seen varying recommendations by the World Health Organization over time. Despite certain studies questioning its efficacy, others highlight potential benefits. The objective of this study was to gauge the impact of remdesivir on clinical outcomes in a Pakistani tertiary care hospital.

Methods

An analytical cross-sectional study was conducted on 108 COVID-19 patients at Mayo Hospital Lahore between September 2020 and August 2021. Of these, 52 received remdesivir. The study employed a structured proforma for data collection, with analyses conducted using SPSS version 26, considering a p-value of less than 0.05 as statistically significant.

Results

Demographic distribution between remdesivir-treated and untreated groups was similar. Significant improvement was observed in the remdesivir cohort in terms of oxygen saturation (58%), ferritin levels (58.2%), chest X-ray results (67.8%), and discharge rates (66.7%) when compared to the untreated group. Stratification based on disease severity showed that remdesivir was particularly beneficial for moderate illness cases in several parameters.

Conclusion

This study suggests that remdesivir can be associated with improved outcomes, especially in patients with moderate COVID-19 severity. The data emphasizes the importance of the disease stage when considering therapeutic interventions and calls for more region-specific research to guide health responses amid diverse epidemiological landscapes.

Impact of Remdesivir on inflammatory and prognostic markers of COVID-19: Findings of an event-monitoring study

Introduction

Remdesivir is currently approved for treating hospitalised patients with COVID-19. However, it is a priority to monitor its safety and effectiveness in various clinical settings. This study was undertaken to assess the impact of remdesivir on inflammatory and prognostic markers of COVID-19.

Materials and Methods

A hospital-based prospective longitudinal study was conducted over two months comprising event monitoring of COVID-19 patients administered remdesivir as per standard guidelines. The demographic details, risk factors and all baseline parameters were collected. The patients were followed up for the appearance of any adverse drug reactions (ADRs) after the start of remdesivir therapy from Day 1 to discharge or death every day. Repeat Lab tests were done on days 2, 4, 6 and 10 days to assess the impact of remdesivir on inflammatory and prognostic markers of COVID-19 over time. Significant predictors of survival in the cohort were also assessed.

Results

A total of 60 COVID-19 patients were administered remdesivir. The mean age of the patients was 59.2 (+13.7) years. There was a significant improvement in the serum creatinine (decreased from 0.9 to 0.7 mg/dL), lymphocyte count {decreased from 9.2 to 7.3 (109 cells/L)} and serum sodium (increased from 134.6 to 137.4) of the patients over six days after the administration of remdesivir. The significant survival predictors were multiple organ failure (P 0.046) and WBC count on Day 10 (P 0.001).

Conclusion

Remdesivir administration improved the prognostic biomarker profile in COVID-19 patients.

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records

BACKGROUND

There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to "hyperinflammation" associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes.

OBJECTIVE

The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management.

METHODS

This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission.

RESULTS

A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit-level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83.

CONCLUSIONS

Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

Th-1, Th-2, Th-9, Th-17, Th-22 type cytokine concentrations of critical COVID-19 patients after treatment with Remdesivir

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly spread around the world causing a pandemic known as coronavirus disease 2019 (COVID-19). Cytokine storm was directly correlated with severity of COVID-19 syndromes. We evaluated the levels of 13 cytokines in ICU hospitalized COVID-19 patients (n=29) before, and after treatment with Remdesivir as well as in healthy controls (n=29). Blood samples were obtained from ICU patients during ICU admission (before treatment) and 5 days after treatment with Remdesivir. A group of 29 age- and gender-matched healthy controls was also studied. Cytokine levels were evaluated by multiplex immunoassay method using a fluorescence labeled cytokine panel. In comparison to cytokine levels measured at ICU admission, serum levels were reduced of IL-6 (134.75 pg/mL vs. 20.73 pg/mL, P< 0.0001), TNF-α (121.67 pg/mL vs. 10.15 pg/mL, P< 0.0001) and IFN-γ (29.69 pg/mL vs. 22.27 pg/mL, P= 0.005), whereas serum level was increased of IL-4 (8.47 pg/mL vs. 12.44 pg/mL, P= 0.002) within 5 days after Remdesivir treatment. Comparing with before treatment, Remdesivir significantly reduced the levels of inflammatory (258.98 pg/mL vs. 37.43 pg/mL, P< 0.0001), Th1-type (31.24 pg/mL vs. 24.46 pg/mL, P= 0.007), and Th17-type (36.79 pg/mL vs. 26.22 pg/mL, P< 0.0001) cytokines in critical COVID-19 patients. However, after Remdesivir treatment, the concentrations of Th2-type cytokines were significantly higher than before treatment (52.69 pg/mL vs. 37.09 pg/mL, P< 0.0001). In conclusion, Remdesivir led to decrease levels of Th1-type and Th17-type cytokines and increase Th2-type cytokines in critical COVID-19 patients 5 days after treatment.

Current Status and Future Perspectives of Lactate Dehydrogenase Detection and Medical Implications: A Review

The demand for glucose uptake and the accompanying enhanced glycolytic energy metabolism is one of the most important features of cancer cells. Unlike the aerobic metabolic pathway in normal cells, the large amount of pyruvate produced by the dramatic increase of glycolysis in cancer cells needs to be converted to lactate in the cytoplasm, which cannot be done without a large amount of lactate dehydrogenase (LDH). This explains why elevated serum LDH concentrations are usually seen in cancer patient populations. LDH not only correlates with clinical prognostic survival indicators, but also guides subsequent drug therapy. Besides their role in cancers, LDH is also a biomarker for malaria and other diseases. Therefore, it is urgent to develop methods for sensitive and convenient LDH detection. Here, this review systematically summarizes the clinical impact of lactate dehydrogenase detection and principles for LDH detection. The advantages as well as limitations of different detection methods and the future trends for LDH detection were also discussed.

Probiotics for preventing or treating COVID-19; a systematic review of research evidence and meta-analyses of efficacy for preventing death, severe disease, or disease progression

Background: COVID-19 variants threaten health globally. Despite improving vaccines and treatments, there is an urgent need for alternative strategies to prevent or reduce the severity of COVID-19. Potential strategies include probiotics, which are safe, inexpensive, globally available and have been studied previously in relation to respiratory infections. Methods: We performed a systematic review and meta-analyses of experimental, trial or observational research evidence evaluating probiotics compared with control groups for preventing or treating COVID-19. We searched PubMed, ProQuest, Google Scholar and Web of Science bibliographic databases for studies published until December 6, 2021. We then performed meta-analyses for outcomes reported consistently across studies. Outcomes reported inconsistently or not amenable to meta-analysis were compared descriptively. Results: We identified six eligible studies, which were all published in 2020 and 2021: one randomized controlled trial and five retrospective cohort studies. The only randomized controlled trial reported that groups that ingested probiotics compared with control groups that did not ingest probiotics did not differ significantly with respect to death, severe disease requiring admission to an intensive care unit or disease progression (all p>0.5). The five retrospective cohort studies reported various apparently beneficial and harmful COVID-19 outcome associations with probiotic ingestion. Meta-analyses revealed no significant associations between probiotic use and death, severe disease, or disease progression caused by COVID-19. Descriptive data revealed that probiotic ingestion was associated with a trend towards worsened duration of hospital stay, improvements in measures of respiratory condition and worsened disease duration. The evidence for these contradictory associations was weak because all studies were prone to bias and none were considered to be of high quality. Conclusions: Current evidence does not suggest that probiotics affect COVID-19 severity or mortality. However, additional higher quality studies need to be conducted to definitively determine if probiotics would be a useful adjunctive treatment for COVID-19.

Ultraviolet-coupled advanced oxidation processes for anti-COVID-19 drugs treatment: Degradation mechanisms, transformation products and toxicity evolution

Remdesivir (RDV), dexamethasone (DEX) and hydroxychloroquine (HCQ) were widely used in the treatment of COVID-19 pneumonia, possibly causing environmental risks and drug-resistance viruses. This study elucidated the degradation mechanisms and potential toxicity risks of the three anti-COVID-19 drugs by UV and ultraviolet-coupled advanced oxidation processes (UV/AOPs). All the drugs could be degraded by more than 98% within 3 min under the following optimal conditions: pH of 5.0 and drug-to-oxidant (H₂O₂) molar ratio of 1:200. Combined with density functional theory (DFT) analysis and high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS), twenty-four transformation products (TPs) were detected and the main degradation pathways were investigated. Based on bacterial luminescence inhibition test and the peak-area evolution of TPs, RDV and HCQ showed an obvious toxicity-increase region when TPs were generated in large quantities, while the toxicity of DEX continued to decline during degradation processes. By QSAR predictions, the main contributors to the toxicity evolution during the UV/AOPs were predicted. Halogen-containing TPs showed significantly higher toxicity than other TPs, and thus the chlorine-containing structure in HCQ presented the potential toxicity. Appropriate reaction parameters and adequate reaction time for the UV/AOPs could eliminate the toxicity of TPs and ensure environmental safety. This study could play a positive role in the treatment of anti-COVID-19 drugs and their environmental hazard assessment.

In Silico Prediction of Andrographolide Dosage Regimens for COVID-19 Treatment

Andrographolide (APE) has been used for COVID-19 treatment in various clinical settings in South-East Asia due to its benefits on reduction of viral clearance and prevention of disease progression. However, the limitation of APE clinical use is the high incidence of adverse events. The objective of this study was to find the optimal dosage regimens of APE for COVID-19 treatment. The whole-body physiologically-based pharmacokinetic (PBPK) models were constructed using data from the published articles and validated against clinical observations. The inhibitory effect of APE was determined for the potency of drug efficacy. For prevention of pneumonia, multiple oral doses such as 120[Formula: see text]mg for three doses, followed by 60[Formula: see text]mg three times daily for 4 consecutive days, or 200[Formula: see text]mg intravenous infusion at the rate of 20 mg/h once daily is advised in patients with mild COVID-19. For prevention of pneumonia and reduction of viral clearance time, the recommended dosage regimen is 500[Formula: see text]mg intravenous infusion at the rate of 25[Formula: see text]mg/h once daily in patients with mild-to-moderate COVID-19. One hundred virtual populations (50 males and 50 females) were simulated for oral and intravenous infusion formulations of APE. The eligible PBPK/PD models successfully predicted optimal dosage regimens and formulations of APE for prevention of disease progression and/or reduction of viral clearance time. Additionally, APE should be co-administered with other antiviral drugs to enhance therapeutic efficacy for COVID-19 treatment.

Interpatient variability in the pharmacokinetics of remdesivir and its main metabolite GS-441524 in treated COVID-19 subjects

Background

Remdesivir is the first antiviral drug against SARS-CoV-2 approved for use in COVID-19 patients.

Objectives

To study the pharmacokinetic inter-individual variability of remdesivir and its main metabolite GS-441524 in a real-world setting of COVID-19 inpatients and to identify possible associations with different demographic/biochemical variables.

Methods

Inpatients affected by SARS-CoV-2 infections, undergoing standard-dose remdesivir treatment, were prospectively enrolled. Blood samples were collected on day 4, immediately after (C₀) and at 1 h (C₁) and 24 h (C₂₄) after infusion. Remdesivir and GS-441524 concentrations were measured using a validated UHPLC-MS/MS method and the AUC_0–24 was calculated. At baseline, COVID-19 severity (ICU or no ICU), sex, age, BMI and renal and liver functions were assessed. Transaminases and estimated glomerular filtration rate (e-GFR) were also evaluated during treatment. Linear regression, logistic regression and multiple linear regression tests were used for statistical comparisons of pharmacokinetic parameters and variables.

Results

Eighty-five patients were included. The mean (CV%) values of remdesivir were: C₀ 2091 (99.1%) ng/mL, C₁ 139.7 (272.4%) ng/mL and AUC_0–24 2791 (175.7%) ng·h/mL. The mean (CV%) values of GS-441524 were: C₀ 90.2 (49.5%) ng/mL, C₁ 104.9 (46.6%) ng/mL, C₂₄ 58.4 (66.9) ng/mL and AUC_0–24 1976 (52.6%) ng·h/mL. The multiple regression analysis showed that age (P < 0.05) and e-GFR (P < 0.01) were independent predictors of GS-441524 plasma exposure.

Conclusions

Our results showed a high interpatient variability of remdesivir and GS-441524 likely due to both age and renal function in COVID-19 inpatients. Further research is required to understand whether the pharmacokinetics of remdesivir and its metabolites may influence drug-related efficacy or toxic effect.

Linear mixed model analysis to evaluate correlations between remdesivir adverse effects with age and gender of patients with mild Covid‐19 pneumonia

We aimed to assess longitudinal changes in clinical indexes of corona disease 2019 (Covid‐19) patients with mild pulmonary infection during 5 days of remdesivir therapy and determine the effect of age and gender on remdesivir adverse effects (AE). Patients' clinical data including inflammatory markers, liver and renal function tests, and heart rate (HR) were extracted from medical records. Linear mixed model (LMM) was used to analyze longitudinal changes in patients' clinical indexes. Gender and age were inserted in LMM as covariates to find their correlation with AE and clinical indexes. Of 84 patients, 35 patients met our criteria for the study. There were significant increases in mean levels of white blood cell (WBC; p = 0.005), alanine aminotransferase (ALT; p = 0.001), aspartate aminotransferase (p = 0.001), blood urea nitrogen (BUN; p = 0.001), and creatinine (p = 0.006), whereas mean levels of erythrocyte sedimentation rate (p = 0.005), C‐reactive protein (p = 0.001), alkaline phosphatase (p = 0.001), and potassium (p = 0.003) decreased significantly. Estimated glomerular filtration rate (p = 0.001) and HR (p = 0.001) showed a notable decline over the course of treatment. LMM analysis showed that mean changes in WBC (β = 0.94, p = 0.029), creatinine (β = 0.12, p = 0.020), and HR (β = 6.47, p = 0.008) were greater in males than in females. Also, age of patients had a significant effect on the mean changes of WBC (β = −0.02, p = 0.023), sodium (β = −0.06, p = 0.010), BUN (β = 0.23, p = 0.001), and HR (β = −0.29, p = 0.001). Despite no renal and liver dysfunction, Covid‐19 patients with mild pulmonary infection may develop some remdesivir AE and attributed side effects might be affected by gender and age of patients.