Skip to main content
Download PDF

Immunomodulatory and clinical effects of receptor-interacting protein kinase 1 (RIPK1) inhibitor eclitasertib (SAR443122) in patients with severe COVID-19: a phase 1b, randomized, double-blinded, placebo-controlled study

Respiratory Research volume 25, Article number: 107 (2024) Cite this article

Abstract

Background

Targeting receptor-interacting serine/threonine protein kinase 1 could mitigate the devastating sequelae of the hyperinflammatory state observed in severe cases of COVID-19. This study explored the immunomodulatory and clinical effects of the receptor-interacting serine/threonine protein kinase 1 inhibitor SAR443122 (eclitasertib) in patients with severe COVID-19.

Methods

In this Phase 1b, double-blinded, placebo-controlled study (NCT04469621) a total of 82 patients were screened, of whom 68 patients were eligible and randomized (2:1) to receive eclitasertib 600 mg (300 mg twice daily) or placebo up to 14 days. Primary outcome was relative change in C-reactive protein from baseline to Day 7. Time to clinical improvement using 7-point ordinal scale, ventilator/respiratory failure-free days, change in SpO2/FiO2 ratio, and biomarkers of severe COVID-19 were explored.

Results

Geometric mean ratio (point estimate [90% confidence interval]) of the relative change from baseline in C-reactive protein with eclitasertib vs. placebo on Day 7 was 0.85 (0.49–1.45; p = 0.30). Median time to 50% decrease in C-reactive protein from baseline was 3 days vs. 5 days (p = 0.056) with eclitasertib vs. placebo. Median time to ≥ 2-point improvement on 7-point clinical symptoms scale was 8 days vs. 10 days with eclitasertib vs. placebo (p = 0.38). Mean ventilator/respiratory failure-free days, change in baseline-adjusted SpO2/FiO2 ratio, and clinical biomarkers showed consistent numerical improvements with eclitasertib vs. placebo. The most frequently reported treatment-emergent adverse events were gastrointestinal disorders and condition aggravated/worsened COVID-19 pneumonia.

Conclusions

Eclitasertib was well tolerated with consistent trends toward more rapid resolution of inflammatory biomarkers and clinical improvement in severe COVID-19 patients than placebo.

ClinicalTrials.gov identifier

NCT04469621, first posted on clinicaltrials.gov on July 14, 2020.

Background

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with influenza-like initial symptoms such as cough, fever, fatigue, headache, myalgias, and diarrhoea [1]. Patients with COVID-19 may become critically ill with acute respiratory distress syndrome (ARDS), which typically begins approximately 7–10 days after symptom onset [2] and is reported to occur in 29% of severe COVID-19 cases during the second week of hospitalisation [3]. Hyperinflammation in COVID-19, characterised by reactive hemophagocytic lymphohistiocytosis, may cause cytopenia, coagulopathy, tissue damage, liver injury, and macrophage activation [4,5,6,7,8]. The overwhelming production of inflammatory cytokines causes organ dysfunction and, eventually, death [9]. Several therapeutic interventions have been proposed to mitigate this inflammatory organ injury in viral pneumonia, and the value of glucocorticoids has been widely debated [10].

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is an intracellular protein that regulates the downstream signalling of tumour necrosis factor receptor 1 (TNFR1), toll-like receptors (TLRs) 3 and 4, and interferon receptors (IFNRs), by exhibiting both kinase activity-dependent and kinase activity-independent functions [11]. RIPK1-mediated signalling promotes inflammation and induces apoptotic or necroptotic cell death [12,13,14,15]. Both RIPK1 kinase-driven inflammation and cell death are key contributors to tumour necrosis factor-alpha (TNF-α)-induced systemic inflammatory response syndrome (SIRS) [16,17,18,19]. Moreover, RIPK1 kinase inhibition may suppress vascular dysfunction, endothelial/epithelial cell damage and exacerbated inflammatory signalling [16, 20]. It could complement antiviral therapy by inhibiting the inflammatory surge and necroptosis of pulmonary epithelial cells [21, 22], preventing, or reducing the effect of severe inflammation on respiratory function and other organ failure. Since RIPK1 is considered a master regulator of proinflammatory cell death, selectively targeting its kinase activity was hypothesised to mitigate the devastating sequelae of the hyperinflammatory state observed in late-stage severe COVID-19.

SAR443122 (eclitasertib) is a highly potent, selective oral inhibitor of RIPK1 kinase activity under development as an immunomodulatory drug for cutaneous lupus erythematosus and ulcerative colitis. We hypothesised that the use of eclitasertib to target patients with severe COVID-19 at an increased risk of SIRS would reduce inflammatory signalling and improve clinical outcomes. This double-blind, placebo-controlled, phase 1b study evaluated the immunomodulatory and clinical effect of eclitasertib vs. placebo in adult patients hospitalised with severe COVID-19.

Methods

Study design and patients

This multinational, multi-center, double-blind, placebo-controlled, randomized study enrolled adult patients hospitalised with severe COVID-19 (NCT04469621, first posted on clinicaltrials.gov on July 14, 2020). The study included three periods: screening period (up to 4 days), treatment period (up to 14 days + one end-of-treatment [EoT] day), and post-intervention observation period (up to 13 days) (Supplementary Fig. 1). Hospitalised adults (18–80 years) with severe COVID-19 infection, confirmed by RT-PCR, or other commercial or public health assay, who had laboratory signs consistent with systemic inflammation (C-reactive protein [CRP] > 50 mg/L) and required oxygen supplementation were enrolled in this study. Patients were excluded from enrolment if they (a) were unlikely to survive 48 h according to the investigator; (b) required the use of invasive or non-invasive positive pressure ventilation or more than 40% fraction of inhaled oxygen (FiO2) and more than 6 L/min of oxygen flow rate; (c) had significant liver enzyme abnormalities, thrombocytopenia, or anaemia; (d) were receiving immunomodulatory therapies (including, but not limited to, anti-IL-6, anti-IL-6R antagonists, Janus kinase inhibitors inhibitors, B-cell depleting agents, anakinra, abatacept, TNF inhibitors, alkylating agents cyclosporine, azathioprine, mycophenolate mofetil, methotrexate, intravenous immunoglobulin or convalescent serum), and/or chronic systemic corticosteroids at a dose higher than prednisone 10 mg or equivalent, for a non-COVID-19-related condition; (e) were pregnant or breastfeeding; or (f) had tuberculosis/non-tuberculous mycobacterial infections or suspected/known active systemic bacterial or fungal infections.

The study was approved by the Institutional Review Board/Institutional Ethics Committee of each study site (Supplementary Table 1), and the study was performed according to consensus ethics principles derived from international ethics guidelines, including the Declaration of Helsinki and the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines for Good Clinical Practice (GCP), all applicable laws, rules, and regulations. A signed written informed consent form was obtained from each patient before conducting any study-related procedures.

Procedure

Enrolled patients were randomized (stratified by site) using an interactive response technology (IRT) system in a 2:1 ratio to receive either eclitasertib 600 mg or a matching placebo daily for 14 days or up to discharge from the hospital, whichever came first. Each hospitalised patient was evaluated daily based on various factors including supplemental oxygen use, and the clinical status of each patient was assessed using a 7-point ordinal scale as follows: 1 = death; 2 = hospitalised, on invasive mechanical ventilation or extracorporeal membrane oxygenation (ECMO); 3 = hospitalised, on non-invasive ventilation or high-flow oxygen devices; 4 = hospitalised, requiring supplemental oxygen; 5 = hospitalised, not requiring supplemental oxygen – requiring ongoing medical care (COVID-19 related or otherwise); 6 = hospitalised, not requiring supplemental oxygen – no longer requires ongoing medical care; and 7 = not hospitalised. Concomitant therapy including thrombolytic therapy and vasopressor treatment was recorded.

Biomarker assessments included clinical laboratory variables (CRP, laboratory markers of severe COVID-19 [D-dimer] [23], and haematology parameters [white blood cell count, differential blood lymphocytes, and neutrophil/lymphocyte ratio]). Blood samples for laboratory assessments, including haematology and quantification of CRP and D-dimer levels, were collected on Days 1 (pre-dose), 3, 5, 7, and 15. Blood samples for lactate dehydrogenase (LDH) and ferritin were collected on Day 1 (pre-dose) and then as and when available per clinical care. For pharmacodynamic assessment, blood samples for cytokine and chemokine biomarker analysis were collected on Days 1 (pre-dose), 3, 5, 7, and 15. The quantitative viral load of SARS-CoV-2 was measured at baseline and on Days 3, 7, and 15/ EoT using reverse transcription-polymerase chain reaction.

The assessment of pharmacokinetics (PK) included the measurement of eclitasertib plasma concentrations (using a validated liquid chromatography–mass spectrometry [LC/MS] method) over 2 weeks of treatment at selected timepoints: within 2–5 h after the first morning dose (around maximum concentration [Cmax]) on Day 1, before the morning dosing on Day 3 (trough concentration [Ctrough]), and before the morning dose and within 2–5 h after the morning dose on Days 7 and 14. Moreover, a PK sample was obtained within 1 h before discharge when this occurred before Day 14. Individual PK parameters such as Cmax, Ctrough, and area under the curve from 0 to 12 h (AUC0–12 h) were calculated using a Maximum a posteriori Bayesian estimation and a population PK model.

Outcomes

The primary endpoint was a relative change in CRP level from baseline to Day 7, defined as the ratio of CRP level on Day 7 vs. CRP level at baseline. The key secondary endpoints included: (a) time to 50% decrease in CRP level from baseline; (b) time to improvement in oxygenation as measured by oxygen saturation ≥ 92% breathing in room air over 48 h or until discharge; and (c) change in peripheral blood oxygen saturation/fraction of inspired oxygen (SpO2/FiO2) ratio from baseline to Day 7. Other secondary endpoints included: (a) number of days without the need for oxygen support and alive within 28 days post-randomization (defined as any calendar day with oxygen saturation ≥ 92% breathing in room air) up to Day 28; (b) number of ventilator-free and respiratory failure-free days (VFDs/RFFDs) and alive up to Day 28; (c) incidence of death up to Day 28; (d) percentage of patients receiving thrombolytic and vasopressor treatment up to Day 28; and (e) change in inflammatory biomarkers (white blood cell count, blood neutrophil and lymphocytes counts, and neutrophil/lymphocyte ratio), and markers of severe COVID-19 (D-dimer) from baseline to Day 7 and at EoT. The exploratory endpoints included: (a) change in ferritin and LDH from baseline to Day 7 and at EoT; (b) time to clinical improvement on the 7-point ordinal scale; (c) change in peripheral cytokine and biomarker levels from baseline up to EoT; (d) quantitative SARS-CoV-2 viral load in blood at baseline and on Days 3, 5, and 7 and at EoT; and (e) eclitasertib plasma pharmacokinetic (PK) parameters (for Cmax, area under the curve over the dosing interval [AUC0–12 h], and Ctrough). Safety was evaluated up to 28 days post the initial dosing. Adverse event coding was performed using the Medical Dictionary for Regulatory Activities version 23.1.

VFDs were defined as the number of calendar days within the 28 days after randomization for which the patient was alive and without the use of invasive mechanical ventilation or non-invasive mechanical ventilation or extracorporeal life support. RFFDs were defined as the number of days for which the patient was alive and not on invasive mechanical ventilation, non-invasive mechanical ventilation, or high-flow nasal cannula at ≥ 50% FiO2 and ≥ 30 L/min of oxygen flow during the 28-day study period.

Statistical analyses

A sample size of 60 evaluable patients allocated in a 2:1 ratio was required for a t-test to achieve an overall power of about 80% at one-sided significance level alpha = 0.05 by assuming a standard deviation of 1.04 and a true difference between treatment groups in mean log-relative change from baseline in CRP of log (0.5) or equivalently a 50% reduction in geometric mean relative to placebo. The efficacy population included all randomized patients who received at least one complete dose of the study drug with at least one post-study drug administration measurement and without a major protocol deviation. Safety population included all randomized patients exposed to the study drug. The PK analysis population included all patients who received any study drug and who had at least one non-missing result following the first dose of the study drug. For the primary analysis, the relative change in CRP from baseline was analysed using a linear mixed model with repeated measurements (MMRM) fitted on log-relative change from baseline for Days 3, 5, 7, and 15.

Kaplan-Meier analysis was performed to determine the following variables: (a) time to 50% decrease in CRP level from baseline; (b) time to improvement in oxygenation as measured by oxygen saturation ≥ 92% breathing in room air over 48 h or until discharge; and (c) time to improvement by at least two points on the 7-point clinical scale. Treatment arms were compared in an exploratory fashion using the log-rank test.

For the change from baseline in SpO2/FiO2 ratio, a linear MMRM was fitted based on observed values for Days 2 through 7 and 15. For other secondary endpoints, data were summarised by treatment arms with descriptive statistics except for laboratory markers of inflammation and severe COVID-19.

The data were analysed using SAS® (Unix, Version 9.4, SAS Institute, NC USA) and R software (R Core Team [2018]). This study is registered with the ClinicalTrials.gov registry, NCT04469621.

Results

Between 17 July 2020 and 23 October 2020, 82 patients were screened in 10 centers in five countries (Argentina, Brazil, Chile, Mexico, and the Russian Federation), of whom 68 patients were eligible and randomized (Fig. 1). Data for 67 and 60 patients were available for the safety and efficacy analyses, respectively. On Day 7, data for only 57.9% (11 of 19 patients) and 48.8% (20 of 41 patients) were available in the placebo and treatment groups, respectively, mainly because the patients were discharged from the hospital after COVID-19 recovery. Demographic and other baseline characteristics including medical history profiles specific to this study were generally similar between the two treatment groups (Table 1), although the baseline mean CRP level was slightly higher in the placebo group than the eclitasertib group (133.5 mg/L vs. 105.6 mg/L). Corticosteroids, as the standard of care, were administered in 65% and 63.8% of the patients in the placebo and eclitasertib groups, respectively.

Fig. 1
figure 1

Trial profile

Full size image
Table 1 Demographics and patient characteristics at baseline (safety population)
Full size table

The primary analysis showed that the decrease in CRP from baseline was numerically greater in the eclitasertib than the placebo group on Days 3, 5, and 7 (Fig. 2). A similar trend was observed after imputing missing CRP values using last observation carried forward (LOCF) analysis. In the primary MMRM analysis (efficacy population), the ratio of the baseline-adjusted relative change in CRP in the eclitasertib vs. placebo group from baseline to Day 7 was 0.85 (90% CI: 0.49–1.45). Although not significant (p = 0.302), the decrease in CRP in the eclitasertib vs. placebo group from baseline to Day 7 tended to be rapid.

Fig. 2
figure 2

Boxplot of raw value of CRP over time (efficacy population).The teal diamond corresponds to the group arithmetic mean. The horizontal line in the box interior represents the group median. The length of the box represents the interquartile range (the distance between the 25th and 75th percentiles). The other symbols correspond to patient values

Full size image

Kaplan-Meier curves for time to 50% improvement in CRP levels, improvement in oxygenation, time to liberation from supplemental oxygen, and improvement by at least two points in the category of 7-point scale are presented in Fig. 3. Median time to 50% decrease in CRP level from baseline was 3 vs. 5 days in the eclitasertib vs. placebo group (p = 0.056; Fig. 3A). Eclitasertib achieved faster improvement in oxygenation (SpO2/FiO2 ratio) than placebo (Fig. 3B). Time to liberation from supplemental oxygen was 6 vs. 7 days (p = 0.185) in the eclitasertib vs. placebo group, respectively (Fig. 3C). Median time for ≥ 2-point improvement on the 7-point scale was 8 vs. 10 days (p = 0.38) in the eclitasertib vs. placebo group (Fig. 3D).

Fig. 3
figure 3

Time to 50% improvement in CRP levels (3 A), time to improvement of oxygenation (SpO2 /FiO2 ratio) (3B), time to liberation from supplemental oxygen (3 C), and improvement by at least two points on 7-point clinical scale (3D) for efficacy population. Event times for patients not meeting this criterion was censored at the last observation time point. For patients who died during the study without experiencing the event, the last observation collected is carried forward to the longest duration of follow-up for any patient plus 1 day. Event is defined as 50% decrease relative to baseline CRP level (3 A), presence of SpO2 ≥92% without use of any supplemental oxygen device on two consecutive days or on the day of discharge (3 C), and an improvement of at least 2 points in category of 7-point clinical scale from baseline (3D)

Full size image

Baseline-adjusted mean change in the SpO2/FiO2 ratio from baseline to Day 7 was numerically greater in the eclitasertib than the placebo group (Table 2). A similar result favouring eclitasertib over placebo was observed at all visits modelled using MMRM, with the largest increase observed on Day 6 of 28.71 (90% CI: −15.14 to 72.56). Imputation analysis using LOCF also suggested more rapid improvement in SpO2/FiO2 ratio in the eclitasertib vs. placebo group.

Table 2 Change from baseline in SpO2/FiO2 ratio (efficacy population)
Full size table

During the 28-day study period, eclitasertib treatment was favoured over the placebo in terms of the number of days without need for oxygen support (2.5 days reduction); mean VFD (2.6 days reduction) and mean RFFD (2.6 days reduction) also showed similar trends (Table 3). During the 15-day treatment period, a difference of 1 day in the mean days (SD) without need for oxygen support (placebo: 7.8 [5.3] vs. eclitasertib 600 mg: 8.8 [4.6]), VFDs (12.4 [5.3] vs. 13.9 [4.0]), and RFFDs (12.3 [5.4] vs. 13.9 [4.0]) was observed in the eclitasertib group and placebo group. The proportion of patients receiving anti-thrombotic treatment up to Day 28 was slightly lower in the eclitasertib group (42.1%) than the placebo group (48.8%). Moreover, fewer patients received vasopressor treatment in the eclitasertib group (2.4% vs. 15.8%) than in the placebo group (Table 3).

Table 3 Summary of secondary efficacy endpoints (efficacy population)
Full size table

Regarding laboratory markers of severe COVID-19, numerically larger decreases in the baseline-adjusted geometric means of relative changes from baseline were observed in the eclitasertib vs. placebo group for the following: D-dimer on Day 7 only, leukocytes on Day 7 only, neutrophil/lymphocyte ratio on Day 7 eclitasertib at EoT, and LDH on Day 7 and at EoT. Differences in ferritin levels between the eclitasertib and placebo groups were not meaningful (Table 4).

Table 4 Summary of laboratory markers of severe COVID-19 (efficacy population)
Full size table

Relative changes from baseline in peripheral cytokines and biomarkers were analysed for the two treatment groups over time up to EoT (Day 15) (Supp Table 2). The decreases in plasma IL-6, IL-10, and IL-6/IL-10 ratio were numerically greater in the eclitasertib than the placebo group (Supp Fig. 2). The viral load tended to decrease, and the number of negative SARS-CoV-2 tests increased over time in the eclitasertib group compared with the placebo group (Supp Table 3). Eclitasertib was associated with faster improvement on the 7-point ordinal scale vs. placebo (Supp Fig. 3).

In patients with severe COVID-19, after administration of eclitasertib 300 mg BID for up to 14 days, steady state was reached on Day 3 with mean (SD) values of 2025 (783) ng/mL for Ctrough, 5169 (1056) ng/mL for Cmax, and 42,214 (10,949) ng·h/mL for AUC0–12 h.

Overall, 4 (5.9%) deaths, all due to COVID-19 complications or worsening of COVID-19, occurred during the study up to Day 28. Two deaths each were reported in the placebo group (10.0%) and the eclitasertib group (4.3%). The most frequently reported treatment-emergent adverse events were gastrointestinal disorders (placebo vs. eclitasertib: 20% vs. 12.8%), condition aggravated/worsened COVID-19 pneumonia (placebo vs. eclitasertib: 20.0% vs. 8.5%), elevated alanine transaminase (placebo vs. eclitasertib: 10.0% vs. 12.8%), and infections (placebo vs. eclitasertib: 25% vs. 8.5%) (Supp Table 4). Most reported treatment-emergent adverse events were considered not related to the study drug by the principal investigators.

Discussion

To our knowledge, this is the first study to explore the immunomodulatory and clinical effects of a RIPK1 inhibitor (eclitasertib) vs. placebo in patients with severe COVID-19. This study did not demonstrate a statistically significant difference in the selected primary endpoint of relative change in CRP on Day 7 from baseline between the treatment and placebo groups. However, the relative CRP decrease from baseline was numerically greater in the eclitasertib group vs. placebo on Day 7, despite the prespecified primary analysis showing about 50% missing data due to a high proportion of patients recovering and being discharged earlier from the hospital than anticipated. A time-to-event Kaplan-Meier analysis, which is less sensitive to missing data, showed an earlier decrease in CRP for eclitasertib on Day 7; the difference to placebo approached statistical significance (p = 0.056). Of note, this effect was observed despite systemic glucocorticoids, known to decrease CRP concentrations, being used in approximately 65% of patients in both groups. Eclitasertib also achieved numerically greater improvement in other inflammatory biomarkers including some of the markers of COVID-19 severity (leukocytes, neutrophil/lymphocyte ratio, LDH, IL-6, IL-8, and IL-6/IL-10) than placebo. Consistent trends toward greater improvements in clinical endpoints were noted in the eclitasertib group than the placebo group with a trend towards faster improvement in oxygenation (SpO2/FiO2 ratio), along with improvements in SpO2 and 7-point clinical scale scores over the treatment period (Fig. 3). Moreover, eclitasertib showed slight improvements in VFDs and RFFDs, with a treatment difference of 2.6 days for each compared with placebo.

Current COVID-19 therapies such as baricitinib, dexamethasone, remdesivir, anakinra, and tocilizumab reduce 28-day mortality [10, 24,25,26,27,28,29]. In the current study, the 28-day mortality was 4.3% (2/47) in the eclitasertib 600 mg group and 10% (2/19) in the placebo group. However, the small sample size precludes any meaningful interpretation of these data and warrants larger, adequately powered studies.

In the current study, the median time to an improvement by at least 2 points on the 7-point clinical scale was also numerically shorter in patients treated with eclitasertib than in those treated with placebo, with a difference of 2 days (p = 0.377). The median time to an improvement by one category on the ordinal scale was reported to be 6 days in the baricitinib plus remdesivir group and 8 days in the placebo group (rate ratio: 1.21; 95% CI: 1.06–1.39) [24]. Patients treated with remdesivir had a shorter time to improvement in one or two categories on the ordinal scale from baseline than patients in the placebo group (one-category improvement: median: 7 vs. 9 days and two-category improvement: median: 11 vs. 14 days) [25]. The present study analysed patients not requiring mechanical or high-flow oxygen ventilation at study entry. Thus, the maximum number of VFDs or RFFDs was theoretically 28 days through the study period. A difference of 2 days between eclitasertib and placebo in RFFD is considered clinically relevant [30]. In the current study, the observed mean RFFD was 23.3 days in the placebo group vs. 25.9 days in the eclitasertib group. In our study, new use of mechanical ventilation or extracorporeal membrane oxygenation (ECMO) was observed in 1 (2.4%) patient in the eclitasertib group and 3 (15.8%) patients in the placebo group. The incidence of new use of mechanical ventilation or ECMO in the baricitinib plus remdesivir and placebo group is reported as 10.0% and 15.2%, respectively [24]. The incidence of new use of mechanical ventilation or ECMO in the remdesivir and placebo group is reported to be 13.0% and 23%, respectively [25].

High neutrophil counts and marked lymphopenia (elevated neutrophil/lymphocyte ratio) are associated with severe COVID-19 disease and the risk of developing sepsis with rapid progression. Moreover, a high baseline level of pro-inflammatory biomarkers and an increase in LDH are associated with COVID-19 disease progression and poor outcomes. In the current study, the leukocyte count, neutrophil/lymphocyte ratio, and LDH level as well as plasma IL-6, IL-8 and IL-6/IL-10 showed a trend towards being lower in the eclitasertib than placebo group. The trend towards a decrease in neutrophil/lymphocyte ratio and in levels of pro-inflammatory biomarkers in the eclitasertib group are coherent with the observed lesser use of vasopressors (norepinephrine) in the eclitasertib than the placebo group.

It is to be noted that Xu et al. (2021) recently found evidence of RIPK1 activation in human COVID-19 lung pathological samples, cultured human lung organoids and ACE2 transgenic mice infected by SARS-CoV-2 and as one of the drivers of inflammatory cytokine storm [31]. Strikingly activated RIPK1 were enriched in ciliated epithelial cells in the airways of severe COVID-19 patients which corresponded to elevated levels of proinflammatory cytokines in the BALF, lung and PBMCs in patients as compared to that of control individuals. Inhibition of RIPK1 with Nec-1s reduced mortality and lung viral load while blocking CNS manifestation of SARS-CoV-2 in ACE2 transgenic mice [31]. Inhibition of RIPK1 kinase activity reduced the viral load and inflammation in lung organoids infected with SARS-CoV-2. Mechanistically, in cells with a high viral load, they found that the RNA-dependent RNA polymerase of SARS-CoV-2, NSP12, a highly conserved central component of coronaviral replication and transcription machinery, promoted the activation of RIPK1. In addition, they showed that inhibition of RIPK1 downregulated the transcriptional induction of proinflammatory cytokines and host factors including ACE2 and EGFR that in turn promote viral entry into cells. It was demonstrated that Nec-1s inhibits propagation of NSP12 323P and 323 L variants (that have increased ability to activate RIPK1) SARS-CoV-2 and inflammation [32, 33]. Overall, these data support the rationale for RIPK1 inhibition as a potential therapeutic option for the treatment of COVID-19. While there were no statistically significant differences in primary and key secondary outcomes in this study, numerical differences consistently favoured eclitasertib group over placebo. Whether RIPK1 inhibition can emerge as an effective therapeutic strategy, either alone or in combination with other immunomodulatory therapies, in patients with severe COVID-19 warrants further investigation.

The incidence of AEs was similar between eclitasertib and placebo groups. Notably, the incidence of worsening of COVID-19 and infections and infestations were lower in patients treated with eclitasertib compared with those in the placebo group. The safety data suggest that overall, eclitasertib had a favourable safety profile and was well-tolerated.

The present study with eclitasertib has some limitations inherent with proof-of-mechanism, Phase 1b studies. The number of patients included in the study was limited, leading to low statistical power to detect differences in clinical outcomes. The ability to collect data was limited during the pandemic. The prespecified analysis on Day 7 for the primary endpoint was compromised by about 50% missing data due to the early discharge of patients from the hospital.

Conclusions

Eclitasertib was well-tolerated. Consistently greater numerical improvements were observed across multiple biomarkers and clinical outcomes for eclitasertib than placebo. Larger confirmatory trials are needed to further assess the clinically significant effects of eclitasertib.

Data availability

The qualified researchers may request access to patient-level data and related documents [such as clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan, and dataset specifications]. Patient-level data will be anonymised, and study documents will be redacted to protect the privacy of trial patients. Further details on Sanofi’s data sharing criteria, eligible studies, and process for requesting access can be found at https://vivli.org/.

References

  1. Caporali R, Crepaldi G, Codullo V, Benaglio F, Monti S, Todoerti M, et al. 20 years of experience with tumour necrosis factor inhibitors: what have we learned? Rheumatology (Oxford). 2018;57(57 Suppl 7):vii5–vii10.

    Article  CAS  PubMed  Google Scholar 

  2. Berlin DA, Gulick RM, Martinez FJ. Severe Covid-19. N Engl J Med. 2020;383(25):2451–60.

    Article  CAS  PubMed  Google Scholar 

  3. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Huang KJ, Su IJ, Theron M, Wu YC, Lai SK, Liu CC, et al. An interferon-gamma-related cytokine storm in SARS patients. J Med Virol. 2005;75(2):185–94.

    Article  CAS  PubMed  Google Scholar 

  5. Tisoncik JR, Korth MJ, Simmons CP, Farrar J, Martin TR, Katze MG. Into the eye of the cytokine storm. Microbiol Mol Biol Rev. 2012;76(1):16–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Guo T, Fan Y, Chen M, Wu X, Zhang L, He T, et al. Cardiovascular implications of fatal outcomes of patients with Coronavirus Disease 2019 (COVID-19). JAMA Cardiol. 2020;5(7):811–8.

    Article  PubMed  Google Scholar 

  7. Hashizume M. Outlook of IL-6 signaling blockade for COVID-19 pneumonia. Inflamm Regen. 2020;40:24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Moore JB, June CH. Cytokine release syndrome in severe COVID-19. Science. 2020;368(6490):473–4.

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Scaglioni V, Soriano ER. Are superantigens the cause of cytokine storm and viral sepsis in severe COVID-19? Observations and hypothesis. Scand J Immunol. 2020;92(6):e12944.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Group RC, Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, et al. Dexamethasone in hospitalized patients with Covid-19. N Engl J Med. 2021;384(8):693–704.

    Article  Google Scholar 

  11. Degterev A, Ofengeim D, Yuan J. Targeting RIPK1 for the treatment of human diseases. Proc Natl Acad Sci U S A. 2019;116(20):9714–22.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mifflin L, Ofengeim D, Yuan J. Receptor-interacting protein kinase 1 (RIPK1) as a therapeutic target. Nat Rev Drug Discov. 2020;19(8):553–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kondylis V, Pasparakis M. RIP kinases in liver cell death, inflammation and Cancer. Trends Mol Med. 2019;25(1):47–63.

    Article  CAS  PubMed  Google Scholar 

  14. Pasparakis M, Vandenabeele P. Necroptosis and its role in inflammation. Nature. 2015;517(7534):311–20.

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Newton K. Multitasking kinase RIPK1 regulates cell death and inflammation. Cold Spring Harb Perspect Biol. 2020;12(3).

  16. Zelic M, Roderick JE, O’Donnell JA, Lehman J, Lim SE, Janardhan HP, et al. RIP kinase 1-dependent endothelial necroptosis underlies systemic inflammatory response syndrome. J Clin Invest. 2018;128(5):2064–75.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Takahashi N, Duprez L, Grootjans S, Cauwels A, Nerinckx W, DuHadaway JB, et al. Necrostatin-1 analogues: critical issues on the specificity, activity and in vivo use in experimental disease models. Cell Death Dis. 2012;3(11):e437.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Duprez L, Takahashi N, Van Hauwermeiren F, Vandendriessche B, Goossens V, Vanden Berghe T, et al. RIP kinase-dependent necrosis drives lethal systemic inflammatory response syndrome. Immunity. 2011;35(6):908–18.

    Article  CAS  PubMed  Google Scholar 

  19. Newton K, Dugger DL, Maltzman A, Greve JM, Hedehus M, Martin-McNulty B, et al. RIPK3 deficiency or catalytically inactive RIPK1 provides greater benefit than MLKL deficiency in mouse models of inflammation and tissue injury. Cell Death Differ. 2016;23(9):1565–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Delvaeye T, De Smet MAJ, Verwaerde S, Decrock E, Czekaj A, Vandenbroucke RE, et al. Blocking connexin43 hemichannels protects mice against tumour necrosis factor-induced inflammatory shock. Sci Rep. 2019;9(1):16623.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  21. Jin L, Liu P, Yin M, Zhang M, Kuang Y, Zhu W. RIPK1: a rising star in inflammatory and neoplastic skin diseases. J Dermatol Sci. 2020;99(3):146–51.

    Article  CAS  PubMed  Google Scholar 

  22. Lin J, Kumari S, Kim C, Van TM, Wachsmuth L, Polykratis A, et al. RIPK1 counteracts ZBP1-mediated necroptosis to inhibit inflammation. Nature. 2016;540(7631):124–8.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. Paliogiannis P, Mangoni AA, Dettori P, Nasrallah GK, Pintus G, Zinellu A. D-Dimer concentrations and COVID-19 severity: a systematic review and Meta-analysis. Front Public Health. 2020;8:432.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kalil AC, Patterson TF, Mehta AK, Tomashek KM, Wolfe CR, Ghazaryan V, et al. Baricitinib plus Remdesivir for hospitalized adults with Covid-19. N Engl J Med. 2021;384(9):795–807.

    Article  CAS  PubMed  Google Scholar 

  25. Beigel JH, Tomashek KM, Dodd LE, Mehta AK, Zingman BS, Kalil AC, et al. Remdesivir for the treatment of Covid-19 - final report. N Engl J Med. 2020;383(19):1813–26.

    Article  CAS  PubMed  Google Scholar 

  26. Investigators R-C, Gordon AC, Mouncey PR, Al-Beidh F, Rowan KM, Nichol AD, et al. Interleukin-6 receptor antagonists in critically ill patients with Covid-19. N Engl J Med. 2021;384(16):1491–502.

    Article  Google Scholar 

  27. Group RC. Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. Lancet. 2021;397(10285):1637–45.

    Article  Google Scholar 

  28. Kyriazopoulou E, Poulakou G, Milionis H, Metallidis S, Adamis G, Tsiakos K, et al. Early treatment of COVID-19 with anakinra guided by soluble urokinase plasminogen receptor plasma levels: a double-blind, randomized controlled phase 3 trial. Nat Med. 2021;27(10):1752–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kyriazopoulou E, Huet T, Cavalli G, Gori A, Kyprianou M, Pickkers P, et al. Effect of anakinra on mortality in patients with COVID-19: a systematic review and patient-level meta-analysis. Lancet Rheumatol. 2021;3(10):e690–e7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Tomazini BM, Maia IS, Cavalcanti AB, Berwanger O, Rosa RG, Veiga VC, et al. Effect of dexamethasone on days alive and Ventilator-Free in patients with moderate or severe Acute Respiratory Distress Syndrome and COVID-19: the CoDEX Randomized Clinical Trial. JAMA. 2020;324(13):1307–16.

    Article  CAS  PubMed  Google Scholar 

  31. Xu G, Li Y, Zhang S, Peng H, Wang Y, Li D, et al. SARS-CoV-2 promotes RIPK1 activation to facilitate viral propagation. Cell Res. 2021;31(12):1230–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pachetti M, Marini B, Benedetti F, Giudici F, Mauro E, Storici P, et al. Emerging SARS-CoV-2 mutation hot spots include a novel RNA-dependent-RNA polymerase variant. J Transl Med. 2020;18(1):179.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Priesemann V, Balling R, Brinkmann MM, Ciesek S, Czypionka T, Eckerle I, et al. An action plan for pan-european defence against new SARS-CoV-2 variants. Lancet. 2021;397(10273):469–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the following individuals from Sanofi: Inoncent Agueusop who contributed to data analysis and interpretation, Manon Bouet and Charlene Buffat who contributed to study conduct and data acquisition, Marie Renouard and Clementine Chopin who contributed to the provision of study drug, and Daniel Wilson who provided toxicological insights to ensure patient safety. The authors would like to thank the following Sanofi employees at the time of study setup and conduct: John Caminis, Sabine Schnyder, Keith Harris, and Pierre-Philippe Luyet. Eclitasertib is under license from Denali Therapeutics. Medical writing support was provided by Rakesh Ojha, PhD, of Sanofi.

Funding

The study was funded by Sanofi. The funder of the study was involved in the study design, data collection, data analysis, data interpretation, and writing of the report.

Author information

Author notes

  1. Peter Florian

    Present address: Head of Fibrotic Disease Research, Boehringer Ingelheim Vetmedica GmbH, Global AH Research, Ingelheim, Germany

Authors and Affiliations

  1. Translational Medicine and Early Development (TMED)/Clinical Pharmacology (TMCP) and Neuro and Neuro-Immunology, 371 Rue du Professeur Blayac, Sanofi, Montpellier, 34080, France

    Pierre-Francois Clot

  2. TMED Pharmacokinetics Dynamics and Metabolism, Sanofi, Montpellier, France

    Christine Farenc

  3. Early Clinical Development Immunology and Inflammation, Sanofi, Cambridge, MA, United States of America

    Benjamin T. Suratt

  4. CEP Germany, Sanofi Deutschland GmbH, Frankfurt, Germany

    Tillmann Krahnke

  5. Early Development Operations, Sanofi, Montpellier, France

    Agnes Tardat

  6. Type 1/17 Immunology and Arthritis, Sanofi Deutschland GmbH, Frankfurt, Germany

    Peter Florian

  7. TMED Biomarkers and Clinical Bioanalysis, Sanofi, Framingham, MA, United States of America

    Robert Pomponio

  8. Global Development in Immunology and Inflammation, Sanofi, Cambridge, MA, United States of America

    Naimish Patel

  9. Immunology and Inflammation Development Franchise, Sanofi, Bridgewater, NJ, United States of America

    Maria Wiekowski & Heribert Staudinger

  10. Sanofi, Bridgewater, NJ, United States of America

    Yong Lin

  11. Department of Internal Medicine, Cochin Hospital, Paris, France

    Benjamin Terrier

Authors
  1. Pierre-Francois Clot
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christine Farenc
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benjamin T. Suratt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tillmann Krahnke
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Agnes Tardat
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Peter Florian
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Robert Pomponio
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Naimish Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Maria Wiekowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yong Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Benjamin Terrier
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Heribert Staudinger
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PFC, CF, BS, TK, AT, PF, RP, NP, MW, YL, BT, and HS were involved in the design of the study, data analysis and interpretation, and critical revision of the manuscript. AT led the operational setup and conduct of the clinical trial. All authors had full access to the data in the study and accept final responsibility to submit the manuscript for publication.

Corresponding author

Correspondence to Pierre-Francois Clot.

Ethics declarations

Ethical approval and consent to participate

The study was approved by the Institutional Review Board/Institutional Ethics Committee of each study site, and the study was performed according to consensus ethics principles derived from international ethics guidelines, including the Declaration of Helsinki and the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines for Good Clinical Practice (GCP), all applicable laws, rules, and regulations. A signed written informed consent form was obtained from each patient before conducting any study-related procedures.

Consent for publication

Not applicable.

Competing interests

Clot PF, Farenc C, Krahnke T, Staudinger H, Suratt BT, Pomponio R, Tardat A, Patel N, Wiekowski M, Lin Y: Sanofi employees, may hold stock and/or stock options in the company. Florian P: Sanofi employee when this work was conducted and holds Sanofi stock; now works for Boehringer Ingelheim Vetmedica GmbH. Terrier B: Received consulting fees from Roche, Chugai, GSK, AstraZeneca, BMS, Terumo BCT, Sanofi, LFB, Grifols.

This work was previously presented at

European Respiratory Society – 31st Annual Congress (ERS 2021).

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Clot, PF., Farenc, C., Suratt, B.T. et al. Immunomodulatory and clinical effects of receptor-interacting protein kinase 1 (RIPK1) inhibitor eclitasertib (SAR443122) in patients with severe COVID-19: a phase 1b, randomized, double-blinded, placebo-controlled study. Respir Res 25, 107 (2024). https://doi.org/10.1186/s12931-024-02670-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12931-024-02670-z

Keywords

Respiratory Research

ISSN: 1465-993X

Contact us