Serum IL-8 as a Determinant of Response to Phosphodiesterase-4 Inhibition in Chronic Obstructive Pulmonary Disease

Mirco Govoni; Michele Bassi; Debora Santoro; Sinéad Donegan; Dave Singh

doi:10.1164/rccm.202301-0071OC

. 2023 May 16;208(5):559–569. doi: 10.1164/rccm.202301-0071OC

Serum IL-8 as a Determinant of Response to Phosphodiesterase-4 Inhibition in Chronic Obstructive Pulmonary Disease

Mirco Govoni ^1,^✉, Michele Bassi ¹, Debora Santoro ¹, Sinéad Donegan ², Dave Singh ³

PMCID: PMC10492261 PMID: 37192443

Abstract

Rationale

Phosphodiesterase-4 (PDE4) inhibitors have demonstrated increased efficacy in patients with chronic obstructive pulmonary disease who had chronic bronchitis or higher blood eosinophil counts. Further characterization of patients who are most likely to benefit is warranted.

Objective

To identify determinants of response to the PDE4 inhibitor tanimilast.

Methods

A PDE4 gene expression signature in blood was developed by unsupervised clustering of the ECLIPSE study dataset (ClinicalTrials.gov ID: NCT 00292552; Gene Expression Omnibus Series ID: GSE76705). The signature was further evaluated using blood and sputum transcriptome data from the BIOMARKER study (NCT 03004417; GSE133513), enabling validation of the association between PDE4 signaling and target biomarkers. Predictivity of the associated biomarkers against clinical response was then tested in the phase-2b PIONEER tanimilast study (NCT 02986321).

Measurements and Main Results

The PDE4 gene expression signature developed in the ECLIPSE dataset classified subgroups of patients associated with different PDE4 signaling in the BIOMARKER cohort with an area under the receiver operator curve of 98%. In the BIOMARKER study, serum IL-8 was the only variable that was consistently associated with PDE4 signaling, with lower levels associated with higher PDE4 activity. In the PIONEER study, the exacerbation rate reduction mediated by tanimilast treatment increased up to twofold in patients with lower IL-8 levels; 36% versus 18%, reaching statistical significance at ⩽20 pg/ml (P = 0.035). The combination with blood eosinophils ⩾150 μl⁻¹ or chronic bronchitis provided further additive exacerbation rate reduction: 45% (P = 0.013) and 47% (P = 0.027), respectively.

Conclusions

Using selected heterogeneous datasets, this analysis identifies IL-8 as an independent predictor of PDE4 inhibition, as tanimilast had a greater effect on exacerbation prevention in patients with chronic obstructive pulmonary disease who had lower baseline serum IL-8 levels. Testing of this biomarker in other datasets is warranted.

Clinical trial registered with www.clinicaltrials.gov (NCT00292552 [Gene Expression Omnibus Series ID: GSE76705], NCT03004417 [GSE133513], and NCT02986321).

Keywords: chronic obstructive pulmonary disease, phosphodiesterase 4 inhibitors, IL-8, blood eosinophils, chronic bronchitis

At a Glance Commentary

Scientific Knowledge on the Subject

There is a high unmet medical need for patients who are still symptomatic despite treatment with inhaled corticosteroids and bronchodilators. Different studies investigated the potential of both inhaled and oral phosphodiesterase-4 (PDE4) inhibitors (tanimilast and roflumilast, respectively) to provide an additional beneficial effect in specific subpopulations. As of now, clinical studies have shown an enriched biological and clinical effect in patients characterized by features of chronic bronchitis and/or by higher eosinophil counts. However, further characterization of patients most likely to benefit is warranted.

What This Study Adds to the Field

In this study, serum IL-8 was identified as a possible independent determinant of response to PDE4 inhibition after a systematic stepwise analysis of different datasets for chronic obstructive pulmonary disease (COPD). First, an inverse biological association between circulating IL-8 and PDE4 signaling was identified. Then, an increased benefit of tanimilast was shown in decreasing exacerbations in patients with COPD who had lower baseline serum IL-8. Along with chronic bronchitis and blood eosinophil counts, serum IL-8 may represent an accessible, noninvasive biomarker, enabling an optimized therapeutic approach for the use of drugs such as PDE4 inhibitors.

Patients with chronic obstructive pulmonary disease (COPD) who exacerbate despite receiving inhaled triple therapy—an inhaled glucocorticoid (ICS), a long-acting β2-agonist (LABA), and a long-acting muscarinic-receptor antagonist (LAMA) (1, 2)—have limited additional pharmacological treatment options. The oral phosphodiesterase-4 (PDE4) inhibitor roflumilast is the only approved antiinflammatory drug that reduces exacerbation rates when added to triple therapy (3–8). However, roflumilast treatment has a high rate of discontinuation because of class-related side effects, including diarrhea, nausea, weight loss, and psychiatric symptoms (9, 10).

Tanimilast (the nonproprietary name of CHF6001) is a novel highly potent and selective inhaled PDE4 inhibitor that has demonstrated antiinflammatory effects without class-related systemic side effects in early-phase COPD clinical trials (11–15). Tanimilast is in phase-3 clinical development as a treatment, when added to triple therapy, to prevent exacerbations in patients with COPD who have a history of exacerbations (16, 17).

Biomarkers can help identify patients who are more likely to respond positively to a pharmacological intervention; for example, blood eosinophil counts (BECs), used with exacerbation history, predict which patients with COPD are more likely to benefit from ICS treatment (18). BECs identify patients with COPD with increased type-2 airway inflammation, providing an explanation for the differential response to ICS (19, 20). Combining clinical phenotype information with biological information (endotyping) enables a precision medicine approach to optimize the therapeutic index (benefit-to-risk ratio) on an individual level. Analyses of clinical trials involving tanimilast or roflumilast have identified subpopulations with greater clinical responses to treatment, notably features of chronic bronchitis and higher BECs (12, 21–23).

Ideally, a predictive biomarker of pharmacological response should be easily accessible, such as from blood, and provide reproducible results (24). PDE4 inhibitors are unlikely to be effective in individuals without activation of the PDE4 pathway. Identification of blood-based biomarkers associated with PDE4 activation may enable an optimized therapeutic approach for PDE4 inhibitors.

This paper describes the identification of predictive biomarkers of response to tanimilast through a systematic stepwise analysis of different COPD datasets. First, we identified biomarkers in subgroups of patients that were associated with different PDE4-related signaling patterns. Then, we tested the predictivity of the identified biomarkers against clinical response by a post hoc analysis of the phase-2b PIONEER study of tanimilast (12).

Methods

Patients’ characteristics, statistical and bioanalytical methods, dataset processing, quality control, clustering and functional enrichment analyses, PDE4 gene expression signature (PDE4-GES), and composite score generation are detailed elsewhere (see the online supplement).

Development of a PDE4-GES Associated with PDE4 Signaling; ECLIPSE Dataset

Training data were retrieved from patients with COPD who were enrolled in the ECLIPSE study (ClinicalTrials.gov ID: NCT00292552; the National Center for Biotechnology Information’s Gene Expression Omnibus Series GSE76705) (25). Two-dimensional gene expression unsupervised hierarchical clustering analysis in blood was performed using the most variable genes (MVGs) to capture major sources of variation within the dataset (26). A PDE4 functional gene set was built using Ingenuity Pathway Analysis (QIAGEN) by “growing” direct and indirect connections to genes up- and downstream of the PDE4 complex/isoforms and the cAMP-mediated signaling pathway. The identified biologically relevant subtypes were assigned a class label (“PDE4^high” or “PDE4^low”) on the basis of the association of related gene clusters with PDE4 functional genes and Gene Ontology (GO) inflammatory response biological processes. Subtype association with higher or lower PDE signaling was confirmed by differential expression analysis of the full gene dataset testing whether downregulated genes (PDE4^low vs. PDE4^high; P value adjusted for false discovery rate using the Benjamini-Hochberg method [pFDR] < 0.05, |fold change| > 1.3)—but not upregulated ones—in the PDE4^low group would be significantly enriched for PDE4 functional genes and GO inflammatory response biological processes (pFDR < 0.05).

Six independent PDE4-GESs were developed to identify the class labels PDE4^low and PDE4^high in the ECLIPSE dataset as dichotomous endpoints. Performance metrics (true-positive rate, area under the receiver operator curve [AUROC], test-positive rate, sensitivity, specificity, positive predictive value, and negative predictive value; see Table E1 in the online supplement) and the proportion of genes that are biologically relevant to the PDE4 pathway were evaluated to select the best PDE4-GES to be carried forward.

Validation of PDE4-GES; BIOMARKER Datasets

The BIOMARKER study inclusion criteria required patients to have chronic bronchitis, to have a COPD Assessment Test score ⩾10, and to be treated with ICS/LABA/LAMA therapy for at least 2 months before enrollment (ClinicalTrials.gov ID: NCT 03004417) (13, 15). Study samples were collected at least 6 weeks after an exacerbation or a lower respiratory tract infection. BIOMARKER data at baseline in blood (Gene Expression Omnibus Series GSE133513) were submitted to three independent hierarchical analyses, each originated using a different gene set for clustering: Analysis 1, of MVGs previously used to cluster the ECLIPSE dataset (MVG-ECLIPSE); Analysis 2, of MVGs of the BIOMARKER study dataset (MVG-BIOMARKER); and Analysis 3, of genes that were previously found to be differentially expressed after tanimilast treatment (DEG-BIOMARKER) (15). Each patient cluster was allocated to lower or higher PDE4 signaling (PDE4^low or PDE4^high) according to their mean PDE4-GES composite scores. The ability of the PDE4-GES to detect differences associated with PDE4 signaling/activity was validated by differential expression analysis both in blood and sputum between patients allocated to the PDE4^low clusters in all three clustering analyses versus the remainder of the cohort (PDE4^high) and by testing whether downregulated genes (pFDR < 0.05, |fold change| > 1.3 in blood; and P < 0.05, |fold change| > 1.3 in sputum) in the PDE4^low group were significantly associated to PDE4 functional genes and GO inflammatory response biological processes (pFDR < 0.05).

Discovery of Biomarkers Associated with PDE4-GES; BIOMARKER Study

An ANOVA model or chi-square test for continuous or discrete variables, respectively, obtained from the BIOMARKER study (13) before randomization, was applied to evaluate the association with patient clusters resulting from the three independent clustering analyses in blood of the BIOMARKER dataset (MVG-ECLIPSE, MVG-BIOMARKER, and DEG-BIOMARKER). Variables included clinical factors such as pulmonary function, age, and smoking status; differential cell counts; and soluble markers in sputum and blood (24 key COPD inflammatory mediators such as IL-8 [or CXCL8], IL-6, and tumor necrosis factor α [TNF-α]) (see Table E2). Markers that were found to be statistically significantly associated (P < 0.05) with patient clusters in all three clustering analyses and significantly correlated with the PDE4-GES scores (Pearson r ⩾ 0.35, P < 0.05) were taken forward for testing as predictive biomarkers of drug response in the PIONEER study.

Testing the Predictivity of Biomarkers against Clinical Response; PIONEER Study

Predictivity of the identified biomarkers against moderate-to-severe COPD exacerbation rate and time to first exacerbation were tested post hoc in a placebo-controlled dose-ranging phase-2b trial, the PIONEER study (ClinicalTrials.gov ID: NCT 02986321) (12). Patients were randomized to tanimilast (four dose groups), budesonide, or placebo in addition to formoterol. IL-8 measurements were assessed at baseline at least 6 weeks after an exacerbation or a lower respiratory tract infection. Full inclusion and exclusion criteria and the main outcomes of the clinical trial have been previously reported (12).

Results

A flowchart summarizing the workflow and key outcomes of this study is depicted in Figure 1.

Development of a PDE4-GES Associated with PDE4 Signaling; ECLIPSE Dataset

Clustering analysis of blood samples collected from patients with COPD in the ECLIPSE dataset resolved into three patient clusters (termed A, B, and C) and three gene clusters (termed 1, 2, and 3) (Figure 2A). Functional enrichment analysis showed that gene cluster 3 was significantly enriched for the PDE4 functional genes (469 genes connected to PDE4 complex/isoforms and cAMP signaling; see Table E3) (pFDR < 7 × 10⁻¹⁵) and for GO inflammatory response processes (pFDR = 7.46 × 10⁻¹¹). Gene cluster 3 was also significantly enriched for other relevant biological processes associated with PDE4 functional genes such as immune response, interferon signaling and response, Toll-like signaling, TNF/TNF superfamily cytokine production, regulation of IL-8 production, and response to bacteria. Patient cluster A had a low expression of gene cluster 3 (Figure 2A, green box). Differential expression analysis of patient cluster A versus cluster B + C using the full gene dataset (Figure 2B) showed that downregulated genes in cluster A, but not upregulated ones, were significantly enriched for PDE4 functional genes (pFDR = 2.43 × 10⁻⁶) and regulation of inflammatory response processes (pFDR = 3.20 × 10⁻²), indicating lower PDE4 signaling/activity in cluster A (PDE4^low) in comparison with the rest of the cohort (cluster B + C; PDE4^high). Patient cluster A (PDE4^low) was also characterized by significantly higher blood IL-8 expression levels (pFDR = 4 × 10⁻³) (Figure 2C).

Figure 2. — Identification of subgroups of patients associated with different phosphodiesterase-4 (PDE4) signaling in the ECLIPSE dataset (Gene Expression Omnibus Series GSE76705) in blood. (A) Unsupervised clustering analysis using the most variable genes within the dataset; patient cluster A has lower expression in gene cluster 3, which is significantly enriched for PDE4 functional genes and Gene Ontology inflammatory response processes. (B) Differentially expressed gene (DEG) analysis of patient cluster A versus cluster B + C using the full gene dataset; downregulated genes, but not upregulated ones (P value adjusted for false discovery rate < 0.05, |fold change| > 1.3), in patient cluster A are significantly enriched for PDE4 functional genes, indicating a distinct background biology associated with lower PDE4 signaling/activity of this group of patients (patient cluster A; PDE4^low) in comparison with the rest of the cohort (patient cluster B + C; PDE4^high). (C) Distribution of IL-8 gene expression levels in patient cluster A versus cluster B + C; patient cluster A (PDE4^low) is characterized by significantly higher IL-8 expression levels in comparison with the rest of the cohort (patient cluster B + C; PDE4^high). Data in plots are means ± 95% confidence interval. *P < 0.05. DEG = differentially expressed gene.

Performance metrics of six different GESs associated with patient clusters defined by PDE4 activity (PDE4^high against PDE4^low) in the ECLIPSE dataset are depicted elsewhere (see Figure E1 and Table E1). The signature composed of the highest proportion of genes functionally associated with PDE4 activity was identified, which comprised an expression algorithm of 14 genes, with 12 belonging to the PDE4 functional gene set (see Figure E2). The signature was able to predict PDE4^high and PDE4^low in the ECLIPSE dataset with an AUROC = 0.923.

Validation of PDE4-GES; BIOMARKER Datasets

Figure 3A presents the heatmaps of standardized expression intensities from the clustering of blood samples taken before randomization in the BIOMARKER study following three independent analyses, each using a different gene set (MVG-ECLIPSE, MVG-BIOMARKER, and DEG-BIOMARKER). None of the identified patient clusters was associated with technical metrics associated with sample quality, indicating that these clusters were driven by biological differences. The heatmaps demonstrated sample clusters that were significantly associated with the PDE4 signature score (P ⩽ 6.8 × 10⁻⁵); this enabled patient clusters with lower or higher PDE4 signaling (PDE4^low or PDE4^high) according to their mean PDE4-GES composite scores to be identified (Figure 3B). To validate the ability of the PDE4-GES to detect differences associated with PDE4 signaling, patients were dichotomized by identifying the subgroup of individuals that were allocated to PDE4^low in all three analyses versus the remainder of the cohort (PDE4^high). This corresponded to approximately 25% of subjects who were in cluster A of all three analyses. Differential expression analysis in blood showed that downregulated genes, but not upregulated ones, in the PDE4^low group were significantly enriched for PDE4 functional genes (pFDR = 1.63 × 10⁻²) and inflammatory response processes (pFDR = 1.80 × 10⁻⁵), indicating lower PDE4 signaling/activity in patients associated with lower PDE4-GES (PDE4^low) (Figure 4A).

Figure 4. — Validation of the phosphodiesterase-4 gene expression signature (PDE4-GES) in the BIOMARKER study. Patients were dichotomized by identifying the subgroup of individuals that were allocated to PDE4^low in all the three clustering analyses of the BIOMARKER dataset (∼25% of patients; Figure 2A) versus the remainder of the cohort (PDE4^high). (A and B) Blood (A) and sputum (B) differential expression analysis shows that downregulated genes, but not upregulated ones (P value adjusted for false discovery rate < 0.05, |fold change| > 1.3 in blood; and P < 0.05, |fold change| > 1.3 in sputum), in the cluster classified as PDE4^low are significantly enriched for PDE4 functional genes, indicating a distinct background biology associated with lower PDE4 signaling/activity of this group of patients (PDE4^low) in comparison with the rest of the cohort. (C and D) Both the PDE4-GES score (C) and IL-8 expression (D) in blood can predict patients in the two groups (PDE4^low vs. PDE4^high) with an AUROC > 0.8. AUROC = area under the receiver operator curve; DEGs = differentially expressed genes; ROC = receiver operator curve.

The same analysis in the same patients from the BIOMARKER study in sputum resulted in a similar outcome to that for blood: Downregulated genes, but not upregulated ones, in the PDE4^low group were significantly enriched for PDE4 functional genes (pFDR = 4.45 × 10⁻⁷) and inflammatory response processes (pFDR = 8.45 × 10⁻⁵) indicating lower PDE4 signaling/activity in patients associated with lower PDE4-GES (PDE4^low) also in sputum (Figure 4B). An overlap of the downregulated and upregulated genes was observed between blood and sputum; 11% and 18%, respectively (Table E4). Analysis of differential cell counts showed a similarity in the distribution of sputum eosinophils and neutrophils in the PDE4^low and PDE4^high groups, although sputum neutrophil percentage (but not absolute count) was marginally higher in the PDE4^high group (85.2 vs. 76.6%, P = 0.002) (Table E5).

Both the PDE4-GES score and blood IL-8 expression could predict patients in the two groups (PDE4^low vs. PDE4^high), with AUROC = 0.978, P < 0.0001; and AUROC = 0.824, P = 0.0003, respectively (Figures 4C and 4D).

Discovery of Biomarkers Associated with PDE4-GES; BIOMARKER Study

Heatmaps resulting from each of the three independent analyses of the BIOMARKER dataset in blood (Figure 3A) were tested for associations of patient clusters with clinical and biological variables (Table E2). Serum IL-8 protein was the only variable significantly associated with patient clusters in all three analyses (MVG-ECLIPSE, MVG-BIOMARKER, and DEG-BIOMARKER) (P ⩽ 0.047) (Figure 3C). Along with protein levels, IL-8 gene expression in blood was significantly associated to patient clusters (P ⩽ 2.0 × 10⁻⁴) (Figure 3D). Both blood IL-8 gene expression and protein concentrations were inversely correlated to the PDE4-GES score (r = −0.37, P = 0.004; and r = −0.36, P = 0.006, respectively) (Figure 5). Sample clusters or IL-8 serum concentrations were not associated with sputum IL-8, blood or sputum eosinophil counts, age, smoking status, or pulmonary function (Pearson r < 0.35; see Tables E2 and E6).

Figure 5. — PDE4 gene expression signature scores and systemic IL-8 are inversely correlated. (A and B) Serum IL-8 concentrations (A) and blood IL-8 gene-expression levels (B); correlation with regression line and 95% confidence intervals. PDE4 = phosphodiesterase-4.

Testing the Predictivity of Biomarkers against Clinical Response; PIONEER Study

Predictivity of serum IL-8 against clinical response was tested in the PIONEER clinical study (12). A total of 1,126 patients (mean age = 64; 70% males; 53% current smokers) had available baseline samples (746 randomized to one of the four tanimilast doses, 187 randomized to budesonide, and 193 randomized to placebo). Differential effect of the treatment was first evaluated by dividing the study population into subgroups of equal size using a baseline IL-8 median value of 17.3 pg/ml. The threshold was then raised to 20 pg/ml and 23 pg/ml to increase the proportion of patients with lower IL-8 levels to values similar to those obtained in the cluster analysis of the ECLIPSE and BIOMARKER datasets (i.e., approximately 65% and 75%, respectively). The majority of patients (88–100%) remained in the same IL-8 subgroup category after 12 and 24 weeks of dosing, regardless of the study treatment received (see Table E7).

Tanimilast (pooled analysis of all treatment doses) showed a numerically lower rate of moderate-to-severe exacerbation in comparison with placebo; rate ratio reduction was 18% (P = 0.218). In patients with lower serum IL-8 levels, the exacerbation rate reduction was 34% for patients with IL-8 levels ⩽17.3 pg/ml (P = 0.064), 36% for IL-8 levels ⩽20 pg/ml (P = 0.035), and 27% for IL-8 levels ⩽23 pg/ml (P = 0.099) (Figures 6A and E3A). The reduction of exacerbation rate increased further when IL-8 was combined with the clinical phenotype (chronic bronchitis) or a higher BECs. In the chronic bronchitis phenotype (56% of patients), the reduction was 29% (P = 0.155), with higher levels in patients who also had lower IL-8 concentrations (e.g., 48% for patients with IL-8 levels ⩽17.3 pg/ml, P = 0.033) (Figures 6B and E3B). In patients with BECs ⩾150 μl⁻¹ (67% of patients), the reduction was 31% (P = 0.054), with higher levels in patients who also had lower IL-8 concentrations (e.g., 44% for subjects with IL-8 levels ⩽17.3 pg/ml, P = 0.033) (Figures 6C and E3C).

Figure 6. — Exacerbation rate reduction is greater in patients having lower serum IL-8 concentrations in the PIONEER study. (*A–C*) Graphs represent the adjusted reduction of the moderate-to-severe exacerbation rate compared with placebo in the overall population and in subgroups of patients with serum IL-8 levels (⩽17.3 pg/ml, ⩽20 pg/ml, and ⩽23 pg/ml) (A) alone or in combination with other known determinants of response, (B) the CB phenotype or (C) blood EOS counts ⩾150 μl⁻¹. *P < 0.05. CB = chronic bronchitis; CI = confidence interval; EOS = eosinophil; RR = rate ratio.

Consistent with exacerbation rate, the hazard ratio (HR) that was associated with the risk of experiencing a moderate-to-severe exacerbation over time, compared with placebo, was lower in patients with IL-8 levels ⩽17.3 pg/ml (0.64, P = 0.054), ⩽20 pg/ml (0.61, P = 0.022), and ⩽23 pg/ml (0.67, P = 0.035) than it was in the overall population (0.75, P = 0.088) (Figures 7A and E3D). The combination of IL-8 with BECs ⩾150 μl⁻¹ or the chronic bronchitis phenotype led to a further significant increase in exacerbation prevention (HR, ∼0.5) (Figures 7B, 7C, E3E, and E3F).

Figure 7. — The risk of experiencing an exacerbation over time is lower in patients having lower serum IL-8 concentrations in the PIONEER study. (*A–C*) Graphs represent the hazard ratio associated with the risk of experiencing a moderate-to-severe exacerbation over time compared with placebo in the overall population and in subgroups of patients with serum IL-8 levels (⩽17.3 pg/ml, ⩽20 pg/ml, and ⩽23 pg/ml) (A) alone or in combination with other known determinants of response, (B) the CB phenotype or (C) blood EOS counts ⩾150 μl⁻¹. *P < 0.05. CB = chronic bronchitis; EOS = eosinophil.

A trend toward an increased efficacy was also observed for the budesonide treatment: For patients with IL-8 levels ⩽23 pg/ml, the reduction of the exacerbation rate increased from 39% (P = 0.030) to 49% (P = 0.0134), whereas HR associated with the risk of experiencing an exacerbation over time decreased from 0.62 (P = 0.038) to 0.51 (P = 0.013).

IL-8 serum concentrations were not associated with any biological or clinical factor, including BECs, COPD phenotype, smoking status, age, or severity of the disease (Table E6).

Discussion

This analysis used data from three different COPD cohorts to identify serum IL-8 as a potential biomarker of the treatment response to an inhaled PDE4 inhibitor. Unsupervised clustering analysis of the COPD blood transcriptome from the ECLIPSE study (25) identified patient subgroups with different patterns of PDE4 signaling pathway activation, allowed the development of the PDE4-GES, and demonstrated IL-8 as a potential biomarker that was increased in PDE4^low subjects. The PDE4-GES was further evaluated using transcriptome data from the BIOMARKER study (13, 15) in both blood and sputum, enabling validation of the association with IL-8 levels by gene expression and serum protein measurement. Finally, a post hoc analysis of the PIONEER study (12) demonstrated that lower serum IL-8 levels were associated with greater clinical responses to tanimilast. Overall, these results implicate lower serum IL-8 levels as a biomarker of higher PDE4 activation and thereby greater potential for a clinical response to PDE4 inhibition.

In the ECLIPSE and BIOMARKER cohorts, lower blood IL-8 gene expression was associated with increased PDE4 signaling. The BIOMARKER population were receiving inhaled triple therapy and had chronic bronchitis (13, 15). In this dataset, clinical characteristics, sputum cell counts, and inflammatory markers in sputum and serum were tested for association with the identified subgroups; only serum IL-8 was associated with PDE4 signaling. This association does not explain the mechanisms involved. It has been reported that patients with lower IL-8 levels (27) have an increased response to antitumor therapies (27–29). IL-8 was primarily expressed in peripheral blood mononuclear myeloid cells, and high IL-8 expression was associated with reduced adaptive immunity, downregulated interferon-inducible genes, and poor clinical response (28, 29). Similarly, the ECLIPSE dataset showed that higher IL-8 expression was associated with downregulation of interferon pathway genes. This potential imbalance of adaptive versus innate immunity in patients with elevated IL-8 levels was associated with PDE4 signaling/activity.

The sputum analysis showed that downregulated genes in the PDE4^low group were enriched for PDE4 functional genes, compatible with the findings in blood samples. These findings provide confidence that serum IL-8 levels reflect PDE4 signaling in the lungs.

The associations between PDE4-GES and IL-8 levels support the hypothesis that lower IL-8 levels are related to a greater clinical response to PDE4 inhibition (12). This hypothesis was tested in the PIONEER study (12) by first using a median threshold of 17.3 pg/ml and then using 20 pg/ml and 23 pg/ml to evaluate proportions of patients with lower IL-8 levels similar to those in the clustering analysis of the ECLIPSE and BIOMARKER datasets (approximately 65% and 75%, respectively). Similar IL-8 thresholds have been used to discriminate between patients with different responses to antitumor therapies (27). Lower serum IL-8 concentrations were associated with increased treatment effects; for example, up to a twofold increased effect on exacerbation prevention using ⩽20 pg/ml. The absolute exacerbation rate reduction by tanimilast at lower serum IL-8 levels was more than 25% using different thresholds. This is greater than the effect of long-acting bronchodilators on exacerbation prevention (30) and comparable with the effect of ICS at higher BECs (18).

The analysis here, from subgroup data, could potentially improve the individual prediction of treatment benefit when used with other clinical and biological information. In a post hoc analysis of the PIONEER study, tanimilast had a greater effect on exacerbation prevention in COPD patients with chronic bronchitis and BECs ⩾150 μl⁻¹ (12). This was consistent with previous observations for the oral PDE4 inhibitor roflumilast (23). Although the role of IL-8 in the activation and recruitment of neutrophils is well characterized (31), its relationship with eosinophils is not yet fully elucidated (32). Consistent with these findings, there was an additive enrichment of the treatment response when IL-8 was combined with BECs or the chronic bronchitis phenotype, implicating IL-8 as an independent determinant of response, with the exacerbation rate reduction being approximately 50%.

IL-8 levels are elevated in the blood and sputum of patients with COPD (33–35). Studies have shown a positive association between sputum and serum IL-8 and bacterial colonization in COPD (36, 37). Bacterial colonization, particularly with proteobacteria, appears to skew the airway inflammation profile away from eosinophilic inflammation and toward a neutrophil-predominant state (18, 38). This has implications for therapeutic responses in COPD, as higher eosinophil counts predict greater responses to both ICS and PDE4 inhibitors (12, 18, 23). A trend toward an increased benefit in patients with lower serum IL-8 levels was also observed for budesonide in the PIONEER analysis, again suggesting that the responsive population might overlap for ICS and PDE4 inhibitors. A link between systemic IL-8 levels and bacterial activity was evidenced in the clustering analysis of the ECLIPSE datasets where patient subgroups were associated with differential expression of genes enriched for response to bacterium. Serum IL-8 levels may, therefore, be influenced by bacterial colonization, altering potential therapeutic responses (38).

A phase-2 clinical trial showed that IL-8 inhibition with a monoclonal antibody had no clinical benefits (39), although there was no subject enrichment to increase the probability of response, such as higher IL-8 levels (31, 40). In the PIONEER study, serum IL-8 was not affected by tanimilast or budesonide treatments (12), and for the majority of patients (at least 88%), the concentrations remained relatively stable over the 6-month study period.

A strength of this analysis is that a hypothesis was generated on the basis of biological evidence and then tested using the PIONEER study data. It is important to note that subgroups identified in the BIOMARKER study were not associated to sample quality metrics, indicating a split purely driven by biological differences. This important feature is not frequently reported in clustering analysis and can cause misinterpretation of results. The analysis of sputum transcriptomic data (BIOMARKER study) allowed validation of the association between systemic IL-8 levels and pulmonary PDE4 signaling/activity.

A limitation of the study was that the hypothesis was built on data retrieved from heterogenous datasets with marked differences in the proportion of patients with chronic bronchitis. To some extent, the different populations used may help to generalize the findings to the overall COPD population, but confirmatory studies with the target population for PDE4 inhibitors are required. In addition, subjects were followed for 24 weeks of treatment; larger and longer exacerbation studies are necessary to understand the relationship between serum IL-8 concentrations and treatment response. Although we have evaluated serum IL-8 in the context of pharmacological response, datasets with broader populations with COPD would be needed to evaluate the prediction of clinical outcomes (including prognosis) such as exacerbation rates. Serum IL-8 may represent an accessible and noninvasive biomarker enabling a personalized therapeutic approach for the use of drugs such as PDE4 inhibitors. Although this biomarker is routinely and easily measured in most bioanalytical laboratories for research purposes by standard immunoassay, future work would be needed to enable rapid IL-8 measurements in clinical practice. It would be valuable to study the potential of serum IL-8 as a biomarker of response to roflumilast.

In conclusion, this analysis has identified a potential role for serum IL-8 as an independent determinant of response to PDE4 inhibition after a systematic stepwise analysis of different COPD datasets. First, we identified a consistent association of serum IL-8 with subgroups of patients with differing PDE4 signaling/activity. Then, we showed the predictivity of IL-8 against clinical response in a post hoc analysis of the PIONEER study with tanimilast treatment.

Acknowledgments

Acknowledgment

The authors would like to acknowledge the invaluable bioinformatics and statistical assistance provided by Dr. Rui Benfeitas and Dr. Stefano Vezzoli in the completion of this scientific paper and thank the investigators and patients at the investigative sites for their support of this study.

Footnotes

Supported by Chiesi Farmaceutici SpA. D. Singh is supported by the National Institute for Health Research Manchester Biomedical Research Centre.

Author Contributions: The study was conceived and designed by M.G. Data were analyzed by M.G., M.B., D.S., and S.D. M.G. and D.S. interpreted the data and wrote the manuscript. All authors revised the manuscript for intellectual content, and approved the submitted version.

Data sharing statement: Chiesi commits to conducting legitimate research and sharing with qualified scientific and medical researchers patient-level data, study-level data, the clinical protocol, and the full clinical study report of Chiesi Farmaceutici S.p.A.–sponsored interventional clinical trials in patients for medicines and indications approved by the European Medicines Agency and/or the U.S. Food and Drug Administration after January 1, 2015, following the approval of any received research proposal and the signature of a Data Sharing Agreement. Chiesi provides access to clinical trial information consistently with the principle of safeguarding commercially confidential information and patient privacy. To date, the present study is out of the scope of the Chiesi policy on clinical data sharing. Other information on Chiesi’s data-sharing commitment, access, and the approval process for research requests are available in the Clinical Trial Transparency section of http://www.chiesi.com/en/research-and-development/.

This article has a related editorial.

This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.

Originally Published in Press as DOI: 10.1164/rccm.202301-0071OC on May 16, 2023

Author disclosures are available with the text of this article at www.atsjournals.org.

References

1. Vestbo J, Papi A, Corradi M, Blazhko V, Montagna I, Francisco C, et al. Single inhaler extrafine triple therapy versus long-acting muscarinic antagonist therapy for chronic obstructive pulmonary disease (TRINITY): a double-blind, parallel group, randomised controlled trial. Lancet . 2017;389:1919–1929. doi: 10.1016/S0140-6736(17)30188-5. [DOI] [PubMed] [Google Scholar]
2. Langham S, Lewis J, Pooley N, Embleton N, Langham J, Han MK, et al. Single-inhaler triple therapy in patients with chronic obstructive pulmonary disease: a systematic review. Respir Res . 2019;20:242. doi: 10.1186/s12931-019-1213-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Calverley PM, Rabe KF, Goehring UM, Kristiansen S, Fabbri LM, Martinez FJ, M2-124 and M2-125 study groups Roflumilast in symptomatic chronic obstructive pulmonary disease: two randomised clinical trials. Lancet . 2009;374:685–694. doi: 10.1016/S0140-6736(09)61255-1. [DOI] [PubMed] [Google Scholar]
4. Sturton G, Fitzgerald M. Phosphodiesterase 4 inhibitors for the treatment of COPD. Chest . 2002;121:192S–196S. doi: 10.1378/chest.121.5_suppl.192s. [DOI] [PubMed] [Google Scholar]
5. Martinez FJ, Calverley PMA, Goehring UM, Brose M, Fabbri LM, Rabe KF. Effect of roflumilast on exacerbations in patients with severe chronic obstructive pulmonary disease uncontrolled by combination therapy (REACT): a multicentre randomised controlled trial. Lancet . 2015;385:857–866. doi: 10.1016/S0140-6736(14)62410-7. [DOI] [PubMed] [Google Scholar]
6. Bardin P, Kanniess F, Gauvreau G, Bredenbröker D, Rabe KF. Roflumilast for asthma: efficacy findings in mechanism of action studies. Pulm Pharmacol Ther . 2015;35:S4–S10. doi: 10.1016/j.pupt.2015.08.006. [DOI] [PubMed] [Google Scholar]
7. Martinez FJ, Rabe KF, Sethi S, Pizzichini E, McIvor A, Anzueto A, et al. Effect of roflumilast and inhaled corticosteroid/long-acting β2-agonist on chronic obstructive pulmonary disease exacerbations (RE2SPOND). A randomized clinical trial. Am J Respir Crit Care Med . 2016;194:559–567. doi: 10.1164/rccm.201607-1349OC. [DOI] [PubMed] [Google Scholar]
8. Rennard SI, Calverley PMA, Goehring UM, Bredenbröker D, Martinez FJ. Reduction of exacerbations by the PDE4 inhibitor roflumilast—the importance of defining different subsets of patients with COPD. Respir Res . 2011;12:18. doi: 10.1186/1465-9921-12-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Rogliani P, Calzetta L, Cazzola M, Matera MG. Drug safety evaluation of roflumilast for the treatment of COPD: a meta-analysis. Expert Opin Drug Saf . 2016;15:1133–1146. doi: 10.1080/14740338.2016.1199683. [DOI] [PubMed] [Google Scholar]
10. Muñoz-Esquerre M, Diez-Ferrer M, Montón C, Pomares X, López-Sánchez M, Huertas D, et al. Roflumilast added to triple therapy in patients with severe COPD: a real life study. Pulm Pharmacol Ther . 2015;30:16–21. doi: 10.1016/j.pupt.2014.10.002. [DOI] [PubMed] [Google Scholar]
11. Facchinetti F, Civelli M, Singh D, Papi A, Emirova A, Govoni M. Tanimilast, a novel inhaled Pde4 inhibitor for the treatment of asthma and chronic obstructive pulmonary disease. Front Pharmacol . 2021;12:740803. doi: 10.3389/fphar.2021.740803. [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Singh D, Emirova A, Francisco C, Santoro D, Govoni M, Nandeuil MA. Efficacy and safety of CHF6001, a novel inhaled PDE4 inhibitor in COPD: the PIONEER study. Respir Res . 2020;21:246. doi: 10.1186/s12931-020-01512-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Singh D, Beeh KM, Colgan B, Kornmann O, Leaker B, Watz H, et al. Effect of the inhaled PDE4 inhibitor CHF6001 on biomarkers of inflammation in COPD. Respir Res . 2019;20:180. doi: 10.1186/s12931-019-1142-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Mariotti F, Govoni M, Lucci G, Santoro D, Nandeuil MA. Safety, tolerability, and pharmacokinetics of single and repeat ascending doses of CHF6001, a novel inhaled phosphodiesterase-4 inhibitor: two randomized trials in healthy volunteers. Int J Chron Obstruct Pulmon Dis . 2018;13:3399–3410. doi: 10.2147/COPD.S174156. [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Govoni M, Bassi M, Vezzoli S, Lucci G, Emirova A, Nandeuil MA, et al. Sputum and blood transcriptomics characterisation of the inhaled PDE4 inhibitor CHF6001 on top of triple therapy in patients with chronic bronchitis. Respir Res . 2020;21:72. doi: 10.1186/s12931-020-1329-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.PILASTER. 2020. https://clinicaltrials.gov/ct2/show/NCT04636801
17.PILLAR. 2020. https://clinicaltrials.gov/ct2/show/NCT04636814
18. Singh D, Agusti A, Martinez FJ, Papi A, Pavord ID, Wedzicha JA, et al. Blood eosinophils and chronic obstructive pulmonary disease: a Global Initiative for Chronic Obstructive Lung Disease Science Committee 2022 review. Am J Respir Crit Care Med . 2022;206:17–24. doi: 10.1164/rccm.202201-0209PP. [DOI] [PubMed] [Google Scholar]
19. Higham A, Beech A, Wolosianka S, Jackson N, Long G, Kolsum U, et al. Type 2 inflammation in eosinophilic chronic obstructive pulmonary disease. Allergy . 2021;76:1861–1864. doi: 10.1111/all.14661. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Higham A, Dungwa J, Pham T-H, McCrae C, Singh D. Increased mast cell activation in eosinophilic chronic obstructive pulmonary disease. Clin Transl Immunology . 2022;11:e1417. doi: 10.1002/cti2.1417. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Singh D, Watz H, Beeh KM, Kornmann O, Leaker B, Colgan B, et al. COPD sputum eosinophils: relationship to blood eosinophils and the effect of inhaled PDE4 inhibition. Eur Respir J . 2020;56:2000237. doi: 10.1183/13993003.00237-2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Singh D, Bassi M, Balzano D, Lucci G, Emirova A, Nandeuil MA, et al. COPD patients with chronic bronchitis and higher sputum eosinophil counts show increased type-2 and PDE4 gene expression in sputum. J Cell Mol Med . 2021;25:905–918. doi: 10.1111/jcmm.16146. [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Martinez FJ, Rabe KF, Calverley PMA, Fabbri LM, Sethi S, Pizzichini E, et al. Determinants of response to roflumilast in severe COPD: pooled analysis of two randomized trials. Am J Respir Crit Care Med . 2018;198:1268–1278. doi: 10.1164/rccm.201712-2493OC. [DOI] [PubMed] [Google Scholar]
24. Stockley RA, Halpin DMG, Celli BR, Singh D. Chronic obstructive pulmonary disease biomarkers and their interpretation. Am J Respir Crit Care Med . 2019;199:1195–1204. doi: 10.1164/rccm.201810-1860SO. [DOI] [PubMed] [Google Scholar]
25. Chang Y, Glass K, Liu YY, Silverman EK, Crapo JD, Tal-Singer R, et al. COPD subtypes identified by network-based clustering of blood gene expression. Genomics . 2016;107:51–58. doi: 10.1016/j.ygeno.2016.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA . 1998;95:14863–14868. doi: 10.1073/pnas.95.25.14863. [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Schalper KA, Carleton M, Zhou M, Chen T, Feng Y, Huang SP, et al. Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors. Nat Med . 2020;26:688–692. doi: 10.1038/s41591-020-0856-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
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29. Rizzo M, Varnier L, Pezzicoli G, Pirovano M, Cosmai L, Porta C. IL-8 and its role as a potential biomarker of resistance to anti-angiogenic agents and immune checkpoint inhibitors in metastatic renal cell carcinoma. Front Oncol . 2022;12:990568. doi: 10.3389/fonc.2022.990568. [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Mathioudakis AG, Vestbo J, Singh D. Long-acting bronchodilators for chronic obstructive pulmonary disease: which one(s), how, and when? Clin Chest Med . 2020;41:463–474. doi: 10.1016/j.ccm.2020.05.005. [DOI] [PubMed] [Google Scholar]
31.Brennan K, Zheng J. In: xPharm: the comprehensive pharmacology reference. Enna SJ, Bylund DB, editors. New York: Elsevier; 2007. Interleukin 8; pp. 1–7. [Google Scholar]
32. Petering H, Götze O, Kimmig D, Smolarski R, Kapp A, Elsner J. The biologic role of interleukin-8: functional analysis and expression of CXCR1 and CXCR2 on human eosinophils. Blood . 1999;93:694–702. [PubMed] [Google Scholar]
33. Chung KF. Cytokines in chronic obstructive pulmonary disease. Eur Respir J Suppl . 2001;34:50s–59s. [PubMed] [Google Scholar]
34. Zeng YY, Hu WP, Zuo YH, Wang XR, Zhang J. Altered serum levels of type I collagen turnover indicators accompanied by IL-6 and IL-8 release in stable COPD. Int J Chron Obstruct Pulmon Dis . 2019;14:163–168. doi: 10.2147/COPD.S188139. [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Zhang J, Bai C. The significance of serum interleukin-8 in acute exacerbations of chronic obstructive pulmonary disease. Tanaffos . 2018;17:13–21. [PMC free article] [PubMed] [Google Scholar]
36. Susantyo S, Ilyas M, Bakri S, Udaya W, Kasim H, Faridin HP, et al. Association between bacterial colonization and serum level of interleukin-8 in stable chronic obstructive pulmonary disease in Makassar. Int J Med Rev Case Rep . 2019;3:707–710. [Google Scholar]
37. Yusuf K, Ilyas M, Tabri A, Katu S, Hp F, Kasim H, et al. The correlation between bacterial colonization and interleukin-8 serum levels in acute exacerbation of COPD. Int J Med Rev Case Rep . 2019;3:702–706. [Google Scholar]
38. Kolsum U, Donaldson GC, Singh R, Barker BL, Gupta V, George L, et al. Blood and sputum eosinophils in COPD; relationship with bacterial load. Respir Res . 2017;18:88. doi: 10.1186/s12931-017-0570-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Mahler DA, Huang S, Tabrizi M, Bell GM. Efficacy and safety of a monoclonal antibody recognizing interleukin-8 in COPD: a pilot study. Chest . 2004;126:926–934. doi: 10.1378/chest.126.3.926. [DOI] [PubMed] [Google Scholar]
40. Mukaida N. Pathophysiological roles of interleukin-8/CXCL8 in pulmonary diseases. Am J Physiol Lung Cell Mol Physiol . 2003;284:L566–L577. doi: 10.1152/ajplung.00233.2002. [DOI] [PubMed] [Google Scholar]

[bib1] 1. Vestbo J, Papi A, Corradi M, Blazhko V, Montagna I, Francisco C, et al. Single inhaler extrafine triple therapy versus long-acting muscarinic antagonist therapy for chronic obstructive pulmonary disease (TRINITY): a double-blind, parallel group, randomised controlled trial. Lancet . 2017;389:1919–1929. doi: 10.1016/S0140-6736(17)30188-5. [DOI] [PubMed] [Google Scholar]

[bib2] 2. Langham S, Lewis J, Pooley N, Embleton N, Langham J, Han MK, et al. Single-inhaler triple therapy in patients with chronic obstructive pulmonary disease: a systematic review. Respir Res . 2019;20:242. doi: 10.1186/s12931-019-1213-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib3] 3. Calverley PM, Rabe KF, Goehring UM, Kristiansen S, Fabbri LM, Martinez FJ, M2-124 and M2-125 study groups Roflumilast in symptomatic chronic obstructive pulmonary disease: two randomised clinical trials. Lancet . 2009;374:685–694. doi: 10.1016/S0140-6736(09)61255-1. [DOI] [PubMed] [Google Scholar]

[bib4] 4. Sturton G, Fitzgerald M. Phosphodiesterase 4 inhibitors for the treatment of COPD. Chest . 2002;121:192S–196S. doi: 10.1378/chest.121.5_suppl.192s. [DOI] [PubMed] [Google Scholar]

[bib5] 5. Martinez FJ, Calverley PMA, Goehring UM, Brose M, Fabbri LM, Rabe KF. Effect of roflumilast on exacerbations in patients with severe chronic obstructive pulmonary disease uncontrolled by combination therapy (REACT): a multicentre randomised controlled trial. Lancet . 2015;385:857–866. doi: 10.1016/S0140-6736(14)62410-7. [DOI] [PubMed] [Google Scholar]

[bib6] 6. Bardin P, Kanniess F, Gauvreau G, Bredenbröker D, Rabe KF. Roflumilast for asthma: efficacy findings in mechanism of action studies. Pulm Pharmacol Ther . 2015;35:S4–S10. doi: 10.1016/j.pupt.2015.08.006. [DOI] [PubMed] [Google Scholar]

[bib7] 7. Martinez FJ, Rabe KF, Sethi S, Pizzichini E, McIvor A, Anzueto A, et al. Effect of roflumilast and inhaled corticosteroid/long-acting β2-agonist on chronic obstructive pulmonary disease exacerbations (RE2SPOND). A randomized clinical trial. Am J Respir Crit Care Med . 2016;194:559–567. doi: 10.1164/rccm.201607-1349OC. [DOI] [PubMed] [Google Scholar]

[bib8] 8. Rennard SI, Calverley PMA, Goehring UM, Bredenbröker D, Martinez FJ. Reduction of exacerbations by the PDE4 inhibitor roflumilast—the importance of defining different subsets of patients with COPD. Respir Res . 2011;12:18. doi: 10.1186/1465-9921-12-18. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib9] 9. Rogliani P, Calzetta L, Cazzola M, Matera MG. Drug safety evaluation of roflumilast for the treatment of COPD: a meta-analysis. Expert Opin Drug Saf . 2016;15:1133–1146. doi: 10.1080/14740338.2016.1199683. [DOI] [PubMed] [Google Scholar]

[bib10] 10. Muñoz-Esquerre M, Diez-Ferrer M, Montón C, Pomares X, López-Sánchez M, Huertas D, et al. Roflumilast added to triple therapy in patients with severe COPD: a real life study. Pulm Pharmacol Ther . 2015;30:16–21. doi: 10.1016/j.pupt.2014.10.002. [DOI] [PubMed] [Google Scholar]

[bib11] 11. Facchinetti F, Civelli M, Singh D, Papi A, Emirova A, Govoni M. Tanimilast, a novel inhaled Pde4 inhibitor for the treatment of asthma and chronic obstructive pulmonary disease. Front Pharmacol . 2021;12:740803. doi: 10.3389/fphar.2021.740803. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib12] 12. Singh D, Emirova A, Francisco C, Santoro D, Govoni M, Nandeuil MA. Efficacy and safety of CHF6001, a novel inhaled PDE4 inhibitor in COPD: the PIONEER study. Respir Res . 2020;21:246. doi: 10.1186/s12931-020-01512-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib13] 13. Singh D, Beeh KM, Colgan B, Kornmann O, Leaker B, Watz H, et al. Effect of the inhaled PDE4 inhibitor CHF6001 on biomarkers of inflammation in COPD. Respir Res . 2019;20:180. doi: 10.1186/s12931-019-1142-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib14] 14. Mariotti F, Govoni M, Lucci G, Santoro D, Nandeuil MA. Safety, tolerability, and pharmacokinetics of single and repeat ascending doses of CHF6001, a novel inhaled phosphodiesterase-4 inhibitor: two randomized trials in healthy volunteers. Int J Chron Obstruct Pulmon Dis . 2018;13:3399–3410. doi: 10.2147/COPD.S174156. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib15] 15. Govoni M, Bassi M, Vezzoli S, Lucci G, Emirova A, Nandeuil MA, et al. Sputum and blood transcriptomics characterisation of the inhaled PDE4 inhibitor CHF6001 on top of triple therapy in patients with chronic bronchitis. Respir Res . 2020;21:72. doi: 10.1186/s12931-020-1329-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib16] 16.PILASTER. 2020. https://clinicaltrials.gov/ct2/show/NCT04636801

[bib17] 17.PILLAR. 2020. https://clinicaltrials.gov/ct2/show/NCT04636814

[bib18] 18. Singh D, Agusti A, Martinez FJ, Papi A, Pavord ID, Wedzicha JA, et al. Blood eosinophils and chronic obstructive pulmonary disease: a Global Initiative for Chronic Obstructive Lung Disease Science Committee 2022 review. Am J Respir Crit Care Med . 2022;206:17–24. doi: 10.1164/rccm.202201-0209PP. [DOI] [PubMed] [Google Scholar]

[bib19] 19. Higham A, Beech A, Wolosianka S, Jackson N, Long G, Kolsum U, et al. Type 2 inflammation in eosinophilic chronic obstructive pulmonary disease. Allergy . 2021;76:1861–1864. doi: 10.1111/all.14661. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib20] 20. Higham A, Dungwa J, Pham T-H, McCrae C, Singh D. Increased mast cell activation in eosinophilic chronic obstructive pulmonary disease. Clin Transl Immunology . 2022;11:e1417. doi: 10.1002/cti2.1417. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib21] 21. Singh D, Watz H, Beeh KM, Kornmann O, Leaker B, Colgan B, et al. COPD sputum eosinophils: relationship to blood eosinophils and the effect of inhaled PDE4 inhibition. Eur Respir J . 2020;56:2000237. doi: 10.1183/13993003.00237-2020. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib22] 22. Singh D, Bassi M, Balzano D, Lucci G, Emirova A, Nandeuil MA, et al. COPD patients with chronic bronchitis and higher sputum eosinophil counts show increased type-2 and PDE4 gene expression in sputum. J Cell Mol Med . 2021;25:905–918. doi: 10.1111/jcmm.16146. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib23] 23. Martinez FJ, Rabe KF, Calverley PMA, Fabbri LM, Sethi S, Pizzichini E, et al. Determinants of response to roflumilast in severe COPD: pooled analysis of two randomized trials. Am J Respir Crit Care Med . 2018;198:1268–1278. doi: 10.1164/rccm.201712-2493OC. [DOI] [PubMed] [Google Scholar]

[bib24] 24. Stockley RA, Halpin DMG, Celli BR, Singh D. Chronic obstructive pulmonary disease biomarkers and their interpretation. Am J Respir Crit Care Med . 2019;199:1195–1204. doi: 10.1164/rccm.201810-1860SO. [DOI] [PubMed] [Google Scholar]

[bib25] 25. Chang Y, Glass K, Liu YY, Silverman EK, Crapo JD, Tal-Singer R, et al. COPD subtypes identified by network-based clustering of blood gene expression. Genomics . 2016;107:51–58. doi: 10.1016/j.ygeno.2016.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib26] 26. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA . 1998;95:14863–14868. doi: 10.1073/pnas.95.25.14863. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib27] 27. Schalper KA, Carleton M, Zhou M, Chen T, Feng Y, Huang SP, et al. Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors. Nat Med . 2020;26:688–692. doi: 10.1038/s41591-020-0856-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib28] 28. Yuen KC, Liu LF, Gupta V, Madireddi S, Keerthivasan S, Li C, et al. High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade. Nat Med . 2020;26:693–698. doi: 10.1038/s41591-020-0860-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib29] 29. Rizzo M, Varnier L, Pezzicoli G, Pirovano M, Cosmai L, Porta C. IL-8 and its role as a potential biomarker of resistance to anti-angiogenic agents and immune checkpoint inhibitors in metastatic renal cell carcinoma. Front Oncol . 2022;12:990568. doi: 10.3389/fonc.2022.990568. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib30] 30. Mathioudakis AG, Vestbo J, Singh D. Long-acting bronchodilators for chronic obstructive pulmonary disease: which one(s), how, and when? Clin Chest Med . 2020;41:463–474. doi: 10.1016/j.ccm.2020.05.005. [DOI] [PubMed] [Google Scholar]

[bib31] 31.Brennan K, Zheng J. In: xPharm: the comprehensive pharmacology reference. Enna SJ, Bylund DB, editors. New York: Elsevier; 2007. Interleukin 8; pp. 1–7. [Google Scholar]

[bib32] 32. Petering H, Götze O, Kimmig D, Smolarski R, Kapp A, Elsner J. The biologic role of interleukin-8: functional analysis and expression of CXCR1 and CXCR2 on human eosinophils. Blood . 1999;93:694–702. [PubMed] [Google Scholar]

[bib33] 33. Chung KF. Cytokines in chronic obstructive pulmonary disease. Eur Respir J Suppl . 2001;34:50s–59s. [PubMed] [Google Scholar]

[bib34] 34. Zeng YY, Hu WP, Zuo YH, Wang XR, Zhang J. Altered serum levels of type I collagen turnover indicators accompanied by IL-6 and IL-8 release in stable COPD. Int J Chron Obstruct Pulmon Dis . 2019;14:163–168. doi: 10.2147/COPD.S188139. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib35] 35. Zhang J, Bai C. The significance of serum interleukin-8 in acute exacerbations of chronic obstructive pulmonary disease. Tanaffos . 2018;17:13–21. [PMC free article] [PubMed] [Google Scholar]

[bib36] 36. Susantyo S, Ilyas M, Bakri S, Udaya W, Kasim H, Faridin HP, et al. Association between bacterial colonization and serum level of interleukin-8 in stable chronic obstructive pulmonary disease in Makassar. Int J Med Rev Case Rep . 2019;3:707–710. [Google Scholar]

[bib37] 37. Yusuf K, Ilyas M, Tabri A, Katu S, Hp F, Kasim H, et al. The correlation between bacterial colonization and interleukin-8 serum levels in acute exacerbation of COPD. Int J Med Rev Case Rep . 2019;3:702–706. [Google Scholar]

[bib38] 38. Kolsum U, Donaldson GC, Singh R, Barker BL, Gupta V, George L, et al. Blood and sputum eosinophils in COPD; relationship with bacterial load. Respir Res . 2017;18:88. doi: 10.1186/s12931-017-0570-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib39] 39. Mahler DA, Huang S, Tabrizi M, Bell GM. Efficacy and safety of a monoclonal antibody recognizing interleukin-8 in COPD: a pilot study. Chest . 2004;126:926–934. doi: 10.1378/chest.126.3.926. [DOI] [PubMed] [Google Scholar]

[bib40] 40. Mukaida N. Pathophysiological roles of interleukin-8/CXCL8 in pulmonary diseases. Am J Physiol Lung Cell Mol Physiol . 2003;284:L566–L577. doi: 10.1152/ajplung.00233.2002. [DOI] [PubMed] [Google Scholar]

PERMALINK

Serum IL-8 as a Determinant of Response to Phosphodiesterase-4 Inhibition in Chronic Obstructive Pulmonary Disease

Mirco Govoni

Michele Bassi

Debora Santoro

Sinéad Donegan

Dave Singh

Abstract

Rationale

Objective

Methods

Measurements and Main Results

Conclusions

At a Glance Commentary

Scientific Knowledge on the Subject

What This Study Adds to the Field

Methods

Development of a PDE4-GES Associated with PDE4 Signaling; ECLIPSE Dataset

Validation of PDE4-GES; BIOMARKER Datasets

Discovery of Biomarkers Associated with PDE4-GES; BIOMARKER Study

Testing the Predictivity of Biomarkers against Clinical Response; PIONEER Study

Results

Figure 1.

Development of a PDE4-GES Associated with PDE4 Signaling; ECLIPSE Dataset

Figure 2.

Validation of PDE4-GES; BIOMARKER Datasets

Figure 3.

Figure 4.

Discovery of Biomarkers Associated with PDE4-GES; BIOMARKER Study

Figure 5.

Testing the Predictivity of Biomarkers against Clinical Response; PIONEER Study

Figure 6.

Figure 7.

Discussion

Acknowledgments

Acknowledgment

Footnotes

References

ACTIONS

PERMALINK

RESOURCES

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