Antifibrotic therapies for idiopathic pulmonary fibrosis
Abstract
Objectives
This is a protocol for a Cochrane Review (intervention). The objectives are as follows:
To investigate the efficacy and safety of antifibrotic drugs in individuals with idiopathic pulmonary fibrosis.
Background
Description of the condition
Interstitial lung diseases (ILD) are a heterogenous group of parenchymal lung diseases characterised by varying degrees of inflammation and fibrosis, resulting in breathlessness, reduced exercise tolerance, reduced lung capacity, and at the end stages, respiratory failure and death.
ILDs may be associated with autoimmune conditions including connective tissue disease (connective tissue disease‐related interstitial lung disease; CTD‐ILD) or sarcoidosis, exposure to environmental antigens (hypersensitivity pneumonitis; HP), smoking (respiratory‐bronchiolitis interstitial lung disease; RB‐ILD), or idiopathic, including idiopathic interstitial pneumonia or idiopathic pulmonary fibrosis (IPF) (Barratt 2018).
IPF, which is characterised by a chronic, progressive fibrosing interstitial pneumonia (ATS 2000), is one of the most common causes of ILD, with a global incidence of 2.8 to 9.3 persons per 100,000 per year (Raghu 2014). The incidence of IPF increases exponentially with age and most commonly presents in the sixth and seventh decades (Raghu 2018). Clinical manifestations include chronic exertional dyspnoea, bibasilar inspiratory crackles, cough, and clubbing, without constitutional symptoms suggesting a multisystem disease (Raghu 2018). Lung function tests demonstrate a restrictive defect, and over time a reduction in exercise tolerance and hypoxaemia develops. Those with IPF are at risk of acute exacerbations (sudden progressive dyspnoea, hypoxia, and ground glass infiltrates on computed tomography (CT)) which significantly increases mortality risk (Juarez 2015). IPF is a chronic, progressive, irreversible disease; without treatment it often progresses to respiratory failure, with a mean overall survival of four years (95% confidence interval 3.7 to 4.6) from the time of diagnosis (Khor 2020).
Whilst termed idiopathic, emerging evidence is beginning to uncover the pathogenesis, genetic, and environmental risk factors contributing to the development of the condition. A family history of IPF (known as familial interstitial pneumonia) increases the likelihood of the development of IPF as well as mortality from the condition (Kropski 2015). Genetic studies indicate that up to 20% of IPF cases are familial (García‐Sancho 2011), and genome‐wide association studies in these clusters have led to the discovery of surfactant and telomere‐related genetic variants which contribute to disease (Noth 2013). In addition to intrinsic genetic risk factors, extrinsic environmental factors appear to increase the risk of IPF, including smoking (associated with epithelial micro‐injury, epithelial‐to‐mesenchymal transition, production of transforming growth factor beta (TGF‐β), and acceleration of telomere shortening), micro‐aspiration of gastric contents (epithelial injury), viral infections (e.g. Epstein‐Barr virus, cytomegalovirus, human herpesvirus‐7, and human herpesvirus‐8, which increase TGF‐β production), inhaled particulate dust, and altered lung microbial composition. IPF is more common in males (hypothesised due to increased risk of relevant exposures such as smoking, dust, and presence of male sex hormones) and in older individuals (likely associated with telomere shortening in ageing) (Zaman 2018).
The beginning of the disease may arise from the epithelium and that of impaired epithelial‐mesenchymal crosstalk (Costabel 2016). Repeated micro‐injury to the alveolar epithelium may result from smoking, inorganic dust and fume exposure, viral proteins (Epstein‐Barr virus), and inherited genetic mutations (telomerase reverse transcriptase (TERT), telomerase RNA component (TERC), poly(A)‐specific ribonuclease (PARN), regulator of telomere elongation helicase 1 (RTEL1)) (Hecker 2011; Kannengiesser 2015; Stuart 2015), leading to intracellular stress (DNA damage and endoplasmic reticulum stress), premature cellular senescence, and abnormal re‐epithelisation (Garcia 2011). This is followed by defective mechanisms of regeneration, including activation of pulmonary fibroblasts, myofibroblast differentiation, and the production of extracellular matrix and collagen, stimulated by TGF‐β, connective tissue growth factor, and platelet‐derived growth factor (PDGF). Once established, alveolar damage and pro‐fibrotic factors lead to a feed‐forward loop of ongoing fibrosis (Wijsenbeek 2020).
The diagnosis of IPF is made by meeting defined criteria as outlined in international consensus guidelines (ATS 2000; Raghu 2018), which includes identification of a usual interstitial pneumonia (UIP) pattern on high‐resolution computed tomography (HRCT) or surgical lung biopsy, and exclusion of another more likely ILD. These guidelines have evolved over time to favour UIP pattern on HRCT over reliance on surgical lung biopsy, which carries a significant morbidity and mortality risk (Hutchinson 2016; Pastre 2021).
Description of the intervention
The early conceptualisation of IPF as an inflammatory alveolitis led to the use of immunosuppressive treatment (Brown 1971), initially with corticosteroids, beginning in the 1960s (Liebow 1965), and later with the addition of cytotoxic and immunomodulatory agents, including azathioprine, chlorambucil, cyclophosphamide, cyclosporin, and D‐penicillamine (Richeldi 2003; Travis 2002). These recommendations were made on very little initial trial evidence, and subsequent data suggest lack of efficacy and potential risk of harm.
In the early 2000s, a multitude of trials examined the use of additional agents, including N‐acetylcysteine (NAC) monotherapy; combination NAC, prednisolone, and azathioprine therapy; warfarin, bosentan, ambrisentan, and imatinib, and found a lack of effect, and in some cases an increased risk of harm (The IPF Clinical Research Network 2012). These treatments are not recommended in IPF, and leave clinicians with little therapeutic options beyond supportive care.
More recently, clinical trials and real‐world observational data examining antifibrotic agents including pirfenidone and nintedanib have reported potential efficacy in IPF (Cameli 2020; King 2014; Krauss 2020; Richeldi 2014; Wuyts 2019). These studies suggest that antifibrotic agents may reduce disease progression, slow lung function decline, improve exercise tolerance, and affect progression‐free survival. Trials examining their efficacy also demonstrate adverse events, including gastrointestinal effects, photosensitivity, and liver dysfunction.
How the intervention might work
Antifibrotic drugs target the downstream pathways of the fibrogenesis process by inhibiting the recruitment, proliferation, and differentiation of fibroblasts and fibrocytes and inhibit the deposition of extracellular matrix (Costabel 2016). Two antifibrotic drugs have been studied in people with IPF: pirfenidone and nintedanib.
Pirfenidone
Pirfenidone was developed as a systemic antifibrotic drug, and is used in fibrotic conditions affecting the heart, lungs, liver, kidneys, and skin. The exact mechanism of action of pirfenidone remains unclear, but it appears to modulate fibrogenesis through anti‐transforming growth factor (anti‐TGF) and antiplatelet derived effects (Moeller 2008). Pirfenidone has been shown to reduce pulmonary fibrosis through in vitro and in vivo animal models in experimental condition (Schaefer 2011), partially through the modulation of cytokines, TGF‐β, basic fibroblast growth factor (Oku 2008), and tumour necrosis factor alpha (TNF‐α) (Iyer 2000; Oku 2002; Oku 2008). It also reduces the expression, synthesis, and accumulation of collagen (Iyer 1999), and inhibits the recruitment and expression of extracellular matrix‐producing cells (including fibroblasts) (Kakugawa 2004). Moreover, pirfenidone is believed to have an anti‐inflammatory effect that protects against oxidative stress (Carter 2011). Pirfenidone is largely metabolised by the hepatic CYP1A2 enzymes, therefore concomitant prescription of certain antibiotics (ciprofloxacin) and proton pump inhibitors (omeprazole) may increase the plasma concentration of the drug (Costabel 2016).
Nintedanib
Nintedanib was first developed as an anti‐angiogenesis agent in lung, ovarian, and colorectal cancer, hepatocellular carcinoma, and renal cell carcinoma (Costabel 2016). Nintedanib is an intracellular tyrosine kinase inhibitor which blocks tyrosine kinases, including platelet‐derived growth factor receptors and B, vascular endothelial growth factor (VEGF) receptors 1 to 3 and fibroblast growth factor (FGF) receptors 1 to 3, FMS‐like tyrosine kinase 3 (FLT3), and the non‐receptor tyrosine kinases Src, Lyn, and Lck, which are involved in lung fibrosis (Wollin 2015).
Nintedanib yields varying effects on multiple signalling pathways that play a key role in the development and progression of fibrosis (Cottin 2015). TGF‐β is a key molecule in the pathogenesis of lung fibrosis, the effects of which are mediated by FGF release and upregulation of FGF receptor 1 and 2 expression (Wollin 2015). Studies using in vitro fibroblasts from individuals with IPF demonstrates that nintedanib interferes with fibroblast proliferation, migration, and differentiation to myofibroblasts, and the secretion of extracellular matrix (Ahluwalia 2014; Wollin 2014). Moreover, in IPF patients, nintedanib inhibits TGF‐β‐induced transformation of fibroblasts to myofibroblasts (Ahluwalia 2014; Wollin 2014). Nintedanib is metabolised by CYP3A4, and cleared hepatically, which in some patients increases the risk of transaminitis. Risk of bleeding or gastrointestinal perforation is increased due to the inhibition of VEGF (Costabel 2016).
Why it is important to do this review
IPF is a progressive fibrotic interstitial lung disease with a poor prognosis. Few therapeutic agents are available beyond supportive care. Antifibrotic agents may be efficacious in slowing disease progression, but have adverse effects. This topic was prioritised by the Cochrane Airways Priority Setting Group.
Objectives
To investigate the efficacy and safety of antifibrotic drugs in individuals with idiopathic pulmonary fibrosis.
Methods
Criteria for considering studies for this review
Types of studies
We will include randomised controlled trials (RCTs), including cluster‐ and cross‐over RCTs. We will include studies reported in full text, those published as abstract only, and unpublished data, written in any language.
Types of participants
We will include adults (over age 18 years) with a diagnosis of IPF as defined by a contemporary international consensus guideline at the time of study conduct, with the presence of UIP pattern on HRCT or surgical lung biopsy, and excluding another ILD or other airways diseases.
Types of interventions
We will include studies comparing any antifibrotic agent (by any route, in any dose), for at least one week, with either placebo or another pharmacological treatment.
We will include any usual care co‐interventions (including pharmacological therapies such as immunosuppressive agents, oxygen therapy, pulmonary rehabilitation), provided they are not part of the randomised treatment.
Types of outcome measures
We will analyse the following outcomes in the review, but will not use them as a basis for study inclusion or exclusion. As outlined in the Background, IPF results in breathlessness, cough, reduced exercise tolerance, increased risk of acute exacerbation of IPF, and ultimately respiratory failure. It is also understood that antifibrotic treatments have known side effects. We will therefore examine the following outcomes.
Primary outcomes
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Mortality (subgrouped as all‐cause mortality, respiratory‐related mortality, and adverse event‐related mortality)
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Change in forced vital capacity (FVC) (% predicted and mL)
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Change in exercise tolerance: either change in 6‐minute walk distance, or an alternative validated measure
Secondary outcomes
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Acute exacerbations
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A categorical > 10% decline in FVC
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Change in diffusing capacity of the lungs for carbon monoxide (DLCO) (% predicted and mL)
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Breathlessness, by any validated scale (e.g. visual analogue scale, Borg score)
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Cough, by any validated scale (e.g. Leicester Cough Questionnaire)
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Quality of life, by any validated scale (e.g. King’s Brief Interstitial Lung Disease questionnaire, St George's Respiratory Questionnaire)
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Serious adverse events (that require hospitalisation or lead to death)
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Adverse events leading to drug discontinuation
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Total adverse events, including:
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gastrointestinal effects (including diarrhoea, abdominal pain, reflux, perforation, anorexia, bleeding);
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neurological (e.g. dizziness);
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cardiac;
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dermatological (e.g. photosensitivity, rash);
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elevated liver function test abnormalities.
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We will use outcomes measured at 12 months for the main analysis, and present any additionally reported time points as secondary analyses. Where multiple events per participant is possible (i.e. for exacerbations and adverse events), we will preferentially report the rate ratio calculated from count data (i.e. the ratio of the number of events divided by person time of follow‐up in the intervention arm to the control arm).
Search methods for identification of studies
Electronic searches
We will search for studies in the following databases and trial registries:
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Cochrane Airways Trials Register (Cochrane Airways 2019), via the Cochrane Register of Studies, all years to date;
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Cochrane Central Register of Controlled Trials (CENTRAL), via the Cochrane Register of Studies, all years to date;
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MEDLINE Ovid SP 1946 to date;
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Embase Ovid SP 1974 to date;
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US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov (www.clinicaltrials.gov/);
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World Health Organization International Clinical Trials Registry Platform (trialsearch.who.int/).
The proposed MEDLINE search strategy is listed in Appendix 1. We will adapt this strategy for use in the other databases. The search strategy was developed by the Cochrane Airways Information Specialist in collaboration with the review authors.
We will search all databases and trials registries from their inception to the present, with no restrictions on language or type of publication. We will identify handsearched conference abstracts and grey literature through the Cochrane Airways Trials Register and CENTRAL.
Searching other resources
We will check the reference lists of all primary studies and review articles for additional references. We will search relevant manufacturers' websites for study information.
We will search on PubMed for errata or retractions from included studies published in full text, and report the date this was done within the review.
Data collection and analysis
Selection of studies
We plan to use Cochrane’s Screen4Me workflow to help assess the search results. Screen4Me comprises three components: known assessments – a service that matches records in the search results to records that have already been screened in Cochrane Crowd and been labelled as an RCT or as Not an RCT; the RCT classifier – a machine learning model that distinguishes RCTs from non‐RCTs; and if appropriate, Cochrane Crowd (crowd.cochrane.org) – Cochrane’s citizen science platform where the Crowd help to identify and describe health evidence. More detailed information about the Screen4Me components can be found in the following publications: Marshall 2018; McDonald 2017; Noel‐Storr 2018; Thomas 2017.
Following this initial assessment, three review authors (IA, JM, DN) will independently screen the titles and abstracts of the remaining search results and code them as 'retrieve' (eligible or potentially eligible/unclear) or 'do not retrieve'. We will retrieve the full‐text study reports of all potentially eligible studies, and three review authors (IA, JM, DN) will independently screen them for inclusion, recording the reasons for exclusion of ineligible studies. Any disagreements will be resolved through discussion or by consulting a third person/review author (HB) if required. We will identify and exclude duplicates and collate multiple reports of the same study so that each study, rather than each report, is the unit of interest in the review. We will record the selection process in sufficient detail to complete a PRISMA flow diagram and 'Characteristics of excluded studies' table (Moher 2009).
Data extraction and management
We will use a data collection form for study characteristics and outcome data that has been piloted on at least one study in the review. Two review authors (IA and JM or DN) will extract the following study characteristics from the included studies.
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Methods: study design, total duration of study, details of any 'run‐in' period, number of study centres and location, study setting, withdrawals, and date of study.
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Participants: N, mean age, age range, gender, severity of condition, diagnostic criteria, baseline lung function, smoking history, inclusion and exclusion criteria.
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Interventions: intervention, comparison, concomitant medications, and excluded medications.
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Outcomes: primary and secondary outcomes specified and collected, and time points reported.
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Notes: funding for studies and notable conflicts of interest of the trial authors.
Two review authors (IA and JM or DN) will independently extract outcome data from the included studies. We will note in the 'Characteristics of included studies' table if outcome data are not reported in a useable way. Any disagreements will be resolved by consensus or by involving a third person/review author (HB). One review author (IA) will transfer data into Review Manager Web (RevMan Web 2020). We will double‐check that data are entered correctly by comparing the data presented in the systematic review with the study reports. A second review author (HB) will spot‐check study characteristics for accuracy against the study report.
Assessment of risk of bias in included studies
Two review authors (IA, DN) will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). Any disagreements will be resolved by discussion or by involving another review author (HB). We will use Version 2 of the Cochrane risk of bias tool for randomised trials (RoB 2) (Sterne 2019), which is based on the following domains.
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Domain 1: Risk of bias arising from the randomisation process
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Domain 2: Risk of bias due to deviations from the intended interventions (effect of assignment to intervention)
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Domain 3: Missing outcome data
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Domain 4: Risk of bias in measurement of the outcome
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Domain 5: Risk of bias in selection of the reported result
We will use the RoB 2 Excel tool to complete RoB 2 assessment and the robvis tool to create weighted bar plots of the distribution of risk of bias judgements within each bias domain (McGuinness 2021). We will use RevMan Web to generate traffic light plots of the domain‐level judgements for each outcome.
Our effect of interest is assignment to the intervention at baseline, and our main outcomes are those listed in the summary of findings table (see below). We will judge each outcome as being at low risk, some concerns, or high risk according to the RoB 2 algorithm. We will provide a quote from the study report, together with a justification for our judgement, in the risk of bias table. Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the risk of bias table. In order to detect reporting bias, we will compare the study protocol with the published report if possible. We will attempt to contact study authors to identify missing or partially reported data. If more than 10 studies are included in the meta‐analysis, we will create a funnel plot to explore publication bias.
Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the risk of bias table. The risk of bias assessment will be incorporated in the 'Results' section of the review and will also form part of the GRADE assessment of the certainty of evidence (along with precision, directness, consistency, and publication bias). When considering treatment effects, we will take into account the risk of bias for the studies that contribute to that outcome. Our primary analysis will include all studies without taking the risk of bias judgement into account.
Assessment of bias in conducting the systematic review
We will conduct the review according to this published protocol and justify any deviations from it in the 'Differences between protocol and review' section of the systematic review.
Measures of treatment effect
We will analyse dichotomous data as odds ratios (OR) and continuous data as the mean difference (MD) or standardised mean difference (SMD). If data from rating scales are combined in a meta‐analysis, we will ensure they are entered with a consistent direction of effect (e.g. lower scores always indicate improvement).
We will undertake meta‐analyses only where this is meaningful, that is if the treatments, participants, and the underlying clinical question are similar enough for pooling to make sense.
We will describe skewed data narratively (e.g. as medians and interquartile ranges for each group).
Where multiple trial arms are reported in a single study, we will include only the relevant arms. If two comparisons (e.g. drug A versus placebo and drug B versus placebo) are combined in the same meta‐analysis, we will either combine the active arms or halve the control group to avoid double‐counting.
If adjusted analyses are available (analysis of variance (ANOVA) or analysis of covariance (ANCOVA)), we will prefer these for use in our meta‐analyses. If both change‐from‐baseline and endpoint scores are available for continuous data, we will use change from baseline unless there is low correlation between measurements in individuals. If a study reports outcomes at multiple time points, we will use outcomes measured at 12 months for the main analysis, and present additional time points as secondary analyses.
We will use intention‐to‐treat (ITT), or 'full analysis set' analyses where they are reported (i.e. those where data have been imputed for participants who were randomly assigned but did not complete the study) instead of completer or per‐protocol analyses.
Unit of analysis issues
For dichotomous outcomes, we will use participants rather than events as the unit of analysis (i.e. number of participants admitted to hospital, rather than number of admissions per participant). Where multiple events per participant is possible (i.e. for exacerbations and adverse events), we will preferentially report the rate ratio calculated from count data (i.e. the ratio of the number of events divided by person time of follow‐up in the intervention arm to the control arm). We will only meta‐analyse data from cluster‐RCTs if the available data have been adjusted (or can be adjusted) to account for the clustering.
Dealing with missing data
We will contact investigators or study sponsors in order to verify key study characteristics and to obtain missing numerical outcome data where possible (e.g. when a study is identified as an abstract only). Where this is not possible, and the missing data are thought to introduce serious bias, we will take this into consideration in the GRADE rating for affected outcomes.
Assessment of heterogeneity
We will use the I² statistic to measure heterogeneity amongst studies in each analysis. If we identify substantial heterogeneity, we will report it and explore the possible causes by prespecified subgroup analysis.
Assessment of reporting biases
If we are able to pool more than 10 studies, we will create and examine a funnel plot to explore possible small‐study and publication biases.
Data synthesis
We will include all eligible studies in the primary analyses, with the risk of bias depicted in the forest plots. In addition, we will perform sensitivity analyses including only studies at low risk of bias.
We will use a random‐effects model and perform a sensitivity analysis with a fixed‐effect model.
Subgroup analysis and investigation of heterogeneity
We plan to present total adverse events by organ‐specific subgroups.
We plan to carry out the following subgroup analyses where data are available.
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By antifibrotic therapy (i.e. pirfenidone versus placebo and nintedanib versus placebo)
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Mild disease versus severe disease (FVC > 80% versus 50% to 80% versus < 50% predicted)
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Duration of therapy (< 6 months versus 6 to 12 months versus > 12 months)
We will use the following outcomes in the subgroup analyses.
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Mortality
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Change in FVC % predicted
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Change in exercise tolerance by change in 6‐minute walk distance
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Exacerbations
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Serious adverse events (that require hospitalisation or lead to death)
We will use the formal test for subgroup interactions in Review Manager Web (RevMan Web 2021).
Sensitivity analysis
We plan to carry out the following sensitivity analyses.
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We will compare the results from a fixed‐effect model with a random‐effects model.
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We will compare the results from studies at low risk of bias with all studies.
Summary of findings and assessment of the certainty of the evidence
We will create a summary of findings table using the following outcomes.
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Mortality
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Change in FVC % predicted
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6‐minute walk distance
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Exacerbations
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Quality of life, by any validated scale (e.g. St George's Respiratory Questionnaire)
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Adverse events leading to drug discontinuation
We will use the five GRADE considerations (within‐study risk of bias (based on the RoB 2 assessment), consistency of effect, imprecision, indirectness, and publication bias) to assess the quality of a body of evidence as it relates to the studies that contribute data for the prespecified outcomes. The GRADE approach specifies four levels of certainty: high, moderate, low, and very low. We will use the methods and recommendations described in Section 8.5 and Chapter 12 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021), employing GRADEpro GDT software (GRADEpro GDT). We will justify all decisions to downgrade the certainty of the evidence using footnotes and will make comments to aid the reader's understanding of the review where necessary.
