| Literature DB >> 26903594 |
Nicole Mayer-Hamblett1, Michael Boyle2, Donald VanDevanter3.
Abstract
Cystic fibrosis (CF) is a life-shortening genetic disease affecting approximately 70,000 individuals worldwide. Until recently, drug development efforts have emphasised therapies treating downstream signs and symptoms resulting from the underlying CF biological defect: reduced function of the CF transmembrane conductance regulator (CFTR) protein. The current CF drug development landscape has expanded to include therapies that enhance CFTR function by either restoring wild-type CFTR protein expression or increasing (modulating) the function of mutant CFTR proteins in cells. To date, two systemic small-molecule CFTR modulators have been evaluated in pivotal clinical trials in individuals with CF and specific mutant CFTR genotypes that have led to regulatory review and/or approval. Advances in the discovery of CFTR modulators as a promising new class of therapies have been impressive, yet work remains to develop highly effective, disease-modifying modulators for individuals of all CF genotypes. The objectives of this review are to outline the challenges and opportunities in drug development created by systemic genotype-specific CFTR modulators, highlight the advantages of sweat chloride as an established biomarker of CFTR activity to streamline early-phase development and summarise options for later phase clinical trial designs that respond to the adoption of approved genotype-specific modulators into standard of care. An optimal development framework will be needed to move the most promising therapies efficiently through the drug development pipeline and ultimately deliver efficacious and safe therapies to all individuals with CF. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/Entities:
Keywords: Cystic Fibrosis; Rare lung diseases
Mesh:
Substances:
Year: 2016 PMID: 26903594 PMCID: PMC4853537 DOI: 10.1136/thoraxjnl-2015-208123
Source DB: PubMed Journal: Thorax ISSN: 0040-6376 Impact factor: 9.139
Figure 1Distributions of genotypes containing the 20 most prevalent mutant cystic fibrosis transmembrane conductance regulator (CFTR) alleles among individuals in the US with CF. Genotypes (each consisting of two CFTR alleles) are shown for individuals with CF followed in the 2012 US CF Foundation Patient Registry (CFFPR).19 The 20 most prevalent mutant CFTR alleles are shown in the same order on each axis with each possible genotype represented by only one bar and homozygous genotypes found on the front diagonal. The most prevalent genotype is F508del/F508del (far left). F508del compound heterozygotes are the remainder of bars along the first CFTR allele ‘wall’. Those genotypes for which there is currently an approved CFTR modulator are highlighted with dark bars. The same data are depicted in (A and B), but with the Y-axis shown as linear in (A) and log10 scale in (B).
Cystic fibrosis biomarkers studied with approved cystic fibrosis transmembrane conductance regulator (CFTR) modulators
| Biomarker | Ivacaftor in G551D | Lumacaftor–ivacaftor in F508del/F508del | References |
|---|---|---|---|
| Sweat chloride | √ | √ | |
| Nasal potential difference | √ | √ | |
| Mucociliary clearance | √ | ||
| Duodenal pH | √ | ||
| Sputum inflammatory markers* | √ | ||
| Lung clearance index | √ | ||
| Exhaled NO | √ | ||
| Sweat rate | √ |
*Free neutrophil elastase, interleukin 8, others.22
Figure 2Mean treatment-associated changes in sweat chloride concentration observed in three trials of cystic fibrosis transmembrane conductance regulator (CFTR) modulators. Modulators and CF genotypes studied are identified above observed values. Studies (A)26 and (B)16 were blinded, randomised and placebo-controlled. Study (C)22 was observational. Bars around point estimates represent 95% CIs.
Strengths and weaknesses of change in sweat chloride as a phase II biomarker for modulator candidates
| Strengths | Weaknesses |
|---|---|
| Highly standardised test | Not a clinical endpoint |
| Widely available | No 1:1 correlation with a clinical endpoint |
| Non-invasive | Not measured at site of desired clinical effect |
| Measure of effect on CFTR function | |
| Indicative of systemic drug bioavailability | |
| Rapid time to response (days) | |
| Rapid washout (days) | |
| Acceptable dynamic range (10–60 mmol/L) | |
| Low variability relative to effect size |
CFTR, cystic fibrosis transmembrane conductance regulator.
Comparison of sweat chloride and FEV1% predicted as phase II endpoints assuming thresholds of effect comparable with those observed for lumacaftor–ivacaftor
| Change in sweat chloride | Change in FEV1% predicted | |
|---|---|---|
| Hypothesised treatment effect (new modulator—placebo) | 10 mmol/L | 3.0% predicted |
| Estimated SD* | 10 | 6.5 |
| Subjects per group† | 16 | 74 |
| Treatment duration | 3–7 days | 14–28 days |
*SD estimates of the 28-day change in sweat chloride and FEV1% predicted derived from prior trials.26
†1:1 randomisation, two-sided t test with 80% power and two-sided 0.05 level of significance.
Figure 3Possible pivotal trial study designs for a candidate cystic fibrosis transmembrane conductance regulator (CFTR) modulator as influenced by genotype target, the existing modulator landscape and anticipated clinical efficacy. For any candidate modulator and CFTR genotype pairing, a series of queries (white boxes) and responses (black boxes) will inform which of five study designs (grey boxes) might be considered.
Sample sizes for superiority study designs. The hypothesis is that the new modulator is more efficacious than a comparator (either placebo or an active comparator)
| Anticipated FEV1 | SD of the change in FEV1 | Sample size per group† | |
|---|---|---|---|
| Scenario A | 3 | 7.3 | 125 |
| Scenario B | 5 | 7.3 | 45 |
| Scenario C | 10 | 7.3 | 12 |
Scenarios estimate the sample size for a 1:1 randomised study with a primary endpoint of absolute change in FEV1% predicted.
*SDs approximated from the 6-month changes in FEV1 observed in previous trials.16 21
†Estimated using a two-sample t test assuming 90% power and a two-sided 0.05 level of significance.
Sample sizes for active-comparator non-inferiority study designs. The hypothesis is that the new modulator is no more than a ‘small amount’ less efficacious than an active comparator, quantified by a non-inferiority (NI) margin
| Previously observed | FEV1 SD of the change in FEV1 | % of Lowest treatment effect to preserve | NI margin† | Sample size per group‡ | |
|---|---|---|---|---|---|
| Scenario A: non-inferiority study vs an active comparator which has efficacy comparable with lumacaftor–ivacaftor | |||||
| 3.0% (1.6 to 4.4) | 7.3 | 75% | 0.75×1.6=1.2% | 778 | |
| Scenario B: non-inferiority study vs an active comparator which has efficacy comparable with ivacaftor | |||||
| 10.6% (8.6 to 12.6) | 7.0 | 50% | 0.50×8.6=4.3% | 56 | |
*Treatment effects and SDs approximated from the 6-month changes in FEV1 observed in previous trials.16 21
†The NI margin is derived based on preserving a percentage of the lowest possible treatment effect observed in the placebo-controlled trial, as captured by the lower bound of the 95% CI. NI margins must be negotiated with regulators and ensure that a clinically meaningful effect size will be maintained.
‡Assuming there is truly no difference between the new modulator and the active comparator, sample size estimates are generated with 90% power to ensure that the lower limit of a one-sided 97.5% CI will be above the NI margin.
Figure 4One-month washout design for a second-generation modulator studied in a population with access to a robust approved modulator. Subjects receiving the approved modulator are randomised 1:1 to receive the candidate modulator or placebo. Subjects can be optionally rolled over to active modulator for additional information.