Stratification of patient samples for efficient clinical trials
The progressive fibrosing form of interstitial lung disease (ILD) is a devastating and frequently fatal condition and may develop from any one of a number of different ILD variants. There are limited markers available for ongoing progressive ILD, but no predictive markers to identify at risk ILD patients, which would severely complicate any trials for preventive medication. In this article researchers identified a protein signature with high predictive value for PF-ILD, allowing the enrichment of a clinical trial. By adding only patients which are prone to develop the fast progressing from of ILD, it would be possible to decrease the recruitment of patients by 80%, saving time and cost for the clinical trial.
One of the cost-driving milestones in the drug development process are clinical trials with the recruitment of patients from phase I to phase III. During the Corona pandemic we have learnt how difficult and time-consuming patient recruitment can be. Studies have estimated that the cost of a single clinical trial patient enrolee is approximately $50,000, so it is unsurprising that the number of subjects needed to generate the data required for eventual market introduction is an important factor in overall development costs. Moreover, should a drug fail to meet requirements in clinical trials, this represents a catastrophic, late failure in the drug development project, wasting years of work and incurring huge costs for the company.
One way to maximize the chances of success and significantly reduce the number of required trial enrolees is to pre-stratify potential subjects to ensure that those most likely to respond to the drug are included in the trial. In addition to significant cost-saving and effectivization of the clinical trial process, this is also in-line with the principles of precision medicine, where therapies are tailored towards specific groups of patients. Protein biomarkers that can stratify patients with different disease endotypes or prognostic pathways, or even directly predict responses to a specific drug, having an enormous potential to pre-stratify subjects prior to enrolment.
Interstitial lung disease
One striking recent example of this is a study of interstitial lung disease (ILD).
Interstitial Lung Disease (ILD) is a heterogeneous group of disorders that cause lung scars, affecting an individual’s ability to breathe. These scars are at the lowest level of lung organization, a functional unit called the alveolus (alveoli plural). The lung interstitium is the connective tissue outside this air sac, and oxygen travels through the alveolus and the interstitium to the surrounding capillaries.
Symptoms include shortness of breath, chest pain, extreme tiredness, and dry cough, and range in severity from mild to life-threatening, and affects approximately 100,000 individuals in the United States every year. Symptoms of this disease are treated with a collection of at least 18 therapeutics and oxygen therapy, with lung transplant being a final (and drastic) option. About 30% of all single lung transplants in the US are driven by idiopathic pulmonary fibrosis which indicates the severe need for additional development of therapeutics.
A theoretical randomised controlled trial with 1:1 randomisation designed without regard to proteomic signature would require 676 patients to detect a 50% reduction in FVC decline at 90% power, assuming a standard deviation of 200 mL and two-tailed α of 0·05. A similar trial restricted to patients with a high-risk proteomic signature would require 142 patients, assuming the same parameters.
There are several types of ILD, some driven by genetics, the majority with no known cause (called idiopathy pulmonary fibrosis or IPF), others caused by dust or mold in the environment, including asbestos-related lung diseases. As a heterogeneous disorder with poor treatment options several efforts are underway to develop improved treatments for this disease.
The progressive fibrosing form of ILD (PF-ILD) is a devastating and frequently fatal condition and may develop from any one of a number of different ILD variants. Previously, there were no predictive markers to identify at risk ILD patients, which would severely complicate any clinical trials for preventive medication. A team from University of California Davis used Olink to measure ~350 plasma proteins in ILD patients and applied machine learning algorithms to identify a 12-protein signature with high predictive value for PF-ILD that was validated in an independent patient cohort. With a negative predictive value of 0.91, the authors calculated that pre-stratification of patients with this protein signature prior to enrolment in a clinical trial for a potential PF-ILD therapeutic would reduce the required size of the trial cohort by 80%, potentially saving >$26M.