Clinical trials to establish the efficacy and safety of a new therapeutic represent a critical and hugely expensive milestone in the drug development process. 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.
How to reduce the size of clinical trials through pre-stratification
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, have an enormous potential to pre-stratify subjects prior to enrolment.
One striking recent example of this is a study of interstitial lung disease (ILD). 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 enrollment in a clinical trial for a potential PF-ILD therapeutic would reduce the required size of the trial cohort by 80%, potentially saving >$26M.
Speed drug development through stratifying clinical trial enrollment
Bowman WS, Newton CA, Linderholm AL, et al.Proteomic biomarkers of progressive fibrosing interstitial lung disease: a multicentre cohort analysis. (2022) Lancet Respiratory Medicine, DOI: 10.1016/S2213-2600(21)00503-8
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.
– Bowman WS et al. Lancet Respir Med (2022)
Proteomics at the heart of multiomics strategies
Systems biology approaches addressing multiple molecular and cellular components are adding vital insights into the dynamic biology underlying human health and disease.