CNS is the second largest area of pharmaceutical research, but the success rate has been low and approvals rare. In CNS drug development, it is common for investigational therapies to show promising effects in earlier trials, only to fail in the final stages of testing. Some of the reasons can be attributed to the technical difficulty of getting drug molecules to their targets across the blood-brain barrier, which in protecting the brain also protects the disease. However, the number of failed Phase III trials following exceptional Phase II results point to another underlying problem in CNS: having a drug that works is necessary to demonstrate efficacy at the magnitude that merits approval.
The data on non-adherence are staggering: 25%-50% of patients do not take their medication. Add to that the high rates of placebo response in psychiatric disorders, and it is no wonder that mental illness contributes the highest proportion of healthcare costs. Pharmaceutical companies now recognize the transformative potential of taking approved compounds and delivering them in innovative ways that help patients stay compliant. It is an applied approach to a patient-centered model of drug development.
The medical, social, and financial value of unlocking treatment adherence is one important takeaway; another important takeaway is methodological. The outcomes in CNS trials are subjective compared to other areas of medicine, with primary outcome measures based on clinician-observed or patient-reported questionnaires.
Despite significant advancements, our trials are still haunted by the specter of high placebo response, patient misclassification, and unreliable outcomes.
A thriving niche has been established over the last decade in looking for solutions to these measurement problems. Our colleagues in this area have tried many approaches: making patient selection independent or consensus-based, improving outcome measures themselves, using technology solutions to enhance detection of change, training investigators to achieve standardization, training patients and caregivers to improve reliability of reporting, patenting adaptive protocol designs, etc. Similar to clinical practice, we have found that none of these solutions work if applied in isolation. With Phase II data, we can analyze underlying trends and quantify risks to outcome data. The important next step is to remain uncompromising in the focus of applying those insights to the selection of a calibrated set of measurement tools to be used in the trial.
Recent successes portend a new era of drug development, one in which treatment efficacy is established in the context of the patient’s life and behavior. It is time for measurement science to follow and embrace the fact that how we measure outcomes will evolve from applying one solution to finding an empirical basis for a highly customized application of all of them, enabled by technology. Personalized medicine needs a personalized approach to measurement. l
