When an endpoint is just the beginning: How preclinical endpoints affect clinical success
In neuroscience drug discovery and development, there are two fundamental problems that researchers are acutely aware of: a high unmet need of successful therapies and a historic difficulty in bringing novel treatments to market. A study by BIO emphasizes the latter point; across all indications, only 9.6 percent of candidates in Phase I reached approval. For neurodegenerative diseases, that number is even smaller (8.4 percent).
One significant challenge in CNS is the disconnect between how preclinical programs are executed compared to how they are executed in the clinic. Some of the important differences include:
- Endpoints: Preclinical studies are done with very tight, objective endpoints, whereas clinical studies are done using more subjective endpoints.
- Population Makeup: Preclinical studies are done with highly homogeneous populations, unlike clinical studies that are conducted with highly heterogeneous populations.
- Age Discrepancies: Preclinical studies are often conducted with young research models, even in diseases that primarily impact the elderly in the clinic.
- Dosing Schedules: Dosing routes and timelines in research models do not always correspond with what is done in the clinic.
Of these areas, one of the most critical discrepancies to address is endpoints—if preclinical and clinical studies are measuring success differently, predicting clinical success based on preclinical outcomes is challenging. While it is important to identify promising targets and candidates for the treatment of neurodegenerative disorders, it is just as important to utilize preclinical data to advise clinical execution. New strategies are emerging that are helping to bridge the divide: both to increase the probability of clinical success, and better advise go/no-go decisions on candidates.
One of the most important strategies is to move novel therapeutics to the clinic with accompanying biomarkers. Target engagement biomarkers, such as position-emission tomography (PET) imaging tracers, allow researchers to know if they are hitting the target of interest to the extent that supports clinical testing. Moreover, they provide a quantitative and non-species-specific measure that can be used to bridge preclinical and clinical studies. Imaging and fluid-based approaches to quantify engagement are also being used with success. Fluid-based biomarkers can be used to get an early read on the success of a long duration clinical trial, which can allow research to make decisions much earlier in the progression.
As an example, if a putative disease-modifying therapeutic for a neurodegenerative condition, such as Alzheimer’s disease, is being tested, biomarkers can be used to confirm early success or potential failure. Identifying success early on in a clinical study allows the researchers to prepare for the subsequent stages of the drug development process. Conversely, identifying failure early can lead to significant savings and reduce the number of patients being exposed to treatments that are not beneficial. It can also allow researchers to modify the study design to ensure that the underlying hypothesis is being tested appropriately.
Neuroscience clinical trials are trending towards being conducted based on more objective measures. This may be most obvious in disorders that effect motor functions, such as Parkinson’s disease, where wearable devices are being employed with increasing frequency. These devices allow clinicians to continuously monitor the effect of treatments on patients, providing more information than would be gained from rating scale data alone. The data obtained from wearable devices can be mimicked in research models using telemetric devices, home cage monitoring, quantitative EEG, camera tracking systems or other quantitative systems that provide objective behavioral readouts.
We are in the midst of a change in the way that preclinical studies are used to design and interpret clinical studies. The use of objective endpoints in the clinic, including both biomarkers and rapidly progressing technologies, will result in an increased ability to translate preclinical endpoints to the human condition, and ultimately lead to an increased success rate of developing novel treatments for neuroscience diseases.