As the general population continues to age, the economic burden of chronic pain and neurodegenerative diseases is increasing and becoming unsustainable. Unfortunately, the pipeline of new therapeutics has thinned over the past several years due to several clinical stage failures. Reasons for these failures may include insufficient understanding of neurobiological mechanisms, poor translation of preclinical data, clinical trial challenges, and the complexity and variability of the human brain and neurological diseases.
The intense medical need for new and better treatments for the aging population has led to neuroscience being one of the largest and fastest growing focus areas in drug development, predicted to take center stage in the coming years, a place currently occupied by oncology.
As drug developers race to develop the next therapeutic, many are looking to incorporate more translational approaches to their development strategy. In this article, we approach 3 ways drug developers can approach translation.
During preclinical stages, researchers utilize disease models that aim to replicate the human condition. In theory, these models would be able to replicate the human disease, share underlying biological mechanisms with human disease, and have predictive value in safety and efficacy in human.
These criteria are often unmet, or at least not met fully within one model.
Challenges often include:
Increasing the clinical translatability of preclinical data requires models that accurately reflect the human condition. Much of current research is conducted in rodents, but the anatomy and physiology that differs from humans. Rodents have lower brain function, shorter lifespan, smaller bodies, different immune mechanisms, less complex CNS systems etc.
While rodents provide valuable data and will always occupy a place in the process, drug developers must consider adding more non-rodent species that have greater similarities to the human condition when possible. Large animal models often reproduce key features of human biology and disease more closely. Non-human primates however are costly, difficult to access, and have long lead times. Due to this, attention has turned to pig models.
The pig shares valuable similarities with humans, including skin and neurological characteristics, nerve stimulation and behavioral changes. Consider some of the major challenges within pain research specifically to see how large animals can bridge the gap between preclinical and clinical studies.
The data below shows an example of the predictability of the pig models. Meloxicam (HTX-009) and Bupivacaine (HTX-002) were evaluated in a flank incision model in the pig. Meloxicam shows no activity in the pig and almost no activity in the human as well. In contrast, Bupivacaine is slightly active in both the pig and human at the beginning. HTX-011 (a combination of Meloxicam and Bupivacaine) shows a synergistic effect in both pig and human.
The use of biomarkers has the potential to increase value early in drug development by extracting more data and information from each study. This can increase efficiency, reduce costs, guide decisions, boost the translational power of preclinical studies, and effectively enable more drug candidates to move forward.
Between 2015-2019, over half of the approvals by the FDA (59%) and EMA (69%) were accompanied by at least one biomarker (source: clinicaltrials.gov). While advances have been made in biomarker development for other disorders (eg cancer), neuroscience biomarkers lag behind due to the complexity of the nervous system and access to tissue and the blood brain barrier.
A growing range of biological measures that include proteomic and cell-based signatures are being explored as potential biomarkers in the field of pain and neurodegenerative conditions. Drug developers can incorporate target biomarkers, mechanism biomarkers, and disease biomarkers into preclinical studies to objectively measure physiological processes, pathophysiological processes, or pharmacological responses.
In the data below, Immunohistochemical analysis of intra-epidermal nerve fiber (IENF) density provides insight into changes in nerve fiber density between naive and STZ-diabetic animals. In the clinic, IENF density may serve as a histological biomarker of nociceptive nerve fiber involvement in peripheral neuropathy.
Using outcomes that are comparable to those measured in patients, researchers can increase the translational value and predictability of preclinical studies.
Electrophysiology (EP), an in vivo biomarker that is used in the clinic, provides the ability to study neurological dysfunctions, how the nervous system is affected, and how it responds to various treatments. Electrophysiology can be conducted to study the functional properties of neurons, which allows researchers to better understand the neurological disorder and the potential of neuroprotective or neurodegenerative therapies.
When adding electrophysiology to a preclinical study, researchers are able to see what is otherwise missed when relying on clinical scores alone. In the data example below, tcSEP (sensory evoked potentials) are monitored using electrophysiology. EP allows researchers to distinguish between mild and severe disease (using motor evoked potentials) and can be used as a diagnostic tool to identify early signs and symptoms before clinical scores appear.
As drug developers aim to improve the success rate of new CNS and pain therapies, the focus on translational approaches will increase. Just as with other indications, larger animal models, biomarker panels and clinical endpoints have the potential to transform neuroscience drug development and the medical landscape.
MD Biosciences, a preclinical neuroscience CRO, has the expertise to design programs that incorporate these translational approaches and help de-risk clinical studies.