The future of precision psychiatry

The mental health clinical trials market is expected to double to $4.27 billion in 2028, up from $2.15 billion in 2018, and grow more than 8% annually. Drug development in this area is costly and is marked by high failure rates and low efficacy.
Decades of sub-par clinical trial results and patient outcomes are leading researchers to explore newer, data-driven, objective methods in their clinical trial designs based on the fast-growing field of precision psychiatry. This evolving discipline relies on biomarkers to identify and treat mental health conditions, rather than the traditional psychiatric approach of relying on patients’ reported symptoms as interpreted through a clinician. Biomarkers under investigation include genes, hormones, gut microbiomes, voice variation, and brain function measures, to name a few.
One approach to measuring brain activity is to focus on resting-state measures obtained using magnetic resonance imaging (fMRI) and electroencephalography (EEG). Resting state measures derived from both MRI and EEG both appear reliable. The validity of resting state MRI-based measures is supported by localisation of function within and across networks, as well as variability in network activation as a function of task-related demands. That said, scaling MRI-based measures of resting state will be expensive. On the other hand, EEG measures are far less expensive. However, resting-state measuresd derived from EEG has a limitation: very few measures have a clear functional meaning to support treatment or drug research and development decisions.
Enter event-related potentials (ERPs): Functional brain measurement from EEG
More recently, dozens of studies in high-impact clinical journals have demonstrated that multiple event-related potentials (ERPs) could serve as relevant biomarkers for understanding depression, anxiety, and schizophrenia.
ERPs, too, can be measured with EEG. However, ERPs reflect brain activity elicited by specific brain functions. ERPs are measured by presenting visual images, sounds, or other salient information. As an example, ERPs have been used recently in the context of autobiographical memories and mental imagery associated with distress triggers.
ERPs have been shown to accurately and reliably measure brain function in individuals with the most common mental health conditions, such as major depressive disorder and anxiety disorders. In turn, these biomarkers can help pharmaceutical companies and affiliated contract research organisations identify unique subtypes within highly heterogeneous conditions before trials begin.
By identifying the specific “neurotype” of potential trial participants, researchers can more quickly determine their novel therapy’s efficacy and tolerance among the targeted group and help bring that drug to market more efficiently.
Evidence base growing
Evidence of how ERP-based brain function measurements can enable precision psychiatry is growing. For example, a 2019 meta-analysis published in the Annual Review of Clinical Psychology concluded that ERPs are valuable for categorising neurotypes within a broader condition, identifying those at greater risk for psychopathology, and charting the potential development of conditions beginning as young as childhood. According to the authors, one such ERP measurement, error-related negativity, is instrumental in understanding anxiety disorders and can drive more personalised treatment.
A similar review published a year later in the International Journal of Psychophysiology pinpoints several ERP components repeatedly associated with emotion regulation. The authors contend that ERPs are instrumental for scientifically valid mental health research and could be applied to personalise treatment and help patients control their brain activity.
Lastly, to help accelerate and improve the efficiency of ERP-related research, a group of scholars published in Neuroimage developed a standardised process for conducting related studies. Their protocol offers data, experimental scripts, and data processing pipelines across various ERP components to promote replicable and comparable studies, enabling findings to be more easily understood and tested.
These few articles, however, only scratch the surface of the recent and ongoing research surrounding ERPs and mental health. Based on this activity and other related advancements in wearable biosensors and data science, it is clear that the momentum toward precision psychiatry has only accelerated.
Applying EEG + ERPs to clinical trials
EEG and ERPs are emerging as optimal neural measures and biomarkers to help pharmaceutical companies overcome the significant challenges faced in developing psychiatric drugs, which generate only a 6% success rate compared to the industry standard of 13%. The high heterogeneity of mental health conditions, lack of trackable biomarkers, and lack of treatment outcome predictor methodology contribute to this high failure rate.
Shortening the clinical trial participant selection, recruitment, and screening process can effectively reduce costly delays and shorten the time to discovery.
Within mental health, finding ideal participants with an indicated condition for a novel drug is often the most time-consuming, due to this high heterogeneity of mental health conditions. The Tufts Center for the Study of Drug Development at the Tufts University School of Medicine estimates that each day of delay for a potentially market-approved prescription drug costs the company $500,000 and $40,000 in direct clinical trial costs, according to a 2024 estimate.
However, measuring multiple ERPs can help researchers categorise potential participants (i.e., neurotype participants based on reliable measures of specific brain functions) and target the individuals who would benefit most from the drug in development.
Once an appropriate cohort is assembled, ERPs can be employed during the screening process to ensure randomisation of participants for the gold-standard double-blind placebo-controlled study. Such rigour is required to demonstrate the novel therapy’s efficacy and earn regulatory approval.
After the trial, researchers can measure ERPs in participants again to detect changes in their brain function following treatment. The biomarkers have even been shown to accurately predict treatment response in specific development models. Thus, at every phase and afterward, researchers can be confident that their therapies are being administered to and metabolised by the exact neurotype of a patient who would be prescribed the approved drug in the real world.
A precise path forward
A data-driven and unbiased clinical trial process for psychiatric drugs can help pharmaceutical companies find the most effective therapies more efficiently and shorten the time to market launch. More importantly, a more targeted and personalised drug development journey can help patients experience improved outcomes sooner, with less trial and error, delivering the outcomes each person seeks and deserves.