Can Precision Psychiatry Give Us New Ways To Treat Mental Health Disorders?
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Our ability to treat disorders of the body has advanced significantly in the 21st century. But that progress has not been matched by new therapies for psychiatric conditions. atai Life Sciences, a clinical-stage biopharmaceutical company, recently launched a new company that aims to correct that imbalance.
PsyProtix, Inc, was established with Duke University spinout Chymia, LLC. PsyProtix takes an alternative route to treating brain disorders based on principles of precision medicine. To discover more about PsyProtix and this precision approach, we spoke with Dr. Rolando Gutierrez-Esteinou, chief medical officer at atai and chief executive officer at PsyProtix.
Molly Campbell (MC): For our readers that may be unfamiliar, can you outline what precision psychiatry is?
Rolando Gutierrez-Esteinou (RG): Precision psychiatry strives for greater personalization in treatment by considering individual clinical and biological characteristics. This differs from more traditional practices in diagnosing and treating mental health disorders—grouping diverse and heterogeneous sets of patients with similar symptom profiles together and treating them in similar ways.
While other areas of medicine, such as oncology, have benefited from greater precision, the challenges and complexity of treating mental health disorders have been a barrier to innovation. Since 2015, there have been around 100 new drug approvals in oncology, many of which allow for greater precision in cancer treatment, while in that time there have only been 7 FDA approvals for psychiatric disorders.1
Since neuropsychiatric disorders are so heterogenous, the approach of trying to treat single biological targets will not sufficiently treat the diversity of patients in need of effective therapies. This is why many companies and researchers are now seeking to apply machine learning (ML) and artificial intelligence (AI) to identify clusters of biological and phenotypic features that personalize effective treatments.
To optimize learning from AI technologies, we need better data inputs. Research has come a long way in understanding the biology of complex mental health disorders, and with this understanding comes detectable biological signals, i.e., biomarkers. Biomarkers can be genetic profiles as well as the presence of proteins and metabolites that can be detected in patients’ samples like tissue, blood or cerebrospinal fluid. There are vast numbers of putative biomarkers implicated in neuropsychiatric disorders (none yet FDA approved) measured through genetic and epigenetic, transcriptomic, proteomic, metabolomic and neuroimaging assessments. Taking the lead from discoveries in oncology, researchers in precision psychiatry hope to develop “liquid biopsies” for psychiatric conditions, but we are still in early phases, generating knowledge required for a paradigm shift in psychiatry.
At atai, we’re also leveraging multi-modal technologies, such as neuroimaging technologies like magnetic resonance imaging (MRI) and electroencephalograms (EEG) to understand how particular brain networks may be perturbed in patients and how those perturbations condition response to treatment.
Better biomarkers, we believe, can add to the efficacy of existing treatments. Additionally, biomarker-based approaches may enable earlier, and possibly, preventative interventions, improving the patient’s prognosis.
Ruairi Mackenzie (RM): How would healthcare improve for patients using a precision psychiatry approach?
RG: As we move to more personalized treatments, we hope to better match individual patient needs to treatment. In doing so, we believe we can significantly reduce the disease burden for patients.
Far too many patients with mental health disorders undergo an arduous process, trying multiple treatment regimens before finding something that works, if they ever do. This process of trial and error to find a treatment can be extremely inefficient and burdensome. Besides the anguish and disappointment that patients undergo as they seek healing, this lost time can have dire consequences in a variety of ways. Many people living with a poorly treated mental health condition have trouble living their day-to-day lives. Their employment and productivity may suffer, as well as their relationships. And for many, mental health disorders can be complicated by comorbidities; with one mental health disorder compounding another mental health condition, and/or increasing the likelihood of a physical ailment like stroke or heart disease. This interplay of mental and physical health can result in a spiral of poor health and behaviors, and worsening outcomes for patients.2
MC: What do we know thus far about the metabolic mechanisms that potentially underlie depression?
RG: For some people who live with depression, a primary cause may be the presence or absence of a gene, that codes for a specific enzyme. In these patients, certain metabolic pathways may be disrupted resulting in neurons that don’t develop correctly, or that might fire improperly, too much or too little, leading to their depression.
These patients may have a relatively straightforward, single-factor depression—in contrast to others who live with depression that has a more complex, multifactorial causation. But even though their depression is “simpler” or “unifactorial”, its cause has been unknown. As a result, these patients may get little to no benefit from available treatments because the mechanism causing their specific form of depression is far removed from the molecular underpinnings of current treatments.
Our PsyProtix team is focused on the application of metabolomic profiling to better treat depression. Metabolomics—the large-scale study of substrates and products of metabolism (metabolites), and how they are influenced by both genetic and environmental factors—has demonstrated tremendous promise in delivering robust quantitative information regarding differences in metabolism associated with disease onset/progression and drug intervention. Metabolites are dynamic (unlike DNA) and impact almost every factor of the phenotype (from genetics to the microbiome) to exert an influence. Altered metabolite levels may be helpful biomarkers for certain heterogenous and polygenic diseases. By casting a wide net, metabolomics may provide potential biomarkers, highlighting affected pathophysiological pathways.
One example of a metabolic factor that we believe could offer key insight into a specific form of depression, impacting a subpopulation of treatment-resistant depression (TRD) patients, is the role of acylcarnitines. These are a type of lipid that play a role in cellular energetics by helping to transport fatty acids into the mitochondria. We know that through this important role in mitochondrial energetics, acylcarnitines are involved in the regulation of numerous cell pathways, including the release of neurotransmitters in the brain. Several studies have pointed to acylcarnitine, at both a metabolite and genetic level, as having an important link to major depressive disorder, severity of symptoms and its treatment.3-6
Importantly, in conjunction with developing novel therapeutic candidates for patients with TRD, metabolomic profiling obtained prior to, during or after ingestion of a trialed compound may provide mechanistic information, biomarkers or patterns associated with a patient’s response to that intervention. This information will also provide an evidence-based feedback loop to inform the selection of future drugs for development.
RM: Your press release mentions that precision psychiatry factors in genes, metabolism, environment and lifestyle. How will PsyProtix weigh these factors in deciding the most effective treatment when our understanding of the causes of many mental health disorders remains limited?
RG: PsyProtix’s metabolomic approach will look for presence, absence, increase or decrease of certain metabolites in a patient’s body. These biomarkers may help explain the metabolic concomitants of some forms of depression and may even identify a subset of people with TRD with particular treatment needs.
Our studies will focus on the identification of a role for acylcarnitines in depression and predicting treatment response. As we have seen, baseline levels of short chain acylcarnitine have been shown to predict treatment outcomes.
In our clinical trials, we will have the ability to follow patients longitudinally. Since most other published studies to date have been cross-sectional, we anticipate being better able to explore prognostic metabolic biomarkers with less variability in the sample. This may allow us to identify metabolites that indicate the likelihood of a future clinical event (i.e., relapse of a depressive episode).
In addition, metabolomics and linked genetic analysis highlighted key enzymes that seem to be implicated in subtypes of depression and resistance to treatment. Targeting this system, possibly in combination with other molecules involved in mitochondrial energetics, could provide effective new approaches for treatment of depression that do not respond to currently used treatments.
MC: What are some of the challenges associated with shifting from a “one-size-fits-all” approach to an arguably more individualized approach for treating psychiatric disorders?
RG: Innovative, more precise treatments would certainly be a substantial shift from the current standard of care. However, if research can show clear improvements in efficacy and safety for patients through a more targeted and individualized approach, we’re certain the shift would be beneficial to patients. Patients, caregivers and healthcare providers recognize the crisis across mental health, and all want to bring better treatments to patients in need.
References:
1. U.S. Food and Drug Administration. New Drugs at FDA: CDER’s New Molecular Entities and New Therapeutic Biological Products. Accessed February 7, 2022. https://www.fda.gov/drugs/development-approval-process-drugs/new-drugs-fda-cders-new-molecular-entities-and-new-therapeutic-biological-products.
2. Centers for Disease Control and Prevention. Heart Disease and Mental Health Disorders. https://www.cdc.gov/heartdisease/mentalhealth.htm. Published May 6, 2020. Accessed February 7, 2022.
3. Milaneschi Y, Arnold M, Kastenmüller G, et al. Genomics-based identification of a potential causal role for acylcarnitine metabolism in depression. https://www.medrxiv.org/content/10.1101/2021.10.18.21265157v1.full-text. Preprint posted October 19, 2021. Accessed February 7, 2022.
4. Nasca C, Bigio B, Lee FS, et al. Acetyl-l-carnitine deficiency in patients with major depressive disorder. Proc Natl Acad Sci U S A. 2018;115(34):8627-8632.doi: 10.1073/pnas.1801609115.
5. Cassol E, Misra V, Morgello S, Kirk GD, Mehta SH, Gabuzda D. Altered Monoamine and Acylcarnitine Metabolites in HIV-Positive and HIV-Negative Subjects With Depression. J Acquir Immune Defic Syndr. 2015;69(1):18-28. doi: 10.1097/QAI.0000000000000551.
6. MahmoudianDehkordi S, Ahmed AT, Bhattacharyya S, et al. Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression. Transl Psychiatry. 2021;11(1):153. doi:10.1038/s41398-020-01097-6.
Dr. Rolando Gutierrez-Esteinou was speaking to Ruairi J Mackenzie and Molly Campbell, Senior Science Writers for Technology Networks
