Theragenomic medicine: Accurate treatment response prediction in depressed patients

Theragenomic medicine: Accurate treatment response prediction in depressed patients

Last Updated on November 7, 2017 by Joseph Gut – thasso

November 05, 2017 – For some patients knowingly or not knowingly suffering from depression, the upcoming period of the year (i.e., the winter season in the Northern Hemisphere) with grey days and over-porportionally long nights, might be a difficult one. Should such patients enter a antidepressant treatment, then currently the dire

Depressed and hopeless. Seemingly nowhere to go.

reality is that in about 50% of patients the first antidepressant drug prescribed does not work. On top of that, around one-third of patients do not respond to any types of antidepressant drugs (although psychological therapies may be useful). For the moment, the only way to know if a patient is a responder to therapy is to try the antidepressant drug and see, what the clinical response might be (trial and error approach). This is frustratingly inaceptable, given the high percentage of non-responders, the long wait of about three month until a treatment response or non-response becomes recognizable, and the frequent potential for adverse drug reactions (ADRs), sometimes even serious, that go along with antidepressant drugs.

This might change dramatically, as new research in the -omics field of patients with depression reveals. According to a study published in the Journal Neuropsychopharmacology (Catteneo et al., 2016), absolute measurements of Macrophage Migration Inhibitory Factor (MMIF) and Interleukin-1-β (IL-1β) mRNA levels, used as blood-based biomarkers, can accurately predict treatment response in depressed patients.

In the study, absolute mRNA values (a reliable quantitation of the number of molecules) of MMIF and IL-1β were measured in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. Linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of treatment with antidepressant drugs was then applied. As MMIF and IL-1β might be involved in different molecular pathways, a protein-protein interaction network by the search tool (STRING) for the Retrieval of Interacting Genes/Proteins was then constructed.

Cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values (PPV) and specificity for nonresponders of 100% in both samples (negative predictive value (NPV) =82% to 85%, sensitivity=52% to 61%) were found. Using network analysis, different clusters of targets for these two cytokines, with MMIF interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and IL-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration, were identified.

Leaving the all scientific slang aside, the take home message of this study for patients is the following: The data provide a clinically suitable approach to the personalization of antidepressant therapy. Patients who have absolute mRNA levels of MMIF and IL-1β above the suggested cutoff values will be non-responders  to a randomly selected first antidepressant drug. This finding is huge: It will spare 50% of patients with depression of un-efficacious treatments, and guides them towards earlier access to more assertive antidepressant strategies, including the addition of other antidepressants or antiinflammatory drugs. This in itself demonstrates the power of theragenomic medicine, i.e., the use of genomic methods to guide therapeutic approaches.



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Ph.D.; Professor in Pharmacology and Toxicology. Senior expert in theragenomic and personalized medicine and individualized drug safety. Senior expert in pharmaco- and toxicogenetics. Senior expert in human safety of drugs, chemicals, environmental pollutants, and dietary ingredients.