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Pharmacogenomics in migraine: catching biomarkers for a predictable disease control

What’s beyond the fog on candidate genes in migraine? Pharmacological predictivity in headache therapy always represented a chimeric goal for clinicians [1]. The fact that only 50% of migraine patients adequately respond to acute and prophylaxis therapies always suggested questions that only pharmacogenomics (PGx) is able to clarify. The great expectations held by researchers in the field of migraine genetics turned into a widespread disenchantment, since the application goal has been missing [2]. Indeed, after a decade of brilliant genetic evidence on rare forms of migraine, such as familial hemiplegic migraine, very little or almost nothing has been translated to therapeutic management of the most common migraine forms [2–6]. Moreover, it is evident that migraine patients react differently to given drugs. A patient’s efficacy for a therapeutic substance are subject to its interaction with DNA/ RNA variations of the patient themselves and might produce significant relapses, not only in terms efficacy but also according to a safe reduction for what concerns global risk of adverse events and a certain profit in terms of health expenses [2,7,8]. Clinical response to a specific drug is caused by the interaction of several gene products, including transporters, metabolizing enzymes and detoxification pathways. Hence, polymorphisms that are present in one or more of the involved genes could possibly affect an individual’s response to therapy. Therefore, the mission of PGx is to research and exploit peculiar genetic profiles in clinical practice in order to optimize and personalize therapies. However, genotyping becomes relevant to prevision in case of a relation between few polymorphisms and clinical manifestation, but also when nongenetic parameters can be estimated. In a multifactorial disease, genetic, environmental and behavioral factors contribute to the definition of a specific metabolic background responsible for drug efficacy and side effects: the possibility to acquire a real, personalized and complex image of the patient’s metabolic power should rely on a diagnostic system where genomic analysis is part of an integrated platform able to evaluate different levels of the drug metabolic pathway.