Accepted

Migraine is a common and highly disabling headache disorder associated with a substantial socioeconomic burden. Migraine treatments can be categorized as preventive treatment, aimed at reducing the frequency and severity of migraine attacks, and acute therapy, intended to abort attacks. Traditionally, acute treatment can be classified as specific (ergot derivatives and triptans) or nonspecific (analgesics and nonsteroidal anti-inflammatory drugs). Triptans, a class of 5-HT receptor agonists with some affinity for the 5-HT receptor subtype, have been proven to be efficacious for acute treatment of moderate to severe migraine and have been deemed the gold standard. The availability of triptans in non-oral formulations, such as subcutaneous (SC) and intranasal forms, can be beneficial for patients who suffer from prominent nausea or vomiting, have a suboptimal response to oral agents, and/or seek a more rapid onset of treatment effects. However, triptans are contraindicated in patients with preexisting cardiovascular and/or cerebrovascular diseases due to their 5-HT-mediated vasoconstrictive action. For this reason, studies have focused on the development of ditans, a group of antimigraine drugs targeting 5-HT and 5-HT receptors. Unfortunately, 5-HT receptor agonists have been shown to be ineffective in the acute treatment of migraine. Several ditans targeting the 5-HT receptor have been developed and have shown no vasoconstrictive effect in preclinical studies, but only two of them, lasmiditan and LY334370, have been tested in clinical trials for migraine, and only lasmiditan has reached to Phase III clinical trials. These Phase III trials have demonstrated the efficacy and safety of lasmiditan, a selective 5-HT receptor agonist, in acute migraine treatment. Lasmiditan might offer an alternative migraine therapy without cardiovascular risks. This review will summarize the development of agents targeting the 5-HT and 5-HT receptors and the clinical evidence supporting the use of these agents for acute migraine treatment.

Machine learning (ML) is largely used to develop automatic predictors in migraine classification but automatic predictors for medication overuse (MO) in migraine are still in their infancy. Thus, to understand the benefits of ML in MO prediction, we explored an automated predictor to estimate MO risk in migraine. To achieve this objective, a study was designed to analyze the performance of a customized ML-based decision support system that combines support vector machines and Random Optimization (RO-MO). We used RO-MO to extract prognostic information from demographic, clinical and biochemical data. Using a dataset of 777 consecutive migraine patients we derived a set of predictors with discriminatory power for MO higher than that observed for baseline SVM. The best four were incorporated into the final RO-MO decision support system and risk evaluation on a five-level stratification was performed. ROC analysis resulted in a c-statistic of 0.83 with a sensitivity and specificity of 0.69 and 0.87, respectively, and an accuracy of 0.87 when MO was predicted by at least three RO-MO models. Logistic regression analysis confirmed that the derived RO-MO system could effectively predict MO with ORs of 5.7 and 21.0 for patients classified as probably (3 predictors positive), or definitely at risk of MO (4 predictors positive), respectively. In conclusion, a combination of ML and RO – taking into consideration clinical/biochemical features, drug exposure and lifestyle – might represent a valuable approach to MO prediction in migraine and holds the potential for improving model precision through weighting the relative importance of attributes.