The future of pharmacogenetics in the treatment of migraine

New management strategies for primary headache disorders: Insights from P4 medicine

Primary headache disorder is the main cause of headache attacks, leading to significant disability and impaired quality of life. This disorder is increasingly recognized as a heterogeneous condition with a complex network of genetic, environmental, and lifestyle factors. However, the timely diagnosis and effective treatment of these headaches remain challenging. Precision medicine is a potential strategy based on P4 (predictive, preventive, personalized, and participatory) medicine that may bring new insights for headache care. Recent machine learning advances and widely available molecular biology and imaging data have increased the usefulness of this medical strategy. Precision medicine emphasizes classifying headaches according to their risk factors, clinical presentation, and therapy responsiveness to provide individualized headache management. Furthermore, early preventive strategies, mainly utilizing predictive tools, are critical in reducing headache attacks and improving the quality of life of individuals with headaches. The current review comprehensively discusses the potential application value of P4 medicine in headache management.

Atogepant and sumatriptan: no clinically relevant drug-drug interactions in a randomized, open-label, crossover trial

Aim: To evaluate pharmacokinetic interactions of atogepant with sumatriptan, an open-label, randomized, crossover study was conducted. Patients & methods: Thirty healthy adults received atogepant 60 mg, sumatriptan 100 mg, or coadministered drugs. Primary end point was geometric mean ratios (GMRs) and 90% CIs of interventions for area under the plasma concentration-time curve from time 0 to t (AUC_0-t) or infinity (AUC_0-∞) and peak plasma concentration (C_max). Results: Atogepant GMRs for AUC_0-t and AUC_0-∞ versus with sumatriptan were within 90% CI 0.80-1.25, indicating no interaction; atogepant C_max was reduced by 22% (GMR: 0.78; 90% CI: 0.69-0.89) with sumatriptan. Sumatriptan GMRs for AUC_0-t, AUC_0-∞ and C_max versus with atogepant were within 90% CI 0.80-1.25. Conclusion: Atogepant with sumatriptan had no clinically relevant pharmacokinetic interactions.

Inflammatory complications of CGRP monoclonal antibodies: a case series

BACKGROUND

Calcitonin gene-related peptide (CGRP) is expressed throughout the body and is a known mediator of migraine, exerting this biological effect through activation of trigeminovascular, meningeal and associated neuronal pathways located in close proximity to the central nervous system. Monoclonal antibodies (mAb) targeting the CGRP pathway are an effective new preventive treatment for migraine, with a generally favourable adverse event profile. Pre-clinical evidence supports an anti-inflammatory/immunoregulatory role for CGRP in other organ systems, and therefore inhibition of the normal action of this peptide may promote a pro-inflammatory response.

CASES

We present a case series of eight patients with new or significantly worsened inflammatory pathology in close temporal association with the commencement of CGRP mAb therapy.

CONCLUSION

This case series provides novel insights on the potential molecular mechanisms and side-effects of CGRP antagonism in migraine and supports clinical vigilance in patient care going forward.

DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

BACKGROUND

Prediction of the drug-target interaction (DTI) is a critical step in the drug repurposing process, which can effectively reduce the following workload for experimental verification of potential drugs' properties. In recent studies, many machine-learning-based methods have been proposed to discover unknown interactions between drugs and protein targets. A recent trend is to use graph-based machine learning, e.g., graph embedding to extract features from drug-target networks and then predict new drug-target interactions. However, most of the graph embedding methods are not specifically designed for DTI predictions; thus, it is difficult for these methods to fully utilize the heterogeneous information of drugs and targets (e.g., the respective vertex features of drugs and targets and path-based interactive features between drugs and targets).

RESULTS

We propose a DTI prediction method DTI-HeNE (DTI based on Heterogeneous Network Embedding), which is specifically designed to cope with the bipartite DTI relations for generating high-quality embeddings of drug-target pairs. This method splits a heterogeneous DTI network into a bipartite DTI network, multiple drug homogeneous networks and target homogeneous networks, and extracts features from these sub-networks separately to better utilize the characteristics of bipartite DTI relations as well as the auxiliary similarity information related to drugs and targets. The features extracted from each sub-network are integrated using pathway information between these sub-networks to acquire new features, i.e., embedding vectors of drug-target pairs. Finally, these features are fed into a random forest (RF) model to predict novel DTIs.

CONCLUSIONS

Our experimental results show that, the proposed DTI network embedding method can learn higher-quality features of heterogeneous drug-target interaction networks for novel DTIs discovery.

Early Management of OnabotulinumtoxinA Treatment in Chronic Migraine: Insights from a Real-Life European Multicenter Study

Introduction

OnabotulinumtoxinA (BT-A) quarterly was the first treatment approved specifically for chronic migraine (CM). It is unclear whether three cycles are better than two to assess early BT-A response.

Methods

We performed a retrospective analysis on real-life prospectively collected data in 16 European headache centers. All the centers provided data on patients treated with BT-A for CM over the first three cycles of treatment. For each treatment cycle we defined patients as “good responders” if reporting a ≥ 50% reduction in monthly headache days compared with the three months before starting BT-A, “partial responders” if reporting a 30–49% reduction in monthly headache days, and “non-responders” if reporting a < 30% reduction in monthly headache days or stopping the treatment before the third cycle.

Results

We included 2879 patients. Seven hundred and eighty-four (64.6%) of the 1213 patients reporting a good response during the first and/or the second cycle had a good response during the third cycle; 309 (49.3%) of the 627 patients reporting a partial response (but no good response) during the first and/or the second cycle had a good response during the third cycle; only 65 (6.3%) of the 1039 patients who did not respond during both the first two cycles achieved a good response during the third cycle. Multivariate analyses showed that partial or good response during the first or the second cycle were independently associated with good response during the third cycle.

Conclusions

Our data suggest that patients with CM responding to BT-A during the first two cycles will likely benefit from the third cycle of treatment, while the probability that non-responders to the first two cycles start responding during the third cycle is low. These results can help guide the individual decision to stop or continue treatment after the second cycle in patients who have not responded to the first two cycles.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40122-021-00253-0.