Eosinophilia: Rare cause of arterial thrombosis and cardioembolic stroke in childhood

Dupilumab-induced hypereosinophilia: review of the literature and algorithm proposal for clinical management

INTRODUCTION

Dupilumab is a human monoclonal antibody that targets both IL-4 and IL-13 signaling. It is currently indicated for the treatment of asthma, moderate-to-severe atopic dermatitis, and chronic rhinosinusitis with nasal polyps (CRSwNP). Eosinophilia has been reported as a potential adverse event in treated patients.

AREAS COVERED

A selective search on PubMed and Medline up to January 2022 was performed, by focusing on dupilumab-induced hypereosinophilia described in clinical trials, real-life studies, and case reports. The possible mechanisms underlying dupilumab-induced hypereosinophilia and the eosinophil-related morbidity have also been explored.

EXPERT OPINION

Dealing with dupilumab-induced hypereosinophilia represents a clinical challenge for clinicians managing patients on dupilumab therapy. An algorithm for the practical management of dupilumab-induced hypereosinophilia has been proposed, in order to properly investigate potential eosinophil-related morbidity and avoid unnecessary drug discontinuation.

Literature reviews of stroke with hypereosinophilic syndrome

Hypereosinophilic syndrome (HES) is defined by persistently elevated blood eosinophil levels and is associated with evidence of organ damage. Cardiovascular involvement in HES is most commonly associated with Loffler endocarditis (cardiac HES). Cardiac HES is typically characterized by progressive subendocardial fibrosis with overlying mural thrombus formation, leading to restrictive dysfunction of the left ventricle. The thrombus from cardiac HES could result in cardiogenic stroke; however, most of the stroke cases with HES were not associated with huge thromboembolism rather multiple infarcts in the watershed area. The major clinical features of 97 previously reported cases of stroke with HES are as follows: the median age was 52 years, of which 61 (63%) were men; the initial presenting symptoms were neurological (73%), followed by headache (16%), respiratory symptoms (9%), and visual symptoms (9%). Almost half of the cases were diagnosed with cardiac HES. The characteristics of cardiac findings were mural thrombi, endomyocardial fibrosis, and a restrictive pattern of heart failure. Cerebral findings revealed 78 cases (80%) were described as multiple infarctions and 55 cases (57 %) were involved with watershed areas, whereas 11 cases (11％) were described as embolic stroke for one proximal large-vessel occlusion. Regarding treatment, 71 (73%), 28 (29%), and 16 (16%) patients were treated with steroids, anticoagulants, and antiplatelets, respectively. The overall mortality and recovery rates were 11% and 89%, respectively. Physicians should know most cases of stroke with HES are characterized by multiple infarctions in the watershed area, and cardiac HES is not always associated with stroke.

Inferring new drug indications using the complementarity between clinical disease signatures and drug effects

BACKGROUND

Drug repositioning is the process of finding new indications for existing drugs. Its importance has been dramatically increasing recently due to the enormous increase in new drug discovery cost. However, most of the previous molecular-centered drug repositioning work is not able to reflect the end-point physiological activities of drugs because of the inherent complexity of human physiological systems.

METHODS

Here, we suggest a novel computational framework to make inferences for alternative indications of marketed drugs by using electronic clinical information which reflects the end-point physiological results of drug's effects on the biological activities of humans. In this work, we use the concept of complementarity between clinical disease signatures and clinical drug effects. With this framework, we establish disease-related clinical variable vectors (clinical disease signature vectors) and drug-related clinical variable vectors (clinical drug effect vectors) by applying two methodologies (i.e., statistical analysis and literature mining). Finally, we assign a repositioning possibility score to each disease-drug pair by the calculation of complementarity (anti-correlation) and association between clinical states ("up" or "down") of disease signatures and clinical effects ("up", "down" or "association") of drugs. A total of 717 clinical variables in the electronic clinical dataset (NHANES), are considered in this study.

RESULTS

The statistical significance of our prediction results is supported through two benchmark datasets (Comparative Toxicogenomics Database and Clinical Trials). We discovered not only lots of known relationships between diseases and drugs, but also many hidden disease-drug relationships. For example, glutathione and edetic-acid may be investigated as candidate drugs for asthma treatment. We examined prediction results by using statistical experiments (enrichment verification, hyper-geometric and permutation test P<0.009 in Comparative Toxicogenomics Database and Clinical Trials) and presented evidences for those with already published literature.

CONCLUSION

The results show that electronic clinical information is a feasible data resource and utilizing the complementarity (anti-correlated relationships) between clinical signatures of disease and clinical effects of drugs is a potentially predictive concept in drug repositioning research. It makes the proposed approach useful to identity novel relationships between diseases and drugs that have a high probability of being biologically valid.