OCRELIZUMAB THERAPY IN PATIENTS WITH ANTI-HBC ANTIBODIES - A PRELIMINARY STUDY

OBJECTIVE

Aim: Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disease resulting in cognitive impairment, physical disabilities, and neurological symptoms. Ocrelizumab is an effective drug used in MS treatment. However, it causes a risk of hepatitis B reactivation in anti-HBc positive patients. We describe the impact of entecavir and tenofovir on HBV reactivation during treatment with ocrelizumab.

PATIENTS AND METHODS

Materials and methods: Our study included eight patients (aged 18-70 years) with positive anti-HBc antibodies who were diagnosed with MS based on the 2017 McDonald criteria. The subjects were treated with ocrelizumab and were given anti-HBV prophylaxis with nucleoside analogs. The mean time from the beginning of therapy with nucleoside analogs to the initiation of ocrelizumab treatment was 27.5 days. Patients were administered ocrelizumab and none of them was diagnosed with HBV reactivation.

RESULTS

Results: None of the laboratory parameters worsened. No severe adverse effects were observed. These results suggest that entecavir and tenofovir are effective in HBV reactivation prophylaxis. Additionally, positive anti-HBc antibodies do not rule out treatment with ocrelizumab.

CONCLUSION

Conclusions: In patients with positive anti-HBc antibodies, nucleoside analogs, such as entecavir or tenofovir, should be administered before ocrelizumab administration to reduce the risk of viral reactivation. Further studies on simultaneous treatment with ocrelizumab and nucleoside analogs are required to confirm our findings.

[Multiple sclerosis : therapy update]

Multiple sclerosis is still a severe disease potentially associated with a short- or long-term disability in young adults. Since a few years therapeutic progresses are considerable. New drugs and new therapy rationale considerably improved our knowledge and patient's care. Early treatment is a key within dedicated specialized and multidisciplinary units. Clinical and neuroradiological no evidence of disease activity (NEDA) is a goal, which is more often reached. Patient's evolution and follow-up is completely changed in recent years with more efficacy.

Multiple Sclerosis Lesions Segmentation in Magnetic Resonance Imaging using Ensemble Support Vector Machine (ESVM)

Background

Multiple Sclerosis (MS) syndrome is a type of Immune-Mediated disorder in the central nervous system (CNS) which destroys myelin sheaths, and results in plaque (lesion) formation in the brain. From the clinical point of view, investigating and monitoring information such as position, volume, number, and changes of these plaques are integral parts of the controlling process this disease over a period. Visualizing MS lesions in vivo with Magnetic Resonance Imaging (MRI) has a key role in observing the course of the disease.

Material and Methods

In this analytical study, two different processing methods were present in this study in order to make an effort to detect and localize lesions in the patients' FLAIR (Fluid-attenuated inversion recovery) images. Segmentation was performed using Ensemble Support Vector Machine (SVM) classification. The trained data was randomly divided into five equal sections, and each section was fed into the computer as an input to one of the SVM classifiers that led to five different SVM structures.

Results

To evaluate results of segmentation, some criteria have been investigated such as Dice, Jaccard, sensitivity, specificity, PPV and accuracy. Both modes of ESVM, including first and second ones have similar results. Dice criterion was satisfied much better with specialist's work and it is observed that Dice average has 0.57±.15 and 0.6±.12 values in the first and second approach, respectively.

Conclusion

An acceptable overlap between those results reported by the neurologist and the ones obtained from the automatic segmentation algorithm was reached using an appropriate pre-processing in the proposed algorithm. Post-processing analysis further reduced false positives using morphological operations and also improved the evaluation criteria, including sensitivity and positive predictive value.