Myasthenia gravis is an autoimmune neurological disease caused by autoantibodies primarily directed against components of the postsynaptic membrane of the neuromuscular junction. Approximately 85% of patients have antibodies directed against the acetylcholine receptor (anti-AChR).
Anti-AChR antibodies act through three distinct mechanisms:
1. Activation of the classical complement pathway: Formation of membrane-attack complexes (MACs) results in the destruction of the postsynaptic membrane. 2. Mechanical blockade: Anti-AChR antibodies block the acetylcholine binding site on its receptor. 3. Internalization and lysosomal degradation: Bivalent IgG causes cross-linking of adjacent receptors leading to internalization and degradation of AChRs (antigenic modulation).
Patient mortality has significantly reduced due to effective treatments preventing severe exacerbations of myasthenic symptoms.
In the past five years, the FDA and EMA have approved complement inhibitors for the treatment of generalized myasthenia gravis with anti-AChR antibody positivity. Eculizumab, a humanized monoclonal antibody, binds to the complement fragment C5, inhibiting its cleavage into C5a and C5b, and preventing the formation of the terminal complement complex C5b-9 (MAC).
Currently, Eculizumab is approved in Italy for generalized myasthenia gravis associated with anti-acetylcholine receptor antibody positivity.
This class of drugs is generally more effective than conventional immunosuppressive therapies, though it comes with higher costs.
There is heterogeneity among patients in their response to complement inhibitor therapies. Currently, there is no specific evidence indicating which patients may benefit most from this class of treatments. Personalized therapy, considering the predominant pathogenic mechanisms of anti-AChR in individual patients, seems necessary. Interindividual heterogeneity in the autoantibody repertoire could underlie different responses to complement inhibitor therapies. For example, inhibition of the complement cascade in patients whose autoantibodies also block receptors might result in an unsatisfactory treatment response. Moreover, C5 gene polymorphisms could explain a lack of response to these new drugs. Investigating the immune, genetic, and cellular profile of myasthenic patients eligible for these new pharmacological therapies could be useful for identifying predictive markers of response and personalizing therapeutic choices.
Age ≥18 years;
Diagnosis of generalized anti-AChR positive Myasthenia Gravis;
Need for therapy with complement inhibitor drugs according to the therapeutic indications approved by AIFA (16);
Ability to perform follow-up at the reference center;
Age <18 years;
Poor compliance with drug therapy;
Concomitant autoimmune diseases;
Insufficient availability of clinical information;
Patients with generalized Myasthenia Gravis positive for anti-AChR antibodies undergoing therapy with complement inhibitor drugs
Cohort
Prospective
Not listed
Identification of Clinical Markers of Favorable Response to Complement Inhibitors Therapy in Patients With Generalized Myasthenia Gravis
\- Analyze the clinical status of patients comparing the results of MG-ADL and QMG clinical scales pre/post complement inhibitors therapy.
Identification of Biological and Cellular Markers of Favorable Response to Complement Inhibitors Therapy in Patients With Generalized Myasthenia Gravis
Analyze the differences between complement proteins pre/post therapy with complement inhibitor drugs.
Identification of Biological and Cellular Markers of Favorable Response to Complement Inhibitors Therapy in Patients With Generalized Myasthenia Gravis
Measure the levels of proteins involved in the pathogenesis of the disease.
Identification of Genetic Markers of Favorable Response to Complement Inhibitors Therapy in Patients With Generalized Myasthenia Gravis
Investigate the presence of polymorphisms in the gene encoding the complement protein C5 in patients refractory to therapy with complement inhibitors.
Predictive algorithm of favorable response
Create a predictive algorithm for response to complement inhibitor therapies by combining clinical, cellular, biological, and genetic parameters
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