Analysis on the risk of myasthenia gravis related to immune checkpoint inhibitors based on the US FDA Adverse Event Reporting System.

Abstract

To evaluate the risk of myasthenia gravis (MG) associated with immune checkpoint inhibitors (ICI). Adverse event (AE) reports related to MG, myasthenic syndrome, and MG crisis for durvalumab, atezolizumab, pembrolizumab, nivolumab, avelumab, and ipilimumab in the US FDA Adverse Event Reporting System (FAERS) from Q1 2004 to Q3 2022 were collected. The proportional reporting odds ratio (PRR) method was used to evaluate the correlation between the six drugs and the three AEs. Statistical significance was defined as having reports ≥3, PRR ≥ 2, and chi-square (χ2 ) ≥ 4. A total of 36, 78, 276, 380, 5, and 53 AE reports were collected for durvalumab, atezolizumab, pembrolizumab, nivolumab, avelumab, and ipilimumab, respectively. For myasthenic syndrome, the PRR values reflecting the correlation with the drugs were 27.83 (χ2 = 102.66), 26.20 (χ2 = 235.67), 44.17 (χ2 = 1313.98), 32.09 (χ2 = 1229.54), 21.31 (χ2 = 151.15), and 0 for durvalumab, atezolizumab, pembrolizumab, nivolumab, avelumab, and ipilimumab, respectively. For MG, the PRR values reflecting the correlation with the drugs were 24.21 (χ2 = 682.04), 18.34 (χ2 = 900.27), 39.32 (χ2 = 7945.15), 26.93 (χ2 = 6636.45), 14.73 (χ2 = 566.47), and 15.69 (χ2 = 54.77) for durvalumab, atezolizumab, pembrolizumab, nivolumab, avelumab, and ipilimumab, respectively. For MG crisis, there were no data for durvalumab, atezolizumab, avelumab, and ipilimumab; the PRR values reflecting the correlation with the drugs were 16.54 (χ2 = 225.23) and 9.20 (χ2 = 119.14) for pembrolizumab and nivolumab, respectively. All six drugs were statistically correlated with their corresponding AEs. ICI may lead to ICIs-associated MG during therapy. Analysis of FAERS data identified signals for AEs of MG with ICI regimens. Practitioners should consider the factors that may increase the likelihood of MG. The findings support a continued surveillance and risk factor identification.