Discovery of functionally distinct anti-C7 monoclonal antibodies and stratification of anti-nicotinic AChR positive Myasthenia Gravis patients.

Myasthenia Gravis (MG) is mediated by autoantibodies against acetylcholine receptors that cause loss of the receptors in the neuromuscular junction. Eculizumab, a C5-inhibitor, is the only approved treatment for MG that mechanistically addresses complement-mediated loss of nicotinic acetylcholine receptors. It is an expensive drug and was approved despite missing the primary efficacy endpoint in the Phase 3 REGAIN study. There are two observations to highlight. Firstly, further C5 inhibitors are in clinical development, but other terminal pathway proteins, such as C7, have been relatively understudied as therapeutic targets, despite the potential for lower and less frequent dosing. Secondly, given the known heterogenous mechanisms of action of autoantibodies in MG, effective patient stratification in the REGAIN trial may have provided more favorable efficacy readouts. We investigated C7 as a target and assessed the in vitro function, binding epitopes and mechanism of action of three mAbs against C7. We found the mAbs were human, cynomolgus monkey and/or rat cross-reactive and each had a distinct, novel mechanism of C7 inhibition. TPP1820 was effective in preventing experimental MG in rats in both prophylactic and therapeutic dosing regimens. To enable identification of MG patients that are likely to respond to C7 inhibition, we developed a patient stratification assay and showed in a small cohort of MG patients (n=19) that 63% had significant complement activation and C7-dependent loss of AChRs in this in vitro set up. This study provides validation of C7 as a target for treatment of MG and provides a means of identifying patients likely to respond to anti-C7 therapy based on complement-activating properties of patient autoantibodies.

Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power.

Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (DLR) appeared to be an effective way to predict whether F0 immigrants could be identified for a particular pair of populations using a given set of markers.