PCSK9 inhibition-mediated reduction in Lp(a) with evolocumab: an analysis of 10 clinical trials and the LDL receptor's role.

Lipoprotein (a) [Lp(a)] is independently associated with CVD risk. Evolocumab, a monoclonal antibody (mAb) to proprotein convertase subtilisin/kexin type 9 (PCSK9), decreases Lp(a). The potential mechanisms were assessed. A pooled analysis of Lp(a) and LDL cholesterol (LDL-C) in 3,278 patients from 10 clinical trials (eight phase 2/3; two extensions) was conducted. Within each parent study, biweekly and monthly doses of evolocumab statistically significantly reduced Lp(a) at week 12 versus control (P < 0.001 within each study); pooled median (quartile 1, quartile 3) percent reductions were 24.7% (40.0, 3.6) and 21.7% (39.9, 4.2), respectively. Reductions were maintained through week 52 of the open-label extension, and correlated with LDL-C reductions [with and without correction for Lp(a)-cholesterol] at both time points (P < 0.0001). The effect of LDL and LDL receptor (LDLR) availability on Lp(a) cell-association was measured in HepG2 cells: cell-associated LDL fluorescence was reversed by unlabeled LDL and Lp(a). Lp(a) cell-association was reduced by coincubation with LDL and PCSK9 and reversed by adding PCSK9 mAb. These studies support that reductions in Lp(a) with PCSK9 inhibition are partly due to increased LDLR-mediated uptake. In most situations, Lp(a) appears to compete poorly with LDL for LDLR binding and internalization, but when LDLR expression is increased with evolocumab, particularly in the setting of low circulating LDL, Lp(a) is reduced.

Evaluation of statistical methods for safety signal detection: a simulation study.

Proactive evaluation of drug safety with systematic screening and detection is critical to protect patients' safety and important in regulatory approval of new drug indications and postmarketing communications and label renewals. In recent years, quite a few statistical methodologies have been developed to better evaluate drug safety through the life cycle of the product development. The statistical methods for flagging safety signals have been developed in two major areas - one for data collected from spontaneous reporting system, mostly in the postmarketing area, and the other for data from clinical trials. To our knowledge, the methods developed for one area have not been applied to the other one so far. In this article, we propose to utilize all such methods for flagging safety signals in both areas regardless of which specific area they were originally developed for. Therefore, we selected eight typical methods, that is, proportional reporting ratios, reporting odds ratios, the maximum likelihood ratio test, Bayesian confidence propagation neural network method, chi-square test for rates comparison, Benjamini and Hochberg procedure, new double false discovery rate control procedure, and Bayesian hierarchical mixture model for systematic comparison through simulations. The Benjamini and Hochberg procedure and new double false discovery rate control procedure perform best overall in terms of sensitivity and false discovery rate. The likelihood ratio test also performs well when the sample sizes are large.