Inhibition or degradation of anti-apoptotic protein BCL-XL is a viable strategy for cancer treatment. Despite the recent development of PROTACs for degradation of BCL-XL, the E3 ligases are confined to the commonly used VHL and CRBN. Herein we report the development of MDM2-BCL-XL PROTACs using MDM2 as E3 ligase for degradation of BCL-XL. Three MDM2-BCL-XL PROTACs derived from MDM2 inhibitor Nutlin-3, which can also upregulate p53, and BCL-2/BCL-XL inhibitor ABT-263 with different linker length were designed, synthesized, and evaluated in vitro. We found BMM4 exhibited potent, selective degradation activity against BCL-XL and stabilized tumor suppressor p53 in U87, A549 and MV-4-11 cancer cell lines. Moreover, combination of BMM4 and BCL-2 inhibitor ABT-199 showed synergistic antiproliferative activity. The unique dual-functional PROTACs offers an alternative strategy for targeted protein degradation.
In clinical trials, the primary analysis is often either a test of absolute/relative change in a measured outcome or a corresponding responder analysis. Though each of these tests may be reasonable, determining which test is most suitable for a particular research study is still an open question. These tests may require different sample sizes, define different clinically meaningful differences, and most importantly, lead to different study conclusions. This paper aims to compare a typical non-inferiority test using absolute change as the study endpoint to the corresponding responder analysis in terms of sample size requirements, statistical power, and hypothesis testing results. From numerical analysis, using absolute change as an endpoint generally requires a larger sample size; therefore, when the sample size is the same, the responder analysis has higher power. The cut-off value and non-inferiority margin are critical which can meaningfully impact whether the two types of endpoints yield conflicting conclusions. Specifically, an extreme cut-off value is more likely to cause different conclusions. However, this impact decreases as population variance increases. One important reason for conflicting conclusions is that the population distribution is not normal. To eliminate conflicting results, researchers should pay attention to the population distribution and cut-off value selection.