Clinical candidates of small molecule p38 MAPK inhibitors for inflammatory diseases

Main Article Content

Li Xing *
(*) Corresponding Author:
Li Xing | li.xing@pfizer.com

Abstract

The trigger and etiology of chronic inflammatory diseases are not well understood, hindering the development of efficient therapeutic approaches. The observation that abnormal activity of the p38 MAPK is common to all inflammatory diseases raised the expectation that p38 inhibitors would serve as general anti-inflammatory therapeutics. A large number of inhibitors were consequently discovered. Several compounds of different scaffolds, blocking the p38 MAPK signaling pathway, have entered phase II clinical trials for rheumatoid arthritis, chronic obstructive pulmonary disease, pain, cardiovascular diseases, and cancer. As I review here, in almost all cases the clinical trials have failed, leading to re-design of compounds and re-evaluation of p38 as a suitable target. I describe how structural features, unique to p38α, have been employed in the inhibitor design and achieved high degree of kinome selectivity. I then focus on some of the drugs that reached human trials and summarize their in vitro/in vivo pharmacological profiles and the related outcomes from clinical investigations. These compounds include VX-745, VX-702, RO-4402257, SCIO- 469, BIRB-796, SD-0006, PH-797804, AMG-548, LY2228820, SB-681323 and GW-856553. Finally, I discuss novel suggested approaches for the use of p38 inhibitors such as combining p38 inhibition with inhibiting other targets that function in parallel inflammatory pathways for achieving efficacy in treating inflammatory diseases.

Downloads

Download data is not yet available.

PlumX Metrics

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.


Article Details