Abstract
Inflammation is a natural response to external stimuli related to the
protection of the organism. However, their exaggerated reaction can cause severe
damage to the body and is related to several diseases, including allergies, rheumatoid
arthritis, diabetes, cancer, and various infections. Furthermore, inflammation is mainly
characterized by increased temperature, pain, flushing, and edema due to the
production of pro-inflammatory cytokines, such as prostaglandins, and can be
controlled using anti-inflammatory drugs. In this sense, selective prostaglandin E2
(PGE2
) inhibition has been targeted and explored for designing new compounds for
anti-inflammatory drugs because it can show fewer side effects than non-steroidal antiinflammatory drugs (NSAIDs) and corticosteroids. It is a bioactive lipid overproduced
during an inflammatory process, produced mainly by COX-1, COX-2, and microsomal
prostaglandin E2
synthase-1 (mPGES-1). Recently, studies have demonstrated that
mPGES-1 inhibition is an excellent strategy for designing anti-inflammatory drugs,
which could protect against pain, arthritis, acute inflammation, autoimmune diseases,
and different types of cancers. Also, in recent years, Computer-Aided Drug Design
(CADD) approaches have been increasingly used to design new inhibitors, decreasing
costs and increasing the probability of discovering active substances and constantly
applying them to discover mPGES-1 inhibitors. Thus, here, this chapter will approach
the latest advances in computational methods to discover new mPGES-1 inhibitors that
can be promising against several inflammatory conditions. The focus is on techniques
such as molecular docking and dynamics, virtual screenings, pharmacophore modeling, fragment-based drug design, quantitative structure-activity relationship (QSAR), and
others explored by researchers worldwide that can lead to the design of a promising
drug against this target.