DP Technology announced the nomination of DPT0415, a novel small molecule targeting Lipoprotein-associated phospholipase A2 (Lp-PLA2), as a preclinical candidate compound for the treatment of diabetic retinopathy (DR) and diabetic macular edema (DME). Lp-PLA2 belongs to group VII of the PLA2 superfamily, primarily secreted by macrophages and circulates in the blood as a complex with low-density lipoprotein (LDL) and high-density lipoprotein (HDL). Lp-PLA2 hydrolyzes oxidized phospholipids, typically on low-density lipoprotein (LDL) to generate lysophosphatidylcholine (lysoPC).

LysoPC induces vascular inflammation, leading to damage to the blood-retinal barrier (BRB). DR is a microvascular complication of diabetes and a major cause of vision loss in middle-aged and elderly people. One-third of people with diabetes have DR. DME can occur at any stage of DR, which is caused by excess fluid and lipid accumulation in the macula due to a breakdown in the blood-retinal barrier.

When the edema extends into the fovea, the patient becomes symptomatic with metamorphopsia and vision loss. The advent of intraocular anti-vascular endothelial growth factor (anti-VEGF) drugs has revolutionized the treatment of DME. However, many patients with DME do not show complete resolution of fluid despite multiple injections, probably because DR is also an inflammatory disease with many cytokines and chemokines involved in the process[6].

Thus, the molecular mechanisms beyond VEGF should be explored. As a Lp-PLA2 inhibitor, oral administration of Darapladib modestly reduced edema and improved vision in a center-involved DME phase IIa study. DPT0415 is a highly potent, selective and safe Lp-PLA2 inhibitor with a novel scaffold.

It demonstrated sufficient target engagement at 0.3 mpk, and resecured retinopathy in the STZ-induced rat DR model. The compound exhibits higher potency, better ADME and physicochemical properties than Darapladib, and demonstrated sufficient safety margin in preclinical studies. The RiDYMO drug design platform integrates various AI and physical algorithms, dedicated to the development of drugs for "undruggable" targets and "best-in-class" molecules.

As one of its core algorithms, Reinforced Dynamics (RiD) has a significant advantage in the sampling efficiency of molecular dynamics simulation. By fully leveraging the high-dimensional representation capabilities of neural networks, RiD can efficiently capture dynamic conformational changes in complicated biomolecular systems. The RiDYMO® platform is dedicated to studying the dynamics of biological systems and revealing cryptic binding sites, encompassing a range of challenging systems including protein-protein interactions (PPIs), intrinsically disordered proteins (IDPs), membrane proteins, RNA, and others. Its effectiveness has been confirmed through validation on challenging targets, including the c-Myc protein, c-Myc RNA, GPX4 protein, Kv1.3 protein, and others.