Probing the dark chemical matter against PDE4 for the management of psoriasis using in silico, in vitro and in vivo approach.

The potential downsides for the present treatment for psoriasis are drug resistance, reduced efficacy, risk of mental episodes, and drug interactions. Hence, this study aims to discover a new drug for psoriasis by considering global research efforts and exploring underrepresented chemical space regions. The objective was to identify novel PDE4D inhibitors from the dark chemical matter (DCM) database for treating psoriasis. To address this we have coupled molecular docking and pharmacophore screening with molecular dynamics (MD) to identify hit molecules. Additionally, pharmacokinetics optimization was performed using machine learning and artificial intelligence which are key parts of drug discovery and development processes. The 139,353 DCM molecules were evaluated for their binding mode and interaction with critical residues such as GLN369, ILE336, PHE340, and PHE372 of the phosphodiesterase-4D (PDE4D) enzyme. Here, 15 hits were obtained through successive virtual screening procedures and all the 15 molecules were subjected to MD simulations for hit identification. In the MD studies, a stable root mean square deviation (RMSD) and ligand-protein interactions were found with four molecules, namely 027230, 060628, 060576, and 085881. The ligand 085881 was found promising because it inhibits LPS-induced IL-6 and TNF-alpha secretion from THP-1 cells with IC₅₀ of 18.41 μM and 34.43 μM, respectively. In vivo erythema grading showed that 085881 possesses mild to moderate anti-psoriatic action. This study demonstrates the effective use of computational techniques to discover novel PDE4D inhibitors and provides insight into their therapeutic potential for treating inflammatory diseases such as psoriasis.