Development of Pharmacophore Models for the Important Off-Target 5-HT_2B Receptor

Abstract

Toxicity is a major cause of attrition in the development of pharmaceuticals, and the off-target effects are a frequent contributor. The 5-HT 2B receptor agonism is known to be responsible for a variety of safety concerns including valvular heart disease which was the cause for the withdrawal of several compounds from the market. An early detection of potential binding to this receptor is thus desirable. Herein, we present the identification of key amino acid residues in the active site of 5-HT 2B by molecular dynamics simulations, the development of pharmacophore models and their performance on in-house data, and a structurally highly diverse subset of Enamine REAL labeled for 5-HT 2B activity by a machine learning model. These models may be used as filters employed on screening compound sets for the early filtration of compounds with potential 5-HT 2B off-target liabilities.

2B Determined: The Future of the Serotonin Receptor 2B in Drug Discovery

“…In 2023, Schieferdecker and Vock reported detailed 5-HT 2B pharmacophore models which are likely to aid in the development of next-generation ligands with robust subtype selectivity. 95 Clinical Compounds. With respect to the clinical development of more advanced molecules, thiophenylpyrimidine PRX-08066 (Figure 4) is a potent and selective 5-HT 2B antagonist, developed by Epix Pharmaceuticals, which was shown to be highly effective in the treatment of druginduced PAH and VHD in rats.…”

“… As before, drug-development-enabling PK information is not reported. In 2023, Schieferdecker and Vock reported detailed 5-HT 2B pharmacophore models which are likely to aid in the development of next-generation ligands with robust subtype selectivity …”

Pareto optimization to accelerate multi-objective virtual screening

“…While the use of docking to off-targets as a proxy for selectivity is well precedented, 18–20 the high false negative rates of docking screens ( i.e. , binders predicted to be nonbinding) pose a risk for experimental validation of molecules predicted to be selective.…”

“…Docking to off-targets has been applied to improve the selectivity profiles of identified compounds. 18–20 These demonstrations have revealed that falsely predicted non-binders may incorrectly be categorized as selective because scoring functions designed for hit-finding typically aim to minimize the false positive rate ( i.e. , weak binders predicted to bind strongly), not the false negative rate ( i.e.…”

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

“…Methods vary from relation extraction from clinical notes to hybrid computational pipelines using molecular docking and machine learning . Pharmacophore modeling is also used, where toxicity and off-target concerns for specific receptors are modeled. , ADR and toxicity databases like SIDER, T3DB, and DrugBank, have enabled the development and evaluation of numerous machine learning methods. A few notable approaches include REMAP, ToxiM, TargeTox, and eToxPred .…”