A Multiple Classifier System Identifies Novel Cannabinoid CB2 Receptor Ligands

open access articleDrugs have become an essential part of our lives due to their ability to improve people’s health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) for an identified protein target. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in-silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). In this work, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1.834.362 compounds), was virtually screened to identify 48.432 potential active molecules using D2-MCS. This list was subsequently clustered based on circular fingerprints and from each cluster, the most active compound was maintained. From these, the top 60 were kept, and 21 novel compounds were purchased. Experimental validation confirmed six highly active hits (>50% displacement at 10 μM and subsequent Ki determination) and an additional five medium active hits (>25% displacement at 10 μM). D2-MCS hence provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%

A multiple classifier system identifies novel cannabinoid CB2 receptor ligands

Abstract Drugs have become an essential part of our lives due to their ability to improve people’s health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) after a protein target has been identified. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). Here, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1,834,362 compounds), was virtually screened to identify 48,232 potential active molecules using D2-MCS. Identified molecules were ranked to select 21 promising novel compounds for in vitro evaluation. Experimental validation confirmed six highly active hits (> 50% displacement at 10 µM and subsequent Ki determination) and an additional five medium active hits (> 25% displacement at 10 µM). Hence, D2-MCS provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%.Dutch Scientific Council | Ref. VENI 14410Xunta de Galicia | Ref. ED431C2018/55-GR

High-titer production of lathyrane diterpenoids from sugar by engineered Saccharomyces cerevisiae

Euphorbiaceae are an important source of medically important diterpenoids, such as the anticancer drug ingenol-3-angelate and the antiretroviral drug prostratin. However, extraction from the genetically intractable natural producers is often limited by the small quantities produced, while the organic synthesis of terpene-derived drugs is challenging and similarly low-yielding. While transplanting the biosynthetic pathway into a heterologous host has proven successful for some drugs, it has been largely unsuccessful for diterpenoids due to their elaborate biosynthetic pathways and lack of genetic resources and tools for gene discovery. We engineered casbene precursor production in S. cerevisiae, verified the ability of six Euphorbia lathyris and Jatropha curcas cytochrome P450s to oxidize casbene, and optimized the expression of these P450s and an alcohol dehydrogenase to generate jolkinol C, achieving ~800mg/L of jolkinol C and over 1g/L total oxidized casbanes in millititer plates, the highest titer of oxidized diterpenes in yeast reported to date. This strain enables the semisynthesis of biologically active jolkinol C derivatives and will be an important tool in the elucidation of the biosynthetic pathways for ingenanes, tiglianes, and lathyranes. These findings demonstrate the ability of S. cerevisiae to produce oxidized drug precursors in quantities that are sufficient for drug development and pathway discovery

High-titer production of lathyrane diterpenoids from sugar by engineered Saccharomyces cerevisiae.

Euphorbiaceae are an important source of medically important diterpenoids, such as the anticancer drug ingenol-3-angelate and the antiretroviral drug prostratin. However, extraction from the genetically intractable natural producers is often limited by the small quantities produced, while the organic synthesis of terpene-derived drugs is challenging and similarly low-yielding. While transplanting the biosynthetic pathway into a heterologous host has proven successful for some drugs, it has been largely unsuccessful for diterpenoids due to their elaborate biosynthetic pathways and lack of genetic resources and tools for gene discovery. We engineered casbene precursor production in S. cerevisiae, verified the ability of six Euphorbia lathyris and Jatropha curcas cytochrome P450s to oxidize casbene, and optimized the expression of these P450s and an alcohol dehydrogenase to generate jolkinol C, achieving ~800mg/L of jolkinol C and over 1g/L total oxidized casbanes in millititer plates, the highest titer of oxidized diterpenes in yeast reported to date. This strain enables the semisynthesis of biologically active jolkinol C derivatives and will be an important tool in the elucidation of the biosynthetic pathways for ingenanes, tiglianes, and lathyranes. These findings demonstrate the ability of S. cerevisiae to produce oxidized drug precursors in quantities that are sufficient for drug development and pathway discovery

SAR exploration of the non-imidazole histamine H3 receptor ligand ZEL-H16 reveals potent inverse agonism

Histamine H3 receptor (H3R) agonists without an imidazole moiety remain very scarce. Of these, ZEL-H16 (1) has been reported previously as a high-affinity non-imidazole H3R (partial) agonist. Our structure-activity relationship analysis using derivatives of 1 identified both basic moieties as key interaction motifs and the distance of these from the central core as a determinant for H3R affinity. However, in spite of the reported H3R (partial) agonism, in our hands, 1 acts as an inverse agonist for Gαi signaling in a CRE-luciferase reporter gene assay and using an H3R conformational sensor. Inverse agonism was also observed for all of the synthesized derivatives of 1. Docking studies and molecular dynamics simulations suggest ionic interactions/hydrogen bonds to H3R residues D1143.32 and E2065.46 as essential interaction points