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Bedaquiline
Published in M. Lindsay Grayson, Sara E. Cosgrove, Suzanne M. Crowe, M. Lindsay Grayson, William Hope, James S. McCarthy, John Mills, Johan W. Mouton, David L. Paterson, Kucers’ The Use of Antibiotics, 2017
Jeffrey A. Tornheim, Kelly E. Dooley
Bedaquiline was first identified through a program at Johnson & Johnson (J&J) in which over 70,000 compounds were screened in their chemical library for activity against the rapidly growing mycobacterium M. smegmatis(Andries et al., 2005). Several diarylquinolones had minimum inhibitory concentrations (MIC) against M. tuberculosis less than 0.5 µg/ml, and among these, R207910 (now bedaquiline) was the most active. Bedaquiline’s full chemical name is (1R, 2S)-1-(6-bromo-2 methoxy-3-quinolinyl)-4-(dimethylamino)-2-(1-naphthalenyl)-1-phenyl-2-butanol. It is a pure enantiomer with a heterocyclic nucleus and side chains including tertiary alcohol and amine groups (Figure 138.1). Its molecular formula is C32H31BrN2O2, and it is marketed by J&J under the trade name Sirturo, compounded 1:1 with fumaric acid, with a molecular weight of 671.58 (Andries et al., 2005; Matteelli et al., 2010; Janssen Products, 2015). Its molecular target is a mycobacterial adenosine triphosphate (ATP) synthase whose structure has been well characterized (Preiss et al., 2015).
Third Histamine Receptor: From Discovery to Clinics, Long-Lasting Love Story at INSERM and Bioprojet
Published in Divya Vohora, The Third Histamine Receptor, 2008
From the careful studies of Jean-Michel and Xavier with brain slices or, even, synaptosomes, it had become clear that a number of our antagonists not only blocked completely the inhibitory effects of agonists on 3H-histamine release but also facilitated this release over the basal level. One explanation could have been that these compounds were relieving the brake exerted by endogenous histamine released under these circumstances. But another explanation could have been that the H3 autoreceptor was constitutively active, that is, was exerting a brake on the release system, even in the absence of histamine or agonist. This question was not settled until Lovenberg et al. [14] (see Chapter 8) cloned the H3 receptor and use identified neutral antagonists in our chemical library.
What are the considerations when selecting a model for influenza drug discovery?
Published in Expert Opinion on Drug Discovery, 2023
Woo-Jin Shin, Seongil Choi, Baik-Lin Seong
Successful drug discovery relies on the quality of the small molecular library in use. Although there is extensive information on chemical compounds from a plethora of public sources, it is crucial to 1) select compounds that have drug-likeness properties such as cell permeability, 2) generate a diverse compound library from large chemical library databases such as DrugBank, PubChem, or ChEMBL, and 3) construct a focused library based on the initial hit compounds from the screening campaign or a set of chemical fragments [11]. If the undertaken research is focused on a specific target, it is beneficial to identify the protein function prior to selecting the compound library. For example, it is beneficial to generate a library that interacts with metals when designing the discovery of influenza virus PA endonuclease inhibitors which utilize divalent metal ions, such as magnesium or manganese, as crucial cofactors for the enzymatic function [16].
The chemical diversity and structure-based discovery of allosteric modulators for the PIF-pocket of protein kinase PDK1
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2019
Xinyuan Xu, Yingyi Chen, Qiang Fu, Duan Ni, Jian Zhang, Xiaolong Li, Shaoyong Lu
Rettenmaier et al. had tried to use a structure-based virtual screening which preformed against both crystal structures and comparative models to identify ligands bound to the PIF-pocket of PDK198. Based on the crystal structure of PDK1-2 complex, a group of six structural models of the PIF-pocket were created. A chemical library of 6,300 property-matched molecules which are commercially available was generated. These molecules were docked against all six structural models for virtual screening. Then the selected molecules were tested by FP competitive binding assay to identify whether the hits were bound to the PIF-pocket of PDK1. Compound 1 (15, Figure 7) with an EC50 value of ∼40 μM and compound 3 (16, Figure 7) with an EC50 value of ∼50 μM were identified, respectively. Furthermore, in order to optimise these two molecules, 518 commercially available analogues were extracted from the ZINC database by means of analogue-by-catalogue searching. Finally, 15 analogues were selected based on the scoring equal to or even better the two parent molecules. Among these molecules, RF4 (17, Figure 7) is the most potent compound with an EC50 value of ∼2 μM.
A multiparametric organ toxicity predictor for drug discovery
Published in Toxicology Mechanisms and Methods, 2020
Chirag N. Patel, Sivakumar Prasanth Kumar, Rakesh M. Rawal, Daxesh P. Patel, Frank J. Gonzalez, Himanshu A. Pandya
The pros of this toxicity predictor are (1) An ample amount of chemical space to approach chemical library, (2) To determine the restraint for the procurement of screening-level data, (3) Selection of biological assays with emphasis on resources available that could generate predictive bioactivity profiles, (4) To assess the impact of metabolism on the compounds with proven efficiency in assays, (5) Storage and analyzation of predictive signatures based on a bioinformatics approach, and (6) Preceding the prospective chemicals testing strategy to compete with traditional toxicity testing (Dix et al. 2007). This toxicity predictor recognizes the hypothesis which may guide further work or plan but it will not provide any assurance of successful work so there will be chance of failure.