“Ligandability” of Drug Targets: Assessment of Chemical Tractability via Experimental and
            <i>In Silico</i>
            Approaches

Bauer, Udo; Breeze, Alexander L.

doi:10.1002/9783527677047.ch03

Cited by 4 publications

(5 citation statements)

References 75 publications

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“…The early assessment of the associated drug target risks are thus of upmost importance to positively affect attrition rates through rational risk balancing and by adopting target-specific lead generation approaches to increase the chances of generating high quality leads. Assessing the target druggability, i.e., the ability to modulate a given target by a small molecule with drug-like properties and thereby achieve a therapeutic effect, has subsequently developed into a key activity since it greatly increases our understanding of the chemical tractability of prospective targets entering drug discovery portfolios …”

Section: Introductionmentioning

confidence: 99%

“…Assessing the target druggability, i.e., the ability to modulate a given target by a small molecule with drug-like properties and thereby achieve a therapeutic effect, has subsequently developed into a key activity since it greatly increases our understanding of the chemical tractability of prospective targets entering drug discovery portfolios. 4 The druggability of a target is a composite parameter that includes important factors relating not only to effective target engagement but also to desirable pharmacokinetic, pharmacodynamic, and toxicological properties, which actually might only become apparent once the project is more advanced. A necessary, but not sufficient, component of druggability is the propensity of the drug target to bind to small-molecule compounds with high affinity.…”

Section: ■ Introductionmentioning

confidence: 99%

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Hot-Spotting with Thermal Scanning: A Ligand- and Structure-Independent Assessment of Target Ligandability

et al. 2017

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Evaluating the ligandability of a protein target is a key component when defining hit-finding strategies or when prioritize among drug targets. Computational as well as biophysical approaches based on nuclear magnetic resonance (NMR) fragment screening are powerful approaches but suffer from specific constraints that limit their usage. Here, we demonstrate the applicability of high-throughput thermal scanning (HTTS) as a simple and generic biophysical fragment screening method to reproduce assessments from NMR-based screening. By applying this method to a large set of proteins we can furthermore show that the assessment is predictive of the success of high-throughput screening (HTS). The few divergences for targets of low ligandability originate from the sensitivity differences of the orthogonal biophysical methods. We thus applied a new strategy making use of modulations in the solvent structure to improve assay sensitivity. This novel approach enables improved ligandability assessments in accordance with NMR-based assessments and more importantly positions the methodology as a valuable option for biophysical fragment screening.

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Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Hot-Spotting with Thermal Scanning: A Ligand- and Structure-Independent Assessment of Target Ligandability

et al. 2017

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“…Linearity in semilogarithmic plots of the irreversible inactivation process is observed with GS-ESF concentrations 0.025–0.2 mM. According to Kitz and Wilson kinetics [ 33 ], the following equation describes the reaction between an active site-directed reagent (e.g., GS-ESF) and an enzyme (E) [ 34 , 35 , 36 , 37 ]: 1/k obs = 1/k 3 + K D /(k 3 [GS-ESF]) where k obs is the observed rate of enzyme inactivation for a given concentration of GS-ESF, k 3 is the maximal rate of inactivation (min −1 ), [GS-ESF] is the concentration of S-glutathionyl ethylenesulfonyl fluoride and K D is the apparent dissociation constant of the hGSTM1-1 and GS-ESF complex. A plot of 1/k obs versus 1/[GS-ESF] for several concentrations of GS-ESF yields a straight line with a positive ordinate intercept (k3) of 0.052 ± 0.02 min −1 and a slope corresponding to an apparent dissociation constant of 0.055 ± 0.01 mM ( Figure 4 c).…”

Section: Resultsmentioning

confidence: 99%

“…Conformational fluctuations of GSTs can lead to ensembles of free enzyme, E, and both the ES and EP complexes. Since the binding of substrates and the ligand (e.g., pirimicarb) occur within the same active site, presumably, two simplified scenarios can explain the limiting promiscuity of hGSTM1-1 [ 33 , 42 , 43 ]. Assuming the classic induced-fit model, conformational changes occur only after binding of the substrate, where substrate binding leads to an ES complex with a different conformation to enzyme E. On the other hand, according to the conformational selection model, an ensemble of conformers pre-exists to allow for the binding of a substrate or ligand to a pre-organized enzyme structure suitable for that substrate or ligand.…”

Section: Resultsmentioning

confidence: 99%

Ligandability Assessment of Human Glutathione Transferase M1-1 Using Pesticides as Chemical Probes

Bodourian

Poudel

Papageorgiou

et al. 2022

IJMS

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Glutathione transferases (GSTs; EC 2.5.1.18) form a group of multifunctional enzymes that are involved in phase II of the cellular detoxification mechanism and are associated with increased susceptibility to cancer development and resistance to anticancer drugs. The present study aims to evaluate the ligandability of the human GSTM1-1 isoenzyme (hGSTM1-1) using a broad range of structurally diverse pesticides as probes. The results revealed that hGSTM1-1, compared to other classes of GSTs, displays limited ligandability and ligand-binding promiscuity, as revealed by kinetic inhibition studies. Among all tested pesticides, the carbamate insecticide pirimicarb was identified as the strongest inhibitor towards hGSTM1-1. Kinetic inhibition analysis showed that pirimicarb behaved as a mixed-type inhibitor toward glutathione (GSH) and 1-chloro-2,4-dinitrobenzene (CDNB). To shine a light on the restricted hGSTM1-1 ligand-binding promiscuity, the ligand-free crystal structure of hGSTM1-1 was determined by X-ray crystallography at 1.59 Å-resolution. Comparative analysis of ligand-free structure with the available ligand-bound structures allowed for the study of the enzyme’s plasticity and the induced-fit mechanism operated by hGSTM1-1. The results revealed important structural features of the H-site that contribute to xenobiotic-ligand binding and specificity. It was concluded that hGSTM1-1 interacts preferentially with one-ring aromatic compounds that bind at a discrete site which partially overlaps with the xenobiotic substrate binding site (H-site). The results of the study form a basis for the rational design of new drugs targeting hGSTM1-1.

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“…Coverage includes genetic associations, somatic mutations, know drugs, gene expression, affected pathways, literature mining and animal models. The latest version of Open Target provides information regarding the tractability of a target, which measures the ‘ligandability’ of putative drug targets [ 130 ], and safety risk information associated with selected targets. The Open Targets Platform allows programmatic retrieval of data via a set of REST services or, alternatively, the access to dump files.…”

Section: Introductionmentioning

confidence: 99%

An omics perspective on drug target discovery platforms

Paananen

Fortino

2019

Briefings in Bioinformatics

151

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The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

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“Ligandability” of Drug Targets: Assessment of Chemical Tractability via Experimental and In Silico Approaches

Cited by 4 publications

References 75 publications

Hot-Spotting with Thermal Scanning: A Ligand- and Structure-Independent Assessment of Target Ligandability

Hot-Spotting with Thermal Scanning: A Ligand- and Structure-Independent Assessment of Target Ligandability

Ligandability Assessment of Human Glutathione Transferase M1-1 Using Pesticides as Chemical Probes

An omics perspective on drug target discovery platforms

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