Classification of scaffold-hopping approaches

Sun, Hongmao; Tawa, Gregory J.; Wallqvist, Anders

doi:10.1016/j.drudis.2011.10.024

Cited by 302 publications

(275 citation statements)

References 109 publications

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“…First, new candidate compounds could be uncovered by exploring the potential energy landscape of Eq. 4, and jumping between different energy minima could be related to "scaffold hopping" in drug discovery (38) as the minima would correspond to structures with pharmacophores. Investigating the topology of the energy landscape and those paths that connect distinct basins (39), as well as the statistics of energy minima, could reveal properties of the binding site.…”

Section: Physical Modelmentioning

confidence: 99%

Predicting protein–ligand affinity with a random matrix framework

Lee

Brenner

Colwell

2016

Proc. Natl. Acad. Sci. U.S.A.

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Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.drug discovery | random matrix theory | protein-ligand affinity | computational pharmacology | statistical physics F inding new ligands that bind to a given target is both a crucial step and a major stumbling block in modern drug discovery. Numerous attempts have been made to develop computational algorithms to predict the binding affinity of a ligand to a given receptor, which would allow potential compounds to be screened in silico, reducing costs and saving time. In particular, in response to the wealth of experimental data that exists both within pharmaceutical companies, and also in freely accessible online databases such as ChEMBL (1), approaches that attempt to "learn" from these data are increasingly gaining attention (2).An intuitive data-driven approach builds on the hypothesis that chemical commonalities among the known ligand set reveal salient features of the binding site. A corollary is that ligands with similar chemical functionality are expected to share similar binding affinity toward a particular receptor (3,4). This suggests that the known ligand set of a given target can be used to learn criteria that predict whether a novel ligand will bind to the target. This ligand-based approach is a powerful paradigm that does not require structural information about the receptor, which is potentially arduous to obtain, unlike other more atomistic methods such as docking or molecular dynamics.Any ligand-based method requires a way to quantify the chemical functionalities of a ligand, and various chemical descriptors have been proposed. Examples include a vector of measured or predicted physical properties (5-8), a vector enumerating the presence or absence of known functional groups on the ligand (9, 10), a vectorial representation of connectivities in the molecular graph (11, 12) (known also as molecular fingerprints), and simply the 3D shape of the ligand (13-16). Existing approaches then take the descriptor associated with each ligand and compare ligands with each other, for example through the Tanimoto coefficient (17, 18).Nonetheless, regardless of how ligand chemical func...

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Section: Physical Modelmentioning

confidence: 99%

Predicting protein–ligand affinity with a random matrix framework

Lee

Brenner

Colwell

2016

Proc. Natl. Acad. Sci. U.S.A.

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“…Under these conditions, resonances for bound inhibitors are extremely broad and are not observable, consistent with slow binding on the NMR time scale to a very large biomolecular complex. Thus, after the protein is saturated with ligand, the resonance for free inhibitor will appear in the 19 F NMR spectrum as excess inhibitor is added. When this titration is performed with compound 6, saturation was observed at a 1:1 ratio of IN to inhibitor, consistent with binding of the quinoline NCINI to each available pocket on the IN CCD, in the context of full length IN.…”

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confidence: 99%

“…19 During the course of lead optimization efforts, it was recognized that replacement of the quinoline core of the NCINI scaffold impacted properties other than potency. Herein we describe our scaffold replacement effort that led to the discovery of the pyridine series of NCINIs, whose salient feature is a reduced contribution of enterohepatic recirculation to in vivo clearance.…”

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confidence: 99%

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Minimizing the Contribution of Enterohepatic Recirculation to Clearance in Rat for the NCINI Class of Inhibitors of HIV

Fader

Carson

Morin

et al. 2014

ACS Med. Chem. Lett.

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A scaffold replacement approach was used to identifying the pyridine series of noncatalytic site integrase inhibitors. These molecules bind with higher affinity to a tetrameric form compared to a dimeric form of integrase. Optimization of the C6 and C4 positions revealed that viruses harboring T124 or A124 amino acid substitutions are highly susceptible to these inhibitors, but viruses having the N124 amino acid substitution are about 100-fold less susceptible. Compound 20 had EC 50 values <10 nM against viruses having T124 or A124 substitutions in IN and >800 nM in viruses having N124 substitions. Compound 20 had an excellent in vitro ADME profile and demonstrated reduced contribution of biliary excretion to in vivo clearance compared to BI 224436, the lead compound from the quinoline series of NCINIs. KEYWORDS: NCINI, enterohepatic recirculation, biliary excretion, integrase tetramer S ince the discovery of HIV in 1983, it is estimated that about 36 million people have died from AIDS worldwide.1 There has been a continuous search for antiretroviral (ARV) agents to combat HIV, which has led to the discovery of a number of mechanistic classes of replication inhibitors. 9 Recent work has shown that this class of inhibitor binds to an allosteric pocket on the catalytic core domain (CCD) of IN, shifting the oligomerization equilibrium of IN toward the tetrameric state.10−12 This aberrant multimerization negatively impacts viral maturation, which leads to the NCINI antiviral effect. 13,14Recently we disclosed our discovery of the NCINIs, culminating in the identification of BI 224436, the first compound from this mechanistic class to advance into clinical trials. 15 Compound 1 exemplifies the structural features of the prototypical quinolone series. The methyl substitution at C2 orients the C3 acetic acid group, favoring the bioactive form, and C4 bears an aromatic core. We demonstrated the importance of the C3 and C4 substituents in binding to IN CCD, which included hydrogen bonding interactions between the carboxylic acid and the backbone amide protons of residues E170 and H171 of IN. It was found that this interaction was critical to antiviral potency and that there was no tolerated isosteric replacement for the acid. The presence of a carboxylic acid can complicate the development of a lead series into a marketed drug for a number of reasons. 16,17 In the case of BI 224436, we found that the extremely low in vivo clearance of this molecule in rat is attributable to enterohepatic recirculation involving excretion of parent and the corresponding acyl glucuronide into the bile, reentry into the gastrointestinal tract, and reabsorption of parent back into the hepatic portal system. This phenomenon was common to all members of the NCINI class that were evaluated in rat pharmacokinetic experiments. The potential variability of the contribution of enterohepatic recirculation to in vivo clearance across species introduces uncertainty in allometric scaling and prediction of human PK parameters, which can compli...

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“…1,2 The introduction and/or modification of substituents of a lead are commonly investigated to optimize these properties, but, a more drastic structural change, i.e., scaffold hopping, is occasionally needed to identify drug candidates with the desired properties. [3][4][5][6][7] Although natural products are useful sources of drug candidates, their complex structures sometimes limit their use as clinical drugs. 8 Peptides are also potentially useful for the treatment of diseases, but their low membrane permeability and biological stability often limit their application as clinical drugs.…”

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confidence: 99%

Rational hopping of a peptidic scaffold into non-peptidic scaffolds: structurally novel potent proteasome inhibitors derived from a natural product, belactosin A

et al. 2014

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Rational scaffold hopping of the natural product belactosin A derivative was successfully achieved based on the pharmacophore model constructed. The peptidic scaffold was replaced by significantly simplified non-peptidic scaffolds, by which weak belactosin A (IC 50 = 1440 nM) was converted into highly potent non-peptidic inhibitors (IC 50 = 26-393 nM).In the drug discovery process, not only desired biological effect on target biomolecule, but also other various properties such as metabolic stability, membrane permeability, solubility, toxicity profile, and synthetic accessibility of leads, have to be optimized.

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Classification of scaffold-hopping approaches

Cited by 302 publications

References 109 publications

Predicting protein–ligand affinity with a random matrix framework

Predicting protein–ligand affinity with a random matrix framework

Minimizing the Contribution of Enterohepatic Recirculation to Clearance in Rat for the NCINI Class of Inhibitors of HIV

Rational hopping of a peptidic scaffold into non-peptidic scaffolds: structurally novel potent proteasome inhibitors derived from a natural product, belactosin A

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