Fluorine Scanning by Nonselective Fluorination: Enhancing Raf/MEK Inhibition while Keeping Physicochemical Properties

Hyohdoh, Ikumi; Furuichi, Noriyuki; Aoki, Toshihiro; Itezono, Yoshiko; Shirai, Haruyoshi; Ozawa, Sawako; Watanabe, Fumio; Matsushita, Masayuki; Sakaitani, Masahiro; Ho, Pil‐Su; Takanashi, Koichi; Harada, Naoki; Tomii, Yasushi; Yoshinari, Kiyoshi; Ori, Kazutomo; Tabo, Mitsuyasu; Aoki, Yuko; Shimma, Nobuo; Iikura, Hitoshi

doi:10.1021/ml4002419

Cited by 32 publications

(22 citation statements)

References 23 publications

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“…Examining all of the systems we are considering here, Table (1) shows the experimental data that is available for hydrogen to fluorine mutations taken from the respective papers where these systems were investigated 9,[40][41][42][43][44][45][46][47] . ΔΔG values in Table (1) are calculated from or IC50 values in references, see references for experimental methodologies.…”

Section: Methodsmentioning

confidence: 99%

“…All FEP and PFS calculations performed in this work were made with Fluorify the details for each stage of these calculations are as follows. The co-crystal structure for the nine systems examined here are taken from the Protein Data Bank 48 with PDBIDs shown in Table ( 1). To prepare these systems non-standard residues were converted to their standard equivalents with pdbfixer 49 .…”

Section: System Setupmentioning

confidence: 99%

“…Fluorine scanning is a common technique in medicinal chemistry and involves systematic replacement of hydrogen with fluorine. [1][2][3][4][5][6] It can improve binding affinity as well as ADME properties. 7,8 A striking example of the potential is given by the application of fluorine scanning on factor Xa.…”

Section: Introductionmentioning

confidence: 99%

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Computational Fluorine Scanning Using Free-Energy Perturbation

Wade

Rizzi

Wang

et al. 2019

J. Chem. Inf. Model.

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We present perturbative fluorine scanning, a computational fluorine scanning approach using free-energy perturbation. This method can be applied to molecular dynamics simulations of a single compound and make predictions for the best binders out of numerous fluorinated analogues. We tested the method on nine test systems: Renin, DPP4, Menin, P38, Factor Xa, CDK2, AKT, JAK2, and Androgen Receptor. The predictions were in excellent agreement with more rigorous alchemical free-energy calculations and in good agreement with experimental data for most of the test systems. However, the agreement with experiment was very poor in some of the test systems and this highlights the need for improved force fields in addition to accurate treatment of tautomeric and protonation states. The method is of particular interest due to the wide use of fluorine in medicinal chemistry to improve binding affinity and ADME properties. The promising results on this test case suggest that perturbative fluorine scanning will be a useful addition to the available arsenal of free-energy methods.

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Section: Methodsmentioning

confidence: 99%

Section: System Setupmentioning

confidence: 99%

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Computational Fluorine Scanning Using Free-Energy Perturbation

Wade

Rizzi

Wang

et al. 2019

J. Chem. Inf. Model.

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“…[18][19][20][21][22] As an indirect proof of the latter concept, a fluorine scan approach -a special case of positional scanning -can be highlighted. [23] Most successful fluorine applications in medicinal chemistry included the introduction of fluorine or fluorinated substituents into (hetero)aromatic moieties. [24] For the aliphatic series, the experimental data on the key in vitro parameters of the corresponding fluorine-containing compounds are rapidly growing, but they are still far from being comprehensive.…”

Section: Introductionmentioning

confidence: 99%

Effect of gem‐Difluorination on the Key Physicochemical Properties Relevant to Medicinal Chemistry: The Case of Functionalized Cycloalkanes

Holovach

Melnykov

Skreminskiy

et al. 2022

Chemistry A European J

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Physico‐chemical properties important to drug discovery (pKa, LogP, and aqueous solubility), as well as metabolic stability, were studied for a series of functionalized gem‐difluorinated cycloalkanes and compared to those of non‐fluorinated and acyclic counterparts to evaluate the impact of the fluorination. It was found that the influence of the CF2 moiety on the acidity/basicity of the corresponding carboxylic acids and amines was defined by inductive the effect of the fluorine atoms and was nearly the same for acyclic and cyclic aliphatic compounds. Lipophilicity and aqueous solubility followed more complex trends and were affected by the position of the fluorine atoms, ring size, and even the nature of the functional group present; also, significant differences were found for the acyclic and cyclic series. Also, gem‐difluorination either did not affect or slightly improved the metabolic stability of the corresponding model derivatives. The presented results can be used as a guide for rational drug design employing fluorine and establish the first chapter in a catalog of the key in vitro properties of fluorinated cycloalkanes.

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“…Incorporating fluorine into drug leads is an established practice in drug design and optimization. In fact, so-called fluorine scan is often employed in the development of drug candidates to systematically exploit the benefits of fluorine substitution [80]. As a result, an estimated one-third of the top-performing drugs currently on the market contain fluorine atoms in their structure [81].…”

Section: Resultsmentioning

confidence: 99%

eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

Naderi

Liu

et al. 2019

BMC Pharmacol Toxicol

148

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BackgroundThe efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery.ResultsIn this work, we describe eToxPred, a new approach to reliably estimate the toxicity and synthetic accessibility of small organic compounds. eToxPred employs machine learning algorithms trained on molecular fingerprints to evaluate drug candidates. The performance is assessed against multiple datasets containing known drugs, potentially hazardous chemicals, natural products, and synthetic bioactive compounds. Encouragingly, eToxPred predicts the synthetic accessibility with the mean square error of only 4% and the toxicity with the accuracy of as high as 72%.ConclusionseToxPred can be incorporated into protocols to construct custom libraries for virtual screening in order to filter out those drug candidates that are potentially toxic or would be difficult to synthesize. It is freely available as a stand-alone software at https://github.com/pulimeng/etoxpred.

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Fluorine Scanning by Nonselective Fluorination: Enhancing Raf/MEK Inhibition while Keeping Physicochemical Properties

Cited by 32 publications

References 23 publications

Computational Fluorine Scanning Using Free-Energy Perturbation

Computational Fluorine Scanning Using Free-Energy Perturbation

Effect of gem‐Difluorination on the Key Physicochemical Properties Relevant to Medicinal Chemistry: The Case of Functionalized Cycloalkanes

eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

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