LogP measurement of a highly hydrophobic properfume: evaluation of extrapolation of RP‐HPLC results and impact of chemical moieties on accuracy

Begnaud, Frédéric; Larcinese, Jean-Paul; Fankhauser, Péter; Maddalena, Umberto

doi:10.1002/ffj.3309

Cited by 8 publications

(14 citation statements)

References 33 publications

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“…Specifically, they require accurate experimental measurements of molecules from the chemical space of interest. 26 Experimental methods to obtain these log P measurements can be broadly categorized into two classes: direct or indirect methods. Direct methods, such as the shake-flask and slow-stir methods, obtain a log P measurement from the concentration ratio between water and octanol.…”

Section: ■ Introductionmentioning

confidence: 99%

“…23,27,28 The shakeflask and slow-stir methods are generally considered the gold standard for log P measurement. 26 Experimental uncertainty estimates for log P measurements range from <0.2 to 0.4 log units and depend on the specific experimental setup. 23,29,30 However, both direct and indirect methods struggle to measure log P for poorly soluble, very lipophilic (high log P), or very hydrophilic (low log P) compounds.…”

Section: ■ Introductionmentioning

confidence: 99%

“…23,29,30 However, both direct and indirect methods struggle to measure log P for poorly soluble, very lipophilic (high log P), or very hydrophilic (low log P) compounds. 26 This is wellillustrated by the relatively large fraction of compounds in the in-house data set with a measured log P annotated as "> 4.7" (Figure S1), highlighting the limits of the used experimental method (see the Methods section). Compounds in the extreme ranges of the log P scale are challenging to measure as either the limit of detection in the low-concentration phase may be approached or the detector in the high-concentration phase may be oversaturated.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Direct methods, such as the shake-flask and slow-stir methods, obtain a log P measurement from the concentration ratio between water and octanol . Indirect methods obtain a log P measurement by measuring a correlated quantity such as capacity factors or solute retention volumes. ,, The shake-flask and slow-stir methods are generally considered the gold standard for log P measurement . Experimental uncertainty estimates for log P measurements range from <0.2 to 0.4 log units and depend on the specific experimental setup. ,, …”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity

et al. 2023

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Lipophilicity, as measured by the partition coefficient between octanol and water (log P), is a key parameter in early drug discovery research. However, measuring log P experimentally is difficult for specific compounds and log P ranges. The resulting lack of reliable experimental data impedes development of accurate in silico models for such compounds. In certain discovery projects at Novartis focused on such compounds, a quantum mechanics (QM)-based tool for log P estimation has emerged as a valuable supplement to experimental measurements and as a preferred alternative to existing empirical models. However, this QM-based approach incurs a substantial computational cost, limiting its applicability to small series and prohibiting quick, interactive ideation. This work explores a set of machine learning models (Random Forest, Lasso, XGBoost, Chemprop, and Chemprop3D) to learn calculated log P values on both a public data set and an in-house data set to obtain a computationally affordable, QM-based estimation of drug lipophilicity. The message-passing neural network model Chemprop emerged as the best performing model with mean absolute errors of 0.44 and 0.34 log units for scaffold split test sets of the public and in-house data sets, respectively. Analysis of learning curves suggests that a further decrease in the test set error can be achieved by increasing the training set size. While models directly trained on experimental data perform better at approximating experimentally determined log P values than models trained on calculated values, we discuss the potential advantages of using calculated log P values going beyond the limits of experimental quantitation. We analyze the impact of the data set splitting strategy and gain insights into model failure modes. Potential use cases for the presented models include pre-screening of large compound collections and prioritization of compounds for full QM calculations.

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Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity

et al. 2023

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“…Such chromatographic characterisation approaches can also rely on the use of sets of homologue compounds of the same general formula, with one feature varied in a systematic manner, e.g. the number of functional groups or length of an alkyl chain [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Origin of the selectivity differences of aromatic alcohols and amines of different n-alkyl chain length separated with perfluorinated C8 and bidentated C8 modified silica hydride stationary phases

Kulsing

Nolvachai

Matyska

et al. 2019

Analytica Chimica Acta: X

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Perfluorinated C8-(PerfluoroC8) and bidentate anchored C8-(BDC8)-modified silica hydride stationary phases have been employed for the isocratic separation of homologous phenylalkanols and phenylalkylamines differing in their n -alkyl chain length, using aqueous-acetonitrile (ACN) mobile phases of different ACN contents from 10 to 90% (v/v) in 10% increments. These analytes showed reversed-phase (RP) retention behaviour with mobile phases of <40% (v/v) ACN content with both stationary phases but with the BDC8 stationary phase providing longer retention. The PerfluoroC8, but not the BDC8, stationary phase also exhibited significant retention of these analytes under conditions typical of an aqueous normal phase (ANP) mode ( i.e. with mobile phases of >80% (v/v) ACN content), with the analytes exhibiting overall U-shape retention dependencies on the ACN content of the mobile phase. Further, these stationary phases showed differences in their selectivity behaviour with regard to the n -alkyl chain lengths of the different analytes. These observations could not be explained in terms of p K a , log P , molecular mass or linear solvation energy concepts. However, density functional theory (DFT) simulations provided a possible explanation for the observed selectivity trends, namely differences in the molecular geometries and structural organisation of the immobilised ligands of these two stationary phases under different solvational conditions. For mobile phase conditions favouring the RP mode, these DFT simulations revealed that interactions between adjacent BDC8 ligands occur, leading to a stationary phase with a more hydrophobic surface. Moreover, under mobile phase conditions favouring retention of the analytes in an ANP mode, these interactions of the bidentate-anchored C8 ligands resulted in hindered analyte access to potential ANP binding sites on the BDC8 stationary phase surface. With the PerfluoroC8 stationary phase, the DFT simulations revealed strong repulsion of individual perfluoroC8 ligand chains, with the perfluoroC8 ligands of this stationary phase existing in a more open brush-like state (and with a less hydrophobic surface) compared to the BDC8 ligands. These DFT simulation results anticipated the chromatographic findings that the phenylalkanols and phenylalkylamines had reduced retention in the RP mode with the PerfluoroC8 stationary phase. Moreover, the more open ligand structure of the PerfluoroC8 stationary phase enabled greater accessibility of the analytes to water solvated binding sites on the stationary phase surface under mobile phase conditions favouring an ANP retention mode, leading to retention of the analytes, particularly the smaller phenylalkylamines, via hydrogen bonding and electrostatic effects.

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Limits of rapid log P determination methods for highly lipophilic and flexible compounds

Martel

Begnaud

Schuler

et al. 2016

Analytica Chimica Acta

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LogP measurement of a highly hydrophobic properfume: evaluation of extrapolation of RP‐HPLC results and impact of chemical moieties on accuracy

Cited by 8 publications

References 33 publications

Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity

Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity

Origin of the selectivity differences of aromatic alcohols and amines of different n-alkyl chain length separated with perfluorinated C8 and bidentated C8 modified silica hydride stationary phases

Limits of rapid log P determination methods for highly lipophilic and flexible compounds

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