Estimating the hydrophobicity extent of molecular fragments using reversed-phase liquid chromatography.
Andrea CarottiIna VarfajIlaria PrusciniGhaid W A AbualzulofLaura MercoliniElisa BianconiAntonio MacchiaruloEmidio CamaioniRoccardo SardellaPublished in: Journal of separation science (2023)
A fast HPLC method was developed to study the hydrophobicity extent of pharmaceutically relevant molecular fragments. By this strategy, the reduced amount of sample available for physico-chemical evaluations in early-phase drug discovery programs does not represent a limiting factor. The sixteen acid fragments investigated were previously synthesized also determining potentiometrically their experimental log D values. For four fragments it was not possible to determine such property since their values were outside of the instrumental working range (2 < pK a < 12). An RP-HPLC method was therefore optimized. For each scrutinized method, some derived chromatographic indices were calculated, and Pearson's correlation coefficient (r) allowed to select the so-called "φ 0 index" as the best correlating with the log D. The w s p H ${}_w^spH$ was fixed at 3.5 and a modification of some variables [organic modifier (methanol vs. ACN), stationary phase (octyl vs. octadecyl), presence/absence of the additives n-octanol, n-butylamine, and n-octylamine], allowed to select the best correlation conditions, producing a r = 0.94 (p < 0.001). Importantly, the φ 0 index enabled the estimation of log D values for four fragments which were unattainable by potentiometric titration. Moreover, a series of molecular descriptors were calculated to identify the chemical characteristics of the fragments explaining the obtained φ 0 . The number of hydrogen bond donors and the index of cohesive interaction correlated with the experimental data.
Keyphrases
- simultaneous determination
- liquid chromatography
- drug discovery
- mass spectrometry
- ms ms
- tandem mass spectrometry
- high performance liquid chromatography
- computed tomography
- magnetic resonance imaging
- machine learning
- ionic liquid
- magnetic resonance
- atomic force microscopy
- diffusion weighted imaging
- transition metal