Molecular Simulation Approaches for the Prediction of Unknown Crystal Structures and Solubilities of (R)- and (R,S)-Crizotinib in Organic Solvents

Abstract

In this work, the solubilities of (R)- and (R,S)-crizotinib in six solvents including methanol, ethanol, 1-propanol, 1-butanol, ethyl acetate, and n-hexane were first measured by a static method in the temperature range from 273.15 to 323.15 K at atmospheric pressure. Then, molecular simulations were performed to predict the crystal structures of (R)-crizotinib and (R,S)-crizotinib from their powder X-ray diffractograms and the solubilities in the above organic solvents using the Gibbs free energy calculation approach via the sublimation cycle. The results showed that the solubilities of (R)- and (R,S)-crizotinib in the selected solvents generally increased with the increasing temperature, and at room temperature the molar fraction solubility order was methanol > 1-butanol > 1-propanol > ethanol > ethyl acetate > n-hexane. The (R)-crizotinib crystal had a monoclinic unit cell, while the (R,S)-crizotinib crystal had a triclinic one. In particular, though it was first applied to the solubility estimation for water-insoluble drug compounds in organic solvents, the Gibbs energy calculation approach developed gave a desirable solubility prediction performance with a root-mean-square error (RMSE) of 0.7638 log S units for (R)-crizotinib and 1.1577 log S units for (R,S)-crizotinib in the selected solvents, respectively. Furthermore, the difference in the solubilities of (R)-crizotinib in different solvents mainly resulted from the different Gibbs free energies for solvation between solute and solvent other than those for sublimation. However, as to the difference in the solubilities of (R)- and (R,S)-crizotinib in the same solvent, the Gibbs free energy for sublimation made more of a contribution than that for solvation. Finally, on the basis of the solid-state characterization, the nature of crystalline (R,S)-crizotinib was confirmed to be a racemic compound. All these results shall provide pharmaceutical industry with a better understanding of this chiral system for crystallization resolution.

Experimental and Molecular Dynamics Simulation Study on the Primary Nucleation of Penicillamine Racemate and Its Enantiomers in the Mixture Solvent of Water and Ethanol

“…The samples were scanned from 5 to 50°(2θ) at a scanning rate of 0.02°s −1 . 39 FTIR spectra of (D)-, (L)-, and (D,L)-penicillamine were acquired using a Spectrum Two Fourier-transform infrared spectrometer (PerkinElmer, Norwalk, CT). Ground KBr powder was used as the background in the measurements.…”

“…PXRD was performed using an X’Pert Pro diffractometer (PANalytical, Almelo, The Netherlands) with Cu Kα radiation (λ = 1.5418 Å) at 40 mA and 40 kV. The samples were scanned from 5 to 50° (2θ) at a scanning rate of 0.02° s –1 …”

Direct Crystallization Resolution of Racemates Enhanced by Chiral Nanorods: Experimental, Statistical, and Quantum Mechanics/Molecular Dynamics Simulation Studies

“… 38 PXRD was operated on a TD-3700 diffractometer with Cu Kα radiation (Tongda Instruments, Dandong, China). The step length from 3 to 80° was 0.05°, and the time per step was 5 s. 39 The thermal analyses by DSC and TGA were conducted on an HSC-4 differential scanning calorimeter (Henjiu Experimental Equipment, Bejing, China) and a WRT-12 thermogravimetric analyzer (Beiguang Hongyuan Instruments, Beijing, China), respectively. The heating rate was 5.0 K per minute.…”

“…The solubilities of three species of Asp in water from 288.15 to 323.15 K were determined, referring to ref ( 39 ). Each solubility measurement was performed using 50 mL of double-jacketed crystallizers.…”

Deep Generative Model for Periodic Graphs

“…For example, a geometry-based generative model can generate graphs within the predefined geometric rules but can-not generalize to other geometric properties. However, in many domains, the network properties and generation principles are largely unknown, such as those for explaining the thermal probability (Tabero et al, 2018) and solubilities (Fan et al, 2019a) of materials. Here the lack of predefined principles leads to the incapability of existing methods to model periodic graphs.…”