Dissolution of Bicalutamide Single Crystals in Aqueous Solution: Significance of Evolving Topography in Accelerating Face-Specific Kinetics

The dissolution kinetics of individual microscale bicalutamide (BIC) form-I crystals are tracked over time using in situ atomic force microscopy (AFM), with the evolution of crystal morphology used to obtain quantitative data on dissolution kinetics via finite element method (FEM) modeling of the dissolution reaction-diffusion problem. Dissolution is found to involve pit formation and roughening on all dissolving surfaces of the BIC crystal, and this has a strong influence on the overall dissolution process and kinetics. While all of the exposed faces (100), {051}, and {1̅02} show dissolution kinetics that are largely surface-kinetic controlled, each face has an intrinsic dissolution characteristic that depends on the degree of hydrogen bonding with aqueous solution, with hydrogen bonding promoting faster dissolution. Moreover, as dissolution proceeds with pitting and roughening, the rate accelerates considerably, so that there is an increasing diffusion contribution. Such insight is important in understanding the oral administration of poorly soluble active pharmaceutical ingredients (APIs) in crystal form. Evidently, surface roughening and defects greatly enhance dissolution kinetics, but the evolving crystal topography during dissolution leads to complex time-dependent kinetics that are important for modeling and understanding API release rates

Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data

The Δδ regression approach of Blade et al. [J. Phys. Chem. A 2020, 124(43), 8959–8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured 1H and 13C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδexperimental) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδcalculated) by regression analysis and a t-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule’s charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδexperimental and Δδcalculated are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures