Quantifying Micromolar Crystallinity in Pharmaceutical Materials Utilizing 19F Solid-State NMR.

Quantifying low-level components in solid-state analysis presents a significant challenge for most thermal, diffractometric, vibrational, and spectroscopic techniques. In pharmaceutical analysis, identifying and quantifying the physical form of the drug substance in solid dosages is a critical task to ensure the quality of drug products. For example, recrystallization of active pharmaceutical ingredients in amorphous solid dispersions can compromise the stability and bioavailability of drug products. Herein, we have developed and demonstrated fluorine-19 solid-state nuclear magnetic resonance (19F ssNMR) methods and pushed the boundary to quantify minor crystalline contents in amorphous pharmaceuticals. Calibration curves suggest that 19F direct polarization and 1H-19F cross-polarization ssNMR can readily quantify 0.1% w/w crystalline compound I, a commercial fluorinated drug molecule developed by Merck & Co., Inc., Rahway, NJ, U.S.A., in its amorphous formulation. 1H-19F multiple cross-polarization (MultiCP) has been implemented, for the first time, and compared with conventional cross-polarization methods. Most importantly, a relaxation-filtered 19F ssNMR method was utilized to unambiguously identify and quantify as low as 0.04% w/w crystalline components, that is, 6 µmol in a 100 mg tablet at 25% drug loading, by suppressing the signal from the amorphous counterpart. Such a low level of detection offers high confidence and sensitivity to quantify trace amounts of phase change in pharmaceutical amorphous materials in the solid state, which can facilitate formulation development as well as quality control.

Investigating Crystalline Protein Suspension Formulations of Pembrolizumab from MAS NMR Spectroscopy.

Developing biological formulations to maintain the chemical and structural integrity of therapeutic antibodies remains a significant challenge. Monoclonal antibody (mAb) crystalline suspension formulation is a promising alternative for high concentration subcutaneous drug delivery. It demonstrates many merits compared to the solution formulation to reach a high concentration at the reduced viscosity and enhanced stability. One main challenge in drug development is the lack of high-resolution characterization of the crystallinity and stability of mAb microcrystals in the native formulations. Conventional analytical techniques often cannot evaluate structural details of mAb microcrystals in the native suspension due to the presence of visible particles, relatively small crystal size, high protein concentration, and multicomponent nature of a liquid formulation. This study demonstrates the first high-resolution characterization of mAb microcrystalline suspension using magic angle spinning (MAS) NMR spectroscopy. Crystalline suspension formulation of pembrolizumab (Keytruda, Merck & Co., Inc., Kenilworth, NJ 07033, U.S.) is utilized as a model system. Remarkably narrow 13C spectral linewidth of approximately 29 Hz suggests a high order of crystallinity and conformational homogeneity of pembrolizumab crystals. The impact of thermal stress and dehydration on the structure, dynamics, and stability of these mAb crystals in the formulation environment is evaluated. Moreover, isotopic labeling and heteronuclear 13C and 15N spectroscopies have been utilized to identify the binding of caffeine in the pembrolizumab crystal lattice, providing molecular insights into the cocrystallization of the protein and ligand. Our study provides valuable structural details for facilitating the design of crystalline suspension formulation of Keytruda and demonstrates the high potential of MAS NMR as an advanced tool for biophysical characterization of biological therapeutics.

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability.

Tablet defects encountered during the manufacturing of oral formulations can result in quality concerns, timeline delays, and elevated financial costs. Internal tablet cracking is not typically measured in routine inspections but can lead to batch failures such as tablet fracturing. X-ray computed tomography (XRCT) has become well-established to analyze internal cracks of oral tablets. However, XRCT normally generates very large quantities of image data (thousands of 2D slices per data set) which require a trained professional to analyze. A user-guided manual analysis is laborious, time-consuming, and subjective, which may result in a poor statistical representation and inconsistent results. In this study, we have developed an analysis program that incorporates deep learning convolutional neural networks to fully automate the XRCT image analysis of oral tablets for internal crack detection. The computer program achieves robust quantification of internal tablet cracks with an average accuracy of 94%. In addition, the deep learning tool is fully automated and achieves a throughput capable of analyzing hundreds of tablets. We have also explored the adaptability of the deep learning analysis program toward different products (e.g., different types of bottles and tablets). Finally, the deep learning tool is effectively implemented into the industrial pharmaceutical workflow.