In this work, an amorphous solid dispersion (ASD) formulation was systematically developed to simultaneously enhance bioavailability and mitigate the mechanical instability risk of the selected crystalline form of a development drug candidate, GDC-0334. The amorphous solubility advantage calculation was applied to understand the solubility enhancement potential by an amorphous formulation for GDC-0334, which showed 2.7 times theoretical amorphous solubility advantage. This agreed reasonably well with the experimental solubility ratio between amorphous GDC-0334 and its crystalline counterpart (∼2 times) in buffers of a wide pH range. Guided by the amorphous solubility advantage, ASD screening was then carried out, focusing on supersaturation maintenance and dissolution performance. It was found that although the type of polymer carrier did not impact ASD performance, the addition of 5% (w/w) sodium dodecyl sulfate (SDS) significantly improved the GDC-0334 ASD dissolution rate. After ASD composition screening, stability studies were conducted on selected ASD powders and their hypothetical tablet formulations. Excellent stability of the selected ASD prototypes with or without tablet excipients was observed. Subsequently, ASD tablets were prepared, followed by in vitro and in vivo evaluations. Similar to the effect of facilitating the dissolution of ASD powders, the added SDS improved the disintegration and dissolution of ASD tablets. Finally, a dog pharmacokinetic study confirmed 1.8 to 2.5-fold enhancement of exposure by the developed ASD tablet over the GDC-0334 crystalline form, consistent with the amorphous solubility advantage of GDC-0334. A workflow of developing an ASD formulation for actual pharmaceutical application was proposed according to the practice of this work, which could provide potential guidance for ASD formulation development in general for other new chemical entities.

Lipid nanoparticles (LNPs) are the most widely investigated delivery systems for nucleic acid-based therapeutics and vaccines. Loading efficiency of nucleic acids may vary with formulation conditions, and it is considered one of the critical quality attributes of LNP products. Current analytical methods for quantification of cargo loading in LNPs often require external standard preparations and preseparation of unloaded nucleic acids from LNPs; therefore, they are subject to tedious and lengthy procedures, LNP stability, and unpredictable recovery rates of the separated analytes. Here, we developed a modeling approach, which was based on locally weighted regression (LWR) of ultraviolet (UV) spectra of unpurified samples, to quantify the loading of nucleic acid cargos in LNPs in-situ. We trained the model to automatically tune the training library space according to the spectral features of a query sample so as to robustly predict the nucleic acid cargo concentration and rank loading capacity with similar performance as the more complicated experimental approaches. Furthermore, we successfully applied the model to a wide range of nucleic acid cargo species, including antisense oligonucleotides, single-guided RNA, and messenger RNA, in varied lipid matrices. The LWR modeling approach significantly saved analytical time and efforts by facile UV scans of 96-well sample plates within a few minutes and with minimal sample preprocessing. Our proof-of-concept study presented the very first data mining and modeling strategy to quantify nucleic acid loading in LNPs and is expected to better serve high-throughput screening workflows, thereby facilitates early-stage optimization and development of LNP formulations.