Phase-appropriate Application of Process Analytical Technology for Early Pharmaceutical Development of Oral Solid Dosage Forms-the Case Study of Uniformity Screening of Dosage Units and Blends.

Process analytical technology (PAT) in late-stage drug product development is typically used for real-time process monitoring, in-process control, and real-time release testing. In early research and development (R&D), PAT usage is limited as the manufacturing scale is relatively small with frequent changes and only a few batches are produced on an annual basis. However, process understanding is critical at early R&D in order to identify process and formulation boundaries, so PAT applications could be particularly useful in early-stage R&D. For oral solid dosage form, conventional HPLC-based content uniformity (CU) methods with sampling of 3 tablets per stratified sampling location in early R&D are typically not sufficient to identify these manufacturing process boundaries and temporal profile. Here, we report a screening CU method based on a multivariate model using transmission Raman spectroscopy (TRS) data on a phase-appropriate calibration set of only 16 tablets. This initial model was used for multiple pre-GMP development batches to provide critical information about blend uniformity and content uniformity (CU). In this work, the precision of the TRS method was evaluated; multiple spectral preprocessing approaches were compared regarding their effects on measurement precision as well as their ability to mitigate the photo bleaching effects during precision experiments. Overall, the TRS-based CU method was much faster than a traditional HPLC-based method allowing a much larger number of tablets to be screened. This larger number of analyzed tablets enabled the processes boundaries and temporal changes in CU to be identified while providing proper statistical assurance on product quality.

Spectroscopy-Based Local Modeling Method for High-Throughput Quantification of Nucleic Acid Loading in Lipid Nanoparticles.

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.

Role for dithiolopyrrolones in disrupting bacterial metal homeostasis.

Natural products harbor unique and complex structures that provide valuable antibiotic scaffolds. With an increase in antibiotic resistance, natural products once again hold promise for new antimicrobial therapies, especially those with unique scaffolds that have been overlooked due to a lack of understanding of how they function. Dithiolopyrrolones (DTPs) are an underexplored class of disulfide-containing natural products, which exhibit potent antimicrobial activities against multidrug-resistant pathogens. DTPs were thought to target RNA polymerase, but conflicting observations leave the mechanisms elusive. Using a chemical genomics screen in Escherichia coli, we uncover a mode of action for DTPs-the disruption of metal homeostasis. We show that holomycin, a prototypical DTP, is reductively activated, and reduced holomycin chelates zinc with high affinity. Examination of reduced holomycin against zinc-dependent metalloenzymes revealed that it inhibits E. coli class II fructose bisphosphate aldolase, but not RNA polymerase. Reduced holomycin also strongly inhibits metallo-ß-lactamases in vitro, major contributors to clinical carbapenem resistance, by removing active site zinc. These results indicate that holomycin is an intracellular metal-chelating antibiotic that inhibits a subset of metalloenzymes and that RNA polymerase is unlikely to be the primary target. Our work establishes a link between the chemical structures of DTPs and their antimicrobial action; the ene-dithiol group of DTPs enables high-affinity metal binding as a central mechanism to inhibit metabolic processes. Our study also validates the use of chemical genomics in characterizing modes of actions of antibiotics and emphasizes the potential of metal-chelating natural products in antimicrobial therapy.

Chemical and functional properties of metal chelators that mobilize copper to elicit fungal killing of Cryptococcus neoformans.

A panel of iron (Fe) and copper (Cu) chelators was screened for growth inhibitory activity against the fungal pathogen Cryptococcus neoformans. Select bidentate metal-binding ligands containing mixed O,S or O,N donor atoms were identified as agents that induce cell killing in a Cu-dependent manner. Conversely, structurally similar ligands with O,O donor atoms did not inhibit C. neoformans growth regardless of Cu status. Studies of Cu(ii) and Cu(i) binding affinity, lipophilicity, and growth recovery assays of Cu-import deficient cells identified lipophilicity of thermodynamically stable CuIIL2 complexes as the best predictor of antifungal activity. These same complexes induce cellular hyperaccumulation of Zn and Fe in addition to Cu. The results described here present the utility of appropriate metal-binding ligands as potential antifungal agents that manipulate cellular metal balance as an antimicrobial strategy.