Journal of pharmaceutical sciences最新文献

One of the limitations with an amorphous solid dispersion (ASD) formulation strategy is low drug loading. Hydrophobic drugs have poor wettability and require a substantial amount of polymer to stabilize the amorphous drug and facilitate release. Using grazoprevir and hypromellose acetate succinate as model drug and polymer respectively, the interplay between particle surface composition, particle wettability, and release performance was investigated. A hierarchical particle approach was used where the surfaces of high drug loading ASDs generated by either solvent evaporation or co-precipitation were further modified with a secondary excipient (i.e., polymer or wetting agent). The surface-modified particles were characterized for drug release, wettability, morphology, and surface composition using two-stage dissolution studies, contact angle measurements, scanning electron microscopy, and X-ray photoelectron spectroscopy, respectively. Despite surface modification with hydrophilic polymers, hierarchical cPAD particles did not consistently exhibit good release performance. Contact angle measurements showed that the secondary excipient had a profound impact on particle wettability. Particles with good wettability showed improved drug release relative to particles that did not wet well, even with similar drug loadings. These observations underscore the intricate interplay between particle wettability and performance in amorphous dispersion formulations and illustrate a promising hierarchical particle approach to formulate high drug loading amorphous dispersions with improved dissolution performance.

Light Density Polyethylene (LDPE) bottles with a specific resin were chosen as container closure system (CCS) to fill "Latanoprost ophthalmic solution" (a generic drug product). As an alternative packaging component, additional manufacturer of LDPE bottles with the same characteristics as the previously selected LDPE bottles was chosen. The appropriateness of both packaging components was evaluated using an extractables and leachable (E&L) study and a formal stability programme that monitored quality of latanoprost ophthalmic solution. The results of relevant quality attributes in stability samples of latanoprost ophthalmic solution packed in both LDPE bottles were compared. It noticed that an unknown impurity in latanoprost ophthalmic solution packaged in LDPE bottles manufactured by an additional manufacturer. Further study revealed that this unknown impurity is Epsilon-caprolactam, a leachable of plastic used in the transportation of LDPE bottles. The leachability was validated through an extraction analysis of a plastic bag used for transportation. Thus, in certain cases, when the source of leachable is not identifiable by an E&L examination of primary, secondary, and tertiary packaging components, the assessment could be extended to include packaging components utilized throughout the supply chain.

Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular delivery for therapeutic proteins. In the pharmaceutical industry, establishing a robust formulation and process to achieve target product performance is a critical component of drug development. Fundamental understanding of the processes for making LNPs and their interactions with biological systems have advanced considerably in the wake of the COVID-19 pandemic. Nevertheless, LNP formulation research remains largely empirical and resource intensive due to the multitude of input parameters and the complex physical phenomena that govern the processes of nanoparticle precipitation, self-assembly, structure evolution, and stability. Increasingly, artificial intelligence and machine learning (AI/ML) are being applied to improve the efficiency of research activities through in silico models and predictions, and to drive deeper fundamental understanding of experimental inputs to functional outputs. This review will identify current challenges and opportunities in the development of robust LNP formulations of nucleic acids, review studies that apply machine learning methods to experimental datasets, and provide discussion on associated data science challenges to facilitate collaboration between formulation and data scientists, aiming to accelerate the advancement of AI/ML applied to LNP formulation and process optimization.

Over recent years, confidence has been gained that predictive stability modeling approaches using statistical tools, prior knowledge and industry experience enable, in many instances, a robust and reliable shelf-life/expiry or retest period prediction for medicinal products. These science and risk-based approaches can compensate for not having a complete real-time stability data set to be included in regulatory applications at the time of initial submission and, thereby, accelerate the availability of new medicines. Examples of predictive stability modeling include accelerated stability assessment procedure (ASAP), advanced kinetic modeling (AKM), and novel modeling approaches that involve the use of Bayesian statistics and Artificial Intelligence (AI) applications such as Machine Learning (ML), with applicability to both synthetic and biological molecules. For biologics, product-specific and platform prior knowledge could be used to overcome model limitations known for non-quantitative stability indicating attributes. A successful ongoing verification approach by comparing the predicted data with real-time stability data would be an appropriate risk management approach which is intended to address regulatory concerns, and further build confidence in the robustness of these predictive modelling approaches with regulatory agencies. Global regulatory acceptance of stability modeling could allow patients to receive potential life-saving medications faster without compromising quality, safety or efficacy.

Immunogenicity of gene therapy and the impacts on safety and efficacy are of increasing interest in the pharmaceutical industry. Unique structural aspects of gene therapy delivery vectors, such as adeno-associated viral (AAV) vectors, are expected to activate the innate immune system. The risk of innate immune activation is critical to understand due to the potential impacts on safety and on subsequent adaptive immune responses. In this study, we investigated the responses of key innate immune players-dendritic cells, natural killer (NK) cells, and the complement system-to AAV8 capsids. Immunogenicity risk was also predicted in the presence empty AAV capsids for AAV gene therapy. Compared to genome-containing "full" AAV8 capsids, empty AAV8 capsids more strongly induced proinflammatory cytokine production and migration by human and mouse dendritic cells, but the "full" capsid increased expression of co-stimulatory markers. Furthermore, in an NK cell degranulation assay, we found mixtures of empty and full AAV8 capsids to activate expression of TNF-α, IFN-γ, and CD107a more strongly in multiple NK cell populations compared to either capsid type alone. Serum complement C3a was also induced more strongly in the presence of mixed empty and full AAV8 capsid formulations. Risk for innate immune activation suggests the importance to determine acceptable limits of empty capsids. Immunogenicity risk assessment of novel biological modalities will benefit from the aforementioned in vitro innate immune activation assays providing valuable mechanistic information.