Pharmaceutical Research最新文献

Improving the bioavailability  of poorly water-soluble drugs presents a significant challenge in pharmaceutical development. Amorphous solid dispersions (ASDs) have garnered substantial attention for their capability to augment the solubility and dissolution rate of poorly water-soluble drugs, thereby markedly enhancing their bioavailability. ASDs, characterized by a metastable equilibrium where the active pharmaceutical ingredient (API) is molecularly dispersed, offer enhanced absorption compared to crystalline forms. This review explores recent research advancements in ASD, emphasizing dissolution mechanisms, phase separation phenomena, and the importance of drug loading and congruency limits on ASD performance. Principal occurrences such as liquid-liquid phase separation (LLPS) and supersaturation are discussed, highlighting their impact on drug solubility, absorption and subsequent bioavailability. Additionally, it addresses the role of polymers in controlling supersaturation, stabilizing drug-rich nanodroplets, and inhibiting recrystallization. Recent advancements and emerging technologies offer new avenues for ASD characterization and production and demonstrate the potential of ASDs to enhance bioavailability and reduce variability, making possible for more effective and patient-friendly pharmaceutical formulations. Future research directions are proposed, focusing on advanced computational models for predicting ASD stability, use of novel polymeric carriers, and methods for successful preparations.

Background: Nonalcoholic fatty liver disease (NAFLD) comprises multiple heterogeneous pathophysiological conditions commonly evaluated by suboptimal liver biopsies. This study aimed to elucidate the role of 13 diverse histological liver scores in assessing NAFLD disease activity using an in silico pharmacometric model-based approach. We further sought to investigate various noninvasive patient characteristics for their ability to reflect all 13 histological scores and the NAFLD activity score (NAS).

Methods: A histological liver score model was built upon 13 biopsy-based pathological features (binary and categorical scores) from the extensive NASH-CRN (Nonalcoholic Steatohepatitis-Clinical Research Network) observational NAFLD Database study (n = 914 adults) using the concept of item response theory. The impact of 69 noninvasive biomarkers potentially reflecting NAFLD activity was quantitatively described across the entire spectrum of all 13 histological scores.

Results: The model suggested that four different disease facets underlie the cardinal NAFLD features (steatosis, inflammation, hepatocellular ballooning (= NAS); fibrosis; highest correlations: corr_{ballooning-fibrosis} = 0.69/corr_{inflammation-ballooning} = 0.62/corr_{steatosis-inflammation} = 0.60). The 13 histological liver scores were best described by contrasting noninvasive biomarkers: Age and platelets best reflected the fibrosis score, while alanine and aspartate aminotransferase best described the NAS, with diverging contributions of the three individual NAS components to the results of the overall NAS.

Conclusions: An in silico histological liver score model allowed to simultaneously quantitatively analyze 13 features beyond NAS and fibrosis, characterizing different disease facets underlying NAFLD and revealing the contrasting ability of 69 noninvasive biomarkers to reflect the diverse histological (sub-)scores.

Metabolic alterations are commonly associated with various cancers and are recognized as contributing factors to cancer progression, invasion, and metastasis. Drug repurposing, a strategy in drug discovery, utilizes existing knowledge to recommend established drugs for new indications based on clinical data or biological evidence. This approach is considered a less risky alternative to traditional drug development. Metformin, a biguanide, is a product of Galega officinalis (French lilac) primarily prescribed for managing type 2 diabetes, is recognized for its ability to reduce hepatic glucose production and enhance insulin sensitivity, particularly in peripheral tissues such as muscle. It also improves glucose uptake and utilization while decreasing intestinal glucose absorption. Statins, first isolated from the fungus Penicillium citrinum is another class of medication mainly used to lower cholesterol levels in individuals at risk for cardiovascular diseases, work by inhibiting the enzyme 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, which is essential for cholesterol biosynthesis in the liver. Metformin is frequently used in conjunction with statins to investigate their potential synergistic effects. Combination of metformin and simvastatin has gathered much attention in cancer research because of its potential advantages for cancer prevention and treatment. In this review, we analyze the effects of metformin and simvastatin, both individually and in combination, on key cancer hallmarks, and how this combination affects the expression of biomolecules and associated signaling pathways. We also summarize preclinical research, including clinical trials, on the efficacy, safety, and potential applications of repurposing metformin and simvastatin for cancer therapy.

Purpose: Epigenetic modifications, such as aberrant DNA methylation, histone alterations, non-coding RNA remodeling, and modulation of transcription factors, are pivotal in the pathogenesis of diverse malignancies. Reactive oxygen species (ROS) have the capacity to impact these epigenetic mechanisms, including DNA methylation, throughout the different stages of cancer development. Therefore, the aim of this review is to address the impact of.

Methods: Published papers were searched in Pubmed and Google Scholar databases using the keywords "epigenetic", or "DNA methylation", or "phytochemicals", or "chemoprevention" to prepare this review.

Results: There is mounting evidence indicating that diminishing ROS accumulation within cells can regulate the function of DNA methyltransferases (DNMTs). Moreover, activation of the cellular defense system can impede and potentially reverse the progression of tumors in cancerous cells. As a result, ROS scavengers, antioxidants, and demethylating agents have emerged as potential therapeutic approaches for specific types of cancer. Additionally, dietary phytochemicals present in fruits, vegetables, and herbs, which have been utilized for centuries, exhibit the capability to modulate transcription factors, decrease inflammation, deliver antioxidant benefits, induce cell-cycle arrest, and stimulate apoptosis.

Conclusion: These phytochemicals can also renew and reprogram the expression of genes that suppress cancer. Thus, prolonged exposure to phytochemicals at low doses represents an innovative therapeutic tactic for the prevention of cancer.

Purpose: Recombinant human B-type natriuretic peptide (rhBNP) has been extensively proven to be an effective mean of heart failure (HF) therapy, but its clinical application is limited by its very short half-life. This study aims to combine in vitro transcribed mRNA (IVT mRNA) and fusion protein technology to develop a rhBNP-Fc mRNA drug with long half-life, high efficiency and few side effects to treat HF.

Methods: The rhBNP-Fc fusion mRNA with IgG4-Fc sequence was produced by IVT technology. rhBNP-Fc mRNA was transfected into HEK293T cells to examine the expression in vitro. rhBNP-Fc mRNA encapsulated in LNP was injected into normal mice to detect the translation efficiency, half-life and negative effects in vivo. Finally, it was injected into doxorubicin-induced HF mice to screen the cardiac protective effect.

Results: The rhBNP-Fc fusion mRNA extended the half-life of rhBNP, showing sustained expression in cell line for at least one day. rhBNP-Fc mRNA translation showed dose-dependent levels, and was still detectable 5 d after injection in vivo. In the HF mouse model, a single administration of rhBNP-Fc mRNA-LNP improved cardiac function, including improving heart ejection and reducing HF biomarkers expression. Additionally, rhBNP-Fc mRNA-LNP treatment mitigated myocardial damage, normalized cardiomyocyte structure, and reduced the levels of pro-inflammatory cytokines.

Conclusion: The rhBNP-Fc mRNA has the potential to serve as an alternative to traditional protein therapies, thereby reducing clinical dosages, injection frequencies, and treatment costs. Our findings offer new insights into the development and application of mRNA drugs, emphasizing their therapeutic potential in long-acting drugs.

Objective: This study aimed to establish an optimal model based on machine learning (ML) to predict Valproic acid (VPA) trough concentrations in Chinese adult epilepsy patients.

Methods: A single-center retrospective study was carried out at the Jinshan Hospital affiliated with Fudan University from January 2022 to December 2023. A total of 102 VPA trough concentrations were split into a derivation cohort and a validation cohort at a ratio of 8:2. Thirteen ML algorithms were developed using 27 variables in the derivation cohort and were filtered by the lowest mean absolute error (MAE) value. In addition, feature selection was applied to optimize the model.

Results: Ultimately, the extra tree algorithm was chosen to establish the personalized VPA trough concentration prediction model due to its best performance (MAE = 13.08). The SHapley Additive exPlanations (SHAP) plots were used to visualize and rank the importance of features, providing insights into how each feature influences the model's predictions. After feature selection, we found that the model with the top 9 variables [including daily dose, last dose, uric acid (UA), platelet (PLT), combination, gender, weight, albumin (ALB), aspartate aminotransferase (AST)] outperformed the model with 27 variables, with MAE of 6.82, RMSE of 9.62, R² value of 0.720, relative accuracy (±20%) of 61.90%, and absolute accuracy (±20 mg/L) of 90.48%.

Conclusion: In conclusion, the trough concentration prediction model for VPA in Chinese adult epileptic patients based on the extra tree algorithm demonstrated strong predictive ability which is valuable for clinicians in medication guidance.

Purpose: The purpose of this research was to develop and characterize dual-drug Isoniazid-Pyridoxine gummies using Semisolid Extrusion (SSE) 3D printing technology, aimed at personalized dosing for a broad patient demographic, from pediatric to geriatric. This study leverages SSE 3D printing, an innovative approach in personalized medicine, to enable precise dose customization and improve patient adherence. By formulating dual drug-loaded gummies, the research addresses the challenges of pill burden and poor palatability associated with traditional tuberculosis regimens, ultimately enhancing the therapeutic experience and effectiveness for patients across various age groups.

Methods: Gummies were formulated using varying ratios of gelatin, carrageenan, and xylitol, and printed using the BIO X 3D printer. Rheological properties were evaluated to confirm printability, shear-thinning behavior, and viscosity recovery. In vitro drug release and stability were assessed under refrigerated (5 ± 3°C) and ambient (25 ± 2°C) storage conditions. FT-IR spectroscopy was used to examine drug-excipient interactions.

Results: The optimized F3 formulation, containing 900 mg Isoniazid and 30 mg Pyridoxine, demonstrated successful printability and structural integrity. Over 80% of both drugs were released within 30 min. Rheological testing confirmed ideal shear-thinning and viscoelastic properties for extrusion-based printing. Suitable textural properties for pediatric patient compliance were observed. Stability studies showed that both drug content and release profiles remained consistent for 30 days under refrigerated storage.

Conclusions: This study determines the potential of SSE 3D printing in fabricating personalized Isoniazid-Pyridoxine-loaded gummies, offering a novel, patient-friendly dosage form for tuberculosis treatment. The optimized formulation exhibited excellent printability, stability, and rapid drug release, positioning 3D-printed gummies as a promising alternative to conventional oral dosage forms in enhancing patient adherence.

Purpose: The purpose of this study is to present a correlative microscopy-tomography approach in conjunction with machine learning-based image segmentation techniques, with the goal of enabling quantitative structural and compositional elucidation of real-world pharmaceutical tablets.

Methods: Specifically, the approach involves three sequential steps: 1) user-oriented tablet constituent identification and characterization using correlative mosaic field-of-view SEM and energy dispersive X-ray spectroscopy techniques, 2) phase contrast synchrotron X-ray micro-computed tomography (SyncCT) characterization of a large, representative volume of the tablet, and 3) constituent segmentation and quantification of the imaging data through user-guided, iterative supervised machine learning and deep learning.

Results: This approach was implemented on a real-world tablet containing 15% API and multiple common excipients. A representative volumetric tablet image was obtained using SyncCT at a 0.36-µm resolution, from which constituent particles and pores were fully segmented and quantified. As validation, the derived tablet formulation composition and porosity agreed with the experimental values, despite the micrometer-scale particle and pore sizes. The approach also revealed the formation of ordered mixture inside the tablet. Notably, the image-derived size distributions of both the agglomerated microcrystalline cellulose and its primary particulate units matched the laser diffraction-based measurements of the as-is material. Key pore attributes including the pore size distribution, spatial anisotropy, and pore interconnectivity were also qualified.

Conclusion: Overall, this study demonstrated that the correlative microscopy-tomography approach, by leveraging phase contrast SyncCT and AI-based image analysis, can deliver new, practically-useful structural and compositional information and facilitate more efficient formulation and process development of tablets.

Introduction: In vitro screening of macrophages for drug-induced effects, such as phospholipidosis, is useful for detecting potentially problematic compounds in the preclinical development of oral inhaled products. High-content image analysis (HCIA) is a multi-parameter approach for cytotoxicity screening. This study provides new insights into HCIA-derived response patterns of murine J774A.1 cells and primary human alveolar macrophages (hAM).

Methods: Several compounds were compared with reference groups (cationic amphiphilic drugs and apoptosis inducers) at different concentrations (0.01 to 10 µM). After incubation, cells were stained with fluorescence markers and HCIA was performed (Cytation™ 5 Cell Imaging System). Ten parameters were analysed: non-adherent cells, increased or reduced mitochondrial activity, membrane permeability, cell area, nuclear area, polynucleated cells, vacuole area, neutral and phospholipid content. A new system of response categorisation was developed for data analysis.

Results: Murine J774A.1 cells exhibited a drug-induced response pattern that was distinct to the corresponding pattern of hAM cells. Comparison with the literature revealed that primary cells (rat or human origin) have similar response patterns, while cell lines (mouse, rat or human) exhibited a different response pattern. Hierarchical clustering revealed toxicologically aligned clusters of compounds, suggesting potential use for understanding mechanisms of drug effects in cell lines and primary cells.

Conclusions: Valuable information for selecting a suitable cell type for HCIA screening of macrophage responses to drug compounds is provided. All cell types were suitable for screening drug-induced phospholipidosis. Still, human primary alveolar macrophages responded differently to drug treatment compared to macrophage cell lines and may be required to evaluate broader response-patterns and mechanisms of toxicity.

Purposes: In the peptide mapping reduction process for monoclonal antibodies (mAbs) and other proteins, the conventional reducing reagents β-mercaptoethanol (β-ME) and dithiothreitol (DTT) pose challenges due to their strong odor and toxicity at high concentrations. Cysteine (Cys), an essential amino acid for new protein synthesis, is an overlooked, nontoxic, and odorless reducing agent. This study presents a novel peptide mapping method using Cys as the reducing agent.

Methods: We explored the reducing effect of Cys at different concentrations and pH levels for peptide mapping analysis of a specific mAb (mAb-1), using DTT as a positive control. RP-HPLC analysis, including UV chromatogram comparison and overall similarity calculation, was conducted for comparison. LC-MS analysis was subsequently utilized to characterize the primary sequence of mAb-1. We also applied the method to other mAbs or proteins to demonstrate its wide applicability.

Results: The UV chromatogram and overall similarity of Cys as a reducing agent at concentrations ranging from 10 to 40 mM and pH levels between 7.0 and 11.0 were consistent with those of the positive control. Reduced concentrations of Cys or lower pH levels compromised reducing efficacy. This novel reducing method proficiently characterized the primary sequence of mAb-1, achieving an overall sequence coverage of 97%. In the analysis of other mAbs or proteins, the peptide mapping results also showed high consistency.

Conclusions: Cys exhibits a reducing ability comparable to DTT and possesses the advantageous characteristics of being nontoxic and odorless, making it a potential alternative for disulfide bond reduction and peptide mapping analysis of proteins and mAbs.