Pharmaceutical Research最新文献

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.

Purpose: Drug-induced liver injury, or DILI, affects numerous patients and also presents significant challenges in drug development. It has been attempted to predict DILI of a chemical by in silico approaches, including data-driven machine learning models. Herein, we report a recent DILI deep-learning effort that utilized our molecular representation concept by manifold embedding electronic attributes on a molecular surface.

Methods: Local electronic attributes on a molecular surface were mapped to a lower-dimensional embedding of the surface manifold. Such an embedding was featurized in a matrix form and used in a deep-learning model as molecular input. The model was trained by a well-curated dataset and tested through cross-validations.

Results: Our DILI prediction yielded superior results to the literature-reported efforts, suggesting that manifold embedding of electronic quantities on a molecular surface enables machine learning of molecular properties, including DILI.

Conclusions: The concept encodes the quantum information of a molecule that governs intermolecular interactions, potentially facilitating the deep-learning model development and training.

Background: High concentration protein formulation (HCPF) development needs to balance protein stability attributes such as conformational/colloidal stability, chemical stability, and solution properties such as viscosity and osmolality.

Methodology: A three-phase design is established in this work. In Phase 1, conformational and colloidal stability are measured by 384-well-based high-throughput (HT) biophysical screening while viscosity reduction screening is performed with HT viscosity screening. Collectively, the biophysical and viscosity screening data are leveraged to design the phase 2 of short-term stability study, executed using 96-well plates under thermal and freeze/thaw stresses. In phase 2, samples are analyzed by stability-indicating assays and processed with pair-wise Student's t-test analyses to choose the final formulations. In phase 3, the final formulations are then confirmed through a one-month accelerated stability in glass vials.

Results: Using a model antibody A (mAb-A), the initial HT screening successfully established the 384-well based platform. A lead formulation was chosen from the second round based on statistical analyses and subsequently tested against the commercial formulation of mAb-A as a control. Compared to the control, the lead formulation reduced the viscosity of mAb-A by 30% and decreased subvisible particles after thermal stress by 80%.

Conclusions: HT biophysical screening in 384-well plates was demonstrated to effectively guide the rational design of a high-throughput stability screening study using 96-well plates. This platform enables the identification of a high concentration formulation within seven weeks within the first two phases of study that strategically balance stability with solution properties, thus achieving a rapid development of HCPF.

Angelica gigas Nakai (AGN) root is a medicinal herbal widely used in traditional medicine in Korea. AGN root ethanolic extracts have been marketed as dietary supplements in the United States for memory health and pain management. We have recently reviewed the pharmacokinetics (PK) and first-pass hepatic metabolism of ingested AGN supplements in humans for the signature pyranocoumarins decursin (D, C_max 1x), decursinol angelate (DA, C_max ~ 10x) and their common botanical precursor and hepatic metabolite decursinol (DOH, C_max ~ 1000x). Here we update in vivo medicinal activities of AGN and/or its pyranocoumarins and furanocoumarin nodakenin in cancer, pain, memory loss, cerebral ischemia reperfusion stroke, metabolic syndrome and vascular endothelial dysfunctions, anxiety, sleep disorder, epilepsy, inflammatory bowel disease, osteoporosis and osteoarthritis. Given their polypharmacology nature, the pertinent mechanisms of action are likely misrepresented by many cell culture studies that did not consider the drug metabolism knowledge. We report here Rho-associated protein kinases (ROCK1/2) as novel targets for DA and DOH. Combining with published inhibitory activity of DOH on acetylcholinesterase, agonist activity of DOH and antagonist/degrader activity of DA/D on androgen and estrogen receptors, D/DA promoting activity for glutamic acid decarboxylase (GAD)- gamma-aminobutyric acid (GABA) inhibitory axis and inhibition of glutamate dehydrogenase (GDH), monoamine oxidase-A (MAO-A) and transient receptor potential vanilloid 1 (TRPV1), we postulate their contributions to neuro-cognitive, metabolic, oncologic, vascular and other beneficial bioactivities of AGN extracts. A clinical trial is being planned for an AGN extract to manage side effects of androgen deprivation therapy in prostate cancer patients.

Objective: Ulcerative colitis (UC) treatment currently faces multiple challenges including adverse effects, prolonged therapy durations, and high costs. Baicalin (BA) has demonstrated anti-inflammatory benefits for inflammatory bowel disease, and the objective of this scholarly work is to address the challenges associated with the poor aqueous solubility and diminished oral bioavailability of the compound in question, thereby offering an innovative therapeutic approach for the management of ulcerative colitis.

Methods: We developed a baicalin-arginine complex (BA-Arg) by screening for suitable basic compounds and utilizing a freeze-drying method, resulting in an amorphous solid dispersion of BA.

Results: Our findings revealed that BA·Arg significantly enhances the intestinal absorption and transmembrane transport of BA without inducing toxicity in Caco-2 cells. Pharmacokinetic studies in healthy Wistar rats demonstrated significantly higher plasma concentrations of BA compared to free BA. In a mouse model induced by 3.5% dextran sodium sulfate, BA·Arg treatment markedly alleviated colitis symptoms as evidenced by reduced inflammatory cell infiltration, decreased lymphocyte aggregation in the colon, and better preservation of intestinal mucosa. This improved the overall anti-colitis efficacy of BA.

Conclusions: Overall, our study presents a simple, eco-friendly formulation process that enhances BA solubility without the need for organic solvents, offering a practical and sustainable solution for developing BA-based therapies for UC.

Objective: Microfluidics has emerged as a promising technique to prepare nanoparticles. However, the current microfluidic devices are mainly chip-based and are often integrated into expensive systems that lack on-the-spot versatility. The aim of this study was to set up a modular microfluidic system based on low-cost capillaries and reusable, easy-to-clean building blocks that can prepare poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles with and without incorporated water-soluble biomacromolecules.

Methods: A two-syringe system variant of the microfluidic system was set up to prepare PLGA particles and to investigate how the flow rates, solvents, and PLGA concentrations impacted the PLGA nanoparticle formation. A three-syringe system was designed to examine the incorporation of proteins into the PLGA particles.

Results: The formation of the nanoparticles was affected by the PLGA concentration in the organic solvent, where an increasing concentration led to larger particle diameters (33-180 nm), and by the total flow rate, where an increase in the total flow rate led to smaller nanoparticles (197-77 nm). Using ultrapure water as the aqueous solvent resulted in precipitation at the outlet at higher PLGA concentrations. Aqueous poly(vinyl alcohol) created neutral particles in contrast to the negatively charged particles obtained with ultrapure water or an ethanol-water mixture. Incorporation of the proteins ovalbumin or lysozyme with a three-syringe system resulted in encapsulation efficiencies above 40%.

Conclusion: A cheap and easily adjustable modular microfluidic system was developed to prepare PLGA nanoparticles with highly reproducible particle diameters that can effectively be loaded with proteins for drug and vaccine delivery.