AAPS PharmSciTech最新文献

We present a calibrated model to scale-up a roller compaction process using minimal experimental data at small scales. The compression properties of three API-laden blends are evaluated for various drug loadings using a laboratory-scale roller compaction simulator (Int J Pharm 269:403–15, 2004). Using the generated blend compression properties, a calibrated model combining the Reynolds roller compaction model (Comput Chem Eng 34:1049–57 2010) and ribbon solid fraction measurements is developed to predict the ribbon solid fraction as a function of the roller compaction conditions (roll force and roll gap). The predictions of this model are compared with measurements from ribbons generated on the Gerteis minipactor at various scales using appropriate methods. The predicted solid fractions achieve good agreement with measured solid fractions for the three drug products. Further, the calibrated model is extended by developing a parametrized regression model where several API properties, formulation properties, and ribbon solid fraction predictions from the Reynolds model are taken as features to generate ribbon solid fractions for partially characterized active blends. The predictions of the hybrid model agree reasonably with measurements on ribbons produced on a Gerteis 3-W-Polygran. This hybrid model can then be deployed to run digital Design of Experiments (DOEs), which is then used to define operating ranges for the roller compaction of two drug products. This method enables the application of digital tools to aid the scale-up of fast-moving drug product processes using minimal experimental data.

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Vaginal disorders in reproductive‑age women, including bacterial vaginosis (affecting 23–29%), endometriosis, etc., are increasing alarmingly day by day worldwide. Vaginal drug delivery (VDD) has garnered notable attention in the pharmaceutical field for the localized and systemic treatment of several hormonal and gynaecological conditions. Conventional treatments remain inadequate due to high relapse rates, local and systemic side effects, compliance, invasiveness, drug resistance, adherence, safety, etc. Additive manufacturing, particularly 3D Printing (3DP), is reshaping pharmaceutical sciences by enabling the fabrication of personalized and novel drug delivery solutions. In addition, 3D Printed VDD systems (VDDS) offer highly customized and patient-specific dosage forms that possess controlled release kinetics and precise control over drug deposition, ultimately leading to increased patient adherence. This review comprehensively examines the state-of-the-art in 3DP technologies and critical considerations such as material selection, impact on drug release mechanisms, mechanical properties, and biocompatibility of the formulations, current regulatory landscape, and future perspectives of 3DP in VDD. This eventually paves the way for better patient-centered therapeutic solutions.

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Breast cancer is a prevalent malignancy that can metastasize to distant organs if left untreated, leading to significant morbidity. While chemotherapy is commonly used, it has drawbacks like low tissue availability, short circulation time, and toxicity to healthy cells. The objective of this study was to develop and characterize three different nanovesicles loaded with doxorubicin hydrochloride and paclitaxel, hypothesizing that this combination would enhance tumor targeting, reduce dosing and toxicity, and improve patient compliance. A validated UPLC method was developed for simultaneous detection and quantitation of both drugs, showing linearity over 3.13–50 µg/mL. Retention times were 1.53 and 4.06 min for doxorubicin hydrochloride and paclitaxel, respectively. Using thin-film hydration technique, blank and drug-loaded liposomes, transfersomes, and niosomes were formulated. Nanovesicles were characterized for size (150–250 nm), polydispersity index (0.14–0.20), and zeta potential (-0.56 to + 0.54 mV). Drug loading was 2.0–2.5% for doxorubicin hydrochloride and 7.0–7.5% for paclitaxel, with high encapsulation efficiency. Drug release studies showed sustained release for up to 72 h. Nanovesicles remained stable for at least seven days at room temperature and 4⁰C. Cytotoxicity and apoptosis studies using MTT assay and flow cytometry on MDA-MB 231 breast cancer cells revealed that the drug-loaded nanovesicles exhibited potent cytotoxicity and induced apoptotic cell death more effectively than individual drug solutions. In conclusion, this study successfully developed three nanovesicular systems co-loaded with doxorubicin hydrochloride and paclitaxel, demonstrating their potential as effective drug delivery systems for breast cancer treatment.

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Created in BioRender. Dang, L. (2025) https://BioRender.com/h14t77

Mini-tablets (MTs) have gained increasing importance as age-appropriate dosage forms, particularly for pediatric patients, due to their ease of swallowing and flexible dosing capabilities. Despite their growing use, regulatory guidance and standardized quality control (QC) methods specifically adapted to MTs, especially extended-release (ER) formulations, are still lacking. This study investigates the suitability of two miniaturized dissolution apparatuses for evaluating ER MTs: a Mini-Paddle system, which is a geometrically downscaled version of the compendial Paddle (USP 2) apparatus, and a small-volume Reciprocating-Holder (USP 7) apparatus. Two commercially available pediatric ER MT drug products containing melatonin and sodium valproate were tested individually and in deliberately manipulated batches to assess the methods’ sensitivity and discriminatory power. The Mini-Paddle apparatus reliably quantified drug release from melatonin MTs but encountered limitations with low-dose sodium valproate MTs, requiring the pooling of multiple units, which masked individual variability. Conversely, the small-volume Reciprocating-Holder enabled sensitive, single-unit testing for both products, effectively detecting batch-to-batch differences despite very low drug loads. Neither apparatus currently holds official compendial status, emphasizing the urgent need for regulatory recognition and standardized methodologies. This study highlights the critical need for sensitive, miniaturized dissolution methods tailored to the unique challenges of MTs to ensure consistent quality, safety, and efficacy, particularly in vulnerable populations. Future work should focus on harmonizing and validating miniaturized dissolution systems to support QC and regulatory acceptance of MT formulations.

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Since Pfizer developed the mRNA vaccine for COVID-19 by leveraging artificial intelligence (AI) for designing the vaccine, integrating AI and allied domains in the drug development process has escalated at an unimaginable rate. Owing to the complex and time-consuming process of drug development, many firms, including big pharma and medium-scale industries, are constantly looking for ways to reduce the time for providing lifesaving medications to patients in need without compromising the safety and efficacy of the product. Formulation of novel drug products in a pharmaceutical R&D and scaling up the process to a large-scale production involves a huge investment and an eye for detail in the intricacies of the processes. Intervention of AI and machine learning (ML) can solve many problems in this aspect. With the rise of Industry 4.0, the relative shift of industry towards process automation, accelerated development has become vital in all domains. The investments in R&D by the large pharmaceutical companies reached up to $190 bn in 2024, according to a report by IQVIA. There is a noted upsurge in investments in the domains interlinking AI and ML with pharmaceutical research. Pharmaceutical formulation development can excel in the early stages, and the productivity can witness a steady growth if AI and ML tools are utilized. Most of the research in this domain remains in the budding stages, and its adoption in the industry needs further refinement by delineating structured guidance from the experts and regulatory agencies. The current review speaks about the current studies reported in the arena of formulation development and also sheds light on some of the areas where the pharmaceutical product development on a larger scale can benefit from AI and ML.

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Fungal infections pose a significant global health challenge, especially in immunocompromised individuals. Luliconazole (LZL), a newly approved drug for topical fungal infections, faces limitations due to poor skin penetration, short skin retention time, and repeated administration. To address this, lipid nanocarrier-based ethosomal gel formulations at 1% w/w strength were developed and extensively characterized through in vitro, ex vivo, and in vivo studies, comparing them with conventional formulations. The optimized ethosomal formulations exhibited a vesicle size of 209.2 ± 8.52 nm, encapsulation efficiency of 81.51 ± 3.62%, and PDI of 0.198 ± 0.001. These ethosomes were incorporated into a gel with adhesiveness of 1.012 mJ, hardness of 0.17 N, and spreadability ranging from 7.9 to 0.52 g·cm/sec. In vitro release studies showed 84.39 ± 1.5% release for the optimized ETs-gel formulation compared to 45.58 ± 0.9% for the LZL-conventional gel. In-vitro activity against Candida albicans demonstrated superior efficacy of the prepared ethosomal formulations over the conventional gel. Ex vivo studies on porcine ear skin indicated enhanced penetration and skin retention time for the optimized ETs-gel compared to the conventional gel. In-vivo antifungal activity on albino rats confirmed the safety, non-irritant nature, and efficacy of the optimized ETs-gel in topical fungal treatment, with no systemic drug circulation observed. Histopathology studies further supported the efficacy of the optimized ETs-gel formulation. Overall, squalene-based ethosomes emerge as promising carriers for enhancing the topical delivery and localized effectiveness of Luliconazole.

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The Active Pharmaceutical Ingredients (API) are highly sensitive molecules and generally have low water solubility and must have their biological performance preserved and enhanced by any process. Hot-melt Extrusion (HME) is one of the preferred methods of preparing Amorphous Solid Dispersions (ASD) for poorly soluble APIs. For HME, the Temperature Profile of Extrusion (TPE) is fundamental to predicting the processability of the mixture among API, polymer, and other ingredients. Dynamic rheological studies can determine the initial TPE from the Soft Point of Mixture (SPM) and the Initial Fusion Plato Temperature (IFPT) by the method of Talmadge-Fitch. Using this approach, the work sought the best extrusion conditions to make blends of Artesunate (AS), Soluplus^®, and Span^®20 compositions using rheological, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffraction (XRD), and scanning electron microscopy (SEM) analysis. Rheology studies associated with conventional ASD formation analysis is a powerful tool for fast and high performance in the extruder. This work shows that using rheology to find the softness point of the mixture is a good tool to set up a fine temperature extrusion profile.

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Glioblastoma (GBM) is the most aggressive and lethal primary brain tumor in adults, characterized by rapid progression, high invasiveness, and resistance to conventional therapies. Despite decades of research, effective treatment remains elusive due to the complex molecular landscape of GBM and the formidable challenge posed by the blood–brain barrier (BBB), which significantly restricts the delivery of therapeutic agents to the central nervous system (CNS). This article provides a comprehensive overview of the current understanding of GBM, focusing on the key oncogenic pathways involved in its pathogenesis, including the PI3K/AKT/mTOR, PD-L1 Evasion and signaling cascades. It also highlights the limitations of traditional drug delivery methods in crossing the BBB and explores the potential of advanced nanocarrier systems such as liposomes, polymeric nanoparticles, dendrimers, and exosomes—in overcoming these barriers to enhance drug bioavailability and therapeutic efficacy. We further examine recent advancements in preclinical and clinical trials involving nanomedicine-based GBM treatments, along with notable patent filings that reflect the growing innovation in this field. Finally, we discuss future directions in personalized medicine, combination therapies, and emerging technologies aimed at improving treatment outcomes for GBM patients. Through a multidisciplinary lens, this article underscores the urgent need for novel strategies to address the unmet clinical challenges in glioblastoma therapy.

Liposomal formulation and intratracheal delivery are promising strategies to enhance the therapeutic efficacy of antimicrobial agents. Tosufloxacin (TFLX), a potent fluoroquinolone, faces challenges due to its low aqueous solubility and poor encapsulation efficiency (EE) in liposomes via conventional passive loading. This study aimed to improve TFLX solubility and liposomal loading using hydroxypropyl-β-cyclodextrin (HP-β-CD). A TFLX/HP-β-CD inclusion complex was prepared by optimizing the molar ratio through phase solubility analysis. The complex was incorporated into the aqueous phase of liposomes using the thin-film hydration method. Physicochemical properties, EE, in vitro drug release, and intracellular antibacterial efficacy against Staphylococcus aureus were evaluated. To confirm complex formation, Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analyses were conducted. Complexation with HP-β-CD enhanced TFLX solubility approximately 25-fold. The resulting TFLX/HP-β-CD-liposomes showed a significantly higher EE (69.7 ± 2.4%) compared to conventional liposomes (17.9 ± 0.8%). Drug release from the formulation was sustained for up to 48 h. Intracellular antibacterial assays demonstrated superior efficacy of TFLX/HP-β-CD-liposomes over TFLX-liposomes. FTIR and XRD analyses supported the formation of the inclusion complex through characteristic spectral shifts and reduced crystallinity. The combination of HP-β-CD complexation and liposomal encapsulation markedly improved TFLX solubility, loading efficiency, and intracellular antimicrobial activity. This dual-delivery strategy offers a promising platform for enhancing the formulation of poorly water-soluble antimicrobial agents.

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The present work is the extended findings of our previous study on valproic acid (VA)-loaded cationic nanoemulsions for HSPiP and Neuro-fuzzy model (ANFIS)-enabled optimized composition to attain targeted permeation flux, the influence of permeation enhancers, and characterizations of the optimized product. The binding efficiency of the formulations with nasal mucus was assessed. The pH dependent in-vitro drug release (dialysis membrane) study, ex-vivo permeation assessment using a goat nasal mucosa, and benchtop physically stable at room temperature, were investigated. The fluorescence microscopy (FM) study compared the penetration potential across the nasal mucosa. The cationic nanoemulsions exhibited low particle size range (29–373 nm), narrow distribution (0.12–0.37), and high zeta potential (22–35.7 mV). HSPiP-generated permeation flux (Y₁) and diffusion coefficients (Y₂) values across nasal mucosa. The trained ANFIS model considered X₁ (oil as SO), X₂ (S_mix), and X₃ (size) for achieving maximal Y₁ (681 µg/cm²/h) and Y₂ (2.74 cm²/s) with high correlation (r² ˃ 0.99) to actual values. The optimized V-PG exhibited desired formulation characteristics and pH-dependent (pH 6.8 ˃ pH 7.4) sustained drug release. V-PG (optimized gel containing polyethylenimine as permeation enhancer, PEI) revealed the highest binding capacity, prolonged mucoadhesion, maximum permeation flux (J_ss ~ 126 µg/cm²/h), enhancement ratio (~ 70), and the desired percent drug deposition (%DD) (22.9 ± 0.7%) than DS (VA suspension). The study revealed maximized cationic V-PG-mediated penetration across nasal mucosal membrane as compared to the suspension, emphasizing its potentiated nose-to-brain delivery to control convulsion in children and aged patients with high patient compliance.

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