AAPS Journal最新文献

Randomized, well-controlled trials are the gold-standard for evaluating novel therapies, but they often fail to fully account for the placebo response, particularly in pain-related conditions such as migraine, where subjective improvement is common in placebo groups. This placebo response is influenced by individual-level factors, such as prior treatment experience, expectations of treatment benefit, and demographic characteristics such as age, race, sex, and clinical trial location. As clinical trials grow increasingly global and diverse, ensuring balanced demographic distribution across treatment arms is essential to accurately assess efficacy. We conducted a meta-analysis of placebo arm data from 14 pivotal Phase 2 and 3 trials that supported approval of six drugs for the preventive treatment of episodic and chronic migraine. Data were stratified by migraine type and analyzed by demographic variables including age, sex, race, menopause status, trial region, prior prophylactic medication use, route of administration, and frequency. Changes from baseline were evaluated for monthly migraine days (primary endpoint) and migraine-related symptoms (e.g., headache days, headache days of moderate/severe intensity, photophobia/phonophobia days, and nausea/vomiting days). Placebo responses were consistently higher in subjects who had not used prior preventive medications, African Americans, and participants enrolled in North America. These findings suggest that placebo response in migraine trials may be modulated by demographic and treatment-related factors, especially when subjective endpoints are used. Accounting for these variables during trial design and subject allocation could help minimize bias, reduce the risk of false negative outcomes, and enhance the likelihood of accurately demonstrating treatment efficacy.

Orodispersible films (ODFs) are increasingly employed for individualized drug delivery due to their ease of administration and precise dosing. However, their drug loading capacity is often limited by the need to maintain thin, flexible structures, posing a particular challenge for incorporating poorly soluble drugs. This study aimed to develop and characterize porous ODF matrices optimized for 3D printing of medicated inks. The primary objective was to investigate the impact of macroporosity on the dissolution kinetics of both poorly soluble and readily soluble drugs, with a focus on enhancing the release of the poorly soluble dexamethasone. Porous ODFs were fabricated via solvent casting using silica- and silicate-based porogens, then loaded with caffeine or dexamethasone through 3D printing. The films were comprehensively characterized using structural (micro-CT, BET), mechanical, and solid-state techniques (SEM, Raman microscopy, FTIR, XRD) to assess porosity, drug crystallization behavior, and drug-matrix compatibility. Drug release was evaluated through dissolution studies. Silica-based porogens yielded films with tunable macroporosity, supporting high drug loads (up to 3-5 times the ink volume). Dexamethasone printed on the SY2 substrate exhibited markedly enhanced dissolution (79.2 ± 1.8%) compared to its powdered form (29.9 ± 11.5%), achieving 61.5% release within 20 min. In contrast, caffeine (readily soluble) showed a transient reduction in dissolution rate during the initial two minutes, attributed to increased particle size and delayed film disintegration. Overall, integrating porous matrix design with 3D printing significantly improved the dissolution of poorly soluble dexamethasone without inducing drug-matrix interactions, confirming that structural modifications drive the enhanced release.

Typically, parent drug is measured for bioequivalence (BE) assessment because it's more sensitive to detect formulation differences, compared to its metabolite(s). For simvastatin immediate release (IR) tablets, current product-specific guidance (PSG) recommends measuring both parent and metabolite but taking metabolite as supportive data. This study aims to utilize physiologically based pharmacokinetic (PBPK) modeling and virtual BE (VBE) simulation to evaluate the sensitivity of parent vs metabolite as analyte on BE assessment, using simvastatin case and explore relevant mechanism. PBPK model was developed to describe drug exposures of parent drug simvastatin (SV) and metabolite simvastatin acid (SVA) in healthy individuals administered with 20-80 mg IR tablets under fasting condition. VBE simulations were conducted to evaluate the sensitivity of SV and SVA as analytes to assess BE between test product and reference listed drug. PBPK model incorporating enzyme- and transporter-mediated kinetics reasonably captures fasting PK profiles for SV and SVA. VBE simulations indicate that parent drug, in general, is more sensitive to demonstrate BE as compared to metabolite. However, this study highlighted the importance of conducting BE analysis using PK data for both SV and SVA when the test product contains certain excipients in the formulation that may impact transporter activity for changing clearance and subsequent drug exposure of metabolite. The VBE simulation results further implied that in some cases, SVA as analyte is more sensitive to show drug exposure differences and may enhance the assessment of formulation effect, as compared to SV. This aligns with current PSG recommendations.

Drug development can take up to 15 years, costing as much as $11 billion USD, and relies heavily on high-quality data. The goal of this investigation of contract research organizations (CROs) was to assess the impact of data management activities (such as curation, quality assessment and integration) on model-informed drug development (MIDD) deliverables. A survey was sent to a diverse sample of CROs, to evaluate their baseline experience with assessing the data quality of sponsor-provided data and the time required to create analysis-ready datasets. It was distributed to 44 colleagues from 32 companies offering pharmacometrics services, including data management. The survey included 11 questions; 9 were multiple choice and 2 open-ended. Responses were gathered anonymously to ensure confidentiality and intellectual property protection and later shared with all participants. Of the 17 survey respondents, most develop data specifications and create analysis-ready datasets. The majority (65%) said the data they received from sponsors was rarely (< 10%) immediately usable due to improper formatting and quality issues like missing data and inconsistencies. Over 50% cited lack of definition/specifications as the primary reason. Assuming an average programming cost of $250/hour, cleaning client data takes CROs 3 to 24 h, costing between $750 and $6000 per dataset. Significant time is spent on rectifying poor-quality data. Automated data quality assessments can improve efficiency checks, though automation alone cannot resolve all quality issues. Better communication, collaboration, and systematic approaches to address data quality issues involving automation and AI are essential to further improve data quality.

International reference standards have been established as gold standards for several biological products. Use of international units can drive consistency and standardization for biosimilar products across manufacturers. However, our research and development for Retacrit® (epoetin alfa-epbx), the first and only biosimilar FDA approved for all indications of Epogen®/Procrit® (epoetin alfa), showed that use of a compendial reference standard overestimated the potency of both the biosimilar and the originator product. Possible causes are or imprecision in the methods used to assign potency, or differences in the isoform composition between the compendial reference standard and product. Tracing the history of international reference standard potency assignment for erythropoietin provided insight into this issue. An internal product specific reference standard and process for qualifying and maintaining future replacements are recommended. Use of specific activity as compared to percent potency or units/mL was a useful tool and can provide a means to normalize data from multiple methods and samples with differences in labelled activity from various manufacturers.

The evaluation of monoclonal antibodies (mAbs) charge variants during their entire life cycle is crucial, as their profiles represent a critical quality attribute of biotherapeutics. While the current scenario still shows marked analytical non-uniformity in the evaluation of charge variants with imaged capillary isoelectric focusing (icIEF) with many "product-specific" methods, regulatory authorities are increasingly encouraging the utilization of horizontal standards, such as Platform Analytical Procedures (PAPs). A practical Analytical Quality by Design (AQbD) workflow is provided, emphasizing the Design of Experiments as a Quality Risk Management tool to develop a PAP based on icIEF, able to accurately measure charge variants pI values. Infliximab was chosen as the leading molecule. The cause-effect matrix, combined with an asymmetric screening design, identified key parameters exerting a critical impact on the Analytical Procedure Attributes. PAP quality measurements were ensured by a 10% risk acceptance level, employing response surface methodology and Monte Carlo simulation. The developed PAP was validated using three independent System Suitability samples and exhibited a low bias in the pI measurement (less than 2%), while maintaining satisfactory separation performance. Good intra-day and inter-day repeatability, combined with a robustness test and an exploratory application to real samples of three different therapeutic mAbs, confirmed its versatility. The study supports regulatory trends by demonstrating the successful application of AQbD in PAP development. This icIEF platform would ensure a systematically consistent analysis of charge variants, where pI is promoted to an objective tool, to be used as an additional reliable parameter in the Quality Control context.

Metronidazole (MTZ) is frequently used in both human and veterinary medicine to treat infections caused by certain protozoa and anaerobic bacteria. This study investigates the pharmacokinetic (PK) profiles of MTZ for available species in the literature by utilizing a linear, allometric, minimal physiologically-based PK (mPBPK) model. High quality PK data for intravenous (IV, n = 13) and oral (PO, n = 10) single doses were collected. Reported clearances (CL) and volumes of distribution (V_SS) were highly correlated (R² = 0.957, 0.969) with body weights (BW) with allometric power coefficients of 0.97 and 0.87. A mPBPK model with one perfusion-limited tissue compartment was used to evaluate MTZ PK using anatomical and physiological parameters for each species. The mPBPK model adequately captured the IV PK profiles when using species-specific CL values and a generalized tissue:plasma partition coefficient (K_p = 0.792 (CV 2.76%)) except for sheep and goats that had very low K_p values. The IV and PO profiles were best fitted jointly with shared physiological parameters and species-specific clearances, K_p values (range 0.55 to 1.44, mean 0.86), and bioavailability (F 0.32 - 1.00, mean 0.73). Overall, successfully integrating allometric scaling into a mPBPK model for diverse species revealed very consistent disposition of MTZ with generally BW-proportional CL values, reasonably conserved K_p values, and a moderate range of absorption rates and high bioavailability.

Immunoglobulin G (IgG) antibodies rely on neonatal Fc receptor (FcRn)-mediated recycling and transcytosis for prolonged serum half-life and tissue distribution. However, high antigen loads during infection may alter FcRn-mediated trafficking, impacting therapeutic efficacy. This study investigates how cognate antigen binding influences FcRn-mediated transport of two SARS-CoV-2-specific (SCoV-2) monoclonal antibodies: Sotrovimab, an Fc-engineered antibody with enhanced FcRn affinity, and B38, a non-engineered comparator. We evaluated antibody binding using ELISA and bio-layer interferometry (BLI) and assessed FcRn-mediated transport through transcytosis and recycling assays in MDCK cells expressing human FcRn. Experiments were conducted with and without SCoV-2 wild-type (WT) spike protein (SP) at a 1:1 molar ratio. Sotrovimab demonstrated superior binding affinity to both SCoV-2 WT SP and FcRn, exhibiting greater baseline transcytosis and recycling efficiency. However, antigen presence significantly reduced transcytosis for both antibodies, with Sotrovimab showing a more pronounced decrease (46.7% vs. 23% for B38). Recycling responses also diverged: Sotrovimab showed a modest, non-significant decrease while recycling of B38 significantly increased. Kinetic analysis revealed antigen binding altered FcRn interactions differently. The higher binding affinity of Sotrovimab was due to reduced dissociation at neutral pH, potentially trapping complexes intracellularly. B38 showed faster association at pH 6.0 without compromised dissociation. These data suggest that cognate antigen binding and interaction of immune complexes (ICs) with FcRn, play a major role in influencing transcytosis and recycling of mAb. These findings emphasize the complex interplay between antigen binding and FcRn function, with implications for antibody dosing strategies during infection to optimize tissue distribution and efficacy.

GT-14, identified as [(E)-4-((1-(1-methyl-1H-indol-2-yl) ethylidene)amino)phenol], is a novel inhibitor targeting the Gα_i2 protein, which is crucial in facilitating cell migration and invasion in prostate, ovarian, and breast cancer cells. therefore a valuable target for treating metastatic castration-resistant prostate cancer (mCRPC). In this study, GT-14's physicochemical properties, permeability, metabolic behavior, and tissue distribution were assessed. The results showed that GT-14 exhibited very slight aqueous solubility at room temperature (0.11 mg/mL) but was soluble in solvents including dimethyl sulfoxide and dimethyl acetamide, and sparingly or slightly soluble in several cosolvents. GT-14 exhibited a distinct pH-dependent solubility profile, being stable across a broad pH range (1.2-7.4) but degrading in strongly basic conditions. It exhibited high permeability (1.3 x 10^-5 cm/s) in Caco-2 cell culture models and therefore identified as a BCS II compound. Hepatic microsomal studies revealed that GT-14 underwent Phase I metabolism, with more than 90% remaining in 60 min incubation in rat liver microsomes. A stable co-solvent formulation was developed to enable intravenous administration for pharmacokinetic studies. Previous pharmacokinetic studies showed that GT-14 exhibited biphasic disposition with a terminal plasma elimination half-life of 268.07 minutes (> 4 hours). Tissue distribution analysis indicated the highest concentration of GT-14 in the prostate, followed by the kidneys, lungs, heart, and liver. Our study presents an early-stage preclinical drug development roadmap that integrates modern technologies for efficiency and success, using GT-14 as a model compound. It showed promising characteristics, reinforcing its potential as a new therapeutic agent for mCRPC.

Model-informed drug development (MIDD) plays an important role in pharmacometrics by leveraging mathematical models to optimize drug dosing strategies. Traditional methods such as nonlinear mixed effects modeling (NONMEM) have long been the gold standard in population pharmacokinetic (PPK) modeling. However, the development of artificial intelligence (AI) presents a potential improvement in predictive performance and computational efficiency. This study evaluates the effectiveness of AI-based MIDD methods for PPK analysis by comparing them against traditional nonlinear mixed-effects (NLME)-based methods (e.g., NONMEM). We tested five machine learning (ML) models, three deep learning (DL) models, and a neural ordinary differential equations (ODE) model on both simulated and real clinical datasets under different scenarios, assessing predictive performance with metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Simulated datasets with known ground truth were created using a two-compartment model, while the real clinical dataset included data from 1,770 patients pooled from multiple clinical trials. Results indicate that AI/ML models often outperform NONMEM, with variations in performance depending on model type and data characteristics. Neural ODE models showed good performance, providing strong performance and explainability with large datasets. These findings underscore the potential of AI/ML methodologies to complement or enhance traditional PPK modeling approaches in MIDD, highlighting their applicability in future pharmacometrics workflows.