AAPS Journal最新文献

Ethinylestradiol (EE) is a common estrogen used in combined oral contraceptives. CYP3A4 and SULT1E1 are the major enzymes that metabolize EE, while CYP2C9 and UGT1A1 have minor contributions. Drug-drug interactions (DDIs) mediated by inhibition or induction of metabolism can adversely impact the safety and efficacy of EE. A physiologically-based pharmacokinetic (PBPK) model was previously developed and extensively verified to predict CYP3A4-mediated DDIs of EE. Recent clinical evidence showed increased EE exposure following coadministration with the SULT1E1 inhibitors etoricoxib and ziritaxestat, highlighting the need to expand the PBPK model to allow for predictions of SULT1E1-mediated DDIs. A PBPK model including SULT metabolism of EE was constructed and the interactions with PBPK models developed for etoricoxib and ziritaxestat were simulated. The observed EE concentrations were within the simulated 95% percentiles for the control and the DDI scenario. The predicted ratios for peak concentration (C_max) and area under concentration-time curve (AUCt) were within 1.5-fold of the observed data. The simulations demonstrated that clinically relevant DDIs may not be expected when EE is co-administered with 120 mg etoricoxib QD but may be expected with 600 mg QD ziritaxestat QD. A simulated dose reduction from 35 µg to 20 µg, when co-administered with ziritaxestat, was predicted to produce EE exposures in a similar range to when 35 µg is administered alone. The developed PBPK models for etoricoxib and ziritaxestat can be used in future applications as probe SULT1E1 precipitants. Incorporation of SULT metabolism into the EE PBPK model may support a more comprehensive assessment of the DDI liability of investigational drugs that affect multiple EE metabolic pathways.

The conventional strategy of prescribing the same dosage to all patients can result in suboptimal efficacy and safety. This is particularly true when considering drug-gene interactions (DGIs), drug-drug interactions (DDIs), or in individuals with compromised organ function. Precision medicine, which aims to tailor drug regimens based on individual patient characteristics, offers a promising alternative by focusing on drug disposition, efficacy, and safety. However, clinical trials face ethical and practical challenges and cannot cover all real-world patient scenarios. Thus, physiological based pharmacokinetic (PBPK) modeling offers a unique framework for enhancing model-informed drug development (MIDD) and precision dosing (MIPD). Despite this, most PBPK applications primarily assess drug pharmacokinetics without evaluating efficacy or safety outcomes. This limits the full potential of mechanistic models. In this study we used integrated PBPK, Quantitative Systems Pharmacology (QSP), and toxicology models to predict risks in scenarios like DGIs, DDIs, and varied renal impairment by simultaneously assessing drug PK, pharmacological effect, and toxicity. The findings underscore the importance of considering pharmacological effects and myotoxicity risks, which differed from changes seen in plasma exposure. This study demonstrates the value of PBPK-QSP models in guiding dose adjustments to optimize the efficacy and safety balance in target patient populations, showcasing their strength in MIDD and MIPD strategies.

Methylphenidate extended-release (ER) products are used in the treatment of attention-deficit/hyperactivity disorder (ADHD). These products exhibit diverse pharmacokinetic (PK) profiles that influence early systemic exposure and clinical response. To ensure bioequivalence (BE) for products such as Concerta and Ritalin LA, both the FDA and EMA recommend the use of pAUC_0-3h, with the EMA additionally suggesting the inclusion of C_max,0-3h. However, it remains unclear whether C_max,0-3h offers added discriminatory value beyond pAUC_0-3h in BE evaluation. The objective was to assess the sensitivity of early exposure metrics (pAUC_0-3h, C_max,0-3h, and C_3h) to changes in the SKAMP (Swanson, Kotkin, Agler, M-Flynn, and Pelham rating scale) response, a pharmacodynamic (PD) endpoint commonly used to assess clinical response in ADHD. PK/PD model simulations were performed for seven methylphenidate ER products. These products were categorized by PK absorption model: parallel dual first-order (e.g., Concerta) and parallel zero-order and first-order (e.g., Metadate CD). The PD model described placebo-adjusted SKAMP response based on simulated plasma concentrations. Simulations were conducted by varying the fast absorption rate constant (ka_Fast) to assess how changes in early exposure metrics affect AUEC_0-3h of the SKAMP response. The results indicated that pAUC_0-3h generally showed greater sensitivity to SKAMP response than C_max,0-3h and C_3h. Although C_max,0-3h exhibited high sensitivity in products with dual first-order absorption, its responsiveness remained inferior to pAUC_0-3h. These findings support the use of pAUC_0-3h as the primary early exposure metric for therapeutic equivalence assessment and suggest that C_max,0-3h may not provide further discriminatory value beyond that of pAUC_0-3h.

Drug permeability across epithelial barriers is critical for predicting oral bioavailability and efficacy. Conventional models, including animals and simple cell cultures, lack intestinal complexity and translational value. Organoids and organ-on-a-chip technologies address these limitations, offering physiologically relevant, human-specific platforms for more accurate drug permeability assessment [1]. Intestinal organoids, derived from stem cells, reproduce key structural and functional features of the gut, including crypt-villus organization, transporter and enzyme expression, and absorptive and secretory functions. Organoids are grown inside of microfluidic chips under dynamic media flow that attempt to recapitulate one or more tissue-specific functions, thereby supporting epithelial maturation and enabling more predictive drug transport measurements. Permeability can be studied by introducing compounds at the apical surface and quantifying their translocation to the basolateral compartment using analytical techniques. Compared with animal models, these approaches yield data of greater human relevance while reducing ethical concerns [2]. Furthermore, they enable the evaluation of passive permeability, active transport, intestinal metabolism, and interactions with microbial products [3]. In this study, permeability was investigated for three model compounds with distinct properties: lisinopril (low permeability), metoprolol (moderate permeability), and fluconazole (high permeability). Experiments were conducted using the Emulate human duodenum-on-a-chip platform and benchmarked against the Ralph Russ Canine Kidney (RRCK) cell line. Apparent permeability (P_app) values were correlated with literature-reported effective permeability (P_eff) using linear regression. This regression was implemented in Simcyp® Simulator and GastroPlus™ to predict systemic exposure, which was compared against observed plasma concentration-time profiles.

The liquid and solid formulations of self-nano-emulsifying drug delivery systems (SNEDDS) have garnered significant attention in the pharmaceutical field for their ability to enhance the solubility and absorption of hydrophobic drugs. While both liquid and solid SNEDDS result in improved bioavailability; portability, patient compliance, desired administration route, and ease of preparation are some factors that contribute to the decision-making of the final SNEDDS dosage form. This review provides a comprehensive analysis of SNEDDS formulations in the liquid and solid state, including production and performance factors that researchers ought to consider when developing their final dosage form. We investigate excipient characteristics, stability concerns, liquid-to-solid preparation methods and their challenges, and in vivo and in vitro comparisons of both dosage forms. Finally, we explore the potential of artificial intelligence in the design of SNEDDS formulations.

In drug development, exposure-response models are widely used to inform decisions in dose optimization processes. Type I error (T1) due to misspecified models can lead to critical and costly decisions. Therefore, a new approach called: "Randomized-exposure mixture-model analysis with type 1 error control (REMIX)" to account for model misspecification is proposed and compared against the standard approach (STA). A total of 82 simulation-estimation scenarios for a hypothetical antidiabetic drug were investigated. T1 rate and power was tested in presence or absence of model misspecification. Moreover, predictive performance of both approaches and accurary of the drug-effec parameter estimates was assessed. Precision and accuracy for the drug-effect parameter were compared to the STA parameter estimates for each structural model with the most patients. REMIX outperformed STA regarding T1 rate inflation (21/82, 44/82 for REMIX and STA, respectively) but led to lower power. In a case study with an example of a clear drug effect and no model misspecification, 27 patients were needed for REMIX compared to 17 for STA to reach 80% power. rRMSE and rBias for full REMIX and STA models were similar in most scenarios if a drug effect was present, but REMIX outperformed STA when no real drug effect was present. Precision and accuracy of parameter estimates were similar for REMIX and STA. Application of REMIX in further studies and comparison to other approaches to control T1 are warranted.

Melanoma is a highly aggressive skin cancer that accounts for only ~ 1% of all skin cancer cases but is responsible for most skin cancer-related deaths. Despite advances in systemic therapies, localized treatment options remain limited. Dacarbazine (DCB), the only FDA-approved chemotherapeutic agent for melanoma, is administered intravenously and is associated with systemic toxicity, poor patient compliance, and nonspecific drug distribution. This study presents a bilayer dissolving microneedle array patch (dMAP) for localized, minimally invasive delivery of DCB to the skin, offering a potential alternative for treating cutaneous melanoma. The tip-casting gel formulation was optimized to ensure sharp, defect-free MAP tips with uniform drug distribution. The optimized bilayer dMAP exhibited strong mechanical properties (< 10% needle deformation) and effective insertion capability, reaching approximately 390 µm in depth within the Parafilm® M model. Ex vivo evaluations using full-thickness neonatal porcine skin demonstrated the complete dissolution of bilayer dMAP tips within 60 min and effective pore formation, as confirmed by methylene blue staining. In ex vivo setup, the bilayer dMAP formulation demonstrated 3.93-fold increase in permeability and a 3.02-fold increase in DCB deposition compared with those of the suspension. Furthermore, bilayer dMAP maintained complete drug stability over 8 days at room temperature under light-protected conditions, whereas free DCB showed approximately 7.5% degradation in aqueous media over the same duration. Therefore, bilayer dMAP provides a stable, minimally invasive, and efficient platform for localized drug delivery to the skin, highlighting its potential as a promising alternative to conventional topical formulations for the treatment of cutaneous melanoma.

Although the use of demographic matching in organ impairment studies is recommended to exclude the confounding effect of demographics on the pharmacokinetic (PK) study results, very little guidance exists on the practical implementation of this approach. Individual matching (IM) as well as several strategies for group matching (GM) have been described in literature. We conducted a systematic review, including 170 renal (RI) and 173 hepatic impairment (HI) studies completed in the last 25 years and characterized used matching methodologies and selected matching criteria. In RI, GM appears more common (70%), while in HI IM was preferred (55%). Age, body weight or body mass index (BMI), and sex were the most commonly used matching criteria, with the most common acceptance margins of ± 10 years for age, ± 10-20% for body weight, and ± 15-20% for BMI, irrespective of matching method used. While IM required notably more subjects in the control group, we did not find a significant effect of various matching strategies on study duration or recruitment between different matching strategies. No matching was used in most of the studies conducted in the target patient population with organ impairment, as opposed to the studies conducted in general organ impairment population without the target indication. Based on the results of this review, we proposed a framework for selection of matching strategy, with IM being a recommended matching method in most cases and GM being suitable for compounds that have well-characterized impact of demographic characteristics on PK and a low between-subject variability.

The tissue response to long-acting injectables (LAIs) suspension injection may impact the product in vivo performance. One such response is the formation of an inflammatory cell layer (ICL) resulting in an envelope around the injected particles. This study aims to use a mechanistic model to describe the clinical in vivo exposure and performance of an intramuscular LAI suspension and evaluate impact of ICL physiological response at the injection site in humans. Aripiprazole lauroxil (AR-L) was used as the model drug. A baseline pharmacokinetics model was built and validated for aripiprazole. The impact of inflammation on the LAI in vivo performance was assessed by including an ICL model. The developed pharmacokinetic model adequately described the observed plasma profiles of AR following intravenous and oral administration in humans. The initial intramuscular predictions assumed that the absorption rate is dependent on the dissolution and partitioning of AR-L into the systemic circulation from the intramuscular (IM) depot. The simulation resulted in a shape mismatch between the simulated and observed data and an earlier predicted T_max. The inclusion of an ICL in the model resulted in adequate predictions (fold errors less than 25%) of the exposure and shape of the plasma concentration-time profiles. Utilizing a time-dependent change in ICL thickness resulted in reasonable predictions of AR pharmacokinetic profiles following IM administration of multiple strengths of the AR-L suspension. This shows the utility of physiologically based pharmacokinetic (PBPK) model in mechanistically describing the in vivo performance of LAIs.

Raman spectroscopy is a proven Process Analytical Technology (PAT) for monitoring mammalian cell culture processes in biopharmaceutical manufacturing. In-line Raman probes provide real-time chemical fingerprints of metabolites and cell culture health analyzed via chemometric modeling. However, Raman hardware (e.g. cables, detectors, optics, probes, and lasers) and software - performing calibration, noise reduction, and cosmic ray removal - impart vendor specific spectral signatures, rendering Raman chemometric models specific to the vendors. This vendor specificity complicates method validation and transfer between manufacturing sites deploying different vendor equipment and impedes upgrades, maintenance, and replacement of obsolete Raman equipment. In this work, we compared two calibration transfer methods to address vendor-to-vendor variation. Piecewise Direct Standardization (PDS) and Spectral Subspace Transformation (SST) methods successfully reduced spectral response variation between previous (Parent) and new (Child) Raman systems. We tested calibration transfer results with offline samples and an established validation approach utilizing paired spectra from Parent and Child Raman systems. Finally, we explored the influence of calibration transfer parameters including training set size, preprocessing position, and window size (number of components) for PDS and SST. Through this investigation we demonstrate the feasibility of this proposed vendor-to-vendor calibration transfer approach as a promising and effective chemometric model transfer between Raman vendors without significant method re-development or re-validation. This approach improves agility within the deployment strategy of chemometric models across supply chain networks and bolsters Raman spectroscopy as a versatile PAT tool for advanced manufacturing within the biopharmaceutical industry.