Search Results (322)

Background: Prophylaxis in children with hemophilia B (HB) lacks quantitative approaches that integrate both plasma exposure and tissue distribution. This study aimed to develop and validate a physiologically based pharmacokinetic (PBPK) model of factor IX (FIX) for pediatric HB. The model incorporated the binding of FIX to type IV collagen (Col4) to characterize its distribution in both plasma and extravascular tissues. Methods: A total of 20 children with severe HB were included, contributing 219 plasma samples. The base PBPK model was first established and verified using adult and plasma-derived FIX (pdFIX) data. It was subsequently extrapolated to children by integrating FIX-CTBB parameters and pediatric observations for model calibration. The validated model was used to characterize plasma pharmacokinetics, predict tissue distribution and target attainment, and simulate alternative prophylactic dosing regimens. Results: The model adequately described the plasma pharmacokinetics of FIX in children and predicted substantial extravascular distribution. Total extravascular exposure was approximately sixfold higher than plasma exposure. Marked heterogeneity in target attainment was identified across tissues. Lower target attainment was observed in the colon, pancreas, and brain, whereas delayed attainment occurred in bone and muscle. Simulations of prophylactic dosing regimens suggested that 75 IU/kg twice weekly may provide a favorable balance among sustained FIX exposure, tissue-level target attainment, and treatment burden. Conclusions: This PBPK model provides a mechanistic and quantitative framework for characterizing plasma and tissue exposure to FIX in children with HB and may support individualized optimization of FIX prophylactic dosing. Full article

Background: In patients with atrial fibrillation receiving long-term anticoagulation therapy with dabigatran etexilate (DABE), moxifloxacin (MFLX) is frequently coadministered to treat concurrent infections; however, the potential drug–drug interaction (DDI) between these agents remains unclear. Herein, we examined the underlying mechanism by which MFLX attenuates DABE pharmacokinetics in rats; subsequently, we elucidated the DDI in humans by establishing a physiologically based pharmacokinetic (PBPK) model based on these animal data. Methods: The 3- and 14-day effects of 40 mg/kg MFLX once daily and secondary bile acid (SBA)-containing dietary intervention on the pharmacokinetic profile of DABE and its active form, dabigatran (DAB), were examined in a rat model. Ileum tissues were harvested to measure the expression of P-glycoprotein (P-gp), pregnane X receptor (PXR), and peroxisome proliferator-activated receptor alpha (PPARα). In addition, we examined the effects of secondary bile acids (SBAs) on P-gp expression and quantified P-gp-mediated DABE efflux transport activity in Caco-2 cells. A PBPK model was used to predict the risk of DAB exposure under this DDI scenario and under combined high-risk conditions, including renal impairment and advanced age. Results: Treatment with MFLX for 3 and 14 days inhibited SBA-producing gut microbiota, thereby suppressing the conversion of primary bile acids to SBAs. Concurrently, a marked reduction in intestinal P-gp expression was observed, along with a significant enhancement of the oral bioavailability of DABE. These effects were reversed by SBA-containing diets. In vitro experiments using Caco-2 cells revealed that physiologically relevant concentrations of SBA significantly upregulated P-gp expression and function, whereas MFLX incubation alone showed no direct modulatory effect on these transporters or regulators. PBPK simulation results showed that in vivo exposure of DAB would increase by 41.7%, 104%, 251%, and 115% when coadministered with MFLX alone, with coexisting mild renal impairment, moderate renal impairment, and aging, respectively. Conclusions: MFLX increases DAB exposure by reducing SBA-regulated intestinal P-gp function. PBPK simulations suggest a low risk of DDI from MFLX coadministration alone; however, caution is warranted in patients with aging or renal impairment. Full article

Background/Objectives: Di(2-ethylhexyl) adipate (DEHA), a biocompatible ester plasticizer, has gained interest as a potential pharmaceutical excipient, yet its pharmacokinetics remain poorly characterized. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model for DEHA and its primary metabolite, mono(2-ethylhexyl) adipate (MEHA), in rats by integrating in vitro, in vivo, in silico, and physiological data. Methods: In vitro hydrolysis was evaluated across tissues using bis-(p-nitrophenyl) phosphate (BNPP) to distinguish BNPP-sensitive and -insensitive metabolism, and tissue-specific clearances were extrapolated to the whole body using a circulatory topology-based framework accounting for sequential extraction across tissues, venous blood, and lungs. Results: Both adipates underwent rapid BNPP-sensitive hydrolysis across multiple tissues, whereas DEHA additionally exhibited BNPP-insensitive metabolism. The framework yielded reasonable estimates of the observed arterial clearance in vivo. Following oral administration, DEHA showed a reproducible double-peak plasma profile, with lymphatic transport identified as the predominant absorption route responsible for the prolonged terminal phase. The final model adequately reproduced the plasma and mesenteric lymph concentration-time profiles of DEHA and MEHA following both intravenous and oral administration. Conclusions: By integrating multi-tissue metabolism, circulatory topology, and lymphatic absorption, this study provides a quantitative framework applicable to rapidly hydrolyzed, highly lipophilic ester compounds, including pharmaceutical excipients and ester prodrugs. Full article

Marine macroalgae represent abundant, renewable reservoirs of structurally unique bioactive compounds, such as sulfated polysaccharides, phlorotannins, and carotenoids, with immense potential for sustainable functional foods. However, their industrial exploitation is severely bottlenecked by complex, repeat-rich genomes, recalcitrant genetic transformation tools, and environmental cultivation variability. Synthesizing evidence from 180 high-quality studies spanning from 1961 to 2026, this review provides a comprehensive synthesis of how artificial intelligence (AI) and synthetic biology may contribute to overcoming these challenges. We highlight key advances across the bioengineering pipeline, including the application of metabolic engineering strategies for enhancing valuable compound production in engineered algal systems. For example, a CrtYB-based metabolic engineering approach achieved β-carotene accumulation of 22.8 mg/g in the microalga Chlamydomonas reinhardtii, providing important insights for future metabolic engineering of marine macroalgae. In addition, AI-assisted approaches show promising potential for enzyme discovery, metabolic pathway prediction, and multi-omics-guided optimization of bioactive compound production. We further discuss critical downstream challenges, including the low gastrointestinal absorption (~14%) and extensive metabolic transformation of seaweed-derived phenolic compounds, as well as the potential application of AI-integrated physiological modeling for improving bioavailability prediction and safety assessment. This review provides a pioneering, data-driven synthesis of how the convergence of AI and synthetic biology is overcoming these roadblocks. Moving beyond generic descriptions, we highlight key empirical milestones across the bioengineering pipeline, including multi-fold yield enhancements in target pigments (up to 22.8 mg/g) and the AI-driven discovery of novel polysaccharide-degrading enzymes. Furthermore, we confront critical downstream challenges, specifically addressing the characteristically low (~14%) gastrointestinal absorption bottleneck and extensive metabolic biotransformation of seaweed phenolics. We demonstrate that integrating digital twins with reinforcement learning-driven physiologically based pharmacokinetic (PB-PK) modeling can compress the R&D cycles of these seaweed functional ingredients by over 60%. Unlike previous reviews that treat these technologies as independent entities, this article proposes a macroalgae-focused approach that delivers a unique, macroalgae-specific computational and experimental framework, providing a future roadmap toward intelligent smart marine biotechnology and sustainable development to drive the global blue bioeconomy. Full article

The rapid expansion of the global nutraceutical industry has heightened the urgency for rigorous yet efficient safety assessment of food-derived bioactives. Conventional toxicological methods, while indispensable, are often costly, time-consuming, and insufficient to keep pace with the rapid product innovation in this sector. In this context, in silico models for predicting Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), alongside broader computational toxicology tools, are increasingly recognized as scalable and cost-effective strategies for early-stage safety evaluation of nutraceuticals. Techniques such as quantitative structure–activity relationship (QSAR) modeling, molecular docking, and physiologically based pharmacokinetic (PBPK) simulation enable early prioritization of candidate compounds, forecasting of potential enzyme- or receptor-mediated interactions, and prediction of bioactive metabolite profiles, prior to costly experimental or clinical evaluation. Yet, their application in the nutraceutical domain presents unique challenges. Food matrices are chemically complex, bioactive data remain sparse and unevenly curated, and long-term, low-dose effects are difficult to capture with existing computational frameworks. These limitations constrain predictive accuracy and raise questions about general applicability across diverse classes of compounds. Therefore, this review critically examines both the opportunities and limitations of computational safety assessment for nutraceuticals. It synthesizes representative case studies, evaluates current methodological limitations, and discusses the evolving regulatory landscape alongside emerging integrative frameworks such as New Approach Methodologies (NAMs) that may guide the future of nutraceutical safety assessment. Full article

Nanostructured drug-delivery materials—liposomes, polymeric nanoparticles, dendrimers, and inorganic carriers—have become central to pharmaceutical strategies for crossing the blood–brain barrier (BBB), where most candidate therapeutics fail to reach their targets. Their biological performance hinges on a coupled chain of vascular transport, BBB translocation, tissue diffusion, cellular uptake, and intracellular release, each of which is shaped by the nanocarrier’s size, surface chemistry, charge, and ligand functionalization. Physiologically based pharmacokinetic (PBPK) models describe this chain mechanistically but are limited by parameter uncertainty, simplified representations of the BBB, and coarse regional resolution. Machine learning (ML) can close these gaps by extracting nonlinear structure–transport–exposure relationships from heterogeneous experimental and clinical datasets. This review examines emerging ML–PBPK hybrid frameworks for predicting the brain biodistribution of nanostructured drug carriers. We compare regression, kernel, and deep learning approaches for parameter inference, model correction, and surrogate modeling; assess strategies for feature selection, uncertainty quantification, and interpretability; and discuss documented failure cases that bound the conditions under which these methods can be trusted. The review closes with recommendations on dataset standardization, software platform selection, and the responsible use of generative AI in pharmaceutical modeling, thus providing guidance for translating nanostructured material design into safer, more effective brain-targeted therapies. Full article

Background/Objectives: Pemetrexed–platinum chemotherapy is a key treatment option for non-squamous non-small cell lung cancer (NSCLC); however, its use is often limited by hematologic toxicity, particularly neutropenia. We aimed to develop a model-informed framework for assessing neutrophil dynamics using routinely available clinical data and pemetrexed exposure. Methods: This real-world investigation included 86 patients with NSCLC who received pemetrexed–platinum chemotherapy for model development, and 83 patients who received the same chemotherapy plus pembrolizumab or bevacizumab for validation. We developed a nonlinear mixed-effects model to predict neutrophil dynamics during the first cycle following pemetrexed–platinum chemotherapy, using patient-specific clinical data collected before chemotherapy initiation and pemetrexed pharmacokinetic parameters derived from physiologically based pharmacokinetic (PBPK) modeling. Results: The final model suggested that the area under the curve (AUC)_0–24 >175 μg·h/mL for pemetrexed, blood urea nitrogen, and concomitant use of renin–angiotensin system inhibitors influenced neutrophil suppression and delayed recovery. The receiver operating characteristic curve (AUROC) for identifying patients with a neutrophil count <1500/μL immediately before the anticipated next treatment cycle was 0.768 (95% CI: 0.639–0.898) in the development cohort, and 0.718 (95% CI: 0.545–0.891) in the validation cohort. Conclusions: This model-informed framework, based on PBPK-derived pemetrexed exposure and routinely available clinical factors, may help identify patients at risk of clinically relevant neutropenia that could delay the initiation of the next treatment cycle. Full article

Background/Objectives: Given the potential for achieving high concentrations at the target site while limiting systemic exposure, delivering drugs directly to the female reproductive tract (FRT) is emerging as a promising strategy for enhancing women’s reproductive health. However, quantitative data describing matrix-specific solubility, matrix-specific binding, and permeability across FRT tissues remain limited, constraining development of physiologically based pharmacokinetic (PBPK) models for intravaginal and intrauterine therapies. Methods: Our work was conducted to help fill this critical gap, evaluating four model drugs with diverse physicochemical and transporter profiles, dapivirine (DPV), levonorgestrel (LNG), MK-2048, and 4′-ethynyl-2-fluoro-2′-deoxyadenosine (EFdA; also known as islatravir or MK-8591) in in vitro and ex vivo human models. Plasma solubility, matrix-specific binding in plasma, cervicovaginal fluid, and FRT tissues, and bidirectional permeability across FRT tissues were quantified. Results: Our results demonstrated that the plasma solubility varied markedly across compounds, following lipophilicity trends, with DPV (34.14 ± 1.04 µg/mL) and EFdA (1808.02 ± 67.36 µg/mL) exhibiting the lowest and highest solubility, respectively. Hydrophobicity-dependent solubility enhancement by plasma proteins (~2× to >30× higher comparing to aqueous solubility in the literature) was observed for all four model drugs. Apparent binding in plasma, cervicovaginal fluid, and FRT tissues was highly correlated with the model compounds’ lipophilicity, with DPV having the most highly matrix-specific binding (97–99%) and EFdA having the least matrix-specific binding with the greatest variability (15–62%). Regional permeability differed significantly across FRT tissues: the human ectocervix, myometrium, endometrium, and fallopian tubes demonstrated distinct transport patterns consistent with epithelial architecture and the transporter-substrate status of the model compounds. Efflux transporter involvement was evident for MK-2048 and EFdA in Caco-2 models (efflux ratios 2.59 and 7.14, respectively), but was less pronounced in the 3D vaginal model and ex vivo tissues. Across all datasets, permeability and binding were strongly influenced by drug lipophilicity and ionization characteristics. Conclusions: Collectively, these findings demonstrate the interplay among solubility, matrix-specific binding, and tissue permeability in governing local drug distribution within the FRT. The experimentally derived parameters provide quantitative inputs for FRT PBPK model development, and are expected to inform design of safe and effective localized therapies for women’s reproductive health. Full article

Background: Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic tool used to predict how a drug moves through the body by incorporating real human physiology, including organ sizes, blood flows, tissue compositions, and enzyme activities. It has been widely employed to estimate drug exposure in different populations with organ impairment, genotype variabilities, and physiological variations. Metoclopramide is an antiemetic and prokinetic agent that is subject to CYP2D6 polymorphism. The study aims to develop PBPK models for several CYP2D6 variants to predict changes in the pharmacokinetic (PK) behavior of metoclopramide. Methods: To conduct this study, a literature review was conducted, and the retrieved physicochemical, biochemical, and PK data were integrated into PK-Sim to develop a PBPK model. Initially, a non-genotype-specific model was developed and extrapolated to genotype-based models. The models were verified using a Visual Predicted Check (VPC), mean predicted-to-observed ratio (R_pre/obs) values, and mean relative deviation (MRD). Results: The simulated profiles were aligned with the reported data, and all the predicted and observed PK parameters were comparable, as the R_pre/obs values were within the 0.5–2 range and MRD values were <2. Moreover, an increasing trend in AUC_0–∞ was observed across CYP2D6*wt/*wt, CYP2D6*wt/*10, CYP2D6*10/*10, and CYP2D6*5/*10, with approximately 1.63-, 2.64-, and 2.88-fold increases compared with the CYP2D6*wt/*wt genotype. Conclusions: The models have adequately estimated the PK behavior of metoclopramide across different CYP2D6 variants. These models might be helpful for populations with diverse CYP2D6 genotypes in dose optimization. Full article

Pulmonary drug delivery has become a vital route for both local and systemic treatments because of the unique structure and function of the respiratory system. Unlike oral and injectable dosage forms, inhalation offers a non-invasive, direct route to deliver medicines to the lungs, bypassing gastric degradation and first-pass hepatic metabolism. Common forms such as aerosols, solutions, suspensions, and dry powders are frequently used to treat respiratory diseases like asthma and chronic obstructive pulmonary disease (COPD). However, their effectiveness is often limited by physiological and biopharmaceutical barriers, such as mucociliary clearance, enzymatic degradation, and nonspecific deposition, which reduce drug retention and bioavailability. These issues are especially critical for poorly soluble or sensitive molecules, leading to lower drug concentrations at the target site and necessitating frequent dosing. To address these challenges, advanced nanoparticle-based delivery systems are being developed to improve drug stability, targeting, and controlled release within the lungs. At the same time, computational methods, including deposition modeling, physiologically based pharmacokinetic (PBPK) simulations, and AI-driven optimization, are increasingly used in formulation development to predict in vivo performance and boost translational success. This review covers the physiological and biological barriers to pulmonary drug delivery, explores major inhalation routes and dosage forms, and discusses new therapeutic strategies and nanoparticle platforms. It also highlights the growing role of in silico modeling and AI in accelerating the design and optimization of pulmonary treatments, while addressing current challenges, limitations, and regulatory issues in translating pulmonary nanomedicine into clinical practice. Full article

Background/Objectives: Selecting appropriate doses for pediatric patients remains one of the most complex challenges in drug development because developmental changes in physiology, metabolism, organ function, and pharmacodynamics substantially influence drug exposure and response. This review summarizes current evidence-based approaches to pediatric dose selection across the developmental continuum and evaluates contemporary model-informed strategies for individualized dosing. Methods: A narrative review of the literature was conducted focusing on pediatric dose-scaling methodologies, developmental pharmacology, physiologically based pharmacokinetic (PBPK) modeling, population pharmacokinetic (PopPK) approaches, exposure–response analysis, therapeutic drug monitoring, and regulatory extrapolation frameworks. Special populations and clinical scenarios relevant to pediatric dose optimization were also evaluated. Results: Simple body weight-based scaling from adult doses inadequately accounts for developmental changes in drug disposition and response. Allometric scaling combined with maturation functions provides improved dose prediction in neonates and infants, while PBPK and PopPK modeling support mechanistic and data-driven dose optimization across pediatric age groups. Fat-free-mass (FFM)-based scaling is preferred over total body weight for many drugs in children with obesity. Additional considerations including obesity, biologics, formulation and excipient safety, pharmacogenomics, critical illness, therapeutic hypothermia, extracorporeal support, therapeutic drug monitoring, and drug–drug interactions substantially influence pediatric dosing strategies. Regulatory frameworks including ICH E11A increasingly support model-informed pediatric extrapolation and precision dosing approaches. Conclusions: Pediatric dose selection has evolved from empirical weight-based dosing toward integrated model-informed strategies incorporating developmental physiology, pharmacometrics, and regulatory science. Allometry, maturation functions, FFM-based scaling, PBPK, PopPK, and therapeutic drug monitoring provide complementary tools for rational pediatric dose optimization, although drug- and pathway-specific validation remains essential, particularly in neonates and critically ill children. Full article

Background: SYHA1805 is a potent farnesoid X receptor (FXR) agonist currently in development for Metabolic Dysfunction-Associated Steatohepatitis (MASH). Methods: To determine the first-in-human (FIH) dose and guide its clinical development, an integrated approach combining in vitro and in vivo ADME and toxicological characterizations, cross-species allometric scaling (AS), and physiologically based pharmacokinetic (PBPK) modeling was employed. Results: Using monkeys and rats as extrapolation species, AS predicted a human intravenous clearance of 20.7 L/h and a steady-state volume of distribution of 15.1 L. Based on body surface area and exposure-based modeling, an FIH dosing regimen for single-dose administration was proposed, ranging from a 30 mg starting dose to a 3000 mg maximum, with an effective dose of 1150 mg. These dosing strategies were further supported by PBPK models, which accurately estimated human systemic exposure. The model simulations were subsequently validated by clinical trial data from a single ascending dose (SAD) study (CTR20202354). Conclusions: These findings establish a robust pharmacokinetic foundation for the continued clinical advancement of SYHA1805. Full article

Allometric scaling provides a practical framework for predicting human pharmacokinetic (PK) parameters from animal data by relating physiological processes to body size through power-law equations. Despite its simplicity and widespread use in first-in-human (FIH) dose selection, its predictive performance is limited by species-specific differences in absorption, distribution, metabolism, and excretion (ADME). This review summarizes the mathematical foundations, workflows, and diagnostics of allometric scaling, while critically examining where the approach succeeds and where it fails. Core concepts, including clearance, volume of distribution, correction factors, and the rule of exponents, are discussed alongside complementary methods: in vitro–in vivo extrapolation (IVIVE), physiologically based pharmacokinetic (PBPK) modelling, and the Wajima normalized time-course method. Historical clinical failures, including fialuridine, TGN1412, BIA 10-2474, and rofecoxib, illustrate the limits of relying solely on allometry, while thalidomide and the fenfluramine combination exemplify toxicodynamic species-selection failures. Modern advances, including the Extended Clearance Classification System (ECCS), target-mediated drug disposition, FcRn recycling, and emerging artificial intelligence and machine-learning methods, are integrated within a framework. Overall, the review treats allometric scaling as a disciplined starting hypothesis that must be triangulated with mechanistic, experimental, and regulatory evidence to support safer and more reliable human translation. Full article

Background: Prostate cancer remains one of the most prevalent malignancies worldwide, with high mortality in advanced and metastatic stages. Drug repurposing offers a cost-effective and time-efficient strategy to identify new therapeutic options. Objectives: This study aimed to apply physiologically based pharmacokinetic (PBPK) modeling to evaluate repurposed antiretroviral drugs efavirenz (EFV), etravirine (ETV), and saquinavir (SAQ) in prostate cancer, and to assess potential drug–drug interactions (DDIs) between EFV and ETV. Methods: PBPK models for EFV and SAQ were obtained and an ETV was developed and validated using literature and ADMET Predictor^® data. Prostate tissue models were modified to simulate malignant conditions, and population-based simulations examined the influence of age and obesity. The GastroPlus^® DDI module was applied to explore mechanistic interactions between EFV and ETV under different physiological scenarios. Results: Tumor-specific prostate tissue alterations produced minimal systemic pharmacokinetic changes but increased total drug accumulated in simulated tissue, with differences in unbound concentrations, while demographic variables such as age and weight significantly affected drug exposure, which are comorbidities in prostate cancer. Lighter individuals exhibited higher plasma concentrations across all drugs, consistent with known previously reported pharmacokinetic trends in obese individuals. DDI simulations indicated only minor changes in ETV pharmacokinetics when combined with EFV, with no clinically significant interaction detected. Conclusions: The integration of PBPK modeling, population variability, and DDI analysis highlights the potential of SAQ, EFV, and ETV as viable drugs for prostate cancer repurposing, but with a heavy focus on dosing personalization. In silico approaches provide a useful framework for early preclinical evaluation and the optimization of repurposed drugs, supporting the early evaluation of repurposed drug candidates in oncology. Full article

Background/Objectives: In this study, we present an analysis of ayahuasca, a psychedelic preparation containing N,N-dimethyltryptamine (DMT) and β-carbolines, such as harmine (HRM), a reversible monoamine oxidase A (MAO-A) inhibitor that enables the oral bioavailability of DMT. CYP2D6 is a highly polymorphic enzyme associated with interindividual variability in drug exposure, but its influence on the pharmacokinetics of ayahuasca alkaloids remains poorly understood. Methods: Using physiologically based pharmacokinetic (PBPK) modeling, we simulated scenarios for poor (PM), normal (NM), and ultra-rapid (UM) metabolizers by adjusting CYP2D6 enzyme expression for each phenotype. Results: PMs showed increased systemic exposure to DMT (AUC +53.3%; Cmax +40.5%) and HRM (AUC +30.6%; Cmax +22.8%), while UMs exhibited reduced exposure to both compounds. Conclusions: These findings highlight the significant impact of CYP2D6 polymorphisms on the pharmacokinetics of DMT and HRM, reinforcing the value of PBPK modeling for predicting interindividual variability and potential clinical risks. Full article