Search Results (30)

Background: Accurately determining pediatric dosing is essential prior to initiating clinical trials or administering medications in routine clinical settings. In children, ethical considerations demand careful evaluation of both safety and effectiveness. Typically, dosing recommendations for therapeutic proteins, such as monoclonal antibodies (mAbs), are derived from adult dosages using body weight as a scaling factor. However, this method overlooks key physiological and biochemical distinctions between pediatric and adult patients. Therefore, this could lead to the underexposure of mAbs, limiting their efficacy in this population. Additional methods are necessary to predict pediatric doses mechanistically. For small molecules, physiologically based pharmacokinetic (PBPK) models have been extensively used to predict pediatric doses based on physiological age-related changes and enzymes/transporters ontogeny. This study aims to evaluate the ability of PBPK models to predict mAbs’ pediatric exposure. Methods: Three mAbs were used for model development and validation: bevacizumab, infliximab, and atezolizumab. The PBPK models were built using GastroPlus^© Biologics module. For each mAb, the PBPK model was developed based on observed data in healthy and/or patient adults. Then, the physiological parameters were scaled to describe the pediatric physiology to predict exposure to the pediatric populations. Predicted plasma concentration–time courses were overlaid with reported observed data to assess the ability of the PBPK model to predict pediatric exposure. Results: Results showed that PBPK models accurately predicted pediatric pharmacokinetics for mAbs. Conclusions: This research marks a significant step in validating mechanistic extrapolation methods for biologics exposure prediction in children using PBPK models. Full article

Background: Biomimetic strategies have gained increasing attention for their ability to enhance the delivery, stability, and functionality of nutraceuticals by emulating natural biological systems. However, the literature remains fragmented, often focusing on isolated technologies without integrating regulatory, predictive, or translational perspectives. Objective: This review aims to provide a comprehensive and multidisciplinary synthesis of biomimetic and bio-inspired nanocarrier strategies for nutraceutical delivery, while identifying critical gaps in standardization, scalability, and clinical translation. Results: We present a structured classification matrix that maps biomimetic delivery systems by material type, target site, and bioactive compound class. In addition, we analyze predictive design tools (e.g., PBPK modeling and AI-based formulation), regulatory frameworks (e.g., EFSA, FDA, and GSRS), and risk-driven strategies as underexplored levers to accelerate innovation. The review also integrates ethical and environmental considerations, and highlights emerging trends such as multifunctional hybrid systems and green synthesis routes. Conclusions: By bridging scientific, technological, and regulatory domains, this review offers a novel conceptual and translational roadmap to guide the next generation of biomimetic nutraceutical delivery systems. It addresses key bottlenecks and proposes integrative strategies to enhance design precision, safety, and scalability. Full article

Physiologically based pharmacokinetic (PBPK) modelling for biologics, such as monoclonal antibodies and therapeutic proteins, involves capturing complex processes, including target-mediated drug disposition (TMDD), FcRn-mediated recycling, and tissue-specific distribution. The Simcyp Designer Biologics PBPK Platform Model offers an intuitive and efficient platform for constructing mechanistic PBPK models with pre-defined templates and automated model assembly, reducing manual input and improving reproducibility. This tutorial provides a step-by-step guide to using the platform, highlighting features such as cross-species scaling, population variability simulations, and flexibility for model customization. Practical case studies demonstrate the platform’s capability to streamline workflows, enabling rapid, mechanistic model development to address key questions in biologics drug development. By automating critical processes, this tool enhances decision-making in translational research, optimizing the modelling and simulation of large molecules across discovery and clinical stages. Full article

Background/Objectives: Physiologically based pharmacokinetic (PBPK) modeling is an important tool in biologic drug development. However, a standardized modeling strategy is currently missing. A cross-industry collaboration developed PBPK models for seven case studies, including monoclonal antibodies, antibody–drug conjugates, and bispecific T-cell engagers, to identify key parameters and establish a workflow to simulate biologic drugs in monkeys and in humans. Methods: PBPK models were developed in the monkey with limited data, including the molecular weight, the binding affinity to FcRn, and the additional systemic clearance of IgG, which is 20% of the total clearance. The binding affinity was only available for human FcRn and corrected for the known species-dependent differences in IgG binding. The strategy of monkey simulations was evaluated with an additional 14 studies published in the literature. Three different scenarios were simulated in humans afterwards: without, with allometrically scaled, and with optimized additional systemic clearance. Results: The plasma peak concentration and the area under the curve were predicted within 50% of the observed data for all studied case examples in the monkey, which demonstrates that sparse input parameters are sufficient for successful predictions in the monkey. Simulations in humans demonstrated the need for additional systemic clearance, because drug exposure was highly overpredicted without an additional systemic clearance term. Allometric scaling improved the predictions, but optimization led to the best fit, which is currently a limitation in the translation from animals to humans. Conclusions: This work highlights the importance of understanding the general mechanisms of drug uptake in different tissue types and cells in both target-dependent and -independent processes. Full article

Objectives: To investigate the pharmacokinetics (PK) of the monoclonal antibody (mAb) in male reproductive tissues and develop a translational physiologically based pharmacokinetic (PBPK) model to characterize the PK data. Method: The PK of a non-cross-reactive antibody (trastuzumab) was investigated in human FcRn-expressing male mice following a 10 mg/kg intravenous dose. The PK in plasma and male reproductive tissues (i.e., epididymis, testes, vas deferens, seminal vesicles, and prostate glands) were evaluated. The observed PK data in mice were mathematically characterized using a novel PBPK model for antibodies that contained male reproductive systems. The mouse PBPK model was scaled to rats, monkeys, and humans to predict the PK of antibodies in male reproductive organs across animal species. Results: Plasma and tissue PK data generated in mice suggest that antibody distribution in male reproductive tissues is generally lower compared to that of most of the organs. The antibody exposure in the testes was 1.70%, in the epididymis was 2.57%, in the vas deferens was 2.01%, in the seminal vesicle was 0.42%, and in the prostate gland was 0.52% of the plasma exposure. The plasma and tissue PK data were simultaneously characterized using the PBPK model, which incorporated the novel male reproductive system. All the predicted PK profiles were within two-fold of the observed data, as indicated by percentage prediction error (%PE) values. The mouse model was successfully translated to bigger animals, and the model was used to simulate the PK of antibodies in rat, monkey, and human male reproductive systems. Conclusions: The combination of the experimental data and novel PBPK model presented here provides unprecedented insights into the antibody distributions in different male reproductive tissues. The PBPK model can serve as a crucial tool for advancing the development of antibody-based therapies for treating sexually transmitted infections (STIs), cancers, and contraceptives. Full article

Autoimmune diseases (AIDs) are a group of disorders in which the immune system attacks the body’s own tissues, leading to chronic inflammation and organ damage. These diseases are difficult to treat due to variability in drug PK among individuals, patient responses to treatment, and the side effects of long-term immunosuppressive therapies. In recent years, pharmacometrics has emerged as a critical tool in drug discovery and development (DDD) and precision medicine. The aim of this review is to explore the diverse roles that pharmacometrics has played in addressing the challenges associated with DDD and personalized therapies in the treatment of AIDs. Methods: This review synthesizes research from the past two decades on pharmacometric methodologies, including Physiologically Based Pharmacokinetic (PBPK) modeling, Pharmacokinetic/Pharmacodynamic (PK/PD) modeling, disease progression (DisP) modeling, population modeling, model-based meta-analysis (MBMA), and Quantitative Systems Pharmacology (QSP). The incorporation of artificial intelligence (AI) and machine learning (ML) into pharmacometrics is also discussed. Results: Pharmacometrics has demonstrated significant potential in optimizing dosing regimens, improving drug safety, and predicting patient-specific responses in AIDs. PBPK and PK/PD models have been instrumental in personalizing treatments, while DisP and QSP models provide insights into disease evolution and pathophysiological mechanisms in AIDs. AI/ML implementation has further enhanced the precision of these models. Conclusions: Pharmacometrics plays a crucial role in bridging pre-clinical findings and clinical applications, driving more personalized and effective treatments for AIDs. Its integration into DDD and translational science, in combination with AI and ML algorithms, holds promise for advancing therapeutic strategies and improving autoimmune patients’ outcomes. Full article

Background: Diphenhydramine is an anti-tussive used periodically to treat seasonal colds, contact dermatitis, and anaphylactic reactions. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of diphenhydramine in predicting its systemic exposure among healthy pediatrics (children and adolescents) by leveraging data files from adults (young and elderly). Methods: The data profiles comprising serum/plasma concentration over time and parameters related to diphenhydramine were scrutinized via exhaustive literature analysis and consolidated in the PK-Sim software version 11.1. This modeling methodology commences with developing an adult model and then translating it to the pediatrics which compares the predicted concentration–time datasets with the reported values. Results: The accuracy of model anticipations was then assessed for each pharmacokinetics (PK) variable, i.e., the area under the curve from 0 to infinity (AUC_0-∞), maximal serum/plasma concentration (C_max), and clearance of the diphenhydramine in plasma (CL) by employing the predicted/observed ratios (R_pre/obs), and average fold error (AFE), which fell within the pre-defined benchmark of 2-fold. The predicted and observed C_max values for pediatrics were 3-fold greater in comparison to the young adults following a 25 mg dose depicting a need to monitor dosage schedules among children closely. Conclusions: These model-based anticipations confirmed the authenticity of the developed pediatric model and enhanced the comprehension of developmental variations on PK of diphenhydramine. This may assist healthcare professionals in ensuring the significance of lifespan applicability in personalized dose regimens, promoting therapeutic efficacy and minimizing side effects in chronic conditions among children. Full article

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure–activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer. Full article

The objective of this work was to develop a population physiologically based pharmacokinetic (popPBPK) model to characterize the variability in the clinical PK of monoclonal antibodies (mAbs) following intravenous (IV) and subcutaneous (SC) administration. An extensive literature search was conducted and clinical PK data for FDA-approved as well as non-approved mAbs were collected. Training and validation datasets of 44 and 9 mAbs exhibiting linear pharmacokinetics were used for model development. The variability in antibody PK was captured by accounting for different rate constants of pinocytosis (CL_up) and intracellular degradation (k_deg) for different mAbs. Typical values for CL_up and k_deg and their respective inter-antibody variabilities (${\omega }_{Clup}$, ${\omega }_{Kdeg})$ were estimated to be 0.32 L/h/L and 26.1 ${\mathrm{h}}^{-1}$ (73% and 46%). Varied absorption profiles following SC dosing were characterized by incorporating inter-antibody variability in local degradation (k_SC) and rate of lymphatic uptake (S_Lu) of mAbs. Estimates for typical k_SC and S_Lu values, and ${\omega }_{Ksc},{\omega }_{S\_Lu},$ were found to be 0.0015 ${\mathrm{h}}^{-1}$ and 0.54 (193%, and 49%). FDA-approved mAbs showed less local degradation (0.0014 ${\mathrm{h}}^{-1}$ vs. 0.0038 ${\mathrm{h}}^{-1}$) compared with other clinically tested mAbs, whereas no substantial differences in physiological processes involved in disposition were observed. To evaluate the generalizability of estimated PK parameters and model validation, the final popPBPK model was used to simulate the range of expected PK for mAbs following SC administration of nine different mAbs that were not used for model-building purposes. The predicted PK of all nine mAbs was within the expected range specified a priori. Thus, the popPBPK model presented here may serve as a tool to predict the clinical PK of mAbs with linear disposition before administering them to humans. The model may also support preclinical-to-clinical translation and ‘first-in-human’ dose determination for mAbs. Full article

The aim of this study was to predict the cytochrome P450 3A (CYP3A)-mediated drug–drug interactions (DDIs) between saxagliptin and nicardipine using a physiologically based pharmacokinetic (PBPK) model. Initially, in silico and in vitro parameters were gathered from experiments or the literature to construct PBPK models for each drug in rats. These models were integrated to predict the DDIs between saxagliptin, metabolized via CYP3A2, and nicardipine, exhibiting CYP3A inhibitory activity. The rat DDI PBPK model was completed by optimizing parameters using experimental rat plasma concentrations after co-administration of both drugs. Following co-administration in Sprague–Dawley rats, saxagliptin plasma concentration significantly increased, resulting in a 2.60-fold rise in AUC, accurately predicted by the rat PBPK model. Subsequently, the workflow of the rat PBPK model was applied to humans, creating a model capable of predicting DDIs between the two drugs in humans. Simulation from the human PBPK model indicated that nicardipine co-administration in humans resulted in a nearly unchanged AUC of saxagliptin, with an approximate 1.05-fold change, indicating no clinically significant changes and revealing a lack of direct translation of animal interaction results to humans. The animal-to-human PBPK model extrapolation used in this study could enhance the reliability of predicting drug interactions in clinical settings where DDI studies are challenging. Full article

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood–brain barrier (BBB) permeability evaluation tools are needed to explore drugs’ ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs. Full article

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research. Full article

Interspecies translation of monoclonal antibodies (mAbs) pharmacokinetics (PK) in presence of target-mediated drug disposition (TMDD) is particularly challenging. Incorporation of TMDD in physiologically based PK (PBPK) modeling is recent and needs to be consolidated and generalized to provide better prediction of TMDD regarding inter-species translation during preclinical and clinical development steps of mAbs. The objective of this study was to develop a generic PBPK translational approach for mAbs using the open-source software (PK-Sim^® and Mobi^®). The translation of bevacizumab based on data in non-human primates (NHP), healthy volunteers (HV), and cancer patients was used as a case example for model demonstration purpose. A PBPK model for bevacizumab concentration-time data was developed using data from literature and the Open Systems Pharmacology (OSP) Suite version 10. PK-sim^® was used to build the linear part of bevacizumab PK (mainly FcRn-mediated), whereas MoBi^® was used to develop the target-mediated part. The model was first developed for NHP and used for a priori PK prediction in HV. Then, the refined model obtained in HV was used for a priori prediction in cancer patients. A priori predictions were within 2-fold prediction error (predicted/observed) for both area under the concentration-time curve (AUC) and maximum concentration (C_max) and all the predicted concentrations were within 2-fold average fold error (AFE) and average absolute fold error (AAFE). Sensitivity analysis showed that FcRn-mediated distribution and elimination processes must be accounted for at all mAb concentration levels, whereas the lower the mAb concentration, the more significant the target-mediated elimination. This project is the first step to generalize the full PBPK translational approach in Model-Informed Drug Development (MIDD) of mAbs using OSP Suite. Full article

The rapid onset of action of nifedipine causes a precipitous reduction in blood pressure leading to adverse effects associated with reflex sympathetic nervous system (SNS) activation, including tachycardia and worsening myocardial and cerebrovascular ischemia. As a result, short acting nifedipine preparations are not recommended. However, importantly, there are no modified release preparations of nifedipine authorised for paediatric use, and hence a paucity of clinical studies reporting pharmacokinetics data in paediatrics. Pharmacokinetic parameters may differ significantly between children and adults due to anatomical and physiological differences, often resulting in sub therapeutic and/or toxic plasma concentrations of medication. However, in the field of paediatric pharmacokinetics, the use of pharmacokinetic modelling, particularly physiological-based pharmacokinetics (PBPK), has revolutionised the ability to extrapolate drug pharmacokinetics across age groups, allowing for pragmatic determination of paediatric plasma concentrations to support drug licensing and clinical dosing. In order to pragmatically assess the translation of resultant dissolution profiles to the paediatric populations, virtual clinical trials simulations were conducted. In the context of formulation development, the use of PBPK modelling allowed the determination of optimised formulations that achieved plasma concentrations within the target therapeutic window throughout the dosing strategy. A 5 mg sustained release mini-tablet was successfully developed with the duration of release extending over 24 h and an informed optimised dosing strategy of 450 µg/kg twice daily. The resulting formulation provides flexible dosing opportunities, improves patient adherence by reducing frequent administration burden and enhances patient safety profiles by maintaining efficacious levels of consistent drug plasma levels over a sustained period of time. Full article

Benzene is a known human carcinogen and one of the ten chemicals of major public health concern identified by the World Health Organization. Our objective was to evaluate benzene’s carcinogenic and non-carcinogenic health risks (current and projected) in highly exposed children in Yucatan, Mexico. Benzene exposure was estimated through a reverse-translation, four-compartment, physiologically based pharmacokinetic model (PBPK) based on previously performed urine trans, trans-muconic acid (benzene metabolite) determinations. Using a risk assessment methodology, the carcinogenic and non-carcinogenic risks of benzene were estimated for 6–12-year-old children from a family of shoemakers. The children’s hazard quotients for decreased lymphocyte count were 27 and 53 for 4 and 8 h/day exposure, respectively, and 37 for the projected 8 h/day exposure in adults. The risks of developing leukemia were 2–6 cases in 1000 children exposed 4 h/day; 4–10 cases in 1000 children exposed 8 h/day, and 2–9 cases in 1000 adults with an 8 h/day lifetime exposure. Children in Yucatan working in shoe-manufacturing workshops, or living next to them, are exposed to benzene concentrations above the reference concentration and have unacceptably high risks of presenting with non-carcinogenic and carcinogenic hematologic symptoms, now and in the future. Interventions to prevent further exposure and mitigate health risks are necessary. Full article