J Pharmacokinet Pharmacodyn [JOURNAL]
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200 papers
Optimizing Subcutaneous Antibody Dosing Regimens Through Operating Space Maps: rHuPH20 Case Study.
Nolan RP, Chintakuntla H
J Pharmacokinet Pharmacodyn · 2026 Jun · PMID 42373825 · Full text
Therapeutic antibody dosing regimens are often constrained by formulation and administration factors that limit flexibility in dose and schedule. Subcutaneous (SC) delivery offers advantages over intravenous (IV) adminis...
Therapeutic antibody dosing regimens are often constrained by formulation and administration factors that limit flexibility in dose and schedule. Subcutaneous (SC) delivery offers advantages over intravenous (IV) administration-such as improved convenience and potentially more favorable pharmacokinetic (PK) profiles-but is typically restricted by injection volume, requiring lower doses and more frequent dosing. Recombinant human hyaluronidase PH20 (rHuPH20) enables rapid administration of high-dose, high-volume therapeutics, thereby expanding the feasible SC delivery landscape. Despite these advances, biopharmaceutical development teams often lack tools to quantify the strategic impact of SC-enabling technologies and interpret PK simulations in a decision-ready format. This work introduces Operating Space Maps, a visual PK simulation framework that organizes multiple dosing scenarios into an intuitive landscape of dose-frequency options. Using rHuPH20 as a case study, a two-compartment PK model is applied to simulate representative antibody regimens for two illustrative scenarios: converting IV regimens to SC with rHuPH20 and extending SC dosing intervals beyond standard limits with rHuPH20. For example, relative to a 1000 mg IV benchmark regimen, a 1400 mg SC dose with rHuPH20 maintains equivalent average exposure while reducing C by approximately 50%. The methodology is broadly applicable to other SC-enabling technologies for which sufficient foundational data exist to parameterize the model. By making trade-offs between C, C, and C explicit and integrating SC delivery constraints, Operating Space Maps enable cross-functional development teams to evaluate regimen feasibility, optimize target product profiles, and guide strategic decisions early in development.
Mechanistic modeling of FcRn-dependent IgG drug interactions: Clinical applications and dosing implications.
Gupta P, Ayyar VS
J Pharmacokinet Pharmacodyn · 2026 Jun · PMID 42350716 · Publisher ↗
Concomitant use of intravenous immunoglobulin (IVIG) with monoclonal antibodies (mAbs) or bispecific T-cell engagers (TCEs) is routinely encountered across autoimmune diseases, B-cell malignancies, immunodeficiencies, an...
Concomitant use of intravenous immunoglobulin (IVIG) with monoclonal antibodies (mAbs) or bispecific T-cell engagers (TCEs) is routinely encountered across autoimmune diseases, B-cell malignancies, immunodeficiencies, and transplantation. These therapies share a key salvage pathway through the neonatal Fc receptor (FcRn), raising the potential for pharmacokinetic (PK) and pharmacodynamic (PD) interactions. A mechanistic model of IgG disposition originally developed in mice was extended and revalidated with an array of human datasets to investigate how IVIG dose, timing, and IgG pool dynamics influence therapeutic IgG behavior and PK/PD outcomes. The model captures endogenous and exogenous IgG kinetics via FcRn-mediated recycling in a peripheral (endosomal) compartment, coupled with first-order catabolism of unbound IgG. Simultaneous model-fitting against clinical data reproduced serum IgG kinetics and endogenous/pathogenic IgG reductions across autoimmune and transplant settings. It recapitulated the ~36% decrease in tesidolumab exposure observed during IVIG co-administration in renal transplant recipients, largely explaining associated PD alterations. In pemphigus vulgaris, model-predicted total IgG increases following 2 g/kg IVIG aligned with observed values. Predicted endogenous IgG decline mirrored the time-course of anti-BP180 autoantibody reduction. Upon suitably adapting and requalifying the model, it was applied to examine two co-therapy scenarios: (i) IVIG supplementation in multiple myeloma patients receiving the TCE teclistamab, and (ii) immunosuppressive rituximab-IVIG co-therapy for transplant desensitization. Simulations revealed how IVIG co-therapy may alter mAb effects depending on IVIG regimen, IgG pool dynamics, and the exposure-response relationship of the mAb. This generalizable framework supports model-informed drug development and prospective management of therapeutic IgG interactions.
Comparing heavy-tailed residual error models for outlier handling in population PK modeling.
Cheng Y, Li Y
J Pharmacokinet Pharmacodyn · 2026 Jun · PMID 42307836 · Publisher ↗
BACKGROUND: Reliable population pharmacokinetic (PopPK) estimation is often compromised by outliers under Gaussian error models. While post hoc filtering using conditional weighted residuals (CWRES) is common, this appro...
BACKGROUND: Reliable population pharmacokinetic (PopPK) estimation is often compromised by outliers under Gaussian error models. While post hoc filtering using conditional weighted residuals (CWRES) is common, this approach is often insensitive due to model "masking" from variance inflation. METHODS: We implemented a one-compartment model in Monolix using a custom likelihood workaround to benchmark four distributions: Normal, Laplace, Generalized Error Distribution (GED), and Student's t. We assessed CWRES sensitivity under extreme contamination and compared estimation performance using theoretical tail-behavior analysis, controlled simulation studies spanning multiple contamination severities, and a real-world caffeine PK case study with influential terminal-phase deviations. RESULTS: Simulations revealed that CWRES-based diagnostics are unreliable; extreme outliers frequently produced |CWRES| < 6 because the Normal model inflated residual variance, masking the contamination. Exponential-tail models (Laplace, GED) improved robustness for moderate outliers but failed under extreme deviations due to insufficiently heavy tails. Conversely, the Student's t model, utilizing power-law tail behavior, maintained stable and minimally biased structural parameter estimates across the contamination settings examined. These patterns were confirmed in the caffeine case study. CONCLUSIONS: Reliance on CWRES-driven residual screening alone is methodologically fragile. Among the models evaluated, exponential-tail distributions are insufficient for extreme outliers, whereas the Student's t distribution provided the most consistent stability across the contamination settings examined here and showed the most robust overall performance among the residual-error models evaluated when influential outliers were present.
Personalized prophylactic therapy optimization in hemophilia A using a hybrid PK-PD-TTE model and deep RL.
Rabbani M, Razavi SE, Goharimanesh M … +1 more , Razavi SE
J Pharmacokinet Pharmacodyn · 2026 Jun · PMID 42243590 · Publisher ↗
Hemophilia A is a genetic bleeding disorder caused by a deficiency or absence of Factor VIII, leading to recurrent and spontaneous hemorrhages. Standard treatment typically involves regular prophylactic infusions of clot...
Hemophilia A is a genetic bleeding disorder caused by a deficiency or absence of Factor VIII, leading to recurrent and spontaneous hemorrhages. Standard treatment typically involves regular prophylactic infusions of clotting factors to prevent bleeding episodes. However, individual variations in treatment response and reliance solely on plasma Factor VIII levels provide an imprecise assessment of bleeding risk. This study presents an intelligent dose-control system utilizing Deep Reinforcement Learning (DRL) integrated with a hybrid Pharmacokinetic-Pharmacodynamic Time-to-Event (PK-PD-TTE) environment. Within this framework, the control policy is learned using a Deep Q‑Network (DQN), enabling the agent to adapt treatment decisions through interaction with the simulated physiological environment. Unlike conventional methods, this framework allows the agent to observe continuous physiological states via Endogenous Thrombin Potential (ETP) and learn optimal dosing policies through trial-and-error. The proposed DQN agent was benchmarked against standard prophylaxis, a Fuzzy Logic controller, and a Bayesian Adaptive Model-Informed Precision Dosing (MIPD) strategy. Simulation results from a virtual cohort (N = 200) demonstrate that the DQN agent achieves a safety profile comparable to Bayesian MIPD while significantly improving factor utilization efficiency. Notably, in patients with low bleeding‑risk phenotypes, the DRL‑based approach achieved a similar bleeding rate to MIPD while reducing annual factor VIII consumption by approximately 39% relative to MIPD and by up to 70% compared to the reference prophylaxis protocol adopted as the simulation baseline. These findings suggest that incorporating reinforcement learning with mechanistic PD feedback can act as a powerful complementary layer to established clinical protocols, facilitating personalized and cost-effective hemophilia management.
Pediatric oral cavity physiologically based pharmacokinetic model to predict pharmacokinetics of mucoadhesive atropine gel to treat sialorrhea.
Parrot M, Murphy N, Rower JE … +5 more , Reilly CA, Green D, Tarrell A, Watt K, Yellepeddi V
J Pharmacokinet Pharmacodyn · 2026 Jun · PMID 42243578 · Publisher ↗
Sialorrhea significantly impairs quality of life in children with neurodisabilities, including cerebral palsy, yet safe and effective pharmacologic treatment options remain limited. Although atropine is widely used for s...
Sialorrhea significantly impairs quality of life in children with neurodisabilities, including cerebral palsy, yet safe and effective pharmacologic treatment options remain limited. Although atropine is widely used for sialorrhea, it is most commonly used off-label as ophthalmic drops administered intraorally, an approach constrained by poor mucosal retention, frequent dosing, medication-error risk, and systemic anticholinergic adverse effects. To address these limitations, we developed a novel mucoadhesive atropine oral gel (0.01% weight/weight (w/w)) to enhance intraoral residence time and support controlled local and systemic exposure. The pharmacokinetics and safety of the atropine gel were evaluated in a Phase I clinical trial in healthy adults, which informed the development and validation of a physiologically based pharmacokinetic (PBPK) model incorporating a mechanistic oral cavity framework. The oral cavity PBPK model accounts for salivary flow, mucosal absorption, swallowing, and saliva-tissue exchange across six compartments, enabling predictions of local and systemic exposure and evaluation of formulation-relevant determinants of intraoral absorption. Pediatric PBPK simulations were scaled from the adult model using Population Estimates for Age-Related Physiology™ (PEAR Physiology™) to support model-informed dose selection. Simulations identified a minimum pediatric dose range of 0.25 mg/kg once or twice daily, with twice-daily dosing to maintain plasma concentrations within the established minimum effective and maximum tolerated concentration range. Collectively, this work demonstrates the utility of PBPK modeling as a translational tool to integrate formulation attributes, oral cavity physiology, and pediatric dose selection, and supports the clinical advancement of mucoadhesive atropine gel as a safer alternative to off-label intraoral atropine eye drops.
Exposure-safety analyses of talazoparib in combination with enzalutamide in patients with metastatic castration-resistant prostate cancer (mCRPC) in the TALAPRO-2 trial.
Wang Y, Hadigol M, Lincha VR … +7 more , Hoffman J, Azad AA, Agarwal N, Matsubara N, Zohren F, DeAnnuntis L, Wang DD
J Pharmacokinet Pharmacodyn · 2026 Jun · PMID 42243460 · Full text
The TALAPRO-2 trial (NCT03395197) showed that the addition of talazoparib, a potent PARP inhibitor, to enzalutamide significantly improved radiographic progression-free survival in patients with mCRPC. Due to adverse eve...
The TALAPRO-2 trial (NCT03395197) showed that the addition of talazoparib, a potent PARP inhibitor, to enzalutamide significantly improved radiographic progression-free survival in patients with mCRPC. Due to adverse events (AEs), approximately 62% of patients experienced dose interruption and 53% of patients experienced dose reduction of talazoparib in TALAPRO-2 trial. Hematologic AEs such as anemia, thrombocytopenia, and neutropenia were the most common AEs leading to dose interruptions or reductions. We investigated the relationship between talazoparib exposure as well as baseline patients/disease characteristics and Grade ≥ 3 anemia, thrombocytopenia, and neutropenia using graphical examination and Cox proportional hazard models. The results indicated that higher talazoparib exposure, C, was associated with a higher risk of Grade ≥ 3 anemia, thrombocytopenia, and neutropenia. Additionally, among the other baseline factors, we observed that higher risk of all tested safety endpoints was associated with lower baseline hemoglobin. In addition, higher risk of anemia was associated with lower baseline body weight and higher baseline lactate dehydrogenase. Higher risk of neutropenia was associated with lower baseline absolute neutrophil count and lower baseline body weight. These findings support the proposed dose modification algorithm as an effective approach for management of AEs.
Development of a novel rabeprazole modified release formulation to reduce the dosing frequency: A trial and triumph with physiologically based biopharmaceutics modeling.
Maddukuri H, Boddu R, Chaudhari SM … +7 more , Choudhury AA, Kambam V, Khan SM, Naidu VR, Singh SK, Saini AK, Kollipara S
J Pharmacokinet Pharmacodyn · 2026 May · PMID 42143202 · Publisher ↗
Advanced in silico tools, such as physiologically based biopharmaceutics models (PBBM) and physiologically based pharmacokinetic models (PBPK), play a pivotal role in model-informed formulation development (MIFD). In the...
Advanced in silico tools, such as physiologically based biopharmaceutics models (PBBM) and physiologically based pharmacokinetic models (PBPK), play a pivotal role in model-informed formulation development (MIFD). In the present study, these methodologies were utilized in the development of a novel rabeprazole modified-release (MR) formulation for improved patient compliance by reducing the dosing frequency relative to existing delayed-release (DR) formulation. A MR formulation containing combination of delayed release and pulsatile release components was designed based on the hypothetical dissolution targets. A PBBM was first established using literature-derived clinical data and used to simulate the performance of formulations with varied dissolution profiles. Initial MR formulation prototypes (once a day) were evaluated in pilot-1 bioequivalence (BE) study against reference formulation (twice a day) but results indicated infra bioequivalence outcome. After pilot-1 study, the formulation and the dissolution method were optimized and then model was refined to predict the pilot 1 outcomes. The validated model was then employed to select suitable pilot-2 prototypes and to prospectively predict their BE outcomes under fasted and fed conditions. The predicted BE ratios were in good agreement with the observed pilot-2 study results. Therefore, the same model was used to make prospective predictions with increased subjects to understand the in vivo performance of pivotal test formulation and lead to the successful bioequivalence outcome, product commercialization, ultimately aiding in increased patient compliance. This work highlights the value of a model-informed strategy in accelerating formulation optimization, reducing development timelines and costs, and strengthening decision-making within pharmaceutical development programs.
Multi-objective optimization in population pharmacokinetic model selection and optimization: application of NSGA-II in pyDarwin.
Li X, Sale M, Craig J … +3 more , Nieforth K, Mazur A, Bies RR
J Pharmacokinet Pharmacodyn · 2026 May · PMID 42143189 · Full text
The selection of a "good" model usually involves a combination of objective and subjective criteria. Although many aspects of model quality can be expressed numerically, certain desirable characteristics remain difficult...
The selection of a "good" model usually involves a combination of objective and subjective criteria. Although many aspects of model quality can be expressed numerically, certain desirable characteristics remain difficult-or even impossible-to quantify precisely. Multi-objective optimization (MOO) provides a systematic way to handle this challenge by explicitly incorporating and balancing both objective (measurable) and subjective (judgment-based) considerations when choosing among candidate solutions. The generated Pareto front represents a set of non-dominated models where no single solution can be improved in one objective without sacrificing the performance in another objective. Using the non-dominated sorting genetic algorithm II (NSGA-II), an implementation of MOO, we simultaneously considered objective function value and number of estimated parameters as competing criteria. Concentration measurements of 17-DMAG, quetiapine, clozapine and ziprasidone were applied to build population pharmacokinetic models through traditional stepwise search, machine learning based single-objective hybrid genetic algorithm (SOHGA) and MOO. Local downhill search with MOO was also assessed in this study. While both objectives improved, models with lower objective function value generally contained more estimated parameters. The number of non-dominated solutions for DMAG, ziprasidone, clozapine, and quetiapine was 17, 9, 9, and 13, respectively. The optimal model selected by SOHGA appeared on the Pareto front for DMAG, ziprasidone and clozapine datasets. Overall, MOO provides objective transparency to the cost of tradeoffs between competing model objectives, allowing researchers to better contextualize subjective criteria (e.g., biological plausibility, improvements in diagnostic plots) when aligning model selection with clinical context.
A missed synthesis from the 1930s: Teorell, Mayneord, and the origins of physiological PK-TGI modelling.
Mistry HB
J Pharmacokinet Pharmacodyn · 2026 May · PMID 42143187 · Publisher ↗
The historical origins of mechanistic pharmacokinetic–tumour growth inhibition (PK–TGI) modelling are usually located in the later twentieth century. However, many of the essential ideas were already present in the 1930s...
The historical origins of mechanistic pharmacokinetic–tumour growth inhibition (PK–TGI) modelling are usually located in the later twentieth century. However, many of the essential ideas were already present in the 1930s, although not brought together in a single formal framework. In 1937, Torsten Teorell published a physiological theory of drug absorption, distribution, and elimination based on transport across biological boundaries and exchange between anatomical spaces. Five years earlier, W. V. Mayneord showed that the growth of Jensen’s rat sarcoma was more naturally described on a linear dimension than on tumour volume and explained this behaviour mechanistically by assuming proliferation within a thin outer rim surrounding a necrotic core. Read together, these papers contain the key ingredients of a physiological PK–TGI model: Teorell provides the concentration–time function, C(t), and Mayneord provides a growth law expressed on tumour radius, dR/dt = g. The missing link is an effect term coupling exposure to inhibition of radial growth. In its simplest form, this yields dR/dt = g - ψ(C). This article is not intended as an exhaustive review. Rather, it argues that the 1930s should be recognised as a formative period in mechanistic biomedicine in which the conceptual foundations of physiological PK–TGI modelling were already available in print. Selected later examples are used only to show that, even when tumour growth is recast in exponential, Gompertzian, reaction-diffusion, agent-based, or modern semi-mechanistic PK–TGI language, the field repeatedly returns to the same central point: for a solid tumour, net growth is governed by the behaviour of a viable outer region rather than by the bulk alone.
Adaptive PK/PD model optimization: a comparative analysis of bounded optimization methods for individual BIS prediction.
Tu Z, Jeffries S, Pelletier E … +4 more , Cafferty O, Morse J, Sinha A, Hemmerling T
J Pharmacokinet Pharmacodyn · 2026 May · PMID 42143179 · Publisher ↗
This study investigates the feasibility of adaptive optimization in pharmacokinetic/pharmacodynamic (PK/PD) models for predicting individual Bispectral Index (BIS) values during sedation and anesthesia by refining establ...
This study investigates the feasibility of adaptive optimization in pharmacokinetic/pharmacodynamic (PK/PD) models for predicting individual Bispectral Index (BIS) values during sedation and anesthesia by refining established models—specifically the Schnider, Marsh, Schuttler, Eleveld model for propofol and Minto model for remifentanil — using bounded optimization techniques. We adapt these population-based models through cumulative intraoperative BIS data to derive an effective set of PK/PD parameters for improved patient-specific BIS prediction. Comparative analyses using datasets from sedation and general anesthesia demonstrate that wider parameter bounding constraints improve model performance, as shown by higher Pearson correlations, and lower prediction errors, while maintaining computational efficiency. Our experiment results showed that Sequential Least Squares Quadratic Programming (SLSQP) and Least Squares methods excel in accuracy and computational efficiency, having the highest Pearson correlation of 0.56 and lowest root mean square error of 10.3. Real-time updates to PK/PD models using continuous BIS data may improve patient-specific BIS prediction during sedation and general anesthesia. These findings support the feasibility of adaptive calibration of population PK/PD models as a component of future model-based anesthetic management. Individualized dosing strategies could support closed-loop anesthesia systems and advance personalized medicine in perioperative care.
Tracer kinetic modeling of a 14 kDa anti-CD8 PET tracer (2C8v144) for in vivo estimation of CD8 expression in tissue and blood, binding affinity, and total body receptor occupancy.
Port RE, Boswell CA, Ogasawara A … +6 more , Gill H, Marik J, Tarantal AF, Berg E, Williams SP, Ferl GZ
J Pharmacokinet Pharmacodyn · 2026 Apr · PMID 42014564 · Publisher ↗
We are developing a 14.3 kDa anti-CD8 VHH tracer, [[Formula: see text]F]-2C8v144 (binding tracer, B), along with a nonbinding control tracer, [[Formula: see text]F]-2C8v145 (C), in order to track CD8+ (“cytotoxic”) T cel...
We are developing a 14.3 kDa anti-CD8 VHH tracer, [[Formula: see text]F]-2C8v144 (binding tracer, B), along with a nonbinding control tracer, [[Formula: see text]F]-2C8v145 (C), in order to track CD8+ (“cytotoxic”) T cells in malignant tumors during immunotherapy by Positron Emission Tomography (PET). Arterial blood concentrations of C and B were monitored in three rhesus monkeys upon i.v. injection of mass doses/kg varying approximately 5–fold for both tracers. Plasma concentrations, calculated from individual hematocrit measurements assuming no cellular uptake, were analyzed by mixed–effects compartmental modeling, by assuming the parameters of non-specific distribution and elimination (estimated based on the PET-based arterial blood concentrations) to be the same for B as for C. C exhibited linear three–compartment kinetics with a mean residence time of 26 min. Nonlinear kinetics of B suggesting saturable, reversible binding outside of circulating blood were described by an average association rate constant, [Formula: see text], and an equilibrium dissociation constant, [Formula: see text]. Binding in blood to circulating CD8+ cells was described by a different association rate constant, [Formula: see text], and the same [Formula: see text]. The total body content of CD8 receptors was estimated at 1.7 nmol/kg body weight, with 2.7% of the total present in blood. The estimate of [Formula: see text], 0.0097/(min·nM), was > 50–fold lower than [Formula: see text] determined in vitro by Surface Plasmon Resonance (SPR, 0.50/(min·nM), yet the estimated [Formula: see text], 0.23 nM, was similar to the SPR estimate (0.13 nM) suggesting that the model informs about affinity. The model also yields predicted total body receptor occupancies and plasma concentrations of unbound B, i.e. the arterial input function needed for analyzing tracer kinetics in malignant tumors.
Mitigating antibiotic resistance from missed doses.
Gilsoul JA, Tung HR, Lawley SD
J Pharmacokinet Pharmacodyn · 2026 Apr · PMID 41961338 · Publisher ↗
Medication nonadherence and antibiotic resistance are well-recognized threats to public health. In this paper, we use modeling and simulation to investigate how nonadherence affects the emergence of antibiotic resistance...
Medication nonadherence and antibiotic resistance are well-recognized threats to public health. In this paper, we use modeling and simulation to investigate how nonadherence affects the emergence of antibiotic resistance. Using a pharmacokinetic (PK) and pharmacodynamic (PD) model that tracks the stochastic bacteria population, we find that (a) missing just one or two doses may significantly increase the risk of resistance, and (b) this risk can be alleviated by taking double doses after missed doses. Furthermore, we quantify how such double dosing increases the antibiotic plasma concentration based on the antibiotic half-life and dosing schedule. Though the effects of missed doses depend on PKPD parameters, this study presents a framework to probe how adherence affects resistance for specific antibiotics. More generally, our results highlight the need to reexamine conventional recommendations for handling missed doses.
A structure-preserving hybrid learning framework for compartmental pharmacokinetic models.
Al Lawati H, Al-Lawatia M
J Pharmacokinet Pharmacodyn · 2026 Apr · PMID 41961126 · Publisher ↗
Pharmacokinetic (PK) models are widely used in drug development and dose planning, but they often face a trade-off between interpretability and flexibility. Traditional physiology-based PK models are transparent but may...
Pharmacokinetic (PK) models are widely used in drug development and dose planning, but they often face a trade-off between interpretability and flexibility. Traditional physiology-based PK models are transparent but may be too simple to capture complex or nonlinear behavior, while AI models are more flexible but can violate basic physical constraints such as nonnegativity or mass balance. In this work, we propose a constrained hybrid AI-PK approach in which small AI components are embedded within the PK equations as bounded corrections to mechanistic rates. The correction network is constructed to be internally conservative across compartments and to act as a controlled perturbation of the base model. We provide a simple mathematical analysis showing that this design preserves key qualitative properties, including boundedness and, under suitable conditions, positivity and stability. Numerical experiments on simulated and reported PK datasets show that the constrained hybrid model improves prediction accuracy and yields more stable extrapolation than both purely mechanistic and unconstrained hybrid models, while respecting the underlying physical structure.
Development of a population pharmacokinetics model and an R-Shiny simulation platform for moxifloxacin pharmacokinetics in non-human primates.
Garhyan A, Leishman D
J Pharmacokinet Pharmacodyn · 2026 Apr · PMID 41920374 · Publisher ↗
Assessment of potential liability of prolonged cardiac repolarization for a new drug is critical in drug research and development. Moxifloxacin is used as a positive control in well controlled in-vivo experiments to eval...
Assessment of potential liability of prolonged cardiac repolarization for a new drug is critical in drug research and development. Moxifloxacin is used as a positive control in well controlled in-vivo experiments to evaluate cardiac safety in preclinical species. A limited number of pharmacokinetic (PK) samples are collected in these experiments due to practical challenges, thus limiting the full utilization of extensive QT data collected every minute. Therefore, the objective of this work was to develop a population pharmacokinetics model of moxifloxacin in non-human primates (NHP) to enable concentration-QTc evaluation of all available telemetered ECG data. A user-friendly R Shiny simulation platform was developed which allows researchers to predict full PK profiles using limited PK data. A cardiovascular telemetry study was conducted in NHPs (n = 48, 50% females) with pharmacokinetic samples from 47 NHPs collected on 4 separate periods after an 80 mg/kg oral dose of moxifloxacin. Periods 1 (at 6 and 24 h on Day 3) and 2 (at 6 and 24 h on Day 10) had sparse PK samples and Periods 3 and 4 (Days 15 and 17) collected serial PK samples for 48 h post dose. Population PK modeling was performed using R package nlmixr2, R package rxode2 (Version 2.1.3) was used for simulations, and R Version 4.3.0 was used. The simulation platform was created using Shiny for R. Moxifloxacin PK in NHPs (body weight range: 2.83–6.67 kg) was best described with a one compartment, first-order absorption, and first-order elimination model. Between-subject variabilities were estimated for absorption rate (ka), clearance (CL), and volume (V) with a correlation between CL and V. Body weight (BW) was used to allometrically scale CL and V. BW was a covariate on ka; ka decreased with increase in BW. Fidelity of the model was confirmed using goodness of fit plots, visual predictive check, and parameters had an acceptable standard error. As only one dose level of moxifloxacin was used in modeling, PK data in literature at other dose levels (10, 30, 50, and 90 mg/kg) were overlaid on simulations to confirm dose-linearity and externally validate the PK model. An R Shiny simulation platform was developed that simulates the PK profiles of moxifloxacin after oral dosing. In addition, users can input sparse PK data in the simulation platform to generate a full PK profile for the NHP based on estimated post-hoc PK parameters. In summary a comprehensive population PK model to describe moxifloxacin PK in NHPs was developed and validated against published data not used in the model development. A user-friendly R Shiny simulation platform implemented the developed model to allow users to generate full PK profiles.
A hybrid PKPD agent-based model of the tumour immune interaction: effects of anti-cancer combination therapy.
Truong VT, Lythe G, Vicini P … +2 more , Yates JWT, Dubois VFS
J Pharmacokinet Pharmacodyn · 2026 Mar · PMID 41874878 · Full text
We have developed a three dimensional hybrid multiscale agent-based ODE PDE model including tumour immune interaction, cell cycle phases, oxygen and drug diffusion dynamics, the pharmacodynamics of chemotherapy, targeted...
We have developed a three dimensional hybrid multiscale agent-based ODE PDE model including tumour immune interaction, cell cycle phases, oxygen and drug diffusion dynamics, the pharmacodynamics of chemotherapy, targeted therapies, immunotherapy, radiotherapy and the respective systemic exposure levels of pharmacological treatments (described by pharmacokinetic modelling). The aim of this model is to support understanding of the spatial-temporal dynamic interactions between cancer cells, the relevant immune cells, and targeted therapies’ molecular moieties, which interact simultaneously in the tumour microenvironment. This interaction is further investigated in the context of combination therapies, thus making some inroads in the mechanistic understanding of positive and negative synergies when multiple therapies are administered. This work addresses the role of temporal sequencing in combination therapies, which requires the simultaneous modelling of multiple system components.
Early exposure-response modeling of an interferon-beta monoclonal antibody (dazukibart) in adults with dermatomyositis.
Prybylski JP, Zhu J', Banfield C … +2 more , Mukherjee A, Purohit V
J Pharmacokinet Pharmacodyn · 2026 Mar · PMID 41874840 · Full text
Dermatomyositis (DM) is a rare and debilitating inflammatory disease associated with muscle weakness and skin manifestations. As with all rare diseases, clinical trials in DM are challenging due to the small number of pa...
Dermatomyositis (DM) is a rare and debilitating inflammatory disease associated with muscle weakness and skin manifestations. As with all rare diseases, clinical trials in DM are challenging due to the small number of patients available for study, and DM patients may even present with skin- or muscle-predominant disease. A Phase 2 study of an anti-interferon-beta monoclonal antibody (dazukibart) in DM was recently completed. Most trial data were collected in skin-predominant DM patients, measuring the Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI); the Total Improvement Score (TIS) was only measured in a small subset of muscle-predominant DM patients. Because TIS is a more holistic endpoint, planning for further dazukibart development depended on a robust understanding of TIS response. This analysis aimed to develop an exposure-response model for dazukibart in DM patients. The model was intended to describe the timecourses of all relevant clinical responses, including CDASI and TIS, using the available data to collectively inform the exposure-response relationships. The model provided evidence for a TIS response in DM patients treated with dazukibart that was consistent with the observed data and supportive of further development. The model used here could be applied directly to model-based meta-analysis of other DM trials, and the general approach can be used in rare diseases with multiple endpoints.Trial registration numbers: NCT03181893, NCT02766621.
Utility of a physiologically-based pharmacokinetic model for investigating time-dependent variability in the fetal-to-maternal plasma concentration ratio of digoxin.
Okuba K, Ikeda K
J Pharmacokinet Pharmacodyn · 2026 Mar · PMID 41874774 · Full text
The purpose of this study was to elucidate the mechanisms underlying the wide variability in fetal-to-maternal (F/M) concentration ratios of digoxin by comprehensively characterizing maternal and fetal pharmacokinetics u...
The purpose of this study was to elucidate the mechanisms underlying the wide variability in fetal-to-maternal (F/M) concentration ratios of digoxin by comprehensively characterizing maternal and fetal pharmacokinetics using a physiologically based pharmacokinetic (PBPK) model that explicitly incorporates pregnant women, the placenta, and the fetus, with particular emphasis on placental transfer processes and time-dependent concentration dynamics. Maternal–fetal digoxin pharmacokinetics were simulated using a maternal–placental–fetal PBPK model implemented in Simcyp™. Placental transfer was described by separating passive diffusion, informed by human placental perfusion data, and P-gp–mediated active efflux scaled from in vitro data using quantitative proteomics. Gestation-dependent fetal renal excretion and amniotic fluid pathways were incorporated. Model predictions were verified against reported clinical maternal pharmacokinetic data. Global sensitivity analysis and pathway contribution analysis were performed to identify key determinants of fetal exposure. Placental P-gp activity selectively influenced fetal digoxin exposure without affecting maternal pharmacokinetics, and model predictions were consistent with clinical maternal data. Fetal exposure was primarily governed by direct placental transfer, whereas amniotic fluid–mediated pathways contributed only minimally. Although F/M (AUC ratio) remained relatively stable, instantaneous F/M values exhibited marked time-dependent variability. These findings indicate that a single time-point F/M may not adequately reflect fetal exposure for digoxin. Time-resolved PBPK analyses provide a mechanistic framework to complement conventional F/M-based assessments of maternal–fetal drug exposure.
Modeling, Simulation, and Extrapolation to Optimize Pediatric Development Plan: Sarilumab Polyarticular-Course juvenile arthritis Application.
Xu C, Zhang M, Liu Y … +5 more , Baret-Cormel L, De Benedetti F, Abdallah H, Kanamaluru V, Meng Z
J Pharmacokinet Pharmacodyn · 2026 Mar · PMID 41874761 · Full text
Polyarticular-course juvenile idiopathic arthritis (pcJIA) is a chronic condition that manifests before the age of 16 years, with pathology similar to that of adult rheumatoid arthritis (RA). Sarilumab is an interleukin-...
Polyarticular-course juvenile idiopathic arthritis (pcJIA) is a chronic condition that manifests before the age of 16 years, with pathology similar to that of adult rheumatoid arthritis (RA). Sarilumab is an interleukin-6 receptor inhibitor approved for RA and pcJIA. To obtain the approval for pcJIA, a single-arm, multiple-dose phase 2 study was conducted to determine the sarilumab dose for pcJIA. The population pharmacokinetics analysis of the phase 2 dose-finding portion data showed comparable pharmacokinetics; exposure–response analyses demonstrated similar or greater efficacy (JIA-ACR30/50/70), and consistent safety (reduction in absolute neutrophil count) in patients with pcJIA compared with adult patients with RA at similar sarilumab exposure. Based on the adult-to-pediatric extrapolation concept and justifications using modeling and simulation, the phase 2 sample size was increased to generate sufficient efficacy and safety data and evidence at selected doses and the requirement for a randomized controlled study in patients with pcJIA was waived. This novel approach enabled the pcJIA dose proposal by aligning with adult RA exposures, streamlined clinical development by removing the control arm requirement and minimized children participation while ensuring robustness of sarilumab efficacy and safety evidence for approval.
Assessing the impact of bacterial heterogeneity on bacteriophage population dynamics.
Beldjenna M, Guedj J, van Hasselt JGC … +1 more , Guo T
J Pharmacokinet Pharmacodyn · 2026 Mar · PMID 41803524 · Full text
Bacteriophages follow complex replication dynamics in codependence with their host cells. The corresponding dynamics are usually described with three key parameters: the adsorption rate φ, the burst size β and the latent...
Bacteriophages follow complex replication dynamics in codependence with their host cells. The corresponding dynamics are usually described with three key parameters: the adsorption rate φ, the burst size β and the latent period τ. Single-cell experiments have shown cellular variability in latent period and burst size. Yet, those parameters are traditionally modelled as uniform values for the whole bacterial population, which may introduce bias in the predicted profiles. Here, we systematically assessed the relevance of modelling this cell-level variability in the context of bacteriophage dynamics. We developed a comprehensive modelling framework, aiming to: (i) identify which parameter distributions impact population dynamics, (ii) quantify how difference in distribution can influence predictions, and (iii) inform model selection by comparing relevant modelling approaches. A distributed delay differential equation model with randomly distributed latent period, burst size and adsorption rate within the cell population was developed. It also included logistic growth of the host cell, saturable adsorption rate and intracellular replication dynamics. This model was compared to classical models, both fixed delay differential equation and transit compartments models, over typical ranges of phage parameters. Only the distribution of the latent period and its influence on burst size impacted population dynamics. The two classical models proved to be good approximations of dynamics, but only at low cellular variability. As such, although the classical approaches can be preferred at lower variability, this distributed framework is warranted when high variability is expected, or when the other approaches fail to accurately capture the dynamics.
On the existence conditions of interaction indices in response surface models.
Yumuk E, Ionescu C
J Pharmacokinet Pharmacodyn · 2026 Mar · PMID 41772298 · Full text
Response-surface models (RSMs) are widely used to characterize the combined effects of two agents and to classify their interaction as additivity, synergy, or antagonism. In this context, an interaction index is frequent...
Response-surface models (RSMs) are widely used to characterize the combined effects of two agents and to classify their interaction as additivity, synergy, or antagonism. In this context, an interaction index is frequently used as a quantitative measure. However, estimation of the interaction index outside feasible ranges may result in complex-valued or biologically implausible predictions in certain regions of the input domain, thereby limiting the validity of the model. This paper introduces a feasibility analysis framework that distinguishes between isobole-level feasibility (i.e. the existence of well-defined isoboles at a given effect level) and global feasibility (i.e. well-posedness across the entire domain). The analysis explicitly characterizes the singular sets that arise when feasibility conditions are violated, thereby explaining when and how models fail. The approach is demonstrated on three canonical models, i.e. Greco, Minto, and Finney, and supported by numerical illustrations, offering practical guidance for systematic and robust parameter selection in drug combination studies, toxicology, and process engineering.
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