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Population Pharmacokinetics: Where Are We Now?

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A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmacokinetics and Pharmacodynamics".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 7603

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Special Issue Editors

Dr. Su-jin Rhee

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Guest Editor

College of Pharmacy, Wonkwang University, Iksan 54538, Republic of Korea
Interests: clinical pharmacokinetics (PK) and pharmacodynamics (PD); population PK/PD modeling and simulation; physiologically-based pharmacokinetic (PBPK) modeling and simulation; individualized pharmacotherapy; early phase clinical trial

Dr. Eunsol Yang

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Guest Editor

Department of Bioengineering and Therapeutic Sciences, University of California, San Fransisco, CA, USA
Interests: pharmacometrics; clinical pharmacology; PK/PD modeling; translational modeling; model-informed dose optimization

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our upcoming Special Issue, “Population Pharmacokinetics: Where Are We Now?”.

Population pharmacokinetics has become increasingly integrated across drug development, regulatory decision-making, and patient care. Its role in addressing knowledge gaps in both preclinical and clinical settings and thereby advancing global health continues to grow. Population pharmacokinetics provides robust quantitative foundations for dose selection and optimization, clinical trial design, and personalized therapy. This Special Issue aims to showcase recent methodological advances and innovative, impactful applications that reflect the current state and future direction of the field.

We welcome submissions aligned with, but not limited to, the following themes:

Methodological advances

- Incorporation of machine learning into conventional population pharmacokinetic approaches.

- Innovative and automated approaches for covariate identification and model building.

Model-informed drug development

- Translational pharmacokinetic/pharmacodynamic modeling to efficiently bridge preclinical findings to optimal human dose selection and clinical trial design.

- Population pharmacokinetics and exposure–response modeling to identify sources of variability and support rationale-driven development decisions.

Model-informed advancements in current treatment strategies

- Population pharmacokinetics focused on special populations, such as pediatrics and pregnancy, to advance understanding and improve dosing strategies.

- Real-world data-based population pharmacokinetics in patient populations to enhance clinical practice.

We look forward to receiving your valuable contributions that advance the science and clinical applications of population pharmacokinetics.

Dr. Su-jin Rhee
Dr. Eunsol Yang
Guest Editors

Manuscript Submission Information

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Keywords

pharmacometrics
population pharmacokinetics
pharmacokinetics/pharmacodynamics
machine learning
model-informed drug development
model-informed dose optimization
special populations
real-world data
personalized therapy

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Published Papers (6 papers)

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Research

19 pages, 1676 KB

Open AccessArticle

Residual Error Coding in NONMEM Can Mislead Diagnostic Residuals: Impact of W Definition on IWRES, WRES, and CWRESI

by Nicolas Simon and Katharina von Fabeck

Pharmaceutics 2026, 18(5), 590; https://doi.org/10.3390/pharmaceutics18050590 - 10 May 2026

Viewed by 628

Abstract

Background and Objective: In NONMEM, the residual error model is implemented in the $ERROR block, where the user defines the prediction equation, Y, and a scaling factor, W, used to compute the individual weighted residual. This residual is reported in the diagnostic output as IWRES and corresponds to the individual residual divided by W. The residual error variance entering the likelihood is determined solely by the EPS and SIGMA structure of Y, independently of W. Multiple coding approaches for W are encountered in the literature, but no systematic analysis has examined how these choices affect diagnostic residuals. The aim of this study was to characterize the impact of W coding on three commonly used residual diagnostics in NONMEM, namely, IWRES, WRES, and CWRESI, across additive, proportional, and combined residual error models. Methods: Three population pharmacokinetic datasets (500 subjects; 6000 observations each) were simulated from a one-compartment oral model under additive (σ_add = 0.5 mg/L), proportional (CV = 20%), and combined (σ_prop = 0.15, σ_add = 0.5 mg/L) residual error structures. The following nine estimation runs were performed in NONMEM 7.6 (FOCE-I), each differing only in the $ERROR coding of W: normalized SIGMA-based, non-normalized, and THETA-based variants. Diagnostic residuals were compared pairwise by examining observation-by-observation ratios, standard deviations, and Pearson correlations. Results: For additive and proportional models, non-normalized W coding produced IWRES compressed by a constant multiplicative factor equal to sqrt(SIGMA(1,1)), reducing SD(IWRES) from 0.933 to 0.269 for the proportional model, while leaving WRES and CWRESI entirely unaffected. THETA-based normalized codings produced IWRES equivalent to SIGMA-based normalized codings. For the combined model, all three coding variants produced similar IWRES, but CWRESI differed by up to 0.586 units between the two-EPS (VAR.1) and one-EPS parameterizations, reflecting differences in NONMEM’s internal variance–covariance matrix structure. The SD coding additionally produced 19 extreme IWRES values (range: −59 to +74) at low predicted concentrations, attributable to the linear approximation of the combined standard deviation. Conclusions: The coding of W in NONMEM substantially affects IWRES but not WRES or CWRESI for simple error models. Cross-run comparisons of IWRES are invalid when W is not consistently normalized. For the combined model, the two-EPS VAR.1 parameterization is recommended for population-level diagnostics. These findings provide a practical framework for consistent and interpretable residual error coding in NONMEM. Full article

(This article belongs to the Special Issue Population Pharmacokinetics: Where Are We Now?)

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20 pages, 1093 KB

Open AccessArticle

PKGPT: Expert-Orchestrated Recursive LLM Agent for Automated NONMEM PopPK Modeling with Human Benchmarking

by Hoyoung Kwack, Hyunseung Kong, Jiwoo Lim, Byoung-Tak Zhang, Jongsung Hahn and Min Jung Chang

Pharmaceutics 2026, 18(4), 501; https://doi.org/10.3390/pharmaceutics18040501 - 18 Apr 2026

Viewed by 990

Abstract

Background/Objectives: Population pharmacokinetic (PopPK) modeling in NONMEM requires iterative, expertise-dependent workflows. Naïve zero-shot prompting of general-purpose large language models (LLMs) typically produces NONMEM code that fails to execute. This study introduces PKGPT, a recursive agentic LLM system designed to automate NONMEM-based PopPK model development and benchmarks its performance against human expert models. Methods: PKGPT, powered by Google’s Gemini 3.0 Flash, embeds pharmacometrics expertise into phase-specific expert-agent prompts orchestrated across five sequential phases: base model establishment, structural diagnostics, overfitting reduction, random-effects optimization, and covariate analysis. The system recursively executes NONMEM, parses outputs, and iteratively refines control streams. PKGPT was evaluated on three public datasets (warfarin, theophylline, and tobramycin) and benchmarked against independently developed human expert models. Results: PKGPT consistently produced executable, converging NONMEM models across all three datasets. In warfarin, both PKGPT and the human expert selected a one-compartment oral structure (ADVAN2), but the expert achieved a lower OFV (294.41 vs. 484.43) via covariate scaling. In theophylline, PKGPT produced parameter estimates close to the expert solution (Ka = 1.59 vs. 1.46 h⁻¹; CL = 0.0399 vs. 0.0404 L/h/kg). In tobramycin, PKGPT correctly identified a two-compartment structure but produced physiologically implausible peripheral volume estimates (V2 = 149 L vs. expert’s 13.2 L). Across datasets, PKGPT did not identify clinically established covariates, and run-to-run reproducibility was variable. Conclusions: PKGPT substantially improves the robustness and usability of LLM-generated NONMEM code compared with naïve zero-shot prompting, accelerating model drafting and iterative refinement, but physiological plausibility and clinical interpretability still require a human-in-the-loop oversight. Full article

(This article belongs to the Special Issue Population Pharmacokinetics: Where Are We Now?)

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10 pages, 523 KB

Open AccessArticle

Calvert Formula Modification for Optimized Carboplatin Dosing in Breast Cancer with Preserved Renal Function

by Jihyun Jeon, Jiyeon Jeon, Huong Tra Dang, Eun-hyang Choi, Sang Yull Kang, Hwi-yeol Yun, Jung-woo Chae, Jae Hyun Kim and Soyoung Lee

Pharmaceutics 2026, 18(4), 398; https://doi.org/10.3390/pharmaceutics18040398 - 24 Mar 2026

Viewed by 654

Abstract

Background/Objectives: Although the Calvert formula has been widely used to guide carboplatin dosing, it may yield inaccurate dose predictions in certain patient populations. We aimed to evaluate the adequacy of the conventional Calvert formula and to propose structural modifications to enhance dosing accuracy in breast cancer patients with preserved renal function (CrCL ≥ 55 mL/min). Methods: A systematic review and meta-analysis were conducted to integrate published pharmacokinetic models in patients with breast cancer. Two retrospective datasets (n = 154) were combined into a single analysis dataset and used to calculate carboplatin doses based on the conventional formulas using creatinine clearance (CrCL) or estimated glomerular filtration rate (eGFR), as well as a modified formula incorporating an additional constant (α). Performance was assessed by the proportion of subjects achieving target area under the curve (AUC) attainment (4–6 mg·min/mL), underexposure (<4 mg·min/mL), and overexposure (≥7 mg·min/mL). All AUC metrics were derived from model-based predictions rather than measured carboplatin concentrations, and no clinical toxicities or efficacy outcomes were used for validation. Results: Meta-analysis yielded fixed-effect parameter estimates (CL: 131.8 mL/min, V₁: 15.39 L, K₁₂: 0.002 min⁻¹, and K₂₁: 0.003 min⁻¹) with a random effect model. The conventional CrCL-based formula yielded 66.0% target attainment, 22.1% underexposure, and 4.5% overexposure. Switching to eGFR improved attainment to 88.3%, reduced underexposure to 5.8%, and lowered overexposure to 0.65%. A modified formula with α = 1 further decreased underexposure (4.5%) while target attainment and overexposure remained unchanged. Conclusions: Replacing CrCL with Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)-derived eGFR in the Calvert formula markedly improved dosing accuracy, while modest structural modification offered additional benefit. The incremental benefit of α = 1 should be considered hypothesis-generating and requires prospective validation with measured carboplatin concentrations and clinical outcomes before applying it in practice. These findings support adopting eGFR-based dosing in breast cancer and suggest the need for future clinical validation. Full article

(This article belongs to the Special Issue Population Pharmacokinetics: Where Are We Now?)

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15 pages, 2312 KB

Open AccessArticle

Towards Personalized Lymphodepletion: A Population Pharmacokinetic Fludarabine Model in Patients Receiving CAR T-Cell Therapy

by Javier Varela-González-Aller, Mario Andrés Sánchez-Salinas, Iñaki Troconiz, Gloria Iacoboni, Carla Alonso-Martínez, Maria-Josep Carreras-Soler, Cecilia Carpio, Anna Farriols-Danes, María Guerra-González, Alfredo Rivas-Delgado, Lucas Rivera Sanchez, Samantha Feijoo, Carolina Valdivia, Pere Barba and Marta Miarons

Pharmaceutics 2025, 17(12), 1592; https://doi.org/10.3390/pharmaceutics17121592 - 10 Dec 2025

Viewed by 983

Abstract

Background/Objectives: Optimal fludarabine dosing in the conditioning regimen based on population pharmacokinetic analysis (popPK) can predict outcomes in patients receiving hematopoietic stem cell transplantation. To date, there is no popPK tailored for patients receiving fludarabine as part of the lymphodepleting regimen before chimeric antigen receptor (CAR) T-cell infusion. The objective of this study was to develop a PopPK model of fludarabine in patients receiving CAR T-cell therapy. Methods: A prospective study was conducted at a tertiary hospital, from January 2021 to July 2022. Demographic, clinical, and analytical variables were collected. Blood samples were obtained on days 1 and 3 of the lymphodepleting regimen at 1.5, 2, 7 and 24 h post-fludarabine doses, and 30 min prior to CART-cell infusion. Fludarabine levels were analyzed through an ultra-performance liquid chromatography tandem mass spectrometry assay based on liquid–liquid extraction. Population pharmacokinetic analysis modeling was performed using nonlinear mixed-effects models (NONMEM). Results: Fifty-six patients (59% male) with a median age of 59 years (range 23–82) received CAR T-cell therapy (38 [68%] axicabtagene ciloleucel, 18 [32%] tisagenlecleucel) for relapsed/refractory large B-cell lymphoma. A total of 348 samples were collected for model development. A three-compartment model with first-order elimination best described the data. Body size, as represented by weight (WGT) with allometric scaling, was a significant predictor of all pharmacokinetic parameters (p < 0.05). Estimated glomerular filtration rate (eGFR) and the CAR T-cell construct type also showed statistical significance for fludarabine clearance (CL) (p < 0.05). Clearance was differentiated into non-renal and renal components. Estimates of V1, V2 and V3 volumes (the apparent volume of distribution of the central, shallow and deep compartments) were 41.2, 14.5 and 10.8 L, respectively. Conclusions: WGT, eGFR and type of CAR-T were predictors of fludarabine pharmacokinetics. This model offers a step toward precision-guided lymphodepletion and might support individualized dosing to optimize efficacy and minimize toxicity. Full article

(This article belongs to the Special Issue Population Pharmacokinetics: Where Are We Now?)

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22 pages, 5615 KB

Open AccessArticle

Pharmacokinetic Evaluation of GB-5001, a Long-Acting Injectable Formulation of Donepezil, in Healthy Korean Participants: Population Pharmacokinetics with Phase 1 Study

by Ye Chan Park, Eunyoung Seol, Jongmi Lee, Jang Hee Hong, Jin-Gyu Jung and Jung Sunwoo

Pharmaceutics 2025, 17(12), 1517; https://doi.org/10.3390/pharmaceutics17121517 - 25 Nov 2025

Viewed by 1557

Abstract

Background/Objectives: Oral donepezil, an acetylcholinesterase (AChE) inhibitor for Alzheimer’s disease, faces adherence challenges. Long-acting injectable (LAI) formulations like GB-5001 aim to enhance adherence by reducing dosing frequency. This Phase 1, open-label, active-controlled, dose-escalation study evaluated the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of GB-5001 in healthy male adults. Methods: Participants were assigned to cohorts receiving GB-5001A or GB-5001D (LAI formulations) via intramuscular (IM) or subcutaneous (SC) injection, or oral Aricept^®. Safety, PK, and PD (AChE inhibition) were assessed. The influence of CYP2D6 phenotype was explored, and modeling/simulation was performed. Results: Fifty healthy male participants completed the study. After IM administration, GB-5001A (70 mg, 140 mg, 280 mg) showed dose-dependent increases in exposure (AUC_inf and C_max), resulting in significantly extended exposure compared to oral Aricept^® 10 mg. No serious adverse events were reported; the most common AEs were mild injection site reactions, which occurred in all treatment groups except the GB-5001A IM 70 mg group and the Aricept group. GB-5001A also demonstrated sustained AChE inhibition. Conclusions: GB-5001A, an LAI donepezil, showed favorable safety, dose-proportional PK, and sustained plasma exposure. It achieved a 3–4-fold longer half-life than oral donepezil. These findings, supported by modeling, highlight GB-5001A’s potential as a once-monthly IM alternative for Alzheimer’s disease treatment. Full article

(This article belongs to the Special Issue Population Pharmacokinetics: Where Are We Now?)

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15 pages, 3734 KB

Open AccessArticle

Toward Genotype-Informed Dosing of Voriconazole: Head-to-Head Simulations Across CYP2C19 Phenotypes Using Population Pharmacokinetic Models

by Yeobin Lee, Nai Lee, Su-jin Rhee and Yun Kim

Pharmaceutics 2025, 17(11), 1398; https://doi.org/10.3390/pharmaceutics17111398 - 28 Oct 2025

Viewed by 1434

Abstract

Background/Objective: Voriconazole exhibits nonlinear pharmacokinetics and wide interindividual variability driven by CYP2C19 phenotype and clinical covariates, necessitating early therapeutic drug monitoring (TDM). This study aimed to assess how the choice of population pharmacokinetic (PopPK) models influences genotype-stratified voriconazole exposure under a standardized adult regimen, and to delineate model-specific implications for clinical prescribing. Methods: Five CYP2C19-informed PopPK models (Yun, Ling, Wang, Dolton, Friberg) were evaluated under one oral dosing scenario with an identical extensive metabolizers (EM)/intermediate metabolizer (IM)/poor metabolizers (PM) cohort; steady-state exposure metrics were compared across models, with sensitivity checks using model-specific cohorts. Results: Yun predicted the highest exposures with the steepest EM–IM–PM gradient, suggesting a need for caution against upper-tail exceedance when genotype effects are pronounced. Ling yielded intermediate exposures with a modest gradient, consistent with adult central tendencies, thus supporting its use for standard adult initial dosing. Wang primarily distinguished between EM and PM, proving useful for lower-bound checks where underexposure risk or limited genotype information is a concern. Friberg (and Dolton) demonstrated lower exposures with limited genotype separation, offering insights when persistent underexposure is suspected. Conclusions: These model-specific patterns indicate that PopPK model choice can influence initial dose-band selection and the timing of early TDM in routine adult care. Ling can serve as a baseline for standard adult initiation, whereas Yun is appropriate for safety-first scenarios when upper-tail risk from strong genotype effects is anticipated; Wang assists when IM data are lacking or when lower-bound checks are needed. Generalizability beyond standardized adult dosing (e.g., special populations) remains limited. Full article

(This article belongs to the Special Issue Population Pharmacokinetics: Where Are We Now?)

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