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Ju G, Liu X, Peng Y, Yang W, Xu N, He Q, Zhang C, Chen L, Yang N, Zhang G, Li C, Su P, Zhu X, Ouyang D. Optimized Sampling Strategies for Isoniazid in East Asian Pediatric Populations Using Population Pharmacokinetics-Informed Approaches. Drug Des Devel Ther 2025; 19:3555-3576. [PMID: 40330817 PMCID: PMC12051976 DOI: 10.2147/dddt.s503987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2024] [Accepted: 04/14/2025] [Indexed: 05/08/2025] Open
Abstract
Objective Isoniazid exposure in vivo is significantly affected by NAT2 genotypes and has ethnic differences. To optimize the sampling strategy for isoniazid in East Asian pediatric populations. We employed a model-informed optimization approach based on INH population pharmacokinetic (PopPK) models. Methods We selected PopPK models for children and East Asian adults and optimized the sampling strategy using PopED (Population Experimental Design), a method that helps identify the most efficient sampling points for maximizing parameter estimation accuracy. Virtual patients with varying NAT2 phenotypes were created, and real-world pediatric scenarios were evaluated using questionnaire data, sampling windows, and stochastic simulations. Results From eight analyzed models (four for East Asian adults and four for non-East Asian pediatrics), we simplified two over-parameterized models using lumping without loss of performance. The optimized clinical sampling strategy involved collecting samples at 0.25 [0-0.5], 1.5 [1-2], 6 [3-8], 12 [9-14], and 24 [22-24] hours post-dose. Simulation verification showed that re-estimated major PK parameters had acceptable relative biases and relative standard error (<30%). Conclusion Traditional adult sampling strategies are inadequate for East Asian pediatric populations. A tailored strategy involving up to five samples can accurately estimate INH PopPK parameters and should be considered for clinical implementation to optimize treatment and reduce patient sampling burden.
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Affiliation(s)
- Gehang Ju
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Changsha, People’s Republic of China
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
| | - Xin Liu
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Yeheng Peng
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Wenyu Yang
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Nuo Xu
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Qingfeng He
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Chenchen Zhang
- School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Lulu Chen
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
- Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
| | - Nan Yang
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
- Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
- Phamark Data Technology Co., Ltd., Changsha, People’s Republic of China
| | - Gufen Zhang
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
- Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
- Phamark Data Technology Co., Ltd., Changsha, People’s Republic of China
| | - Chao Li
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
- Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
- Phamark Data Technology Co., Ltd., Changsha, People’s Republic of China
| | - Pan Su
- Hunan Chest Hospital, Changsha, People’s Republic of China
| | - Xiao Zhu
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China
| | - Dongsheng Ouyang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
- Institute of Clinical Pharmacology, Central South University, Changsha, People’s Republic of China
- Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
- Changsha Duxact Biotech Co., Ltd., Changsha, People’s Republic of China
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Visintainer R, Fochesato A, Boaretti D, Giampiccolo S, Watson S, Levi M, Reali F, Marchetti L. stormTB: a web-based simulator of a murine minimal-PBPK model for anti-tuberculosis treatments. Front Pharmacol 2025; 15:1462193. [PMID: 39845781 PMCID: PMC11750688 DOI: 10.3389/fphar.2024.1462193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 12/18/2024] [Indexed: 01/24/2025] Open
Abstract
Introduction Tuberculosis (TB) poses a significant threat to global health, with millions of new infections and approximately one million deaths annually. Various modeling efforts have emerged, offering tailored data-driven and physiologically-based solutions for novel and historical compounds. However, this diverse modeling panorama may lack consistency, limiting result comparability. Drug-specific models are often tied to commercial software and developed on various platforms and languages, potentially hindering access and complicating the comparison of different compounds. Methods This work introduces stormTB: SimulaTOr of a muRine Minimal-pbpk model for anti-TB drugs. It is a web-based interface for our minimal physiologically based pharmacokinetic (mPBPK) platform, designed to simulate custom treatment scenarios for tuberculosis in murine models. The app facilitates visual comparisons of pharmacokinetic profiles, aiding in assessing drug-dose combinations. Results The mPBPK model, supporting 11 anti-TB drugs, offers a unified perspective, overcoming the potential inconsistencies arising from diverse modeling efforts. The app, publicly accessible, provides a user-friendly environment for researchers to conduct what-if analyses and contribute to collective TB eradication efforts. The tool generates comprehensive visualizations of drug concentration profiles and pharmacokinetic/pharmacodynamic indices for TB-relevant tissues, empowering researchers in the quest for more effective TB treatments. stormTB is freely available at the link: https://apps.cosbi.eu/stormTB.
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Affiliation(s)
- Roberto Visintainer
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Anna Fochesato
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Trento, Italy
| | - Daniele Boaretti
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Stefano Giampiccolo
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Information Engineering and Computer Science (DISI), University of Trento, Trento, Italy
| | - Shayne Watson
- Gates Medical Research Institute, Cambridge, MA, United States
| | - Micha Levi
- Gates Medical Research Institute, Cambridge, MA, United States
| | - Federico Reali
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Luca Marchetti
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
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Kwon YK, Kim MJ, Choi YJ, Yoon SH, Oh KS, Shin YM. Lead exposure estimation through a physiologically based toxicokinetic model using human biomonitoring data and comparison with scenario-based exposure assessment: A case study in Korean adults. Food Chem Toxicol 2024; 191:114829. [PMID: 38955257 DOI: 10.1016/j.fct.2024.114829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/04/2024]
Abstract
Pb toxicity is linked to cardiovascular and nephrotoxicity issues. Exposure to this heavy metal can occur through food and drinking water. Therefore, this study aimed to evaluate Pb exposure and assess health risks in Korean adults using a physiologically based toxicokinetic (PBTK) model. Human blood Pb concentrations were monitored using the Korean National Environmental Health Survey (KoNEHS) Cycle 4. The average Pb exposure in Korean adults was 0.520 μg/kg bw/day. The PBTK results were compared with scenario-based results from the 2021 risk assessment report of five heavy metals, including Pb, conducted by the MFDS. Exposure determined through reverse dosimetry was approximately two times higher than scenario-based exposure (0.264 μg/kg bw/day). The higher exposure levels obtained during PBTK analysis may be attributed to sustained exposure within historically more contaminated living environments and the long half-life of Pb. These findings suggest that the PBTK-based method can quantify aggregated exposure levels in the body over time, potentially serving as a complementary tool to address the constraints of scenario-based assessment methods for integrated risk assessment. Moreover, this model is convenient and cost-effective compared with scenario-based exposure estimation. These findings can facilitate the application of model for tracking continuous national changes in hazardous substance levels.
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Affiliation(s)
- Yong-Kook Kwon
- Food Safety Risk Assessment Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Osong Health Technology Administration Complex, 187 Osongsaengmyeong2(i)-ro, Osong-eup, Heungdoek-gu, Cheongju-si, Chungcheongbuk-do, 25159, Republic of Korea
| | - Min-Ju Kim
- Food Safety Risk Assessment Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Osong Health Technology Administration Complex, 187 Osongsaengmyeong2(i)-ro, Osong-eup, Heungdoek-gu, Cheongju-si, Chungcheongbuk-do, 25159, Republic of Korea
| | - Yun Ju Choi
- Food Safety Risk Assessment Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Osong Health Technology Administration Complex, 187 Osongsaengmyeong2(i)-ro, Osong-eup, Heungdoek-gu, Cheongju-si, Chungcheongbuk-do, 25159, Republic of Korea
| | - Sang Hyeon Yoon
- Food Safety Risk Assessment Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Osong Health Technology Administration Complex, 187 Osongsaengmyeong2(i)-ro, Osong-eup, Heungdoek-gu, Cheongju-si, Chungcheongbuk-do, 25159, Republic of Korea
| | - Keum-Soon Oh
- Food Safety Risk Assessment Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Osong Health Technology Administration Complex, 187 Osongsaengmyeong2(i)-ro, Osong-eup, Heungdoek-gu, Cheongju-si, Chungcheongbuk-do, 25159, Republic of Korea
| | - Yeong Min Shin
- Food Safety Risk Assessment Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Osong Health Technology Administration Complex, 187 Osongsaengmyeong2(i)-ro, Osong-eup, Heungdoek-gu, Cheongju-si, Chungcheongbuk-do, 25159, Republic of Korea.
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Reali F, Fochesato A, Kaddi C, Visintainer R, Watson S, Levi M, Dartois V, Azer K, Marchetti L. A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis. Front Pharmacol 2024; 14:1272091. [PMID: 38239195 PMCID: PMC10794428 DOI: 10.3389/fphar.2023.1272091] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/04/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.
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Affiliation(s)
- Federico Reali
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Anna Fochesato
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Povo, Italy
| | - Chanchala Kaddi
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Roberto Visintainer
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Shayne Watson
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Micha Levi
- Gates Medical Research Institute, Cambridge, MD, United States
| | | | - Karim Azer
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Luca Marchetti
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Povo, Italy
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