Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis-Reference-Cited by-同舟云学术

Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis

Published:2024-07 Issue:7 Volume:63 Page:1055-1063
ISSN:0312-5963
Container-title:Clinical Pharmacokinetics
language:en
Short-container-title:Clin Pharmacokinet

Author:

Tang Bo-Hao,Zhang Xin-Fang,Fu Shu-Meng,Yao Bu-Fan,Zhang Wei,Wu Yue-E.,Zheng Yi,Zhou Yue,van den Anker John,Huang Hai-Rong,Hao Guo-Xiang,Zhao Wei^ORCID

Funder

National Key R&D Program of China

the National Key Research and Development Program of China

National Natural Science Foundation of China

Distinguished Young and Middle-aged Scholar of Shandong University

Innovation and Development Joint Fund of Natural Science Foundation of Shandong Province

Natural Science Foundation of Shandong Province

the Capital’s Funds for Health Improvement and Research

Beijing High-Level Public Health Talent Program

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40262-024-01400-4.pdf

Reference38 articles.

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3. Soedarsono S, Jayanti RP, Mertaniasih NM, Kusmiati T, Permatasari A, Indrawanto DW, et al. Development of population pharmacokinetics model of isoniazid in Indonesian patients with tuberculosis. Int J Infect Dis. 2022;117:8–14.

4. Rogers Z, Hiruy H, Pasipanodya JG, Mbowane C, Adamson J, Ngotho L, et al. The non-linear child: ontogeny, isoniazid concentration, and NAT2 genotype modulate enzyme reaction kinetics and metabolism. EBioMedicine. 2016;11:118–26.

5. Kinzig-Schippers M, Tomalik-Scharte D, Jetter A, Scheidel B, Jakob V, Rodamer M, et al. Should we use N-acetyltransferase type 2 genotyping to personalize isoniazid doses? Antimicrob Agents Chemother. 2005;49(5):1733–8.