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Tang BH, Zhang XF, Fu SM, Yao BF, Zhang W, Wu YE, Zheng Y, Zhou Y, van den Anker J, Huang HR, Hao GX, Zhao W. Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis. Clin Pharmacokinet 2024; 63:1055-1063. [PMID: 38990504 DOI: 10.1007/s40262-024-01400-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2024] [Indexed: 07/12/2024]
Abstract
INTRODUCTION Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C2h), as an indicator of safety and efficacy, are important for optimizing therapy. OBJECTIVE The objective of this study was to establish machine learning (ML) models to predict the C2h, that can be used for establishing an individualized dosing regimen in clinical practice. METHODS Published population pharmacokinetic (PopPK) models for adults were searched based on PubMed and ultimately four reliable models were selected for simulating individual C2h datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C2h obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C2h. Real-world data were used for predictive performance evaluations. Virtual trials were used to compare ML-optimized doses with PopPK model-optimized doses. RESULTS Categorical boosting (CatBoost) exhibited the highest prediction ability. Target C2h can be predicted using the ML model combined with the dosing regimen and three covariates (N-acetyltransferase 2 [NAT2] genotypes, weight and race [Asians and Africans]). Real-world data validation results showed that the ML model can achieve an overall prediction accuracy of 93.4%. Using the final ML model, the mean absolute prediction error value decreased by 45.7% relative to the average of PopPK models. Using the ML-optimized dosing regimen, the probability of target attainment increased by 43.7% relative to the PopPK model-optimized dosing regimens. CONCLUSION Machine learning models were developed with great predictive performance, which can be used to determine the individualized initial dose of isoniazid in adult patients.
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Affiliation(s)
- Bo-Hao Tang
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xin-Fang Zhang
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Shu-Meng Fu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bu-Fan Yao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhang
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue Zhou
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Departments of Pediatrics, Pharmacology and Physiology, Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Department of Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Hai-Rong Huang
- National Clinical Laboratory on Tuberculosis, Beijing Key Laboratory on Drug-Resistant Tuberculosis, Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumor Research Institute, Capital Medical University, Beijing, China
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhao
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
- NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Shandong University, Jinan, China.
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Ju G, Liu X, Yang W, Xu N, Chen L, Zhang C, He Q, Zhu X, Ouyang D. Model-Informed Precision Dosing of Isoniazid: Parametric Population Pharmacokinetics Model Repository. Drug Des Devel Ther 2024; 18:801-818. [PMID: 38500691 PMCID: PMC10946406 DOI: 10.2147/dddt.s434919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 03/07/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use. Methods A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca. Results Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00-200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC0-24 and 83.2% decrease in Cmax (Infants), and a 49.3% reduction in AUC0-24 and 73.5% reduction in Cmax (Adults). Discussion Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.
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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 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
| | - 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
| | - 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
| | - Chenchen Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Qingfeng He
- Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, 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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Hoa PQ, Kim HK, Jang TW, Seo H, Oh JY, Kim HC, Shin AY, Min J, Jayanti RP, Hung TM, Anh NK, Ahn S, Long NP, Cho YS, Shin JG. Population pharmacokinetic model of rifampicin for personalized tuberculosis pharmacotherapy: Effects of SLCO1B1 polymorphisms on drug exposure. Int J Antimicrob Agents 2024; 63:107034. [PMID: 37977236 DOI: 10.1016/j.ijantimicag.2023.107034] [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: 04/10/2023] [Revised: 09/27/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Rifampicin (RIF) exhibits high pharmacokinetic (PK) variability among individuals; a low plasma concentration might result in unfavorable treatment outcomes and drug resistance. This study evaluated the contributions of non- and genetic factors to the interindividual variability of RIF exposure, then suggested initial doses for patients with different weight bands. METHODS This multicenter prospective cohort study in Korea analyzed demographic and clinical data, the solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotypes, and RIF concentrations. Population PK modeling and simulations were conducted using nonlinear mixed-effect modeling. RESULTS In total, 879 tuberculosis (TB) patients were divided into a training dataset (510 patients) and a test dataset (359 patients). A one-compartment model with allometric scaling for effect of body size best described the RIF PKs. The apparent clearance (CL/F) was 16.6% higher among patients in the SLCO1B1 rs4149056 wild-type group than among patients in variant group, significantly decreasing RIF exposure in the wild-type group. The developed model showed better predictive performance compared with previously reported models. We also suggested that patients with body weights of <40 kg, 40-55 kg, 55-70 kg, and >70 kg patients receive RIF doses of 450, 600, 750, and 1050 mg/day, respectively. CONCLUSIONS Total body weight and SLCO1B1 rs4149056 genotypes were the most significant covariates that affected RIF CL/F variability in Korean TB patients. We suggest initial doses of RIF based on World Health Organization weight-band classifications. The model may be implemented in treatment monitoring for TB patients.
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Affiliation(s)
- Pham Quang Hoa
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Hyun Kuk Kim
- Department of Internal Medicine, Division of Pulmonology, Inje University Haeundae Paik Hospital, Busan, Republic of Korea
| | - Tae Won Jang
- Department of Internal Medicine, Pulmonary Division, Kosin University Gospel Hospital, Busan, Republic of Korea
| | - Hyewon Seo
- Department of Internal Medicine, Division of Pulmonary Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Jee Youn Oh
- Department of Internal Medicine, Division of Pulmonology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Ho Cheol Kim
- Department of Internal Medicine, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Changwon, Republic of Korea
| | - Ah Young Shin
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jinsoo Min
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Rannissa Puspita Jayanti
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Tran Minh Hung
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Ky Anh
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Sangzin Ahn
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Phuoc Long
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Yong-Soon Cho
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
| | - Jae-Gook Shin
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, Republic of Korea.
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Niesyto K, Keihankhadiv S, Mazur A, Mielańczyk A, Neugebauer D. Ionic Liquid-Based Polymer Matrices for Single and Dual Drug Delivery: Impact of Structural Topology on Characteristics and In Vitro Delivery Efficiency. Int J Mol Sci 2024; 25:1292. [PMID: 38279291 PMCID: PMC10816880 DOI: 10.3390/ijms25021292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/15/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024] Open
Abstract
Previously reported amphiphilic linear and graft copolymers, derived from the ionic liquid [2-(methacryloyloxy)ethyl]trimethylammonium chloride (TMAMA_Cl‾), along with their conjugates obtained through modification either before or after polymerization with p-aminosalicylate anions (TMAMA_PAS‾), were employed as matrices in drug delivery systems (DDSs). Based on the counterion type in TMAMA units, they were categorized into single drug systems, manifesting as ionic polymers with chloride counterions and loaded isoniazid (ISO), and dual drug systems, featuring ISO loaded in self-assembled PAS conjugates. The amphiphilic nature of these copolymers was substantiated through the determination of the critical micelle concentration (CMC), revealing an increase in values post-ion exchange (from 0.011-0.063 mg/mL to 0.027-0.181 mg/mL). The self-assembling properties were favorable for ISO encapsulation, with drug loading content (DLC) ranging between 15 and 85% in both single and dual systems. In vitro studies indicated ISO release percentages between 16 and 61% and PAS release percentages between 20 and 98%. Basic cytotoxicity assessments using the 2,5-diphenyl-2H-tetrazolium bromide (MTT) test affirmed the non-toxicity of the studied systems toward human non-tumorigenic lung epithelial cell line (BEAS-2B) cell lines, particularly in the case of dual systems bearing both ISO and PAS simultaneously. These results confirmed the effectiveness of polymeric carriers in drug delivery, demonstrating their potential for co-delivery in combination therapy.
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Affiliation(s)
| | | | | | | | - Dorota Neugebauer
- Department of Physical Chemistry and Technology of Polymers, Faculty of Chemistry, Silesian University of Technology, 44-100 Gliwice, Poland; (K.N.); (S.K.); (A.M.); (A.M.)
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Li J, Cai X, Chen Y, Wang C, Jiao Z. Parametric population pharmacokinetics of isoniazid: a systematic review. Expert Rev Clin Pharmacol 2023; 16:467-489. [PMID: 36971782 DOI: 10.1080/17512433.2023.2196401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
INTRODUCTION Isoniazid (INH) plays an important role in prevention and treatment of tuberculosis (TB). However, large pharmacokinetic (PK) variations are observed in patients receiving standard INH dosages. Considering the influence of PK variations on INH efficacy or adverse reactions, we reviewed the population PK studies of INH and explored significant covariates that influence INH PK. METHODS The PubMed and Embase databases were systematically searched from their inception to 30 January 2023. PPK studies on INH using a parametric nonlinear mixed-effect approach were included in this review. The characteristics and identified significant covariates of the included studies were summarized. RESULTS Twenty-one studies conducted in adults, and seven in pediatrics were included. A two-compartment model with first-order absorption and elimination was the frequently used structural model for INH. NAT2 genotype, body size, and age were identified as significant covariates affecting INH PK variation. The median clearance (CL) value in the fast metabolizers was 2.55-fold higher than that in the slow metabolizers. Infants and children had higher CL per weight values than adults with the same metabolic phenotype. In pediatric patients, CL value increased with postnatal age. CONCLUSIONS Compared with slow metabolizers, the daily dose of INH should be increased by 200-600 mg in fast metabolizers. To achieve effective treatment, pediatric patients need a higher dose per kilogram than adults. Further PPK studies of anti-tuberculosis drugs are needed to comprehensively understand the covariates that affect their PK characteristics and to achieve accurate dose adjustments.
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Soedarsono S, Mertaniasih NM, Kusmiati T, Permatasari A, Ilahi WK, Anggraeni AT. Characteristics of Previous Tuberculosis Treatment History in Patients with Treatment Failure and the Impact on Acquired Drug-Resistant Tuberculosis. Antibiotics (Basel) 2023; 12:antibiotics12030598. [PMID: 36978465 PMCID: PMC10044547 DOI: 10.3390/antibiotics12030598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/09/2023] [Accepted: 03/15/2023] [Indexed: 03/19/2023] Open
Abstract
Tuberculosis (TB) treatment failure is a health burden, as the patient remains a source of infection and may lead to the development of multi-drug resistance (MDR). Information from cases of treatment failure that develop into MDR, which is related to a history of previous TB treatment, in accordance with the pharmacokinetic aspect, is one important thing to prevent TB treatment failure and to prevent drug resistance. This was an observational descriptive study in an acquired MDR-TB patient who had a prior history of treatment failure. A structured questionnaire was used to collect information. The questionnaire consisted of a focus on the use of TB drug formulas during the treatment period, as well as when and how to take them. This study included 171 acquired MDR-TB patients from treatment failure cases. An amount of 64 patients received the separated TB drug, and 107 patients received the fixed dose combination (FDC) TB drug. An amount of 21 (32.8%) patients receiving separated TB drug and six (5.6%) patients receiving FDC TB drug took their drug in divided doses. In addition, three (4.7%) patients receiving separated TB drug and eight (7.5%) patients receiving FDC TB drug took their drug with food. An amount of 132 out of 171 (77.2%) patients had a history of incorrect treatment that developed into MDR-TB. Education on how to take the correct medication, both the separate version and the FDC TB drug, according to the pharmacokinetic aspect, is important before starting TB treatment.
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Affiliation(s)
- Soedarsono Soedarsono
- Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya 60131, Indonesia
- Sub-Pulmonology Department of Internal Medicine, Faculty of Medicine, Hang Tuah University, Surabaya 60244, Indonesia
- Correspondence:
| | - Ni Made Mertaniasih
- Department of Clinical Microbiology, Faculty of Medicine, Universitas Airlangga, Surabaya 60131, Indonesia
- Tuberculosis Study Group, Institute of Tropical Disease, Universitas Airlangga, Surabaya 60131, Indonesia
| | - Tutik Kusmiati
- Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya 60131, Indonesia
- Tuberculosis Study Group, Institute of Tropical Disease, Universitas Airlangga, Surabaya 60131, Indonesia
| | - Ariani Permatasari
- Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya 60131, Indonesia
- Tuberculosis Study Group, Institute of Tropical Disease, Universitas Airlangga, Surabaya 60131, Indonesia
| | - Wiwik Kurnia Ilahi
- Division of Pulmonary Medicine, Ibnu Sina General Hospital, Gresik 61121, Indonesia
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Soedarsono S, Jayanti RP, Mertaniasih NM, Kusmiati T, Permatasari A, Indrawanto DW, Charisma AN, Lius EE, Yuliwulandari R, Quang Hoa P, Ky Phat N, Thu VTA, Ky Anh N, Ahn S, Phuoc Long N, Cho YS, Shin JG. Development of population pharmacokinetics model and Bayesian estimation of rifampicin exposure in Indonesian patients with tuberculosis. Tuberculosis (Edinb) 2023; 139:102325. [PMID: 36841141 DOI: 10.1016/j.tube.2023.102325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/04/2023] [Accepted: 02/12/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND Interindividual variability in the pharmacokinetics (PK) of anti-tuberculosis (TB) drugs is the leading cause of treatment failure. Herein, we evaluated the influence of demographic, clinical, and genetic factors that cause variability in RIF PK parameters in Indonesian TB patients. METHODS In total, 210 Indonesian patients with TB (300 plasma samples) were enrolled in this study. Clinical data, solute carrier organic anion transporter family member-1B1 (SLCO1B1) haplotypes *1a, *1b, and *15, and RIF concentrations were analyzed. The population PK model was developed using a non-linear mixed effect method. RESULTS A one-compartment model with allometric scaling adequately described the PK of RIF. Age and SLCO1B1 haplotype *15 were significantly associated with variability in apparent clearance (CL/F). For patients in their 40s, each 10-year increase in age was associated with a 10% decrease in CL/F (7.85 L/h). Patients with the SLCO1B1 haplotype *15 had a 24% lower CL/F compared to those with the wild-type. Visual predictive checks and non-parametric bootstrap analysis indicated good model performance. CONCLUSION Age and SLCO1B1 haplotype *15 were significant covariates of RIF CL/F. Geriatric patients with haplotype *15 had significantly greater exposure to RIF. The model could optimize TB pharmacotherapy through its application in therapeutic drug monitoring (clinical trial no. NCT05280886).
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Affiliation(s)
- Soedarsono Soedarsono
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Sub-pulmonology Department of Internal Medicine, Faculty of Medicine, Hang Tuah University, Surabaya, 60244, Indonesia; Institute of Tropical Disease, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia.
| | - Rannissa Puspita Jayanti
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Ni Made Mertaniasih
- Institute of Tropical Disease, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia; Department of Clinical Microbiology, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia
| | - Tutik Kusmiati
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Institute of Tropical Disease, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Ariani Permatasari
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Institute of Tropical Disease, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Dwi Wahyu Indrawanto
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Anita Nur Charisma
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Elvina Elizabeth Lius
- Department of Pulmonology & Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60131, Indonesia; Dr. Soetomo General Hospital, Surabaya, 60131, Indonesia
| | - Rika Yuliwulandari
- Department of Pharmacology, Faculty of Medicine, YARSI University, Jakarta, 10510, Indonesia; Genetic Research Center, YARSI Research Institute, YARSI University, Jakarta, 10510, Indonesia
| | - Pham Quang Hoa
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Nguyen Ky Phat
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Vo Thuy Anh Thu
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Nguyen Ky Anh
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Sangzin Ahn
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Nguyen Phuoc Long
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Yong-Soon Cho
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea.
| | - Jae-Gook Shin
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea; Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, 47392, Republic of Korea
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Thu VTA, Dat LD, Jayanti RP, Trinh HKT, Hung TM, Cho YS, Long NP, Shin JG. Advancing personalized medicine for tuberculosis through the application of immune profiling. Front Cell Infect Microbiol 2023; 13:1108155. [PMID: 36844400 PMCID: PMC9950414 DOI: 10.3389/fcimb.2023.1108155] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 01/17/2023] [Indexed: 02/12/2023] Open
Abstract
While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics' potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.
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Affiliation(s)
- Vo Thuy Anh Thu
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Ly Da Dat
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Rannissa Puspita Jayanti
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Hoang Kim Tu Trinh
- Center for Molecular Biomedicine, University of Medicine and Pharmacy at Ho Chi Minh, Ho Chi Minh City, Vietnam
| | - Tran Minh Hung
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Yong-Soon Cho
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea,*Correspondence: Jae-Gook Shin, ; Nguyen Phuoc Long,
| | - Jae-Gook Shin
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea,Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, Republic of Korea,*Correspondence: Jae-Gook Shin, ; Nguyen Phuoc Long,
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9
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Baracaldo-Santamaría D, Calderon-Ospina CA, Ortiz CP, Cardenas-Torres RE, Martinez F, Delgado DR. Thermodynamic Analysis of the Solubility of Isoniazid in (PEG 200 + Water) Cosolvent Mixtures from 278.15 K to 318.15 K. Int J Mol Sci 2022; 23:ijms231710190. [PMID: 36077585 PMCID: PMC9456571 DOI: 10.3390/ijms231710190] [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: 08/03/2022] [Revised: 09/01/2022] [Accepted: 09/01/2022] [Indexed: 11/16/2022] Open
Abstract
The solubility of drugs in cosolvent systems of pharmaceutical interest is of great importance for understanding and optimizing a large number of processes. Here, we report the solubility of isoniazid in nine (PEG 200 + water) cosolvent mixtures at nine temperatures (278.15, 283.15, 288.15, 293.15, 298.15, 303.15, 308.15, and 318.15 K) determined by UV-vis spectrophotometry. From the solubility data, the thermodynamic solution, mixing, and transfer functions were calculated in addition to performing the enthalpy-entropy compensation analysis. The solubility of isoniazid depends on the concentration of PEG 200 (positive cosolvent effect) and temperature (endothermic process) reaching its maximum solubility in pure PEG 200 at 318.15 K and the lowest solubility in pure water at 278.15 K. The solution process is favored by the solution entropy and according to the enthalpy-entropy compensation analysis it is driven by entropy in mixtures rich in water and by enthalpy in mixtures rich in PEG 200.
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Affiliation(s)
- Daniela Baracaldo-Santamaría
- Pharmacology Unit, Department of Biomedical Sciences, School of Medicine and Health Sciences, Universidad del Rosario, Bogotá 111221, Colombia
| | - Carlos Alberto Calderon-Ospina
- Pharmacology Unit, Department of Biomedical Sciences, School of Medicine and Health Sciences, Universidad del Rosario, Bogotá 111221, Colombia
- GENIUROS Research Group, Center for Research in Genetics and Genomics (CIGGUR), School of Medicine and Health Sciences, Universidad del Rosario, Bogotá 111221, Colombia
| | - Claudia Patricia Ortiz
- Programa de Administración en Seguridad y Salud en el Trabajo, Grupo de Investigación en Seguridad y Salud en el Trabajo, Corporación Universitaria Minuto de Dios-UNIMINUTO, Neiva 410001, Colombia
| | - Rossember Edén Cardenas-Torres
- Grupo de Fisicoquímica y Análisis Matemático, Facultad de Ciencias y Humanidades, Fundación Universidad de América, Bogotá 111221, Colombia
| | - Fleming Martinez
- Grupo de Investigaciones Farmacéutico-Fisicoquímicas, Departamento de Farmacia, Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Carrera 30 No. 45-03, Bogotá 111221, Colombia
| | - Daniel Ricardo Delgado
- Programa de Ingeniería Civil, Grupo de Investigación de Ingenierías UCC-Neiva, Facultad de Ingeniería, Universidad Cooperativa de Colombia, Sede Neiva, Neiva 410001, Colombia
- Correspondence: ; Tel.: +57-321-910-4471
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10
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Influence of N-acetyltransferase 2 (NAT2) genotype/single nucleotide polymorphisms on clearance of isoniazid in tuberculosis patients: a systematic review of population pharmacokinetic models. Eur J Clin Pharmacol 2022; 78:1535-1553. [PMID: 35852584 PMCID: PMC9482569 DOI: 10.1007/s00228-022-03362-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/29/2022] [Indexed: 11/19/2022]
Abstract
Purpose Significant pharmacokinetic variabilities have been reported for isoniazid across various populations. We aimed to summarize population pharmacokinetic studies of isoniazid in tuberculosis (TB) patients with a specific focus on the influence of N-acetyltransferase 2 (NAT2) genotype/single-nucleotide polymorphism (SNP) on clearance of isoniazid. Methods A systematic search was conducted in PubMed and Embase for articles published in the English language from inception till February 2022 to identify population pharmacokinetic (PopPK) studies of isoniazid. Studies were included if patient population had TB and received isoniazid therapy, non-linear mixed effects modelling, and parametric approach was used for building isoniazid PopPK model and NAT2 genotype/SNP was tested as a covariate for model development. Results A total of 12 articles were identified from PubMed, Embase, and hand searching of articles. Isoniazid disposition was described using a two-compartment model with first-order absorption and linear elimination in most of the studies. Significant covariates influencing the pharmacokinetics of isoniazid were NAT2 genotype, body weight, lean body weight, body mass index, fat-free mass, efavirenz, formulation, CD4 cell count, and gender. Majority of studies conducted in adult TB population have reported a twofold or threefold increase in isoniazid clearance for NAT2 rapid acetylators compared to slow acetylators. Conclusion The variability in disposition of isoniazid can be majorly attributed to NAT2 genotype. This results in a trimodal clearance pattern with a multi-fold increase in clearance of NAT2 rapid acetylators compared to slow acetylators. Further studies exploring the generalizability/adaptability of developed PopPK models in different clinical settings are required.
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Jayanti RP, Long NP, Phat NK, Cho YS, Shin JG. Semi-Automated Therapeutic Drug Monitoring as a Pillar toward Personalized Medicine for Tuberculosis Management. Pharmaceutics 2022; 14:pharmaceutics14050990. [PMID: 35631576 PMCID: PMC9147223 DOI: 10.3390/pharmaceutics14050990] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/26/2022] [Accepted: 05/02/2022] [Indexed: 12/10/2022] Open
Abstract
Standard tuberculosis (TB) management has failed to control the growing number of drug-resistant TB cases worldwide. Therefore, innovative approaches are required to eradicate TB. Model-informed precision dosing and therapeutic drug monitoring (TDM) have become promising tools for adjusting anti-TB drug doses corresponding with individual pharmacokinetic profiles. These are crucial to improving the treatment outcome of the patients, particularly for those with complex comorbidity and a high risk of treatment failure. Despite the actual benefits of TDM at the bedside, conventional TDM encounters several hurdles related to laborious, time-consuming, and costly processes. Herein, we review the current practice of TDM and discuss the main obstacles that impede it from successful clinical implementation. Moreover, we propose a semi-automated TDM approach to further enhance precision medicine for TB management.
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Affiliation(s)
- Rannissa Puspita Jayanti
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
| | - Nguyen Phuoc Long
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
| | - Nguyen Ky Phat
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
| | - Yong-Soon Cho
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
| | - Jae-Gook Shin
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan 47392, Korea; (R.P.J.); (N.P.L.); (N.K.P.); (Y.-S.C.)
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea
- Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan 47392, Korea
- Correspondence: ; Tel.: +82-51-890-6709; Fax: +82-51-893-1232
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12
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Nguyen VL, Ahn S, Hoa PQ, Long NP, Ahn S, Cho YS, Shin JG. Center for Personalized Precision Medicine for Tuberculosis: Smart Research and Development Workstation. Healthc Inform Res 2022; 28:176-180. [PMID: 35576986 PMCID: PMC9117804 DOI: 10.4258/hir.2022.28.2.176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/28/2022] [Accepted: 03/30/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Van Lam Nguyen
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan,
Korea
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan,
Korea
| | - Sangjin Ahn
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan,
Korea
| | - Pham Quang Hoa
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan,
Korea
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan,
Korea
| | - Nguyen Phuoc Long
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan,
Korea
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan,
Korea
| | - Sangzin Ahn
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan,
Korea
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan,
Korea
| | - Yong-Soon Cho
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan,
Korea
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan,
Korea
- Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan,
Korea
| | - Jae-Gook Shin
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan,
Korea
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan,
Korea
- Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan,
Korea
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