Development of Population Pharmacokinetics Model of Isoniazid in Indonesian Tuberculosis Patients

ABSTRACT This study was designed to describe the population pharmacokinetics of rifapentine (RFP) and 25-desacetyl RFP in a South African pulmonary tuberculosis patient population. Special reference was made to studying the influence of previous exposure to rifampin (RIF) and the variability in pharmacokinetic parameters between patients and between occasions and the influence of different covariates. Patients were included in the study if they had been receiving first-line antimycobacterial therapy (rifampin, isoniazid, pyrazinamide, and ethambutol) for not less than 4 weeks and not more than 6 weeks and were divided into three RFP dosage groups based on weight: 600 mg, <45 kg; 750 mg, 46 to 55 kg; and 900 mg, >55 kg. Participants received a single oral dose of RFP together with concomitant antimycobacterial agents, excluding RIF, on study days 1 and 5 after they ingested a soup-based meal. The RFP and 25-desacetyl RFP concentration-time data were analyzed by nonlinear mixed-effect modeling using NONMEM. The pharmacokinetics of the parent drug were modeled separately, and the individual pharmacokinetic parameters were used as inputs for the 25-desacetyl RFP pharmacokinetic model. A one-compartment disposition model was found to best describe the data for both the parent and the metabolite, and the metabolite was assumed to be formed only from the central compartment of the parent drug. Prior treatment with RIF did not alter the pharmacokinetics of RFP but appeared to increase the excretion of 25-desacetyl RFP in a nonlinear fashion. The RFP oral clearance and volume of distribution were found to increase by 0.049 liter/h and 0.691 liter, respectively, with a 1-kg increase from the median weight of 50 kg. The oral clearance of 25-desacetyl RFP was found to be 35% lower in female patients. The model developed here describes the population pharmacokinetics of RFP and its primary metabolite in tuberculosis patients and includes the effects of prior administration with RIF and covariate factors.

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Effects of type 2 diabetes mellitus on the population pharmacokinetics of rifampin in tuberculosis patients

Tuberculosis ◽

10.1016/j.tube.2014.10.013 ◽

2015 ◽

Vol 95 (1) ◽

pp. 54-59 ◽

Cited By ~ 12

Author(s):

Min Jung Chang ◽

Jung-woo Chae ◽

Hwi-yeol Yun ◽

Jangik I. Lee ◽

Hye Duck Choi ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Population Pharmacokinetics ◽

Tuberculosis Patients

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Population Pharmacokinetics of Rifampin in Pulmonary Tuberculosis Patients, Including a Semimechanistic Model To Describe Variable Absorption

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.00461-07 ◽

2008 ◽

Vol 52 (6) ◽

pp. 2138-2148 ◽

Cited By ~ 90

Author(s):

Justin J. Wilkins ◽

Radojka M. Savic ◽

Mats O. Karlsson ◽

Grant Langdon ◽

Helen McIlleron ◽

...

Keyword(s):

Pulmonary Tuberculosis ◽

Population Pharmacokinetics ◽

South African ◽

Transit Time ◽

Mean Transit Time ◽

Volume Of Distribution ◽

Fixed Dose ◽

Time Data ◽

Multiple Dosing ◽

Tuberculosis Patients

ABSTRACT This article describes the population pharmacokinetics of rifampin in South African pulmonary tuberculosis patients. Three datasets containing 2,913 rifampin plasma concentration-time data points, collected from 261 South African pulmonary tuberculosis patients aged 18 to 72 years and weighing 28.5 to 85.5 kg and receiving regular daily treatment that included administration of rifampin (450 to 600 mg) for at least 10 days, were pooled. A compartmental pharmacokinetic model was developed using nonlinear mixed-effects modeling. Variability in the shape of the absorption curve was described using a flexible transit compartment model, in which a delay in the onset of absorption and a gradually changing absorption rate were modeled as the passage of drug through a chain of hypothetical compartments, ultimately reaching the absorption compartment. A previously described implementation was extended to allow its application to multiple-dosing data. The typical population estimate of oral clearance was 19.2 liters·h−1, while the volume of distribution was estimated to be 53.2 liters. Interindividual variability was estimated to be 52.8% for clearance and 43.4% for volume of distribution. Interoccasional variability was estimated for CL/F (22.5%) and mean transit time during absorption (67.9%). The use of single-drug formulations was found to increase both the mean transit time (by 104%) and clearance (by 23.6%) relative to fixed-dose-combination use. A strong correlation between clearance and volume of distribution suggested substantial variability in bioavailability, which could have clinical implications, given the dependence of treatment effectiveness on exposure. The final model successfully described rifampin pharmacokinetics in the population studied and is suitable for simulation in this context.

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Population pharmacokinetics and target attainment analysis of linezolid in multidrug‐resistant tuberculosis patients

British Journal of Clinical Pharmacology ◽

10.1111/bcp.15102 ◽

2021 ◽

Author(s):

Anna K. Tietjen ◽

Niklas Kroemer ◽

Dario Cattaneo ◽

Sara Baldelli ◽

Sebastian G. Wicha

Keyword(s):

Population Pharmacokinetics ◽

Multidrug Resistant ◽

Target Attainment ◽

Tuberculosis Patients ◽

Resistant Tuberculosis ◽

Multidrug Resistant Tuberculosis

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Population Pharmacokinetic Analyses of Isoniazid in Tuberculosis Treatment: a Systematic Review

VNU Journal of Science Medical and Pharmaceutical Sciences ◽

10.25073/2588-1132/vnumps.4278 ◽

2021 ◽

Vol 37 (1) ◽

Author(s):

Le Anh Tuan ◽

Bui Son Nhat ◽

Nguyen Hong Long ◽

Nguyen Thi Ngan ◽

Nguyen Thi Lien Huong ◽

...

Keyword(s):

World Health Organization ◽

Population Pharmacokinetics ◽

South African ◽

Antimicrobial Agents ◽

Compartment Model ◽

World Health ◽

Population Pharmacokinetic ◽

Pharmacokinetic Variability ◽

Tuberculosis Patients ◽

Health Organization

The aims of this systematic review are to provide knowledge concerning population pharmacokinetics of isoniazid (INH) and to identify factors influencing INH pharmacokinetic variability. Pubmed and Embase databases were systematically searched from inception to July, 2017. Relevant articles from reference lists were also included. All population pharmacokinetic studies of INH written in English, conducted in human (either healthy subjects or pulmonary tuberculosis patients) were included in this review. Ten studies were included in this review. Most studies characterized a two-compartment model with first-order kinetics for INH with a transit-compartment model for absorption suggested. Frequently reported significant predictors for INH clearance is NAT2 acetylator types (slow/intermediate/fast), while weight is a significant covariate for INH volume of distribution (both central and peripheral). In children, enzyme maturation had a profound affect on INH clearance. Keywords: Population pharmacokinetics, Isoniazid. References [1] World Health Organization, Global Tuberculosis Report 2019. https://apps.who.int/iris/bitstream/handle/10665/329368/9789241565714-eng.pdf (accessed 18 December 2019).[2] United Nations, Transforming our world: The 2030 agenda for sustainable development, New York, USA, 2015.[3] K. Takayama, L. Wang, H.L. David, Effect of isoniazid on the in vivo mycolic acid synthesis, cell growth, and viability of Mycobacterium tuberculosis, Antimicrob Agents Chemother 2.1 (1972) 29-35. https://doi.org/10.1128/aac.2.1.29 [4] A. Jindani, V.R. Aber, E. A. Edwards, D. A. Mitchison, The early bactericidal activity of drugs in patients with pulmonary tuberculosis. Am Rev Respir Dis 121(6) (1980) 939-49. https://doi.org/10.1164/arrd.1980.121.6.939 [5] P.R. Donald, The influence of human N-acetyltransferase genotype on the early bactericidal activity of isoniazid. Clin Infect Dis 39(10) (2004) 1425-30. https://doi.org/10.1086/424999 [6] D.A. Mitchison, Basic mechanisms of chemotherapy, Chest 76(6 Suppl) (1979) 771-81. https://doi.org/10.1378/chest.76.6_supplement.771 [7] H. McIlleron et al., Determinants of rifampin, isoniazid, pyrazinamide, and ethambutol pharmacokinetics in a cohort of tuberculosis patients, Antimicrob Agents Chemother 50(4) (2006) 1170-7. https://doi.org/10.1128/aac.50.4.1170-1177.2006 [8] S. Chideya et al., Isoniazid, rifampin, ethambutol, and pyrazinamide pharmacokinetics and treatment outcomes among a predominantly HIV-infected cohort of adults with tuberculosis from Botswana, Clin Infect Dis 48(12) (2009) 1685-94. https://doi.org/10.1086/599040 [9] N. Singh et al., Study of NAT2 gene polymorphisms in an Indian population: association with plasma isoniazid concentration in a cohort of tuberculosis patients. Mol Diagn Ther 13(1) (2009) 49-58. https://doi.org/10.1007/bf03256314 [10] N. Buchanan, C. Eyberg, M.D. Davis, Isoniazid pharmacokinetics in kwashiorkor. S Afr Med J 56(8) (1979) 299-300.[11] U.S. Food and Drug Administration (1999), "Guidance for Industry. Populationpharmacokinetics",Retrieved from http://www.fda.gov/downloads/Drugs/.../Guidances/UCM072137.pdf[12] D. R Mould, R. N. Upton, Basic concepts in population modeling, simulation, and model‐based drug development, CPT: pharmacometrics & systems pharmacology 1(9) (2012) 1-14. https://doi.org/10.1038/psp.2012.4 [13] P. Denti et al., Pharmacokinetics of isoniazid, pyrazinamide, and ethambutol in newly diagnosed pulmonary TB patients in Tanzania, PLoS ONE 10(10) (2015), e0141002. https://doi.org/10.1371/journal.pone.0141002 [14] B. Guiastrennec et al., Suboptimal Antituberculosis Drug Concentrations and Outcomes in Small and HIV-Coinfected Children in India: Recommendations for Dose Modifications, Clin Pharmacol Ther 104(4) (2017), 733-741. https://doi.org/10.1002/cpt.987 [15] M. Kinzig-Schippers et al., Should we use N-acetyltransferase type 2 genotyping to personalize isoniazid doses?, Antimicrobial Agents and Chemotherapy 49(5) (2005), 1733-1738. https://doi.org/10.1128/aac.49.5.1733-1738.2005 [16] J.J. Kiser et al., Isoniazid pharmacokinetics, pharmacodynamics, and dosing in South African infants, Therapeutic Drug Monitoring 34(4) (2012) 446-451. https://doi.org/10.1097/ftd.0b013e31825c4bc3 [17] L. Lalande, Population modeling and simulation study of the pharmacokinetics and antituberculosis pharmacodynamics of isoniazid in lungs, Antimicrobial Agents and Chemotherapy 59(9) (2015) 5181-5189. https://doi.org/10.1128/aac.00462-15 [18] C. Magis-Escurra et al., Population pharmacokinetics and limited sampling strategy for first-line tuberculosis drugs and moxifloxacin, International Journal of Antimicrobial Agents 44(3) (2014) 229-234. https://doi.org/10.1016/j.ijantimicag.2014.04.019 [19] C.A. Peloquin et al., Population pharmacokinetic modeling of isoniazid, rifampin, and pyrazinamide, Antimicrobial Agents and Chemotherapy 41(12) (1997) 2670-2679. https://doi.org/10.1128/aac.41.12.2670 [20] K.Y. Seng et al., Population pharmacokinetic analysis of isoniazid, acetylisoniazid, and isonicotinic acid in healthy volunteers, Antimicrobial Agents and Chemotherapy 59(11) (2015) 6791-6799. https://doi.org/10.1128/aac.01244-15 [21] J.J. Wilkins et al., Variability in the population pharmacokinetics of isoniazid in South African tuberculosis patients, British Journal of Clinical Pharmacology 72(1) (2011) 51-62. https://doi.org/10.1111/j.1365-2125.2011.03940.x [22] S.P. Zvada et al., Population pharmacokinetics of rifampicin, pyrazinamide and isoniazid in children with tuberculosis: In silico evaluation of currently recommended doses, Journal of Antimicrobial Chemotherapy 69(5) (2014) 1339-1349. https://doi.org/10.1093/jac/dkt524 [23] World Health Organization, Guidance for national tuberculosis programmes on the management of tuberculosis in children (No. WHO/HTM/TB/2014.03). World Health Organization, 2014.[24] World Health Organization, & Stop TB Initiative (World Health Organization), Treatment of tuberculosis: guidelines. World Health Organization, 2010.[25] J.S. Starke, S.M, Tuberculosis in: James D. Cherry, Ralph D. Feigin (Eds.), Textbook of Pediatric Infectious Diseases., Saunders: Philadelphia, 1998 pp. 1196-1238. [26] J.G. Pasipanodya, S. Srivastava, T. Gumbo, Meta-analysis of clinical studies supports the pharmacokinetic variability hypothesis for acquired drug resistance and failure of antituberculosis therapy, Clinical Infectious Diseases 55(2) (2012) 169-177. https://doi.org/10.1093/cid/cis353

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Population Pharmacokinetics of Rifampicin on Pulmonary Tuberculosis Patients

VNU Journal of Science Medical and Pharmaceutical Sciences ◽

10.25073/2588-1132/vnumps.4168 ◽

2019 ◽

Vol 35 (2) ◽

Author(s):

Bui Son Nhat ◽

Vu Dinh Hoa ◽

Le Anh Tuan ◽

Le Thi Luyen

Keyword(s):

Clinical Pharmacology ◽

Pulmonary Tuberculosis ◽

Population Pharmacokinetics ◽

South African ◽

Pharmacokinetic Model ◽

Antimicrobial Agents ◽

Population Pharmacokinetic Analysis ◽

Pharmacokinetic Analysis ◽

Population Pharmacokinetic ◽

Tuberculosis Patients

Abstract: This study aimed to establish a reasonable population pharmacokinetic model for rifampicin taken orally by patients with pulmonary tuberculosis, estimate pharmacokinetic parameters as well as influencing covariates. Blood samples of patients were collected at day 10 – 14 after commencing treatment. Time – concentration data were handled using non-linear mixed-effect model with Monolix 2018. An one-compartment, linear elimination, absorption with transit compartments model was found to be the most suitable for rifampicin. Volume of distribution (33,5 L) and clearance (9,62 L) were found to be influenced by fat-free mass (calculated using Janmahasatian’s method). Absorption-related parameters (Ktr, mean transit time and Ka) were found to have high inter-individual variability. Keywords Rifampicin, population pharmacokinetics, pulmonary tuberculosis. References [1] Christian Lienhardt et al, Target regimen profiles for treatment of tuberculosis: a WHO document (2017).[2] J.G. Pasipanodya et al, Serum drug concentrations predictive of pulmonary tuberculosis outcomes, The Journal of infectious diseases 208(9) (2013) 1464-1473. https://doi.org/10.1093/infdis/jit352[3] Jonathan Reynolds, Scott K Heysell (2014), Understanding pharmacokinetics to improve tuberculosis treatment outcome, Expert opinion on drug metabolism & toxicology 10(6) (2014) 813-823. https://doi.org/10.1517/17425255.2014.895813[4] E.F. Egelund, A.B. Barth, C.A. Peloquin (2011), Population pharmacokinetics and its role in anti-tuberculosis drug development and optimization of treatment, Current pharmaceutical design 17(27) (2017) 2889-2899. https://doi.org/10.2174/138161211797470246.[5] J.F. Murray, D.E. Schraufnagel, P.C. Hopewell, Treatment of tuberculosis. A historical perspective, Annals of the American Thoracic Society 12(12) (2015) 1749-1759. https://doi.org/10.1513/AnnalsATS.201509-632PS[6] K.E. Stott, et al, Pharmacokinetics of rifampicin in adult TB patients and healthy volunteers: a systematic review and meta-analysis, Journal of Antimicrobial Chemotherapy 73(9) (2018) 2305-2313. https://doi.org/10.1093/jac/dky152.[7] Le Thi Luyen, Ta Manh Hung et al, Simultaneous Determination of Pyrazinamide, Rifampicin, Ethambutol, Isoniazid and Acetyl Isoniazid in Human Plasma by LC-MS/MS Method, Journal of Applied Pharmaceutical Science 8(09) (2018) 061-073. https://doi.org/ 10.7324/JAPS.2018.8910.[8] M.T. Chirehwa et al, Model-based evaluation of higher doses of rifampin using a semimechanistic model incorporating autoinduction and saturation of hepatic extraction, Antimicrobial agents and chemotherapy 60(1) (2016) 487-494. https://doi.org/10.1128/AAC.01830-15.[9] Paolo Denti et al, A population pharmacokinetic model for rifampicin auto-induction, The 3rd international workshop on clinical pharmacology of TB drugs (2010).[10] Y. Jing et al, Population pharmacokinetics of rifampicin in Chinese patients with pulmonary tuberculosis, The Journal of Clinical Pharmacology 56(5) (2016) 622-627. https://doi.org/10.1002/jcph.643.[11] S.R.C. Milán et al, Population pharmacokinetics of rifampicin in Mexican patients with tuberculosis, Journal of clinical pharmacy and therapeutics 38(1) (2013) 56-61. https://doi.org/10.1111/jcpt.12016.[12] Anushka Naidoo et al, Effects of genetic variability on rifampicin and isoniazid pharmacokinetics in South African patients with recurrent tuberculosis, Pharmacogenomics(00) (2013). https://doi.org/10.2217/pgs-2018-0166.[13] Neesha Rockwood et al, HIV-1 coinfection does not reduce exposure to rifampin, isoniazid, and pyrazinamide in South African tuberculosis outpatients, Antimicrobial agents and chemotherapy 60(10) (2016) 6050-6059. https://doi.org/10.1128/AAC.00480-16.[14] Alessandro Schipani et al, A simultaneous population pharmacokinetic analysis of rifampicin in Malawian adults and children, British Journal of Clinical Pharmacology 81(4) (2016) 679-687. https://doi.org/10.1111/bcp.12848.[15] Kok-Yong Seng et al, Population pharmacokinetics of rifampicin and 25-deacetyl-rifampicin in healthy Asian adults, Journal of Antimicrobial Chemotherapy 70(12) (2015) 3298-3306. https://doi.org/10.1093/jac/dkv268.[16] J.J. Wilkins et al, Population pharmacokinetics of rifampin in pulmonary tuberculosis patients, including a semimechanistic model to describe variable absorption, Antimicrobial agents and chemotherapy 52(6) (2008)2138-2148. https://dx.doi.org/10.1128%2FAAC.00461-07.[17] Sylvain Goutelle et al, Population modeling and Monte Carlo simulation study of the pharmacokinetics and antituberculosis pharmacodynamics of rifampin in lungs, Antimicrobial agents and chemotherapy 53(7) (2009) 2974-2981. https://doi.org/10.1128/AAC.01520-08.[18] R.M. Savic et al, Implementation of a transit compartment model for describing drug absorption in pharmacokinetic studies, Journal of pharmacokinetics and pharmacodynamics 34(5) (2007) 711-726. https://doi.org/10.1007/s10928-007-9066-0.[19] B.J. Anderson, N.H.G. Holford, Mechanism-based concepts of size and maturity in pharmacokinetics, Annu. Rev. Pharmacol. Toxicol 48 (2008) 303-332. https://doi.org/10.1146/annurev.pharmtox.48.113006.094708.[20] Kok-Yong Seng et al, Population pharmacokinetic analysis of isoniazid, acetyl-isoniazid and isonicotinic acid in healthy volunteers, Antimicrobial agents and chemotherapy, pp. AAC. (2015) 01244-15. https://doi.org/10.1128/AAC.01244-15.[21] Sarayut Janmahasatian et al, Quantification of lean bodyweight, Clinical pharmacokinetics 44(10), (2005) 1051-1065. https://doi.org/10.2165/00003088-200544100-00004.[22] Kidola Jeremiah et al, Nutritional supplementation increases rifampin exposure among tuberculosis patients coinfected with HIV, Antimicrobial agents and chemotherapy 58(6) (2014) 3468-3474. https://doi.org/10.1128/AAC.02307-13

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Population Pharmacokinetics of Ethambutol in South African Tuberculosis Patients

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.00274-11 ◽

2011 ◽

Vol 55 (9) ◽

pp. 4230-4237 ◽

Cited By ~ 32

Author(s):

Siv Jönsson ◽

Alistair Davidse ◽

Justin Wilkins ◽

Jan-Stefan Van der Walt ◽

Ulrika S. H. Simonsson ◽

...

Keyword(s):

Renal Function ◽

Body Weight ◽

Population Pharmacokinetics ◽

South African ◽

Plasma Concentrations ◽

Spectrometric Method ◽

Mass Spectrometric Method ◽

Oral Clearance ◽

Tuberculosis Patients ◽

Tandem Mass Spectrometric

ABSTRACTEthambutol, one of four drugs in the first-line antitubercular regimen, is used to protect against rifampin resistance in the event of preexisting resistance to isoniazid. The population pharmacokinetics of ethambutol in South African patients with pulmonary tuberculosis were characterized using nonlinear mixed-effects modeling. Patients from 2 centers were treated with ethambutol (800 to 1,500 mg daily) combined with standard antitubercular medication. Plasma concentrations of ethambutol were measured following multiple doses at steady state and were determined using a validated high-pressure liquid chromatography-tandem mass spectrometric method. The data comprised 189 patients (54% male, 12% HIV positive) weighing 47 kg, on average (range, 29 to 86 kg), and having a mean age of 36 years (range, 16 to 72 years). The estimated creatinine clearance was 79 ml/min (range, 23 to 150 ml/min). A two-compartment model with one transit compartment prior to first-order absorption and allometric scaling by body weight on clearance and volume terms was selected. HIV infection was associated with a 15% reduction in bioavailability. Renal function was not related to ethambutol clearance in this cohort. Interoccasion variability exceeded interindividual variability for oral clearance (coefficient of variation, 36 versus 20%). Typical oral clearance in this analysis (39.9 liters/h for a 50-kg individual) was lower than that previously reported, a finding partly explained by the differences in body weight between the studied populations. In summary, a population model describing the pharmacokinetics of ethambutol in South African tuberculosis patients was developed, but additional studies are needed to characterize the effects of renal function.

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