Laure Lalande (1), Laurent Bourguignon (1,2), Pascal Maire (1,2), Sylvain Goutelle (1,2)
(1) UMR CNRS 5558, Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, Villeurbanne, France, (2) Service Pharmacie, Groupement hospitalier de gériatrie, Hospices Civils de Lyon, Lyon, France
Objectives: To develop a pulmonary pharmacokinetic (PK) model of isoniazid (INH) and to simulate the ability of various dosage regimens to attain published target exposure of antimicrobial efficacy against Mycobacterium tuberculosis.
Methods: Population analysis of plasma, epithelial lining fluid (ELF) and alveolar cell (AC) data [1,2,3] from 89 subjects who received oral INH was performed using Monolix software (SAEM algorithm). The covariate selection was performed using a stepwise procedure. Matlab was then used to perform 1000-subject simulations with different dosing regimens in fast (FA) and slow INH acetylators (SA). Individual AUC24/MIC (area under the concentration-curve divided by minimal inhibitory concentration) ratios were calculated in plasma and ELF for 8 MIC values ranging from 0,016 to 2 mg/L[4]. The AUC/MIC ratios were then compared to target ratios associated with a 90% killing effect on M. tuberculosis in a murine (567)[5] and an in vitro study (707,13)[6].
Results: A 3-compartment model with first-order oral absorption and linear elimination was selected. INH acetylator status significantly influenced the drug elimination and was the only covariate included in the final model. The model displayed acceptable predictive performance with mean error (ME, in mg/L) and root mean squared error (RMSE, in mg/L) of -0,14 and 1,17; 0,76 and 3,77; -1,18 and 5,24 for population predictions in plasma, ELF and AC respectively. In the simulations, a 300mg INH dose was associated with a probability of target attainment (PTA) in plasma of 53,6% in FA and 74,1% in SA for a low MIC of 0.016 mg/L, and only 0,05% in FA and 0,2% in SA for a high MIC of 0.125 mg/L. In the ELF, the PTA was 94,9% and 97,2% for the low MIC and 54,8% and 68,1% for the high MIC in FA and SA respectively. A 600mg INH dose was associated with PTA in the two acetylator groups (FA/SA) of 83,1%/92,5% for the low MIC, and 5,3%/13,5% for the high MIC in plasma. In the ELF, those respective PTA were 94,9%/97,2% for the low MIC and 54,8%/68,1% for the high MIC. The PTA were significantly lower in FA than in SA for all those simulations (χ-2 test, p<10-3).
Conclusions: This is the first population model describing INH PK in plasma, ELF and AC. Simulations performed indicated that INH acetylator status and M. tuberculosis MIC may greatly influence the ability to reach INH exposures that are optimal for bacterial killing. The adult standard dose of 300mg appears to be suboptimal, especially in FA.
References:
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[2] O’Brien JK, Doerfler ME, Harkin TJ, Rom WN. Isoniazid levels in the bronchoalveolar lavage fluid of patients with pulmonary tuberculosis. Lung (1998) 176:205-211
[3] Katiyar SK, Bihari S, Prakash S. Low-dose inhaled versus standard dose oral form of anti-tubercular drugs: concentrations in bronchial epithelial lining fluid, alveolar macrophage and serum. J Postgrad Med (2008):54(3):245-246
[4] Gumbo T. New susceptibility breakpoint for first-line antituberculosis drugs based on antimicrobial pharmacokinetic/pharmacodynamics science and population pharmacokinetic variability. Antimicrob Agents Chemother (2010) 54(4):1484-91
[5] Jayaram R, Shandil RK, Gaonkar S, Kaur P, Suresh BL, Mahesh BN, Jayashree R, Nandi V, Bharath S, Kantharaj E, Baladubramaniam V. Isoniazid pharmacokinetics-pharmacodynamics in an aerosol infection model of tuberculosis. Antimicrob Agents Chemother (2004) 48(8):2951-57
[6] Gumbo T, Louie A, Liu W, Brown D, Ambrose PG, Bhavnani SM, Drusano GL. Isoniazid bactericidal activity and resistance emergence: integrating pharmacodynamics and pharmacogenomics to predict efficacy in different ethnic populations. Antimicrob Agents Chemother (2007) 51(7):2329-36
Reference: PAGE 23 () Abstr 3078 [www.page-meeting.org/?abstract=3078]
Poster: Drug/Disease modeling - Infection