Population pharmacokinetics of colistin: implications for clinical use for Gram-negative pathogens

Methods A clinical trial (MagicBullet) was conducted at the ICU of Virgen del Rocío University Hospital, Spain and Attikon University Hospital, Greece. Critically ill adult patients who met the following inclusion criteria were enrolled: >96h of mechanical ventilation, ventilator-associated pneumonia defined by clinical and radiological criteria, CPIS >4, tracheo-bronchial respiratory culture; pregnancy was an exclusion criterion. The following information was collected: gender, age, creatinine concentration (Cr) and creatinine clearance (CrCl) on days 1, 3, 7, 14 and 21 (Cockcroft-Gault equation). Serial venous blood collections were taken immediately at 1, 2, 4, 6 and 8 h after the beginning of the CMS infusion on day 1 and at 1, 2, 4, 6 and 8 h, at steady state. Colistin A and B and their prodrugs, CMS A and CMS B were measured using LC-MS/MS. A multi-compartment PK model was fitted to the data using the program Pmetrics. The differential equations driving the distribution of CMS and colistin are shown in equations (1) to (3). R1 represents the infusion rate of CMS, x1 and x2 the mass of CMS in the central and peripheral compartments, respectively, and x3 the mass of colistin in the single compartment for this drug. A graphical description of the model is shown in Figure 1: For the Monte Carlo simulations, a semiparametric sampling method was used. A colistin free fraction of 34% was used. The probability target attainment (PTA) was assessed for each dosing regimen using a range of fAUC0-24h/MIC targets determined from experimental models for both P. aeruginosa and A. baumannii (JAC, Cheah et al. 2015). Population Pharmacokinetics of Colistin: Implications for Clinical Use For Gram Negative Pathogens Pachón-Ibáñez ME1, Docobo-Pérez, F2, Blanquero Rafael3, Garnacho-Montero, J4; Sandra Benítez Peña3, Dimopoulos, G5; Rosso, C6; Gutiérrez A1, Lopez-cortes LF1, Cisneros-Herreros, J.M1. 1Unit of Infectious Diseases, Microbiology and Preventive Medicine, Biomedical Institute of Seville, University Hospital Virgen del Rocío/CSIC/University of Sevilla, Seville, Spain; 2Unit of Infectious Diseases and Clinical Microbiology, University Hospital Virgen Macarena, Seville, Spain; 3Department of Statistics and Operations Research, University of Seville; 4Unit of Critical Care and Emergency, University Hospital Virgen del Rocío of Seville, Spain; 5Unit of Critical Care and Emergency, University Hospital Attikon, Athens, Greece; 6 Clinical Trial Unit, Clinical Pharmacology Department, University Hospital Virgen del Rocío, Seville, Spain. Conclusion Our model suggests that a 4.5 MU (360 mg) loading dose, followed by 3 MU (240 mg)/Q8h, produced have a low PTA against A. baumannii and P. aeruginosa when the MIC are higher than 0.125 mg/L. These results can be used to provide decision support for establishing breakpoints for colistin against these nosocomial pathogens. Introduction and Purpose The objective of this study was to characterize the pharmacokinetics of colistin methanesulphonate (CMS) and colistin in critically ill patients following the administration of a 4.5 MU CMS loading dose follow by 3MU CMS Q8. A population PK model and Monte Carlo simulation were used to calculate the probability of target attainment (PTA) against Acinetobacter baumannii and Pseudomonas aeruginosa by considering a range of MIC values seen in the clinic. Results Demographic and clinical data are shown in Table 1. Nineteen patients were enrolled in the study. None of the patients required renal replacement therapy during the treatments. The coefficients of determination (r2) for observed versus individual fitted CMS and colistin plasma concentrations were 0.528 and 0.812, respectively. The mean and standard deviation of the parameters generated in the simulation were 31.52 (26.15) for V1, 31.31 (28.69) for V3, 6.99 (2.45) for K12, 2.00 (1.55) for K21, 1.11 (1.10) for K13, 6.13 (5.26) for SCL1 and 8.95 (4.47) for SCL3. PTA against A. baumannii and P. aeruginosa for the treatment of pneumonia are shown in figure 2. Figure 2. PTA against A. baumannii and P. aeruginosa for the treatment of pneumonia. Loading dose 4.5 MU CMS (360 mg) and 3 MU CMS (240 mg) Q8h. Static effect (triangle), 1-log (square) and 2-log (diamond) CFU/ml of bacterial reduction. Figure 2. Visual predictive check for plasma concentrations of CMS and colistin. Blue dots show the observed data, lines show percentiles (5th, 75th and 95th). A. baumannii P. aeruginosa Table 2. Population pharmacokinetic parameters Contact information: Address to María E. Pachón-Ibáñez. Dept. Infectious Diseases, Microbiology and Preventive Medicine. IBiS/HUVR of Seville, Spain. E-mail: [email protected] Parameter Mean SD %CV Median V1 33.034 25.862 78.287 21.547 V3 48.717 33.085 67.912 45.658 K12 4.952 3.207 64.760 4.991 K21 1.214 1.144 94.224 1.190 K13 0.390 0.178 45.729 0.310 SCL1 11.986 6.764 56.434 11.895 SCL3 13.965 4.379 31.359 15.333 CMS 𝑑𝑥1 𝑑𝑡 =𝑅1− 𝐾12 𝑥1− 𝑆𝐶𝐿1 𝑉 1 𝑥1− 𝐾13𝑥1+𝐾21 𝑥2 𝑑𝑥2 𝑑𝑡 = 𝐾12 𝑥1− 𝐾21 𝑥2 Colistin 𝑑𝑥3 𝑑𝑡 = 𝐾13 𝑥1− 𝑆𝐶𝐿3 𝑉 3 𝑥3 Figure 1. Graphical description of the PK model Patient´ s no. Gender Age (years) BMI APACH E II score Mortalit y 28 days Creatinine concentration (mg/dL) Creatinine Clearance (mL/min) Isolated microorganisms SV Day 1 Day 3 Day 7 Day 14 Day 21 Day 1 Day 3 Day 7 Day 14 Day 21 01 - 001 M 68 39,6 29 Alive 0.7 0.5 0.9 0.4 0.5 179.1 222.2 126.3 260.9 260.9 A. baumannii 01 - 002 M 47 25.3 10 Alive 0.8 0.8 1.2 3.5 2.7 137.8 137.8 82.7 29.3 38.7 A. baumannii 01 - 016 F 67 40,0 17 Alive 0.5 0.4 0.3 - - 152.1 198.9 221.6 - - A. baumannii 01 - 026 F 56 20.2 24 Dead 0.9 1.2 - - - 62.8 48.7 - - - - 01 - 035 M 54 29.2 8 Alive 0.8 0.9 1,0 0.4 0.4 151.2 121.9 119.4 265.4 322.8 Streptococcus spp. 01 - 047 M 68 55.7 22 Dead 0.5 0.7 0.5 - - 319.1 220.6 306.1 - - - 21 - 072 F 86 19.3 18 Dead 0.2 0.3 0.4 0.3 - 194.4 129.6 97.2 129.6 - A. baumannii P. aeruginosa 21 - 079 F 79 34.5 11 Alive 0.5 0.7 0.7 0.7 - 122.4 87.5 87.4 87.4 - Klebsiella pneumoniae 21 - 087 M 18 23.1 10 Alive 0.6 0.6 0.6 0.7 - 211.8 211.8 211.8 181.5 - - 01 - 096 F 79 31.3 11 Alive 0.7 0.6 0.5 0.2 0.3 81.1 91.4 110.8 250.5 169.4 Burkholderia cepacia 21 - 103 M 38 25.1 17 Alive 0.5 0.4 0.7 0.6 - 212.5 265.6 151.8 177.1 - - 01 - 112 M 65 30.1 17 Alive 1.1 0.9 0.8 0.6 - 83.0 104.2 110.3 135.8 - - 21 - 140 F 28 38.8 11 Alive 0.3 0.3 0.3 0.2 - 493.6 493.6 493.6 740.4 - - 01 - 149 F 71 32,0 17 Alive 0.8 0.7 0.5 - - 80.4 89.3 120.7 - - Staphylococcus aureus 01 - 172 M 69 21.9 18 Alive 0.3 0.6 0.5 - - 170.0 79.5 93.0 - - Enterobacter aerogenes 01 - 175 F 58 33.3 16 Alive 0.3 0.3 0.4 0.2 - 256.2 300.4 212.5 414.9 - - 21 - 176 M 55 32.2 14 Alive 0.6 0.5 0.9 - - 200.7 240.8 133.8 - - - 01 - 197 M 42 26.1 11 Alive 1.3 - - - - 54.7 - - - - - 01 - 213 M 79 29.4 14 Alive 0.4 0.4 0.4 - - 247.5 277.2 253.2 - - E. coli Table 1. Demographic and clinical data P1229 MagicBullet is a project funded by the European Union - Directorate General for Research and Innovation through the Seventh Framework Program for Research and Development (Grant Agreement 278232) and has been running since January 1st 2012 (duration 36 months).