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- SUPPLEMENTAL MATERIAL Sputum processing for time to detection data
- Pharmacokinetic analyses
- Testing for co-linearity amongst pharmacokinetic variables
- REFERENCES
- Savic RM, Jonker DM, Kerbusch T, and Karlsson MO.
| The impact of non-linear interactions of pharmacokinetics and MICs on sputum bacillary kill rates as a marker of sterilizing effect in tuberculosis
Emmanuel Chigutsa, Jotam G. Pasipanodya, Marianne E. Visser, Paul D. van Helden, Peter J. Smith, Frederick A. Sirgel, Tawanda Gumbo, Helen McIlleron SUPPLEMENTAL MATERIAL Sputum processing for time to detection data All sputum samples were processed in the same laboratory which had standard operating procedures in place. Sputum samples from patients were decontaminated using a combination of 6% sodium hydroxide and 2.9% sodium citrate. Volumes of the decontamination solution added were adjusted to match the volume of sputum, so that it would become a 1 in 2 dilution in all cases. The mixture was allowed to stand for 20 minutes before adding phosphate buffer (pH 6.8) and centrifuging for 15 minutes at 3200 rpm. The supernatant was carefully decanted and 0.5 mL of phosphate buffer was added to the residue. 0.5 mL of the resultant mixture was measured out and put into the MGIT system. Therefore, equal volumes of the final mixture were inoculated into the MGIT system and incubated until a positive result was recorded, or up to 42 days, whichever occurred sooner. MIC determination Serial two-fold dilutions were prepared in sterile water for each drug. Standard Mycobacteria Growth Indicator Tubes (MGIT 960) with a pH of 6.8 were inoculated with 100 µl of the respective dilutions to obtain final drug concentrations ranging from: 0.01–0.1 mg/L for isoniazid, 0.016–1.0 mg/L for rifampin and 0.3–5.0 mg/L for ethambutol. MICs for pyrazinamide was determined in BACTEC MGIT 960 PZA medium at pH 5.9 (Becton Dickinson Biosciences, Sparks) and the test concentrations ranged from 6.25–100 mg/L. All tests were done in duplicate and repeated if discrepancies were observed. M. tuberculosis H37Rv (ATCC 27294) was included with each batch as reference strain to verify the reproducibility of the MGIT 960 PZA MIC test. MARS analysis The optimal MARS model is the one with the lowest GCV which is a balance between goodness-of-fit and number of BFs. In addition to the naïve R-squared value to assess the goodness of fit of the model on the original dataset, MARS implemented using the Salford Predictive Modeler® also provides a cross-validated R-squared value which can be used to assess the robustness of the final model and reliability of parameter estimates. MARS can also perform classification into categories, based on maximum homogeneity. Hence, local models describing the respective partitioned data regions with high variance are combined in a global model which achieves a good balance between bias and variance, and in addition to identification of global predictors, it can identify variable effects within discrete regions of the data. Pharmacokinetic analyses Measurement of drug plasma concentrations was carried out using high performance liquid chromatography-tandem mass spectrometric (LC/MS/MS) methods. Validated and published methods were used for rifampin, isoniazid, pyrazinamide (1) and ethambutol (2). The drug concentrations were validated in the range of 0.1-30 mg/L for rifampin, 0.1-15 mg/L for isoniazid, 0.2-70 mg/L for pyrazinamide and 0.1-10 mg/L for ethambutol. Inter- and intra-day coefficients of variation were less than 10% for all the drugs. Using pharmacokinetic models described below, the time integral of the drug concentrations (AUC0-24) was obtained by creating an additional compartment in NONMEM where the predicted amount of drug in the central compartment, divided by the volume of distribution, would accumulate into from 0-24 hours after the dose. The Cmax was obtained by using NONMEM to determine the maximum concentration of the predicted drug concentration-time profile. For the patients who had pharmacokinetic sampling performed on two occasions the AUC and Cmax were calculated separately for each occasion and the average of the two predictions was used as the PK variable for the subsequent analyses. Rifampin Twelve percent of the observed data was below the lower limit of quantification (LLOQ=0.1 mg/L). Beal’s M3 method was used to handle data below the LLOQ whereby data below the LLOQ was used to calculate the likelihood that the predicted concentration at that time was less than the LLOQ, whilst measurements greater than the LLOQ were used to calculate the likelihood that the prediction was equal to the measurement using the Laplacian estimation algorithm (3, 4). A transit absorption compartment model was used to account for the absorption delay (5, 6). First order elimination from a one compartment model best described the remaining structural model. A combined additive and proportional error model was used to describe the residual unexplained variability. Further details on the rifampin model are available from Chigutsa et al (7) and this is the same group of patients as those in the reference. For convenience, the parameter estimates and relative standard errors (RSE) from the model in this group of patients are shown in table S1.
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