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The tumor size ratio (TSR), time-to-tumor growth (TTG), and tumor growth rate (kG) are frequently suggested as model-based predictors of overall survival (OS) for different types of tumors. When the tumor metrics are applied in forecasting of the outcome for individual patients at an early stage, the tumor data might be sparse resulting in imprecise prediction. This simulation study aimed to investigate how the tumor follow-up data and estimation approaches influence the accuracy in the tumor size metrics and the predicted hazard of death for individual patients. Longitudinal tumor size and OS data were simulated using tumor growth inhibition and Weibull distribution models, respectively. Based on the model and increasing measurement durations, the accuracy (defined as 80–125% of the simulated “true” value) in individual metrics and hazard was computed. TSR week 6 (TSRw6) accuracy was adequate for 91% of the patients when tumor size was measured up to 12 weeks. For TTG and kG metrics, the highest accuracy observed was lower (43 and 77%, respectively) and occurred later (42 and 60 weeks, respectively). The simultaneous (joint) and sequential estimation approaches resulted in similar accuracies, however, in general, the sequential approach where individual tumor size parameters are fixed, demonstrated inferior estimation properties. The TSRw6 and the model-predicted tumor time course (absolute or relative change) had better forecasting properties than TTG or kG. The population pharmacokinetic (PK) parameters and data approach performed similarly or better than the simultaneous approach and had a better accuracy in estimating individuals' hazard of death than the individual PK parameters method. Title in thesis list of papers of Sreenath Madathil Krishnan: Bayesian forecasting of tumor size metrics and survival

To our knowledge, this is the first large-scale study to report on HR in Gram-negative bacteria. We described the prevalence of colistin HR in a large sample of carbapenem-resistant A. baumannii isolates, the evolution of many colistin HR isolates to a resistant phenotype following colistin exposure and withdrawal, and the clinical consequences of colistin HR.

Objectives In vitro models showing synergism between polymyxins and carbapenems support combination treatment for carbapenem-resistant Gram-negative (CRGN) infections. We tested the association between the presence of in vitro synergism and clinical outcomes in patients treated with colistin plus meropenem. Methods This was a secondary analysis of AIDA, a randomized controlled trial comparing colistin with colistin–meropenem for severe CRGN infections. We tested in vitro synergism using a checkerboard assay. Based on the fractional inhibitory concentration (ΣFIC) index for each colistin–meropenem combination, we categorized results as synergistic, antagonistic or additive/indifferent. The primary outcome was clinical failure at 14 days. Secondary outcomes were 14- and 28-day mortality and microbiological failure. Results The sample included 171 patients with infections caused by carbapenem-resistant Acinetobacter baumannii (n = 131), Enterobacteriaceae (n = 37) and Pseudomonas aeuruginosa (n = 3). In vitro testing showed synergism for 73 isolates, antagonism for 20 and additivism/indifference for 78. In patients who received any colistin plus meropenem, clinical failure at 14 days was 59/78 (75.6%) in the additivism/indifference group (reference category), 54/73 (74.0%) in the synergism group (adjusted odds ratio (aOR) 0.76, 95% CI 0.31–1.83), and 11/20 (55%) in the antagonism group (aOR 0.77, 95% CI 0.22–2.73). There was no significant difference between groups for any secondary outcome. Comparing the synergism group to patients treated with colistin monotherapy, synergism was not protective against 14-day clinical failure (aOR 0.52, 95% CI 0.26–1.04) or 14-day mortality (aOR1.09, 95% CI 0.60–1.96). Discussion In vitro synergism between colistin and meropenem via checkerboard method did not translate into clinical benefit.

In the article referenced above [Fransson MN, Green H, Litton J-E, and Friberg LE (2011) Drug Metab Dispos 39: [247–255][1]], in [Table 3][2], the RSE value for IIVf6aOH/fmpac and the Parameter IIVf6aOH/fmpac are incorrect. The corrected table appears below. View this table: TABLE 3 Parameter

BackgroundToxicity and therapeutic effects of paclitaxel vary greatly between patients and remain a clinically relevant problem with regard to the handling of dose delay/reduction or termination of treatment. We investigated the notion that single nucleotide polymorphisms (SNPs) in CYP2C8 could be partly responsible for this variation. Paclitaxel is mainly metabolized by CYP2C8; SNPs have been investigated in this context before but conclusions are still lacking. We present a prospective study of paclitaxel clearance (CL) in 93 Caucasian females with epithelial ovarian cancer with regard to the CYP2C8 *1b, *1c, *3 and *4 genotypes.Material and methodsAll patients were diagnosed with primary ovarian/peritoneal cancer and received 175mg/m2 paclitaxel over 3 hrs plus carboplatin (AUC5-6) q3w. All patients gave written and verbal consent. The study was approved by ethics committees in Denmark and Sweden. Blood was sampled at 3hrs, 5-8 hrs and 18-24hrs after start of infusion. Total plasma paclitaxel was quantified using HPLC. CremophorEL® (CrEL) was determined as described by Sparreboom et al.1998. CL of unbound paclitaxel was estimated using total concentrations, CrEL and other parameters in the model described by Henningsson et al.2003 using NONMEM VI. Genotypes were determined using Pyrosequencing. Genotypes were in HW equilibrium, except *1b (p=0.01).ResultsThe PK model predicted the data well. The CL of unbound paclitaxel was lower for patients with the CYP2C8*3 and *4 variants (pCYP2C8variantNPaclitaxel CL geometric mean (l/h)95% c.iP-value(T-test of log transformed CL)Wt/Wt49395.0[370.3;421.4] Wt/*1b + *1b/*1b(n=1)44374.5[348.8;402.2]0.267Wt/Wt69382.4[362.1;404.0] Wt/*1c24393.1[355.4;434.8]0.617Wt/Wt74394.7[375.2;415.3] Wt/*319350.0[310.0;395.3]0.041*Wt/Wt86390.9[372.1;410.7] Wt/*47320.8[281.7;365.3]0.028*Note: for the one patient carrying both the *3 and *4 variant CL was 269.7 l/hConclusionsThis study implies reduced elimination of paclitaxel in Caucasian female patients with the CYP2C8*3 and *4 genotypes. This confirms several in vitro studies and pilot studies but is different to Henningsson et al 2005 and Marsh et al 2006 which could be explained by differences in dose ranges, infusion times and/or related to gender. The finding is important in terms of understanding inter individual variability of paclitaxel pharmacokinetics and might in the future provide useful information for individualized chemotherapy.