Improvement of therapeutic drug monitoring of imatinib by Bayesian prediction of trough levels. Personalized drug dosage

Aims: Plasma concentrations of imatinib differ largely between patients despite same dosage, owing to large inter-individual variability in pharmacokinetic (PK) parameters. As the drug concentration at the end of the dosage interval (Cmin) correlates with treatment response and tolerability, monitoring of Cmin is suggested for therapeutic drug monitoring (TDM) of imatinib. Due to logistic difficulties, random sampling during the dosage interval is however often performed in clinical practice, thus rendering the respective results not informative regarding Cmin values.Objectives: (I) To extrapolate randomly measured imatinib concentrations to more informative Cmin using classical Bayesian forecasting. (II) To extend the classical Bayesian method to account for correlation between PK parameters. (III) To evaluate the predictive performance of both methods.Methods: 31 paired blood samples (random and trough levels) were obtained from 19 cancer patients under imatinib. Two Bayesian maximum a posteriori (MAP) methods were implemented: (A) a classical method ignoring correlation between PK parameters, and (B) an extended one accounting for correlation. Both methods were applied to estimate individual PK parameters, conditional on random observations and covariate-adjusted priors from a population PK model. The PK parameter estimates were used to calculate trough levels. Relative prediction errors (PE) were analyzed to evaluate accuracy (one-sample t-test) and to compare precision between the methods (F-test to compare variances).Results: Both Bayesian MAP methods allowed non-biased predictions of individual Cmin compared to observations: (A) - 7% mean PE (CI95% - 18 to 4 %, p = 0.15) and (B) - 4% mean PE (CI95% - 18 to 10 %, p = 0.69). Relative standard deviations of actual observations from predictions were 22% (A) and 30% (B), i.e. comparable to the intraindividual variability reported. Precision was not improved by taking into account correlation between PK parameters (p = 0.22).Conclusion: Clinical interpretation of randomly measured imatinib concentrations can be assisted by Bayesian extrapolation to maximum likelihood Cmin. Classical Bayesian estimation can be applied for TDM without the need to include correlation between PK parameters. Both methods could be adapted in the future to evaluate other individual pharmacokinetic measures correlated to clinical outcomes, such as area under the curve(AUC).

Prediction of free imatinib concentrations based on total plasma concentrations in patients with gastrointestinal stromal tumours.

AIM: Total imatinib concentrations are currently measured for the therapeutic drug monitoring of imatinib, whereas only free drug equilibrates with cells for pharmacological action. Due to technical and cost limitations, routine measurement of free concentrations is generally not performed. In this study, free and total imatinib concentrations were measured to establish a model allowing the confident prediction of imatinib free concentrations based on total concentrations and plasma proteins measurements. METHODS: One hundred and fifty total and free plasma concentrations of imatinib were measured in 49 patients with gastrointestinal stromal tumours. A population pharmacokinetic model was built up to characterize mean total and free concentrations with inter-patient and intrapatient variability, while taking into account α1 -acid glycoprotein (AGP) and human serum albumin (HSA) concentrations, in addition to other demographic and environmental covariates. RESULTS: A one compartment model with first order absorption was used to characterize total and free imatinib concentrations. Only AGP influenced imatinib total clearance. Imatinib free concentrations were best predicted using a non-linear binding model to AGP, with a dissociation constant Kd of 319 ng ml(-1) , assuming a 1:1 molar binding ratio. The addition of HSA in the equation did not improve the prediction of imatinib unbound concentrations. CONCLUSION: Although free concentration monitoring is probably more appropriate than total concentrations, it requires an additional ultrafiltration step and sensitive analytical technology, not always available in clinical laboratories. The model proposed might represent a convenient approach to estimate imatinib free concentrations. However, therapeutic ranges for free imatinib concentrations remain to be established before it enters into routine practice.

Prise en charge de la pneumonie communautaire [Management of community acquired pneumonia]

Une grande partie des patients présentant une pneumonie communautaire ont un bon pronostic et peuvent être suivis dans la pratique ambulatoire moyennant un nombre limité d'examens paracliniques. Le diagnostic repose sur une clinique suggestive en présence d'un infiltrat compatible avec une pneumonie à la radiographie de thorax. Une anamnèse de voyage et une bonne connaissance de résistances locales du pneumocoque aux antibiotiques sont déterminantes pour le choix du traitement. Pour les patients de moins de 50 ans sans comorbidités et sans signes de sévérité, il est généralement recommandé de prescrire un macrolide ou une fluoroquinolone de 3e ou de 4e génération (fluoroquinolones «respiratoires»). Pour les patients plus âgés, et pour ceux qui présentent des comorbidités ou des critères de sévérité, une évaluation à l'hôpital est indiquée. La majorité des patients hospitalisés en soins généraux peuvent être traités par l'association d'une bêta-lactame et d'un macrolide, ou par une fluoroquinolone «respiratoire» seule, après prélèvements d'expectorations et de sang pour culture.

Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation

High-throughput prioritization of cancer-causing mutations (drivers) is a key challenge of cancer genome projects, due to the number of somatic variants detected in tumors. One important step in this task is to assess the functional impact of tumor somatic mutations. A number of computational methods have been employed for that purpose, although most were originally developed to distinguish disease-related nonsynonymous single nucleotide variants (nsSNVs) from polymorphisms. Our new method, transformed Functional Impact score for Cancer (transFIC), improves the assessment of the functional impact of tumor nsSNVs by taking into account the baseline tolerance of genes to functional variants.

Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

Pseudomonas aeruginosa serotypes in nosocomial pneumonia: prevalence and clinical outcomes.

INTRODUCTION: Pseudomonas aeruginosa frequently causes nosocomial pneumonia and is associated with poor outcome. The purpose of this study was to assess the prevalence and clinical outcome of nosocomial pneumonia caused by serotype-specific P. aeruginosa in critically ill patients under appropriate antimicrobial therapy management. METHODS: A retrospective, non-interventional epidemiological multicenter cohort study involving 143 patients with confirmed nosocomial pneumonia caused by P. aeruginosa. Patients were analyzed for a period of 30 days from time of nosocomial pneumonia onset. Fourteen patients fulfilling the same criteria from a phase IIa studyconducted at the same time/centers were included in the prevalence calculations but not in the clinical outcome analysis. RESULTS: The prevalence of serotypes was: O6 (29%), O11 (23%), O10 (10%), O2 (9%), and O1 (8%). Serotypes with a prevalence of less than 5% were found in 13% of patients, 8% were classified as not typeable. Across all serotypes, 19% mortality, 70% clinical resolution, 11% clinical continuation, and 5% clinical recurrence were recorded. Age and higher APACHE II (Acute Physiology and Chronic Health Evaluation II) were predictive risk factors associated with probability of death and lower clinical resolution for P. aeruginosa nosocomial pneumonia. Mortality tends to be higher with O1 (40%) and lower with O2 (0%); clinical resolution tends to be better with O2 (82%) compared to other serotypes. Persisting pneumonia with O6 and O11 was, respectively, 8% and 21%; clinical resolution with O6 and O11 was, respectively, 75% and 57%. CONCLUSIONS: In P. aeruginosa nosocomial pneumonia, the most prevalent serotypes were O6 and O11. Further studies including larger group sizes are needed to correlate clinical outcome with virulence factors of P. aeruginosa in patients with nosocomial pneumonia caused by various serotypes; and to compare O6 and O11, the two serotypes most frequently encountered in critically ill patients.

Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks

Abstract Background: Many complex systems can be represented and analysed as networks. The recent availability of large-scale datasets, has made it possible to elucidate some of the organisational principles and rules that govern their function, robustness and evolution. However, one of the main limitations in using protein-protein interactions for function prediction is the availability of interaction data, especially for Mollicutes. If we could harness predicted interactions, such as those from a Protein-Protein Association Networks (PPAN), combining several protein-protein network function-inference methods with semantic similarity calculations, the use of protein-protein interactions for functional inference in this species would become more potentially useful. Results: In this work we show that using PPAN data combined with other approximations, such as functional module detection, orthology exploitation methods and Gene Ontology (GO)-based information measures helps to predict protein function in Mycoplasma genitalium. Conclusions: To our knowledge, the proposed method is the first that combines functional module detection among species, exploiting an orthology procedure and using information theory-based GO semantic similarity in PPAN of the Mycoplasma species. The results of an evaluation show a higher recall than previously reported methods that focused on only one organism network.

Symptomatic intracranial hemorrhage after stroke thrombolysis: comparison of prediction scores.

BACKGROUND AND PURPOSE: Several prognostic scores have been developed to predict the risk of symptomatic intracranial hemorrhage (sICH) after ischemic stroke thrombolysis. We compared the performance of these scores in a multicenter cohort. METHODS: We merged prospectively collected data of patients with consecutive ischemic stroke who received intravenous thrombolysis in 7 stroke centers. We identified and evaluated 6 scores that can provide an estimate of the risk of sICH in hyperacute settings: MSS (Multicenter Stroke Survey); HAT (Hemorrhage After Thrombolysis); SEDAN (blood sugar, early infarct signs, [hyper]dense cerebral artery sign, age, NIH Stroke Scale); GRASPS (glucose at presentation, race [Asian], age, sex [male], systolic blood pressure at presentation, and severity of stroke at presentation [NIH Stroke Scale]); SITS (Safe Implementation of Thrombolysis in Stroke); and SPAN (stroke prognostication using age and NIH Stroke Scale)-100 positive index. We included only patients with available variables for all scores. We calculated the area under the receiver operating characteristic curve (AUC-ROC) and also performed logistic regression and the Hosmer-Lemeshow test. RESULTS: The final cohort comprised 3012 eligible patients, of whom 221 (7.3%) had sICH per National Institute of Neurological Disorders and Stroke, 141 (4.7%) per European Cooperative Acute Stroke Study II, and 86 (2.9%) per Safe Implementation of Thrombolysis in Stroke criteria. The performance of the scores assessed with AUC-ROC for predicting European Cooperative Acute Stroke Study II sICH was: MSS, 0.63 (95% confidence interval, 0.58-0.68); HAT, 0.65 (0.60-0.70); SEDAN, 0.70 (0.66-0.73); GRASPS, 0.67 (0.62-0.72); SITS, 0.64 (0.59-0.69); and SPAN-100 positive index, 0.56 (0.50-0.61). SEDAN had significantly higher AUC-ROC values compared with all other scores, except for GRASPS where the difference was nonsignificant. SPAN-100 performed significantly worse compared with other scores. The discriminative ranking of the scores was the same for the National Institute of Neurological Disorders and Stroke, and Safe Implementation of Thrombolysis in Stroke definitions, with SEDAN performing best, GRASPS second, and SPAN-100 worst. CONCLUSIONS: SPAN-100 had the worst predictive power, and SEDAN constantly the highest predictive power. However, none of the scores had better than moderate performance.

Prediction of Childhood Asthma Using Conditional Probability and Discrete Event Simulation

Abstract: Asthma prevalence in children and adolescents in Spain is 10-17%. It is the most common chronic illness during childhood. Prevalence has been increasing over the last 40 years and there is considerable evidence that, among other factors, continued exposure to cigarette smoke results in asthma in children. No statistical or simulation model exist to forecast the evolution of childhood asthma in Europe. Such a model needs to incorporate the main risk factors that can be managed by medical authorities, such as tobacco (OR = 1.44), to establish how they affect the present generation of children. A simulation model using conditional probability and discrete event simulation for childhood asthma was developed and validated by simulating realistic scenario. The parameters used for the model (input data) were those found in the bibliography, especially those related to the incidence of smoking in Spain. We also used data from a panel of experts from the Hospital del Mar (Barcelona) related to actual evolution and asthma phenotypes. The results obtained from the simulation established a threshold of a 15-20% smoking population for a reduction in the prevalence of asthma. This is still far from the current level in Spain, where 24% of people smoke. We conclude that more effort must be made to combat smoking and other childhood asthma risk factors, in order to significantly reduce the number of cases. Once completed, this simulation methodology can realistically be used to forecast the evolution of childhood asthma as a function of variation in different risk factors.

Investigation of Rapid Thermal Analysis Procedures for Prediction of the Service Life of PCCP Carbonate Coarse Aggregate, HR-337, 1993

The major objective of this research project was to use thermal analysis techniques in conjunction with x-ray analysis methods to identify and explain chemical reactions that promote aggregate related deterioration in portland cement concrete. Twenty-two different carbonate aggregate samples were subjected to a chemical testing scheme that included: • bulk chemistry (major, minor and selected trace elements) • bulk mineralogy (minor phases concentrated by acid extraction) • solid-solution in the major carbonate phases • crystallite size determinations for the major carbonate phases • a salt treatment study to evaluate the impact of deicer salts Test results from these different studies were then compared to information that had been obtained using thermogravimetric analysis techniques. Since many of the limestones and dolomites that were used in the study had extensive field service records it was possible to correlate many of the variables with service life. The results of this study have indicated that thermogravimetric analysis can play an important role in categorizing carbonate aggregates. In fact, with modern automated thermal analysis systems it should be possible to utilize such methods on a quality control basis. Strong correlations were found between several of the variables that were monitored in this study. In fact, several of the variables exhibited significant correlations to concrete service life. When the full data set was utilized (n = 18), the significant correlations to service life can be summarized as follows ( a = 5% level): • Correlation coefficient, r, = -0.73 for premature TG loss versus service life. • Correlation coefficient, r, = 0.74 for relative crystallite size versus service life. • Correlation coefficient, r, = 0.53 for ASTM C666 durability factor versus service life. • Correlation coefficient, r, = -0.52 for acid-insoluble residue versus service life. Separation of the carbonate aggregates into their mineralogical categories (i.e., calcites and dolomites) tended to increase the correlation coefficients for some specific variables (r sometimes approached 0.90); however, the reliability of such correlations was questionable because of the small number of samples that were present in this study.

Prediction of cottonseed longevity

The objective of this work was to determine the viability equation constants for cottonseed and to detect the occurrence and depletion of hardseededness. Three seedlots of Brazilian cultivars IAC-19 and IAC-20 were tested, using 12 moisture content levels, ranging from 2.2 to 21.7% and three storage temperatures, 40, 50 and 65ºC. Seed moisture content level was reached from the initial value (around 8.8%) either by rehydration, in a closed container, or by drying in desiccators containing silica gel, both at 20ºC. Twelve seed subsamples for each moisture content/temperature treatment were sealed in laminated aluminium-foil packets and stored in incubators at those temperatures, until complete survival curves were obtained. Seed equilibrium relative humidity was recorded. Hardseededness was detected at moisture content levels below 6% and its releasing was achieved either naturally, during storage period, or artificially through seed coat removal. The viability equation quantified the response of seed longevity to storage environment well with K E = 9.240, C W = 5.190, C H = 0.03965 and C Q = 0.000426. The lower limit estimated for application of this equation at 65ºC was 3.6% moisture content.

Improvement of walking speed prediction by accelerometry and altimetry, validated by satellite positioning.

Activity monitors based on accelerometry are used to predict the speed and energy cost of walking at 0% slope, but not at other inclinations. Parallel measurements of body accelerations and altitude variation were studied to determine whether walking speed prediction could be improved. Fourteen subjects walked twice along a 1.3 km circuit with substantial slope variations (-17% to +17%). The parameters recorded were body acceleration using a uni-axial accelerometer, altitude variation using differential barometry, and walking speed using satellite positioning (DGPS). Linear regressions were calculated between acceleration and walking speed, and between acceleration/altitude and walking speed. These predictive models, calculated using the data from the first circuit run, were used to predict speed during the second circuit. Finally the predicted velocity was compared with the measured one. The result was that acceleration alone failed to predict speed (mean r = 0.4). Adding altitude variation improved the prediction (mean r = 0.7). With regard to the altitude/acceleration-speed relationship, substantial inter-individual variation was found. It is concluded that accelerometry, combined with altitude measurement, can assess position variations of humans provided inter-individual variation is taken into account. It is also confirmed that DGPS can be used for outdoor walking speed measurements, opening up new perspectives in the field of biomechanics.