We demonstrate the role of interactions in driving the relaxation of an isolated integrable quantum system following a sudden quench. We consider a family of integrable hard-core lattice anyon models ...that continuously interpolates between noninteracting spinless fermions and strongly interacting hard-core bosons. A generalized Jordan-Wigner transformation maps the entire family to noninteracting fermions. We find that, aside from the singular free-fermion limit, the entire single-particle density matrix and, therefore, all one-body observables relax to the predictions of the generalized Gibbs ensemble (GGE). This demonstrates that, in the presence of interactions, correlations between particles in the many-body wave function provide the effective dissipation required to drive the relaxation of all one-body observables to the GGE. This relaxation does not depend on translational invariance or the tracing out of any spatial domain of the system.
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CMK, CTK, FMFMET, IJS, NUK, PNG, UM
We use the coordinate Bethe ansatz to exactly calculate matrix elements between eigenstates of the Lieb-Liniger model of one-dimensional bosons interacting via a two-body delta-potential. We ...investigate the static correlation functions of the zero-temperature ground state and their dependence on interaction strength, and analyze the effects of system size in the crossover from few-body to mesoscopic regimes for up to seven particles. We also obtain time-dependent nonequilibrium correlation functions for five particles following quenches of the interaction strength from two distinct initial states. One quench is from the noninteracting ground state and the other from a correlated ground state near the strongly interacting Tonks-Girardeau regime. The final interaction strength and conserved energy are chosen to be the same for both quenches. The integrability of the model highly constrains its dynamics, and we demonstrate that the time-averaged correlation functions following quenches from these two distinct initial conditions are both nonthermal and moreover distinct from one another.
Poor U.K. lung cancer survival rates may, in part, be due to late diagnosis.
To evaluate the effectiveness of a mixed-method community-based social marketing intervention on lung cancer diagnoses.
A ...public awareness campaign in conjunction with brief intervention training in general practices was piloted in six localities with a high lung cancer incidence. End points were self-reported awareness of lung cancer symptoms; intention to seek healthcare; chest x-ray referral rates in primary care; secular trends in the incidence of lung cancer and stage at diagnosis, compared before and after the intervention.
21% (128/600) (95% CI 18% to 25%) of the targeted population recalled something about the campaign. Compared with a responder in the control area, the odds of a responder in the intervention area saying that they would visit their general practitioner and request a chest x-ray for a cough was 1.97 times (95% CI 1.18 to 3.31, p=0.01). Primary care chest x-ray referral rates increased by 20% in the targeted practices in the year following the intervention compared with a 2% fall in the control practices. The difference was highly significant, with an incidence rate ratio of 1.22 (95% CI 1.12 to 1.33, p=0.001). There was a 27% increase in lung cancer diagnoses in the intervention area compared with a fall in the control area. The incidence rate ratio was 1.42 (95% CI 0.83 to 2.44 p=0.199).
This is encouraging early evidence that an awareness and early recognition initiative may facilitate lung cancer diagnosis.
Title. Diagnostic delay in lung cancer: a qualitative study
Aim. This paper is a report of a study to identify factors influencing delay in reporting symptoms of lung cancer.
Background. Lung ...cancer accounts for approximately 5% of deaths in the Western world. For up to 80% of patients, their disease is inoperable because it has been diagnosed too late. This suggests that reducing diagnostic delay could reduce mortality.
Methods. This qualitative study was conducted from July 2005 to May 2006 in community and hospital settings in the United Kingdom. A purposive sample of people diagnosed with lung cancer in the previous 6 months (n = 18), and two 18‐month survivors took part in individual interviews.
Findings. Participants reported a range of interrelating factors that influenced delay in reporting symptoms of lung cancer. Barriers to symptom reporting included symptom experience, lack of knowledge and fear. Blame and stigma because of smoking were also prevalent influences, as well as cultural factors, non‐standard patterns of healthcare utilization and underlying stoical attitudes. The only factor to emerge as helpful in overcoming delay was the role families played.
Conclusion. Lack of knowledge and awareness about lung cancer could be addressed by better education of the public. Social marketing is a way of developing education messages which tackle cultural influences on treatment‐seeking delay. Nurses have a potential role in developing and disseminating those messages.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK, VSZLJ
Pharmacokinetic drug-drug interactions are frequently characterized and quantified by an AUC ratio (Rauc). The typical value of the AUC ratio in case of cytochrome-mediated interactions may be ...predicted by several approaches, based on
in vitro
or
in vivo
data. Prediction of the interindividual variability of Rauc would help to anticipate more completely the consequences of a drug-drug interaction. We propose and evaluate a simple approach for predicting the standard deviation (sd) of Ln(Rauc), a metric close to the interindividual coefficient of variation of Rauc. First, a model was derived to link sd(Ln Rauc) with the substrate fraction metabolized by each cytochrome and the potency of the interactors, in case of induction or inhibition. Second, the parameters involved in these equations were estimated by a Bayesian hierarchical model, using the data from 56 interaction studies retrieved from the literature. Third, the model was evaluated by several metrics based on the fold prediction error (PE) of sd(Ln Rauc). The median PE was 0.998 (the ideal value is 1) and the interquartile range was 0.96–1.03. The PE was in the acceptable interval (0.5 to 2) in 52 cases out of 56. Fourth, a surface plot of sd(Ln Rauc) as a function of the characteristics of the substrate and the interactor has been built. The minimal value of sd(Ln Rauc) was about 0.08 (obtained for Rauc = 1) while the maximal value, 0.7, was obtained for interactions involving highly metabolized substrates with strong interactors.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Summary
Introduction
Sunitinib is a multikinase inhibitor active in various cancers types including renal cancers and endocrine tumors. The study analyzed the influence of the lean body mass (LBM) ...and of pharmacogenetic variants on the exposure to sunitinib and its active metabolite, SU12662, and on sunitinib toxicity and clinical activity.
Materials and Methods
Exposure to sunitinib and SU12662 was assessed on days 10 and 21 during the first treatment cycle. Acute toxicity was graded using the NCI 4.0 CTCAE ver. 4.0. The LBM and 14 common single nucleotide polymorphisms in the
CYP3A4/3A5, NR1I2, NR1I3, ABCB1,
and
ABCG2
genes were analyzed according to the drug exposure at day 10. Determinants (including sunitinib exposure and pharmacogenetic variants) for toxicities were assessed, as well as the relationship between drug exposure and survival in renal cancer patients.
Results
Ninety-two patients (60 % with renal cancer) were assessable for pharmacokinetics, toxicity and survival, and 66 for genetic analysis. The LBM (
p
< 0.0001) and a polymorphism in the ABCG2 transporter (421C>A) (
p
= 0.014) were two independent parameters accounting for the variability of composite (sunitinib + SU12662) exposure. Advanced age (OR = 1.47 1.01–2.15,
p
= 0.048) and high sunitinib exposure (OR = 1.16 1.05–1.28,
p
= 0.005) were independently associated with any grade ≥ 3 acute toxicity, and high SU12662 exposure was associated with grade ≥ 2 thrombocytopenia (OR = 1.27 1.03–1.57,
p
= 0.028). A high composite area under the curve (AUC) >1,973 ng/mL∙h at day 21 was associated with a doubled survival (35.2 vs 16.7 months; log-rank
p
= 0.0051) in renal cancer patients.
Conclusions
This study indicates that LBM and drug monitoring may be helpful in the management of sunitinib-treated patients.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Sorafenib, a new oral multikinase inhibitor with antiangiogenic properties, has demonstrated preclinical and clinical activity against several tumor types. The aims of this study were to validate a ...method for the measurement of sorafenib in plasma from cancer patients, then to test this method in clinical practice. Following liquid–liquid extraction, the compounds were separated with gradient elution (on a C18 ultrasphere ODS column using a mobile phase of acetonitrile/20
mM ammonium acetate), then detected at 255
nm. The calibration was linear in the range 0.5–20
mg/L. Intra- and inter-assay precision was lower than 7 and 10%, respectively, at 0.5, 3 and 20
mg/L. Plasma sorafenib concentrations were measured in 22 cancer patients (99 samples). The mean trough sorafenib concentration (
C
min) and concentration at peak were 4.3
±
2.5
mg/L (
n
=
68, CV
=
57.5%) and 6.2
±
3.0
mg/L (
n
=
31, CV
=
47.5%), respectively. Mean sorafenib
C
min in eight patients who experienced grade 3 drug-related adverse events was approximately 1.5-fold greater than that observed in the remaining patients (7.7
±
3.6
mg/L
vs. 4.4
±
2.4
mg/L,
P
=
0.0083). In conclusion, the method was successfully used in routine practice to monitor plasma concentrations of sorafenib in cancer patients. Finally, large interindividual variability and higher exposure in patients experiencing severe toxicity support the need for therapeutic drug monitoring to ensure an optimal exposure to sorafenib.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Changes in serum tumor biomarkers may indicate treatment efficacy. Traditional tumor markers may soon be replaced by novel serum biomarkers, such as circulating tumor cells (CTCs) or circulating ...tumor nucleic acids. Given their promising predictive values, studies of their kinetics are warranted. Many methodologies meant to assess kinetics of traditional marker kinetics during anticancer treatment have been reported. Here, we review the methodologies, the advantages and the limitations of the analytical approaches reported in the literature. Strategies based on a single time point were first used (baseline value, normalization, nadir, threshold at a time t), followed by approaches based on two or more time points half-life (HL), percentage decrease, time-to-events…. Heterogeneities in methodologies and lack of consideration of inter- and intra-individual variability may account for the inconsistencies and the poor utility in routine. More recently, strategies based on a population kinetics approach and mathematical modeling have been reported. The identification of equations describing individual kinetic profiles of biomarkers may be an alternative strategy despite its complexity and higher number of necessary measurements. Validation studies are required. Efforts should be made to standardize biomarker kinetic analysis methodologies to ensure the optimized development of novel serum biomarkers and avoid the pitfalls of traditional markers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We propose a framework to enable quantitative prediction of the impact of CYP2D6 polymorphisms on drug exposure. It relies mostly on in vivo data and uses two characteristic parameters: one for the ...drug and the other for the genotype. The metric of interest is the ratio of drug area under the curve (AUC) in patients with mutant genotype to the AUC in patients with wild‐type genotype. Any combination of alleles, as well as duplications, may be accommodated in the framework. Estimates of the characteristic parameters were obtained by orthogonal regression for 40 drugs and five classes of genotypes, respectively, including poor, intermediate, and ultrarapid metabolizers (PMs, IMs, and UMs). The mean prediction error of AUC ratios was −0.05, and the mean prediction absolute error was 0.20. An external validation was also carried out. The model may be used to predict the variations in exposure induced by all drug–genotype combinations. An application of this model to a rare combination of alleles (*4*10) is described.
Clinical Pharmacology & Therapeutics (2011) 90 4, 582–587. doi:10.1038/clpt.2011.147
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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