Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10−8) or suggestively genome wide (p < 2.3 × 10−6). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.

Background:Plasma exposure of sunitinib shows large inter-individual variation. Therefore, a pharmacokinetic (PK) study was performed to determine safety and feasibility of sunitinib dosing based on PK levels.Methods:Patients were treated with sunitinib 37.5 mg once daily. At days 15 and 29 of treatment, plasma trough levels of sunitinib and N-desethyl sunitinib were measured. If the total trough level (TTL) was <50 ng ml−1 and the patient did not show any grade ⩾3 toxicity, the daily sunitinib dose was increased by 12.5 mg. If the patient suffered from grade ⩾3 toxicity, the sunitinib dose was lowered by 12.5 mg.Results:Twenty-nine out of 43 patients were evaluable for PK assessments. Grade ⩾3 adverse events were experienced in seven patients (24%) at the starting dose and in nine patients (31%) after dose escalation. TTLs were below target in 15 patients (52%) at the starting dose. Of these, five patients (17%) reached target TTL after dose escalation without additional toxicity.Conclusions:In a third of the patients that were below target TTL at standard dose, the sunitinib dose could be increased without additional toxicities. This could be the basis for future studies and the implementation of a PK-guided dosing strategy in clinical practice.

Interpatient variability in the pharmacokinetics (PK) of sunitinib is high. Single nucleotide polymorphisms (SNPs) in PK candidate genes have been associated with the efficacy and toxicity of sunitinib, but whether these SNPs truly affect the PK of sunitinib remains to be elucidated. This multicenter study involving 114 patients investigated whether these SNPs and haplotypes in genes encoding metabolizing enzymes or efflux transporters are associated with the clearance of sunitinib and its active metabolite SU12662. SNPs were tested as covariates in a population PK model. From univariate analysis, we found that the SNPs in CYP3A4, CYP3A5, and ABCB1 were associated with the clearance of both sunitinib and SU12662. In multivariate analysis, CYP3A4*22 was found to be eliminated last with an effect size of -22.5% on clearance. Observed effect sizes are below the interindividual variability in clearance and are therefore too limited to directly guide individual dosing of sunitinib.

Background:Tyrosine kinase inhibitors (TKIs) are associated with prolongation of the QTc interval on the electrocardiogram (ECG). The QTc-interval prolongation increases the risk of life-threatening arrhythmias. However, studies evaluating the effects of TKIs on QTc intervals are limited and only consist of small patient numbers.Methods:In this multicentre trial in four centres in the Netherlands and Italy we screened all patients who were treated with any TKI. To evaluate the effects of TKIs on the QTc interval, we investigated ECGs before and during treatment with erlotinib, gefitinib, imatinib, lapatinib, pazopanib, sorafenib, sunitinib, or vemurafenib.Results:A total of 363 patients were eligible for the analyses. At baseline measurement, QTc intervals were significantly longer in females than in males (QTcfemales=404 ms vs QTcmales=399 ms, P=0.027). A statistically significant increase was observed for the individual TKIs sunitinib, vemurafenib, sorafenib, imatinib, and erlotinib, after the start of treatment (median ΔQTc ranging from +7 to +24 ms, P<0.004). The CTCAE grade for QTc intervals significantly increased after start of treatment (P=0.0003). Especially patients who are treated with vemurafenib are at increased risk of developing a QTc of ⩾470 ms, a threshold associated with an increased risk for arrhythmias.Conclusions:These observations show that most TKIs significantly increase the QTc interval. Particularly in vemurafenib-treated patients, the incidence of patients at risk for arrhythmias is increased. Therefore, especially in case of combined risk factors, ECG monitoring in patients treated with TKIs is strongly recommended.

AIMSPreviously published pharmacokinetic (PK) models for sunitinib and its active metabolite SU12662 were based on a limited dataset or lacked important elements such as correlations between sunitinib and its metabolite. The current study aimed to develop an improved PK model that circumvented these limitations and to prove the utility of the PK model in treatment optimization in clinical practice. METHODSOne thousand two hundred and five plasma samples from 70 cancer patients were collected from three PK studies with sunitinib and SU12662. A semi-physiological PK model for sunitinib and SU12662 was developed incorporating pre-systemic metabolism using non-linear mixed effects modelling (NONMEM). Allometric scaling based on body weight was applied. The final model was used for simulation of the PK of different treatment regimens. RESULTSSunitinib and SU12662 PK were best described by a one and two compartment model, respectively. Introduction of pre-systemic formation of SU12662 strongly improved model fit, compared with solely systemic metabolism. The clearance of sunitinib and SU12662 was estimated at 35.7 (relative standard error (RSE) 5.7%) l h −1 and 17.1 (RSE 7.4%) l h −1 , respectively for 70 kg patients. Correlation coefficients were estimated between inter-individual variability of both clearances, both volumes of distribution and between clearance and volume of distribution of SU12662 as 0.53, 0.48 and 0.45, respectively. Simulation of the PK model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy. CONCLUSIONSA semi-physiological PK model for sunitinib and SU12662 in cancer patients was presented including pre-systemic metabolism. The model was superior to previous PK models in many aspects. WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• The necessity and evidence of therapeutic drug monitoring has been reported for sunitinib and its active metabolite SU12662.• Pharmacokinetic modelling has proved its utility in therapeutic drug monitoring.• Limitations existed for a previous published pharmacokinetic model for sunitinib and SU12662 in the application of therapeutic drug monitoring. WHAT THIS STUDY ADDS• We presented a superior pharmacokinetic model for sunitinib and SU12662 including pre-systemic metabolism.• The simulation of the current model obtained the correct concentration range for sunitinib and SU12662.• The model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy in clinic observations.

Background: Lean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce the power to identify genetic signals having an association with both lean mass and fat mass. Objectives: To determine the impact of different fat mass adjustments on genetic architecture of LM and identify additional LM loci. Methods: We performed genome-wide association analyses for whole-body LM (20 cohorts of European ancestry with n = 38,292) measured using dual-energy X-ray absorptiometry) or bioelectrical impedance analysis, adjusted for sex, age, age 2 , and height with or without fat mass adjustments (Model 1 no fat adjustment; Model 2 adjustment for fat mass as a percentage of body mass; Model 3 adjustment for fat mass in kilograms). Results: Seven single-nucleotide polymorphisms (SNPs) in separate loci, including one novel LM locus (TNRC6B), were successfully replicated in an additional 47,227 individuals from 29 cohorts. Based Conclusions: In conclusion, we identified one novel LM locus (TNRC6B). Our results suggest that a genetically determined increase in lean mass might exert either harmful or protective effects on metabolic traits, depending on its relation to fat mass.

Phenotype tests for ABCB1 and CYP3A4 did not explain inter-individual variability of sunitinib exposure sufficiently. However, the correlation between sunitinib clearance and the occurrence of severe toxicity suggests a direct exposure-toxicity relationship.

The anti-estrogen tamoxifen is characterized by a large variability in response, partly due to pharmacokinetic differences. We examined circadian variation in tamoxifen pharmacokinetics in mice and breast cancer patients. Pharmacokinetic analysis was performed in mice, dosed at six different times (24-h period). Tissue samples were used for mRNA expression analysis of drug-metabolizing enzymes. In patients, a cross-over study was performed. During three 24-h periods, after tamoxifen dosing at 8 a.m., 1 p.m., and 8 p.m., for at least 4 weeks, blood samples were collected for pharmacokinetic measurements. Differences in tamoxifen pharmacokinetics between administration times were assessed. The mRNA expression of drug-metabolizing enzymes showed circadian variation in mouse tissues. Tamoxifen exposure seemed to be highest after administration at midnight. In humans, marginal differences were observed in pharmacokinetic parameters between morning and evening administration. Tamoxifen C max and area under the curve (AUC)0–8 h were 20 % higher (P < 0.001), and tamoxifen t max was shorter (2.1 vs. 8.1 h; P = 0.001), indicating variation in absorption. Systemic exposure (AUC0–24 h) to endoxifen was 15 % higher (P < 0.001) following morning administration. The results suggest that dosing time is of marginal influence on tamoxifen pharmacokinetics. Our study was not designed to detect potential changes in clinical outcome or toxicity, based on a difference in the time of administration. Circadian rhythm may be one of the many determinants of the interpatient and intrapatient pharmacokinetic variability of tamoxifen.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-015-3452-x) contains supplementary material, which is available to authorized users.