An aqueous Rapamycin clinical trial check standard was also analysed at the start and end of each analytical run and after every ten samples (except for mercury analysis). Participation in external quality assurance

schemes was also undertaken both in the UK TEQAS organised by the University of Surrey and the German G-EQUAS, organised by University of Erlangen (elements where quality assurance certification was achieved are stated in Table 2). Participation in external quality assurance schemes for creatinine measurements was also undertaken in a UK scheme (RIQAS organised by Randox Laboratories Limited, Belfast, N. Ireland). The limit of detection (LOD) for each analyte was calculated as three times the standard deviation of the blanks run throughout all analyses. The limit of quantification (LOQ) in this GDC-0199 solubility dmso report is calculated as the LOQ in an undiluted urine sample and can

be defined as three times the standard deviation of all of the blank samples run throughout the analyses (i.e. the LOD) multiplied by the dilution factor of the urine sample (which varied from 10 to 20), i.e. this is the lowest quantifiable concentration measured in a urine sample (Table 3). For some elements, a proportion of the measurements fell below the LOQ. Such measurements are referred to as left censored. A common method of dealing with left-censored measurements is to substitute in the value of half the LOQ, however this method lacks rigour and can lead to biased before estimates of the true variability of the measurements. Bayesian methods have gained popularity in recent years and can handle censored data more naturally than classical likelihood-based methods. As such, a Bayesian approach using Markov Chain Monte Carlo (Gilks et al., 1996) has been used for dealing with the censored data. It is common practice in biological monitoring to adjust the urinary concentrations for dilution. Statistical modelling allows the investigation of the effectiveness of this correction. One such approach is to compare the estimates of variability that arise from modelling

corrected and uncorrected concentrations; for elements where the variability decreases with creatinine correction, the correction may be beneficial. As repeat samples were taken on some individuals thus resulting in correlation between their measurements, a mixed effects model was used in the analysis to account for correlation and to model inter-individual variability via random effects. The urinary concentrations were assumed to be lognormally distributed, as is common in biomonitoring (Leese et al., 2013). The effects of smoking and gender were considered, resulting in a mixed effects model of the form: ln(Yij)=μ+βgIg,ij+βsIs,ij+wi+ϵijwi∼N(0,σ12)ϵij∼N(0,σ22)where the elemental urinary concentration (either creatinine-corrected or uncorrected) is denoted by Yij , (the subscripts denote the j th measurement on the i th subject).