hERG Channels · July 5, 2022

Other abbreviations are as follows: CRP, C-reactive concentration at inclusion; LL, difference in ln-likelihood for each covariate; MTX, methotrexate cotreatment; was influenced by methotrexate cotreatment, pre-infusion DAS28, and pre-infusion CRP concentration

Other abbreviations are as follows: CRP, C-reactive concentration at inclusion; LL, difference in ln-likelihood for each covariate; MTX, methotrexate cotreatment; was influenced by methotrexate cotreatment, pre-infusion DAS28, and pre-infusion CRP concentration. useful in RA. is the number of model parameters to estimate. The use of AIC is based on the parsimony between a best fit to the data and a limited number of parameters. The OFV was ?2.ln-likelihood (C2LL). The model with the lowest AIC was selected. Interindividual modelThe interindividual variability of pharmacokinetic parameters was described using an exponential model: = TV exp(is the estimated ITM2A individual parameter, TV is the typical value of the parameter and is the random effect for the were assumed to be normally distributed, with mean 0 and variance 2. For each parameter, 2 was fixed to 0 if 2 or could not be estimated properly. Error modelAdditive, proportional and mixed additiveCproportional models were tested. For example, the combined additiveCproportional model CG-200745 was implemented as follows: = (1 + prop,and are observed and predicted and add,are proportional and additive errors, which are assumed to follow a Gaussian distribution with mean 0 and variances prop2 and add2, respectively. CovariatesThe influence of the following covariates was tested in the population CG-200745 excluding the ATI+ patients: (i) binary covariates, i.e. sex (SX), association with methotrexate and/or corticosteroids; and (ii) continuous covariates, i.e. age, bodyweight (WT), height, disease duration, infliximab treatment duration, CRP concentration and DAS28, which is the disease activity score on 28 joints [11]. The influence of a binary CG-200745 covariate (CAT) on TV was implemented as ln(TV) = ln(CAT=0) + CAT=1, where CAT=0 is the value of for an arbitrary reference category and CAT=1 is the value of TV for the other category. Continuous covariates (COV) were centred on their median as follows: i = 0 (COV/med(COV))cov, where 0 is value of for a median subject, COV quantifies the influence of COV on and med(COV) is the median value of COV in the population. Model comparison and covariate selectionInterindividual, residual and covariate models were compared using OFV and AIC. From pairs of nested models, the one with the lowest OFV was chosen. This was assessed by a likelihood ratio test (LRT), in which the difference in OFV between two models (OFV) is assumed to follow a 2 distribution. The influence of patient characteristics (covariates) was assessed in two steps, as follows. A univariate step, in which the influence of each factor on pharmacokinetic parameters associated with interindividual variability was tested. Covariates were separately included into the base model. Covariates showing a significant influence ( 0.1) were included in the model (full model). A multivariate step, in which a backward stepwise elimination was performed; the covariates of the full model were removed one by one. Covariates whose removal resulted in a statistically significant increase in the OFV ( 0.01) were retained in the model. Model goodness of fit and CG-200745 evaluationThe goodness of fit was assessed for each model by plotting population-predicted (PRED) and individually predicted (IPRED) concentrations for each pharmacokinetic parameter). In addition, the distribution of residuals was evaluated by graphical inspection of population (PWRES) and individual weighted residual distributions (IWRES), visual predictive checks (VPC) and normalized prediction distribution errors (NPDE) [12]. These residuals should follow a standard normal distribution to confirm a satisfactory fit of the model to the data and (ii) to allow a 2 distribution for LRT tests. Results Patients Eighty-four patients treated with infliximab and who were assumed to be at steady state were included (Table ?(Table1).1). A total of 412 serum samples were available for analysis. Median (range) pre-infusion dose, dosing interval and infliximab concentrations were 3.6 mg kg?1 (2.5C6.8), 8.0 weeks (5.0C13.0) and 1.3 mg l?1 ( 0.014C12.0), respectively. Antibodies toward infliximab were detected in the pre-infusion serum of three patients (nos 34, 55 and 79); two of these three patients were also treated with methotrexate and one was not. Of note, the infliximab concentrations for these three patients were below the limit of detection within 4 weeks after infliximab administration (data not shown). As the number of ATI+ patients was too small to add ATI status as a covariate and the pharmacokinetic profiles of ATI+ patients were very different from those.