As tacrolimus has a rather narrow therapeutic range and high individual variability in its pharmacokinetics, it is important to determine the cause of the variation in tacrolimus pharmacokinetics. The purpose of this study was to establish a population pharmacokinetic-pharmacogenetic model of tacrolimus and identify covariates that affect pharmacokinetic parameters to prevent fluctuations in the tacrolimus trough concentration during the early period after transplantation. Data from 1501 trough concentrations and 417 densely collected concentrations were compiled from 122 patients who were on post-operative days 10-20 and analysed with a nonlinear mixed-effect model. The first-order conditional estimation (FOCE) with interaction method was used to fit the model using the NONMEM program. Clinical/laboratory data were also collected for the same period, and CYP3A5 and ABCB1 genotypes were analysed for use in modelling from all included patients. An empirical Bayesian approach was used to estimate individual pharmacokinetic profiles. A one-compartment model with first absorption and elimination and lag time best described the data. The estimated population mean of clearance (CL/F), volume of distribution (V/F) and absorption rate (K a ) were 21.9 L/hr, 205 L, and 3.43/hr, respectively, and the lag time was fixed at 0.25 hr. Clearance increased with days after transplantation and decreased with CYP3A5*3/*3 about 18.4% compared with CYP3A5*1 carriers (p < 0.001). A population pharmacokinetic model was developed for tacrolimus in early post-kidney transplantation recipients to identify covariates that affect tacrolimus pharmacokinetics. Post-operative days and CYP3A5 genotype were confirmed as critical factors of tacrolimus pharmacokinetics.

To our knowledge, this is the first PPK study to suggest that GSTA1 polymorphisms in adults are associated with busulfan PK.

Background Methylphenidate, a stimulant used to treat attention deficit hyperactivity disorder, has the potential to be used nonmedically, such as for studying and recreation. In an era when many people actively use social networking services, experience with the nonmedical use or side effects of methylphenidate might be shared on Twitter. Objective The purpose of this study was to analyze tweets about the nonmedical use and side effects of methylphenidate using a machine learning approach. Methods A total of 34,293 tweets mentioning methylphenidate from August 2018 to July 2019 were collected using searches for “methylphenidate” and its brand names. Tweets in a randomly selected training dataset (6860/34,293, 20.00%) were annotated as positive or negative for two dependent variables: nonmedical use and side effects. Features such as personal noun, nonmedical use terms, medical use terms, side effect terms, sentiment scores, and the presence of a URL were generated for supervised learning. Using the labeled training dataset and features, support vector machine (SVM) classifiers were built and the performance was evaluated using F1 scores. The classifiers were applied to the test dataset to determine the number of tweets about nonmedical use and side effects. Results Of the 6860 tweets in the training dataset, 5.19% (356/6860) and 5.52% (379/6860) were about nonmedical use and side effects, respectively. Performance of SVM classifiers for nonmedical use and side effects, expressed as F1 scores, were 0.547 (precision: 0.926, recall: 0.388, and accuracy: 0.967) and 0.733 (precision: 0.920, recall: 0.609, and accuracy: 0.976), respectively. In the test dataset, the SVM classifiers identified 361 tweets (1.32%) about nonmedical use and 519 tweets (1.89%) about side effects. The proportion of tweets about nonmedical use was highest in May 2019 (46/2624, 1.75%) and December 2018 (36/2041, 1.76%). Conclusions The SVM classifiers that were built in this study were highly precise and accurate and will help to automatically identify the nonmedical use and side effects of methylphenidate using Twitter.

The objective of the study was to investigate the pharmacokinetic drug-drug interactions between tacrolimus (TAC) and mycophenolate mofetil (MMF) in healthy Korean male volunteers. Seventeen volunteers participated in a three-period, single-dose, and fixed sequence study. They sequentially received MMF, TAC, and the combination. Concentrations of TAC, mycophenolic acid (MPA), and its metabolites MPA 7-O-glucuronide and MPA acyl glucuronide were measured. The variants of CYP3A4, CYP3A5, SLCO1B1, SLCO1B3, ABCC2, UGT1A9, and UGT2B7 were genotyped. Drug interaction was evaluated with a non-compartmental analysis and population pharmacokinetic modelling to quantify the interaction effect. A total of 1,082 concentrations of those analytes were analysed. AUC0-inf of TAC increased by 22.1% (322.4 ± 174.1 to 393.6 ± 121.7 ng·h/mL; P < 0.05) when co-administered with MMF, whereas the pharmacokinetic parameters of MPA and its metabolites were not changed by TAC. Apparent clearance (CL/F) of TAC was 17.8 L/h [relative standard error (RSE) 11%] or 13.8 L/h (RSE 11%) without or with MMF, respectively. Interaction was explained by the exponential model. The CYP3A5 genotype was the only significant covariate. The population estimate of CL/F of TAC was 1.48-fold (RSE 16%) in CYP3A5 expressers when compared to nonexpressers. CL/F of TAC was decreased when co-administered with MMF in these subjects.

No abstract