Therapeutic drug monitoring of imatinib in patients with chronic myeloid leukemia (CML) is an ongoing debate. We studied the influence of imatinib trough levels on therapeutic response in 206 newly diagnosed patients with CML. We also compared the drug levels in patients taking branded and generic imatinib. Imatinib levels were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Marked inter-individual variability was seen in imatinib levels (coefficient of variation = 69%). Trough levels were significantly higher in patients who attained complete cytogenetic response than those who did not (2213.9 ± 1101 vs. 1648.6 ± 1403.4ng/mL; p < .001). Patients with major molecular response (MMR) had higher trough levels than those without MMR (2333.4 ± 1112 vs. 1643.4 ± 1383.9ng/mL; p = .001). Patients with trough levels ≤1000ng/mL were at high risk for failure of imatinib therapy [RR =1.926; 95%CI (1.562, 2.374); p < .001]. Trough levels emerged as an independent predictor of imatinib response in multivariate analysis. To conclude, imatinib trough levels significantly influence cytogenetic and molecular response and might emerge as a potential biomarker for therapeutic response in CML.

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Therapeutic drug monitoring was found to be useful in practice, in tailoring drug dosage in accordance with the needs of individual patient, in distinguishing nonresponders from noncompliants, and in aiding in making critical decisions. However, the "reference range" of these antiepileptic drugs was not reliable in predicting the occurrence of breakthrough seizures and clinical symptoms of suspected drug toxicity.

Fentanyl exhibits interindividual variability in its dose requirement due to various nongenetic and genetic factors such as single nucleotide polymorphisms (SNPs). This study aims to develop and cross-validate robust predictive models for postoperative fentanyl analgesic requirement and other related outcomes in patients undergoing major breast surgery. Data regarding genotypes of 10 candidate SNPs, cold pain test (CPT) scores, pupillary response to fentanyl (PRF), and other common clinical characteristics were recorded from 257 patients undergoing major breast surgery. Predictive models for 24-hour fentanyl requirement, 24-hour pain scores, and time for first analgesic (TFA) in the postoperative period were developed using 4 different algorithms: generalised linear regression model, linear support vector machine learning (SVM-Linear), random forest (RF), and Bayesian regularised neural network. The variant genotype of OPRM1 (rs1799971) and higher CPT scores were associated with higher 24-hour postoperative fentanyl consumption, whereas higher PRF and history of hypertension were associated with lower fentanyl requirement. The variant allele of COMT (rs4680) and higher CPT scores were associated with 24-hour postoperative pain scores. The variant genotype of CTSG (rs2070697), higher intraoperative fentanyl use, and higher CPT scores were associated with significantly lower TFA. The predictive models for 24-hour postoperative fentanyl requirement, pain scores, and TFA had R-squared values of 0.313 (SVM-Linear), 0.434 (SVM-Linear), and 0.532 (RF), respectively. We have developed and cross-validated predictive models for 24-hour postoperative fentanyl requirement, 24-hour postoperative pain scores, and TFA with satisfactory performance characteristics and incorporated them in a novel web application.