Prediction of stable acenocoumarol dose by a pharmacogenetic algorithm.

AIM: To develop an acenocoumarol (ACN) dosing algorithm for patients with atrial fibrillation or venous thromboembolism, considering the influence on the stable ACN dose of clinical factors and gene polymorphisms, including CYP2C9*2/*3, VKORC1, CYP4F2*3, ABCB1, APOE, CYP2C19*2/*17, and GGCX. METHODS AND RESULTS: A retrospective observational study was carried out to obtain clinical and pharmacogenetic dose algorithms by multiple linear regression of results in a cohort of 134 patients under treatment with a stable ACN dose for atrial fibrillation or venous thromboembolism and to test them in an independent validation cohort of 30 patients.The pharmacogenetic dosing algorithm included CYP2C9, VKORC1, and APOE, which explained 56.6% of the variability in the stable ACN dose. Lower deviation from the stable dose and increased accuracy were shown by the pharmacogenetic algorithm, which correctly classified 67% of patients with a deviation of up to 20%. CONCLUSION: The variability in the stable ACN dose was better explained by a pharmacogenetic algorithm including clinical and genetic factors (CYP2C9, VKORC1, and APOE) than by a clinical algorithm, providing a more accurate dosage prediction.

Effect of CYP4F2, VKORC1, and CYP2C9 in Influencing Coumarin Dose: A Single-Patient Data Meta-Analysis in More Than 15,000 Individuals.

The cytochrome P450 (CYP)4F2 gene is known to influence mean coumarin dose. The aim of the present study was to undertake a meta-analysis at the individual patients level to capture the possible effect of ethnicity, gene-gene interaction, or other drugs on the association and to verify if inclusion of CYP4F2*3 variant into dosing algorithms improves the prediction of mean coumarin dose. We asked the authors of our previous meta-analysis (30 articles) and of 38 new articles retrieved by a systematic review to send us individual patients' data. The final collection consists of 15,754 patients split into a derivation and validation cohort. The CYP4F2*3 polymorphism was consistently associated with an increase in mean coumarin dose (+9% (95% confidence interval (CI) 7-10%), with a higher effect in women, in patients taking acenocoumarol, and in white patients. The inclusion of the CYP4F2*3 in dosing algorithms slightly improved the prediction of stable coumarin dose. New pharmacogenetic equations potentially useful for clinical practice were derived.

Extrapolation of acenocoumarol pharmacogenetic algorithms.

INTRODUCTION: Acenocoumarol (ACN) has a narrow therapeutic range that is especially difficult to control at the start of its administration. Various dosing pharmacogenetic-guided dosing algorithms have been developed, but further work on their external validation is required. The aim of this study was to evaluate the extrapolation of pharmacogenetic algorithms for ACN as an alternative to the development of a specific algorithm for a given population. MATERIAL AND METHODS: The predictive performance, deviation, accuracy, and clinical significance of five pharmacogenetic algorithms (EU-PACT, Borobia, Rathore, Markatos, Krishna Kumar) were compared in 189 stable ACN patients representing all indications for anticoagulant treatment. RESULTS: The correlation between the dose predictions of the five pharmacogenetic models ranged from 7.7 to 70.6% and the percentage of patients with a correct prediction (deviation ≤20% from actual ACN dose) ranged from 5.9 to 40.7%. EU-PACT and Borobia pharmacogenetic dosing algorithms were the most accurate in our setting and evidenced the best clinical performance. CONCLUSIONS: Among the five models studied, the EU-PACT and Borobia pharmacogenetic dosing algorithms demonstrated the best potential for extrapolation.

MET: a new promising biomarker in non-small-cell lung carcinoma.

Non-small-cell lung cancer (NSCLC) leads cancer-related deaths worldwide. Mutations in the kinase domain of the EGFR gene provide sensitivity to tyrosine kinase inhibitors (TKI) drugs. TKI show initial response rates over 75% in mutant EGFR-NSCLC patients, although most of these patients acquire resistance to EGFR inhibitors after therapy. EGFR-TKI resistance mechanisms include amplification in MET and its ligand, and also MET mutations. MET signaling dysregulation has been involved in tumor cell growth, survival, migration and invasion, angiogenesis and activation of several pathways, therefore representing an attractive target for anticancer drug development. In this review, we will discuss MET-related mechanisms of EGFR-TKI resistance in NSCLC, as well as the main drugs targeted to inhibit MET pathway.