Pharmacogenomics
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This study attempted to identify predictors of S-warfarin clearance (CL[S]) and to make a pharmacokinetic evaluation of genotype-based dosing algorithms in African-Americans. ⋯ African-Americans possess independent predictors of CL(S), possibly leading to a prediction error of any dosing algorithm that excludes African-specific variant(s). Original submitted 3 September 2014; Revision submitted 3 November 2014.
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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. ⋯ 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.
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Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. ⋯ A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area.
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Cancer treatments are increasingly being targeted to specific patient populations based on the molecular and genetic features of their tumor, so called precision or personalized cancer medicine. Preclinical cancer models are essential tools for cancer research, but unfortunately our current models often fail to effectively represent patient tumors and can be poorly predictive of clinical responses. In this perspective, we discuss the use of new in vitro 3D cell models called 'organoids' as preclinical cancer models in the context of other commonly used models, namely cancer cell lines and patient-derived xenografts. We consider the relative strengths and limitations of each model, and discuss how organoid culture models could facilitate the personalization of cancer medicine.