Current Drug Metabolism - Volume 12, Issue 5, 2011

Drug metabolic enzyme is a key factor to determine the intracorporal processes of drugs. The polymorphism of the drug metabolic enzymes affects almost 30% of the drug metabolism; some susceptibility genes of a disease will be possibly found though systematic analysis of these SNPs. As SNPs may determine the tiny difference between the gene sequences of an individual and a group of people, the researches on SNPs of drug metabolic enzymes will also lead a new way to the personalized drug design and personalized medicine. Recently, significant advance has been made in the areas of pharmacogenetics, functional genomics, structural biology as well as bioinformatics which have been used to study the SNPs of drug metabolic enzymes and their relationship with personalized medicine. We found it necessary to summarize the recent progresses by a special issue on “Current Drug Metabolism”. The main subtopics include: Usage of SNPs in pharmacogenomics; Analyze the SNPs of CYP families in humans; SNPs analysis for prediction of drug efficacy; Construction of SNPs databases for researches of drug response; Prediction of SNPs in drug-response genes; Identification of SNPs for personalized drug design; New methods used for studying SNPs of drug metabolic enzymes Progresses and clinical consequences of personalized medicine and so on.

This review focuses on identification of the important active site residues of CYP2D6 in terms of CYP2D6 polymorphism. A meta-analysis was performed on the reported literature regarding (1) values of the Michaelis-Menten constant (Km), maximal velocity (Vmax), and intrinsic clearance (Vmax/Km) for 41 metabolic reactions of 31 substrates mediated by human cytochrome P450 2D6 and its variants and mutants and (2) inhibition constants (Ki) for 15 inhibitors. The mean ratios of Vmax/Km values with respect to the wild type (CYP2D6.1) for CYP2D6.2 (R296C/S486T), CYP2D6.10 (P34S/S486T), CYP2D6.17 (T107I/R296C/S486T), CYP2D6.31 (R296C/R440H/S486T), CYP2D6.34 (R296C), CYP2D6.36 (P34S/S486T and 6 other amino acids substitutions), CYP2D6.49 (P34S /F120I/S486T), and P34S and G42R mutants but not CYP2D6.39 (S486T) were in the range 0.03-0.61, and the median ratios were in the range 0.03-0.57. More than 90% of Vmax/Km values for CYP2D6.10, CYP2D6.17, and CYP2D6.36 were less than half of those for CYP2D6.1. In addition, 20-59% of Vmax/Km values for these variants were less than one-tenth those of the wild type. These results suggest that the CYP2D6 polymorphism may affect the metabolic activities of many compounds. However, the kinetic behaviors of these variants and mutants depended on the metabolic reaction. The Ki values of many of the inhibitors of CYP2D6.10 and CYP2D6.17 were comparable with or higher than those for CYP2D6.1. Collectively, these findings provide insights into the contributions of CYP2D6 polymorphisms to drug metabolism and adverse drug interactions.

Among all the drug metabolic enzymes, cytochrome P450 (CYP450) superfamily acts as an important role responsible for the oxidation of almost 90% currently used drugs. As variations of Single Nucleotide Polymorphism (SNPs) in human CYP450 genes will cause different drug effects and even adverse effects, studies on SNPs of human CYP450 genes can be used for indicating the most possible genes associated with human diseases and relevant therapeutic targets, predicting the drug efficacy and adverse drug response, investigating individual gene specific properties and then providing personalized and optimal clinic therapies. Recently, some new bioinformatics methods are introduced in SNPs researches, which significantly facilitate the development of drug and medicine. The review will focus on a brief introduction of the SNPs of human drug metabolic enzymes and their relationships with personalized medicine. Besides, common bioinformatics analysis methods and some latest progresses and applications in this area will also be discussed.

The Human Genome and the Hap Map Projects as well as the extensive use of deep resequencing worldwide, have contributed to a massive catalogue of reported single nucleotide polymorphisms (SNPs) and other genetic variations in the human genome. Pharmacogenomics is an emerging field that combines genetics with pharmacokinetics and pharmacodynamics of the drug in attempt to understand inter-individual differences among patients and develop more accurate drug dosing. However, only for the minority of those variations an association with phenotype has been established. Here, we provide an overview of genes and genetic variants that influence inter- individual dosing of three of the most widely used drugs, namely warfarin, irinotecan and thiopurine drugs, to highlight a tangible benefit of translating genomic knowledge into clinical practice. Therefore, particular SNPs in vitamin K epoxide reductase complex subunit 1 (VKORC1), cytochrome P450 2C9 (CYP2C9), uridine diphosphate glucoronosyltransferase 1A1 (UGT1A1) and thiopurine Smethyltransferase (TPMT) genes has proven to be applicable for optimising the dosage in pursuit of maximum efficacy and minimum adverse effects. Thus, they set an important paradigm of implementation of pharmacogenomics in the mainstream clinical practice.

The association between gene expression and clinical characteristics of non-small cell lung cancer (NSCLC) are uncovered. These genes are critical elements in carcinoma physiological processes, including DNA synthesis, DNA repair and mitosis. Genes such as ERCC1, RRM1, TYMS, TUBB3 and STMN1 are predictive biomarker candidates for chemotherapy sensitivity in patients with NSCLC. Suitable gene expression analyzing technology is key factor for the personalize medicine to become a reality. This mini-review will describe and discuss critically on most currently widely used gene expression analyzing technologies, involving immunohistochemistry (IHC), reverse-transcription quantitative PCR (RT-qPCR) and branch-DNA technology (bDNA).

Over 10 million SNPs have been discovered to date as the result of both a private and public effort in the past two decades. Extensive investigations on SNPs have been performed to assess clinical applications for pharmacogenomics and Personalized Medicine. Recently, around the 10th anniversary of the first publication by the Human Genome Project, Hamburg and Collins addressed questions regarding the progress of the genomics field and its impact on pharmacogenomics / Personalized Medicine. Similar questions remain around the potential link of SNPs to Personalized Medicine applications, and the extent to which they have impacted “real world” clinical practices. Built upon these previous efforts, and to achieve our objectives of describing and assessing the role of SNPs and their impact on Personalized Medicine, this article analyzes and summarizes the clinical relevance, molecular mechanisms, clinical evidence, and preliminary regulatory and clinical guideline information of relevant SNPs. In addition, it focuses on two applications directly related to Personalized Medicine drug therapeutics: predictive biomarkers for patient stratification and dose selection. In summary, this article attempts to provide a general and comprehensive view of the role of SNPs in pharmacogenomics and Personalized Medicine, as well as a practical view of their impact on clinical practice today.

In the field of pharmacogenetics, we currently have a few markers to guide physicians as to the best course of therapy for patients. For the most part, these genetic variants are within a drug metabolizing enzyme that has a large effect on the degree or rate at which a drug is converted to its metabolites. For many drugs, response and toxicity are multi-genic traits and understanding relationships between a patient's genetic variation in drug metabolizing enzymes and the efficacy and/or toxicity of a medication offers the potential to optimize therapies. This review will focus on variants in drug metabolizing enzymes with predictable and relatively large impacts on drug efficacy and/or toxicity; some of these drug/gene variant pairs have impacted drug labels by the United States Food and Drug Administration. The challenges in identifying genetic markers and implementing clinical changes based on known markers will be discussed. In addition, the impact of next generation sequencing in identifying rare variants will be addressed.

Single Nucleotide Polymorphisms (SNPs) are the most abundant form of genetic variations observed in the human genome. With the advent of high-throughput genotyping arrays and next-generation sequencing (NGS) platforms, tens of millions of SNPs have been uncovered in several human populations. However, the huge amount of SNPs bring new challenges in subsequent analysis. In reality, a number of SNPs may not be genotyped, and non-mutant bases may be falsely reported as SNPs in the microarray and NGS platforms. Furthermore, the identification of disease susceptibility genes are often confounded by numerous SNPs correlated by chance. In this paper, we review existing approaches for calling SNPs using microarrays and next-generation sequencing (NGS) platforms. Methods for measuring linkage disequilibrium (LD) and applications of the LD structure are discussed. Finally, we compare methods for inferring haplotypes from genotypes using microarray and NGS platforms and present the challenges of using SNPs in large-scale association studies.