The genetics of human drug response

Leukemia Stem Cell Drug Discovery

The relative survival of cancer patients, when considering the tumoral stage at diagnosis, has not changed significantly in the last three decades, in spite of our increasingly detailed knowledge of the molecular alterations occurring in human tumors. In parallel, despite a growing number of clinical trials being conducted, the absolute number of drugs that are effective in humans is declining, and many new drugs move into the market without having enough evidence of their benefit on survival or quality of life. In part, this failure is due to the discordance between the results from preclinical and clinical trial phases, therefore leading to a high percentage of apparently promising lead compounds being abandoned in the transfer to the clinic. This discordance is caused, to a large degree, by the use of inappropriate animal models in the first stages of drug development. In this chapter, we discuss how the development of cancer therapies needs to be redesigned in order to achieve cancer cure, and how this redesign must involve the generation of better animal models, based on the tenets of the cancer stem cell theory, and capable of recapitulating all the aspects of human cancer. The use of such improved models should increase the likelihood of success in drug development, reducing the number of agents that go into trial, and the amount of patients undergoing useless trials.

TPMT Polymorphism: When Shield Becomes Weakness

Thiopurine methyltransferase (TPMT) is a cytoplasmic transmethylase present in both prokaryotes and eukaryotes. In humans, it shows its presence in almost all of the tissues, predominantly in liver and kidney. TPMT is one of the important metabolic enzymes of phase II metabolic pathway and catalyzes methylation of thiopurine drugs such as azathioprine, 6-thioguanine and 6-mercaptopurine, which are used to treat patients with neoplasia and autoimmune disease as well as transplant recipients. In this sense, TPMT acts as shield against toxic effect of these drugs. Pharmacogenomic studies revealed that genetic polymorphism in TPMT is responsible for variable and, in some cases, adverse drug reaction. Those human groups who carry variants of TPMT (i.e., [Formula: see text], [Formula: see text], [Formula: see text]) are at high risk, because they are unable to metabolize thiopurine drugs thus becoming a weakness of patients against these drugs. Keeping in the mind the importance of TPMT, this review discusses the existence and distribution of various TPMT variants throughout different ethnic groups and risk of adverse drug reactions to them, and how they can avoid this risk of side effects. The review also highlighted factors responsible for variable reactions of TPMT, how this TPMT polymorphism can be considered in drug designing process to avoid toxic effects, designing precautions against them and more importantly designing personalized medicine.

GENE-LEVEL PHARMACOGENETIC ANALYSIS ON SURVIVAL OUTCOMES USING GENE-TRAIT SIMILARITY REGRESSION

Gene/pathway-based methods are drawing significant attention due to their usefulness in detecting rare and common variants that affect disease susceptibility. The biological mechanism of drug responses indicates that a gene-based analysis has even greater potential in pharmacogenetics. Motivated by a study from the Vitamin Intervention for Stroke Prevention (VISP) trial, we develop a gene-trait similarity regression for survival analysis to assess the effect of a gene or pathway on time-to-event outcomes. The similarity regression has a general framework that covers a range of survival models, such as the proportional hazards model and the proportional odds model. The inference procedure developed under the proportional hazards model is robust against model misspecification. We derive the equivalence between the similarity survival regression and a random effects model, which further unifies the current variance-component based methods. We demonstrate the effectiveness of the proposed method through simulation studies. In addition, we apply the method to the VISP trial data to identify the genes that exhibit an association with the risk of a recurrent stroke. TCN2 gene was found to be associated with the recurrent stroke risk in the low-dose arm. This gene may impact recurrent stroke risk in response to cofactor therapy.

The role of Multidrug Resistance-1 (MDR1) variants in response to atorvastatin among Jordanians

The MDR1 gene encodes for P-glycoprotein (P-gp), which is an efflux transporter at the cell membrane. The P-gp has wide substrate specificity for multiple medications including the lipid lowering drug, atorvastatin. In this study, we investigated the possible association between three common MDR1 gene polymorphisms (G2677T, C3435T, and C1236T), and the lipid lowering effect of atorvastatin among Jordanians. Lipid and lipoproteins were measured in blood samples collected from patients (n = 201) at baseline and during atorvastatin treatment. MDR1 polymorphisms were genotyped using polymerase chain reaction-restriction fragment length polymorphism. Both the TT genotype of G2677T and the TT genotype of the C3435T polymorphisms were associated with lower levels of low-density lipoproteins after atorvastatin treatment. However, the effects of atorvastatin on the levels of total cholesterol, triglycerides, and high-density lipoprotein, were not correlated with any of the genotypes in both polymorphisms. Finally, the C1236T polymorphism was not associated with the lipid lowering effect of atorvastatin. In conclusion, the MDR1 gene polymorphisms G2677T, and C3435T, but not C1236T were associated with the lipid lowering effect of atorvastatin among Jordanians.

Investigation of CYP2C19 allele and genotype frequencies in Iranian population using experimental and computational approaches

CytochromeP4502C19 is a genetically polymorphic gene with prominent role in drug metabolism. Regarding its critical medical importance, this study was conducted to achieve accurate CYP2C19allele frequencies in Iranian population and hereby paving the way for a tailor-made CYP2C19 DNA test. Iran is a large multi-ethnic country, however, its population structure for CYP2C19 alleles is calculated as nearly zero (Fwc (st)=0.001). The Study was conducted on 691 individuals in Tehran, the conurbation in which total population structure is significantly eroded by massive immigration and DNA was analyzed by TaqMan SNP genotyping assay. A cumulative meta-analysis was then conducted to achieve less than five percent variation range in allele frequencies with 99.9% confidence level. High degree of Hardy-Weinberg equilibrium in pooled data proved the authenticity of meta-analysis. By cumulative meta-analysis the average frequencies of CYP2C19*2 and CYP2C19*3 alleles were calculated as 0.125[99.9% CI, 0.112-0.139] and 0.006[99.9% CI, 0.004-0.009], respectively. According to the solid frequency data obtained by pooling the data and meta-analysis and comparing with other ethnicities, Iranian population's CYP2C19 allele frequencies completely differ from other Asian ethnicities and matches African and European ethnicities the most. Since this is the biggest CYP2C19 allele frequency study in the Middle East, the results of this study will also be useful in cross-population and regional CYP2C19 genetic variation studies.

Ridge regression in prediction problems: automatic choice of the ridge parameter

To date, numerous genetic variants have been identified as associated with diverse phenotypic traits. However, identified associations generally explain only a small proportion of trait heritability and the predictive power of models incorporating only known-associated variants has been small. Multiple regression is a popular framework in which to consider the joint effect of many genetic variants simultaneously. Ordinary multiple regression is seldom appropriate in the context of genetic data, due to the high dimensionality of the data and the correlation structure among the predictors. There has been a resurgence of interest in the use of penalised regression techniques to circumvent these difficulties. In this paper, we focus on ridge regression, a penalised regression approach that has been shown to offer good performance in multivariate prediction problems. One challenge in the application of ridge regression is the choice of the ridge parameter that controls the amount of shrinkage of the regression coefficients. We present a method to determine the ridge parameter based on the data, with the aim of good performance in high-dimensional prediction problems. We establish a theoretical justification for our approach, and demonstrate its performance on simulated genetic data and on a real data example. Fitting a ridge regression model to hundreds of thousands to millions of genetic variants simultaneously presents computational challenges. We have developed an R package, ridge, which addresses these issues. Ridge implements the automatic choice of ridge parameter presented in this paper, and is freely available from CRAN.

Finasteride metabolism and pharmacogenetics: new approaches to personalized prevention of prostate cancer

Incidences of prostate cancer in most countries are increasing owing to better detection methods; however, prevention with the use of finasteride, a very effective steroid 5α-reductase type II inhibitor, has been met with mixed success. A wide interindividual variation in response exists and is thought to be due to heritable factors. This article summarizes the literature that attempts to elucidate the molecular mechanisms of finasteride in terms of its metabolism, excretion and interaction with endogenous steroid molecules. We describe previously reported genetic variations of steroid-metabolizing genes and their potential association with finasteride efficacy. Based on the literature, we outline directions of research that may contribute to understanding the interindividual variation in finasteride prevention and to the future development of personalized medicine.

Neural markers of genetic vulnerability to drug addiction

This chapter will summarize genetics findings derived from various strategies and highlight important neural markers (or correlates) in some specific and extensively studied genes. Most studies highlighted here focus on alcohol and nicotine dependence (AD and ND, respectively). AD and ND are among the most prevalent addictive disorders worldwide, are among the best studied, and are also associated globally with the largest socioeconomic impact.We describe different mechanisms through which genes can have an impact on the addictive behaviors, distinguishing between the genes that inscribe the proteins affecting the metabolism of the addictive substance (e.g., ADH/ALDH for alcohol or CYP2A6 for nicotine) and genes that code for the brain transmitter systems, such as genes involved in cerebral neurotransmission thought to be involved in addiction (e.g., brain reward system, mood regulation, opioid system). Strategies include linkage analyses, association studies, whole genome association studies as well as intermediate/endophenotype studies. Moreover, some important findings derived from animal studies and from neuroimaging studies are highlighted. In conclusion, we provide the reader with an overview of most important studies related to AD and ND and give an outlook how these findings may become useful and beneficial in the future.

Recent development in pharmacogenomics: from candidate genes to genome-wide association studies

Genetic diversity, most notably through single nucleotide polymorphisms and copy-number variation, together with specific environmental exposures, contributes to both disease susceptibility and drug response variability. It has proved difficult to isolate disease genes that confer susceptibility to complex disorders, and as a consequence, even fewer genetic variants that influence clinical drug responsiveness have been uncovered. As such, the candidate gene approach has largely failed to deliver and, although the family-based linkage approach has certain theoretical advantages in dealing with common/complex disorders, progress has been slower than was hoped. More recently, genome-wide association studies have gained increasing popularity, as they enable scientists to robustly associate specific variants with the predisposition for complex disease, such as age-related macular degeneration, Type 2 diabetes, inflammatory bowel disease, obesity, autism and leukemia. This relatively new methodology has stirred new hope for the mapping of genes that regulate drug response related to these conditions. Collectively, these studies support the notion that modern high-throughput single nucleotide polymorphism genotyping technologies, when applied to large and comprehensively phenotyped patient cohorts, will readily reveal the most clinically relevant disease-modifying and drug response genes. This review addresses both recent advances in the genotyping field and highlights from genome-wide association studies, which have conclusively uncovered variants that underlie disease susceptibility and/or variability in drug response in common disorders.

Pharmacogenomics of beta2-agonist: key focus on signaling pathways

Asthma is one of the most common respiratory diseases, where inhalation and exhalation are obstructed due to narrowing of the airways by broncho-constriction or by inflammation. Among all the available anti-asthma therapies, beta2-agonists are the most effective bronchodilators available, and give rapid relief of asthma symptoms. Evidence suggests that the degree of beta2-agonist response varies greatly between patients and genetic factors have a major role in it. Despite several studies on the beta2-agonist pharmacogenetics, significant gaps in knowledge still remain and need to be resolved before the pharmacotyping of beta2-agonist responsiveness comes to clinical practice. As we know, beta2-agonists show their influence by targeting beta2-adrenergic receptors, leading to the activation of beta2-adrenergic receptors and its downstream cascade. Signaling through beta2-adrenergic receptors mediates numerous airway functions by regulating broncho-constriction and dilation pathways. Therefore, it is an important prerequisite to understand these pathways, which will assist in defining the variability in therapeutic responses for beta2-agonists. Owing to the complexity of the action of a beta2-agonist and its therapeutic response, a broader genomics approach will help in optimizing therapy for the individual patient. This might be achieved by considering and focusing on receptor/s at which the drug binds directly, signal transduction cascades or downstream proteins and proteins involved in the relaxation and constriction of the airway smooth muscle. Considering that a drug response may involve a large number of proteins, it seems unlikely that a single polymorphism or haplotype in a single gene would explain a high degree of drug response variability in a consistent fashion. Thus, it shows that a polygenic approach will be more appropriate. In order to follow this, the mode of action of the beta2-agonist and its downstream signaling cascade should essentially be assessed to resolve the beta2-agonist enigma.

Genetics of antipsychotic treatment emergent weight gain in schizophrenia

Classic and modern antipsychotics can induce substantial weight gain causing diabetes, lipid abnormalities and psychological distress. Treatment emergent weight gain varies within the broad class of antipsychotics; however, an individual's propensity to develop weight gain largely depends on genetic factors. The first part of this review highlights current ideas and concepts related to antipsychotic-induced weight gain, including principles on energy homeostasis. The second part summarizes genetic findings emphasizing studies published after 2003 as prior studies have been reviewed in detail elsewhere. Candidate gene studies have produced significant findings in the 5-hydroxytryptamin 2C (5HT2C) and adrenergic alpha2a (ADRalpha2a) receptor genes, as well as in the leptin, guanine nucleotide binding protein (GNB3) and synaptomal-associated protein 25kDa (SNAP25) genes. Results from genome-wide association and linkage studies point to several chromosomal regions (e.g., 12q24) and some specific genes (e.g., promelanin concentrating hormone [PMCH], polycyctic kidney and hepatic disease 1 [PKHD1], peptidylglycine alpha-amidating monooxygenase [PAM]). However, more efforts are needed before risk prediction and personalized medicine can be made available for antipsychotic-induced weight gain.