Pharmacogenetic tests in asthma therapy

Chapter 7: Pharmacogenomics

There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.

GSNO reductase and beta2-adrenergic receptor gene-gene interaction: bronchodilator responsiveness to albuterol

BACKGROUND

Short-acting inhaled beta2-agonists such as albuterol are used for bronchodilation and are the mainstay of asthma treatment worldwide. There is significant variation in bronchodilator responsiveness to albuterol not only between individuals but also across racial/ethnic groups. The beta2-adrenergic receptor (beta2AR) is the target for beta2-agonist drugs. The enzyme, S-nitrosoglutathione reductase (GSNOR), which regulates levels of the endogenous bronchodilator S-nitrosoglutathione, has been shown to modulate the response to beta2-agonists.

OBJECTIVE

We hypothesized that there are pharmacogenetic interactions between GSNOR and beta2AR gene variants that are associated with variable response to albuterol.

METHODS

We performed family-based analyses to test for association between GSNOR gene variants and asthma and related phenotypes in 609 Puerto Rican and Mexican families with asthma. In addition, we tested these individuals for pharmacogenetic interaction between GSNOR and beta2AR gene variants and responsiveness to albuterol using linear regression. Cell transfection experiments were performed to test the potential effect of the GSNOR gene variants.

RESULTS

Among Puerto Ricans, several GSNOR SNPs and a haplotype in the 3'UTR were significantly associated with increased risk for asthma and lower bronchodilator responsiveness (P=0.04-0.007). The GSNOR risk haplotype affects expression of GSNOR mRNA and protein, suggesting a gain of function. Furthermore, gene-gene interaction analysis provided evidence of pharmacogenetic interaction between GSNOR and beta2AR gene variants and the response to albuterol in Puerto Rican (P=0.03), Mexican (P=0.15) and combined Puerto Rican and Mexican asthmatics (P=0.003). Specifically, GSNOR+17059*beta2AR+46 genotype combinations (TG+GG*AG and TG+GG*GG) were associated with lower bronchodilator response.

CONCLUSION

Genotyping of GSNOR and beta2AR genes may be useful in identifying Latino individuals, who might benefit from adjuvant therapy for refractory asthma.

Docking studies on a refined human beta(2) adrenoceptor model yield theoretical affinity values in function with experimental values for R-ligands, but not for S-antagonists

G-protein coupled receptors (GPCR) belong to the largest group of membrane proteins involved in signal transduction. These receptors are implicated in diverse physiological and pathological events. The human beta(2) adrenergic receptor (hbeta(2)AR) is one of the few GPCRs whose 3-D structures are available on the Protein Data Bank. Because there is great interest by drug developers for hbeta(2)AR as a target, it is necessary to study its ligand-recognition process at the atomic level. The hbeta(2)AR can recognize both R/S enantiomeric ligands, R-agonists result in a greater activation than do S-agonists (eutomers and distomers for activation, respectively), according to experimental results. In this work is reported the ligand recognition on a refined hbeta(2)AR-structure of a set of well-known R/S-ligands by means of docking studies. Data obtained in silico were analyzed and compared with those reported in vitro. The theoretical affinity values were reproduced for agonists, but not for antagonist (or inverse agonists). However, theoretical data for R-antagonists are in function to experimental data. The theoretical results confirm the role of amino acids previously reported by mutagenesis studies due to their important roles in drug affinity and stereoselectivity.