Drug ADME-associated protein database as a resource for facilitating pharmacogenomics research

Development and validation of LC-MS/MS assay for the simultaneous determination of methotrexate, 6-mercaptopurine and its active metabolite 6-thioguanine in plasma of children with acute lymphoblastic leukemia: Correlation with genetic polymorphism

Individualized therapy is a recent approach aiming to specify dosage regimen for each patient according to its genetic state. Cancer chemotherapy requires continuous monitoring of the plasma concentration levels of active forms of cytotoxic drugs and subsequent dose adjustment. In order to attain optimum therapeutic efficacy, correlation to pharmacogenetics data is crucial. In this study, a specific, accurate and sensitive liquid chromatography tandem mass spectrometry (LC-MS/MS) has been developed for determination of methotrexate (MTX), 6-mercaptopurine (MP) and its metabolite 6-thioguanine nucleotide (TG) in human plasma. Based on the basic character of the studied compounds, solid phase extraction using a strong cation exchanger was found the optimum approach to achieve good extraction recovery. Chromatographic separation was carried out using RP-HPLC and isocratic elution by acetonitrile: 0.1% aqueous formic acid (85:15v/v) with a flow rate of 0.8mL/min at 40°C. The detection was performed by tandem mass spectrometry in MRM mode via electrospray ionization source in positive ionization mode. Analysis was carried out within 1.0min over a concentration range of 6.25-200.00ng/mL for the studied analytes. Validation was carried out according to FDA guidelines for bioanalytical method validation and satisfactory results were obtained. The applicability of the assay for the monitoring of the MTX, MP and TG and subsequent application to personalized therapy was demonstrated in a clinical study on children with acute lymphoblastic leukemia (ALL). Results confirmed the need for implementation of reliable analysis tools for therapeutic dose adjustment.

Report of new haplotype for ABCC2 gene: rs17222723 and rs8187718 in cis

The ATP-binding cassette, subfamily C [CFTR/MRP], member 2 (ABCC2) gene is a member of the ATP-binding cassette transporters and is involved in the transport of molecules across cellular membranes. Substrates transported by ABCC2 include antiepileptics, statins, tenofovir, cisplatin, irinotecan, and carbamazepine. Because of the pharmacogenomics implications, we developed a clinical laboratory-developed assay to test for seven variants in the ABCC2 gene: c.3563T>A (p.V1188E, rs17222723), c.1249G>A (p.V417I, rs2273697), c.3972C>T (p.I1324I, rs3740066), c.2302C>T (p.R768W, rs56199535), c.2366C>T (p.S789F, rs56220353), c.-24C>T (5'UTR, rs717620), and c.4544G>A (p.C1515Y, rs8187710). During the validation process, we noted several DNA samples, obtained from the Coriell Cell Repository, that contained both c.3563T>A, c.4544G>A, and a third variant, suggesting that c.3563T>A and c.4544G>A are in cis on the chromosome in some individuals. We obtained DNA samples from a trio (father, mother, and child), tested their ABCC2 variants, and confirmed that c.3563T>A and c.4544G>A were in cis on the same chromosome. Here, we report a new haplotype in ABCC2.

Metabolism, pharmacokinetics, tissue distribution, and stability studies of the prodrug analog of an anti-hepatitis B virus dinucleoside phosphorothioate

The alkoxycarbonyloxy dinucleotide prodrug R(p), S(p)-2 is an orally bioavailable anti-hepatitis B virus agent. The compound is efficiently metabolized to the active dinucleoside phosphorothioate R(p), S(p)-1 by human liver microsomes and S9 fraction without cytochrome P450-mediated oxidation or conjugation. The conversion of R(p), S(p)-2 to R(p), S(p)-1 appears to be mediated by liver esterases, occurs in a stereospecific manner, and is consistent with our earlier reported studies of serum-mediated hydrolytic conversion of R(p), S(p)-2 to R(p), S(p)-1. However, further metabolism of R(p), S(p)-1 does not occur. The presence of a minor metabolite, the desulfurized product 10 was noted. The prodrug R(p), S(p)-2 was quite stable in simulated gastric fluid, whereas the active R(p), S(p)-1 had a half-life of <15 min. In simulated intestinal fluid, the prodrug 2 was fully converted to 1 in approximately 3 h, whereas 1 remained stable. To ascertain the tissue distribution of the prodrug 2 in rats, the synthesis of (35)S-labeled R(p), S(p)-2 was undertaken. Tissue distribution studies of orally and intravenously administered radiolabeled [(35)S]2 demonstrated that the radioactivity concentrates in the liver, with the highest liver/plasma ratio in the intravenous group at 1 h being 3.89 (females) and in the oral group at 1 h being 2.86 (males). The preferential distribution of the dinucleotide 1 and its prodrug 2 into liver may be attributed to the presence of nucleoside phosphorothioate backbone because phosphorothioate oligonucleotides also reveal a similar tissue distribution profile upon intravenous administration.

Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

Computational methods for predicting compounds of specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) property are useful for facilitating drug discovery and evaluation. Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property. These methods are particularly useful for compounds of diverse structures to complement QSAR methods, and for cases of unavailable receptor 3D structure to complement structure-based methods. A number of studies have demonstrated the potential of these methods for predicting such compounds as substrates of P-glycoprotein and cytochrome P450 CYP isoenzymes, inhibitors of protein kinases and CYP isoenzymes, and agonists of serotonin receptor and estrogen receptor. This article is intended to review the strategies, current progresses and underlying difficulties in using machine learning methods for predicting these protein binders and as potential virtual screening tools. Algorithms for proper representation of the structural and physicochemical properties of compounds are also evaluated.

Pharmacogenomics and nutrigenomics: synergies and differences

The success of the Human Genome Project and the spectacular development of broad genomics tools have catalyzed a new era in both medicine and nutrition. The terms pharmacogenomics and nutrigenomics are relatively new. Both have grown out of their genetic forbears as large-scale genomics technologies have been developed in the last decade. The aim of both disciplines is to individualize or personalize medicine and food and nutrition, and ultimately health, by tailoring the drug or the food to the individual genotype. This review article provides an overview of synergies and differences between these two potentially powerful science areas. Individual genetic variation is the common factor on which both pharmacogenomics and nutrigenomics are based. Each human is genetically (including epigenetics) unique and phenotypically distinct. One of the expectations of both technologies is that a wide range of gene variants and related single-nucleotide polymorphism will be identified as to their importance in health status, validated and incorporated into genotype based strategies for the optimization of health and the prevention of disease. Pharmacogenomics requires rigorous genomic testing that will be regulated and analyzed by professionals and acted on by medical practitioners. As further information is obtained on the importance of the interaction of food and the human genotype in disease prevention and health, pharmacogenomics can provide an opportunity driver for nutrigenomics. As we move from disease treatment to disease prevention, the two disciplines will become more closely aligned.

Pharmacogenetics and the concept of individualized medicine

Adverse drug reaction in patients causes more than 2 million hospitalizations including 100,000 deaths per year in the United States. This adverse drug reaction could be due to multiple factors such as disease determinants, environmental and genetic factors. In order to improve the efficacy and safety and to understand the disposition and clinical consequences of drugs, two rapidly developing fields--pharmacogenetics (focus is on single genes) and pharmacogenomics (focus is on many genes)--have undertaken studies on the genetic personalization of drug response. This is because many drug responses appear to be genetically determined and the relationship between genotype and drug response may have a very valuable diagnostic value. Identification and characterization of a large number of genetic polymorphisms (biomarkers) in drug metabolizing enzymes and drug transporters in an ethnically diverse group of individuals may provide substantial knowledge about the mechanisms of inter-individual differences in drug response. However, progress in understanding complex diseases, its negative psychosocial consequences, violation of privacy or discrimination, associated cost and availability and its complexity (extensive geographic variations in genes) may become potential barriers in incorporating this pharmacogenetic data in risk assessment and treatment decisions. In addition, it requires increased enthusiasm and education in the clinical community and an understanding of pharmacogenetics itself by the lay public. Although individualized medications remain as a challenge for the future, the pharmacogenetic approach in drug development should be still continued. If it becomes a reality, it delivers benefits to improve public health and allow genetically subgroup diseases thereby avoiding adverse drug reactions (by knowing in advance who should be treated with what drug and how).