The Affymetrix DMET Plus platform reveals unique distribution of ADME-related variants in ethnic Arabs

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.

Deciphering DMET genetic data: comprehensive assessment of Northwestern Han, Tibetan, Uyghur populations and their comparison to eleven 1000 genome populations

We investigated the allele frequencies of drug absorption, distribution, metabolism and elimination (ADME)-related drug-metabolizing enzymes and transporters (DMET) genes in the Northwestern Han, Tibetan and Uyghur populations and compared the related genes in these three populations with those in eleven 1000 Genome populations. We examined 1936 single nucleotide polymorphisms of 225 DMET genes involved in ADME processes and found 732, 679 and 804 sites were polymorphic in Han, Tibetan and Uyghur. Tibetan differed from Han in only four sites (p < .05), whereas Uyghur differed from Han and Tibetan in 24 and 21 sites, respectively (p < .05). The distributions of 1058 genotyping data of 245 individuals from Han, Tibetan and Uyghur were compared with 1207 other individuals from the eleven 1000 Genomes populations. The top four populations in Han that exhibited the smallest pairwise Fst values were CHB, Tibetan, CHD and JPT; those in Tibetan were Han, CHB, Uyghur and CHD; and those in Uyghur were Han, Tibetan, GIH and CEU. MEGA results revealed that CHB, CHD, JPT, Han, Tibetan and Uyghur were grouped in cluster 1. GIH, MEX, CEU and TSI were grouped in cluster 2. MKK, ASW, LWK and YRI were grouped in cluster 3.

CHST3 and CHST13 polymorphisms as predictors of bosentan-induced liver toxicity in Japanese patients with pulmonary arterial hypertension

Bosentan, an endothelin receptor antagonist, has been widely used as a first-line drug for the treatment of pulmonary arterial hypertension (PAH). In addition, bosentan is approved for patients with digital ulcers related to systemic sclerosis. Liver dysfunction is a major adverse effect of bosentan and may lead to discontinuation of therapy. The purpose of this study was to identify genomic biomarkers to predict bosentan-induced liver injury. A total of 69 PAH patients were recruited into the study. An exploratory analysis of 1936 single-nucleotide polymorphisms (SNPs) in 231 genes involved in absorption, distribution, metabolism, and elimination of multiple medications using Affimetrix DMET™ (Drug Metabolism Enzymes and Transporters) chips was performed. We extracted 16 SNPs (P <  0.05) using the Jonckheere-Terpstra trend test and multiplex logistic analysis; we identified two SNPs in two genes, CHST3 and CHST13, which are responsible for proteoglycan sulfation and were significantly associated with bosentan-induced liver injury. We constructed a predictive model for bosentan-induced liver injury (area under the curve [AUC]: 0.89, sensitivity: 82.61%, specificity: 86.05%) via receiver operating curve (ROC) analysis using 2 SNPs and 2 non-genetic factors. Two SNPs were identified as potential predictive markers for bosentan-induced liver injury in Japanese patients with pulmonary arterial hypertension. This is the first pharmacogenomics study linking proteoglycan sulfating genes to drug-induced liver dysfunction, a frequently observed clinical adverse effect of bosentan therapy. These results may provide a way to personalize PAH medicine as well as provide novel mechanistic insights to drug-induced liver dysfunction.

UGT1A1 polymorphisms associated with prolactin response in risperidone-treated children and adolescents with autism spectrum disorder

The aim of this study was to investigate the association of drug-metabolizing enzyme and transporter (DMET) polymorphisms with the risperidone-induced prolactin response using an overlapping gene model between serum prolactin level and hyperprolactinemia in autism spectrum disorder (ASD) patients. Eighty-four ASD patients who were receiving risperidone for at least 1 month were recruited and then assigned to either the normal prolactin group or the hyperprolactinemia group based on their serum prolactin level. The genotype profile of 1936 (1931 single nucleotide polymorphisms (SNPs) and 5 copy number variation (CNVs) drug metabolism markers was obtained using the Affymetrix DMET Plus GeneChip microarray platform. Genotypes of SNPs used to test the accuracy of DMET genotype profiling were determined using TaqMan SNP Genotyping Assay kits. Eighty-four patients were selected for the allelic association study after microarray analyses (51 in the normal prolactin group, and 33 in the hyperprolactinemia group). An overlapping allelic association analysis of both analyses discovered five DMET SNPs with a suggestive association (P < 0.05) with risperidone-induced prolactin response. Three UGT1A1 SNPs (UGT1A1*80c.-364C > T, UGT1A1*93 c.-3156G > A, and UGT1A1 c.-2950A > G, showed a suggestive association with the risperidone-induced prolactin response and found to be in complete linkage disequilibrium (D' value of 1). In this DMET microarray platform, we found three UGT1A1 variants with suggestive evidences of association with the risperidone-induced prolactin response both measured by hyperprolactinemia and by prolactin level. However, due to the lack of validation studies confirmation and further exploration are needed in future pharmacogenomic studies.

DMET-Miner: Efficient discovery of association rules from pharmacogenomic data

Microarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient's samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation.