OBJECTIVE: We aimed to investigate the genetic predisposition of co-trimoxazole-induced SCAR.

METHODS: We conducted a multicountry case-control association study that included 151 patients with of co-trimoxazole-induced SCAR and 4631 population controls from Taiwan, Thailand, and Malaysia, as well as 138 tolerant controls from Taiwan. Whole-genome sequencing was performed for the patients and population controls from Taiwan; it further validated the results from Thailand and Malaysia.

RESULTS: The whole-genome sequencing study (43 case patients vs 507 controls) discovered that the single-nucleotide polymorphism rs41554616, which is located between the HLA-B and MICA loci, had the strongest association with co-trimoxazole-induced SCAR (P = 8.2 × 10-9; odds ratio [OR] = 7.7). There were weak associations of variants in co-trimoxazole-related metabolizing enzymes (CYP2D6, GSTP1, GCLC, N-acetyltransferase [NAT2], and CYP2C8). A replication study using HLA genotyping revealed that HLA-B∗13:01 was strongly associated with co-trimoxazole-induced SCAR (the combined sample comprised 91 case patients vs 2545 controls [P = 7.2 × 10-21; OR = 8.7]). A strong HLA association was also observed in the case patients from Thailand (P = 3.2 × 10-5; OR = 3.6) and Malaysia (P = .002; OR = 12.8), respectively. A meta-analysis and phenotype stratification study further indicated a strong association between HLA-B∗13:01 and co-trimoxazole-induced drug reaction with eosinophilia and systemic symptoms (P = 4.2 × 10-23; OR = 40.1).

RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.

OBJECTIVE: This study sought to detect CYP2B6 and OPRM1 variants and their genotypes, as major contributors to inter-variability in methadone responsiveness and methadone dose requirements.

METHODS: We carried out a prospective experimental one-phase pharmacogenetic study in four addiction clinics in Malaysia. Patients on stable methadone maintenance therapy were recruited. The prevalence of the CYP2B6 and OPRM1 polymorphisms was determined using a nested polymerase chain reaction (PCR), followed by genotyping. A two-step multiplex PCR method was developed to simultaneously detect the 26 SNPs in these two genes.

RESULTS: 120 males were recruited for this study. The patients were between 21and 59 years old, although the majority of the patients were in their 30s. C64T and G15631T in CYP2B6and G31A, G691C, and A118G in OPRM1 were found to be polymorphic, and the allelic frequencies of each were calculated. We further detected eight new haplotypes.

METHODS: Nevirapine population pharmacokinetics was modelled with Pmetrics. A total of 708 observations from 112 patients were included in the model building and validation analysis. Evaluation of the model was based on a visual inspection of observed versus predicted (population and individual) concentrations and plots weighted residual error versus concentrations. Accuracy and robustness of the model were evaluated by visual predictive check (VPC). The median parameters' estimates obtained from the final model were used to predict individual nevirapine plasma area-under-curve (AUC) in the validation dataset. The Bland-Altman plot was used to compare the AUC predicted with trapezoidal AUC.

RESULTS: The median nevirapine clearance was of 2.92 L/h, the median rate of absorption was 2.55/h and the volume of distribution was 78.23 L. Nevirapine pharmacokinetics were best described by one-compartmental with first-order absorption model and a lag-time. Weighted residuals for the model selected were homogenously distributed over the concentration and time range. The developed model adequately estimated AUC.