Narrow Results

We have shown that the in vitro hepatic microsomal metabolism of pyranocoumarin compound decursinol angelate (DA) to decursinol (DOH) exclusively requires cytochrome P450 (CYP) enzymes, whereas the conversion of its isomer decursin (D) to DOH can be mediated by CYP and esterase(s). To provide insight into specific isoforms involved, here we show with recombinant human CYP that 2C19 was the most active at metabolizing D and DA in vitro followed by 3A4. With carboxylesterases (CES), D was hydrolyzed by CES2 but not CES1, and DA was resistant to both CES1 and CES2. In human liver microsomal (HLM) preparation, the general CYP inhibitor 1-aminobenzotriazole (ABT) and respective competitive inhibitors for 2C19 and 3A4, (+)-N-3-benzylnirvanol (NBN) and ketoconazole substantially retarded the metabolism of DA and, to a lesser extent, of D. In healthy human subjects from a single-dose pharmacokinetic (PK) study, 2C19 extensive metabolizer genotype (2C19*17 allele) tended to have less plasma DA AUC_0-48h and poor metabolizer genotype (2C19*2 allele) tended to have greater DA AUC_0-48h. In mice given a single dose of D/DA, pretreatment with ABT boosted the plasma and prostate levels of D and DA by more than an order of magnitude. Taken together, our findings suggest that CYP isoforms 2C19 and 3A4 may play a crucial role in the first pass liver metabolism of DA and, to a lesser extent, that of D in humans. Pharmacogenetics with respect to CYP genotypes and interactions among CYP inhibitor drugs and D/DA should therefore be considered in designing future translation studies of DA and/or D.

Pharmacogenetics studies how genetic variation leads to variability in drug response. Guidelines for selecting the right drug and right dose for patients based on their genetics are clinically effective, but are widely unused. For some drugs, the normal clinical decision making process may lead to the optimal dose of a drug that minimizes side effects and maximizes effectiveness. Without measurements of genotype, physicians and patients may adjust dosage in a manner that reflects the underlying genetics. The emergence of genetic data linked to longitudinal clinical data in large biobanks offers an opportunity to confirm known pharmacogenetic interactions as well as discover novel associations by investigating outcomes from normal clinical practice. Here we use the UK Biobank to search for pharmacogenetic interactions among 200 drugs and 9 genes among 200,000 participants. We identify associations between pharmacogene phenotypes and drug maintenance dose as well as differential drug response phenotypes. We find support for several known drug-gene associations as well as novel pharmacogenetic interactions.

The history and current status of consensus arylamine N-acetyltransferase (NAT) gene nomenclature is outlined, describing the contributions of the NAT committee over the years. The NAT nomenclature website, hosted by the Democritus University of Thrace, Department of Molecular Biology and Genetics (http://nat.mbg.duth.gr/), is presented. This website consolidates annotated information about prokaryotic and eukaryotic NAT genes, as well as a complete listing of NAT polymorphic alleles in human and other mammalian model species. Detailed instructions about the correct species-specific naming of NAT genes and alleles are also provided on the NAT website. Scientists who wish to name new NAT sequences should follow those guidelines and contact the committee who will approve the official symbols of new NAT genes or alleles. New developments towards a standardised nomenclature across the entire spectrum of pharmacogenetics-associated genes, including human NAT1 and NAT2, will be discussed in view of recent advancements in genomic medicine.

In this paper, we report on adapting the JREX relation extraction engine, originally developed For the elicitation of protein-protein interaction relations, to the domains of pharmacogenetics and pharmacogenomics. We propose an intrinsic and an extrinsic evaluation scenario which is based on knowledge contained in the PHARMGKB knowledge base. Porting JREX yields favorable results in the range of 80% F-score for Gene-Disease, Gene-Drug, and Drug-Disease relations.

The investigation of phenotypes in model organisms has the potential to reveal the molecular mechanisms underlying disease. The large-scale comparative analysis of phenotypes across species can reveal novel associations between genotypes and diseases. We use the PhenomeNET network of phenotypic similarity to suggest genotype–disease association, combine them with drug–gene associations available from the PharmGKB database, and infer novel associations between drugs and diseases. We evaluate and quantify our results based on our method's capability to reproduce known drug–disease associations. We find and discuss evidence that levonorgestrel, tretinoin and estradiol are associated with cystic fibrosis (p < 2:65 · 10⁻⁶, p < 0:002 and p < 0:031, Wilcoxon signedrank test, Bonferroni correction) and that ibuprofen may be active in chronic lymphocytic leukemia (p < 2:63 p < 0:03110⁻²³ Wilcoxon signed-rank test, Bonferroni correction). To enable access to our results, we implement a web server and make our raw data freely available. Our results are the first steps in implementing an integrated system for the analysis and prediction of drug–disease associations for rare and orphan diseases for which the molecular basis is not known.

Pharmacogenetics represents one of the most promising areas of precision medicine, with several guidelines for genetics-guided treatment ready for clinical use. Despite this, implementation has been slow, with few health systems incorporating the technology into their standard of care. One major barrier to uptake is the lack of education and awareness of pharmacogenetics among clinicians and patients. The introduction of large language models (LLMs) like GPT-4 has raised the possibility of medical chatbots that deliver timely information to clinicians, patients, and researchers with a simple interface. Although state-of-the-art LLMs have shown impressive performance at advanced tasks like medical licensing exams, in practice they still often provide false information, which is particularly hazardous in a clinical context. To quantify the extent of this issue, we developed a series of automated and expert-scored tests to evaluate the performance of chatbots in answering pharmacogenetics questions from the perspective of clinicians, patients, and researchers. We applied this benchmark to state-of-the-art LLMs and found that newer models like GPT-4o greatly outperform their predecessors, but still fall short of the standards required for clinical use. Our benchmark will be a valuable public resource for subsequent developments in this space as we work towards better clinical AI for pharmacogenetics.