Hepatic mixed-function oxidase metabolism of the ubiquitous pollutant polychlorinated biphenyls (PCBs) is implicated in their toxification and detoxification. We used dichlorobiphenyls (DCBs) as models to investigate the effect of the chloro substituent sites on this metabolism experimentally and by molecular orbital calculations. Reconstituted, purified cytochrome P-450 PB-B and BNF-B, the major terminal oxidase isozymes of this system, from phenobarbital (PB)- and beta-naphthoflavone (BNF)-induced rats were used to investigate this metabolism. Both isozymes are also induced by PCBs. High-performance liquid chromatography (HPLC) was used to detect, quantify, and isolate metabolites. Metabolite structures were identified by mass spectrometry, dechlorination to identifiable hydroxybiphenyls, and HPLC retention times. All DCBs yielded 3- and 4- but no 2-monohydroxylated metabolites (3,3'-DCB also yielded a dihydroxy metabolite). Di-o-chloro-substituted DCBs were metabolized primarily by cytochrome P-450 PB-B, mono-o-chloro substituted DCBs by both isozymes approximately equivalently, and DCBs without o-chloro substituents by BNF-B primarily. Thus PB-B preferentially metabolizes noncoplanar DCBs and BNF-B coplanar DCBs. The cytochrome isozymes exhibited differing regioselectivities for DCB metabolism - PB-B hydroxylated unchlorinated phenyl rings and BNF-B chlorinated rings. Incorporation of epoxide hydrolase yielded DCB dihydrodiols, and hydroxy metabolite patterns were consistent with those calculated from ring-opened arene oxide intermediates. Thus the rates and regioselectivities of metabolism and thus possibly the toxicity and carcinogenicity of DCBs are dependent on the cytochrome P-450 isozymes induced.

Objective. To develop, implement, and evaluate "Test2Learn" a program to enhance pharmacogenomics education through the use of personal genomic testing (PGT) and real genetic data. Design. One hundred twenty-two second-year doctor of pharmacy (PharmD) students in a required course were offered PGT as part of a larger program approach to teach pharmacogenomics within a robust ethical framework. The program added novel learning objectives, lecture materials, analysis tools, and exercises using individual-level and population-level genetic data. Outcomes were assessed with objective measures and pre/post survey instruments. Assessment. One hundred students (82%) underwent PGT. Knowledge significantly improved on multiple assessments. Genotyped students reported a greater increase in confidence in understanding test results by the end of the course. Similarly, undergoing PGT improved student's self-perceived ability to empathize with patients compared to those not genotyped. Most students (71%) reported feeling PGT was an important part of the course, and 60% reported they had a better understanding of pharmacogenomics specifically because of the opportunity. Conclusion. Implementation of PGT in the core pharmacy curriculum was feasible, well-received, and enhanced student learning of pharmacogenomics.

The Pharmacogene Variation Consortium (PharmVar) is now providing star (*) allele nomenclature for the highly polymorphic human SLCO1B1 gene encoding the organic anion transporting polypeptide 1B1 (OATP1B1) drug transporter. Genetic variation within the SLCO1B1 gene locus impacts drug transport, which can lead to altered pharmacokinetic profiles of several commonly prescribed drugs. Variable OATP1B1 function is of particular importance regarding hepatic uptake of statins and the risk of statin‐associated musculoskeletal symptoms. To introduce this important drug transporter gene into the PharmVar database and serve as a unified reference of haplotype variation moving forward, an international group of gene experts has performed an extensive review of all published SLCO1B1 star alleles. Previously published star alleles were self‐assigned by authors and only loosely followed the star nomenclature system that was first developed for cytochrome P450 genes. This nomenclature system has been standardized by PharmVar and is now applied to other important pharmacogenes such as SLCO1B1. In addition, data from the 1000 Genomes Project and investigator‐submitted data were utilized to confirm existing haplotypes, fill knowledge gaps, and/or define novel star alleles. The PharmVar‐developed SLCO1B1 nomenclature has been incorporated by the Clinical Pharmacogenetics Implementation Consortium (CPIC) 2022 guideline on statin‐associated musculoskeletal symptoms.

Introduction As key experts in supporting medication-decision making, pharmacists are well-positioned to support the incorporation of pharmacogenomics into clinical care. However, there has been little study to date of pharmacists’ information needs regarding pharmacogenomics. Understanding those needs is critical to design information resources that help pharmacists effectively apply pharmacogenomics information. Objectives We sought to understand the pharmacogenomics information needs and resource requirements of pharmacists. Methods We conducted qualitative inquiries with 14 pharmacists representing 6 clinical environments, and used the results of those inquiries to develop a model of pharmacists’ pharmacogenomics information needs and resource requirements. Results The inquiries identified 36 pharmacogenomics-specific and pharmacogenomics-related information needs that fit into four information needs themes: background information, patient information, medication information, and guidance information. The results of the inquiries informed a model of pharmacists’ pharmacogenomics resource requirements, with 3 themes: structure of the resource, perceptions of the resource, and perceptions of the information. Conclusion Responses suggest that pharmacists anticipate an imminently growing role for pharmacogenomics in their practice. Participants value information from trust-worthy resources like FDA product labels, but struggle to find relevant information quickly in labels. Specific information needs include clinically relevant guidance about genotypes, phenotypes, and how to care for their patients with known genotypes. Information resources supporting the goal of incorporating complicated genetic information into medication decision-making goals should be well-designed and trustworthy.

Si nanowires (NWs) integrated in a field effect transistor device structure are characterized using scanning electron (SEM), atomic force, and scanning Kelvin probe force (KPFM) microscopy. Reactive ion etching (RIE) and vapor-liquid-solid (VLS) growth were used to fabricate NWs between predefined electrodes. Characterization of Si NWs identified defects and/or impurities that affect the surface electronic structure. RIE NWs have defects that both SEM and KPFM analysis associate with a surface contaminant as well as defects that have a voltage dependent response indicating impurity states in the energy bandgap. In the case of VLS NWs, even after aqua regia, Au impurity levels are found to induce impurity states in the bandgap. KPFM data, when normalized to the oxide-capacitance response, also identify a subset of VLS NWs with poor electrical contact due to nanogaps and short circuits when NWs cross that is not observed in AFM images or in current-voltage measurements when NWs are connected in parallel across electrodes. The experiments and analysis presented outline a systematic method for characterizing a broad array of nanoscale systems under device operation conditions.

Little of the variation between children in age of first sleeping through the night could be explained by these nine factors.

Pharmacogenomics is a tool for practitioners to provide precision pharmacotherapy using genomics. All providers are likely to encounter genomic data in practice with the expectation that they are able to successfully apply it to patient care. Pharmacogenomics tests for genetic variations in genes that are responsible for drug metabolism, transport, and targets of drug action. Variations can increase the risk for drug toxicity or poor efficacy. Pharmacogenomics can, therefore, be used to help select the best medication or aid in dosing. Nephrologists routinely treat cardiovascular disease and manage patients after kidney transplantation, two situations for which there are several high-evidence clinical recommendations for commonly used anticoagulants, antiplatelets, statins, and transplant medications. Successful use of pharmacogenomics in practice requires that providers are familiar with how to access and use pharmacogenomics resources. Similarly, clinical decision making related to whether to use existing data, whether to order testing, and if data should be used in practice is needed to deliver precision medicine. Pharmacogenomics is applicable to virtually every medical specialty, and nephrologists are well positioned to be implementation leaders.

Pharmacotherapy for traumatic brain injury (TBI) is focused on resuscitation, prevention of secondary injury, rehabilitation and recovery. Pharmacogenomics may play a role in TBI for predicting therapies for sedation, analgesia, seizure prevention, intracranial pressure-directed therapy and neurobehavioral/psychiatric symptoms. Research into genetic predictors of outcomes and susceptibility to complications may also help clinicians to tailor therapeutics for high-risk individuals. Additionally, the expanding use of genomics in the drug development pipeline has provided insight to novel investigational and repurposed medications that may be useful in the treatment of TBI and its complications. Genomics in the context of treatment and prognostication for patients with TBI is a promising area for clinical progress of pharmacogenomics.