Search

Aims: To assess knowledge and attitudes toward pharmacogenomics (PGx) of incoming doctoral pharmacy students, to evaluate the internal structure and reliability of the PGx survey and to identify variables associated with the different responses. Methods: A PGx survey based on the core pharmacist competencies in PGx was created. Results: Of 83.2% analyzable responses, 91% believed PGx is a useful tool and relevant to future practice but over 70% stated they lack confidence in clinical PGx knowledge. This 38-item PGx survey included three factors showing high reliability. Prior genetic/PGx testing and unsatisfactory medication experiences were associated with a more positive attitude toward PGx. Conclusion: The majority of students have positive attitudes toward PGx, but lack knowledge in genetic concepts and clinical PGx.

Several healthcare organizations across Minnesota have developed formal pharmacogenomic (PGx) clinical programs to increase drug safety and effectiveness. Healthcare professional and student education is strong and there are multiple opportunities in the state for learners to gain workforce skills and develop advanced competency in PGx. Implementation planning is occurring at several organizations and others have incorporated structured utilization of PGx into routine workflows. Laboratory-based and translational PGx research in Minnesota has driven important discoveries in several therapeutic areas. This article reviews the state of PGx activities in Minnesota including educational programs, research, national consortia involvement, technology, clinical implementation and utilization and reimbursement, and outlines the challenges and opportunities in equitable implementation of these advances.

Aim: Multiple genetic variants have been associated with variation in tacrolimus (TAC) trough concentrations. Unfortunately, additional studies do not confirm these associations, leading one to question if a reported association is accurate and reliable. We attempted to validate 44 published variants associated with TAC trough concentrations., Materials & Methods: Genotypes of the variants in our cohort of 1923 kidney allograft recipients were associated with TAC trough concentrations., Results: Only variants in CYP3A4 and CYP3A5 were significantly associated with variation in TAC trough concentrations in our validation., Conclusion: There is no evidence that common variants outside the CYP3A4 and CYP3A5 loci are associated with variation in TAC trough concentrations. In the future rare variants may be important and identified using DNA sequencing.

Aim: Determine the effect of the genetic variants beyond CYP3A5*3 on tacrolimus disposition., Patients & Methods: We studied genetic correlates of tacrolimus trough concentrations with POR*28, CYP3A4*22 and ABCC2 haplotypes in a large, ethnically diverse kidney transplant cohort (n = 2008)., Results: Subjects carrying one or more CYP3A5*1 alleles had lower tacrolimus trough concentrations (p = 9.2 × 10(-75)). The presence of one or two POR*28 alleles was associated with a 4.63% reduction in tacrolimus trough concentrations after adjusting for CYP3A5*1 and clinical factors (p = 0.037). In subset analyses, POR*28 was significant only in CYP3A5*3/*3 carriers (p = 0.03). The CYP3A4*22 variant and the ABBC2 haplotypes were not associated., Conclusion: This study confirmed that CYP3A5*1 was associated with lower tacrolimus trough concentrations. POR*28 was associated with decreased tacrolimus trough concentrations although the effect was small possibly through enhanced CYP3A4 enzyme activity. CYP3A4*22 and ABCC2 haplotypes did not influence tacrolimus trough concentrations. Original submitted 19 December 2014; Revision submitted 2 April 2015.

Aim: Tacrolimus is an immunosuppressant used in transplantation. This article reports the validation of the authors' recently developed genetics-based tacrolimus equation that predicts troughs., Methods: Validation was performed in an independent cohort of 795 kidney transplant recipients receiving tacrolimus. The performance of the equation to predict initial troughs was assessed by calculating the bias and precision of the equation. For all troughs in the first 6 months post-transplant, a comparison was made between the troughs predicted using the equation versus those predicted using a basic apparent clearance model with no covariates., Results: For initial troughs, the equation had a low bias (0.2 ng/ml) and high precision (1.8 ng/ml). For all troughs, the equation predicted troughs significantly better than the basic apparent clearance model., Conclusion: The tacrolimus equation had good bias and precision in predicting initial troughs and performed better than a basic apparent clearance model for all the troughs.