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3012 Functional consequences of pravastatin isomerization on OATP1B1-mediated transport
- Jonathan Wagner, Melissa Ruggiero, J. Steven Leeder, Bruno Hagenbuch
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- Journal:
- Journal of Clinical and Translational Science / Volume 3 / Issue s1 / March 2019
- Published online by Cambridge University Press:
- 26 March 2019, p. 107
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- Article
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OBJECTIVES/SPECIFIC AIMS: In the present study, we examined the functional consequences of 3α-PVA on OATP1B1-mediated PVA transport. To elucidate this, we determined the effect of SLCO1B1 genotype on PVA transport and the role of 3αPVA as a competitive inhibitor of OATP1B1, which could serve as another covariate that disrupts the systemic and hepatic exposure of pravastatin in children and adults. METHODS/STUDY POPULATION: Site directed mutagenesis was performed to generate SLCO1B1 genotypes of interest (*1a, *1b, *5, *15). Human embryonic kidney (HEK293) cells were grown and plated at 200 000 cells per well in 24-well plates. Twenty-four hours later the cells were transfected with the aforementioned plasmids. Forty-eight hours later cell-based transport was performed with radiolabelled [3H]-pravastatin sodium salt. Non-radioactive pravastatin sodium salt and 3’α-iso-pravastatin sodium salt was used for PVA transport and 3αPVA studies, respectively. Cells were washed 3 times with warm uptake buffer, incubated for 1 minute with uptake solutions containing PVA and 3αPVA at varying concentrations. Transport was terminated by four 1-ml washes with ice-cold uptake buffer. Cells were lysed with 300 µl 1% Triton X-100 in PBS at room temperature for 30 minutes prior to analysis. Radioactivity was measured in a MicroBeta2 liquid scintillation counter. The remaining cell lysates were transferred to 96-well plates to determine total protein concentration using the bicinchonic acid protein assay. All transport measurements were corrected by the total protein concentration. All experiments were performed 3 to 4 times independently with 2-3 determinations. Data were analyzed for significant differences amongst genotype groups using ANOVA followed by Tukey’s multiple comparisons test. IC50 and kinetic parameters were calculated using non-linear regression analysis. RESULTS/ANTICIPATED RESULTS: Pravastatin transport in SLCO1B1 variants (*5, *15) was significantly decreased compared to the reference genotype *1a and *1b (Km [µM]: *1a 18.2 ± 0.9; *1b 17.9 ± 3.3; *5 34.2 ± 9.7; *15 34.1 ± 6.1; p≤0.05; Vmax [pmol/mg/min]: *1a 104.9 ± 13.1; *1b 93.7 ± 16.7; *5 44.8 ± 15.9; *15 62.3 ± 22.5; p≤0.05). *1a and *1b were not significantly different with respect to pravastatin transport. Intrinsic clearance was diminished nearly 4 to 5-fold in SLCO1B1 variants compared to reference genotypes (Vmax/Km [µl/min/mg]: *1a 5.8 ± 0.8; *1b 5.7 ± 1.9; *5 1.3 ± 0.2; *15 1.8 ± 0.3; p≤0.01). Pravastatin transport was inhibited by 3αPVA for all genotypes. However, there was more pronounced inhibition in the SLCO1B1 variant genotypes compared to reference genotypes (IC50 [µM]: *1a 15.9 ± 1.9; *1b 18.6 ± 5.7; *5 3.9 ± 2.0; *15 4.4 ± 0.8; p≤0.01; Ki: *1a 15.0 ± 1.8; *1b 17.5 ± 5.4; *5 3.8 ± 2.9; *15 4.3 ± 0.8; p≤0.01). DISCUSSION/SIGNIFICANCE OF IMPACT: In vitro PVA transport is altered according to SLCO1B1 genotype, consistent with previous in vitro and human experience. Our data suggest that the significantly different maximal transport velocity (Vmax) in variant versus non-variant genotypes is consistent with decreased membrane expression of OATP1B1 with the variant c.521T>C allele. However, in contrast to data involving typical model substrates (e.g. estrone-3-sulfate), the PVA binding affinity (Km) was significantly different between variant and non-variant genotypes, consistent with altered binding of the substrate to OATP1B1. Collectively, we conclude that decreased OATP1B1 expression and function in variant genotypes influence altered transport for PVA. Finally, the functional consequences of 3αPVA formation on PVA transport was confirmed in our study. Mechanistically, we confirmed our observation in humans that 3αPVA inhibits OATP1B1 transport. However, this effect is more pronounced in variant genotypes as shown by lower IC50 values compared to the reference genotypes. This highlights another source of variation that must be taken into consideration when trying to optimize the pravastatin dose-exposure relationship in humans.
22 - Application of Pharmacogenetics and Pharmacogenomics in Pediatrics: What Makes Children Different?
- from II - Therapeutic Areas
- Edited by Russ B. Altman, Stanford University, California, David Flockhart, Indiana University, David B. Goldstein, Duke University, North Carolina
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- Book:
- Principles of Pharmacogenetics and Pharmacogenomics
- Published online:
- 05 June 2012
- Print publication:
- 23 January 2012, pp 249-262
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Summary
Historically, submissions to the Food and Drug Administration (FDA) have been based on safety and efficacy data obtained from clinical trials conducted in adults with limited or no data from children. As a result, pediatricians and other health care professionals have relied on empiric therapeutic strategies, largely the consequence of treatment on a trial-and-error basis. In essence, the absence of information in the product label forces pediatricians to choose between avoiding the use of a medication that may be beneficial and using a medication “off-label” in the absence of evidence-based safety and efficacy data with the accompanying potential for ineffective and harmful outcomes.
During the past fifteen years, new federal laws and regulations have increased the level of scientific and clinical rigor of investigations aimed at ensuring that the use of medications by children is, indeed, safe and effective. Interested readers are referred to a detailed chronology of events occurring between 1994 and 2002 (1), and a contemporary discussion of the issues surrounding more recent legislative activities, such as the Pediatric Research Equity Act (PREA) of January 2003 (2). In general, it is recognized that growth and development are accompanied by changes in the physiological and biochemical processes determining drug disposition and response, for example, drug absorption, distribution, metabolism, excretion, and targets of drug response (3). However, the acquisition of information that can inform safe and efficacious use of medications in children of different ages or developmental stages has been a relatively recent development, increasing dramatically in recent years as a consequence of the various legislative initiatives. It is now apparent that extrapolation of adult data to pediatric populations is quite inappropriate because drug clearance may be greater than or, in some cases, less than that observed in adults (4). Thus, even though weight-based dosing strategies are becoming more sophisticated and have improved abilities to use adult data to infer drug clearance in children (5), they are unlikely to provide consistent dosing guidelines across all pediatric age groups or chemical classes. This is largely due to the variability in the developmental patterns of expression of the various drug-metabolizing enzymes and transporters involved in the disposition of individual compounds. Furthermore, evidence that the response to some medications may be different in children relative to adults despite comparable drug exposure is beginning to accrue (e.g., buspirone [4]), implying that age-related differences in drug targets and downstream signal transduction pathways may also be present.