PharmGKB summary

Goswami, Srijib; Gong, Li; Giacomini, Kathleen M.; Altman, Russ B.; Klein, Teri E.

doi:10.1097/fpc.0000000000000048

Cited by 31 publications

(19 citation statements)

References 38 publications

(58 reference statements)

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“…An elementary consequence of standard enzyme kinetics is that molecules using the same protein may compete with or inhibit each other, in this case each other's transport. This is a simply vast topic, so (notwithstanding earlier critiques of summarizing via the enormous review literature), we here simply point out several useful and recent reviews (from the last 3 years only) that describe in detail the many named and genetically identified transporters that are involved in DDI (Han, 2011 ; Kido et al, 2011 ; Klatt et al, 2011 ; König, 2011 ; Maeda et al, 2011 ; Marzolini et al, 2011 ; Müller and Fromm, 2011 ; Riches et al, 2011 ; Shitara, 2011 ; Zhang et al, 2011b ; Bi et al, 2012 ; Elsby et al, 2012 ; Feng et al, 2012 , 2013 , 2014 ; Fromm, 2012 ; Grandvuinet et al, 2012 ; Karlgren et al, 2012 ; Keogh, 2012 ; Lepist and Ray, 2012 ; Nies et al, 2012 ; Sissung et al, 2012 ; Sprowl and Sparreboom, 2012 , 2014 ; Takanohashi et al, 2012 ; Varma et al, 2012 ; Yeo et al, 2012 , 2013 ; Yoshida et al, 2012 , 2013 ; Kis et al, 2013 ; König et al, 2013 ; Maeda and Sugiyama, 2013 ; Sugiyama and Steffansen, 2013 ; Tang et al, 2013 ; Zamek-Gliszczynski et al, 2013 ; Goswami et al, 2014 ; Tannenbaum and Sheehan, 2014 ; Vildhede et al, 2014 ). We are not aware of any papers that showed such DDI based on any measured competition for transport via the phospholipid bilayer.…”

Section: Some Further Areas Where the Hypothesis Of Dominant Transpormentioning

confidence: 99%

How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion

Kell¹,

Oliver²

2014

Front. Pharmacol.

147

169

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One approach to experimental science involves creating hypotheses, then testing them by varying one or more independent variables, and assessing the effects of this variation on the processes of interest. We use this strategy to compare the intellectual status and available evidence for two models or views of mechanisms of transmembrane drug transport into intact biological cells. One (BDII) asserts that lipoidal phospholipid Bilayer Diffusion Is Important, while a second (PBIN) proposes that in normal intact cells Phospholipid Bilayer diffusion Is Negligible (i.e., may be neglected quantitatively), because evolution selected against it, and with transmembrane drug transport being effected by genetically encoded proteinaceous carriers or pores, whose “natural” biological roles, and substrates are based in intermediary metabolism. Despite a recent review elsewhere, we can find no evidence able to support BDII as we can find no experiments in intact cells in which phospholipid bilayer diffusion was either varied independently or measured directly (although there are many papers where it was inferred by seeing a covariation of other dependent variables). By contrast, we find an abundance of evidence showing cases in which changes in the activities of named and genetically identified transporters led to measurable changes in the rate or extent of drug uptake. PBIN also has considerable predictive power, and accounts readily for the large differences in drug uptake between tissues, cells and species, in accounting for the metabolite-likeness of marketed drugs, in pharmacogenomics, and in providing a straightforward explanation for the late-stage appearance of toxicity and of lack of efficacy during drug discovery programmes despite macroscopically adequate pharmacokinetics. Consequently, the view that Phospholipid Bilayer diffusion Is Negligible (PBIN) provides a starting hypothesis for assessing cellular drug uptake that is much better supported by the available evidence, and is both more productive and more predictive.

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Section: Some Further Areas Where the Hypothesis Of Dominant Transpormentioning

confidence: 99%

How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion

Kell¹,

Oliver²

2014

Front. Pharmacol.

147

169

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“… 23 Among children, morphine clearance was significantly lower in homozygote carriers of loss‐of‐function OCT1 variants. 24 …”

Section: Organic Cation Transporter Isoformmentioning

confidence: 99%

Pharmacogenomics and Morphine

Ofoegbu

Ettienne

2021

The Journal of Clinical Pharma

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Morphine is an opioid analgesic indicated in the treatment of acute and chronic moderate to severe pain. From a pharmacodynamic standpoint, morphine exerts its effects by agonizing mu-opioid receptors predominantly, resulting in analgesia and sedation. Pharmacokinetically, morphine is primarily metabolized in the liver via glucuronidation by the enzyme uridine diphosphate glucuronosyltransferase family 2 member B7 and encounters the transporter proteins organic cation transporter isoform 1 and P-glycoprotein (adenosine triphosphate-binding cassette subfamily B member 1) as it is being distributed throughout the body. The genes coding for the proteins impacting either the pharmacokinetics or pharmacodynamics of morphine may bear genetic variations, also known as polymorphisms, which may alter the function of the proteins in such a manner that an individual may have disparate treatment outcomes. The purpose of this review is to highlight some of the genes coding for proteins that impact morphine pharmacokinetics and pharmacodynamics and present some treatment considerations.

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“…The SLC22A1 gene encodes for the organic cationic transporter 1 (OCT), a transporter responsible for the hepatic uptake of xenobiotics and for the capture of organic cations from blood to epithelial cells [ 38 ]. There is very little information available regarding atorvastatin and SLC22A1 .…”

Section: Discussionmentioning

confidence: 99%

SLCO1B1 Phenotype and CYP3A5 Polymorphism Significantly Affect Atorvastatin Bioavailability

Zubiaur

Benedicto

Villapalos‐García

et al. 2021

JPM

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Atorvastatin, prescribed for the treatment of hypercholesterolemia, demonstrated overwhelming benefits in reducing cardiovascular morbidity and mortality. However, many patients discontinue therapy due to adverse reactions, especially myopathy. The Dutch Pharmacogenetics Working Group (DPWG) recommends an alternative agent to atorvastatin and simvastatin or a dose adjustment depending on other risk factors for statin-induced myopathy in SLCO1B1 rs4149056 CC or TC carriers. In contrast, the Clinical Pharmacogenetics Implementation Consortium (CPIC) published their guideline on simvastatin, but not on atorvastatin. In this work, we aimed to demonstrate the effect of SLCO1B1 phenotype and other variants (e.g., in CYP3A4/5, UGT enzymes or SLC transporters) on atorvastatin pharmacokinetics. For this purpose, a candidate-gene pharmacogenetic study was proposed. The study population comprised 156 healthy volunteers enrolled in atorvastatin bioequivalence clinical trials. The genotyping strategy comprised a total of 60 variants in 15 genes. Women showed higher exposure to atorvastatin compared to men (p = 0.001), however this difference disappeared after dose/weight (DW) correction. The most relevant pharmacogenetic differences were the following: AUC/DW and Cmax /DW based on (a) SLCO1B1 phenotype (p < 0.001 for both) and (b) CYP3A5*3 (p = 0.004 and 0.018, respectively). As secondary findings: SLC22A1 *2/*2 genotype was related to higher Cmax/DW (ANOVA p = 0.030) and SLC22A1 *1/*5 genotype was associated with higher Vd/F (ANOVA p = 0.032) compared to SLC22A1 *1/*1, respectively. Finally, UGT2B7 rs7439366 *1/*1 genotype was associated with higher tmax as compared with the *1/*3 genotype (ANOVA p = 0.024). Based on our results, we suggest that SLCO1B1 is the best predictor for atorvastatin pharmacokinetic variability and that prescription should be adjusted based on it. We suggest that the CPIC should include atorvastatin in their statin-SLCO1B1 guidelines. Interesting and novel results were observed based on CYP3A5 genotype, which should be confirmed with further studies.

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PharmGKB summary

Cited by 31 publications

References 38 publications

How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion

How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion

Pharmacogenomics and Morphine

SLCO1B1 Phenotype and CYP3A5 Polymorphism Significantly Affect Atorvastatin Bioavailability

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