Cytochrome P450 in Pharmacogenetics: An Update

Tornio, Aleksi; Backman, Janne T.

doi:10.1016/bs.apha.2018.04.007

Cited by 130 publications

(98 citation statements)

References 114 publications

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“…Thus, pharmacokinetics in heterozygous and homozygous variant allele carriers can be compared with that in reference allele carriers, to inform about the relevance of OATP1B1 in the disposition of the investigational drug, and its sensitivity to inhibition of OATP1B1. Of drug transporters and drug‐metabolizing enzymes, at least ABCG2 , CES1 , CYP2C9 , CYP2C19 , CYP2D6 , CYP3A4 , CYP3A5 , DPYD , NAT2 , SLC22A1 , SLCO1B1 , TPMT , UGT1A1 , UGT1A3 , UGT2B10 , and UGT2B17 show significant genetic variability . Variants in these genes could be used to define subject groups in a genotype panel study and genotyping for these genes should be considered if they are likely to be involved in the pharmacokinetics of the victim or perpetrator of a DDI study.…”

Section: Design Of Clinical Ddi Studiesmentioning

confidence: 99%

Clinical Studies on Drug–Drug Interactions Involving Metabolism and Transport: Methodology, Pitfalls, and Interpretation

Tornio

Filppula

Niemi

et al. 2019

Clin Pharma and Therapeutics

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132

139

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Many drug–drug interactions ( DDI s) are based on alterations of the plasma concentrations of a victim drug due to another drug causing inhibition and/or induction of the metabolism or transporter‐mediated disposition of the victim drug. In the worst case, such interactions cause more than tenfold increases or decreases in victim drug exposure, with potentially life‐threatening consequences. There has been tremendous progress in the predictability and modeling of DDI s. Accordingly, the combination of modeling approaches and clinical studies is the current mainstay in evaluation of the pharmacokinetic DDI risks of drugs. In this paper, we focus on the methodology of clinical studies on DDI s involving drug metabolism or transport. We specifically present considerations related to general DDI study designs, recommended enzyme and transporter index substrates and inhibitors, pharmacogenetic perspectives, index drug cocktails, endogenous substrates, limited sampling strategies, physiologically‐based pharmacokinetic modeling, complex DDI s, methodological pitfalls, and interpretation of DDI information.

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Section: Design Of Clinical Ddi Studiesmentioning

confidence: 99%

Clinical Studies on Drug–Drug Interactions Involving Metabolism and Transport: Methodology, Pitfalls, and Interpretation

Tornio

Filppula

Niemi

et al. 2019

Clin Pharma and Therapeutics

Self Cite

132

139

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“…The cytochrome P450 (CYP) plays a major role in drug metabolism with CYP (1A2, 2C9, 2C19, 2D6, and 3A4), mainly responsible for the biotransformation of greater than 90% of drugs in phase-1 metabolism [65], [66]. However, among these P450 families, CYP3A4 is the focus part of this study [67]. The relationship between the rate of elimination of the drug and concentration of the drug in the body is best described by total clearance [68].…”

Section: Discussionmentioning

confidence: 99%

In-SilicoAnalysis of Secondary Metabolites that Modulates Enzymes of Cholesterol Target

Marahatha

Basnet

Bhattarai

et al. 2020

Preprint

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Hypercholesterolemia has posed a serious threat of heart diseases and stroke worldwide. Xanthine oxidase (XO), the rate-limiting enzyme in uric acid biosynthesis, is regarded as the root of reactive oxygen species (ROS) that generates atherosclerosis and cholesterol crystals. β-Hydroxy β-methylglutaryl-coenzyme A reductase (HMGR) is a rate-limiting enzyme in cholesterol biosynthesis. Although some commercially available enzyme inhibiting drugs have effectively reduced the cholesterol level, most of them have failed to meet the requirements of being apt drug candidates. Here, we have carried out an in-silico analysis of secondary metabolites that have already shown good inhibitory activity against XO and HMGR. Out of 118 secondary metabolites reviewed, sixteen molecules inhibiting XO and HMGR were taken based on IC50 values reported in vitro assays. Further, receptor-based virtual screening was carried out against secondary metabolites using GOLD Protein-Ligand Docking Software, combined with subsequent post-docking, to study the binding affinities of ligands to the enzymes. In-Silico ADMET analysis was carried out to study their pharmacokinetic properties, followed by toxicity prediction through ProTox-II. The molecular docking of amentoflavone (1) (GOLD score 70.54), and ganomycin I (9) (GOLD score 59.61) evinced that the drug has effectively bind at the competitive site of XO and HMGR, respectively. Besides, 6-paradol (3) and selgin (4) could be potential drug candidates to inhibit XO. Likewise, n-octadecanyl-O-α-D-glucopyranosyl(6’→1”)-O-α-D-glucopyranoside (10) could be potential drug candidates to maintain serum cholesterol. In-silico ADMET analysis showed that the sixteen metabolites were optimal within the categorical range in comparison to commercially available XO and HMGR inhibitors, respectively. Toxicity analysis through Protox-II revealed that 6-gingerol (2), ganoleucoin K (11), and ganoleucoin Z (12) are toxic for human use. This computational analysis supports earlier experimental evidence towards the inhibition of XO and HMGR by natural products. Further study is necessary to explore the clinical efficacy of these secondary molecules, which might be alternatives for the treatment of hypercholesterolemia.Graphical abstract

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“…At the same time, there is also appreciation for the “fidelity” of DME, transporters, and NHRs, which is evident when a single nucleotide (nonsynonymous) polymorphism greatly impacts protein function; a key single‐nucleotide polymorphism can markedly affect a drug's protein interaction at the (sub)angstrom level and alter its overall PK‐ADME‐DDI profile with impact on efficacy and safety. Such a scenario is most apparent when a drug's major clearance step is dominated by a single polymorphic DME or transporter or when transporter‐DME interplay is mediated by two polymorphic proteins . The concept of fidelity also applies to the various small molecules that interact with DME and transporters, as it has long been understood that relatively subtle changes in structure can greatly impact their solubility, permeability, inhibition, metabolism, transport, and protein‐binding properties …”

Section: Complex Dynamic and High‐fidelity Pk‐adme‐ddi Processesmentioning

confidence: 99%

From Endogenous Compounds as Biomarkers to Plasma‐Derived Nanovesicles as Liquid Biopsy; Has the Golden Age of Translational Pharmacokinetics‐Absorption, Distribution, Metabolism, Excretion‐Drug–Drug Interaction Science Finally Arrived?

Rodrigues

Rowland

2019

Clin Pharma and Therapeutics

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It is now established that a drug's pharmacokinetics (PK) absorption, distribution, metabolism, excretion (ADME) and drug–drug interaction (DDI) profile can be modulated by age, disease, and genotype. In order to facilitate subject phenotyping and clinical DDI assessment, therefore, various endogenous compounds (in plasma and urine) have been pursued as drug‐metabolizing enzyme and transporter biomarkers. Compared with biomarkers, however, the topic of circulating extracellular vesicles as “liquid biopsy” has received little attention within the ADME community; most organs secrete nanovesicles (e.g., exosomes) into the blood that contain luminal “cargo” derived from the originating organ (proteins, messenger RNA, and microRNA). As such, ADME profiling of plasma exosomes could be leveraged to better define genotype‐phenotype relationships and the study of ontogeny, disease, and complex DDIs. If methods to support the isolation of tissue‐derived plasma exosomes are successfully developed and validated, it is envisioned that they will be used jointly with genotyping, biomarkers, and modeling tools to greatly progress translational PK‐ADME‐DDI science.

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Cytochrome P450 in Pharmacogenetics: An Update

Cited by 130 publications

References 114 publications

Clinical Studies on Drug–Drug Interactions Involving Metabolism and Transport: Methodology, Pitfalls, and Interpretation

Clinical Studies on Drug–Drug Interactions Involving Metabolism and Transport: Methodology, Pitfalls, and Interpretation

In-SilicoAnalysis of Secondary Metabolites that Modulates Enzymes of Cholesterol Target

From Endogenous Compounds as Biomarkers to Plasma‐Derived Nanovesicles as Liquid Biopsy; Has the Golden Age of Translational Pharmacokinetics‐Absorption, Distribution, Metabolism, Excretion‐Drug–Drug Interaction Science Finally Arrived?

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