A Mechanistic Approach to Antiepileptic Drug Interactions

Anderson, Gail D.

doi:10.1201/ebk1420085594-c14

Cited by 6 publications

(7 citation statements)

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“…Although the exact mechanisms underlying this individual variability remain elusive, concomitant medications and genetic polymorphisms represent two major causes of this phenomenon (Anderson, 1998;Chung et al, 2008;Nakajima et al, 2004;Patsalos and Perucca, 2003;Perucca, 2006). VPA is metabolized via three routes, catalyzed by various cytochrome P450 (CYP) enzymes, mitochondrionmediated ␤-oxidation, and glucuronidation; this is catalyzed by uridine diphosphate glucuronosyltransferases (UGTs) (Reith et al, 2000;Zaccara et al, 1988).…”

Section: Introductionmentioning

confidence: 99%

The influence of UGT2B7 genotype on valproic acid pharmacokinetics in Chinese epilepsy patients

et al. 2015

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Section: Introductionmentioning

confidence: 99%

The influence of UGT2B7 genotype on valproic acid pharmacokinetics in Chinese epilepsy patients

et al. 2015

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“…20 However, these are approximations and no data is available on the minimal duration required for an inductive treatment to reach maximum induction and the minimal waiting period to see its maximum effects on the substrate.…”

Section: Use Of Repeated Dosingmentioning

confidence: 99%

Pharmacokinetic Drug Interaction Studies Must Consider Pharmacological Heterogeneity, Use of Repeated Dosing, and Translation Into a Message Understandable to Practicing Clinicians

Leon¹,

Spina²,

Díaz³

2009

Journal of Clinical Psychopharmacology

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he Food and Drug Administration set the standards for studying drug-drug interactions (DDIs) in the US by drafting sets of guidelines for the industry. The 2006 guidelines 1 focused on study design, data analysis, and implications for dosing and labeling and provided a general orientation to key issues such as study design, study population, choice of substrate and interacting drugs, route administration, dose selection, and end points, sample size, and statistical considerations. However, it is up to pharmaceutical companies to implement specific studies for their drugs. Dr Greenblatt, 2 the Editor of this journal, has recently provided a summary of some methodological requisites for the design and presentation of pharmacokinetic DDI studies to improve the scientific quality of this journal's DDI articles. Certainly, Dr Greenblatt's summary could be used for any pharmacological journal. In the current editorial, the writers point out that the existing scientific standards for pharmacokinetic DDI studies are not sufficient to meet the needs of unusual patients or their prescribing physicians. There are 3 concepts: (1) pharmacological heterogeneity, (2) use of repeated dosing, and (3) translation into a message understandable to practicing clinicians that can be used to illustrate how these studies could be improved. PHARMACOLOGICAL HETEROGENEITYTo explain what we call pharmacological heterogeneity, we shall begin with what statisticians call heterogeneity; we apologize for having to go back and forth between statistical and pharmacological concepts. As Feinstein 3 (both a physician and a scientist with mathematical training) pointed out, the problem of statistical heterogeneity is largely ignored by statistics textbooks. A statistician analyzing a DDI study would probably assume that the issue of heterogeneity had already been resolved by clinicians or pharmacologists before research data were presented to them. If one calculates the mean and SD of a variable using a sample, one assumes that the sample is homogeneous, including entities of the same type, rather than a heterogeneous mixture of things. The concept of pharmacological heterogeneity in drug metabolism somewhat overlaps with the concept of statistical heterogeneity. For us, pharmacological heterogeneity refers to the concept that some individuals metabolize medications in a way that is different from the average individual. We consider 2 types of pharmacological heterogeneity that have clinical relevance; the first is found in individuals whom statisticians would call outliers and the second in those treated with clinically relevant inhibitors and/or inducers.According to statistics texts, outliers are subjects at the extremes of a distribution, that is, the subjects observed in the tail areas, far beyond the central indexes and the near-central zones of the distribution. 4 This statistical concept has major relevance for pharmacokinetics. To simplify, we will focus on the so-called polymorphic variations. A genetic variation is usually considered a po...

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“…All patients were administering 1-5 other antiepileptic drugs in various combinations. Patients were classified as those who received CYP inducers (phenytoin, carbamazepine, or phenobarbital) and those who received CYP2C19 inhibitors (zonisamide or topiramate) [14,15,23]. Each patient was not receiving any drugs except antiepileptics which potentially affect the pharmacokinetics of CLB, such as CYP3A4 inhibitors or CYP2C19 inhibitors.…”

Section: Pharmacokineticsmentioning

confidence: 99%

Effect of CYP2C19 polymorphisms on the clinical outcome of low-dose clobazam therapy in Japanese patients with epilepsy

Hashi

Yano

Shibata

et al. 2014

Eur J Clin Pharmacol

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A Mechanistic Approach to Antiepileptic Drug Interactions

Cited by 6 publications

References 0 publications

The influence of UGT2B7 genotype on valproic acid pharmacokinetics in Chinese epilepsy patients

The influence of UGT2B7 genotype on valproic acid pharmacokinetics in Chinese epilepsy patients

Pharmacokinetic Drug Interaction Studies Must Consider Pharmacological Heterogeneity, Use of Repeated Dosing, and Translation Into a Message Understandable to Practicing Clinicians

Effect of CYP2C19 polymorphisms on the clinical outcome of low-dose clobazam therapy in Japanese patients with epilepsy

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