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      Meta-analysis of hepatic cytochrome P450 ontogeny to underwrite the prediction of pediatric pharmacokinetics using physiologically based pharmacokinetic modeling

      1 , 2
      The Journal of Clinical Pharmacology
      Wiley

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          Predicting in vivo drug interactions from in vitro drug discovery data.

          In vitro screening for drugs that inhibit cytochrome P450 enzymes is well established as a means for predicting potential metabolism-mediated drug interactions in vivo. Given that these predictions are based on enzyme kinetic parameters observed from in vitro experiments, the miscalculation of the inhibitory potency of a compound can lead to an inaccurate prediction of an in vivo drug interaction, potentially precluding a safe drug from advancing in development or allowing a potent inhibitor to 'slip' into the patient population. Here, we describe the principles underlying the generation of in vitro drug metabolism data and highlight commonly encountered uncertainties and sources of bias and error that can affect extrapolation of drug-drug interaction information to the clinical setting.
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            Polymorphism of human cytochrome P450 2D6 and its clinical significance: Part I.

            Cytochrome P450 (CYP) 2D6 is one of the most investigated CYPs in relation to genetic polymorphism, but accounts for only a small percentage of all hepatic CYPs (approximately 2-4%). There is a large interindividual variation in the enzyme activity of CYP2D6. The enzyme is largely non-inducible and metabolizes approximately 25% of current drugs. Typical substrates for CYP2D6 are largely lipophilic bases and include some antidepressants, antipsychotics, antiarrhythmics, antiemetics, beta-adrenoceptor antagonists (beta-blockers) and opioids. The CYP2D6 activity ranges considerably within a population and includes ultrarapid metabolizers (UMs), extensive metabolizers (EMs), intermediate metabolizers (IMs) and poor metabolizers (PMs). There is a considerable variability in the CYP2D6 allele distribution among different ethnic groups, resulting in variable percentages of PMs, IMs, EMs and UMs in a given population. To date, 74 allelic variants and a series of subvariants of the CYP2D6 gene have been reported and the number of alleles is still growing. Among these are fully functional alleles, alleles with reduced function and null (non-functional) alleles, which convey a wide range of enzyme activity, from no activity to ultrarapid metabolism of substrates. As a consequence, drug adverse effects or lack of drug effect may occur if standard doses are applied. The alleles *10, *17, *36 and *41 give rise to substrate-dependent decreased activity. Null alleles of CYP2D6 do not encode a functional protein and there is no detectable residual enzymatic activity. It is clear that alleles *3, *4, *5, *6, *7, *8, *11, *12, *13, *14, *15, *16, *18, *19, *20, *21, *38, *40, *42, *44, *56 and *62 have no enzyme activity. They are responsible for the PM phenotype when present in homozygous or compound heterozygous constellations. These alleles are of clinical significance as they often cause altered drug clearance and drug response. Among the most important variants are CYP2D6*2, *3, *4, *5, *10, *17 and *41. On the other hand, the CYP2D6 gene is subject to copy number variations that are often associated with the UM phenotype. Marked decreases in drug concentrations have been observed in UMs with tramadol, venlafaxine, morphine, mirtazapine and metoprolol. The functional impact of CYP2D6 alleles may be substrate-dependent. For example, CYP2D6*17 is generally considered as an allele with reduced function, but it displays remarkable variability in its activity towards substrates such as dextromethorphan, risperidone, codeine and haloperidol. The clinical consequence of the CYP2D6 polymorphism can be either occurrence of adverse drug reactions or altered drug response. Drugs that are most affected by CYP2D6 polymorphisms are commonly those in which CYP2D6 represents a substantial metabolic pathway either in the activation to form active metabolites or clearance of the agent. For example, encainide metabolites are more potent than the parent drug and thus QRS prolongation is more apparent in EMs than in PMs. In contrast, propafenone is a more potent beta-blocker than its metabolites and the beta-blocking activity during propafenone therapy is more prominent in PMs than EMs, as the parent drug accumulates in PMs. Since flecainide is mainly eliminated through renal excretion, and both R- and S-flecainide possess equivalent potency for sodium channel inhibition, the CYP2D6 phenotype has a minor impact on the response to flecainide. Since the contribution of CYP2D6 is greater for metoprolol than for carvedilol, propranolol and timolol, a stronger gene-dose effect is seen with this beta-blocker, while such an effect is lesser or marginal in other beta-blockers. Concordant genotype-phenotype correlation provides a basis for predicting the phenotype based on genetic testing, which has the potential to achieve optimal pharmacotherapy. However, genotype testing for CYP2D6 is not routinely performed in clinical practice and there is uncertainty regarding genotype-phenotype, gene-concentration and gene-dose relationships. Further prospective studies on the clinical impact of CYP2D6-dependent metabolism of drugs are warranted in large cohorts of subjects.
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              Clinical Pharmacology of Tramadol

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                Author and article information

                Journal
                The Journal of Clinical Pharmacology
                The Journal of Clinical Pharmacology
                Wiley
                00912700
                March 2016
                March 2016
                October 09 2015
                : 56
                : 3
                : 266-283
                Affiliations
                [1 ]Clinical Pharmacology, Modeling and Simulation; Amgen, Inc.; South San Francisco CA USA
                [2 ]Pharmacokinetics and Drug Metabolism; Amgen, Inc.; Thousand Oaks CA USA
                Article
                10.1002/jcph.585
                26139104
                30c28d6c-0b63-4bbc-baae-7de05357b7f8
                © 2015

                http://doi.wiley.com/10.1002/tdm_license_1.1

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