Genetic variants predict plasma exposure to efavirenz and nelfinavir, and they may predict virologic failure and/or emergence of drug-resistant virus. These associations with treatment responses must be validated in other studies.

The present population pharmacokinetic (PK) and pharmacodynamic (PD) study modeled the effects of covariates including drug adherence and the coadministration of protease inhibitors (PIs) on the pharmacokinetics of efavirenz (EFV) and the relationship between EFV exposure and virological failure in patients who failed initial PI treatment in Adult AIDS Clinical Trial Group (AACTG) study 398. We also report on the population PKs of the PIs nelfinavir (NFV) and indinavir (IDV). AACTG study 398 patients received EFV, amprenavir, adefovir dipivoxil, and abacavir and were randomized to take, in addition, one of the following: NFV, IDV, saquinavir (SQV), or placebo. The PK databases consisted of 531 EFV concentrations (139 patients), 219 NFV concentrations (75 patients), and 66 IDV concentrations (11 patients). Time to virological failure was ascertained for all patients in the PK databases. PK data were fit with a population PK model that

CYP2D6 is the major enzyme involved in the metabolism of venlafaxine. Subjects with a low CYP2D6 activity have increased plasma concentrations of venlafaxine that may predispose them to cardiovascular side effects. In vitro and in vivo studies showed that diphenhydramine, a nonprescription antihistamine, can inhibit CYP2D6 activity. Therefore, the authors investigated in this study a potential drug interaction between diphenhydramine and venlafaxine. Fifteen male volunteers, nine with the extensive metabolizer (EM) and six with the poor metabolizer (PM) phenotype of CYP2D6, received venlafaxine hydrochloride 18.75 mg orally every 12 hours for 48 hours on two occasions (1 week apart): once alone and once during the concomitant administration of diphenhydramine hydrochloride (50 mg every 12 hours). Blood and urine samples were collected for 12 hours under steady-state conditions. In EMs, diphenhydramine decreased venlafaxine oral clearance from 104+/-60 L/hr to 43+/-23 L/hr (mean +/- SD; p < 0.05) without any effect on renal clearance (4+/-1 L/hr during venlafaxine alone and 4+/-2 L/hr during venlafaxine plus diphenhydramine). In PMs, coadministration of diphenhydramine did not cause significant changes in oral clearance and partial metabolic clearances of venlafaxine to its various metabolites. Diphenhydramine disposition was only slightly affected by genetically determined low CYP2D6 activity or concomitant administration of venlafaxine. In conclusion, diphenhydramine, at therapeutic doses, inhibits CYP2D6-mediated metabolism of venlafaxine in humans. Clinically significant interactions could be encountered during the concomitant administration of diphenhydramine and other antidepressant or antipsychotic drugs that are substrates of CYP2D6.

Multilocus genetic interactions between variant drug metabolism and transporter genes may predict efavirenz pharmacokinetics and treatment responses. This finding may have implications for better individualizing antiretroviral therapy.

The effects of gender, time variables, menstrual cycle phases, plasma sex hormone concentrations and physiologic urinary pH on CYP2D6 phenotyping were studied using two widely employed CYP2D6 probe drugs, namely dextromethorphan and metoprolol. Phenotyping on a single occasion of 150 young, healthy, drug-free women and men revealed that the dextromethorphan: dextrorphan metabolic ratio (MR) was significantly lower (P < 0.0001) in 56 female extensive metabolizers (0.008+/-0.021) compared to 86 male extensive metabolizers (0.020 +/-0.040). Urinary pH was a significant predictor of dextromethorphan: dextrorphan MRs in men and women (P < 0.001). Once-a-month phenotyping with dextromethorphan of 12 healthy young men (eight extensive metabolizers and four poor metabolizers) over a 1-year period, as well as every-other-day phenotyping with dextromethorphan of healthy, pre-menopausal women (10 extensive metabolizers and 2 poor metabolizers) during a complete menstrual cycle, did not follow a particular pattern and showed similar intrasubject variability ranging from 24.1% to 74.5% (mean 50.9%) in men and from 20.5% to 96.2% (mean 52.0%) in women, independent of the CYP2D6 phenotype (P = 0.342). Using metoprolol as a probe drug, considerable intrasubject variability (38.6+/- 12.0%) but no correlation between metoprolol: alpha-hydroxymetoprolol MRs and pre-ovulatory, ovulatory and luteal phases (mean +/- SD metoprolol: a-hydroxymetoprolol MRs: 1.086+/- 1.137 pre-ovulatory; 1.159+/-1.158 ovulatory and 1.002+/-1.405 luteal phase; P> 0.9) or 17beta-oestradiol, progesterone or testosterone plasma concentrations was observed. There was a significant inverse relationship between physiologic urinary pH and sequential dextromethorphan: dextrorphan MRs as well as metoprolol: alpha-hydroxymetoprolol MRs in men and women, with metabolic ratios varying up to six-fold with metoprolol and up to 20-fold with dextromethorphan (ANCOVA P < 0.001). We conclude that apparent CYP2D6 activity is highly variable, independent of menstrual cycle phases, sex hormones, time variables or phenotype. Up to 80% of the observed variability can be explained by variations of urinary pH within the physiological range. An apparent phenotype shift as a result of variations in urinary pH may be observed in individuals who have metabolic ratios close to the population antimode.

AIMSThe objectives of this study were to develop a population pharmacokinetic (PopPK) model for tacrolimus in paediatric liver transplant patients and determine optimal sampling strategies to estimate tacrolimus exposure accurately. METHODSTwelve hour intensive pharmacokinetic profiles from 30 patients (age 0.4-18.4 years) receiving tacrolimus orally were analysed. The PopPK model explored the following covariates: weight, age, sex, type of transplant, age of liver donor, liver function tests, albumin, haematocrit, drug interactions, drug formulation and time post-transplantation. Optimal sampling strategies were developed and validated with jackknife.

Partial adherence with a prescribed or randomly assigned dose gives rise to unintended variability in actual drug exposure in clinical practice and during clinical trials. There are tremendous costs associated with incomplete and/or improper drug intake-to both individual patients and society as a whole. Methodology for quantifying the relation between adherence, exposure and drug response is an area of active research. Modeling and statistical approaches have been useful in evaluating the impact of adherence on therapeutics and in addressing the challenges of confounding and measurement error which arise in this context. This paper reviews quantitative approaches to using adherence information in improving therapeutics. It draws heavily on applications in the area of HIV pharmacology.

Mexiletine, a class Ib antiarrhythmic agent, is rapidly and completely absorbed following oral administration with a bioavailability of about 90%. Peak plasma concentrations following oral administration occur within 1 to 4 hours and a linear relationship between dose and plasma concentration is observed in the dose range of 100 to 600 mg. Mexiletine is weakly bound to plasma proteins (70%). Its volume of distribution is large and varies from 5 to 9 L/kg in healthy individuals. Mexiletine is eliminated slowly in humans (with an elimination half-life of 10 hours). It undergoes stereoselective disposition caused by extensive metabolism. Eleven metabolites of mexiletine are presently known, but none of these metabolites possesses any pharmacological activity. The major metabolites are hydroxymethyl-mexiletine, p-hydroxy-mexiletine, m-hydroxy-mexiletine and N-hydroxy-mexiletine. Formation of hydroxymethyl-mexiletine, p-hydroxy-mexiletine and m-hydroxy-mexiletine is genetically determined and cosegregates with polymorphic debrisoquine 4-hydroxylase [cytochrome P450 (CYP) 2D6] activity. On the other hand, CYP1A2 seems to be implicated in the N-oxidation of mexiletine. Various physiological, pathological, pharmacological and environmental factors influence the disposition of mexiletine. Myocardial infarction, opioid analgesics, atropine and antacids slow the rate of absorption, whereas metoclopramide enhances it. Rifampicin (rifampin), phenytoin and cigarette smoking significantly enhance the rate of elimination of mexiletine, whereas ciprofloxacin, propafenone and liver cirrhosis decrease it. Cimetidine, ranitidine, fluconazole and omeprazole do not modify the disposition of mexiletine. Conversely, mexiletine is known to alter the disposition of other drugs, such as caffeine and theophylline. Factors affecting the elimination of mexiletine may be clinically important and dosage adjustments are often necessary.