Pharmacokinetic Drug Interaction Study Research Articles

Drug-drug interactions: it is not only CYP450's which matter

The objectives of this review are: • To highlight that although CYP450's are a major target for drug-drug interactions other mechanisms need to be considered. • To appreciate that important interactions also occur due to a change in pH (gut) and inhibition of non P450 enzymes (eg UGTs). • To discuss the increasing importance of understanding transporter based interactions in the gut, liver, kidney and blood brain barrier. • To outline new data on clinically important drug-drug interactions between antiretrovirals and between antiretrovirals and other drugs. • To discuss how to predict, manage and avoid drug-drug interactions including an outline of on-line resources available for this purpose. Pharmacokinetic drug interaction studies performed during the drug development process, or post-licensing provide the substantive data base from which recommendations regarding the use of certain drug combinations are made. However given the sheer number of potential interactions extrapolation on the basis of potential mechanism of interaction is also important. Thus a foundational knowledge of drug disposition (enzymes, transporters involved etc) is essential so that in vitro data (is the drug a substrate; is the drug an inhibitor of a particular enzyme or transporter) can be used to underpin a clinical study or be the basis for an informed decision re the potential for an interaction. While the major focus in the HIV field has been on CYP450 enzymes (for the obvious reason that many of the drugs are extensively metabolised and/or are inducers/inhibitors) there is a growing awareness of the key role for other proteins -- in particular UDP-glucuronyltransferases (UGTs) and transporters (ABC transporters such as P-pg, MRP1,2,7; SLCO transporters such as OATP1B1, OATP1B3, OATP1A2, OCT1,2, OAT1,2). This is a rapidly emerging field and one which is going to impact not only on our understanding of mechanisms of drug-drug interactions but also on seeing the bigger picture in relation to the role of pharmacogenetics in inter-individual variability. Unexpected interactions will continue to emerge and will need to be managed. Ultimately the key to management of patients on multiple drugs is clinical vigilance, access to adequate resources to help inform (eg web based resources), utility of therapeutic drug monitoring where available and close follow up of patients. Recommended reading: Dickinson L, Khoo S, Back D. Pharmacokinetics and drug-drug interactions of antiretrovirals: An update. Antiviral Res. 2009 Aug 7 (Epub ahead of print).

Clinical pharmacokinetic drug interaction studies of gabapentin enacarbil, a novel transported prodrug of gabapentin, with naproxen and cimetidine

Gabapentin enacarbil, a transported prodrug of gabapentin, provides sustained, dose-proportional exposure to gabapentin. Unlike gabapentin, the prodrug is absorbed throughout the intestinal tract by high-capacity nutrient transporters, including mono-carboxylate transporter-1 (MCT-1). Once absorbed, gabapentin enacarbil is rapidly hydrolyzed to gabapentin, which is subsequently excreted by renal elimination via organic cation transporters (OCT2). To examine the potential for drug-drug interactions at these two transporters, the pharmacokinetics of gabapentin enacarbil were evaluated in healthy adults after administration alone or in combination with either naproxen (an MCT-1 substrate) or cimetidine (an OCT2 substrate). Subjects (n= 12 in each study) received doses of study drug until steady state was achieved; 1200 mg gabapentin enacarbil each day, followed by either naproxen (500 mg twice daily) or cimetidine (400 mg four times daily) followed by the combination. When gabapentin enacarbil was co-administered with naproxen, gabapentin C(ss,max) increased by, on average, 8% and AUC by, on average, 13%. When gabapentin enacarbil was co-administered with cimetidine, gabapentin AUC(ss) increased by 24% and renal clearance of gabapentin decreased. Co-administration with gabapentin enacarbil did not affect naproxen or cimetidine exposure. Gabapentin enacarbil was generally well tolerated. No gabapentin enacarbil dose adjustment is needed with co-administration of naproxen or cimetidine.

Simultaneous RP‐HPLC‐DAD quantification of bromocriptine, haloperidol and its diazepane structural analog in rat plasma with droperidol as internal standard for application to drug‐interaction pharmacokinetics

A simple and rapid RP-HPLC-DAD method was developed and validated for simultaneous determination of the dopamine antagonists haloperidol, its diazepane analog, and the dopamine agonist bromocriptine in rat plasma, to perform pharmacokinetic drug-interaction studies. Samples were prepared for analysis by acetonitrile (22.0 microg/mL) plasma protein precipitation with droperidol as an internal standard, followed by a double-step liquid-liquid extraction with hexane : chloroform (70:30) prior to C-18 separation. Isocratic elution was achieved using a 0.1% (v/v) trifluoroacetic acid in deionized water, methanol and acetonitrile (45/27.5/27.5, v/v/v). Triple-wavelength diode-array detection at the lambda(max) of 245 nm for haloperidol, 254 nm for the diazepane analog and droperidol, and 240 nm for bromocriptine was carried out. The LLOQ of DAL, HAL, and BCT were 45.0, 56.1, and 150 ng/mL, respectively. In rats, the estimated pharmacokinetic parameters (i.e., t(1/2), CL, and V(ss)) of HAL when administered with DAL and BCT were t(1/2) = 16.4 min, V(ss) = 0.541 L/kg for HAL, t(1/2) = 28.0 min, V(ss) = 2.00 L/kg for DAL, and t(1/2) = 24.0 min, V(ss) = 0.106 L/kg for BCT. The PK parameters for HAL differed significantly from those previously reported, which may be an indication of a drug-drug interaction.

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

de Leon, Jose MD*†; Spina, Edoardo MD, PhD‡§; Diaz, Francisco J. PhD∥ Author Information

Preparation of Scientific Reports on Pharmacokinetic Drug Interaction Studies

Preparation of Scientific Reports on Pharmacokinetic Drug Interaction Studies

Effects of Atazanavir/Ritonavir or Fosamprenavir/Ritonavir on the Pharmacokinetics of Rosuvastatin

Rosuvastatin (RSV) is a potent statin with a lower potential for drug interactions. However, recent data have revealed unexpected increases in RSV concentrations with lopinavir/ritonavir. The objective is to study the pharmacokinetic interaction of RSV with atazanavir/ritonavir (ATV/RTV) or fosamprenavir/ritonavir (FPV/RTV). In a prospective pharmacokinetic drug interaction study, six HIV-seronegative, healthy adult volunteers received single 10-mg doses of RSV at baseline and after 6 days of ATV/RTV and FPV/RTV, with 6-day washout periods. Plasma concentrations of RSV and its metabolites, N-desmethyl-RSV and RSV-lactone, were measured by using a internally validated tandem mass spectrometric (LC-MS/MS) method over 24 hours. Compared to baseline, the area under the plasma concentration-time curve (AUC 0-24h) and maximum plasma concentration (Cmax) of RSV increased by 213% and 600%, respectively, and the time to reach Cmax was shorter (1.75 h vs. 2.91 h) when given with ATV/RTV (P < 0.05). However, coadministration with FPV/RTV did not significantly affect the pharmacokinetics of RSV. The AUC 0-24h of N-desmethyl-RSV was not significantly affected by either combinations, but that of RSV-lactone increased (P < 0.05) by 61% and 76% after coadministration with ATV/RTV and FPV/RTV, respectively. ATV/RTV significantly increases the plasma concentrations of rosuvastatin, most likely by increasing rosuvastatin's oral bioavailability. Dose limitations of RSV with ATV/RTV may be needed.

Clinical management of drug interaction with antiretroviral agents

Combination antiretroviral therapy has improved the morbidity and mortality of HIV-infected patients worldwide. As patients live longer, management of HIV infection extends to treatment of a wide spectrum of co-morbid conditions. Pharmacokinetic interactions are common among antiretroviral drugs when they are used in combination and along with treatments for other conditions. This review discusses the clinical significance of drug interactions among antiretroviral drugs and other medications, resources to use in assessing drug interaction potential, and some key principles to follow when managing patients prescribed potentially interacting drugs. Targeted pharmacokinetic drug interaction studies and extrapolations on the basis of potential mechanism of interactions provide an initial basis for recommendations regarding use of certain drug combinations. Some unexpected interactions have emerged in the literature through case reports in which untoward effects were observed. Management of patients on multiple drug therapy can be a challenge. The key to safe and effective therapy relies on the clinician's vigilance in their ongoing assessment of interaction potential among drugs prescribed to each patient, the significance for such interactions, the need for modification to therapy, and close follow up to assess safety and toxicity.

Differential Genotype Dependent Inhibition of CYP2C9 in Humans

The effects of genetic polymorphisms in drug-metabolizing enzymes (e.g., CYP2C9(*)3) on drug clearance have been well characterized but much less is known about whether these polymorphisms alter susceptibility to drug-drug interactions. Previous in vitro work has demonstrated that genotype-dependent inhibition of CYP2C9 mediated flurbiprofen metabolism, suggesting the possibility of genotype-dependent inhibition interactions in vivo. In the current study, flurbiprofen was used as a probe substrate and fluconazole as a prototypical inhibitor to investigate whether genotype-dependent inhibition of CYP2C9 occurs in vivo. From 189 healthy volunteers who were genotyped for CYP2C9 polymorphisms, 11 control subjects (CYP2C9(*)1/(*)1), 9 heterozygous and 2 homozygous for the CYP2C9(*)3 allele participated in the pharmacokinetic drug interaction study. Subjects received a single 50-mg oral dose of flurbiprofen alone or after administration of either 200 or 400 mg of fluconazole for 7 days using an open, randomized, crossover design. Flurbiprofen and fluconazole plasma concentrations along with flurbiprofen and 4'-hydroxyflurbiprofen urinary excretion were monitored. Flurbiprofen apparent oral clearance differed significantly among the three genotype groups (p < 0.05) at baseline but not after pretreatment with 400 mg of fluconazole for 7 days. Changes in flurbiprofen apparent oral clearance after fluconazole coadministration were gene dose-dependent, with virtually no change occurring in (*)3/(*)3 subjects. Analysis of fractional clearances suggested that the fraction metabolized by CYP2C9, as influenced by genotype, determined the degree of drug interaction observed. In summary, the presence of CYP2C9(*)3 alleles (either one or two alleles) can alter the degree of drug interaction observed upon coadministration of inhibitors.

Open‐label steady‐state pharmacokinetic drug interaction study on co‐administered quetiapine fumarate and divalproex sodium in patients with schizophrenia, schizoaffective disorder, or bipolar disorder

To determine whether there is a pharmacokinetic drug interaction between quetiapine fumarate and divalproex sodium. The pharmacokinetics and short-term tolerability and safety of coadministered quetiapine and divalproex were examined in adults with schizophrenia/schizoaffective disorder (Cohort A) or bipolar disorder (Cohort B) in an open-label, parallel, 2-cohort drug-interaction study conducted at three centers in the United States. Cohort A was administered quetiapine (150 mg bid) prospectively for 13 days, with divalproex (500 mg bid) added on days 6-13. Cohort B was administered divalproex (500 mg bid) for 16 days, with quetiapine (150 mg bid) added on days 9-16. Quetiapine and valproic acid plasma concentration-time data over a 12-h steady-state dosing interval were used to determine C(max), T(max), C(min), area under the plasma concentration-time curve (AUC(tau)), and oral clearance (CL/F). In Cohort A (n = 18), addition of divalproex did increase the C(max) of quetiapine by 17% but did not change AUC(tau). In Cohort B (n = 15), addition of quetiapine decreased both total valproic acid C(max) and AUC(tau) by 11%. No differences were observed in adverse events (AEs) with either quetiapine or divalproex monotherapy or their combination. Combination therapy with quetiapine (150 mg bid) and divalproex (500 mg bid) resulted in small and statistically non-significant pharmacokinetic changes.

Modelling the influence of MDR1 polymorphism on digoxin pharmacokinetic parameters

Digoxin is a well-known probe for the activity of P-glycoprotein. The objective of this work was to apply different methods for covariate selection in non-linear mixed-effect models to study the relationship between the pharmacokinetic parameters of digoxin and the genotype for two major exons located on the multi-drug-resistance 1 (MDR1) gene coding for P-glycoprotein. Thirty-two healthy volunteers were recruited in three pharmacokinetic drug interaction studies. The data after a single oral administration of digoxin alone were pooled. All subjects were genotyped for the MDR1 C3435T and G2677T/A genotypes. The concentration-time profile of digoxin was established using 12-16 blood samples taken between 15 min and 72 h after administration. We modelled the pharmacokinetics of digoxin using non-linear mixed-effect models. Parameter estimation was performed using the stochastic approximation EM method (SAEM). We used three methods to select the covariate model: selection from a full model using Wald tests, forward inclusion using the log-likelihood ratio test and model selection using the Bayesian Information Criterion. The three covariate inclusion methods led to the same final model. Carriers of two T alleles for the C3435T polymorphism in exon 26 of MDR1 had a lower apparent volume of distribution than carriers of a C allele. The only other covariate effect was a shorter absorption time-lag in women. The apparent volume of distribution of digoxin is lower in TT subjects, probably reflecting differences in bioavailability. Non-linear mixed-effect models can be useful for detecting the influence of covariates on pharmacokinetic parameters.

Exenatide effects on statin pharmacokinetics and lipid response

Exenatide is an adjunctive treatment for type 2 diabetes. Many patients with type 2 diabetes have dyslipidemia, which requires treatment with three hydroxy-3-methyl glutaryl coenzyme (HMG-CoA) reductase inhibitors (statins), hence, concurrent use of exenatide and statins is likely. Exenatide slows gastric emptying, which may alter the absorption rate of co-administered oral medications. Thus, the potential interaction between exenatide and statins was evaluated in two study settings. In an open-label, fixed-sequence, clinical pharmacology study, the plasma pharmacokinetics of lovastatin (40 mg after breakfast) in the presence and absence of exenatide (10 microg before breakfast and dinner) was evaluated in 21 healthy subjects. In a second clinical setting, changes in lipid profiles and statin dosage over 30 weeks in patients with type 2 diabetes were retrospectively compared (n = 180 exenatide 10 microg twice daily (BID), n = 168 placebo BID) in a combined analysis of three placebo-controlled, randomized exenatide Phase 3 trials. In healthy subjects, exenatide decreased mean lovastatin area under the plasma concentration time curve from zero to infinity (AUC0-infinity) and maximum plasma concentration (Cmax) by 40 and 28%, respectively, and increased median time to maximum plasma concentration (tmax) by 4 hours. In the exenatide Phase 3 trials, 30-week changes from baseline for low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol, triglycerides and statin dosage were not significantly different between the exenatide and placebo groups treated with statins. Despite observed changes in lovastatin bioavailability in the pharmacokinetic drug interaction study, exenatide did not negatively affect long-term lipid profiles or statin dosage in patients with concurrent statin therapy. Thus, co-administration of exenatide does not require adjustment in statin dosage.

Three case reports of a clinical pharmacokinetic interaction with buprenorphine and atazanavir plus ritonavir

Pharmacokinetic drug interactions have the potential to reduce significantly the antiretroviral or drug treatment benefit that patients gain from each therapy, secondary to alterations in either plasma or tissue levels of one or both agents [1]. In this report, we present the first cases of symptomatic adverse side-effects, temporally associated with the induction and stabilization of buprenorphine in patients administered the combination of atazanavir and ritonavir. Although the patients were also taking nucleoside reverse transcriptase inhibitors, there is no predicted interaction between these compounds on the basis of their currently understood pharmacokinetics (Table 1).Table 1: Description of subjects.Patient 1 was inducted with buprenorphine and was titrated to a stable dose of 14 mg/day. Two days after induction, the combination of enteric-coated didanosine 250 mg, tenofovir 300 mg, atazanavir 300 mg and ritonavir 100 mg were all started once a day and co-administered with buprenorphine. The following day, the patient began to experience daytime somnolence and decreased mental functioning. Buprenorphine dosing was not decreased initially, but the patient was offered increased counseling for the next 2 weeks. In recognition of a potential drug interaction of buprenorphine with known inhibitors of CYP3A4, the patient's buprenorphine dose was decreased to 8 mg/day, with tolerance to the sedative effects occurring within 7 days. Patient 2 had for several months received enteric-coated didanosine 250 mg, tenofovir 300 mg, atazanavir 300 mg and ritonavir 100 mg and twice-daily olanzepine 5 mg. On day 2 of induction with buprenorphine (8 mg), the patient reported feeling ‘doped up’. Despite attempts to maintain the patient on 8 mg/day, the buprenorphine dose was reduced to every other day, with an improvement in the feelings of opiate excess. Patient 3 had for several months received the once-daily combination of lamivudine 300 mg, tenofovir 300 mg and atazanavir 400 mg while incarcerated. After release from prison, atazanavir dosing was adjusted to 300 mg and combined with ritonavir 100 mg in recognition of the interaction between tenofovir and atazanavir. To avoid relapse to heroin use, the patient requested buprenorphine and was initiated at 8 mg a day. The patient described dizziness and feeling ‘high’ and ‘overmedicated’, and requested a dose modification. Buprenorphine dosing was successfully changed to 8 mg every other day. Although the dizziness improved on alternate day dosing, a craving for opiates occurred. Therefore, buprenorphine was increased to 8 mg a day and further increased to 12 mg a day. The patient then developed hypersomnolence similar to patients 1 and 2. Despite the hypersomnolence, the patient elected to continue buprenorphine at this dose because it reduced the craving for opiates. Continued supportive counseling was provided and tolerance developed over the following week with the gradual cessation of hypersomnolence. This small case series is the first to suggest and document possible clinical drug interactions between buprenorphine and two protease inhibitors known to inhibit CYP3A4. Briefly, the primary route of buprenorphine phase I metabolism is via CYP3A4 to norbuprenorphine [2,3]. The glucuronidation of buprenorphine metabolites is the most significant part of their phase II metabolism [4]. A small in-vitro study in liver microsomes [5] demonstrated an increase in buprenorphine levels probably caused by the inhibition of buprenorphine metabolism by ritonavir at CYP3A4. Atazanavir also appeared to inhibit the metabolism of compounds at CYP3A4 [6]. In pharmacokinetic studies, atazanavir has not been demonstrated to increase the levels of methadone or result in symptoms of opiate excess [7]. In clinical trials, however, atazanavir increased saquinavir levels, thus requiring reduced dosing of saquinavir when combined [8]. A recent pharmacokinetic study [9] demonstrated that both atazanavir and ritonavir independently boosted saquinavir drug levels, possibly through independent mechanisms. Atazanavir is also an inhibitor and inducer of p-glycoprotein [10] and an inhibitor of uridine diphosphate-glucuronosyl transferase 1A1 [11]. Importantly, uridine diphosphate-glucuronosyl transferase 1A1 is involved in the phase II metabolism of buprenorphine [12], and its inhibition by atazanavir may further increase drug levels. Therefore, in these three cases, it is possible that atazanavir or ritonavir inhibited the major phase I metabolic pathway (CYP3A4), or that atazanavir inhibited the major phase II pathway for buprenorphine, resulting in increased levels and clinical symptoms of opiate excess. Until further data are available, specifically from well-conducted pharmacokinetic drug interaction studies of buprenorphine with ritonavir, atazanavir or both, the use of buprenorphine in combination with ritonavir and atazanavir should be undertaken cautiously. Induction with buprenorphine should begin at reduced dosing, and dose escalation should occur at a slower than usual pace to allow providers to assess for opiate excess and to allow patients to become accustomed to the effects of buprenorphine. Sponsorship: The authors would like to thank the National Institute on Drug Abuse (R01-DA 13805, K24-DA 017072) and the Substance Abuse and Mental Health Services Agency (H79 TI 15767) for their funding and continued support in these research and clinical efforts.

Analysing pharmacokinetic drug interaction or comparability studies with missing values in crossover design

AbstractIn a pharmacokinetic drug interaction study using a three‐period, three‐treatment (drug A, drug B, and drugs A and B concomitantly) crossover design, pharmacokinetic parameters for either drug are only measured in two of the three periods. Similar missing data problems can arise for a four‐period, four‐treatment crossover pharmacokinetic comparability study. This paper investigates whether the usual ANOVA model for the crossover design can be applied under this pattern of missing data. It is shown that the model can still be used, contrary to a belief that a new one is needed. The effect of this type of missing data pattern on the statistical properties of treatment, period and carryover effect estimates was derived and illustrated by means of simulations and an example. Copyright © 2003 John Wiley &amp; Sons, Ltd.

A Pharmacokinetic and Pharmacodynamic Drug Interaction Study of Acamprosate and Naltrexone

Acamprosate and naltrexone have each demonstrated safety and efficacy for alcohol dependence in placebo-controlled clinical trials. There is scientific and clinical interest in evaluating these drugs in combination, given their high tolerability, moderate effect sizes, different pharmacological profiles and potentially different effects on drinking outcomes. Thus, this is the first human pharmacokinetic and pharmacodynamic drug interaction study of acamprosate and naltrexone. Twenty-four normal, healthy adult volunteers participated in a double-blind, multiple dose, within subjects, randomized, 3-way crossover drug interaction study of the standard therapeutic dose of acamprosate (2 g/d) and the standard therapeutic dose of naltrexone (50 mg/d), given alone and in combination, with seven days per treatment condition and seven days washout between treatments. Blood samples were collected on a standardized schedule for pharmacokinetic analysis of naltrexone, 6-β-naltrexol, and acamprosate. A computerized assessment system evaluated potential drug effects on cognitive functioning. Coadministration of acamprosate with naltrexone significantly increased the rate and extent of absorption of acamprosate, as indicated by an average 33% increase in acamprosate maximum plasma concentration, 33% reduction in time to maximum plasma concentration, and 25% increase in area under the plasma concentration-time curve. Acamprosate did not affect the pharmacokinetic parameters of naltrexone or 6-β-naltrexol. A complete absence of negative interactions on measures of safety and cognitive function supports the absence of a contraindication to co-administration of acamprosate and naltrexone in clinical practice.

A review of eprosartan pharmacokinetic and pharmacodynamic drug interaction studies.

A series of clinical pharmacology studies was conducted to characterize potential interactions between eprosartan and other commonly prescribed drugs. Separate studies assessed the effect of eprosartan on the pharmacokinetics of digoxin and hydrochlorothiazide (HCTZ) and the pharmacodynamics of warfarin and glyburide (glibenclamide), as well as the effects of ranitidine, HCTZ, fluconazole, and ketoconazole on eprosartan pharmacokinetics. Eprosartan had no significant effect on the pharmacokinetics of digoxin and HCTZ and the pharmacodynamics of warfarin and glyburide. Thus, no dosing adjustments are necessary during concomitant therapy with these agents. Ranitidine, HCTZ, ketoconazole, and fluconazole had no effect on eprosartan pharmacokinetics. Single or multiple oral doses of eprosartan were safe and well tolerated when coadministered with these agents.

High-performance liquid chromatographic assays for the quantification of amikacin in human plasma and urine

Amikacin, an aminoglycoside antibiotic, is frequently coadministered with penicillins and broad-spectrum cephalosporins to synergize the activity of these agents. Sensitive, selective and reproducible high-performance liquid chromatographic assays have been developed for the quantification of amikacin in plasma and urine collected from human subjects. The plasma method involves the ultrafiltration of plasma prior to derivatization. An aliquot of plasma ultrafiltrate or urine is mixed with dimethyl sulfoxide and tris(hydroxymethyl)aminoethane followed by derivatization of amikacin with 1-fluoro-2,4-dinitrobenzene at 58°C for 30 min. The reaction mixture is then injected directly onto a reversed-phase C 18 column preceded by a guard column. The column is eluted with a mobile phase containing acetonitrile and 2-methoxyethanol in 1% Tris buffer. Amikacin derivative is detected at 340 nm. The methods were applied for the analysis of amikacin in plasma and urine samples from volunteers receiving amikacin and cefepime, a fourth-generation cephalosporin, in a clinical pharmacokinetic drug interaction study.

The Statistical Evaluation of a Three-Period Two-Treatment Crossover Pharmacokinetic Drug Interaction Study

In a pharmacokinetic drug interaction study, the purpose is to determine whether the coadministration of a drug A with a second drug B alters the absorption/distribution/metabolism/elimination profile of either drug. While the usual design for such studies is a three-period crossover, it cannot be analyzed as such, because the plasma-level data of drug B will be 0 when drug A is given alone, and vice versa. The easiest way to proceed is to do two sets of paired analyses, one on the absorption profile of A (A vs AB), and the other on the absorption profile of B (B vs AB). A complete separation of the total sources of variation and degrees of freedom is presented along with a numerical example.

Pharmacokinetic drug interactions.

Drugs labeled with stable isotopes have been successfully used in pharmacokinetic drug interaction studies. This technique provides precise information on the mechanisms responsible for drug interactions, e.g., changes in clearance due to induction or inhibition of drug metabolism, bioavailability, and volume of distribution. It offers the advantage that detailed studies on the pharmacokinetics and metabolism of a drug can be conducted in a clinical setting during steady-state conditions, thus avoiding problems in patient management that can arise from studies in which drug is withdrawn.