Metabolic Drug-drug Interactions Research Articles

In Vitro Assessment of Metabolic Drug-Drug Interaction Potential of Apixaban through Cytochrome P450 Phenotyping, Inhibition, and Induction Studies

Apixaban is an oral, direct, and highly selective factor Xa inhibitor in late-stage clinical development for the prevention and treatment of thromboembolic diseases. The metabolic drug-drug interaction potential of apixaban was evaluated in vitro. The compound did not show cytochrome P450 inhibition (IC(50) values >20 microM) in incubations of human liver microsomes with the probe substrates of CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, or 3A4/5. Apixaban did not show any effect at concentrations up to 20 muM on enzyme activities or mRNA levels of selected P450 enzymes (CYP1A2, 2B6, and 3A4/5) that are sensitive to induction in incubations with primary human hepatocytes. Apixaban showed a slow metabolic turnover in incubations of human liver microsomes with formation of O-demethylation (M2) and hydroxylation products (M4 and M7) as prominent in vitro metabolites. Experiments with human cDNA-expressed P450 enzymes and P450 chemical inhibitors and correlation with P450 activities in individual human liver microsomes demonstrated that the oxidative metabolism of apixaban for formation of all metabolites was predominantly catalyzed by CYP3A4/5 with a minor contribution of CYP1A2 and CYP2J2 for formation of M2. The contribution of CYP2C8, 2C9, and 2C19 to metabolism of apixaban was less significant. In addition, a human absorption, distribution, metabolism, and excretion study showed that more than half of the dose was excreted as unchanged parent (f(m CYP) <0.5), thus significantly reducing the overall metabolic drug-drug interaction potential of apixaban. Together with a low clinical efficacious concentration and multiple clearance pathways, these results demonstrate that the metabolic drug-drug interaction potential between apixaban and coadministered drugs is low.

Canine CYP2B11 metabolizes and is inhibited by anesthetic agents often co-administered in dogs

Medetomidine is a well-established sedative and analgesic for dogs and cats. As a premedicant for anesthesia regimens that also include other agents, medetomidine can also provide a dose-sparing effect. While there are likely several reasons for the dose-sparing effect of medetomidine, the role of metabolic drug-drug interactions at the single enzyme level has not yet been examined. Using a panel of individually expressed canine cytochromes P450 cloned from beagle liver, this report demonstrates that medetomidine is an extremely potent CYP2B11 inhibitor (IC(50) < 10 nm) and that ketamine and midazolam are CYP2B11 substrates with high intrinsic clearances. These in vitro findings suggest that under some circumstances, medetomidine (i.e. 'perpetrator') may inhibit the metabolic clearance of some high metabolic clearance drugs (i.e. 'victims') with which it is commonly co-administered via the CYP2B11 pathway. However, as the dose-sparing effect of medetomidine premedication commonly results in anesthetic dose reduction, any increased plasma concentrations of victim drugs caused by medetomidine would still be lower than if no dose reduction had been made. Further studies are needed to characterize whether medetomidine possesses the potential to cause pharmacokinetic interactions. In conclusion, the ability of recombinant P450s to define canine-specific drug clearance pathways and P450 inhibitors should prove useful in identifying drug combinations that may require dose adjustments in dogs.

Voriconazole pharmacokinetic variability in cystic fibrosis lung transplant patients

Aspergillosis is a high-risk complication in cystic fibrosis (CF) lung transplant patients. Azole antifungal drugs inhibit CYP3A4, resulting in significant metabolic drug-drug interactions. Voriconazole (VRZ) was marketed without therapeutic drug monitoring (TDM) recommendations, consistent with favorable pharmacokinetics, but regular determinations of plasma VRZ concentration were introduced in our center to manage interactions with calcineurin inhibitors and to document the achievement of therapeutic levels. VRZ TDM data analysis for trough concentration (C0) and peak concentration (C2) was carried out, using validated liquid chromatography assay with ultraviolet detection, for 35 CF lung transplant patients (mean age 25 years, mean weight 47 kg, balanced sex ratio) since 2003. Therapeutic range (C0: 1.5 +/- 0.5 - C2 : 4.0 +/- 1.0 mg/L) was expressed relative to pivotal pharmacokinetic trial data. The duration of VRZ treatment ranged from 9 days to 22 months. The recommended standard dose of VRZ (200 mg twice a day, following the loading dose) resulted in significant plasma concentrations (>0.5 mg/L) in 20% of CF lung transplant patients. Therapeutic concentrations were obtained using higher doses (average 570 +/- 160 mg/day, +43%, P<0.01). Despite adaptation, C0 remained <0.5 mg/L (11%), even when the drug was administered intravenously, highlighting the variability of VRZ pharmacokinetics, possibly enhanced by CYP2C19 polymorphism. The risk of inefficacy during periods of underdosage was overcome by treatment with antifungal drug combinations (caspofungin, n=10). The therapeutic index was limited by neurologic effects (14%) and hepatic abnormalities (30%). VRZ concentrations correlated significantly (P<0.01) with aspartate aminotransferase levels but not with bilirubin levels. VRZ acted as a metabolic inhibitor of tacrolimus (C0 to dose ratio 5.8 +/- 2.6, n=31/VRZ versus 1.7 +/- 0.9 alone, P<0.001). Large changes in azole concentration affected the magnitude of the drug-drug interactions and adjustment requirements. TDM is required because VRZ levels are often undetectable in treated CF lung transplant patients, supporting the use of antifungal drug combinations until achievement of VRZ C0 at a steady state between 1 and 2 mg/L. Plasma VRZ concentrations should be determined for the quantitative, individualized management of drug-drug interactions in lung transplant patients, in particular immunosuppressant such as tacrolimus, considering VRZ to be both a target and an inhibitor of CYP3A4.

Assessing the efficiency of mixed effects modelling in quantifying metabolism based drug–drug interactions: using in vitro data as an aid to assess study power

The clinical assessment of metabolic drug-drug interactions (mDDI) may involve population-based pharmacokinetic (POPPK) assessment as part of Phase 3 clinical trials. The elements of such POPPK study design have not been linked to prior information from in vitro experiments. Using in vitro-in vivo extrapolation techniques, implemented within Simcyp algorithms, the influence of POPPK study design (sample size, concentration-time data points, proportion of the population receiving a concomitant medication (COMED)) was studied in relation to the inhibitory potency of the each COMED. Steady-state concentrations of a candidate compound (compound X; mainly metabolized by cytochrome P450 enzymes, CYP3A4 and CYP2D6) in the presence and absence of COMEDs (including ketoconazole, fluconazole, quinidine and paroxetine as inhibitors) were analysed using non-linear mixed effect modelling (NONMEM). The NONMEM operator was blind to the nature of the COMEDs and the inhibitory effects on model parameters were classified as either statistically (p>0.01 for a change in objective function) or kinetically (COMED effect>2 fold) significant. Using a population study size of 2000, no false-positive cases were identified and, except in one case, no false-negative interaction was observed when >2.5% of patients had received an interacting COMED. The findings increase the confidence in the ability of the mixed effects modelling approach to identify 'true' interactions. However, they also emphasize the importance of study design and the potential value of using pre-clinical information from in vitro studies. Recent US Food and Drug Administration guidance on mDDI has put more emphasis on the use of in vitro systems for detecting and anticipating such effects. Combining these data with the framework of non-linear mixed effect modelling seems a natural progression in the field of assessing mDDI.

Antifungal Azole Metabolites: Significance in Pharmaceutical and Biomedical Analysis

Antifungal Azole Metabolites: Significance in Pharmaceutical and Biomedical Analysis Individualised therapy and factors determining such variability among patients are confusing to both physicians and their patients because of the observed therapeutic, metabolic and toxic response. The same is true about antifungal azoles. They are under the influence and become targets of metabolic drug-drug interactions where more than one active form of the drug may be involved. The clinical relevance of these interactions may vary upon the azole involved and upon the intention of drug administration. The pharmacodynamics and pharmacokinetics of azole drugs as indicated by the reviewed data make the need for characterization of all their metabolites even more evident. The health care systems also emphasize the identification and quantitation of the metabolites for a comprehensive understanding of the biological safety of individual metabolites, thus, revealing the need and scope of bioanalytical research in metabolite and toxicity profiling of drugs. Availability of protocols for qualitative and quantitative characterization of all metabolites will have many applications for therapeutic drug monitoring, bioequivalence, toxicological and all related studies. Identification of metabolites may be done by a variety of chromatographic and spectroscopic techniques, either alone or in combination with other techniques. Conventional liquid chromatography has been exploited widely in the field of metabolite profiling. The arrival of hyphenated techniques has revolutionized metabolite profiling, by not only separating but also generating data for the structural identification of metabolites as well. Among all techniques, the most exploited are Liquid Chromatography-Mass Spectroscopy, Nuclear Magnetic Resonance spectroscopy, Liquid Chromatography-Nuclear Magnetic Resonance spectroscopy, Liquid Chromatography-Nuclear Magnetic Resonance spectroscopy-Mass Spectroscopy and Extraction-Nuclear Magnetic Resonance spectroscopy. This compilation provides a tool for the metabolic, bioanalytical and biomedical understanding of antifungal azole metabolites.

Evaluation ofEscherichia coliMembrane Preparations of Canine CYP1A1, 2B11, 2C21, 2C41, 2D15, 3A12, and 3A26 with Coexpressed Canine Cytochrome P450 Reductase

The preparation of bacterial membranes ("Bactosomes") containing expressed canine (beagle) hepatic cytochromes P450 (P450s) is described. cDNAs from seven canine P450s were subcloned into inducible expression plasmids and, for the first time, cotransformed and expressed with a canine P450 oxidoreductase in Escherichia coli to produce active, full-length, native sequence P450s. Enzyme expression levels, although variable, were generally sufficient to enable short incubation times and to limit the total protein present in enzyme incubations. Steady-state kinetics of CYP1A1, 2C21, and 2D15 Bactosomes demonstrated similarities with dog liver microsomes or Baculosomes. However, 3A12 lacked substrate inhibition in the formation of 1'-OH midazolam, and 2B11 displayed non-Michaelis-Menten kinetics, suggesting possible differences in protein interaction effects. In monitoring the metabolites of common P450 substrates, phenacetin deethylation, temazepam demethylation, and bufuralol 1'-hydroxylation were shown to be relatively selective reactions catalyzed by CYP1A1, 2B11, and 2D15, respectively. 1'-OH midazolam was formed in higher quantities by CYP2B11 and 2C21 than by 3A12, raising questions about the use of midazolam as a CYP3A12 probe in vivo. In summary, a panel of recombinant P450s was produced to make up for the lack of commercially available canine P450 isoforms. The Bactosomes are expected to facilitate reaction phenotyping and metabolic drug-drug interaction assessment in canine drug development and to enable the study of interspecies differences in P450-mediated drug metabolism.

P4-406: Tarenflurbil (MPC-7869), a selective Aβ42-lowering agent, has no significant effect on drugs metabolized by CYP2C9

Alzheimer's disease is the most common type of dementia among the elderly. Its histopathologic features include phosphorylated Tau neurofibrillary tangles and amyloid beta senile plaques. In randomized, double-blind Phase II studies, the Selective Aβ42-Lowering Agent (SALA) tarenflurbil (MPC-7869) has demonstrated efficacy and tolerability. In earlier studies, CYP2C9 inhibition was demonstrated in vitro. Therefore, tarenflurbil has the potential to inhibit CYP2C9 in vivo. Sixteen healthy subjects participated in this third-party blind, placebo-controlled, randomized, crossover, Phase I study. Participants received either placebo or two 400-mg oral tablets of tarenflurbil every 12 hours for 9 consecutive days. On Day 6, each subject received one 500-mg oral tablet of tolbutamide, a CYP2C9 probe substrate. After the 14-day washout period, the treatments were switched (ie, subjects received the other treatment). Blood samples were collected throughout the study. Blood and urine samples were obtained on Day 6, prior to the morning dose of tarenflurbil or placebo and tolbutamide, and throughout the 96 hours after tolbutamide administration. The 90% confidence intervals for tolbutamide AUC(0–∞) (110%–120%) and Cmax (88%–112%) were within the standard bioequivalence criteria (80%–125%). Five days after administration of tarenflurbil 800 mg BID, the difference between mean tolbutamide exposure AUC(0–∞) with and without tarenflurbil was less than 15%. Furthermore, the formation clearance of 4-hydroxytolbutamide was similar for the treated and untreated states. Tarenflurbil has low potential for inhibition-based metabolic drug-drug interactions. Because of its amyloid-lowering properties, tarenflurbil holds promise as a disease-modifying agent for Alzheimer's disease.

In Vitro Drug-Drug Interaction Screens for Canine Veterinary Medicines: Evaluation of Cytochrome P450 Reversible Inhibition

Little information regarding the metabolic pathways of pharmaceutical agents administered to dogs, or the inhibition of those metabolic pathways, is available. Without this information, it is difficult to assess how combinations of drugs, whether new or old or approved or nonapproved, may increase the risk for metabolic drug-drug interactions in dogs. Because mammalian xenobiotic metabolism pathways often involve the hepatic cytochrome P450 (P450) monooxgenases, canine liver microsome P450 inhibition screens were tested to evaluate the potential metabolic drug interaction risk of commonly used veterinary medicines. A probe substrate cocktail was developed for four of the five major hepatic canine P450s and used to evaluate their inhibition by 45 canine therapeutic agents in a single-point IC(50) screen. Moderate inhibitors (>25%) were further characterized with an automated ninepoint IC(50) assay that identified ketoconazole, clomipramine, and loperamide as submicromolar CYP2D15 inhibitors. Additional inhibitors belonged to the antiemetic, antimitotic, and anxiolytic therapeutic classes. According to the marker activities, the relative frequency of P450 inhibition by isoform followed the sequence CYP2D15 > CYP2B11 > CYP2C21/41 > CYP3A12/26 > CYP1A1/2. The findings presented suggest there is some overlap in canine and human P450 inhibition specificity. However, occasional differences may give human drugs used off-label in dogs unexpected P450 inhibition profiles and, therefore, cause an unexpected drug-drug interaction risk.

Pharmacokinetic drug interactions of azoles

Drug-drug interactions are a major concern with triazoles. Azoles are potent metabolic inhibitors and interactions commonly occur via metabolizing enzymes (ie, cytochrome P450 isoenzyme superfamily) or drug transporters (ie, P-glycoprotein). However, the clinical relevance of these interactions may vary upon the azole involved and upon the “target” drug. Azoles may also be under influence of and become targets of metabolic drug-drug interactions. Potential interactions between antifungal agents and over-the-counter or alternative medicines and herbs should not be underestimated. Here, we provide a comprehensive overview of different types of pharmacokinetic drug interactions involving azoles with selected examples.

Comparison of different approaches to predict metabolic drug-drug interactions

Three approaches were compared to predict the actual magnitude of drug interaction (the mean fold-change in the area under the curve (AUC)) of reversible or irreversible (mechanism-based) cytochrome P450 (CYP) inhibitors. These were: (1) the pragmatic use of the '[I]/K(i)' approach; (2) the 'Mechanistic-Static Model' (MSM), which is a more complex extension of the '[I]/K(i)' approach that incorporates f(m,CYP), intestinal availability for CYP3A substrates, and mechanism-based inhibition (MBI); and (3) the 'Mechanistic-Dynamic Model' (MDM) which considers the time-variant change in the concentration of the inhibitor by using physiologically-based pharmacokinetic (PBPK) models (as implemented within the Simcyp(R) Population-Based ADME Simulator). The three approaches ([I]/K(i), MSM, and MDM) predicted a 'correct' drug-drug interaction (DDI) result (interaction: Greater than or equal to twofold; no interaction: Less than twofold) in 74, 87, and 80% of the 100 trials evaluated, respectively. Importantly, for trials with a greater than or equal to twofold change in AUC in the presence of the inhibitor (59 trials), the [I]/K(i), MSM, and MDM approaches predicted the mean AUC change within twofold of actual in 17, 53, and 64% of the trials, respectively. Overall, the MDM approach showed an improvement in the prediction of DDI magnitude compared to the other methods evaluated and was useful in its ability to predict variability in DDI magnitude and pharmacokinetic parameters. Moreover, the MDM model allowed the automated prediction of the inhibition of parallel metabolic pathways and simulations of different dosing regimens.

Inhibition of P450 Enzymes: An In Vitro Approach

Drug metabolism is a major determinant of drug clearance, interindividual pharmacokinetic differences and, indirectly, of the clinical efficacy and toxicity of drugs. Altered pharmacokinetics can result in inadequate concentration of the drug at the site of action and/or great variations in clinical response. Therefore, the development of a new drug requires not only an exhaustive characterisation of its pharmacological activity, but also knowledge of major enzymes involved in metabolite formation, and the potential enzyme inhibiting or enzyme inducing properties of the drug. Multi-drug therapy is not uncommon in clinical practice. Simultaneous administration of several drugs may result in metabolic drug-drug interactions having pharmacological and/or toxicological implications. As drugs are metabolised by a limited number of enzymes, they can compete each other as substrates for the same enzyme. Thus, competitive/non-competitive/irreversible inhibition of drug-metabolising enzymes by one of the therapeutic agents will result in elevations in plasma/tissue concentrations of the other drugs. For compounds with a narrow therapeutic index, this can lead to overdosage symptoms and/or toxicity. Cytochrome P450 (P450) enzymes are major players in the oxidative metabolism of therapeutic agents and, consequently, the most common mechanism underlying drug-drug interactions is the inhibition of P450 activities. Several drugs in common use cause large increases in exposure to other drugs. As this is an undesirable feature for a drug candidate, information about P450 inhibition by the compound should be obtained before a drug candidate is considered for the clinical stages of development. A combination of biochemical advances in the understanding of the function and regulation of drug-metabolising enzymes, in particular P450s, and automated analytical technologies are revolutionising drug metabolism research. Keywords: Cytochrome P450, drug-drug interaction, hepatocytes, irreversible inhibitors, microsomes, reversible inhibitors

Pharmacokinetic Profile of Dexloxiglumide

Dexloxiglumide is a potent and selective cholecystokinin type 1 (CCK1) receptor antagonist currently under development in a variety of diseases affecting the gastrointestinal tract such as gastro-oesophageal reflux disease, irritable bowel syndrome (IBS), functional dyspepsia, constipation and gastric emptying disorders. In female patients with constipation-predominant IBS, clinical efficacy has been demonstrated following administration of dexloxiglumide 200 mg three times daily. Dexloxiglumide is rapidly and extensively absorbed after single oral administration in humans with an absolute bioavailability of 48%. The incomplete bioavailability is due to both incomplete absorption and hepatic first-pass effect. Following multiple-dose administration of 200 mg three times daily, the accumulation is predictable, indicating time-independent pharmacokinetics. In addition, dexloxiglumide pharmacokinetics are dose-independent after both single and repeated oral three-times-daily doses in the dose range 100-400 mg. Dexloxiglumide absorption window extends from the jejunum to the colon and the drug is a substrate and a weak inhibitor of P-glycoprotein and multidrug resistance protein 1. Plasma protein binding of dexloxiglumide is 94-98% and the drug has a moderate to low volume of distribution in humans. Systemic clearance of dexloxiglumide is moderate and cytochrome P450 (CYP) 3A4/5 and CYP2C9 have been implicated in the metabolism of dexloxiglumide to produce O-demethyl dexloxi-glumide. This metabolite is further oxidised to dexloxiglumide carboxylic acid. These two major metabolites (accounting for up to 50% of dexloxiglumide elimination) have been identified. However, in human plasma the unchanged drug represents the major (up to 91%) component of the metabolic profile. The parent drug is believed to be the major contributor to the efficacy of the compound, since its major metabolites are pharmacologically inactive. In addition, the drug is a single isomer chiral drug (eutomer) that does not undergo chiral inversion into its pharmacologically inactive enantiomer (distomer). After oral administration of (14)C-dexloxiglumide, radioactivity is mainly excreted in bile and in faeces (74% of dose) with much lower excretion in urine (20% of dose). Renal excretion of unchanged dexloxiglumide is low (7% of dose in urine and faeces, 1% of dose in urine) and is dose-independent in the dose range 100-400 mg. As the kidney is a minor contributor to the elimination of dexloxiglumide and/or its metabolites in humans, the pharmacokinetics of the drug should not be affected in patients with renal insufficiency. The pharmacokinetics of dexloxiglumide are also not affected by age, sex and administration with a high-fat breakfast. Mild and moderate liver impairment do not affect the pharmacokinetics of dexloxiglumide but severe liver impairment causes increases in systemic exposure to dexloxiglumide and O-demethyl dexloxiglumide. Thus, the drug should be prescribed with caution in patients with severe hepatic impairment even though no dose adjustment is warranted. The results of different drug interaction studies have indicated that no clinically relevant metabolic and concomitant drug-drug interactions are expected during the clinical use of dexloxiglumide.

Prediction of the Clearance of Eleven Drugs and Associated Variability in Neonates, Infants and Children

Prediction of the exposure of neonates, infants and children to xenobiotics is likely to be more successful using physiologically based pharmacokinetic models than simplistic allometric scaling, particularly in younger children. However, such models require comprehensive information on the ontogeny of anatomical, physiological and biochemical variables; data that are not available from single sources. The Simcyp software integrates demographic, genetic, physiological and pathological information on adults with in vitro data on human drug metabolism and transport to predict population distributions of drug clearance (CL) and the extent of metabolic drug-drug interactions. The algorithms have now been extended to predict clearance and its variability in paediatric populations by incorporating information on developmental physiology and the ontogeny of specific cytochrome P450s. Values of the clearance (median and variability) of 11 drugs (midazolam [oral and intravenous], caffeine, carbamazepine, cisapride, theophylline, diclofenac, omeprazole, S-warfarin, phenytoin, gentamicin and vancomycin) were predicted for 2,000 virtual subjects (birth to 18 years). In vitro enzyme pharmacokinetic parameters (maximum rate of metabolism [Vmax] and Michaelis-Menten constant [Km]) and in vivo clearance data were obtained from the literature. In neonates 70% (7/10) of predicted median clearance values were within 2-fold of the observed values. Corresponding results for infants, children and adolescents were 100% (9/9), 89% (17/19) and 94% (17/18), respectively. Predicted variability (95% confidence interval) was within 2-fold of the observed values in 70% (7/10), 67% (6/9), 63% (12/19) and 55% (10/18) of cases, respectively. The accuracy of the physiologically based model incorporated in the Simcyp software was superior to that of simple allometry, especially in children <2 years old. The in silico prediction of pharmacokinetic behaviour in paediatric patients is not intended to replace clinical studies. However, it provides a valuable aid to decision-making with regard to first-time dosing in children and study design. The clinical study then becomes 'confirmatory' rather than 'exploratory'.

Oxymorphone Extended Release Does Not Affect CYP2C9 or CYP3A4 Metabolic Pathways

Two 14-day, randomized, open-label, parallel-group studies examined the effects of extended-release (ER) oxymorphone on CYP2C9 or CYP3A4 metabolic activities in healthy subjects. On days -1, 7, and 14, subjects received either a CYP2C9 probe (tolbutamide 500 mg) or CYP3A4 probes (midazolam and [14C N-methyl]-erythromycin for the erythromycin breath test). Subjects were randomized to 5 groups: high-dose oxymorphone ER (3 x 20 mg q12h) + naltrexone (50 mg q24h); low-dose oxymorphone ER (10-20 mg q12h); rifampin (2 x 300 mg q24h), an inducer of CYP2C9 and CYP3A4 activities; naltrexone (50 mg q24h); or CYP probes alone (controls). Probe metabolism was significantly altered by rifampin on days 7 and 14 (P < .05), whereas probe metabolism was not significantly affected by low-dose oxymorphone ER or by high-dose oxymorphone ER plus naltrexone. Oxymorphone ER exhibits a minimal potential for causing metabolic drug-drug interactions mediated by CYP2C9 or CYP3A4.

Metabolism by N‐Acetyltransferase 1 In Vitro and in Healthy Volunteers: A Prototype for Targeted Inhibition

Inhibition of drug metabolism is generally avoided but can be useful in limited circumstances, such as reducing the formation of toxic metabolites. Acetylation is a major pathway for drug elimination that can also convert substrates into toxic species, including carcinogens. Sulfamethoxazole, a widely used antibiotic, is metabolized via arylamine N-acetyltransferase 1. p-Aminosalicylate, used for antitubercular treatment, is also metabolized by N-acetyltransferase 1 and could potentially inhibit sulfamethoxazole metabolism. Human hepatocytes from 4 donors were incubated in vitro with sulfamethoxazole and paminosalicylate at clinically achievable concentrations. p-Aminosalicylate competitively reduced the acetylation of sulfamethoxazole in vitro by 61% to 83% at 200 microM. Four healthy volunteers were studied following doses of 500 mg sulfamethoxazole either alone or during administration of paminosalicylate (4 g ter in die). Plasma concentrations of paminosalicylate exceeded 100 microM. With each subject as his or her own control, p-aminosalicylate reduced by 5-fold the ratio of plasma concentrations of acetylsulfamethoxazole relative to parent drug (P < .001). Metabolic drug-drug interaction studies in vitro successfully predicted inhibition of acetylation via N-acetyltransferase 1 in vivo. Although no specific toxic species was investigated in this work, the potential was demonstrated for improving the therapeutic index of drugs that have toxic metabolites.

Clinical pharmacology: special safety considerations in drug development and pharmacovigilance.

The dose of a drug is a major determinant of its safety, and establishing a safe dose of a novel drug is a prime objective during clinical development. The design of pre-marketing clinical trials precludes the representation of important subpopulations such as children, the elderly and people with co-morbidities. Therefore, postmarketing surveillance (PMS) activities are required to monitor the safety profile of drugs in real clinical practice. Furthermore, individual variations in pharmacogenetic profiles, the immune system, drug metabolic pathways and drug-drug interactions are also important factors in the occurrence of adverse drug reactions. Thus, the safety of a drug is a major clinical consideration before and after it is marketed. A multidisciplinary approach is required to enhance the safety profile of drugs at all stages of development, including PMS activities. Clinical pharmacology encompasses a range of disciplines and forms the backbone of drug safety consideration during clinical drug development. In this review we give an overview of the clinical drug development process and consider its limitations. We present a discussion of several aspects of clinical pharmacology and their application to enhancing drug safety. Pharmacokinetic-pharmacodynamic modelling provides a method of predicting a clinically safe dose; consideration of drug pharmacokinetics in special populations may enhance safe therapeutics in a wider spectrum of patients, while pharmacogenetics provides the possibility of genotype-specific therapeutics. Pharmacovigilance activities are also discussed. Given the complex nature and unpredictability of type B reactions, PMS activities are crucial in managing the risks drugs pose to the general population. The various aspects of clinical pharmacology discussed make a strong case for this field as the backbone of optimising and promoting safe development and use of drugs.

Reaction phenotyping in drug discovery: moving forward with confidence?

For the pharmaceutical industry, one of the challenges in evaluating the risk of future compound attrition at the discovery stage is the successful prediction of the major routes of clearance in humans. For compounds cleared by metabolism, such information will help to avoid the development of compounds that will exhibit large interpatient differences in pharmacokinetics via 1). routes of metabolism catalyzed by functionally polymorphic enzymes and/or 2). clinically significant metabolic drug-drug interactions, in the later stages of development. The degree of intersubject variability that is acceptable for a drug candidate is uncertain in the discovery stage where knowledge of other important factors is limited or unavailable (i.e. therapeutic index, pharmacodynamic variability, etc). Reaction phenotyping is the semi-quantitative in vitro estimation of the relative contributions of specific drug-metabolizing enzymes to the metabolism of a test compound. However, reaction phenotyping in the discovery stage of drug development is complicated by the absence of radiolabelled parent compound or metabolite bioanalytical standards relative to later stages of development. In this commentary, some of the approaches, based on published data, which can be taken to overcome these challenges are discussed. In addition, knowledge of the molecular structure (i.e. specific chemical substituents), physicochemical properties, and routes of clearance in animals can all help in making a successful prediction for the routes of clearance in humans. In combination, the objective of these studies should be to reduce to a minimum the risk of finding significant inter-patient differences in pharmacokinetics at a later stage in development due to significant metabolism by polymorphic enzymes or drug-drug interactions. Consequently, this data should be used to avoid costly late stage attrition.

Physiological approaches to the prediction of drug-drug interactions in study populations.

The prediction of metabolic drug-drug interactions should include quantitative attributes, such as variability in the study populations, and the results should be presented in terms of probability and uncertainty. The simple algebraic equations used to calculate one mean value for the extent of drug-drug interaction are adequate for qualitative or semi-quantitative risk assessment. However, truly quantitative predictions continue to fail. The success of drug-drug interaction predictions requires understanding of the relationship between drug disposition and quantifiable influential factors on the change in systemic exposure. The complex interplay of influential factors, including variability estimates, on successful prediction of drug interaction have not been systematically examined. Therefore, physiologically relevant models of metabolic drug-drug interaction will likely play increasingly important roles in improving quantitative predictions and in the assessment of the influential factors underlying the interactions. The physiologically-based approach, with stochastic considerations, offers a powerful alternative to the empirical calculation of mean values. In addition to quantitative estimation of the interaction for assessing probability of risk, a reasonably validated predictive model is useful for prospective optimization of study designs. As a consequence, the definitive clinical trial would yield more meaningful information to support dosing recommendations. This review focuses on illustrating the importance of an integrated approach to building useful models for prediction of metabolism-based drug-drug interactions in human subjects.

The effects of dose staggering on metabolic drug-drug interactions.

To investigate the effect of dose staggering on metabolic drug-drug interactions (MDDI). Using Matlab, anatomical, physiological and biochemical data relating to human pharmacokinetics were integrated to create a representative virtual healthy subject relevant to in vivo studies. The effects of dose staggering on AUC and C(max) were investigated under various scenarios with respect to pharmacokinetic characteristics of the inhibitor and substrate drugs (e.g. hepatic extraction ratio). Specific cases were also simulated where MDDI had been studied experimentally for combinations of drugs (budesonide and ketoconazole; triazolam and itraconazole). The decrease in the magnitude of the inhibitory effect of the 'perpetrator' drug (inhibitor) on the 'victim' drug (substrate) as a result of 'dose staggering' was greater when the 'perpetrator' was given after the 'victim'. There was reasonable agreement between the predicted extent of the interactions and the observed in vivo data (mean prediction errors of 25 and -14% for AUC and C(max) values, respectively (n=7)). The impact of dose staggering was minimal during continuous dosage of inhibitors with long elimination half-lives (e.g. itraconazole, >20 h). Clinical trial simulations using physiological information may provide useful guidelines for optimal dose staggering when poly-pharmacy is inevitable.

Clinical pharmacokinetic profile of modafinil.

Modafinil is a unique wake-promoting agent for oral administration. Its pharmacological properties are distinct from those of other CNS agents, and it selectively targets neuronal pathways in the sleep/wake centres of the brain. After single or multiple oral doses, modafinil is readily absorbed, reaching maximum plasma concentrations at 2-4 hours after administration and pharmacokinetic steady state within 2-4 days. Its pharmacokinetics are dose-independent between 200 and 600 mg/day. The elimination half-life is approximately 12-15 hours, which is largely reflective of the pharmacokinetics of the longer-lived l-enantiomer. Modafinil is primarily eliminated via metabolism, mainly in the liver, with subsequent excretion in the urine. Less than 10% of the dose is excreted as unchanged drug. Metabolism is largely via amide hydrolysis, with lesser contributions from cytochrome P450 (CYP)-mediated oxidative pathways. In patients who are renally or hepatically compromised, the elimination processes can be slowed, and in a similar manner (although to a lesser extent), elimination in the elderly may be reduced due to normal effects of aging. Because modafinil is administered concomitantly with other medications, the potential for metabolic drug-drug interactions has been examined both in vitro and in vivo. In vitro, modafinil was observed to produce a reversible inhibition of CYP2C19 in human liver microsomes. It also caused a small, but concentration-dependent, induction of CYP1A2, CYP2B6 and CYP3A4 activities and suppression of CYP2C9 activity in primary cultures of human hepatocytes. Clinical studies have been conducted to examine the potential for interactions with methylphenidate, dexamfetamine, warfarin, ethinylestradiol and triazolam. The only substantive interactions observed were with ethinylestradiol and triazolam, apparently through induction of CYP3A4, primarily in the gastrointestinal system. Overall, the results of the interaction studies suggest that modafinil has potential to affect the pharmacokinetics of drugs that are metabolised by certain CYP enzymes. Compounds that induce or inhibit CYP activity are unlikely to have major effects on the pharmacokinetics of modafinil. In summary, the results show that modafinil is a moderately long-lived drug that is well absorbed and extensively metabolised.