Use of in vitro drug metabolism data to evaluate metabolic drug-drug interactions in man: the need for quantitative databases

Predicting the Effect of Renal Function on Systemic Clearance: Is a simple scaling method sufficient?

PURPOSE

To employ a simple scaling method to predict systemic or oral clearance for drugs that are primarily renally cleared knowing the fraction eliminated in urine (f_e) and a patient's renal function relative to healthy controls (S_GFR).

METHODS

Observations evaluating drug clearance as a function of creatinine clearance for renally cleared drugs (f_e >0.3) were obtained from literature sources. The analysis comprised of 82 unique drugs from 124 studies including 31 drugs with replicate studies. A simple scaler for renal function was employed and compared to the linear regression of available data. For drugs in which replicate studies were available, the ability of the linear regression (Cl vs Cl_CR) from one pharmacokinetic study was used to predict observations from an assigned replicate and compared to the scaling approach.

RESULTS

For patients categorized as severe kidney disease (Cl_CR fixed at 20 ml/min), the scalar tended to over predict some observations, but 92% of the predictions were within 50 - 200% of the observed data. For drugs with available replicates, the scalar was as good or better in predicting the influence of Cl_CR on systemic clearance from a separate study when comparing against the linear regression approach.

CONCLUSION

A scaling approach to account for alterations in drug clearance appears to have its advantages and represents a simple and generalizable method for guiding dose adjustments in patients with decreased renal function for drugs that are renally cleared (f_e >0.3). In addition to its use in clinical practice, validation of this approach may have implications in facilitating more efficient drug development processes for designing dose-adjusted pharmacokinetic studies in patients with renal disease.

The Cancer Drug Fraction of Metabolism Database

This study aims to create a database for quantifying the fraction of metabolism of cytochrome P450 isozymes for cancer drugs approved by the US Food and Drug Administration. A reproducible data collection protocol was developed to extract essential information, including both substrate-depletion and metabolite-formation data from publicly available in vitro selective cytochrome P450 enzyme inhibition studies. We estimated the fraction of metabolism from the curated data. To demonstrate the utility of this database, we conducted an in vitro drug interaction prediction for the 42 cancer drugs. In the drug-drug interaction prediction, we identified 31 drug pairs with at least one cancer drug in each pair that had predicted area under concentration ratios > 2. We further found clinical drug interaction pieces of evidence in the literature to support 20 of these 31 drug-drug interaction pairs.

The Reliability of Estimating Ki Values for Direct, Reversible Inhibition of Cytochrome P450 Enzymes from Corresponding IC50 Values: A Retrospective Analysis of 343 Experiments

In the present study, we conducted a retrospective analysis of 343 in vitro experiments to ascertain whether observed (experimentally determined) values of Ki for reversible cytochrome P450 (P450) inhibition could be reliably predicted by dividing the corresponding IC₅₀ values by two, based on the relationship (for competitive inhibition) in which Ki = IC₅₀/2 when [S] (substrate concentration) = Km (Michaelis-Menten constant). Values of Ki and IC₅₀ were determined under the following conditions: 1) the concentration of P450 marker substrate, [S], was equal to Km (for IC₅₀ determinations) and spanned Km (for Ki determinations); 2) the substrate incubation time was short (5 minutes) to minimize metabolism-dependent inhibition and inhibitor depletion; and 3) the concentration of human liver microsomes was low (0.1 mg/ml or less) to maximize the unbound fraction of inhibitor. Under these conditions, predicted Ki values, based on IC₅₀/2, correlated strongly with experimentally observed Ki determinations [r = 0.940; average fold error (AFE) = 1.10]. Of the 343 predicted Ki values, 316 (92%) were within a factor of 2 of the experimentally determined Ki values, and only one value fell outside a 3-fold range. In the case of noncompetitive inhibitors, Ki values predicted from IC₅₀/2 values were overestimated by a factor of nearly 2 (AFE = 1.85; n = 13), which is to be expected because, for noncompetitive inhibition, Ki = IC₅₀ (not IC₅₀/2). The results suggest that, under appropriate experimental conditions with the substrate concentration equal to Km, values of Ki for direct, reversible inhibition can be reliably estimated from values of IC₅₀/2.

'In silico' simulations to assess the 'in vivo' consequences of 'in vitro' metabolic drug-drug interactions

Recently, metabolic drug-drug interactions (M-DDI) have raised some high-profile problems in drug development resulting in restricted use, withdrawal or non-approval by regulatory agencies. The use of in vitro technologies to evaluate the potential for M-DDI has become routine in the drug development process. Nevertheless, in the absence of an integrated approach, their interpretation and value remains the subject of debate, and the vital distinction between a useful "simulation" and a precise "prediction" is not often appreciated. Various in silico softwares are now available for the simulation of M-DDI. However, a concerted effort by the industry is necessary to evaluate their use. The FDA has recently emphasised the importance of such collaboration to improve the crucial path to development of new drugs. In silico simulation of M-DDI has the potential to add significant value to this process.:

“Cocktail” Approaches and Strategies in Drug Development: Valuable Tool or Flawed Science?

There is an increasing interest in the simultaneous administration of several probe substrates to characterize the activity of multiple drug-metabolizing enzymes, the so-called "cocktail" approach. However, this method remains controversial and is being investigated more extensively. No general consensus has emerged on the applicability of this approach in clinical investigation and during drug development. The objective of the article is to review this important yet specialized technique, as well as its merits, drawbacks, and potential application in drug development. Among the two-, three-, four-, five-, and six-drug in vivo cocktails previously evaluated in humans, a variety of substrate probe combinations have been studied. Some probe combinations have been validated not to interact in vivo and have been useful in characterizing drug-drug interaction potential and metabolic enzyme induction in humans. For drug candidates that affect two or more in vitro pathways or are potential gene inducers, the use of a cocktail approach may facilitate the rapid delineation of the drug candidate's drug interaction potential. It may also offer the potential of providing clear guidance on safely conducting larger clinical studies and limiting comedication restrictions to only those likely to be clinically relevant.

Cynomolgus monkey as a potential model to assess drug interactions involving hepatic organic anion transporting polypeptides: in vitro, in vivo, and in vitro-to-in vivo extrapolation

Organic anion-transporting polypeptides (OATP) 1B1, 1B3, and 2B1 can serve as the loci of drug-drug interactions (DDIs). In the present work, the cynomolgus monkey was evaluated as a potential model for studying OATP-mediated DDIs. Three cynomolgus monkey OATPs (cOATPs), with a high degree of amino acid sequence identity (91.9, 93.5, and 96.6% for OATP1B1, 1B3, and 2B1, respectively) to their human counterparts, were cloned, expressed, and characterized. The cOATPs were stably transfected in human embryonic kidney cells and were functionally similar to the corresponding human OATPs (hOATPs), as evident from the similar uptake rate of typical substrates (estradiol-17β-d-glucuronide, cholecystokinin octapeptide, and estrone-3-sulfate). Moreover, six known hOATP inhibitors exhibited similar IC(50) values against cOATPs. To further evaluate the appropriateness of the cynomolgus monkey as a model, a known hOATP substrate [rosuvastatin (RSV)]-inhibitor [rifampicin (RIF)] pair was examined in vitro; the monkey-derived parameters (RSV K(m) and RIF IC(50)) were similar (within 3.5-fold) to those obtained with hOATPs and human primary hepatocytes. In vivo, the area under the plasma concentration-time curve of RSV (3 mg/kg, oral) given 1 hour after a single RIF dose (15 mg/kg, oral) was increased 2.9-fold in cynomolgus monkeys, consistent with the value (3.0-fold) reported in humans. A number of in vitro-in vivo extrapolation approaches, considering the fraction of the pathways affected and free versus total inhibitor concentrations, were also explored. It is concluded that the cynomolgus monkey has the potential to serve as a useful model for the assessment of OATP-mediated DDIs in a nonclinical setting.

Impact of nonlinear midazolam pharmacokinetics on the magnitude of the midazolam-ketoconazole interaction in rats

Numerous groups have described the rat as an in vivo model for the assessment and prediction of drug-drug interactions (DDIs) in humans involving the inhibition of cytochrome P450 3A forms. Even for a well-established substrate-inhibitor pair like midazolam-ketoconazole, however, the magnitude of the DDI in rats (e.g. 1.5- to 5-fold) does not relate to what is observed clinically (e.g. 5- to 16-fold). Because nonlinear substrate pharmacokinetics (PK) may result in a weaker interaction, it was hypothesized that the lower magnitude of interaction observed in rats was due to the saturation of metabolic pathway(s) of midazolam at the doses used (10-20 mg/kg). Therefore, the inhibitory effects of ketoconazole were reevaluated at lower oral (1 and 5 mg/kg) and intravenous (IV) (1 mg/kg) doses of midazolam. In support of the hypothesis, oral exposure at 5 mg/kg dose of midazolam was 18-fold higher compared to that at 1 mg/kg. Furthermore, when the interaction was investigated at the lower midazolam dose (1 mg/kg), ketoconazole increased the IV and oral exposure of midazolam by 7-fold and 11-fold, respectively. A weaker DDI (1.5- to 1.8-fold) was observed at the higher oral midazolam dose. Collectively, these results suggest that the lower reported interaction in rats is likely due to saturation of midazolam clearance at the doses used. Therefore, when the rat is used as a DDI model to screen and differentiate compounds, or predict CYP3A inhibition in humans, it is important to use low doses of midazolam and ensure linear PK.

Identification of intestinal loss of a drug through physiologically based pharmacokinetic simulation of plasma concentration-time profiles

BACKGROUND AND OBJECTIVE

Despite recent advances in understanding of the role of the gut as a metabolizing organ, recognition of gut wall metabolism and/or other factors contributing to intestinal loss of a compound has been a challenging task due to the lack of well characterized methods to distinguish it from first-pass hepatic extraction. The implications of identifying intestinal loss of a compound in drug discovery and development can be enormous. Physiologically based pharmacokinetic (PBPK) simulations of pharmacokinetic profiles provide a simple, reliable and cost-effective way to understand the mechanisms underlying pharmacokinetic processes. The purpose of this article is to demonstrate the application of PBPK simulations in bringing to light intestinal loss of orally administered drugs, using two example compounds: verapamil and an in-house compound that is no longer in development (referred to as compound A in this article).

METHODS

A generic PBPK model, built in-house using MATLAB software and incorporating absorption, metabolism, distribution, biliary and renal elimination models, was employed for simulation of concentration-time profiles. Modulation of intrinsic hepatic clearance and tissue distribution parameters in the generic PBPK model was done to achieve a good fit to the observed intravenous pharmacokinetic profiles of the compounds studied. These optimized clearance and distribution parameters are expected to be invariant across different routes of administration, as long as the kinetics are linear, and were therefore employed to simulate the oral profiles of the compounds. For compounds with reasonably good solubility and permeability, an area under the concentration-time curve for the simulated oral profile that far exceeded the observed would indicate some kind of loss in the intestine.

RESULTS

PBPK simulations applied to compound A showed substantial loss of the compound in the gastrointestinal tract in humans but not in rats. This accounted for the lower bioavailability of the compound in humans than in rats. PBPK simulations of verapamil identified gut wall metabolism, well established in the literature, and showed large interspecies differences with respect to both gut wall metabolism and drug-induced delays in gastric emptying.

CONCLUSIONS

Mechanistic insights provided by PBPK simulations can be very valuable in answering vital questions in drug discovery and development. However, such applications of PBPK models are limited by the lack of accurate inputs for clearance and distribution. This article demonstrates a successful application of PBPK simulations to identify and quantify intestinal loss of two model compounds in rats and humans. The limitation of inaccurate inputs for the clearance and distribution parameters was overcome by optimizing these parameters through fitting intravenous profiles. The study also demonstrated that the large interspecies differences associated with gut wall metabolism and gastric emptying, evident for the compounds studied, make animal model extrapolations to humans unreliable. It is therefore important to do PBPK simulations of human pharmacokinetic profiles to understand the relevance of intestinal loss of a compound in humans.

Integrated in vitro analysis for the in vivo prediction of cytochrome P450-mediated drug-drug interactions

Unbound IC(50) (IC(50,u)) values of 15 drugs were determined in eight recombinantly expressed human cytochromes P450 (P450s) and human hepatocytes, and the data were used to simulate clinical area under the plasma concentration-time curve changes (deltaAUC) on coadministration with prototypic CYP2D6 substrates. Significant differences in IC(50,u) values between enzyme sources were observed for quinidine (0.02 microM in recombinant CYP2D6 versus 0.5 microM in hepatocytes) and propafenone (0.02 versus 4.1 microM). The relative contribution of individual P450s toward the oxidative metabolism of clinical probes desipramine, imipramine, tolterodine, propranolol, and metoprolol was estimated via determinations of intrinsic clearance using recombinant P450s (rP450s). Simulated deltaAUC were compared with those observed in vivo via the ratios of unbound inhibitor concentration at the entrance to the liver to inhibition constants determined against rP450s ([I](in,u)/K(i)) and incorporating parallel substrate elimination pathways. For this dataset, there were 20% false negatives (observed deltaAUC >or= 2, predicted deltaAUC < 2), 77% correct predictions, and 3% false positives. Thus, the [I](in,u)/K(i) approach appears relatively successful at estimating the degree of clinical interactions and can be incorporated into drug discovery strategies. Using a Simcyp ADME (absorption, metabolism, distribution, elimination) simulator (Simcyp Ltd., Sheffield, UK), there were 3% false negatives, 94% correct simulations, and 3% false positives. False-negative predictions were rationalized as a result of mechanism-based inhibition, production of inhibitory metabolites, and/or hepatic uptake. Integrating inhibition and reaction phenotyping data from automated rP450 screens have shown applicability to predict the occurrence and degree of in vivo drug-drug interactions, and such data may identify the clinical consequences for candidate drugs as both "perpetrators" and "victims" of P450-mediated interactions.

Stereoselective bupropion hydroxylation as an in vivo phenotypic probe for cytochrome P4502B6 (CYP2B6) activity

The clearance of racemic bupropion, metabolized selectively by CYP2B6 in vitro, has been used clinically to phenotype CYP2B6 activity, polymorphisms, and drug interactions but has known limitations. Bupropion hydroxylation by CYP2B6 is stereoselective. This investigation assessed the stereoselectivity of bupropion pharmacokinetics and the influence of CYP2B6 induction. Ten healthy volunteers received immediate-release bupropion before and after 7 days of rifampin. Plasma and urine bupropion and hydroxybupropion were analyzed using a stereoselective assay. Plasma area under the curve (AUC(0-infinity)) and maximum concentrations were 3-fold greater for R- than S-bupropion. Bupropion apparent oral clearance was 3- and 2-fold greater for S- than R- and R,S-bupropion, respectively. Hydroxybupropion plasma AUC(0-infinity) and elimination half-life were significantly less for (S,S)- than (R,R)- and the racemate. (S,S)-hydroxybupropion was formation rate limited, whereas (R,R)-hydroxybupropion and the racemate were elimination rate limited. Rifampin doubled both R- and S-bupropion clearance and caused 4-fold increases in both (R,R)- and (S,S)-hydroxybupropion formation clearances. Increases in the plasma hydroxybupropion/bupropion AUC(0-infinity) ratio were greater for (S,S)- than (R,R)-hydroxybupropion. Simplified plasma and urine metrics of stereoselective bupropion metabolism and clearance were identified. Because metabolite formation clearance is the best in vivo metric of enzyme activity and due, therefore, to faster S-bupropion elimination and formation rate-limited (S,S)-hydroxybupropion kinetics, stereoselective S-bupropion hydroxylation and (S,S)-hydroxybupropion formation clearance may be a useful and improved phenotypic probe for CYP2B6.

Lack of effect of ketoconazole-mediated CYP3A inhibition on sorafenib clinical pharmacokinetics

Sorafenib is a novel, small-molecule anticancer compound that inhibits tumor cell proliferation by targeting Raf in the Raf/MEK/ERK signalling pathway, and inhibits angiogenesis by targeting tyrosine kinases such as vascular-endothelial growth factor receptor (VEGFR-2 and VEGFR-3) and platelet-derived growth factor receptor (PDGFR). In vitro microsomal data indicate that sorafenib is metabolized by two pathways: phase I oxidation mediated by cytochrome P450 (CYP) 3A4; and phase II conjugation mediated by UGT1A9. Approximately 50% of an orally administered dose is recovered as unchanged drug in the feces, due to either biliary excretion or lack of absorption. The aim of this study was to evaluate the effect of CYP3A inhibition by ketoconazole on sorafenib pharmacokinetics. This was an open-label, non-randomized, 2-period, one-way crossover study in sixteen healthy male subjects. A single 50 mg dose of sorafenib was administered alone (period 1) and in combination with ketoconazole 400 mg once daily (period 2) (ketoconazole was given for 7 days, and a single 50 mg sorafenib dose was administered concomitantly on day 4). No clinically relevant change in pharmacokinetics of sorafenib and no clinically relevant adverse events or laboratory abnormalities were observed in this study upon co-administration of the two drugs. Plasma concentrations of the main CYP3A4 generated metabolite, sorafenib N-oxide, decreased considerably upon ketoconazole co-administration. This effect is in accordance with the in vitro finding that CYP3A4 is the primary enzyme for sorafenib N-oxide formation. Further, these data indicate that blocking sorafenib metabolism by the CYP3A4 pathway will not lead to an increase in sorafenib exposure. This is consistent with data from a clinical mass-balance study that showed 15% of the administered dose was eliminated by glucuronidation, compared to less than 5% eliminated as oxidative metabolites. Since there was no increase in sorafenib exposure following concomitant administration of the highly potent CYP3A4 inhibitor ketoconazole with low dose sorafenib, it is postulated that higher therapeutic doses of sorafenib may be safely co-administered with ketoconazole, as well as with other inhibitors of CYP3A.

In vitro inhibition of UDP glucuronosyltransferases by atazanavir and other HIV protease inhibitors and the relationship of this property to in vivo bilirubin glucuronidation

Several human immunodeficiency virus (HIV) protease inhibitors, including atazanavir, indinavir, lopinavir, nelfinavir, ritonavir, and saquinavir, were tested for their potential to inhibit uridine 5'-diphospho-glucuronosyltransferase (UGT) activity. Experiments were performed with human cDNA-expressed enzymes (UGT1A1, 1A3, 1A4, 1A6, 1A9, and 2B7) as well as human liver microsomes. All of the protease inhibitors tested were inhibitors of UGT1A1, UGT1A3, and UGT1A4 with IC(50) values that ranged from 2 to 87 microM. The IC50 values found for all compounds for UGT1A6, 1A9, and 2B7 were >100 microM. The inhibition (IC50) of UGT1A1 was similar when tested against the human cDNA-expressed enzyme or human liver microsomes for atazanavir, indinavir, and saquinavir (2.4, 87, and 7.3 microM versus 2.5, 68, and 5.0 microM, respectively). By analysis of the double-reciprocal plots of bilirubin glucuronidation activities at different bilirubin concentrations in the presence of fixed concentrations of inhibitors, the UGT1A1 inhibition by atazanavir and indinavir was demonstrated to follow a linear mixed-type inhibition mechanism (Ki = 1.9 and 47.9 microM, respectively). These results suggest that a direct inhibition of UGT1A1-mediated bilirubin glucuronidation may provide a mechanism for the reversible hyperbilirubinemia associated with administration of atazanavir as well as indinavir. In vitro-in vivo scaling with [I]/Ki predicts that atazanavir and indinavir are more likely to induce hyperbilirubinemia than other HIV protease inhibitors studied when a free Cmax drug concentration was used. Our current study provides a unique example of in vitro-in vivo correlation for an endogenous UGT-mediated metabolic pathway.

Mechanism-Based Inactivation of CYP3A by HIV Protease Inhibitors

Human immunodeficiency virus (HIV) protease inhibitors (PIs) are inhibitors of CYP3A enzymes, but the mechanism is poorly defined. In this study, time- and concentration-dependent decreases in activity as defined by maximum rate of inactivation (k(inact)) and inhibitor concentration that gives 50% maximal inactivation (K(I)) of CYP3A by amprenavir, indinavir, lopinavir, nelfinavir, ritonavir, and saquinavir were quantified using testosterone 6beta-hydroxylation as a marker for CYP3A activity with recombinant CYP3A4(+b(5)), recombinant CYP3A5, and pooled human liver microsomes (HLMs). All the PIs, except indinavir, displayed inactivation with CYP3A4(+b(5)) and HLMs. Ritonavir was the most potent (K(I) = 0.10 and 0.17 microM) and demonstrated high k(inact) values (0.32 and 0.40 min(-1)) with both CYP3A4(+b(5)) and HLMs. Ritonavir was not significantly depleted by high-affinity binding with CYP3A4(+b(5)) and confirmed that estimation of reversible inhibition was confounded with irreversible inhibition. For CYP3A5, nelfinavir exhibited the highest k(inact) (0.47 min(-1)), but ritonavir was the most potent (K(I) = 0.12 microM). Saquinavir and indinavir did not show time- and concentration-dependent decreases in activity with CYP3A5. Spectrophototmetrically determined metabolic intermediate complex formation was observed for all of the PIs with CYP3A4(+b(5)), except for lopinavir and saquinavir. The addition of nucleophilic and free aldehyde trapping agents and free iron and reactive oxygen species scavengers did not prevent inactivation of CYP3A4(+b(5)) by ritonavir, amprenavir, or nelfinavir, but glutathione decreased the inactivation by saquinavir (17%) and catalase decreased the inactivation by lopinavir (39%). In conclusion, all the PIs exhibited mechanism-based inactivation, and predictions of the extent and time course of drug interactions with PIs could be underestimated if based solely on reversible inhibition.

Progress towards prediction of human pharmacokinetic parameters from in vitro technologies

This review provides an academic view of the current status on using in vitro systems for the prediction of human in vivo drug clearance and inhibition interaction potential. It stresses that although in vitro technology continues to develop in an impressive way and expectations are high within the pharmaceutical industry, the potential of prediction process is yet to be fully realized. The principles of scaling and modeling in vitro parameters have a sound base and have been validated by using animal tissue. However, it is clear that the comparatively simple standard approach developed and validated in animal systems, results in a high incidence of underprediction for parameters describing clearance and inhibition interaction potential when applied to humans. There are several challenges to our ability to interpret the human in vitro data that can now be so readily generated, in particular, accommodating the unusual kinetic properties characteristic of CYP3A4 substrates, namely, positive and negative cooperativity, in the assessment of inhibition potential.

Database analyses for the prediction of in vivo drug-drug interactions from in vitro data

AIMS

In theory, the magnitude of an in vivo drug-drug interaction arising from the inhibition of metabolic clearance can be predicted using the ratio of inhibitor concentration ([I]) to inhibition constant (K(i)). The aim of this study was to construct a database for the prediction of drug-drug interactions from in vitro data and to evaluate the use of the various estimates for the inhibitor concentrations in the term [I]/K(i).

METHODS

One hundred and ninety-three in vivo drug-drug interaction studies involving inhibition of CYP3A4, CYP2D6 or CYP2C9 were collated from the literature together with in vitro K(i) values and pharmacokinetic parameters for inhibitors, to allow calculation of average/maximum systemic plasma concentration during the dosing interval and maximum hepatic input plasma concentration (both total and unbound concentration). The observed increase in AUC (decreased clearance) was plotted against the estimated [I]/K(i) ratio for qualitative zoning of the predictions.

RESULTS

The incidence of false negative predictions (AUC ratio > 2, [I]/K(i) < 1) was largest using the average unbound plasma concentration and smallest using the hepatic input total plasma concentration of inhibitor for each of the CYP enzymes. Excluding mechanism-based inhibition, the use of total hepatic input concentration resulted in essentially no false negative predictions, though several false positive predictions (AUC ratio < 2, [I]/K(i) > 1) were found. The incidence of true positive predictions (AUC ratio > 2, [I]/K(i) > 1) was also highest using the total hepatic input concentration.

CONCLUSIONS

The use of the total hepatic input concentration of inhibitor together with in vitro K(i) values was the most successful method for the categorization of putative CYP inhibitors and for identifying negative drug-drug interactions. However this approach should be considered as an initial discriminating screen, as it is empirical and requires subsequent mechanistic studies to provide a comprehensive evaluation of a positive result.

ADMET in silico modelling: towards prediction paradise?

Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.

Quinidine and haloperidol as modifiers of CYP3A4 activity: multisite kinetic model approach

The selection of appropriate substrates for investigating the potential inhibition of CYP3A4 is critical as the magnitude of effect is often substrate-dependent, and a weak correlation is often observed among different CYP3A4 substrates. This feature has been attributed to the existence of multiple binding sites and, therefore, relatively complex in vitro data modeling is required to avoid erroneous evaluation and to allow prediction of drug-drug interactions. This study, performed in lymphoblast-expressed CYP3A4 with oxidoreductase, provides a systematic comparison of the effects of quinidine (QUI) and haloperidol (HAL) as modifiers of CYP3A4 activity using a selection of CYP3A4 substrates: testosterone (TST), midazolam (MDZ), nifedipine (NIF), felodipine (FEL), and simvastatin (SV). The effect of QUI and HAL on CYP3A4-mediated pathways was substrate-dependent, ranging from potent inhibition of NIF (K(i) = 0.25 and 5.3 micro M for HAL and QUI, respectively), weak inhibition (TST), minimal effect (HAL on MDZ/SV) to QUI activation of FEL and SV metabolism. Inhibition of TST metabolite formation occurred but its autoactivation properties were maintained, indicating binding of a QUI/HAL molecule to a distinct effector site. Various multisite kinetic models have been applied to elucidate the mechanism of the drug-drug interactions observed. Kinetic models with two substrate-binding sites have been found to be appropriate to a number of interactions, provided the substrates show hyperbolic (MDZ, FEL, and SV) or substrate inhibition kinetic properties (NIF). In contrast, a three-site model approach is required for TST, a substrate showing positive cooperativity in its binding to CYP3A4.

In vitro tests for predicting drug-drug interactions: the need for validated procedures

Over the past decade, the prediction of drug-drug interactions from in vitro studies has become a rapidly expanding field of research. Numerous papers and excellent review articles (Bertz & Granneman 1997; Ito et al. 1998a & b; Lin 2000; Bachmann & Ghosh 2001; Ekins & Wrighton 2001; Weaver 2001) have been published in this area. Yet like any new and fast-growing subject, this one has been developing with some confusion and without any real, efficient organisation. Depending on the drug tested, the models and extrapolation parameters used, etc., results and conclusions may vary widely from study to study (von Moltke et al. 1998; Weaver 2001). Several authors have called for validation of these procedures (Rodrigues et al. 2001; Kummar & Surapaneni 2001; Pelkonen et al. 2001a & b; Kremers 2002), and regulatory authorities intend to require better traceability and reliability (FDA & EMEA guidelines). A systematic and reliable approach is needed also to allow such protocols to be incorporated into early screening for potential drugs and new chemical entities. There is certainly a great need to standardise these studies and to verify their conclusions, but is true validation possible in this field? The main purpose of the present paper is to discuss this issue and to examine what is possible and what is needed to improve the quality of predictions made from in vitro experiments.

Inhibition kinetics of monoclonal antibodies against cytochromes P450

Monoclonal antibodies (MAbs) inhibitory to individual cytochromes P450 (P450s) are of tremendous utility in identification of P450s responsible for the metabolism of a given drug or drug candidate in pharmaceuticals. In the present study, two inhibitory MAbs against CYP2D6 (MAb(2D6-50,) IgG(2b) and MAb(2D6-184), IgG(2a)) were developed by hybridoma technology to exhibit their high specificity and potency. The MAbs were further employed to assess the quantitative role (47-93%) of CYP2D6 to the metabolism of bufuralol in human liver microsomes from seven donors. Together with the MAb inhibitory to CYP3A4 as previously reported (Mei et al., 1999), the MAbs were used to study the inhibition kinetics of dextromethorphan O-demethylation (CYP2D6), testosterone 6beta-hydroxylation (CYP3A4) and aflatoxin B1 3-hydroxylation (CYP3A4), respectively, with an adequate size of sample measurement. A kinetic model was proposed to fit the experimental observations with three-dimensional nonlinear regression, thereby resulting in a solution of kinetic parameters, i.e., K(I), K(S), V(max), alpha, and beta (changes in K(I) or K(S) and V(max) in the presence of the MAb). As a result, dissociation constants (K(I)) of the MAbs for the enzymes and the maximal inhibition (beta) values for the P450-catalyzed reactions were predicted to have 0.04 to 0.25 microM and > or =94%, respectively. The results have demonstrated that the model can accurately predict the kinetic parameters and provide some insights into the understanding of the mechanism of MAb interaction with P450 enzyme in nature and the applications of the MAbs in qualitative and quantitative identification of P450s involved in drug metabolism.