`

`114
`
`L. L. von Moltke et al.
`
`tations and drawbacks. The issue of risk to human subjects
`is always of concern, even if the actual risk is small and
`acceptable. The increasingly stringent regulatory require(cid:173)
`ments for control and monitoring of premarketing studies,
`as well as the scientific needs for appropriate design and
`adequate sample size, generally cause these studies to be
`costly and time-consuming. As a consequence, there is a
`realistic limit to the number and scope of clinical drug
`interaction studies that can be performed, whether in the
`premarketing phases of drug development or after approval
`for clinical use. Inevitably some important drug interac(cid:173)
`tions will be overlooked simply because they were not
`among those that were studied; the often-cited case of the
`terfenadine-ketoconazole interaction is an important ex(cid:173)
`ample [12]. On the other hand, many clinical interaction
`studies are negative.
`These dilemmas have stimulated the search for alterna(cid:173)
`tive approaches to studying drug interactions. The possibil(cid:173)
`ity of predicting in vivo metabolic events from in vitro
`models has been discussed for decades and some of the
`fundamental principles and assumptions involved in the in
`vitro-in vivo predictions have been described [13-15]. Iden(cid:173)
`tifying the cytochromes involved in biotransformation of a
`specific drug can identify sources of variation in clearance
`secondary to population demographics, genetic polymor(cid:173)
`phisms, extrahepatic enzyme distribution, and known
`chemical inducers or inhibitors. The ability to accurately
`predict drug interactions secondary to inhibition of cyto(cid:173)
`chrome metabolism has been greatly enhanced by the
`knowledge of which cytochrome mediates a specific reac(cid:173)
`tion.
`
`MODEL COMPONENTS AND ASSUMPTIONS
`
`Predictive models for in vitro-in vivo scaling of pharmaco(cid:173)
`kinetic drug interaction can be constructed from a combi(cid:173)
`nation of laboratory and theoretical components. Many of
`the pieces are based on well-established principles, while
`others represent hypotheses and assumptions. The follow(cid:173)
`ing describes the steps in development of a representative
`model.
`
`Biotransformation In Vitro
`
`The objective of the laboratory component is to replicate in
`vitro the principal metabolic pathways of the index sub(cid:173)
`strate as they occur in vivo, and to characterize the mech(cid:173)
`anism and quantitative inhibiting potency of the model
`inhibitor.
`Human liver microsomes appear to be the most applica(cid:173)
`ble in vitro system for purposes of scaling, since the various
`cytochromes are present in proportion to their in vivo
`representation. This is particularly important when more
`than one specific cytochrome contributes to the biotrans(cid:173)
`formation of the index substrate. Microsomal preparations
`from various animal species have been used for predictive
`
`studies of drug interactions, but no species has been
`generally established as a substitute for human tissue.
`Mathematical analysis of in vitro reaction kinetic data in
`the absence of inhibitors usually includes application of the
`Michaelis-Menten (MM) equation, with or without linear(cid:173)
`izing transformation of the data [16]. However, with in(cid:173)
`creasing numbers of data points and/or range of substrate
`concentrations, it may become apparent that the simplest
`form of the MM equation is not applicable. Examples of
`complicating features requiring modification of modeling
`approaches include: simultaneous contribution of two or
`more enzymes with distinct Km values [17, 18]; cooperative
`binding resulting in apparent substrate activation [19-23];
`concentration-dependent inhibition by substrate and/or
`product [21, 22, 24-26]; sequential metabolism of the
`primary metabolite to yield secondary downstream meta(cid:173)
`bolic products. Goodness-oHit criteria from mathematical
`modeling techniques may tentatively identify which one
`(or more) of these features is applicable to a given data set,
`but definitive confirmation of a biochemical mechanism
`requires supplemental information [21]. In any case, com(cid:173)
`plex reaction kinetic mechanisms may compromise the
`conceptual validity of absolute or relative intrinsic clear(cid:173)
`ance values as calculated from the V maxlKm ratio [27].
`It is nonetheless important to recognize that the accuracy
`of potency calculations for specific chemical inhibitors is
`not necessarily compromised importantly when simplifying
`approximations are used to handle complex reaction kinet(cid:173)
`ics. When two distinct enzyme components contribute to a
`specific biotransformation, the component with the lowest
`Km (the "high-affinity" site) may account for a large
`percentage of net intrinsic clearance. In such cases, the
`high-Km enzyme may be approximated by a simple linear
`function, and inhibition of the low-Km enzyme accounts for
`most of the clinically important activity of chemical inhib(cid:173)
`itors [17].
`
`Chemical Inhibitors In Vitro
`
`Coincubation of a candidate chemical inhibitor with the
`target substrate using the same in vitro system can yield an
`estimate of the intrinsic inhibitory potency of the candidate
`compound. The most straightforward approach utilizes a
`fixed concentration of substrate [5] coincubated with var(cid:173)
`ious concentrations of inhibitor (Fig. 1). Analysis of the
`relation between inhibitor concentration [I] and reaction
`velocity decrement (i.e. reaction velocity at that concen(cid:173)
`tration of inhibitor divided by reaction velocity with no
`inhibitor present) may yield an estimated concentration of
`inhibitor corresponding to a 50% decrease in reaction
`velocity (IC50). This approach has the advantage of being
`model independent. That is, 1C50 can be calculated without
`knowledge of the biochemical mechanism of inhibition.
`The 1C50 values are quite useful when comparing the
`relative inhibitory potency of different candidate inhibitors
`of the same chemical class, such as the SSRI antidepres(cid:173)
`sants [17] or azole antifungal agents [28]. On the other
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`118
`
`L. L. von Moltke et al.
`
`TABLE 2. Partitioning of Ketoconazole Between Plasma and
`Liver Tissue
`
`100
`
`80
`
`60
`
`40
`
`20
`
`0
`>
`:>
`Z
`0
`W
`>
`0::
`W
`CI)
`a:J
`0
`
`/
`
`/
`
`/
`
`/
`
`/,
`
`/ab
`
`/
`
`/
`
`/
`
`c
`
`d
`
`/
`
`/
`
`/
`
`/
`
`/
`
`/
`
`/
`
`/
`
`/
`
`/
`
`/
`
`/
`
`/
`
`/e
`
`/
`
`/
`
`/
`
`20
`
`40
`
`60
`
`80
`
`100
`
`PREDICTED IN VITRO
`FIG. 5. Outcome of six studies evaluating the actual inhibition
`of oral desipramine clearance in humans as compared with that
`predicted for the in vitro model. In vitro competitive K; values
`for six SSRls versus desipramine hydroxylation in human liver
`microsomes were determined as described previously [94-96].
`Predicted inhibition (x-axis) was determined using the model
`described in the text, based on K; values and plasma SSRI
`concentrations which were then corrected for anticipated he(cid:173)
`patic uptake. Actual inhibition of desipramine clearance in vivo
`(y-axis) was determined in clinical pharmacokinetic studies.
`Dashed line is the line of identity (y = x). Symbols a and bare
`studies of fluoxetine/norfluoxetine [97,98]; c and d are studies
`of paroxetine [100, 101]; and e and f are studies of sertraline/
`desmethylsertraline [98, 99].
`
`these SSRls [97-100]. Application of the predictive model
`yields reasonably accurate forecasting of in vivo desipramine
`clearance decrements based on in vitro data, provided that
`values of [I] determined from plasma SSRI concentrations
`are adjusted for anticipated liver partitioning, consistent
`with experimental and human autopsy data (Fig. 5). Use of
`unbound or even total SSRI plasma concentrations in the
`predictive model leads to substantial underestimation of
`observed clearance decrements.
`
`SSRI Antidepressants and P450-3A Substrates
`
`In vivo decrements in clearance of alprazolam, a substrate
`for P450-3A isoforms, due to coadministration of fluoxetine
`[101, 102] or fluvoxamine [103] were reasonably well
`predicted from in vitro data [86, 95]. Minimal clinical
`interaction of terfenadine with paroxetine was predicted in
`vitro [104] and confirmed in vivo [105]. However, the
`predictive model forecasted a significant decrement in
`triazolam clearance due to coadministration of fluoxetine
`[82], whereas a clinical study demonstrated only a minimal
`in vivo pharmacokinetic interaction [106]. The reasons for
`these discrepancies are not established. Gastrointestinal
`P450-3A isoforms are likely to account for an important
`
`Experimental system
`Rat, in vivo
`Rat, in vivo
`Rat, in vivo
`Mouse, in vivo
`In vitro: human plasma
`vs liver homogenate
`* At plasma concentrations <3.8 I-Lg/mL, liver:plasma partition ratios exceeded 3.2.
`
`Liver:plasma
`partition ratio
`
`5
`0.5-1.0
`~3.2*
`2.03
`1.12
`
`Ref.
`79
`80
`81
`82
`82
`
`azole (total plasma concentration 2::1 f.Lg/mL) reduced oral
`midazolam clearance by an average of 94%. By applying
`these Ki values along with the total plasma ketoconazole
`concentrations for [I] in the predictive model, the pre(cid:173)
`dicted degrees of oral midazolam clearance inhibition are
`95% [36] and >95% (assuming [S] « Km) [24], respec(cid:173)
`tively. However, use of the projected unbound plasma
`ketoconazole concentration (0.01 X total plasma concen(cid:173)
`tration) yields underestimations of the degree of clearance
`inhibition (16 and 82%, respectively). Reasonable predic(cid:173)
`tions have also been derived, using total plasma ketocon(cid:173)
`azole concentrations and in vitro Ki values, for the clinical
`interactions of ketoconazole with triazolam [82, 84], terfe(cid:173)
`nadine [12, 85], and alprazolam [86, 87]. It is of interest that
`coadministration of ketoconazole reduces clearance of oral
`alprazolam by an average of 69% [87], although absolute
`bioavailability of oral alprazolam is greater than 80%
`[88,89].
`Itraconazole, like ketoconazole, is 99% bound to blood/
`plasma components [78], whereas liver versus total plasma
`concentration ratios in experimental models range from
`11:1 to 20:1 [79]. The competitive Ki for itraconazole versus
`a-OH-midazolam formation was 0.28 f.LM [24], and the
`impairment of midazolam clearance by coadministration of
`itraconazole (total plasma concentrations generally ~0.1
`f.LM) in three clinical studies was 90, 83, and 85% [83, 90,
`91]. These are well predicted by the scaling model if a
`liver:plasma ratio in the range of 10:1 to 20:1 is assumed for
`itraconazole, but poorly predicted based on total or un(cid:173)
`bound plasma itraconazole levels. It is of interest that
`itraconazole coadministration also impairs clearance of
`intravenous midazolam [91].
`
`SSRI Antidepressants and P450-2D6 Substrates
`
`In vitro inhibition of desipramine hydroxylation, a reaction
`mediated largely if not entirely by human cytochrome
`P450-2D6 [92, 93], has been evaluated for SSRI antidepres(cid:173)
`sants and their pertinent metabolites [94, 95]. Based on in
`vitro competitive Ki values, fluoxetine, norfluoxetine, and
`paroxetine are potent inhibitors, while sertraline, desmeth(cid:173)
`ylsertraline, and fluvoxamine are substantially weaker [96].
`Clinical studies have evaluated the impairment of desipra(cid:173)
`mine clearance due to coadministration of a number of
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`Predicting Drug Interactions
`
`119
`
`component of presystemic extraction after oral administra(cid:173)
`tion of drugs such as triazolam. Inhibition of P450-3A
`isoforms by fluoxetine is largely attributable to the metab(cid:173)
`olite norfluoxetine [53, 107], which may have incomplete
`access to gastrointestinal mucosal cells following adminis(cid:173)
`tration of the parent compound. It is also possible that
`fluoxetine and other SSRIs may have inducing effects that
`offset competitive inhibition under some circumstances.
`
`COMMENT
`
`Major advances in the use of in vitro systems to understand
`human drug metabolism have logically led to exploration of
`the application of such systems to focus, supplement, or
`even replace, human pharmacokinetic study programs. In
`vitro models to predict drug interactions based on in vitro
`data have been proposed. The models are preliminary, and
`the drawbacks, limitations, weaknesses, and assumptions
`are numerous. The models can, in principle, forecast the
`magnitude of an interaction, but not its potential clinical
`consequences [96]. Nonetheless, the concept is promising,
`and the potential long-range benefit is a drug-development
`process that is more rapid and cost-effective, with reduced
`risk to human subjects.
`
`This work was supported by Grants MH-34223, DA-OS2S8, MH-
`19924, and RR-000S4 from the Department of Health and Human
`Services, and by a grant-in-aid from Pharmacia & Upjohn, Kalama(cid:173)
`zoo, MI. Dr. von Moltke is the recipient of a Scientist Development
`Award (K21-MH-01237) from the National Institutes of Mental
`Health. Dr. Schmider was the recipient of a Merck Sharp & Dohme
`International Fellowship in Clinical Pharmacology. The authors are
`grateful for the assistance of Su Xiang Duan, Monette M. Cotreau(cid:173)
`Bibbo, and Jeffrey Grassi.
`
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