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Tight Junction

Basolateral (serosal)

Figure 2. A depiction of (i) the absorptive transport routes across intestinal epithelium and (ii) the physical and biochemical barriers to drug absorption.

basis for poor permeability across the intestinal epithelium prior to setting up a screen. The intestinal epithelium, which comprises a single layer of cells, constitutes a formidable barrier to oral absorption of drugs. Although the epithelium contains several different cell types, the polarized columnar cells, known as enterocytes, play a crucial role both in the absorption of nutrients and xenobiotics and in serving as a barrier to their absorption. The barrier role of the enterocytes is two-fold; the cell membrane and the intercellular junctions, known as tight junctions, constitute a physical barrier to entry of hydrophilic compounds into systemic circulation, whereas the efflux transporters present in the apical membrane (e.g. P-glycoprotein (P-gp)) and metabolic enzymes present in cellular compartments (e.g. esterases, peptidases, cytochrome P450 (CYP), sulfotrans-ferases) constitute a biochemical barrier to lipophilic compounds that are capable of traversing the cell membrane and cross the epithelium via the transcellular route (Figure 2). Thus, an in vitro model for intestinal epithelium should mimic the physical and biochemical barrier properties of intestinal epithelium.

Caco-2 cell monolayer, grown on a porous polycarbonate membrane, was initially introduced as an in vitro model for intestinal absorption/transport studies in late 1980's and early 1990's (Hidalgo et al., 1989; Artursson, 1991); subsequently, several cell culture-based in vitro models have been introduced (reviewed in Borchardt et al., 1996; Weinstein et al., 2003). These in vitro models are used extensively in the pharmaceutical industry and academic groups as screening and investigative tools for drug absorption studies. The cells, grown on a porous membrane, differentiate into enterocyte-like cells upon reaching confluence. Absorptive transport across these cell monolayers is typically investigated in a TranswellTM set-up by adding the test compound on the apical

(AP), i.e. lumenal, side and analyzing the compound appearing in the basolateral (BL), i.e. serosal, compartment as a function of time; secretory transport can be studied by reversing the donor and receiver compartments. In addition to the cell culture models, in vitro systems employing intestinal tissue from preclinical species (e.g. everted sac) are also employed with the rationale that such models may mimic the intestinal transport more closely than the cell-based models employing immortalized cell lines. In vitro models to study absorption across intestinal epithelium have been reviewed previously (Borchardt et al., 1996; and Smith 1997), and will not be elaborated here.

Relatively simple experiments can be devised to answer the following questions regarding the transport mechanism of the lead series of compounds: (1) are the compounds traversing the cell monolayers predominantly via the transcellular or the paracellular route? (2) Is the transport mediated by a transporter? (3) Is an efflux transporter affecting the absorptive transport of the compounds? The information derived from these experiments can provide a rational basis for screening the compounds for optimum transport properties.

Paracellular vs. transcellular transport

Typically, neutral lipophilic molecules traverse the intestinal epithelium via the transcellular route by partitioning into the cell membrane and diffusing through the membrane and/or the cytosolic compartment (Figure 2). In contrast, compounds that are very hydrophilic or that have a net charge cannot effectively partition into the cell membrane, and are often translocated across the intestinal epithelium via the paracellular route (Figure 2) unless they are substrates for one of the transporters that carry hydrophilic nutrient molecules across the intestinal epithelium. The paracellular transport is not very efficient due to the relatively small surface area available to compounds for entry into the intercellular space as compared to rather large surface area presented by the apical membrane of the enterocytes with its microvilli. In addition, the paracellular transport of compounds is further restricted by the presence of the tight junction ( reviewed in Powell, 1981, Anderson and Van Itallie, 1995, Ward et al., 2000).

The in vitro cell culture models (e.g. Caco-2 cell monolayers) are well suited to determine the primary route by which a compound is translocated across the intestinal epithelium. The experimental approach that is most commonly employed for this purpose involves measuring the flux (permeability) of a compound across the intact cell monolayer and comparing it with the flux across monolayers in which tight junctions are compromised by removal of extracellular Ca2+ ions, either by using a Ca2+-free buffer or including a Ca2+ chelator such as ethylenediaminetetraacetic acid (EDTA). It is assumed that the compound whose flux increases by several fold when extracellular Ca2+ is removed from the transport buffer must be traversing the cell monolayers predominantly via the paracellular route; conversely, the flux of a transcellularly transported compound does not change significantly when Ca2+ ions are depleted from the transport medium. This approach is criticized by some because it requires extrapolating results obtained from a perturbed cell monolayer system to explain the transport behavior of compounds in the intact system. Furthermore, it is argued that removal of extracellular Ca2+ ions may damage the cell membrane and confound the transport data acquired in the absence of extracellular Ca2+ ions. However, no other simple in vitro method is available currently to assess relative contribution of paracellular vs. transcellular transport of compounds across intestinal epithelium or the cell culture models of intestinal epithelium.

Carrier-mediated transport

A hydrophilic compound often crosses the intestinal epithelium via a carrier-mediated mechanism when it can mimic a physiologic substrate. For hydrophilic compounds which are not able to effectively cross the cell monolayer via the transcellular diffusion process, a transporter-based mechanism may contribute significantly to its absorptive transport (transporters for drug absorption are reviewed in Amidon and Sadee; 1999). A simple experimental approach, in which the flux of the test compound across cell monolayers is measured as a function of concentration, can provide valuable information regarding the likely involvement of a transporter in the absorptive transport of the compound. A hyperbolic relationship between flux and concentration, in which the flux plateaus at high concentrations, suggests involvement of a transporter, provided that the plateau at high concentrations is not reached by exceeding the solubility of the compounds. Often, a diffusive component contributes significantly to the overall transport of a compound. In such a case, the plot of flux vs. concentration does not reach a plateau; instead, it continues to increase in a non-linear fashion even at concentrations that would saturate the transporter. For a compound exhibiting such a behavior, sometime it may be difficult to determine if a transporter is involved as is the case for transport of ranitidine across Caco-2 cells (Lee and Thakker, 1999). However, analyzing the data in terms of permeability (Papp, flux normalized to concentration and area) as opposed to flux can often reveal the involvement of a saturable mechanism much more clearly. If the compound is a substrate for a transporter, the Papp value often exhibits large changes with changes in the concentration of the compound, particularly over the low concentration range where the transporter mechanism is likely to be dominant. This is diagnostic of a transporter-mediated transport mechanism; in contrast, the Papp value remains unchanged over the entire concentration range for a purely diffusive transport mechanism. Thus, determining the Papp at two widely separated concentrations may be sufficient to get an indication of the involvement of a transporter mechanism for the test compound.

Attenuation of absorptive transport by an efflux transporter

Efflux transporters in the apical membrane of intestinal epithelium represent a significant barrier to intestinal absorption of many compounds that are transcel-lularly transported across the epithelium (reviewed in Fromm, 2000; Lin and

Yamazaki, 2003). P-gp is one of the most important and widely studied efflux transporters, and most drug discovery programs employ a screen to assess its effect on intestinal drug absorption (reviewed in Polli and Serabjit-Singh, 2004). These screens involve determination of Papp across cell monolayers such as Caco-2 cells or MDCK cells transfected with the MDR1 gene (MDR-MDCK) in the AP to BL and BL to AP directions. A ratio of Papp in BL to AP and AP to BL direction (efflux ratio) is often used as a parameter to assess the extent of interaction between the test compound and P-gp, and a large efflux ratio is considered a liability with respect to the compound's oral absorption (Polli and Serabjit-Singh, 2004). However, we have observed that attenuation of the absorptive transport (AP to BL) by P-gp and enhancement of their secretory transport (BL to AP) are not equal in magnitude for many compounds (Troutman and Thakker, 2003a). Hence, it is conceivable that absorptive transport of a compound with a high efflux ratio may not be affected much by P-gp and that the large efflux ratio is a consequence of a large effect of P-gp on its secretory transport as is the case with rhodamine 123 and doxorubicin (Troutman and Thakker, 2003b). In such cases, the efflux ratio would over-predict the attenuation of absorptive transport by P-gp. This is evident from the comparison of digoxin and rhodamine 123 transport across Caco-2 cells as shown in Figure 3. Both digoxin and rhodamine 123 are substrates for P-gp, and exhibit large efflux ratios in the Caco-2 cell culture model. However, a comparison of absorptive transport of these compounds in the presence and absence of the P-gp inhibitor, GW918, reveals that P-gp has no effect on the absorptive transport of rhodamine 123, whereas it affects both absorptive and secretory transport of digoxin in a much more symmetrical manner.

3.50E-05

1.50E-05

2.00E-05

1.00E-05

Digoxin control + GW918 (0.5 nM)

Rhodamine

Figure 3. AP to BL and BL to AP transport of digoxin and rhodamine 123 in the absence and presence of the P-gp inhibitor GW918. The experimental design clearly highlights the fact that P-gp affects both absorptive (AP to BL) and secretory (BL to AP) transport of digoxin, but affects only the secretory (BL to AP) transport of rhodamine 123 (adapted from Troutman and Thakker, 2003b).

Based on these observations, we have proposed an alternative approach to screen compounds for P-gp-mediated attenuation of absorptive transport (Troutman and Thakker, 2003c). This approach involves determination of the absorptive permeability (Papp) of compounds and comparing it with the absorptive permeability determined in the presence of sufficiently high concentration of a non-competitive P-gp inhibitor (e.g. GW918) so as to completely inhibit its efflux activity. The permeability value determined in the absence of the P-gp efflux activity would be equivalent to the permeability of the compound due to passive diffusion (PPD) across the cell monolayer, and the difference between PPD and Papp would be equivalent to the attenuation of the absorptive transport by Pgp. This P-gp-mediated attenuation of the absorptive transport (PPD - Papp) can be conveniently expressed by a number between 0 and 1 by normalizing it with respect to PPD. Thus, (PPD - Papp)/PPD, which we refer to as absorptive quotient (AQ), will have the value of 0 when P-gp has no effect on the absorptive permeability of a compound (i.e. PPD = Papp), and the value of 1 when P-gp completely attenuates the absorptive transport of a compound (i.e. Papp = 0). Thus for any P-gp substrates, the AQ value (0 < AQ < 1) will give an indication of the extent to which P-gp is attenuating the absorptive transport of these compounds. Since in a transport screen, one is interested in assessing the effect of P-gp on the intestinal absorption of drug candidates, and not so much the influence of P-gp on secretion, the measurement of AQ is recommended over the measurement of efflux ratio. Recently, Thiel-Demby et al. (2004) have shown that AQ measurement also can correctly identify compounds as P-gp substrates/nonsubstrates relative to the efflux ratio measurement with 80% accuracy while affording two-fold increase in throughput.

Metabolism

Most of the compounds administered as therapeutic agents are transformed by one or more metabolic enzymes into products that are often more hydrophilic and more amenable to excretion. Metabolic transformation can have profound influence on the therapeutic efficacy and toxicity of drugs.

Rapid metabolism can limit the ability of a drug to attain the efficacious concentrations in the blood (with the exception of iv administration) or in the target tissue and to maintain these concentrations for sufficiently long periods of time to be therapeutically useful. For orally administered drugs, extensive metabolic clearance of the drug can occur in the intestine or in liver prior to reaching systemic circulation. This, so called "first pass effect" can contribute significantly to limit the oral bioavailability of a drug. In addition, repeated administration of a drug can lead to induction of certain drug metabolizing enzymes including those involved in the biotransformation of the administered drug (reviewed in Lin and Liu, 2001; Savas et al., 1999). This, in turn, could lead to greater metabolic clearance and less exposure of the drug than initially anticipated at the time of fixing the dose. Induction could also similarly affect

Metabolic Reaction

Functional Group

Metabolic Product

C-H (aliphatic)

Alcohol

C = C (olephenic)

Epoxide

C = C (aromatic)

Phenol (isomerization of arene oxide)

Oxidation

C-H a to N (O, S)

Amine + Aldehyde or Ketone

C-OH

Aldehyde or Ketone

Ar-NH2

Hydroxyl amine, Nitroso, Nitro, N-oxide

r-nh2

Aldehyde (monoamine oxidase catalyzed)

R-S-R

Sulfoxide, Sulfone

R-CHO, R-CO-R

Alcohol

Quinone

Hydroquinone

Reduction

R-NO2 (or Ar-NO2)

Nitroso, hydroxylamine, amine

R-N = N-R'

Hydrazine, amine

R-S-S-R'

Sulfhydryl

C = O(OR)

Carboxylic acid

C = O(SR)

Carboxylic acid

C = O(NH2)

Carboxylic acid

Hydrolysis

O-C = O(OR)

Carboxylic acid

O-C = O(NR)

Carboxylic acid

Ar-O-SO3

Phenol

R-O-PO3

Alcohol

Epoxide

trans-Dihydrodiol

-OH, -COOH, -NH2

Glucuronide

Ar-OH

Sulfate

Conjugation

Epoxide, Ar-Cl

Glutathione conjugate, Mercapturic acid

Ar-C = O(OH)

Amino acid conjugate

-nh2

N-acyl derivative

Table 1. Metabolic Reactions, target functional groups, and metabolic products

Oxidation

Hydrolysis

Cytochrome P450 (CYP)

Esterases

Flavin monooxygenase (FMO)

Proteases

Peroxidase

Peptidases

Monoamine oxidase

Glucuronidase

Xanthine oxidase

Sulfatase

Dehydrogenases

Phosphatases

Aldehyde oxidase

Reduction

Conjugation

Keto reductase

Glucuronosyl transferases

DT diaphorase

Sulfotransferases

Azo reductase

Glutathione transferases

Dehydrogenases

Acetyl transferases

Cytochrome P450 (CYP)

Kinases

Table 2. Drug Metabolizing Enzymes

Table 2. Drug Metabolizing Enzymes other co-administered drugs that are predominantly cleared by the induced enzyme(s).

Metabolism of drugs can lead to adverse effects under the following circumstances: (i) when a metabolite of the drug exhibits undesirable pharmacological activity, (ii) when a chemically reactive metabolite(s) covalently modifies cellular macromolecules, and (iii) when the drug or its metabolite inhibits a metabolic enzyme(s) which plays a key role in the clearance of a co-administered drug, resulting in reduced clearance of the co-administered drug and overexposure to it.

Clearly, in vitro models to assess metabolic stability, induction and inhibition of metabolic enzymes, and formation of reactive metabolites are necessary in order to reduce or eliminate any metabolism-related liability of drug candidates. These in vitro models are discussed in the following sections with an emphasis on their use in making decisions for selection/de-selection or re-design of compounds.

Metabolic stability

In designing an appropriate in vitro screen for metabolic stability, the obvious question that needs to be addressed is: how does one know which enzyme(s) is involved in the metabolic transformation of the drug candidates? The first step toward addressing this question is to recognize that despite rather wide chemical space covered by drug candidates, their metabolic transformation can be grouped under one of four chemical reaction types: (1) oxidation, (2) reduction, (3)

hydrolysis, and (4) conjugation. The in vitro model for metabolic stability should be selected based on the most likely metabolic transformations that might occur for the test compounds. The functionalities that can undergo each class of metabolic transformation are shown in Table 1. This Table does not provide an exhaustive list of all functionalities and their respective metabolic transformation(s); instead, it provides frequently encountered metabolic transformations associated with most common functionalities (see reviews by Parkinson, 1996, and Low, 1998 for metabolic transformations and enzymes). Table 2 shows the metabolic enzymes that catalyze each of these four major chemical transformations. Among these, CYP represent the largest group of enzymes coded by a superfamily of genes, affecting metabolism of a large number of endogenous and xenobiotic compounds representing diverse chemical classes. Liver is the predominant source of these enzymes; however, intestinal epithelium, which was considered to be a source of mostly hydrolytic and certain conjugative enzymes, is now proven to be an important source of the oxidative enzymes including CYP.

Tissue slices represent the most complex in vitro system with intact cellular and intercellular architecture, thus yielding highest probability that most metabolic transformations that are likely to occur in vivo will be reproduced in vitro (reviewed in Ekins, 1996). However, this model is technically most challenging to implement and requires consideration of many factors including cell/tissue viability, uniformity and reproducibility of the tissue preparation, and recovery of entrapped drugs/metabolites. Furthermore, it is not as amenable to high throughput screening for metabolic stability as some of the simpler metabolic systems. Suspended or cultured hepatocytes are often used as sources of (hepatic) metabolic enzymes (see Maurel, 1996; LeCluyse et al., 1996; Houston and Carlile, 1997; LeCluyse, 2001). Like tissue slices, they provide intact cellular architecture and full complement of metabolic enzymes as well as co-factors; however, it is important to recognize that many enzyme activities, including CYP activity, decline rapidly subsequent to harvesting the cells. Much effort has been devoted to develop culture conditions (medium composition, extracellular matrix, and co-cultures) that would maintain enzyme activities for long periods of time to make this system more useful for metabolism studies (Maurel, 1996; LeCluyse et al., 1996; LeCluyse, 2001). It is recognized now that often the rate-limiting step in the metabolic transformation of a compound may be its entry into the cell and that transporters may play an important role as determinants of metabolic transformation of compounds by regulating their traffic into and out of cells. Only the cell-based systems can assess the overall impact of both the transporters and enzymes on the metabolic stability of a compound. Cell-free systems including S-9 fraction (supernatant from 9000 x g centrifugation of cell homogenates), microsomes (endoplasmic reticulum), and expressed enzymes provide simpler systems that are more rugged with respect to enzyme stability and more amenable to high throughput platforms (reviewed in Ansede and Thakker, 2004). Selection of a cell-free system to screen for metabolic stability should be made with a recognition that these systems often lack some of the metabolic enzymes (lost during fractionation or denatured during preparation) and/or co-

factors. Conjugation reactions are often difficult to reproduce in vitro with cell-free systems because each conjugation reaction requires a specific co-factor, requiring multiple enzymes for its biosynthesis. Augmenting the incubation medium by addition of a co-factor may not always lead to an "active" enzyme system because it may not reach the appropriate sub-cellular compartment. Hence, preliminary studies should be done with a more complete system to ensure that the cell-free system will provide adequate complement of enzymes and co-factors. The expressed enzymes can provide insights regarding the role of specific enzymes on the metabolism of test compounds; however, the information on the contribution of individual enzymes toward the overall metabolism is not obtained from studies involving expressed enzymes. Thus it is important to understand the advantages and limitations of various in vitro systems so that an optimum system can be chosen for answering specific questions on metabolic stability or setting up an appropriate screen.

CYP inhibition and induction

Screens for metabolic inhibition that are currently being employed are to assess inhibitory potential of drug candidates toward major human CYP enzymes. Typically, these assays involve testing drug candidates at a fixed concentration (% inhibition) or at a range of concentrations (IC50 or Ki) as inhibitors of individually expressed CYP enzymes against a known substrate for each enzyme. There are commercially available fluorogenic substrates for each CYP enzyme that, upon oxidation, are converted to fluorescent products. High throughput assays have been implemented using these fluorogenic substrates (reviewed in Ansede and Thakker, 2004). However, it is important to note that the substrates employed in these assays are often "non-drug like" in their properties. Thus inhibition measured against these substrates often shows poor correlation to the inhibitory potency measured against more classical "drug-like" substrates (Cohen et al., 2003). Because of such experiences, several companies have resorted to more resource-intensive LC/MS methods to screen drug candidates as inhibitors of CYP enzymes against "drug-like" substrates (reviewed in Ansede and Thakker, 2004).

Inhibitory potency of drug candidates provide important information regarding their drug interaction potential. However, it must be recognized that clinical implications in terms of seriousness and scope of drug interactions resulting from inhibition of different CYP enzymes is considerably varied. For example, a potent inhibitor of CYP3A4, an enzyme that accounts for (i) metabolism of over 50% of all the pharmaceutical agents (Rendic and Di Carlo, 1997) and (ii) over 30% of all CYP enzymes present in human liver ((Shimada et al., 1994), should be considered unsuitable for further development because it is likely to cause drug interactions with a wide range of co-administered drugs. In contrast, inhibitors of CYP enzymes such as 1A1, 2A6, and 2B6 may affect only a few co-administered drugs because each of these CYP enzymes account for metabolism of <1% of all the pharmaceutical agents (Rendic and Di Carlo, 1997), and together constitute less than 5% of total CYP enzymes in human liver

(Shimada et al., 1994). A potent inhibitor of one of these CYP enzymes can be developed provided that a profile of drug interactions is defined by conducting targeted clinical studies and included in the label. A factor that should be considered in assessing the implications of CYP inhibition by likely co-administered drugs on the exposure/plasma level of a test compound is the number of different CYP enzymes that metabolize the test compound. Thus, disposition of a compound that is metabolized by multiple CYP enzymes is not likely to be affected much due to inhibition of one of the CYP enzymes by a co-administered drug. Finally, as is the case with most in vitro parameters, before any decision is made about selection of drug candidates based on their inhibitory potency toward a CYP enzyme, it is important to determine if the in vitro potency corresponds to a similar inhibitory potency in vivo.

Several human CYP enzymes such as CYP1A1/CYP1A2, and CYP2B6, CYP2C9/CYP2C19, CYP2E1, and CYP3A4, are induced in response to repeat administration of certain agents (reviewed in Lin and Liu, 2001; Savas et al., 1999). Significant induction of CYPs can lead to increased clearance of the drug that induces the CYP, or other co-administered drugs that are substrates for the induced CYP. When this increased clearance leads to sufficient decrease in plasma concentrations such that the efficacy of a therapeutic agent is diminished, it can lead to sub-optimal therapeutic response - e.g. loss of efficacy of contraceptive steroids or HIV protease inhibitors due to CYP3A4 induction. Hence, the potential to induce CYPs is evaluated as a part of the candidate selection process. The in vitro model most often used is the one in which primary hepatocytes are cultured with extracellular matrix elements in sandwich-culture configuration (Maurel, 1996; LeCluyse et al., 1996; LeCluyse, 2001; Silva and Nicoll-Griffith, 2002). The cells are exposed to potential inducers for a period of time, after which induction of an enzyme (e.g. CYP3A) is assessed by measuring its activity or by quantifying the corresponding mRNA using quantitative real-time reverse transcriptase polymerase chain reaction (RT-PCR). With improved understanding of the molecular mechanism involving the role of nuclear receptors such as PXR and CAR in the induction of CYP enzymes (Savas et al., 1999, Honkakoksi and Negishi, 2000; Wang and LeCluyse, 2003), efforts are being made to develop and implement high throughput assays based on reporter gene constructs (Moore and Kliewer, 2000). Since there are significant species differences in the induction profile of CYP enzymes, induction potential of test compounds in different species should be evaluated using hepatocytes from the respective species. Better understanding of subtle differences in the regulatory mechanisms for expression of inducible CYP enzymes in different species will provide tools for development of species-specific high throughput assays for CYP induction.

Metabolism-based toxicity

Clinical failure due to toxicity remains a significant risk factor in drug development (Walsh, 2005). Often the toxicity exhibited by a test compound is not due to the parent compound; instead it is caused by a metabolite. The metabolite-induced toxicity is likely to escape detection during preclinical safety assessment since metabolite profiles and metabolite disposition may be different in different species. An important mechanism of metabolite-mediated toxicity is via covalent modification of critical macromolecules by reactive metabolites. It is generally accepted that covalent modification of macromolecules by reactive metabolites is a risk factor with respect to serious toxicity. However, not all covalent modifications lead to toxicity. This lack of well-defined and consistent link between the formation of metabolite-macromolecule adduct and toxicity makes it difficult to use covalent binding of metabolites to macromolecules as a decision tool for selection/de-selection of a drug candidate. However, certain companies, specifically Merck, minimize the risk of reactive metabolite-mediated toxicity by screening for the propensity of lead compounds to form reactive metabolites and "designing it out" by appropriate structural modifications (Evans et al., 2004). Such a screen typically involves incubating test compounds with liver microsomes, trapping any reactive metabolites produced with a nucleophile such as glutathione, and characterizing and quantifying the adduct with LC/MS. Often, a threshold is set (<50 pmol equivalent/mg microsomal protein at Merck) for the formation of glutathione adducts, below which the compound is advanced without further testing (Evans et al., 2004). When the adduct level is above the threshold, further studies are triggered to characterize the reactive metabolite so that medicinal chemistry can design it out of the drug candidate, and to assess covalent binding in vitro and in vivo using a radiolabeled drug. It is clear from Evans et al. (2004) that a compound is not automatically rejected if it produces protein adducts upon metabolism above the 50 pmol equivalent/mg protein; instead, several decision factors are brought to bear in deciding whether to advance such a compound for further development. While such an approach is an important first step toward reducing the risk of toxicity due to reactive metabolites, much remains to be done to elucidate the events after the initial formation of protein adducts that lead to toxic manifestation.

Excretion

The clearance of drugs and/or metabolites from the body occurs via one or both of the two major excretory mechanisms; i.e. biliary and renal excretion. As depicted in Figure 4, biliary excretion involves uptake of compounds across the sinusoidal membrane of hepatocytes via a diffusive or a carrier-mediated mechanism, intracellular disposition which may involve metabolism and/or sequestration, and subsequent efflux or the parent drug and/or metabolites across the canalicular membrane via an active transport process (Zamek-Gliszczynski and Brouwer, 2004). Depending on whether the compound (and/or metabolites) is removed from hepatocytes via efflux across the canalicular or the sinusoidal membrane, it is excreted via the biliary route or the renal route, respectively. Until recently, the biliary excretion potential of compounds could be evaluated only sinusoidal membran blood

Bile Excretion
Figure 4. A depiction of the biliary excretion process involving uptake, cellular disposition, and efflux of a compound in hepatocytes (reproduced from Zamek-Gliszczynski and Brouwer, 2004).

using in vivo preclinical models or in the perfused liver system. Besides being slow for screening purposes, the information obtained in the preclinical species could not be linked easily to biliary excretion in humans. The first in vitro model to assess biliary excretion was developed based on the observation by Liu et al. (1998, 1999a, 1999b), which showed that when hepatocytes are cultured in a sandwich configuration, they develop the architecture that is a two-dimensional representation of the three-dimensional architecture of the liver. The in vitro biliary excretion model can be constructed with hepatocytes from different preclinical species and from humans, thus enabling the assessment of biliary clearance in different species (reviewed in Zamek-Gliszczynski and Brouwer, 2004). This is an important development because in vivo measurement of biliary clearance in preclinical species would not allow prediction of biliary clearance in humans due to interspecies differences in transporter specificities.

Renal clearance of drugs and metabolites involves filtration, secretion, and re-uptake in different segments of the renal tubule. The secretion and uptake processes are mediated by a variety of transporters. Thus far, an in vitro system that can incorporate all the processes involved in renal excretion has not been developed. While in vitro cell culture based systems to assess the role of individual transporters in renal excretion have been reported, none of these systems have been implemented as a screen to assess and predict renal excretion as a part of the lead selection process.

Distribution

Upon entering the systemic circulation, compounds leave the blood stream by traversing across the capillary endothelium and distribute into various tissues. Of course, the compound distributed into tissues can re-enter the bloodstream as the plasma concentration of the compound changes over time. At steady-state, each compound is distributed into tissues with a distinct and unique equilibrium constant. The distribution of compounds into tissues at steady state is often measured in terms of volume of distribution (at steady state), which is defined in the simplest term as the total volume of plasma that would be necessary to contain the entire dose of the compound at a plasma concentration achieved after distribution. The distribution of compounds into the tissue space often involves multiple processes, including passive diffusion across the endothelial cells, leakage of solution through the intercellular space in the capillary endothelium, diffusion through the intercellular matrix and across the cell membrane, transportermediated translocation across the cell membrane into and out of the cell etc. Thus, the extent of distribution of a compound depends on its physicochemical properties and its ability to serve as a substrate for one or more influx and efflux transporters. To date, in vitro models to assess and predict distribution of drug candidates are not well developed although some attempts toward this goal have been made (Ballard et al., 2003a, 2003b, Leahy and Rowland, 2003). In vitro models for distribution into specific organ or tissue have also been attempted, e.g. in vitro measurement of brain and plasma unbound fraction to assess CNS distribution (Kalvass and Maurer, 2002). The brain-to-plasma ratio (Kpbrain) is the most commonly used parameter in drug discovery and development to characterize CNS disposition of a compound. The differences in nonspecific binding between brain and plasma is one possible mechanism underlying different brain-to-plasma ratios among compounds (Rowland, 1985; Waterbeemd et al., 2001a; Waterbeemd et al., 2001b). Since in the absence of distributional impairment,Kpbrain is related to the degree of binding to brain tissue and plasma, it is possible to evaluate in vivo CNS distribution by measuring brain and plasma unbound fraction in vitro. This approach has been used successfully in evaluating the CNS distribution of discovery compounds (Kalvass and Maurer, 2002). Since, distribution of compounds may significantly affect their disposition, and in turn, their efficacy and toxicity, developing good predictive in vitro models to assess distribution should be considered a high priority.

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