Advertisement
Article CommentaryCommentary

Commentary: Theoretical Predictions of Flow Effects on Intestinal and Systemic Availability in Physiologically Based Pharmacokinetic Intestine Models: The Traditional Model, Segregated Flow Model, and QGut Model

K. Sandy Pang and Edwin C. Y. Chow
Drug Metabolism and Disposition October 2012, 40 (10) 1869-1877; DOI: https://doi.org/10.1124/dmd.112.045872

Abstract

Physiologically based pharmacokinetic (PBPK) models for the intestine, comprising of different flow rates perfusing the enterocyte region, were revisited for appraisal of flow affects on the intestinal availability (FI) and, in turn, the systemic availability (Fsys) and intestinal versus liver contribution to the first-pass effect during oral drug absorption. The traditional model (TM), segregated flow model (SFM), and effective flow (QGut) model stipulate that 1.0, ∼0.05 to 0.3, and ≤0.484× of the total intestinal flow, respectively, reach the enterocyte region that houses metabolically active and transporter-enriched enterocytes. The fractional flow rate to the enterocyte region (fQ), when examined under varying experimental conditions, was found to range from 0.024 to 0.2 for the SFM and 0.065 to 0.43 for the QGut model. Appraisal of these flow intestinal models, when used in combination with whole-body PBPK models, showed the ranking as SFM < QGut model < TM in the description of FI, and the same ranking existed for the contribution of the intestine to first-pass removal. However, the ranking for the predicted contribution of hepatic metabolism, when present, to first-pass removal was the opposite: SFM > QGut model > TM. The findings suggest that the fQ value strongly influences the rate of intestinal metabolism (FI and Fsys) and indirectly affects the rate of liver metabolism due to substrate sparing effect. Thus, the fQ value in the intestinal flow models pose serious implications on the interpretation of data on the first-pass effect and oral absorption of drugs.

Introduction

Compartmental models are no longer adequate to address effects of permeability barriers (de Lannoy and Pang, 1986, 1987), intestinal and liver transporters and enzymes (Suzuki and Sugiyama, 2000a,b), and sequential metabolism within the intestine and liver (Pang and Gillette, 1979; Sun and Pang, 2010) during oral drug absorption (for reviews, see Pang, 2003; Pang et al., 2008; Fan et al., 2010; Pang and Durk, 2010; Chow and Pang, 2013). These aspects are especially pertinent when intestinal metabolic activity is substantial relative to that in the liver, and when different extents of induction/inhibition of intestinal and hepatic enzymes or transporters are the result of treatment with the culprit compound, which usually shows a higher induction/inhibition effect with oral administration (Fromm et al., 1996; Paine et al., 1996; Thummel et al., 1996; Eeckhoudt et al., 2002; Mouly et al., 2002; Fang and Zhang, 2010; Liu et al., 2010; Lledó-García et al., 2011; Zhu et al., 2011).

Over the past decade, there have been exciting advances made toward the development of physiologically relevant pharmacokinetic (PBPK) intestinal models to interrelate intestinal transporters, enzymes, and blood flow in the appraisal of their influence on intestinal (FI), liver (FH), and oral systemic (Fsys or FabsFIFH) availability. In this commentary, we revisited several physiologically based intestinal models that are associated with differential flow patterns: the traditional model (TM), in which the entire intestinal flow perfuses the enterocyte region; the segregated flow model (SFM), in which a low enterocyte flow (Qen) perfuses the enterocyte region (fractional flow, fQ or Qen/QPV is ≤0.3) (Cong et al., 2000); and the QGut model, in which the effective flow QGut that perfuses the enterocyte region is at best half the intestinal flow and close in value to the villous flow (Qvilli) (Yang et al., 2006, 2007; Gertz et al., 2010). These three intestinal models are viewed as competent to describe the immediate removal of the formed metabolite by excretion or sequential metabolism within the intestine and/or further processing by liver, for drugs and metabolites exhibiting varying permeability properties (Cong et al., 2000; Yang et al., 2006, 2007; Gertz et al., 2010; Sun and Pang, 2010). The models are more prepared to supply mechanistic insight into the pharmacokinetics of drugs and their metabolites and allow inclusion of transporters into different organ components (apical or basolateral membranes) to discriminate between the permeability properties of the drug and its formed metabolite in permitting or delimiting influx and efflux in drug and metabolite processing (Pang et al., 2008; Darwich et al., 2010; Galetin et al., 2010; Gertz et al., 2010; Rowland Yeo et al., 2010; Chow and Pang, 2013). By virtue of inclusion of transport and eliminatory events, these physiologically based models are able to more accurately describe the net appearance of the formed metabolite into the systemic circulation, because metabolite levels can be drastically reduced as a result of sequential metabolism (Pang and Gillette, 1979).

Intestinal PBPK models have been incorporated into whole body PBPK modeling. The semi-PBPK model proposed by Hall and colleagues (Quinney et al., 2008; Zhang et al., 2009; Quinney et al., 2010) resembles the TM-PBPK and features the intestine and liver tissues separately while minimizing the number of other tissues involved, retaining characteristics of the intestine and liver to describe metabolism, transport, and binding. The semi-PBPK model has been used to describe midazolam inhibition by intestinal and hepatically formed metabolites, N-desmethyldiltiazem from diltiazem in humans (Zhang et al., 2009), and hydroxyitraconazole from itraconazole in rats (Quinney et al., 2008), and in the estimation of the contribution of the intestine (∼30–40%) in furamidine formation from pafuramidine in a prodrug-drug relationship in rats, then humans (Yan et al., 2012). Chow et al. (2011) used the combined TM-PBPK and SFM-PBPK models to predict the 1.8- and 2.6-fold induction of brain and kidney P-glycoprotein (P-gp) protein expression with the vitamin D receptor ligand, 1α,25-dihydroxyvitamin D3, respectively, and demonstrated a superior fit with the SFM-PBPK model in explaining the P-gp-mediated excretion of digoxin. In the perfused rat intestine preparation in which the intestine is the only eliminating tissue, the SFM was found to be superior to the TM in describing morphine glucuronidation (Cong et al., 2000) and digoxin excretion by the P-glycoprotein under induced and noninduced states (Liu et al., 2006). In this commentary, we appraised how these intestinal flow models differed by examining the effects of enterocytic flow on FI and, in turn, Fsys and the extents of intestinal and liver first-pass removal with use of simulations.

Theoretical: The Intestinal Flow Models

The TM and SFM.

Historically, the TM and SFM were first introduced by Cong et al. (2000) to offer an explanation of the higher extent of intestinal metabolism of erythromycin (Lown et al., 1995) and midazolam (Paine et al., 1996) in humans, and enalapril hydrolysis (Pang et al., 1985) and morphine glucuronidation in the vascularly perfused rat intestine preparation (Doherty and Pang, 2000) between oral (po) versus intravenous (iv) dosing of drugs. Both models describe the effects of protein binding, enzymes for parallel and sequential pathways, and passive diffusion and/or transporter-driven permeation in metabolically and transport-competent enterocytes (Cong et al., 2000). In this model, one or more metabolic pathways, denoted as the metabolic intrinsic clearances, CLint,met1,I and CLint,met2,I, for the intestine may exist for precursor drug (P), and, similarly, CLint,met1,H and CLint,met2,H denote parallel metabolic pathways for the liver (Fig. 1). Drug secretion is represented by the CLint,sec,I for the intestinal secretion intrinsic clearance and CLint,sec,H for the liver biliary intrinsic clearance. Figure 1A denotes intestinal removal only, whereas Fig. 1B denotes both intestinal and liver removal; there is no elimination from other organs and tissues, which are lumped as highly and poorly perfused tissues. The influx and efflux clearances are denoted as CLd1, CLd2, CLd3, and CLd4 for the intestine (Fig. 1B, superscript I) and liver (Fig. 1B, superscript H); the unbound fractions in blood, intestine, and liver are denoted as fB, fI, and fH, respectively (although not shown in Fig. 1 for the sake of simplification). The single, significant difference between the TM and SFM is the flow pattern for perfusion of tissue regions of the small intestine. The SFM emphasizes a low flow (fQ ≈ 0.05–0.3× total intestinal flow) that perfuses the enterocyte region, and the remaining flow [(1-fQ)QPV] is shunted to the serosal or nonactive region (Fig. 1). This segregated flow pattern contrasts with the TM that describes the entire flow being able to reach the enterocyte or the total intestinal tissue, that is, fQ = 1 (Cong et al., 2000).

Fig. 1.
Fig. 1.

Whole-body PBPK, with the liver and other lumped compartments (highly perfused, poorly perfused) being connected to the intestine model (TM and SFM), depicting the intestine (A) and intestine and liver (B) as the eliminating tissue(s)/organ(s). The intestine subcompartments are as follows: for TM, subscripts int and intB denote intestinal tissue and intestinal blood, respectively; for SFM, subscripts en and enB denote enterocyte and enterocyte blood, respectively; s and sB denote serosal tissue and serosal blood, respectively. For the liver, subscripts L and LB represent liver tissue and liver blood, respectively; subscript R denotes the reservoir or blood compartment. For TM, the intestine represents a well mixed enterocyte region and receives the entire intestinal blood flow, QI or QPV. For SFM, the intestinal blood flow is segregated to perfuse the enterocyte and serosal regions; the flow to the enterocyte region is denoted as fQQPV, and the serosal region, (1-fQ)QPV. At the basolateral membrane, the drug influx and efflux clearances into or out of the intestine or enterocyte are characterized by the transport clearance parameters CLd1I and CLd2I, respectively. For SFM, additional influx and efflux clearance into or out of the serosal tissue compartment are characterized by the transport clearance parameters, CLd3I and CLd4I. The liver receives blood from hepatic blood artery (QHA) arising from the blood compartment and venous flow, QPV, from the intestine; the summed blood flow exits the liver as QH. The influx and efflux clearances of the drug into or out of the liver are CLd1H and CLd2H, respectively. Intrinsic metabolic clearance of parent drug (P) to form the primary metabolites in the intestine are denoted as CLint,met1,I and CLint,met2,I, and those in liver are CLint,met1,H and CLint,met2,H; the intestine and liver secrete P out via secretory intrinsic clearances, CLint,sec,I and CLint,sec,H, respectively. The bile flow rate is denoted as Qbile. Drug administrated orally (solution form) is administered into the lumen and may be either absorbed into intestine with the rate constant, ka, or degraded in lumen by the rate constant, kg; drug given intravenously directly enters the blood compartment.

Explicit solutions for the area under the curves (AUCs) for the TM- and SFM-PBPK models that feature the intestine as the only eliminating organ (Fig. 1A) were provided by Sun and Pang, (2009, 2010). These AUCs could be further modified by consideration of protein binding (unbound fractions fB and fI) for po and iv dosing.

Embedded Image and Embedded Image

In eqs. 1 and 2, the fB term appears next to CLd1I. It is also recognized that tissue binding effects are apparently nonoperative because the fI term cancels out in both the numerator and denominator. The difference in flow between the TM and SFM is denoted by fQ, the fraction of QPV that perfuses the enterocyte region; for TM, fQ = 1, whereas for SFM, fQ = 0.05 to 0.3. The flow term is absent for AUCpo but present in AUCiv.

Accordingly, the FI and Fsys is as follows: Embedded Image

Likewise, the AUCs for the TM- and SFM-PBPK models that feature both the intestine and liver as eliminating organs (Fig. 1B) have been solved (Sun and Pang, 2010), and their ratio, after consideration given to protein binding, is as follows: Embedded Image

Again, tissue binding effects are nonoperative because fI and fH, or the tissue unbound fractions for the intestine and liver, cancel out in both the numerator and denominator. In eq. 4, the fB term appears next to the influx clearances for the intestine and liver, CLd1I and CLd1H. Increases in fB would generally lower FI according to eqs. 3 and 4.

These solutions for FI (eqs. 3 and 4) revealed the blunting effect due to drug reabsorption, or the factor (1-Fabs), where Fabs or fraction absorbed is ka/(ka + kg) [where kg is the luminal degradation constant that comprises gastrointestinal transit and degradation] (Lin et al., 1999; Sun and Pang, 2009, 2010). The Fabs term has been reported to be highly correlated to the permeability of drug, Papp (Zhu et al., 2002; Corti et al., 2006; Kadono et al., 2010). Apical secretion mediated via the CLint,sec,I was nullified when the fraction absorbed ∼1, rendering the conclusion that FI is affected more by CLint,met,I and not so much by CLint,sec,I (Sun and Pang, 2009, 2010; Chow and Pang, 2013). As emphasized for the SFM, the partial flow suggests a bypass of enterocytes for drugs entering the intestinal tissue from the systemic circulation, whereas by design, drug given orally necessitates passage of the entire absorbed amount through the enterocyte region. This scenario would lead to a greater extent of intestinal removal for the drug given orally versus when the drug is given intravenously (Cong et al., 2000), rendering “route-dependent intestinal removal.”

The QGut Model.

Yang et al. (2007) constructed the “QGut model” based on an effective flow, QGut, to the enterocyte region, by relating this effective QGut to the intestinal availability, FI, or FG in their terminology. The equation for FI is based analogously to the equation for hepatic availability (FH), according to the well stirred liver model (Pang and Rowland, 1977), where fI is the unbound fraction of drug in intestinal tissue and CLint,I, the total, intestinal intrinsic clearance that encompasses both secretion and metabolism.

Embedded Image

The QGut is a hybrid term derived from the actual Qvilli [18 l/h or 300 ml/min (Gertz et al., 2010), representing ∼48.4% of the total intestinal flow (assumed to equal the portal venous flow or QPV, ∼ 620 ml/min) (Valentin, 2002; Yang et al., 2006, 2007)] and drug permeability clearance (CLperm), a parameter that is normally estimated as the area x effective permeability (Peff) assessed from perfused (human) jejunal studies, from Caco-2 cell Papp, or based on physicochemical data such as hydrogen bond donors and polar surface area. The QGut value of midazolam, a drug with high apparent permeability, was estimated to be 16.6 l/h, a value that is 92% of the value of Qvilli (Gertz et al., 2010).

Embedded Image

For a drug that is highly permeable, CLperm ≫ Qvilli, it may be deduced that QGut ≅ Qvilli.

Upon substitution of eq. 6 into eq. 5, the following is obtained: Embedded Image

As originally conceived by Yang et al. (2007), the CLperm term stands collectively for CLd1I and CLd2I but should be replaced appropriately by either CLd1I or CLd2I. Upon comparison of eq. 7 with eq. 3, the CLperm terms for the QGut model could now be assigned. By analogy to eq. 3, it is further recognized that fICLint,I is equivalent to the composite term, fI[CLint,met1,I + CLint,met2,I + (1-Fabs)CLint,sec,I)]. The term fICLint,I, in the QGut model which represents the summed unbound metabolic and secretory intrinsic clearances, fails to consider the intestinal secretion followed by reabsorption of the secreted material in the lumen. Upon consideration of all these missed events: Embedded Image eq. 8 is obtained for the QGut model, in an equivalent format as that for the TM and SFM (eq. 3). Similarities are seen between the SFM/TM and the QGut model. The fQQPV term for the SFM is equivalent to the Qvilli term of the QGut model (300 ml/min), which describes a partial flow (fQ = 0.484) perfusing the enterocyte region.

Results

Comparison of fQ.

A proper comparison of these models has not been made in any rigorous fashion, especially in regard to fQ on FI. The starting point of the comparison is fQ, being of a low value (∼0.05–0.3) for the SFM, ∼0.5 (Qvilli/QPV = 0.484) for the QGut model, and highest (1.0) for the TM. We feel that the fQ term could serve as an important variable for selection of the most appropriate model to best describe the intestine. Upon perusal of the literature, estimates of QGut according to eq. 2 for various drugs range from 2.4, 5.7, 8.6, to 16.6 l/h, corresponding to 6.5 to 43% of the total intestinal flow, with good predictions for midazolam but poor estimation of FI (or FG) for saquinavir in vivo (Gertz et al., 2010). Some of these fQ values for the QGut model are higher than the fQ values of 0.07, 0.024, and 0.2 estimated from fits of the SFM to the data on benzoic acid (Cong et al., 2001), morphine (Cong et al., 2000), and digoxin (Liu et al., 2006), respectively, from vascularly perfused rat small intestine preparations. For digoxin, which is mainly excreted unchanged in the mouse in vivo, a value of 0.16 was found for fQ (Chow et al., 2011). The fQ terms, whether for the SFM or for QGut model, are less than unity (Gertz et al., 2010; Chow and Pang, 2013), with fQ values being higher (>0.3) for the QGut model. Values of fQ for the SFM are lower and correspond better with published evidence that suggests segregated flows for the small intestine, and that a small fraction of flow (5–30%) perfuses the active, mucosal region (Granger et al., 1980).

Simulation of FI.

Equation 1 for the TM and SFM, which consider the intestine as the only eliminating organ, lacks any of the flow terms and suggests that AUCpo is identical among the TM, QGut model, and SFM, whereas the AUCiv intended for the TM/SFM (eq. 2) consists of the flow term, fQQPV for SFM and TM, and Qvilli for the QGut model, by analogy. Thus, different AUCiv values for the QGut model and the SFM result when the flow term is replaced by the appropriate flow rate, fQQPV or Qvilli. Because the rate of intestinal metabolism is dependent on the flow rate for delivery of substrate, it may be concluded that, when a smaller flow reaches the enterocyte region, a smaller intestinal removal rate results with systemic delivery. The ranking of the intestinal removal rate is SFM < QGut model < TM after intravenous dosing. The lower flow rate stipulated by the SFM in bringing the substrate into enterocyte region yields a higher AUCiv (ranking for AUCiv: SFM > QGut model > TM) and consequently a lower FI for the SFM compared with the QGut model and TM for given CLint,met,I values. This view is supported by the simulations (Fig. 2A). The ranking of FI was SFM < QGut model < TM.

Fig. 2.
Fig. 2.

Effects of changing CLint,met1,I (e.g., induction or inhibition of enzymes) on FI according to the TM, QGut model, and SFM with eq. 4 (A) and Fsys with the FI values shown in A under varying conditions of FH = 0.1, 0.5, and 0.9 (B). In this simulation, CLint,sec,I was set as 200 ml/min; QPV = 620 ml/min; fQ = 1.0 (TM), or 0.484 (QGut model) and 0.1 (SFM); Fabs = 0.1, 0.5, or 0.9; CLint,met2,I = 0; CLd1I = CLd2I = 20× QPV, denoting a highly permeable drug.

It is further observed that the solutions for FI are identical for the scenario in which the intestine is the only eliminating organ (eq. 3) and when the intestine and liver are both eliminating organs (eq. 4). These patterns for FI (Fig. 2A) are translated into Fsys for any given FH (= 0.1, 0.5, or 0.9; Fig. 2B). Again, the simulated patterns are consistent with the view that a decreased intestinal extraction ratio is accompanied by an increase in mesenteric flow (Chen and Pang, 1997; Chalasani et al., 2001; Yang et al., 2007; Chow and Pang, 2013); the lower intestinal removal rate due to lower enterocytic flows would result in higher hepatic processing, as observed experimentally by Chen and Pang (1997).

Changing CLd1I or CLd2I on FI.

When we further examined the effects of the basolateral influx (CLd1I) or efflux (CLd2I) transport clearances for drugs that exhibit varying degrees of absorption (described by Fabs = 0.1, 0.5, and 1.0), all models show that FI is attenuated when CLd1I is increased or when CLd2I is decreased (Fig. 3). Increasing the influx basolateral clearance (CLd1I) from low to high (left column, from 1 to 5 and 20× blood flow; Fig. 3) would lead to lower FI values, whereas upon increasing values of CLd2I from low to higher values [from 1 to 5× flow, middle column; then 20× flow, right column (Fig. 3)], higher FI values are attained due to ability of the influxed drug to escape intestinal enzymes intracellularly. The fQ effects from the flow models are apparent again with the simulations, and the ranking for FI values is SFM < QGut model < TM (Fig. 3).

Fig. 3.
Fig. 3.

Effects of varying basolateral transport clearances, CLd1I and CLd2I, on FI according to eq. 3, for drugs that are highly absorbed (Fabs = 0.9). For these simulations, CLint,sec,I is set as 200 ml/min; QPV is set as 620 ml/min, and fQ = 1.0 (for TM), or 0.484 (for QGut model) and 0.1 (for SFM). Values of CLd1I and CLd2I are altered from 1, 5, and 20× QPV. The value of CLint,met2,I, the intrinsic clearance for the alternate metabolic pathway, is set as 0.

Contributions from Intestine and Liver to First-Pass Effect.

To assess the contributions from the intestine versus the liver in first-pass removal among these flow-intestinal models, we further simulated the rates predicted from the mass equations shown below (eqs. 9 and 10) that describe the rates of intestinal (vI) and hepatic (vH) removal. For estimation of the rates, there exists the need to define the flow-averaged portal venous concentration, C̅PV, to account for the partial flow entering the enterocyte region, and for accurate prediction of the intestinal removal rate, vI. Embedded Image Embedded Image Embedded Image Here, E is the extraction ratio for the intestine or liver that equals (1-F), and CA is the arterial concentration. The fractional contributions by the intestine and liver may now be calculated.

The fractional contribution by intestine to the first-pass effect is as follows: Embedded Image and the fractional contribution by liver to the first-pass effect is as follows: Embedded Image

Qvilli replaces fQQPV, in eqs. 9, 10, 12, and 13 for the QGut model, with fQ = 0.484. Again, substitution of fQ (= 1, 0.484, and 0.1 for TM, QGut model, and SFM, respectively) embedded in FI or EI (eq. 3) yields the corresponding fractional removal estimates. Accordingly, the lower intestinal removal rate (vI) predicted by the SFM due to the reduced flow rate results in a correspondingly higher contribution by the liver due to the substrate sparing effect of the intestine (Fig. 4). Whereas for TM, the greater intestinal contribution in removing the drug leads to a lesser removal contribution by the liver due to a substrate depleting effect of the intestine (Fig. 4). Predictions from the QGut model on the intestinal and liver contributions to first-pass removal fall in between those for the SFM and TM, and the patterns are similar when Fabs = 0.1 or 0.9 (Fig. 4).

Fig. 4.
Fig. 4.

Simulation for the fractional contributions of the intestine, vI/(vI + vH) (A), and liver, vH/(vI + vH) (B). The designated drug examples vary from being poorly to highly absorbed (Fabs = 0.1 and 0.9), the hepatic availability (FH) of which vary from 0.1 to 0.9, QHA = 300 ml/min and CLd1I = CLd2I = 20× QPV, with an excretion component CLint,sec,I = 200 ml/min and a nonexistent, alternate metabolic pathway (CLint,met2,I = 0); the assigned flow rates were as follows: QPV = 620 ml/min; fQ = 1.0 (TM) or 0.1 (or Qen/QPV for SFM). Simulations for the QGut model (fQ = 0.484) were intermediate of those for the SFM and TM. See text for details.

Again, the predictions reveal that the fQ values in different intestinal models affect the contributions of the intestine and liver in the first-pass effect. For any given CLint,met1,I, this difference translates to ranking for the intestinal contribution to the first-pass effect as TM > QGut > SFM, and for the liver, the ranking is TM < QGut < SFM. These opposite trends in intestinal versus hepatic contributions to first-pass have been discussed by Xu et al. (1989) and Chen and Pang (1997), who attributed their observations to the anterior positioning of the intestine without recognizing the segregated flow effects. It must be commented that the effect of Fabs is not apparent in altering the contributions of the intestine or liver in first-pass removal in these simulations; the Fabs term affects only the reabsorption of the intestinally secreted drug (eqs. 3 and 4), which has been, for all intent and purpose, a minor pathway (CLint,sec,I = 200 ml/min) relative to values of CLint,met1,I examined.

Effects of Binding.

The mathematical manipulation reveals that tissue binding effects are canceled out because the unbound fraction terms in intestine (fI) or liver (fH) are absent in both the numerator and denominator. As seen from eqs. 3 and 4, only the fB term persists in the equations and is associated with the influx clearances, CLd1, for the intestine and liver (superscripted I and H, respectively). Upon changing fB at three sets of CLint,met1,I values for the various models (Fig. 5), it could be seen that increased values of fB generally lower FI (Fig. 5). Exceedingly similar patterns are observed for Fabs = 0.1 and 0.9.

Fig. 5.
Fig. 5.

Effects of changing fB on FI according to the TM, QGut model, and SFM with eq. 3 at Fabs = 0. 1 (A) or 0.9 (B) at CLint,met1,I = 100, 1000, or 2000 ml/min. In this simulation, CLint,sec,I is set as 200 ml/min; QPV = 620 ml/min; fQ = 1.0 (TM), 0.484 (QGut model) or 0.1 (SFM); CLint,met2,I = 0; CLd1I = CLd2I = 20× QPV, denoting a highly permeable drug. See text for details.

Discussion

This examination reveals that fQ is the key issue in the prediction of FI and contribution of both the intestine and liver to first-pass removal. The QGut model is similar to the SFM in many respects, except that a higher limit exists for fQ. The simulations, based on the various fQ values, show that the predicted intestinal availability of the QGut model falls between those of the TM and SFM models under varying conditions of efflux and influx clearances (Fig. 3). Decreased intestinal availabilities are expected with lower fQ values (Fig. 2), and this effect contributes to a greater proportion of first-pass extraction by the liver, the posterior organ (Fig. 4).

A major issue for the prediction of FI is the choice of the correct fQ value for intestinal models, especially for the QGut model. The problem that the intended QGut term is a hybrid function of Qvilli and CLperm (as shown in eq. 7) could now be circumvented with use of eq. 8. Although literature reports for the QGut model suggest that fQ varies between 0.07 and 0.43, we suggest use of the unambiguous Qvilli term or fQQPV (fQ = 0.484) for the QGut model, with inclusion of the CLd1I and CLd2I terms in lieu of CLperm, in a format similar to those for the SFM and TM (eq. 8) to define the fractional flow and the transport intrinsic clearances. This revelation implies that the effective flow rate to the enterocyte region (fQ = 0.484) for the QGut model is higher than that for the SFM. Another revelation is that fICLint,I in QGut model falls short of the more comprehensive term, [CLint,met,I + (1-Fabs)CLint,sec,I], in the prediction of FG (or FI in our terms). This may be another reason why poor prediction prevails for some drugs that are P-gp substrates (Gertz et al., 2010). Indeed, improved estimation of Peff with use of a P-gp inhibitor seemed to improve the FI prediction of saquinavir (Gertz et al., 2011). The need for fI in the equation for the QGut model is questionable because the term cancels out even when the binding effects of intestinal tissue on efflux, metabolism, or excretion are taken into consideration.

Other theoretical modeling that considers heterogeneity of transporters and enzymes along the length of the small intestine, as in the segmental traditional (STM) and segmental segregated flow (SSFM) models (counterparts of TM and SFM), has revealed that metabolic heterogeneity strongly affects FI (Tam et al., 2003). Wu (2012) has recently commented, in a theoretical examination, that heterogeneity matters in predicting Fsys after comparison of simulations from the TM-PBPK and SSFM-PBPK models on the systemic availability of the parent aglycone during the process of enterohepatic circulation of biliarily excreted glucuronides. The consideration of heterogeneity of transporters and enzymes on intestinal modeling in vivo surfaced much later, possibly due to the difficulty in obtaining population and length-averaged estimates on physiological dimensions of the lumen, surface area, flow, and enzymes and transporters in humans and animals (Badhan et al., 2009; Bruyère et al., 2010). Other compartmental models, when coupled with a refined description on the linear transfer kinetics of state properties of the drug (unreleased or solid form, undissolved or aggregate form, and dissolved or solution form), physicochemical properties (pKa, solubility, particle size, particle density, and permeability), physiological properties (gastric emptying, intestinal transit rate, intestinal metabolism, and luminal transport), and dosage factors (dosage form and dose), in the gastrointestinal tract show much improved predictions of drug kinetics (Agoram et al., 2001; Hendriksen et al., 2003), especially with inclusion of heterogeneity factors in the modeling (Bolger et al., 2009; Abuasal et al., 2012). However, the ability of many of the present models to fully describe metabolite kinetics remains uncertain. We have noted that heterogeneity models such as the SSFM and STM (Tam et al., 2003), whether necessary or not, are more pertinent in cases of enzyme heterogeneity among the segments. In absence of metabolism by the intestine, we found that the STM and SSFM perform as well as the TM and SFM, as found for studies on the absorption of benzoic acid (Cong et al., 2001) and digoxin absorption and efflux by P-gp (Liu et al., 2006) in the vascularly perfused intestine preparation. The presence of metabolite data is an absolute necessity for the discrimination between the SFM and TM.

It can be concluded that the designated flow rate to the enterocyte region of the intestine, defined according to the different intestinal flow models, strongly affects FI and Fsys and the proportions of intestinal and liver in first-pass removal. With the solved equations for the AUCs, it is apparent that predictions on the interplay between intestine and hepatic transporters and enzymes are readily attainable (Pang et al., 2009; Sun and Pang, 2010). Key issues for proper intestinal modeling are the accurate definition of fQ and improved estimates of the transport clearances. The proper definition of fQ is of paramount importance, and this awaits use of sophisticated tools to properly estimate the enterocyte versus the total intestinal flow rate. Notwithstanding the deficiencies persisting in all of the mentioned models, it is rewarding to see how the theoretical refinement in intestinal modeling has advanced our activity and knowledge toward how transporter and enzyme heterogeneity as well as segregated flow patterns affect drug metabolism and excretion by the small intestine and liver in first-pass removal during oral drug absorption.

Authorship Contributions

Participated in research design: Pang and Chow.

Conducted experiments: Pang and Chow.

Performed data analysis: Pang and Chow.

Wrote or contributed to the writing of the manuscript: Pang and Chow.

Footnotes

  • This work was supported by the Canadian Institute for Health Research (to K.S.P.); and the Alexander Graham Bell National Science and Engineering Research Council (NSERC) fellowship, National Science and Engineering Research Council of Canada, NSERC (to E.C.Y.C.).

  • Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.

    http://dx.doi.org/10.1124/dmd.112.045872.

  • ABBREVIATIONS:

    PBPK
    physiologically based pharmacokinetic
    FI or FG
    intestinal availability
    FH
    hepatic availability
    Fsys
    systemic availability
    Fabs
    fraction absorbed
    TM
    traditional model
    SFM
    segregated flow model
    Qen
    low enterocyte flow
    fQ
    fractional flow rate to the enterocyte region
    QPV
    portal venous flow
    Qgut
    effective flow
    Qvilli
    villous flow
    P-gp
    P-glycoprotein
    po
    oral
    iv
    intravenous
    CLint,met
    metabolic intrinsic clearance
    P
    precursor drug
    CLint,met1,H
    metabolic intrinsic clearance for liver
    CLint,sec,I
    intestinal secretion clearance
    CLint,sec,H
    liver biliary intrinsic clearance
    CLd1 and CLd2
    influx and efflux intrinsic clearances
    CLint,I
    total intestinal intrinsic clearance
    CLint,H
    total liver intrinsic clearance
    fB
    unbound fraction in blood
    fI
    unbound fraction in intestine
    fH
    unbound fraction in liver
    AUC
    area under the curve
    ka
    absorption rate constant
    kg
    luminal degradation rate constant
    Papp
    apparent permeability
    CLperm
    drug permeability clearance
    Peff
    effective permeability
    vI
    rate of intestinal removal
    vH
    rate of hepatic removal
    E
    extraction ratio
    CA
    arterial concentration
    STM
    segmental traditional model
    SSFM
    segmental segregated flow model
    C̅PV
    flow-averaged portal venous concentration.

  • Received March 30, 2012.
  • Accepted June 27, 2012.

References

  1. ↵
    1. Abuasal BS,
    2. Bolger MB,
    3. Walker DK,
    4. Kaddoumi A
    (2012) In silico modeling for the nonlinear absorption kinetics of UK-343,664: a P-gp and CYP3A4 substrate. Mol Pharm 9:492–504.
    OpenUrlCrossRefPubMed
  2. ↵
    1. Agoram B,
    2. Woltosz WS,
    3. Bolger MB
    (2001) Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev 50 (Suppl 1):S41–S67.
    OpenUrlCrossRefPubMed
  3. ↵
    1. Badhan R,
    2. Penny J,
    3. Galetin A,
    4. Houston JB
    (2009) Methodology for development of a physiological model incorporating CYP3A and P-glycoprotein for the prediction of intestinal drug absorption. J Pharm Sci 98:2180–2197.
    OpenUrlCrossRefPubMed
  4. ↵
    1. Bolger MB,
    2. Lukacova V,
    3. Woltosz WS
    (2009) Simulations of the nonlinear dose dependence for substrates of influx and efflux transporters in the human intestine. AAPS J 11:353–363.
    OpenUrlCrossRefPubMed
  5. ↵
    1. Bruyère A,
    2. Declèves X,
    3. Bouzom F,
    4. Ball K,
    5. Marques C,
    6. Treton X,
    7. Pocard M,
    8. Valleur P,
    9. Bouhnik Y,
    10. Panis Y,
    11. et al
    . (2010) Effect of variations in the amounts of P-glycoprotein (ABCB1), BCRP (ABCG2) and CYP3A4 along the human small intestine on PBPK models for predicting intestinal first pass. Mol Pharm 7:1596–1607.
    OpenUrlCrossRef
  6. ↵
    1. Chalasani N,
    2. Gorski JC,
    3. Patel NH,
    4. Hall SD,
    5. Galinsky RE
    (2001) Hepatic and intestinal cytochrome P450 3A activity in cirrhosis: effects of transjugular intrahepatic portosystemic shunts. Hepatology 34:1103–1108.
    OpenUrlCrossRefPubMed
  7. ↵
    1. Chen J,
    2. Pang KS
    (1997) Effect of flow on first-pass metabolism of drugs: single pass studies on 4-methylumbelliferone conjugation in the serially perfused rat intestine and liver preparations. J Pharmacol Exp Ther 280:24–31.
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Chow EC,
    2. Pang KS
    (2013) Why we need PBPK modeling to examine intestine and liver oral drug absorption. Curr Drug Metab, in press.
  9. ↵
    1. Chow EC,
    2. Durk MR,
    3. Cummins CL,
    4. Pang KS
    (2011) 1α,25-Dihydroxyvitamin D3 up-regulates P-glycoprotein via the vitamin D receptor and not farnesoid X receptor in both fxr(−/−) and fxr(+/+) mice and increased renal and brain efflux of digoxin in mice in vivo. J Pharmacol Exp Ther 337:846–859.
    OpenUrlAbstract/FREE Full Text
  10. ↵
    1. Cong D,
    2. Doherty M,
    3. Pang KS
    (2000) A new physiologically based, segregated-flow model to explain route-dependent intestinal metabolism. Drug Metab Dispos 28:224–235.
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Cong D,
    2. Fong AK,
    3. Lee R,
    4. Pang KS
    (2001) Absorption of benzoic acid in segmental regions of the vascularly perfused rat small intestine preparation. Drug Metab Dispos 29:1539–1547.
    OpenUrlAbstract/FREE Full Text
  12. ↵
    1. Corti G,
    2. Maestrelli F,
    3. Cirri M,
    4. Furlanetto S,
    5. Mura P
    (2006) Development and evaluation of an in vitro method for prediction of human drug absorption I. Assessment of artificial membrane composition. Eur J Pharm Sci 27:346–353.
    OpenUrlCrossRefPubMed
  13. ↵
    1. Darwich AS,
    2. Neuhoff S,
    3. Jamei M,
    4. Rostami-Hodjegan A
    (2010) Interplay of metabolism and transport in determining oral drug absorption and gut wall metabolism: a simulation assessment using the “Advanced Dissolution, Absorption, Metabolism (ADAM)” model. Curr Drug Metab 11:716–729.
    OpenUrlCrossRefPubMed
  14. ↵
    1. de Lannoy IA,
    2. Pang KS
    (1986) Presence of a diffusional barrier on metabolite kinetics: enalaprilat as a generated versus preformed metabolite. Drug Metab Dispos 14:513–520.
    OpenUrlAbstract
  15. ↵
    1. de Lannoy IA,
    2. Pang KS
    (1987) Effect of diffusional barriers on drug and metabolite kinetics. Drug Metab Dispos 15:51–58.
    OpenUrlAbstract
  16. ↵
    1. Doherty MM,
    2. Pang KS
    (2000) Route-dependent metabolism of morphine in the vascularly perfused rat small intestine preparation. Pharm Res 17:291–298.
    OpenUrlCrossRefPubMed
  17. ↵
    1. Eeckhoudt SL,
    2. Horsmans Y,
    3. Verbeeck RK
    (2002) Differential induction of midazolam metabolism in the small intestine and liver by oral and intravenous dexamethasone pretreatment in rat. Xenobiotica 32:975–984.
    OpenUrlCrossRefPubMed
  18. ↵
    1. Fan J,
    2. Chen S,
    3. Chow EC,
    4. Pang KS
    (2010) PBPK modeling of intestinal and liver enzymes and transporters in drug absorption and sequential metabolism. Curr Drug Metab 11:743–761.
    OpenUrlCrossRefPubMed
  19. ↵
    1. Fang C,
    2. Zhang QY
    (2010) The role of small-intestinal P450 enzymes in protection against systemic exposure of orally administered benzo[a]pyrene. J Pharmacol Exp Ther 334:156–163.
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. Fromm MF,
    2. Busse D,
    3. Kroemer HK,
    4. Eichelbaum M
    (1996) Differential induction of prehepatic and hepatic metabolism of verapamil by rifampin. Hepatology 24:796–801.
    OpenUrlCrossRefPubMed
  21. ↵
    1. Galetin A,
    2. Gertz M,
    3. Houston JB
    (2010) Contribution of intestinal cytochrome P450-mediated metabolism to drug-drug inhibition and induction interactions. Drug Metab Pharmacokinet 25:28–47.
    OpenUrlCrossRefPubMed
  22. ↵
    1. Gertz M,
    2. Harrison A,
    3. Houston JB,
    4. Galetin A
    (2010) Prediction of human intestinal first-pass metabolism of 25 CYP3A substrates from in vitro clearance and permeability data. Drug Metab Dispos 38:1147–1158.
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Gertz M,
    2. Houston JB,
    3. Galetin A
    (2011) Physiologically based pharmacokinetic modeling of intestinal first-pass metabolism of CYP3A substrates with high intestinal extraction. Drug Metab Dispos 39:1633–1642.
    OpenUrlAbstract/FREE Full Text
  24. ↵
    1. Granger DN,
    2. Richardson PD,
    3. Kvietys PR,
    4. Mortillaro NA
    (1980) Intestinal blood flow. Gastroenterology 78:837–863.
    OpenUrlPubMed
  25. ↵
    1. Hendriksen BA,
    2. Felix MV,
    3. Bolger MB
    (2003) The composite solubility versus pH profile and its role in intestinal absorption prediction. AAPS PharmSci 5:E4.
    OpenUrlPubMed
  26. ↵
    1. Kadono K,
    2. Akabane T,
    3. Tabata K,
    4. Gato K,
    5. Terashita S,
    6. Teramura T
    (2010) Quantitative prediction of intestinal metabolism in humans from a simplified intestinal availability model and empirical scaling factor. Drug Metab Dispos 38:1230–1237.
    OpenUrlAbstract/FREE Full Text
  27. ↵
    1. Lin JH,
    2. Chiba M,
    3. Baillie TA
    (1999) Is the role of the small intestine in first-pass metabolism overemphasized? Pharmacol Rev 51:135–158.
    OpenUrlFREE Full Text
  28. ↵
    1. Liu S,
    2. Tam D,
    3. Chen X,
    4. Pang KS
    (2006) P-Glycoprotein and an unstirred water layer barring digoxin absorption in the vascularly perfused rat small intestine preparation: induction studies with pregnenolone-16α-carbonitrile. Drug Metab Dispos 34:1468–1479.
    OpenUrlAbstract/FREE Full Text
  29. ↵
    1. Liu YT,
    2. Hao HP,
    3. Xie HG,
    4. Lai L,
    5. Wang Q,
    6. Liu CX,
    7. Wang GJ
    (2010) Extensive intestinal first-pass elimination and predominant hepatic distribution of berberine explain its low plasma levels in rats. Drug Metab Dispos 38:1779–1784.
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Lledó-García R,
    2. Nácher A,
    3. Casabó VG,
    4. Merino-Sanjuán M
    (2011) A pharmacokinetic model for evaluating the impact of hepatic and intestinal first-pass loss of saquinavir in the rat. Drug Metab Dispos 39:294–301.
    OpenUrlAbstract/FREE Full Text
  31. ↵
    1. Lown KS,
    2. Thummel KE,
    3. Benedict PE,
    4. Shen DD,
    5. Turgeon DK,
    6. Berent S,
    7. Watkins PB
    (1995) The erythromycin breath test predicts the clearance of midazolam. Clin Pharmacol Ther 57:16–24.
    OpenUrlCrossRefPubMed
  32. ↵
    1. Mouly S,
    2. Lown KS,
    3. Kornhauser D,
    4. Joseph JL,
    5. Fiske WD,
    6. Benedek IH,
    7. Watkins PB
    (2002) Hepatic but not intestinal CYP3A4 displays dose-dependent induction by efavirenz in humans. Clin Pharmacol Ther 72:1–9.
    OpenUrlCrossRefPubMed
  33. ↵
    1. Paine MF,
    2. Shen DD,
    3. Kunze KL,
    4. Perkins JD,
    5. Marsh CL,
    6. McVicar JP,
    7. Barr DM,
    8. Gillies BS,
    9. Thummel KE
    (1996) First-pass metabolism of midazolam by the human intestine. Clin Pharmacol Ther 60:14–24.
    OpenUrlCrossRefPubMed
  34. ↵
    1. Pang KS
    (2003) Modeling of intestinal drug absorption: roles of transporters and metabolic enzymes (for the Gillette Review Series). Drug Metab Dispos 31:1507–1519.
    OpenUrlFREE Full Text
  35. ↵
    1. Pang KS,
    2. Durk MR
    (2010) Physiologically-based pharmacokinetic modeling for absorption, transport, metabolism and excretion. J Pharmacokinet Pharmacodyn 37:591–615.
    OpenUrlCrossRefPubMed
  36. ↵
    1. Pang KS,
    2. Gillette JR
    (1979) Sequential first-pass elimination of a metabolite derived from a precursor. J Pharmacokinet Biopharm 7:275–290.
    OpenUrlCrossRefPubMed
  37. ↵
    1. Pang KS,
    2. Cherry WF,
    3. Ulm EH
    (1985) Disposition of enalapril in the perfused rat intestine liver preparation: absorption, metabolism and first pass effect. J Pharmacol Exp Ther 233:788–795.
    OpenUrlAbstract/FREE Full Text
  38. ↵
    1. Pang KS,
    2. Maeng HJ,
    3. Fan J
    (2009) Interplay of transporters and enzymes in drug and metabolite processing. Mol Pharm 6:1734–1755.
    OpenUrlCrossRefPubMed
  39. ↵
    1. Pang KS,
    2. Morris ME,
    3. Sun H
    (2008) Formed and preformed metabolites: facts and comparisons. J Pharm Pharmacol 60:1247–1275.
    OpenUrlCrossRefPubMed
  40. ↵
    1. Pang KS,
    2. Rowland M
    (1977) Hepatic clearance of drugs. I. Theoretical considerations of a “well-stirred” model and a “parallel tube” model. Influence of hepatic blood flow, plasma and blood cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance. J Pharmacokinet Biopharm 5:625–653.
    OpenUrlCrossRefPubMed
  41. ↵
    1. Quinney SK,
    2. Galinsky RE,
    3. Jiyamapa-Serna VA,
    4. Chen Y,
    5. Hamman MA,
    6. Hall SD,
    7. Kimura RE
    (2008) Hydroxyitraconazole, formed during intestinal first-pass metabolism of itraconazole, controls the time course of hepatic CYP3A inhibition and the bioavailability of itraconazole in rats. Drug Metab Dispos 36:1097–1101.
    OpenUrlAbstract/FREE Full Text
  42. ↵
    1. Quinney SK,
    2. Zhang X,
    3. Lucksiri A,
    4. Gorski JC,
    5. Li L,
    6. Hall SD
    (2010) Physiologically based pharmacokinetic model of mechanism-based inhibition of CYP3A by clarithromycin. Drug Metab Dispos 38:241–248.
    OpenUrlAbstract/FREE Full Text
  43. ↵
    1. Rowland Yeo K,
    2. Jamei M,
    3. Yang J,
    4. Tucker GT,
    5. Rostami-Hodjegan A
    (2010) Physiologically based mechanistic modelling to predict complex drug-drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut - the effect of diltiazem on the time-course of exposure to triazolam. Eur J Pharm Sci 39:298–309.
    OpenUrlCrossRefPubMed
  44. ↵
    1. Sun H,
    2. Pang KS
    (2009) Disparity in intestine disposition between formed and preformed metabolites and implications: a theoretical study. Drug Metab Dispos 37:187–202.
    OpenUrlAbstract/FREE Full Text
  45. ↵
    1. Sun H,
    2. Pang KS
    (2010) Physiological modeling to understand the impact of enzymes and transporters on drug and metabolite data and bioavailability estimates. Pharm Res 27:1237–1254.
    OpenUrlCrossRefPubMed
  46. ↵
    1. Suzuki H,
    2. Sugiyama Y
    (2000a) Role of metabolic enzymes and efflux transporters in the absorption of drugs from the small intestine. Eur J Pharm Sci 12:3–12.
    OpenUrlCrossRefPubMed
  47. ↵
    1. Suzuki H,
    2. Sugiyama Y
    (2000b) Transport of drugs across the hepatic sinusoidal membrane: sinusoidal drug influx and efflux in the liver. Semin Liver Dis 20:251–263.
    OpenUrlCrossRefPubMed
  48. ↵
    1. Tam D,
    2. Tirona RG,
    3. Pang KS
    (2003) Segmental intestinal transporters and metabolic enzymes on intestinal drug absorption. Drug Metab Dispos 31:373–383.
    OpenUrlAbstract/FREE Full Text
  49. ↵
    1. Thummel KE,
    2. O'Shea D,
    3. Paine MF,
    4. Shen DD,
    5. Kunze KL,
    6. Perkins JD,
    7. Wilkinson GR
    (1996) Oral first-pass elimination of midazolam involves both gastrointestinal and hepatic CYP3A-mediated metabolism. Clin Pharmacol Ther 59:491–502.
    OpenUrlCrossRefPubMed
  50. ↵
    1. Valentin J
    (2002) Basic Anatomical and Physiological Data for Use in Radiological Protection: Reference Values. Pergamon, Oxford.
  51. ↵
    1. Wu B
    (2012) Use of physiologically based pharmacokinetic models to evaluate the impact of intestinal glucuronide hydrolysis on the pharmacokinetics of aglycone. J Pharm Sci 101:1281–1301.
    OpenUrlCrossRefPubMed
  52. ↵
    1. Xu X,
    2. Hirayama H,
    3. Pang KS
    (1989) First-pass metabolism of salicylamide. Studies in the once-through vascularly perfused rat intestine-liver preparation. Drug Metab Dispos 17:556–563.
    OpenUrlAbstract
  53. ↵
    1. Yan GZ,
    2. Generaux CN,
    3. Yoon M,
    4. Goldsmith RB,
    5. Tidwell RR,
    6. Hall JE,
    7. Olson CA,
    8. Clewell HJ,
    9. Brouwer KL,
    10. Paine MF
    (2012) A semiphysiologically based pharmacokinetic modeling approach to predict the dose-exposure relationship of an antiparasitic prodrug/active metabolite pair. Drug Metab Dispos 40:6–17.
    OpenUrlAbstract/FREE Full Text
  54. ↵
    1. Yang J,
    2. Jamei M,
    3. Heydari A,
    4. Yeo KR,
    5. de la Torre R,
    6. Farré M,
    7. Tucker GT,
    8. Rostami-Hodjegan A
    (2006) Implications of mechanism-based inhibition of CYP2D6 for the pharmacokinetics and toxicity of MDMA. J Psychopharmacol 20:842–849.
    OpenUrlAbstract/FREE Full Text
  55. ↵
    1. Yang J,
    2. Jamei M,
    3. Yeo KR,
    4. Tucker GT,
    5. Rostami-Hodjegan A
    (2007) Prediction of intestinal first-pass drug metabolism. Curr Drug Metab 8:676–684.
    OpenUrlCrossRefPubMed
  56. ↵
    1. Zhang X,
    2. Quinney SK,
    3. Gorski JC,
    4. Jones DR,
    5. Hall SD
    (2009) Semiphysiologically based pharmacokinetic models for the inhibition of midazolam clearance by diltiazem and its major metabolite. Drug Metab Dispos 37:1587–1597.
    OpenUrlAbstract/FREE Full Text
  57. ↵
    1. Zhu Y,
    2. D'Agostino J,
    3. Zhang QY
    (2011) Role of intestinal cytochrome P450 (P450) in modulating the bioavailability of oral lovastatin: insights from studies on the intestinal epithelium-specific P450 reductase knockout mouse. Drug Metab Dispos 39:939–943.
    OpenUrlAbstract/FREE Full Text
  58. ↵
    1. Zhu C,
    2. Jiang L,
    3. Chen TM,
    4. Hwang KK
    (2002) A comparative study of artificial membrane permeability assay for high throughput profiling of drug absorption potential. Eur J Med Chem 37:399–407.
    OpenUrlCrossRefPubMed
View Abstract
Back to top

In this issue

Download PDF
Article Alerts
Email Article
Citation Tools
Article CommentaryCommentary

INTESTINAL FLOW MODELS IN PBPK MODELING

K. Sandy Pang and Edwin C. Y. Chow
Drug Metabolism and Disposition October 1, 2012, 40 (10) 1869-1877; DOI: https://doi.org/10.1124/dmd.112.045872
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo

Jump to section