Dmd054031 1975..1993

Prediction of human pharmacokinetics of new drugs, as well as other disposition attributes, has become a routine practice in drug research and development. Prior to the 1990s, drug disposition science was used in a mostly descriptive manner in the drug development phase. With the advent of in vitro methods and availability of human-derived reagents for in vitro studies, drugdisposition scientists became engaged in the compound design phase of drug discovery to optimize and predict human disposition properties prior to nomination of candidate compounds into the drug development phase. This has reaped benefits in that the attrition rate of new drug candidates in drug development for reasons of unacceptable pharmacokinetics has greatly decreased. Attributes that are predicted include clearance, volume of distribution, halflife, absorption, and drug-drug interactions. In this article, we offer our experience-based perspectives on the tools and methods of predicting human drug disposition using in vitro and animal data. Introduction and History The prediction of human pharmacokinetic and disposition attributes of new chemical entities from preclinical data has become a mainstay of drug metabolism and pharmacokinetics (DMPK) organizations within pharmaceutical research and development operations. In the vast majority of large research and development groups, the nomination of new compounds into the development phase requires a prediction of what the human pharmacokinetics will be. Tools have been developed to assess important human disposition attributes during the drug design phase so that medicinal chemists can simultaneously optimize absorption, distribution, metabolism, and excretion (ADME) properties and pharmacological potency. These tools and methods are standard in modern drug discovery, and are a critical element to successful drug discovery. But this was not always the case. Prior to the 1990s, the focus of the ADME scientists in the pharmaceutical industry was compound characterization. Most work was descriptive rather than mechanistic. Drug candidates were generally not studied until the development phase, and the focus was on gathering pharmacokinetic and toxicokinetic data, and offering a description of the metabolism and excretion of new compounds in humans and laboratory animals. The value that a study of ADME properties of new chemicals could bring to the drug design phase was well recognized by scientists at Pfizer’s Sandwich, UK, research facility (Smith, 2012), and this was enhanced by an organizational structure wherein the Sandwich Drug Metabolism Department reported to the leadership of the local drug discovery organization. The contrast with the sister Drug Metabolism Department at the Pfizer Groton, CT, research facility was notable in that the latter group was under the Toxicology Department, an organization that was very oriented to descriptive data gathering in the drug development phase. Nevertheless, the incredible value that drug-metabolism scientists could bring to the discovery phase was recognized in the early 1990s, because there were many dx.doi.org/10.1124/dmd.113.054031. ABBREVIATIONS: ABC, ATP binding-cassette, ADAM, advanced dissolution, absorption, and metabolism; ADME, absorption, distribution, metabolism, and excretion; AO, aldehyde oxidase; AUC, area under the curve; AUCR, area under the curve ratio; BCRP, breast cancer resistance protein; BCS, Biopharmaceutics Classification System; BDDCS, Biopharmaceutical Drug Disposition Classification System; CAT, compartmental absorption transit; CL, clearance; Clint, intrinsic clearance CLint,u unbound intrinsic clearance; CLr, renal clearance; CYP, cytochrome P450; DDI, drug-drug interaction; DMPK, drug metabolism and pharmacokinetics; EHC, enterohepatic circulation; Fa, fraction absorbed; Fg, fraction passing through the intestine; fm, fractional metabolism; fu, fraction unbound in blood; HPLC, high-performance liquid chromatography; IVIVE, in vitro–in vivo extrapolation; kdeg, natural enzyme degradation rate; MAO, monoamine oxidase; MRP, multidrug resistance protein; MRT, mean residence time; NCE, new chemical entity; OATP, organic anion transporting polypeptide; OCT, organic cation transporter; PBPK, physiologically-based pharmacokinetic; P-gp, P-glycoprotein; PK, pharmacokinetics; PPB, plasma protein binding; PSA, polar surface area; PXR, pregnane X receptor; QSAR, quantitative structure-activity relationship; SCH, sandwich-cultured hepatocytes; SCHH, sandwich-cultured human hepatocytes; SULT, sulfotransferase; t1/2, half-life; TDI, time-dependent inactivation; UGT, uridine 5’-diphospho-glucuronosyltransferase; VD, volume of distribution; VDb, volume of distribution during the terminal elimination phase; VDss, volume of distribution at steady state. 1975 at A PE T Jornals on July 7, 2017 dm d.aspurnals.org D ow nladed from compounds that failed in phase 1 first-in-human studies due to poor pharmacokinetics (i.e., very short half-life and/or very high oral clearance) (Kola and Landis, 2004). This led to a marked expansion of DMPK groups across the industry, as the remit evolved from one of providing descriptive data in the drug development phase to also include working with drug discovery project teams to leverage ADME data in the optimization of drug molecules. A critical contribution that DMPK scientists make to discovery project teams is offering a data-driven opinion as to what the human pharmacokinetics would be prior to nomination of the drug candidate into the more expensive early development phase. In the early days of this contribution, the only approach available was to gather pharmacokinetic data in laboratory animal species and make suppositions from that data (e.g., if clearance was low in rats and dogs, then it would be low in humans, etc.). Allometric scaling (discussed in greater detail below) offered a use for animal pharmacokinetic data that was one level of sophistication greater than merely stating that pharmacokinetic (PK) characteristics in humans would be like those in animals. In allometry, there is a fundamental assumption that differences in pharmacokinetic processes will mirror differences in other attributes across species (i.e., weight, life-span, other), and thereby a quantitative projection of human pharmacokinetic parameters could be made (Boxenbaum, 1982). In the early 1990s, the increasing availability and characterization of human-derived reagents that could be used for in vitro drug metabolism studies revolutionized the approaches to applying ADME to drug discovery. These included subcellular fractions from human liver, such as human liver microsomes, as well as cloned and heterologously expressed human drug metabolizing enzymes. Thus, methods that had been described previously for scaling in vitro data to animal pharmacokinetic data (Rane et al., 1977; Houston, 1994) could be tried with human-derived reagents. Back in the early 1990s, in our company there was disbelief among some leaders in the research organization that techniques such as in vitro–in vivo scaling and allometric scaling could really offer quality predictions of human pharmacokinetics. There was an opinion that compounds should be nominated into the development phase and that phase 1 studies would be the ones to sort out compounds that had appropriate or inappropriate human pharmacokinetics. Thus, the challenge was laid down to DMPK scientists to prove whether preclinical data could really be useful for quantitative predictions of human pharmacokinetics. The response to the challenge was a retrospective analysis of human pharmacokinetic data in view of whether various prediction techniques (in vitro and in vivo) would have been adequately predictive. (“Adequate” prediction methods are those such that the accuracy is good enough for correct and confident decision-making when selecting candidate compounds for the early development phase of drug research. Predicted half-lives need to permit the desired dosing regimen frequency. Predicted oral bioavailability has to permit a dose level that is not higher than what can be absorbed and economically manufactured.) The results of this analysis were promising and were reported in 1997 (Obach et al., 1997). Since that time, scientists at Pfizer have reassessed these techniques as human pharmacokinetic data for new drug candidates became available (Hosea et al., 2009). Around this time, other groups were also pursuing predictions of human pharmacokinetics using in vitro scaling (Iwatsubo et al., 1997) and allometric scaling with in vitro intrinsic clearance corrections (Lave et al., 1997). Thus, the prediction of human pharmacokinetics prior to nomination of new drug candidates into drug development has become an expectation of drug discovery project teams. Furthermore, human liver microsomal liability data are gathered using high-throughput methods for testing of every new chemical made by medicinal chemists, for use in building structure-metabolism relationships and driving compound design to low rates of cytochrome P450 metabolism. Other types of in vitro data to address drug disposition attributes (e.g., membrane penetrability, P450 inhibition, others) are also gathered early using high-throughput methods. We have progressed far from the early times of merely striving for predicting human half-life. We now seek to predict many aspects of drug disposition in humans, including clearance, volume of distribution, half-life, oral absorption, oral bioavailability, drug-drug interactions, impact of genetic polymorphisms on drug disposition, penetration into target organs, efficacious dose, and even complete concentration-versus-time curves with accompanying interindividual variability. Prediction algorithms have evolved from simple equations to complex physiologically based pharmacokinetic (PBPK) modeling. Some parameters, like volume of distribution, can be reasonably predicted from computational methods alone. Other properties that we desire to predict, such as target-tissue free concentrations, are dependent on emerging areas of ADME science (e.g., drug transporters), and our ability to predict such properties is still immature. It is also important to note that methods used to predict human pharmacokinetics and other disposition properties have a level of inaccuracy beyond which distinctions among compounds cannot be made (Beaumont and Smith, 2009). For example, if two compounds are predicted to have pharmacokinetic parameters within 2-fold of each other, these cannot be truly distinguished from each other, and other nonpharmacokinetic attributes may be more important for drawing any distinction. In this commentary, we offer a perspective of predictions of human pharmacokinetic/disposition attributes from a pharmaceutical industry research and development viewpoint. We describe each of the major attributes that we currently seek to predict using preclinical data, an overall assessment of the advantages and shortcomings of various methods that have been used, and a desire for what is needed to increase success in the future. While the advances that started in the early 1990s are well established, it is clear that several areas remain to be explored with basic scientific research so that the development and application of methods can be accomplished. Predicting Half-Life Half-life, in reference to a xenobiotic, is the oldest pharmacokinetic parameter and is used extensively in pharmacokinetic and pharmaceutical literature. It is an important component to help define the dosing frequency (along with knowledge of the pharmacokinetic/ pharmacodynamic relationship). Convenient dosing regimens are strived for in drug discovery, so the half-life in humans is one of the most important parameters to predict. Half-life is a function of the clearance and volume of distribution: two fundamental parameters that will be discussed in the following sections. A major utility for predicted human half-life values is to identify or predict the maximum dosing interval (tmax) to maintain the drug concentration within the therapeutic range (Cupper/Clower). This value is estimated with consideration of the therapeutic index and can be expressed with the following equation (Rowland and Tozer, 1995): tmax 1⁄4 1:44 •t1=2 •ln Cupper Clower ðEq: 1Þ In addition, the predicted human half-life values are used to rank order chemical series, identify the most ideal drug candidate, assess clinical feasibility, and retrospectively determine how well preclinical tools and reagents were applied in predicting the actual human halflife (Hosea et al., 2009). Half-life is frequently the only human pharmacokinetic parameter that can be used to retrospectively evaluate performance of human pharmacokinetic prediction methods that use 1976 Di et al. at A PE T Jornals on July 7, 2017 dm d.aspurnals.org D ow nladed from preclinical tools and reagents. Without corresponding intravenous data, accuracy in clearance (CL) and volume of distribution (VD) predictions cannot be assessed individually (Hosea et al., 2009). To be practical about predicted human half-lives, the accuracy in prediction should be considered within ranges of half-life values (Obach et al., 1997). Nevertheless, half-life predictions should be as accurate as possible. To this extent, two approaches are predominantly being used to predict human half-life. The most widely used method employs the above-mentioned approach using predicted human volume of distribution at steady state (VDss) and clearance values to predict half-life according to Eq. 2 (Hosea et al., 2009). Various prediction methods for VDss and CL are described in other sections within this paper. But, as expected, the accuracy of the half-life prediction is more a function of the accurate prediction of the independent parameters CL and VDss. t1=2 1⁄4 lnð2Þ•ss CL ðEq: 2Þ However, all these reported approaches are retrospective analyses, and the forward predictiveness of the various approaches is not always clear. In one example, it was determined that simple allometry can accurately predict the elimination rate-constant for irbesartan from animal data (Kumar and Srinivas, 2008). In another study, it was concluded that human half-life values can adequately be predicted from rat half-life values using either linear regression or allometric scaling between human and rat (Caldwell et al., 2004). About 76% and 77% of the values were predicted within an average fold error of less than 3 for the regression and allometric method, respectively. The authors also concluded that deriving half-life values from VDss and CL values were not more accurate than predicting half-life directly. However, 77% predictability within an average fold error of less than 3 is on par with other reported successes in predicting half-life from VDss and CL values (Hosea et al., 2009). Regardless of the reported success using simple animal-to-human correlations, using VDss and CL to predict effective half-life should be used preferentially. While human VDss values can be reasonably well-predicted from animal data (see below), CL can vary considerably across species, which can thus confound predictions of half-life from animal data alone. With contemporary drug chemical space more frequently involving transporters as a major CL mechanism, it is likely that a half-life predicted from CL (and VDss) separately could be more accurate. Predicting Volume of Distribution Volume of distribution is a critical PK parameter and is relatively predictable compared with the other, more challenging PK variables, such as clearance. Many comprehensive reviews have been written on this topic (Obach, 2007; Berry et al., 2011; Zou et al., 2012; Lombardo et al., 2013b). Over 30 different in vivo, in vitro, and in silico methodologies are available to predict human VD. The literature information is controversial on what the best approach is, and significant validation and refinements of the methodologies are still ongoing in the field. Here, we discuss a few common methods and their strengths and limitations. It is important to understand that these various methods find their greatest applicability at different times during the drug discovery-development timeline. In general, interspecies scaling with in vivo animal data is more accurate than other approaches and should be used for later stages of drug discovery when accurate prediction of human PK is essential. In silico methods can be effective for early stages of drug discovery, where large number of compounds need to be profiled (or even predicted prior to synthesis). Mechanistic-based PBPK models are particularly useful for whole-body PBPK modeling. In vitro approaches tend to be less frequently employed, since in silico models are predictive without any added experimental cost. It is important to note that VD itself has little impact on compound selection (except in extreme instances such as very high VD values that may indicate excessive tissue partitioning). Compounds should not be designed to manipulate VD; it is free clearance that should be the focus. Rather, the importance of VD is in its contribution to halflife and mean residence time (MRT), which in turn is used to help predict the dosing interval needed for chronically used medication (see above). There are several different VD terms that describe distribution in different ways. The two most commonly described are the VDss and VDb terms. VDss is a more informative value for dosing regimen, as it contributes to mean residence time, a value that better reflects the extent of accumulation that will occur with multiple dosing. VDb contributes to the terminal elimination-phase half-life, which may not have as much impact on the dosing regimen if only a small percentage of the total exposure occurs during this phase. Thus, prediction of VDss is generally more valuable than VDb. Key Determinants of VD. VD is a function of the extent to which the drug binds to plasma components (fb,p) versus the extent to which the drug binds nonspecifically to tissue components (fbT). VD } fb;T fb;p ðEq: 3Þ Because VD is mostly determined by nonspecific binding to plasma and tissue components, key determinants of VD are physicochemical properties rather than specific pharmacophores. Lipophilicity and charge state of a molecule at physiologic pH (dependent on the pKa) are the most important descriptors for prediction of VD. Generally, VD increases with increasing lipophilicity or cationic fraction, and it decreases with increasing anionic fraction at physiologic pH (Lombardo et al., 2006). Transporters have also been shown to have significant impact on VD (Grover and Benet, 2009; Shugarts and Benet, 2009). Prediction of VD Using In Vivo Data from Preclinical Species. Despite the empirical nature of interspecies scaling and the criticisms of this approach for predicting clearance and other PK parameters, it is quite effective in predicting human VD from in vivo animal data. Several methodologies are commonly used by DMPK scientists for interspecies scaling of VD (Lombardo et al., 2013b): 1) single species scaling, 2) allometric scaling with multiple species, 3) equivalency or proportionality approach, 4) Øie-Tozer model (Oie and Tozer, 1979), 5) Wajima method (Wajima et al., 2004), and 6) multiple linear regression approach. There are divergent points of view on interspecies scaling of human VD from preclinical animal data (Obach, 1997; Ward and Smith, 2004; Obach, 2007; Hosea et al., 2009; Sui et al., 2010; Berry et al., 2011; Jones et al., 2011b), raising the following questions: 1) Which method gives the most accurate prediction? 2) Which species or species combination is the best approach? 3) Does correction of plasma protein binding yield better or worse estimation of human VD? Depending on the animals used in the experiments, the diversity of the compounds, and the number of the compounds in the studies, the conclusions can be different (Obach, 1997; Ward and Smith, 2004; Obach, 2007; Hosea et al., 2009; Sui et al., 2010; Berry et al., 2011; Jones et al., 2011b). A recent study with a large set of well-characterized, structurally diverse compounds concluded that monkey was superior to dog or/and rat in all the methods tested, presumably because monkey is evolutionarily closest to human (Lombardo et al., 2013b). Both uptake and efflux transporters in monkey have been shown to be more predictive of human than rodents and dogs (Syvänen et al., 2009; Shen et al., 2013). The Øie-Tozer model gave the best Prediction of Drug Disposition 1977 at A PE T Jornals on July 7, 2017 dm d.aspurnals.org D ow nladed from prediction compared with the other methods. However, for some compounds, aberrant values of fraction unbound in tissues were calculated (either ,0 or .1), potentially due to transporter involvement (Waters and Lombardo, 2010). The abnormal values of fraction unbound in tissues from the Øie-Tozer equation can be used as a diagnostic criterion on whether the method is suitable for a particular compound. Correction for plasma protein binding (PPB) yielded significantly lower performance than those without PPB correction in all the approaches evaluated. This could potentially be due to inaccuracy in PPB data, since they were generated from different laboratories using various devices. In practice, when monkey data are not available, the recommendation is to use dog single species scaling, Øie-Tozer, or Wajima models with rat and dog data to predict human VDss (Oie and Tozer, 1979; Wajima et al., 2004). Overall, prediction of human VDss from preclinical species is reliable and accurate. For most compounds, 70% to 80% of the predictions are within 2to 3-fold of the measured human values (Lombardo et al., 2013b). Prediction of VD Using In Vitro Assays. Prediction of human VD from in vitro tissue binding data has been reported over several years (Obach, 2007; Berry et al., 2011; Berezhkovskiy, 2012). The assumption is that in vitro surrogate measurements of tissue homogenate binding are representative of in vivo tissue binding. The in vitro experiment measures drug binding to tissue homogenates (e.g., adipose, muscle, lung, brain, liver, etc.) using equilibrium dialysis, ultrafiltration, or ultracentrifugation methods. The fraction of tissue homogenate binding was found to correlate with VDss. The in vitro tissue-binding approach for VD determination is not very commonly used, mainly due to the intense resources needed experimentally, and the predictability is comparable with in silico models. Prediction of human VD has also been shown to be successful using chromatographic methods (Hollosy et al., 2006; Valko et al., 2011). Plasma protein binding (log K [human serum albumin]) and phosphate lipid binding (log K [immobilized artificial membrane]) were derived from gradient high-performance liquid chromatography (HPLC) retention time using human serum albumin and phosphatidyl-choline-immobilized artificial membrane columns. The method is based on the assumption that the sum of the albumin and phospholipid binding has the most significant impact on VD. By using two biomimetic HPLC columns, the method provides a high-throughput, reliable approach to estimate VD in early drug discovery. Prediction of VD Using In Silico Methods. VD is reasonably well predicted using in silico approaches, and the methodologies have improved in recent years. The two most commonly used mechanistic PBPK models are based on tissue composition equations. They are the Poulin and Theil (Poulin and Theil, 2002) method enhanced by Berezhkovskiy (Berezhkovskiy, 2004), and the Rodgers and Rowland method (Rodgers et al., 2005; Rodgers and Rowland, 2006, 2007). Both models only require in vitro input or computed physicochemical properties of lipophilicity, plasma protein binding, and blood-to-plasma ratio. Although these methods are generally slightly less accurate than interspecies scaling, mechanistic-based PBPK models do not need in vivo data, which is a significant time and resource saving. In addition, these models can be incorporated into whole-body PBPK models (e.g., Simcyp), and they provide a powerful approach to individualized human PK and pharmacodynamics prediction. There are several assumptions for the tissue composition–based PBPK VD models: 1) no saturation of binding processes, 2) membrane permeation is via passive diffusion only, 3) the binding constituents within the tissues are plasma proteins and lipids, and 4) each tissue has a well-stirred distribution model limited by blood perfusion. The models predict reasonably well for most compounds with some exceptions (;65% within 3-fold of in vivo values) (Berry et al., 2011). For highly lipophilic compounds (log P . 3.5), free VDss tends to be overpredicted by the Rodgers and Rowland method. In some cases when the assumptions do not hold true, (e.g., there is extensive involvement of active transporters or binding to tissue constituents beyond plasma proteins and lipids) the prediction accuracy will be significantly compromised (Rodgers et al., 2012). Other factors contributing to the inaccuracy of the methods are inaccuracies in calculating or measuring physicochemical properties and log P values between octanol and water that do not reflect the partition into tissues with various lipids. Several quantitative structure-activity relationship (QSAR) in silico models have been developed to predict human and rat VDss using various computed descriptors based solely on inputs of molecular structure (Ghafourian et al., 2004; Gleeson et al., 2006; Lombardo et al., 2006; Berellini et al., 2009). The assumptions are that binding to tissues is nonspecific and that models rely heavily on physicochemical properties. The prediction accuracy of the QSAR models decrease when binding is specific or when transporters are involved. The models typically include a large number of compounds in the training set to cover diverse chemical space, and the predictions are, in general, reasonably accurate, with the majority of the compounds within 2–3fold of the measured values. The performance of the models is highly dependent on whether similar molecular structures are in the training set. For completely novel structures, QSAR models are less accurate. Even though the QSAR in silico models are inferior to interspecies scaling in human VDss prediction, they do not require animal data, and thus save time and experimental cost. QSAR models are particularly useful to estimate VDss prior to synthesis, to prioritize chemical series and guide structure activity relationships. Overall, prediction of VDss is important for feeding into half-life predictions. VDss itself is not necessarily a parameter upon which drug design should be based. Among the parameters described in this commentary, VDss appears to be the most amenable to in silico methods at the present time, although the use of animal models will also provide good predictions. Predicting Clearance Among the human pharmacokinetic parameters to predict, clearance is the most important and challenging. For any given eliminating organ (e.g., liver, kidney), clearance is determined by three main factors: the rate at which the drug is delivered to the organ (i.e., blood flow), the extent of drug binding in blood that reduces the ability of the organ to extract the drug, and the intrinsic capability of the organ to clear the drug (via metabolism or transport). Clearing organ blood flows and size are allometrically scalable across species, and there has been considerable focus on allometric scaling as a clearance prediction method (Mordenti, 1986; Mahmood, 1999; Tang and Mayersohn, 2006; Lombardo et al., 2013a). However, intrinsic clearance is reliant upon the activities of specific drug-metabolizing enzymes and transporters. These differ between humans and laboratory animal species (in both substrate specificities and expression levels), thus making it more difficult to reliably predict human clearance from allometric scaling (i.e., “vertical” allometry described by Tang and Mayersohn, 2006), and favoring the use of in vitro methods based on humanderived reagents to make predictions. In our research, allometry is rarely used for human clearance prediction, with the exception of drugs undergoing simple renal filtration as the clearance mechanism, or when intrinsic clearance is so high that the overall clearance will be limited by organ blood flow, which is allometrically scalable. One of the most important insights that a drug-metabolism scientist can bring to the process of predicting human clearance from preclinical data is 1978 Di et al. at A PE T Jornals on July 7, 2017 dm d.aspurnals.org D ow nladed from a notion of what the clearance mechanism for the new compound will be in humans. Clearance mechanisms can be viewed to be largely driven by the polar surface area (PSA) of the molecule (Fig. 1); hydrophilic, high-PSA compounds tend to be less membrane permeable and thus removed by drug transport into excretory fluids (urine, bile). Hydrophobic, low-PSA compounds require biotransformation to more hydrophilic, membrane-impermeable metabolites that can then be transported into excretory fluids. This is a fundamental theme of drug disposition, and has been nicely summarized in the development of the Biopharmaceutical Drug Disposition Classification System (BDDCS) (Wu and Benet, 2005). Insight into the most likely clearance mechanism will assist in the selection of which methods to use to make clearance predictions (Fig. 2). Metabolic Clearance. In the 1970s, the well-stirred model of hepatic clearance was first described (Gillette, 1971; Rowland, et al., 1973; Wilkinson and Shand, 1975, Pang and Rowland, 1977), and while other models of extraction have also been proposed (e.g., parallel tube model, dispersion model), it is the well-stirred model that has been relied upon the most when predicting clearance in drug research, mostly due to its simplicity: CLh 1⁄4 Qh • fu •CLint;u Qh þ fu •CLint;u ðEq: 4Þ The terms Qh, fu, and CLint,u refer to the hepatic blood flow, fraction unbound in blood, and hepatic unbound intrinsic clearance, respectively. The liver is generally the focus in drug discovery, and thus three in vitro measurements are needed to make a prediction of human clearance: free fraction in human plasma, blood/plasma ratio, and a measurement of intrinsic clearance. For cytochrome P450–mediated metabolic clearance, the use of pooled human liver microsomes is commonplace in early drug research. In fact, many research organizations use a human liver microsomal lability assay as one of the first assays used to characterize new compounds. Intrinsic clearance is defined by the enzyme kinetic parameters KM and Vmax: CLint 1⁄4 Vmax KM þ 1⁄2S if 1⁄2S ,,KM; then CLint 1⁄4 Vmax KM ðEq: 5Þ However, determination of KM and Vmax generally requires the measurement of metabolite formation, and while this can typically be done in later drug development (when either authentic standards of metabolites are available for development of bioanalytic methods, or radiolabeled drug is available for use in radiometric HPLC), in drug discovery this is more challenging. To accomplish this, microsomal lability assays are run by monitoring parent compound depletion at a low substrate concentration (usually 1 mM or less) to determine the in vitro half-life (t1/2), which can be converted to CLint and scaled to represent the entire organ (Obach, 1999). Values for the content of microsomal protein in the liver, an essential parameter for scaling, have been proposed to range between 30 and 50 mg/g tissue. Our preferred value is 45 mg/g liver, which was proposed by Houston (Houston, 1994). Some have proposed using empirical scaling factors when estimating in vivo from in vitro, but in our current experience this has not been necessary. While screening for human liver microsomal lability is a useful approach to prioritize compounds for further consideration and to develop SAR for overall CYP metabolism, it is typically desirable to increase the confidence in the prediction of human pharmacokinetics for compounds about to enter the development phase of drug research. The development of cross-species in vitro–in vivo correlations is a powerful means to enhance the confidence in the human pharmacokinetic prediction. Laboratory animal species (e.g., mice, rats, dogs, monkeys, or other) are dosed with the compound of interest and the clearance is determined. Plasma-free fraction, blood/plasma partitioning, and in vitro CLint are determined in these species, and the scaled clearance values are plotted against the actual clearance values (Fig. 3). A linear relationship is developed, and the predicted human in vivo value is extrapolated from this relationship using the value Fig. 1. Generalized diagram of clearance mechanisms as they relate to molecular properties. (It is not intended that there are discrete boundaries defining the categories of drugs; this represents a very general trend that can help frame thinking about clearance mechanisms. Exceptions exist.) Fig. 2. General layout of drug clearance mechanisms. Prediction of Drug Disposition 1979 at A PE T Jornals on July 7, 2017 dm d.aspurnals.org D ow nladed from predicted from the in vitro data alone. A further enhancement of this approach was developed by investigators at Hoffman-LaRoche, wherein in vitro data were combined with allometric scaling principles (Lave et al., 1999). Measurements of nonspecific binding to liver microsomes at concentrations used in the in vitro assays can be important for some types of compounds (especially hydrophobic, cationic compounds), and the CLint values must be corrected by the free-fraction value to yield reliable prediction of clearance (Obach, 1997). Also, to ensure that CLint is not saturable at anticipated doses, measurement of in vitro t1/2 at a lower concentration than 1 mM is typically done. If the in vitro t1/2 at a lower concentration is shorter than at a higher concentration, the potential for a supraproportional dose-exposure relationship is more likely. These approaches have been available and applied in drug research for well over a decade. Throughout that time, advances have been made to expand to other metabolic clearance enzymes, the use of other in vitro systems, and prediction of other attributes related to clearance. In drug discovery, these other systems and methods are not routinely applied, but the astute ADME scientist will recognize the situations in which alternate systems and experiments are needed to appropriately predict human clearance. Prediction of human clearance for compounds that are glucuronidated has been accomplished using alamethicintreated liver microsomes (Kilford et al., 2009). Examples of the use of in vitro approaches to predict human clearance for compounds metabolized by sulfotransferase, aldehyde oxidase, and monoamine oxidase have also been reported (Sawant et al., 2010; Zientek et al., 2010; Cubitt et al., 2011; Akabane et al., 2012). These new approaches are now being applied to drug discovery, as the instance merits. In addition to liver microsomes, other in vitro systems from liver and other drug-metabolizing tissues are used when appropriate. Human liver cytosol serves as a source for aldehyde oxidase. Whole blood can be used in the prediction of human clearance for drugs conjugated by glutathione S-transferase enzymes. Also, recombinant heterologously expressed P450 enzymes are used in predicting clearance (Stringer et al., 2009; Chen et al., 2011). However, the most compelling of all in vitro systems to predict clearance is human hepatocytes. The development of the capability to cryopreserve human hepatocytes has led to the more routine use of this reagent in human clearance prediction. The advantages are obvious in that human hepatocytes are a closer representation of the in vivo situation, since the entire complement of hepatic drug-metabolizing enzymes are present in appropriate proportions, and drug-uptake transporters that may actually be the rate-determining step in clearance for some drugs are operational. Reports of the utility of human hepatocytes for prediction of clearance have been made (Chiba et al., 2009; Hallifax et al., 2010; Poulin and Haddad, 2013). For highly permeable compounds, hepatocytes perform similarly to the enzyme systems (e.g., microsomes, cytosols) with the aforementioned advantage of a complete complement of metabolizing enzymes and cofactors. The scaling factor typically applied to intrinsic clearance is 120 million cells/g of liver for humans (Barter et al., 2007). Preclinical species have higher scaling factors than human with the exception of monkey (same as human). Hepatocytes are particularly useful when nonCYP-mediated pathways are involved (UGT, AO, SULT, MAO, etc.). For compounds that have low passive permeability and/or are efflux transporter substrates, apparent intrinsic clearance in hepatocytes is usually lower than that in enzyme systems, due to lower free hepatocyte concentrations compared with the medium concentrations (Kp,uu , 1). For compounds that are hepatic uptake transporter substrates, intracellular free drug concentration is higher than medium, and apparent intrinsic clearance in hepatocytes is usually higher than that in enzyme systems (Shitara et al., 2006; Kusuhara and Sugiyama, 2009; Di et al., 2012a). Therefore, it is important to understand the interplay between transporter and metabolizing enzymes when interpreting hepatocyte intrinsic clearance data. Metabolism studies with hepatocytes have also been extended to address low clearance challenges by using the relay approach (Di et al., 2012b). In some cases, substrate consumption is too slow to provide an accurate measurement of CLint. A typical suspension hepatocyte incubation will be kinetically competent for 4 hours; with the relay protocol, extended incubation times can be accomplished, and scaled in vivo intrinsic clearance values as low as 2 ml/min/kg cells can be reliably measured. Alternatively, low CLint compounds can be evaluated by measuring metabolite formation; however, as stated earlier, this requires the availability of authentic standards of metabolites for quantitation or the use of radiolabeled substrate. Methods whereby metabolites are biosynthesized, isolated, and concentrations established through the use of quantitative nuclear magnetic resonance spectroscopy are being increasingly used (Walker et al., 2011). Transporter-Mediated Hepatobiliary Clearance. Drug transporters are expressed in a variety of organs, including the intestine, liver, kidney, and brain, and play a key role in the disposition, adverse reactions, and therapeutic efficacy of drugs (Giacomini et al., 2010). The clinical importance of transporters has been recognized, especially Fig. 3. Example of an in vitro–in vivo correlation for clearance for ezlopitant (Obach, 2000). 1980 Di et al. at A PE T Jornals on July 7, 2017 dm d.aspurnals.org D ow nladed from through transporter genetic polymorphism and drug interaction studies, where significant changes in the pharmacokinetics of some drugs and consequent clinical responses are observed (Niemi et al., 2005; Shitara et al., 2006; Shitara and Sugiyama, 2006; Giacomini et al., 2010; Niemi et al., 2011; Elsby et al., 2012; Lai et al., 2012). A number of studies suggested that hepatic uptake could be a rate-determining step in hepatic clearance, not only for the compounds that are metabolically stable, such as rosuvastatin (Bergman et al., 2006) and pravastatin, (Yamazaki et al., 1996; Watanabe et al., 2009a; Varma et al., 2012c), but also for compounds that are extensively metabolized, e.g., repaglinide (Kalliokoski et al., 2008; Varma et al., 2013b) and atorvastatin (Maeda et al., 2011). Characterization of hepatobiliary transport proteins was well advanced by the mid-1990s (Keppler and Arias, 1997; Muller and Jansen, 1997). Such investigations were aided by advancement of transporter molecular biology that enabled experiments to be conducted in vitro to investigate active hepatic uptake and biliary excretion via the study of individual transporters. Many drugs are actively taken up into hepatocytes via specific uptake transporters that include organic anion transporting polypeptide (OATP) 1B1 (SLCO1B1), OATP1B3 (SLCO1B3), and OATP2B1 (SLCO2B1); the organic anion transporter (OAT) 2 (SLC22A7); and the organic cation transporter (OCT) 1 (SLC22A1). ATP binding-cassette (ABC) efflux transporters localized on the canalicular membrane of hepatocytes, such as P-glycoprotein (P-gp) (ABCB1), multidrug resistance-associated protein (MRP) 2 (ABCC2), breast cancer resistance protein (BCRP) (ABCG2), and BSEP (ABCB11), are mainly responsible for canalicular secretion (Ishikawa et al., 1995; Keppler et al., 1997; Muller and Jansen, 1997; Suzuki and Sugiyama, 1999; Chandra and Brouwer, 2004). Based on the analysis of physicochemical property space, ionization state, size, and polarity were noted to be important determinants in the biliary elimination, and these properties are also closely associated with molecular interaction with the hepatic uptake transporters (Yang et al., 2009; Varma et al., 2012a) The extended clearance equations (Equations 6 and 7) can be applied to get an estimate of the effect of transporter involvement in the hepatic disposition and further to predict the overall hepatic clearance (Liu and Pang, 2005; Shitara et al., 2006; Webborn, et al., 2007; Barton et al., 2013). CLh 1⁄4 Kpuu •CLinteffluxþmetab • fub •Qh ðKpuu •CLinteffluxþmetab • fubÞ þ Qh ðEq: 6Þ where CLh is hepatic clearance, CLint,efflux+metab is the sum of biliary intrinsic clearance and metabolic intrinsic clearance, fub is fraction unbound in blood, Qh is hepatic blood flow, and Kpuu represents the unbound concentration in liver relative to the unbound concentration in plasma at steady state, given as: Kpuu 1⁄4 CLintactive þ CLintpassive CLintpassive þ CLintefflux þ CLintmetab ðEq: 7Þ The uptake parameters in these equations (CLint,active and CLint,passive) are obtained from in vitro uptake experiments utilizing hepatocyte or recombinant cell line systems. The efflux parameter can be derived from sandwich culture hepatocyte or transporter vesicle experiments and the CLint,metab from conventional in vitro stability assays (e.g., microsomes). All these parameters are appropriately scaled to in vivo CLint values before being entered into Eq. 6 and 7 using hepatocellularity and liver weight factors (Umehara and Camenisch, 2012; Varma et al., 2013c). Success of mechanistic-based prediction of transportermediated disposition and drug-drug interactions (DDIs) depends upon: 1) the adaptation of experimental tools and study design to obtain in vitro parameters that are relevant in vivo, 2) bridging the functional and protein expression difference between in vitro and in vivo systems, and 3) understanding the uncertainty associated with the parameters obtained from in vitro tools (Barton et al., 2013). Despite many limitations and a lack of a comprehensive demonstration of quantitative in vivo–in vitro extrapolation (IVIVE), in vitro experimental tools have significantly improved the ability to generate relevant transporter affinity parameters to predict transporter-mediated disposition and the associated drug interactions. Currently available in vitro tools increase in complexity from single-gene overexpressing immortalized cell lines to isolated hepatocytes and 3-dimensional cultured hepatocyte systems. Cells expressing individual uptake transporters have been extensively used to determine if a test compound is an inhibitor of a transporter and/or a substrate (Sharma et al., 2012; Soars et al., 2012; Varma et al., 2012a). The studies are readily amenable for obtaining various kinetic parameters, including KM, Vmax, and IC50 of substrates or inhibitors. Experiments may also be designed to obtain the uptake rate by estimating the accumulated amount of compounds in the cells over time. When information on relative expression of transporter proteins between in vitro tools and in the liver becomes available, the in vitro transport kinetics can be directly used in mechanistic models. On the other hand, primary isolated hepatocytes express transporters and phase I/II enzymes, and are generally recognized as the closest in vitro surrogate of the liver for CYP metabolism (Watanabe et al., 2009b). This tool is commonly accepted as a holistic and inexpensive method to assess in vivo liver clearance involving hepatic uptake transport (Lam and Benet, 2004; Li et al., 2008; Soars et al., 2009; Yabe et al., 2011; Menochet et al., 2012). In addition, the suspension system facilitates assessing the impact of P450 metabolic activity, the intraindividual variations caused by genetic polymorphisms, and other pathophysiologic factors (GuguenGuillouzo and Guillouzo, 2010). However, membrane leakage in the suspension hepatocyte model was also shown to negatively impact the active uptake estimates (Kimoto et al., 2012b). A media-loss assay has been developed to measure uptake rate by influx transporters using hepatocyte suspension with very short time points (Soars et al., 2007,2009). The method was found to give better prediction of hepatic clearance of uptake transporter substrates, particularly for acidic compounds, due to OATP uptake into the hepatocytes. However, the media-loss assay was found to be unsuitable for compounds with high passive permeability or weak uptake transporter substrates (Jigorel and Houston, 2012). Overall, for definitive uptake rate determination, an oil spin assay or hepatocyte sandwich culture experiment is recommended to generate reliable data. To investigate the interplay between metabolic clearance, hepatic uptake, and biliary efflux, a tool in which all of these processes are active is desirable. SCH involves culturing primary hepatocytes between two layers of gelled matrix in a sandwich configuration, allowing the hepatocytes to form a bile canalicular network (Bi et al., 2006). While down-regulation of uptake transporters in sandwichcultured rat hepatocytes has been a concern for the reliability of parameters obtained sandwich-cultured human hepatocytes (SCHH) has proven to maintain the expression of both transporters and major CYP enzymes (Li et al., 2010; Kimoto et al., 2011; Kotani et al., 2011). This model can be used to estimate the initial hepatic uptake and biliary secretion (Bi et al., 2012). SCHH allows simultaneous assessment of multiple processes occurring in the hepatocytes in a mechanistic manner and provides context to the relevance of the processes for hepatic disposition of active transporting drugs (Jones et al., 2012). However, since uptake transporters are down-regulated, there can be a lack of an ability to derive quantitative in vitro–in vivo correlations across preclinical species as is done for metabolically cleared compounds. Prediction of Drug Disposition 1981 at A PE T Jornals on July 7, 2017 dm d.aspurnals.org D ow nladed from In our laboratories, a whole PBPK model was developed by incorporating uptake and biliary clearance parameters derived from SCHH experiments together with other ADME and physicochemical properties, to assess the predictability of the clinical disposition for the compounds cleared by hepatic transporters (Jones et al., 2012). In addition to the retrospective analysis, the model was prospectively applied to predict the human PK for four proprietary compounds prior to clinical investigation, with averaged empirical scaling factors from literature compounds. When compared with allometric scaling approaches (Hosea et al., 2009), the approaches resulted in a favorable prediction accuracy (Jones et al., 2012). Further work is required to understand the necessity of employing large scaling factors, and it seems clear that there are factors not yet identified/understood that prevent quantitative extrapolation. During the past decade, numerous attempts have been made to find suitable in vitro models for predicting human biliary secretion. Human bile samples are difficult to obtain in the clinic, and therefore, the extent of biliary secretion of drugs and/or their metabolites in human is commonly measured by collecting feces (Ghibellini et al., 2004). The measurement is confounded by the presence of unabsorbed drugs, or underestimated due to enterohepatic circulation (EHC). As a result, bile clearance in humans is mostly assessed based on in vitro or in vivo preclinical models. Bile duct cannulated preclinical species have been widely used in practice for many years to characterize biliary clearance. Although biliary clearance in rat often overestimates clearance in human, partly because of the higher expression levels of the hepatobiliary transporters and bile flow in rats (Lai, 2009), assessing drug eliminated from bile duct–cannulated rats remains a useful approach to collect qualitative information about biliary excretion (Varma et al., 2012a). The in vitro biliary clearance values obtained from SCH were shown to be linearly correlated to in vivo biliary clearance in rat to a certain extent (Liu et al., 1999; Abe et al., 2008; Fukuda et al., 2008), although considerable misprediction of human biliary clearance were also noted (Jones et al., 2012). Certain studies demonstrated the correlation of clearance between SCHH and human biliary clearance (Ghibellini et al., 2007). In addition, by incorporating the differential expression of hepatobiliary transport (Li et al., 2010) or the use of in vitro ADME data including biliary clearance from SCH within PBPK models (Jones et al., 2012), the biliary clearance prediction from in vitro SCH models can be further improved. In the in vitro hepatocyte systems, the intrinsic transport parameters are derived based on the drug concentration in the incubation medium. This is relevant for calculating uptake transport parameters, but the lack of reliable tools to estimate free intracellular concentration limits estimation of accurate efflux parameters. Nevertheless, given these challenges in predicting and validation of biliary clearance, there are concerted efforts in the industry toward better characterization of hepatobiliary elimination in relation to physiochemical assessment, transporter affinity, and IVIVE in early discovery. These include: 1) further understanding the molecular biology, quantitative expression, and functional activity of hepatobiliary transporters; 2) improving preclinical models to better predict hepatobiliary clearance in humans; 3) managing the capacity for high-throughput in vitro testing, and 4) better understanding of hepatobiliary transporter IVIVE to predict drug disposition and DDIs. Renal Clearance. Renal clearance (CLr) is determined by passive glomerular filtration, active tubular secretion, and passive as well as active reabsorption. Methods for prediction of renal clearance in humans and an understanding of the renal clearance mechanisms are needed to predict plasma concentration-time profiles and the potential for renal DDIs. This is particularly important for compounds that have low or negligible metabolic clearance. Interspecies allometric scaling is widely accepted for extrapolating the pharmacokinetic parameters obtained from animal studies to successfully predict human pharmacokinetic parameters (Lin, 1995). As mentioned above, interspecies scaling is based on empirically observed relationships between physiologic parameters and body weights among animals. Simple allometric scaling of the renal clearance is undoubtedly useful for drugs that are eliminated in the urine by glomerular filtration, because the glomerular filtration rate depends on the molecular size and conforms to allometric scaling across species. In our experience, when renal clearance by glomerular filtration of parent compound is the major clearance pathway, allometric scaling was pursued as the best method to predict renal clearance in human. However, when species differences exist in the mechanism of elimination, simple allometry will likely not accurately predict clearance from animal pharmacokinetic parameters. Specifically, allometry performs more poorly when active processes are primarily involved. Mahmood (1998) used allometry to predict human CLr for 10 compounds that were actively excreted from the kidneys, using data from at least three animal species, and found that the prediction errors ranged from 285% to +72% (Mahmood, 1998). However, it is interesting to note that allometry worked well for prediction of the CLr of creatinine and fluconazole, which involve tubular secretion in humans (Dedrick, 1973; Jezequel, 1994). It is likely allometric scaling works for compounds that have glomerular filtration as the main renal clearance mechanism, with minor active secretion and mainly passive reabsorption. Transporters expressed on basolateral and apical sides of renal proximal tubule cells mediate the active renal secretion and reabsorption of endogenous compounds and xenobiotics. It is known that organic cation transporter 2, as well as organic anion transporters 1 and 3, are major transporters on the basolateral membrane. In contrast, multidrugresistance protein (MRP) 1 and the MRP2 transporter, as well as multidrug and toxin extrusion proteins, are major transporters on the apical membrane. Thus, transporters work together to mediate active renal secretion of compounds. Meanwhile, peptide transporter 2 and system L amino acid transporters, etc., are more associated with active reabsorption. Drug transporters can have large species differences in terms of transport function and transporter expression level. Additionally, transporter capacity shows remarkable heterogeneity along the proximal tubule (Masereeuw and Russel, 2001). Thus, it is difficult to predict renal elimination of actively secreted compounds from in vitro data. Compared with metabolism, the frequency of drug candidates in our research organization that have primarily have a renal clearance pathway is much lower. We studied 10 structurally related, renally excreted amino acid–like compounds, including gabapentin, pregabalin, and 8 of their structural analogs. These compounds are not significantly metabolized in humans, and they are predominantly excreted unchanged in urine with a wide range of net renal clearance values ranging from significant net secretion to extensive net reabsorption. Since these compounds were significantly metabolized by dogs and to a lesser extent by rats, these animal species were not considered to be good preclinical models to predict human PK. Similar to humans, monkeys showed minimal to no metabolism for these compounds. The studies indicated human and monkey clearance can be described using allometric scaling based on body weight; both had the same allometric coefficient for the net clearance. Importantly, the high correlation of renal clearance between monkeys and humans was observed regardless of whether the net renal excretion processes resulted in net secretory, reabsorptive, or neutral profiles relative to glomerular filtration rate. These findings demonstrated that renal active transport mechanisms and compound disposition of these compounds 1982 Di et al. at A PE T Jornals on July 7, 2017 dm d.aspurnals.org D ow nladed from are similar in monkey and humans. Based on these studies, monkey was a good species for predicting human renal clearance with net secretion, reabsorption, or filtration clearance of this series of compounds. However, it should be noted that the compounds used in this study are amino acid–like and differ from typical small molecular weight drugs. Tahara and coworkers (2006) reported that renal and renal tubular secretion clearances of famotidine in humans were successfully estimated by simple allometric scaling using data from rats, dogs, and monkeys (Tahara et al., 2006), although the contribution of the renal transporters differs depending on the species. In this instance monkeys were more appropriate animal species for quantitatively predicting the renal DDI in humans. Regarding basolateral transporters in renal proximal tubule cells, the monkey OATs and OCTs have similar characteristics to the human orthologs. Additionally, the conservation of net secretory processes between monkeys and humans for OAT3 was demonstrated in vitro and in vivo (Tahara et al., 2006), whereas the correlation of the transport activities between rat and human OAT3 was poor. The same group also reported that in terms of substrate specificity and localization in the kidney, there was minimal species difference between monkey OAT1 and OAT3, compared with human orthologs (Tahara et al., 2005). Recently, Paine and coworkers (Paine et al., 2011) compared three animal scaling methods: direct correlations between renal clearance in humans and each of the two main preclinical species (rat and dog), simple allometry, and Mahmood’s renal clearance scaling method, to predict human renal clearance of 36 diverse drugs that show active secretion or net reabsorption. The data suggested the most accurate predictions were obtained by using a direct correlation with the dog renal clearance after correcting for differences in fu and kidney blood flow. This suggested a good species crossover between the transporters involved in any active process for this diverse set of compounds. Overall, prediction confidence of transporter-mediated renal clearance in humans is not high. Since cynomolgus monkey is a closer species to human in evolution than rats or dogs, it is likely that cynomolgus monkey is a suitable animal model for the prediction of renal clearance of compounds with significant active renal secretion. However, studies of other species, as well as allometric scaling, could be valuable for predicting certain compounds with active renal secretion. Additionally, human renal proximal tubule model and human kidney slice could be valuable for predicting renal clearance, although it is a big challenge to maintain the renal transporter functions in these in vitro models. The long-term goal is to develop PBPK models by incorporating renal uptake and efflux clearance using transportertransfected cell lines together with other ADME and physicochemical properties to assess the predictability of transporter-mediated renal clearance in humans. In drug discovery and development, it would be ideal for compounds to have multiple clearance pathways, including a renal clearance component. Renal clearance can be desirable because of the relatively low potential for drug-drug interactions. However, drugs cleared exclusively by the kidney can be subject to high exposure in renally impaired patients. Furthermore, since kidney function decreases naturally with aging, a drug cleared exclusively through the renal route can attain higher exposures in the elderly population. Overall, clearance is probably the most important pharmacokinetic parameter to predict, since it is a contributing property to t1/2, oral bioavailability (via first-pass extraction), and DDI. ADME scientists have achieved a reasonably reliable standard for predicting clearance for P450-metabolized compounds, but other clearance mechanisms require more work for method development. In drug design, optimizing unbound intrinsic clearance should be the primary focus when optimizing ADME, since this is the parameter that will reflect the maximum potential free drug concentrations to which the receptor will be exposed. Intrinsic receptor potency, target tissue penetration, and free intrinsic clearance are the three parameters with which medicinal chemists and their ADME scientist partners must be most concerned when optimizing drug properties through design. Predicting Oral Bioavailability Oral bioavailability (F) is one of the most important determinants of the dosing regimen. The extent to which a drug fails to be absorbed or is removed by first-pass extraction before it can reach the systemic circulation and consequently the pharmacological target compartment will dictate how large a dose must be administered. Thus, during the new drug design phase, considerable effort is expended to optimize oral bioavailability. Solubility and permeability are critical determinants of drug absorption following oral administration. Based on these fundamental properties, Amidon and colleagues proposed the Biopharmaceutics Classification System (BCS), which is extensively used for regulatory and industrial purposes, particularly to waive conducting expensive bioequivalence clinical studies for high solubility–high permeability (class I) drugs. (Amidon et al., 1995; CDER/FDA, 2000; Yu et al., 2002). Class I compounds possess no absorption limitations, and thus the goal of drug research teams is to drive the chemistry space toward the class I behavior through compound design. High-throughput methodologies for measuring solubility and permeability have been used in screening large numbers of compounds in the early drug discovery stage (Bevan and Lloyd, 2000; Roy et al., 2001; Obata et al., 2004; Alsenz and Kansy, 2007; Alsenz et al., 2007). In several high-throughput solubility assays, compounds are introduced as the dimethyl sulfoxide stock solution, and the nonthermodynamic solubility is estimated typically in pH buffer at 7.4 (Obata et al., 2004). However, the solubility data generated under thermodynamic equilibrium conditions represents the best-case scenario (Alsenz and Kansy, 2007), especially in simulated gastrointestinal fluids adjusted to the physiologically relevant pH. Preclinical models, such as the in situ rat intestinal perfusion and in vitro epithelial cell culture models, that are appropriately validated to predict the extent of drug absorption in humans, can be used for permeability assessments (Varma et al., 2004). Cell-based permeability assays utilizing Caco-2, Madin-Darby canine kidney (MDCK), and recently the lowefflux-transporter MDCK cell lines have been employed in discovery settings (Artursson, 1990; Artursson and Magnusson, 1990; Irvine et al., 1999; Di et al., 2011). Similarly, the parallel artificial membrane permeation assay has been established as an alternative to cell-based assays for predicting oral passive absorption (Avdeef, 2005; Avdeef et al., 2005). Sigmoidal relationships, as described by the complete radial mixing (parallel tube) model (Artursson and Karlsson, 1991; Fagerholm et al., 1996; Varma and Panchagnula, 2005), were observed between apparent permeability and human fraction absorbed (Fa) using different cellbased and animal models. For example, Artursson and Karlsson showed a good relationship between the transport across Caco-2 monolayers and human Fa (Artursson and Karlsson, 1991). Irvine and coworkers showed an approximately sigmoidal correlation between permeability and human Fa, and also demonstrated a linear correlation of MDCK and Caco-2 permeability for 55 drugs (Irvine et al., 1999). Recently, using a dataset of over 100 drugs, we showed a good relationship between apparent permeability across MDCK–low efflux cells (cloned for low P-gp expression [Di et al., 2011]) and human Fa, when permeability values at apical pH 6.5 and 7.4 were used for acidic and non-acidic drugs, respectively (Fig. 4) (Varma et al., 2012b). Prediction of Drug Disposition 1983 at A PE T Jornals on July 7, 2017 dm d.aspurnals.org D ow nladed from Also, a logarithmic relationship was established between the in vitro apparent permeability and the in situ human effective permeability (Fig. 4), which could be used to predict rate and extent of oral absorption using dynamic models. Generally, the human effective permeability estimates are several times higher than the in vitro apparent permeability determined across cell monolayers, including Caco-2. A major reason for the fold differences between the estimates could be the larger effective absorptive area of the human intestinal perfusion segment provided by the villi and microvilli, although differences in the diffusion coefficient, membrane thickness, membrane bilayer composition, luminal content, or extracellular mucus layer affecting drug partitioning into the membrane may also contribute (Artursson and Karlsson, 1991; Fagerholm et al., 1996). Also, a major drawback of cell-based models is the associated intraand inter-laboratory variability in the permeability features. Understanding such differences and establishing correlations for the in-house models is therefore required to accurately predict the rate and extent of oral absorption. In situ intestinal perfusion in animals is a more reliable technique than in vitro models because of the intact blood supply and innervation; however, the model is not very amenable for increased throughput and is not commonly used in preclinical development. In vivo pharmacokinetic studies in laboratory animals provide more reliable estimates for oral absorption. In general, fraction of oral dose absorbed in rats and monkeys showed good linear correlations (slope near to unity) with the fraction absorbed in humans (Chiou and Barve, 1998; Chiou and Buehler, 2002). However, dogs show a poor correlation, especially for the hydrophilic drugs, presumably due to a leaky paracellular pathway (Chiou et al., 2000). Although a good correlation was observed with the fraction dose absorbed in monkeys and human, a comparison of oral bioavailability values reveals considerably lower bioavailability in monkeys for several drugs, which was attributed to the greater first-pass metabolism of monkeys, taking place in the gut wall and/or liver (Takahashi et al., 2009). Thus, when using animal data to project human bioavailability, considerable attention must be paid to the potential species differences in metabolism that can occur during first pass, as well as differences in gastrointestinal physiology. One area of increasing interest is that of membrane transporters localized in the intestine. Enterocytes express several transporters belonging to the ABC and solute carrier (SLC) superfamilies on the apical and basolateral membranes for the efflux or influx of endogenous substances and xenobiotics. P-gp, BCRP, MRP2, and MRP4 are localized on the brush-border (apical) membrane, while certain MRPs are expressed on the basolateral membrane of the enterocytes. These efflux transporters limit the enterocytic levels of their substrates. P-gp substrate screening using various in vitro cell-based models is now an integral part of drug discovery, due to the observation that the substrate specificity of this protein is broad (Gupta et al., 2010). Discovery teams design or tailor molecules to reduce substrate specificity to P-gp to improve the oral bioavailability of drugs and brain penetration. Several SLC transporters, such as peptide transporter 1, OATP2B1, and OCTN1/2, localized at the apical surface of enterocytes, were suggested to primarily drive oral absorption of compounds in a particular chemical space (Varma et al., 2011; Tamai, 2012). However, lack of standardized experimental tools and limited knowledge on the in vitro–in vivo extrapolation of transporter kinetics has limited the quantitative prediction of oral absorption when such active mecha-