Active Transport

Transporters should be an integral part of any ADMET modeling program because of their ubiquitous presence on barrier membranes and the substantial overlap between their substrates and many drugs. Unfortunately, because of our limited understanding of transporters, most prediction programs do not have a mechanism to incorporate the effect of active transport. However, interest in these transporters has resulted in a relatively large amount of in vitro data, which in turn have enabled the generation of pharmacophore and QSAR models for many of them. These models have assisted in the understanding of the complex effects of transporters on drug disposition, including absorption, distribution, and excretion. Their incorporation into current mod-

eling programs would also result in more accurate prediction of drug disposition behavior. Readers are referred to a recent review for discussions of in silico strategies in modeling transporters [41].

P-glycoprotein (P-gp) is an ATP-dependent efflux transporter that transports a broad range of substrates out of the cell. It affects drug disposition by reducing absorption and enhancing renal and hepatic excretion [42]. For example, P-gp is known to limit the intestinal absorption of the anticancer drug pacli-taxel [43] and restricts the CNS penetration of human immunodeficiency virus (HIV) protease inhibitors [44]. It is also responsible for multidrug resistance in cancer chemotherapy. Because of its significance in drug disposition and effective cancer treatment, P-gp attracted numerous efforts and has become the most extensively studied transporter, with abundant experimental data [42].

Ekins and colleagues generated five computational pharmacophore models to predict the inhibition of P-gp from in vitro data on a diverse set of inhibitors with several cell systems, including inhibition of digoxin transport and verapamil binding in Caco-2 cells; vinblastine and calcein accumulation in P-gp-expressing LLC-PK1 (L-MDR1) cells; and vinblastine binding in vesicles derived from CEM/VLB100 cells [45, 46]. By comparing and merging all P-gp pharmacophore models, common areas of identical chemical features such as hydrophobes, hydrogen bond acceptors, and ring aromatic features as well as their geometric arrangement were identified to be the substrate requirements for P-gp. Similar transport requirements were reiterated in other works [47, 48]. More recently Cianchetta and colleagues combined alignment-independent 3D descriptors and physicochemical descriptors to model inhibition of calcein accumulation in Caco-2 cells [49]. Using a diverse set of 129 compounds, the authors derived a robust QSAR model that revealed two hydrophobic features, two hydrogen bond acceptors, and the molecular dimension to be essential determinants of P-gp-mediated transport. These identified transport requirements not only to help screen compounds with potential efflux related bioavailability problems, but also to assist the identification of novel P-gp inhibitors, which when coadministered with target drugs would optimize their pharmacokinetic profile by increasing bioavailability. In fact, a recent pharmacophore-based database screening has proposed 28 novel P-gp inhibitors from the Derwent World Drug Index [50]. Our own Catalyst pharmacophore searches of databases have also guided the identification of several currently prescribed drugs that are P-gp inhibitors (|M), which was previously unknown (Fig. 20.2, manuscript in preparation).

20.6.2 BCRP

Breast cancer resistance protein (BCRP) is another ATP-dependent efflux transporter that confers resistance to a variety of anticancer agents, including

Figure 20.2 Pharmacophore models for P-gp inhibition. A. P-gp inhibition pharmacophore aligned with the potent inhibitor LY335979. B. P-gp substrate pharmacophore aligned with verapamil. C. P-gp inhibition pharmacophore 2 aligned with LY335979. Green indicates H-bond acceptor feature, and cyan indicates hydrophobic feature. See color plate.

Figure 20.2 Pharmacophore models for P-gp inhibition. A. P-gp inhibition pharmacophore aligned with the potent inhibitor LY335979. B. P-gp substrate pharmacophore aligned with verapamil. C. P-gp inhibition pharmacophore 2 aligned with LY335979. Green indicates H-bond acceptor feature, and cyan indicates hydrophobic feature. See color plate.

anthracyclines and mitoxantrone [51]. In addition to a high level of expression in hematological malignancies and solid tumors, BCRP is also expressed in intestine, liver, and brain, thus implicating its intricate role in drug disposition behavior. Recently, Zhang and colleagues generated a BCRP 3D-QSAR model by analyzing structure and activity of 25 flavonoid analogs [52]. The model emphasizes very specific structural feature requirements for BCRP such as the presence of a 2,3-double bond in ring C and hydroxylation at position 5. Because the model is only based on a set of closely related structures instead of a diverse set, it should be applied with caution. Satisfying the transport model would render a compound susceptible to BCRP, but not fitting into the model does not necessarily exclude the candidate from BCRP transport. In fact, this caveat should be considered for all predictive in silico models, because no model can cover all possible chemical space.

20.6.3 Nucleoside Transporters

Nucleoside transporters transport both naturally occurring nucleosides and synthetic nucleoside analogs that are used as anticancer drugs (e.g., cladrib-ine) and antiviral drugs (e.g., zalcitabine). There are different types of nucle-oside transporters, including concentrative nucleoside transporters (CNT1, CNT2, CNT3) and equilibrative nucleoside transporters (ENT1, ENT2), each having different substrate specificities. The broad-affinity, low-selective ENTs are ubiquitously located, whereas the high-affinity, selective CNTs are mainly located in epithelia of intestine, kidney, liver, and brain [53], indicating their involvement in drug absorption, distribution, and excretion. The first 3D-QSAR model for nucleoside transporters was generated back in 1990 [54]. It is an oversimplified general model limited by the scarce experimental data at that time. A more comprehensive study generated distinctive models for CNT1, CNT2, and ENT1 with both pharmacophore and 3D-QSAR modeling techniques [55]. All models show the common features required for nucleoside transporter-mediated transport: two hydrophobic features and one hydrogen bond acceptor on the pentose ring. The individual models also reveal the subtle characteristic requirements for each specific transporter. The modeling results also support the previous observation that CNT2 is the most selective transporter whereas ENT1 has the broadest inhibitor specificity. More recently, we performed the same analyses and generated pharmacophore and 3D-QSAR models for CNT3 by assessing the transport activity of 33 nucleoside analogs [55a]. These studies represent a comprehensive evaluation of transport requirements of all three types of CNTs.

20.6.4 hPEPTl

The human peptide transporter (hPEPT1) is a low-affinity high-capacity oli-gopeptide transport system that transports a diverse range of substrates including p-lactam antibiotics [56] and angiotensin-converting enzyme (ACE) inhibitors [57]. It is mainly expressed in intestine and kidney, affecting drug absorption and excretion. A pharmacophore model based on three high-affinity substrates (Gly-Sar, bestatin, and enalapril) recognized two hydro-phobic features, one hydrogen bond donor, one hydrogen bond acceptor, and one negative ionizable feature to be hPEPT1 transport requirements [58]. This pharmacophore model was subsequently applied to screen the CMC database with over 8000 druglike molecules. The antidiabetic repaglinide and HMG-CoA reductase inhibitor fluvastatin were suggested by the model and later verified to inhibit hPEPT1 with submillimolar potency [58]. This work demonstrated the potential of applying in silico models in high-throughput database screening.

20.6.5 ASBT

The human apical sodium-dependent bile acid transporter (ASBT) is a high-efficacy, high-capacity transporter expressed on the apical membrane of intestinal epithelial cells and cholangiocytes. It assists absorption of bile acids and their analogs, thus providing an additional intestinal target for improving drug absorption. Baringhaus and colleagues developed a pharmacophore model based on a training set of 17 chemically diverse inhibitors of ASBT [59]. The model revealed ASBT transport requirements as one hydrogen bond donor, one hydrogen bond acceptor, one negative charge, and three hydrophobic centers. These requirements are in good agreement with a previous 3D-QSAR model derived from the structure and activity of 30 ASBT inhibitors and substrates [60].

20.6.6 OCT

The organic cation transporters (OCTs) facilitate the uptake of many cationic drugs across different barrier membranes from kidney, liver, and intestine epithelia. A broad range of drugs or their metabolites fall into the chemical class of organic cation (carrying a net positive charge at physiological pH) including antiarrhythmics, P-adrenoreceptor blocking agents, antihistamines, antiviral agents, and skeletal muscle-relaxing agents [61]. Three OCTs have been cloned from different species, OCT1, OCT2, and OCT3. A human OCT1 pharmacophore model was developed by analyzing the extent of inhibition of TEA uptake in HeLa cells of 22 diverse molecules. The model suggests the transport requirements of human OCT1 as three hydrophobic features and one positive ionizable feature [62]. Molecular determinants of substrate binding to human OCT2 and rabbit OCT2 were recently reported [63]. Both 2D- and 3D-QSAR analyses were performed to identify and discriminate the binding requirements of the two orthologs. The models showed the same chemical features, highlighting their similarities. However, the orientation of a critical hydrogen bonding feature set the two orthologs apart. This work illustrates the sensitivity of in silico modeling in discriminating similar transporters.

20.6.7 OATP

Organic anion transporting polypeptides (OATPs) influence the plasma concentration of many drugs by actively transporting them across a diverse range of tissue membranes such as liver, intestine, lung, and brain [64]. Because of their broad substrate specificity, OATPs transport not only organic anionic drugs, as originally thought, but also organic cationic drugs. Currently 11 human OATPs have been identified, and the substrate binding requirements of the best-studied OATP1B1 were successfully modeled with the metaphar-macophore approach recently [65]. Through assessing a training set of 18 diverse molecules, the metapharmacophore model identified three hydropho-bic features flanked by two hydrogen bond acceptor features to be the essential requirement for OATP1B1 transport. Similar requirements were derived from another 3D-QSAR study based on rat Oatp1a5 [66].

20.6.8 BBB-Choline Transporter

The BBB-choline transporter is a native nutrient transporter that transports choline, a charged cation, across the BBB into the CNS [67]. Its active transport assists the BBB penetration of cholinelike compounds, and understanding its structural requirements should afford a more accurate prediction of BBB permeation. Even though the BBB-choline transporter has not been cloned, Geldenhuys and colleagues applied a combination of empirical and theoretical methodologies to study its binding requirements [68]. The 3D-QSAR models were built with empirical Ki data obtained from in situ rat brain perfusion experiments with a structurally diverse set of compounds. Three hydrophobic interactions and one hydrogen bonding interaction surrounding the positively charged ammonium moiety were identified to be important for BBB-choline transporter recognition. Even though the model statistical significance is not optimal (q2 < 0.5), it does provide a useful estimation of BBB-choline transporter binding requirements. More accurate in silico models could be generated once higher-quality data from the cloned BBB-choline transporter are available.

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