Molecular switch controlling the binding of anionic bile acid conjugates to human apical sodium-dependent bile acid transporter

Computer-Aided Drug Design Methods

Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

A substrate pharmacophore for the human sodium taurocholate co-transporting polypeptide

Human sodium taurocholate co-transporting polypeptide (NTCP) is the main bile acid uptake transporter in the liver with the capability to translocate xenobiotics. While its inhibitor requirements have been recently characterized, its substrate requirements have not. The objectives of this study were (a) to elucidate NTCP substrate requirements using native bile acids and bile acid analogs, (b) to develop the first pharmacophore for NTCP substrates and compare it with the inhibitor pharmacophores, and (c) to identify additional NTCP novel substrates. Thus, 18 native bile acids and two bile acid conjugates were initially assessed for NTCP inhibition and/or uptake, which suggested a role of hydroxyl pattern and steric interaction in NTCP binding and translocation. A common feature pharmacophore for NTCP substrate uptake was developed, using 14 native bile acids and bile acid conjugates, yielding a model which featured three hydrophobes, one hydrogen bond donor, one negative ionizable feature and three excluded volumes. This model was used to search a database of FDA approved drugs and retrieved the majority of the known NTCP substrates. Among the retrieved drugs, irbesartan and losartan were identified as novel NTCP substrates, suggesting a potential role of NTCP in drug disposition.

Mechanistic interpretation of conventional Michaelis-Menten parameters in a transporter system

The aim was to elucidate how steps in drug translocation by a solute carrier transporter impact Michaelis-Menten parameters Km, Ki, and Vmax. The first objective was to derive a model for carrier-mediated substrate translocation and perform sensitivity analysis with regard to the impact of individual microrate constants on Km, Ki, and Vmax. The second objective was to compare underpinning microrate constants between compounds translocated by the same transporter. Equations for Km, Ki, and Vmax were derived from a six-state model involving unidirectional transporter flipping and reconfiguration. This unidirectional model is applicable to co-transporter type solute carriers, like the apical sodium-dependent bile acid transporter (ASBT) and the proton-coupled peptide cotransporter (PEPT1). Sensitivity analysis identified the microrate constants that impacted Km, Ki, and Vmax. Compound comparison using the six-state model employed regression to identify microrate constant values that can explain observed Km and Vmax values. Results yielded some expected findings, as well as some unanticipated effects of microrate constants on Km, Ki, and Vmax. Km and Ki were found to be equal for inhibitors that are also substrates. Additionally, microrate constant values for certain steps in transporter functioning influenced Km and Vmax to be low or high.

An updated review on drug-induced cholestasis: mechanisms and investigation of physicochemical properties and pharmacokinetic parameters

Drug-induced cholestasis is an important form of acquired liver disease and is associated with significant morbidity and mortality. Bile acids are key signaling molecules, but they can exert toxic responses when they accumulate in hepatocytes. This review focuses on the physiological mechanisms of drug-induced cholestasis associated with altered bile acid homeostasis due to direct (e.g., bile acid transporter inhibition) or indirect (e.g., activation of nuclear receptors, altered function/expression of bile acid transporters) processes. Mechanistic information about the effects of a drug on bile acid homeostasis is important when evaluating the cholestatic potential of a compound, but experimental data often are not available. The relationship between physicochemical properties, pharmacokinetic parameters, and inhibition of the bile salt export pump among 77 cholestatic drugs with different pathophysiological mechanisms of cholestasis (i.e., impaired formation of bile vs. physical obstruction of bile flow) was investigated. The utility of in silico models to obtain mechanistic information about the impact of compounds on bile acid homeostasis to aid in predicting the cholestatic potential of drugs is highlighted.

The SLC10 carrier family: transport functions and molecular structure

The SLC10 family represents seven genes containing 1-12 exons that encode proteins in humans with sequence lengths of 348-477 amino acids. Although termed solute carriers (SLCs), only three out of seven (i.e. SLC10A1, SLC10A2, and SLC10A6) show sodium-dependent uptake of organic substrates across the cell membrane. These include the uptake of bile salts, sulfated steroids, sulfated thyroidal hormones, and certain statin drugs by SLC10A1 (Na(+)-taurocholate cotransporting polypeptide (NTCP)), the uptake of bile salts by SLC10A2 (apical sodium-dependent bile acid transporter (ASBT)), and uptake of sulfated steroids and sulfated taurolithocholate by SLC10A6 (sodium-dependent organic anion transporter (SOAT)). The other members of the family are orphan carriers not all localized in the cell membrane. The name "bile acid transporter family" arose because the first two SLC10 members (NTCP and ASBT) are carriers for bile salts that establish their enterohepatic circulation. In recent years, information has been obtained on their 2D and 3D membrane topology, structure-transport relationships, and on the ligand and sodium-binding sites. For SLC10A2, the putative 3D morphology was deduced from the crystal structure of a bacterial SLC10A2 analog, ASBT(NM). This information was used in this chapter to calculate the putative 3D structure of NTCP. This review provides first an introduction to recent knowledge about bile acid synthesis and newly found bile acid hormonal functions, and then describes step-by-step each individual member of the family in terms of expression, localization, substrate pattern, as well as protein topology with emphasis on the three functional SLC10 carrier members.

Structure-activity relationship for FDA approved drugs as inhibitors of the human sodium taurocholate cotransporting polypeptide (NTCP)

The hepatic bile acid uptake transporter sodium taurocholate cotransporting polypeptide (NTCP) is less well characterized than its ileal paralog, the apical sodium dependent bile acid transporter (ASBT), in terms of drug inhibition requirements. The objectives of this study were (a) to identify FDA approved drugs that inhibit human NTCP, (b) to develop pharmacophore and Bayesian computational models for NTCP inhibition, and (c) to compare NTCP and ASBT transport inhibition requirements. A series of NTCP inhibition studies were performed using FDA approved drugs, in concert with iterative computational model development. Screening studies identified 27 drugs as novel NTCP inhibitors, including irbesartan (Ki = 11.9 μM) and ezetimibe (Ki = 25.0 μM). The common feature pharmacophore indicated that two hydrophobes and one hydrogen bond acceptor were important for inhibition of NTCP. From 72 drugs screened in vitro, a total of 31 drugs inhibited NTCP, while 51 drugs (i.e., more than half) inhibited ASBT. Hence, while there was inhibitor overlap, ASBT unexpectedly was more permissive to drug inhibition than was NTCP, and this may be related to NTCP possessing fewer pharmacophore features. Findings reflected that a combination of computational and in vitro approaches enriched the understanding of these poorly characterized transporters and yielded additional chemical probes for possible drug-transporter interaction determinations.

Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions

PURPOSE

Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles.

METHODS

Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1.

RESULTS

QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71-100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds.

CONCLUSIONS

The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.

Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing

Molecular mechanics force fields are widely used in computer-aided drug design for the study of drug-like molecules alone or interacting with biological systems. In simulations involving biological macromolecules, the biological part is typically represented by a specialized biomolecular force field, while the drug is represented by a matching general (organic) force field. In order to apply these general force fields to an arbitrary drug-like molecule, functionality for assignment of atom types, parameters, and charges is required. In the present article, which is part I of a series of two, we present the algorithms for bond perception and atom typing for the CHARMM General Force Field (CGenFF). The CGenFF atom typer first associates attributes to the atoms and bonds in a molecule, such as valence, bond order, and ring membership among others. Of note are a number of features that are specifically required for CGenFF. This information is then used by the atom typing routine to assign CGenFF atom types based on a programmable decision tree. This allows for straightforward implementation of CGenFF's complicated atom typing rules and for equally straightforward updating of the atom typing scheme as the force field grows. The presented atom typer was validated by assigning correct atom types on 477 model compounds including in the training set as well as 126 test-set molecules that were constructed to specifically verify its different components. The program may be utilized via an online implementation at https://www.paramchem.org/ .

Intrinsic energy landscapes of amino acid side-chains

Amino acid side-chain conformational properties influence the overall structural and dynamic properties of proteins and, therefore, their biological functions. In this study, quantum mechanical (QM) potential energy surfaces for the rotation of side-chain χ(1) and χ(2) torsions in dipeptides in the alphaR, beta, and alphaL backbone conformations were calculated. The QM energy surfaces provide a broad view of the intrinsic conformational properties of each amino acid side-chain. The extent to which intrinsic energetics dictates side-chain orientation was studied through comparisons of the QM energy surfaces with χ(1) and χ(2) free energy surfaces from probability distributions obtained from a survey of high resolution crystal structures. In general, the survey probability maxima are centered in minima of the QM surfaces as expected for sp(3) (or sp(2) for χ(2) of Asn, Phe, Trp, and Tyr) atom centers with strong variations between amino acids occurring in the energies of the minima indicating intrinsic differences in rotamer preferences. High correlations between the QM and survey data were found for hydrophobic side-chains except Met, suggesting minimal influence of the protein and solution environments on their conformational distributions. Conversely, low correlations for polar or charged side-chains indicate a dominant role of the environment in stabilizing conformations that are not intrinsically favored. Data also link the presence of off-rotamers in His and Trp to favorable interactions with the backbone. Results also suggest that the intrinsic energetics of the side-chains of Phe and Tyr may play important roles in protein folding and stability. Analyses on whether intrinsic side-chain energetics can influence backbone preference identified a strong correlation for residues in the alphaL backbone conformation. It is suggested that this correlation reflects the intrinsic instability of the alphaL backbone such that assumption of this backbone conformation is facilitated by intrinsically favorable side-chain conformations. Together our results offer a broad overview of the conformational properties of amino acid side-chains and the QM data may be used as target data for force field optimization.

Deconstructing 14-phenylpropyloxymetopon: minimal requirements for binding to mu opioid receptors

A series of phenylpropyloxyethylamines and cinnamyloxyethylamines were synthesized as deconstructed analogs of 14-phenylpropyloxymetopon and analyzed for opioid receptor binding affinity. Using the Conformationally Sampled Pharmacophore modeling approach, we discovered a series of compounds lacking a tyrosine mimetic, historically considered essential for μ opioid binding. Based on the binding studies, we have identified the optimal analogs to be N-methyl-N-phenylpropyl-2-(3-phenylpropoxy)ethanamine, with 1520 nM, and 2-(cinnamyloxy)-N-methyl-N-phenethylethanamine with 1680 nM affinity for the μ opioid receptor. These partial opioid structure analogs will serve as the novel lead compounds for future optimization studies.

Structural requirements of bile acid transporters: C-3 and C-7 modifications of steroidal hydroxyl groups

The apical sodium dependent bile acid transporter (ASBT) and sodium-taurocholate cotransporting polypeptide (NTCP) are potential prodrug targets, but the structural requirements for these transporters are incompletely defined. The objective of this study was to evaluate the effect of C-3 and C-7 substitution on bile acid interaction with these bile acid transporters. Nineteen bile acid analogs were tested against ASBT and NTCP for binding, as well as translocation. Results indicated that ASBT and NTCP accommodated a wide range of substituents for binding, but all major C-7 modifications resulted in analogs that did not demonstrate active uptake by either ASBT or NTCP. A C-3 modification that was not tolerated at C-7 still afforded translocation via ASBT and NTCP, confirming the relative unacceptability of C-7 modification. Both ASBT and NTCP demonstrated a generally similar binding potency. Results suggest that drug conjugation to the C-3 hydroxyl group, rather than C-7, has potential to lead to a successful prodrug targeting ASBT and NTCP.

Consensus 3D model of μ-opioid receptor ligand efficacy based on a quantitative Conformationally Sampled Pharmacophore

Despite being studied for over 30 years, a consensus structure-activity relationship (SAR) that encompasses the full range peptidic and nonpeptidic μ-opioid receptor ligands is still not available. To achieve a consensus SAR the Conformationally Sampled Pharmacophore (CSP) method was applied to develop a predictive model of the efficacy of μ-opioid receptor ligands. Emphasis was placed on predicting the efficacy of a wide range of agonists, partial agonists, and antagonists as well as understanding their mode of interaction with the receptor. Inclusion of all accessible conformations of each ligand, a central feature of the CSP method, enabled structural features between diverse μ-opioid receptor ligands that dictate efficacy to be identified. The models were validated against a diverse collection of peptidic and nonpeptidic ligands, including benzomorphans, fentanyl (4-anilinopiperidine), methadone (3,3-diphenylpropylamines), etonitazene (benzimidazole derivatives), funaltrexamine (C6-substituted 4,5-epoxymorphinan), and herkinorin. The model predicts (1) that interactions of ligands with the B site, as with the 19-alkyl substituents of oripavines, modulate the extent of agonism; (2) that agonists with long N-substituents, as with fentanyl and N-phenethylnormorphine, can bind in an orientation such that the N substitutent interacts with the B site that also allows the basic N-receptor Asp interaction essential for agonism; and (3) that the μ agonist herkinorin, that lacks a basic nitrogen, binds to the receptor in a manner similar to the traditional opioids via interactions mediated by water or a ion. Importantly, the proposed CSP model can be reconciled with previously published SAR models for the μ receptor.

Computational ligand-based rational design: Role of conformational sampling and force fields in model development

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

Structural determinants for transport across the intestinal bile acid transporter using C-24 bile acid conjugates

The human apical sodium dependent bile acid transporter (hASBT) reabsorbs gram quantities of bile acid daily and is a potential prodrug target to increase oral drug absorption. In the absence of a high resolution hASBT crystal structure, 3D-QSAR modeling may prove beneficial in designing prodrug targets to hASBT. The objective was to derive a conformationally sampled pharmacophore 3D-QSAR (CSP-SAR) model for the uptake of bile acid conjugates by hASBT. A series of bile acid conjugates of glutamyl chenodeoxycholate were evaluated in terms of K(m) and normalized V(max) (normV(max)) using hASBT-MDCK cells. All monoanionic conjugates were potent substrates. Dianions, cations and zwitterions, which bound with a high affinity, were not substrates. CSP-SAR models were derived using structural and physicochemical descriptors, and evaluated via cross validation. The best CSP-SAR model for K(m) included two structural and two physiochemical descriptors, where substrate hydrophobicity enhanced affinity. A best CSP-SAR model for K(m)/normV(max) employed one structural and three physicochemical descriptors, also indicating hydrophobicity enhanced efficiency. Overall, the bile acid C-24 region accommodated a range of substituted anilines, provided a single negative charge was present near C-24. In comparing uptake findings to prior inhibition results, increased hydrophobicity enhanced activity, with dianions and zwitterions hindering activity.

Reliability of inhibition models to correctly identify type of inhibition

PURPOSE

Type of inhibition (e.g. competitive, noncompetitive) is frequently evaluated to understand transporter structure/function relationships, but reliability of nonlinear regression to correctly identify inhibition type has not been assessed. The purpose was to assess the ability of nonlinear regression to correctly identify inhibition type.

METHODS

This aim was pursued through three objectives that compared the competitive, noncompetitive, and uncompetitive inhibition models to best fit simulated competitive and noncompetitive data. The first objective involved conventional inhibition data and entailed simulated data for the common situation where substrate concentration was fixed at a single level but inhibitor concentration varied. The second objective involved Dixon-type data where both substrate and inhibitor concentrations varied. A third objective involved nonconventional inhibition data, where substrate concentration was varied and inhibitor was fixed at a single concentration. Experimental data were also examined.

RESULTS

Nonlinear regression performed poorly in identifying the correct inhibition model for conventional inhibition data, but performed moderately well for Dixon-type data. Interestingly, nonlinear regression performed well for nonconventional inhibition data, particularly at higher inhibitor concentrations. Experimental data support simulation findings.

CONCLUSIONS

Conventional inhibition data is a poor basis to determine inhibition type, while Dixon-type data affords modest success. Nonconventional inhibition data merits further consideration.