Barbara Zdrazil

The N Terminus of Monoamine Transporters Is a Lever Required for the Action of Amphetamines

The serotonin transporter (SERT) terminates neurotransmission by removing serotonin from the synaptic cleft. In addition, it is the site of action of antidepressants (which block the transporter) and of amphetamines (which induce substrate efflux). We explored the functional importance of the N terminus in mediating the action of amphetamines by focusing initially on the highly conserved threonine residue at position 81, a candidate site for phosphorylation by protein kinase C. Molecular dynamics simulations of the wild type SERT, compared with its mutations SERTT81A and SERTT81D, suggested structural changes in the inner vestibule indicative of an opening of the inner vestibule. Predictions from this model (e.g. the preferential accumulation of SERTT81A in the inward conformation, its reduced turnover number, and a larger distance between its N and C termini) were verified. Most importantly, SERTT81A (and the homologous mutations in noradrenaline and dopamine) failed to support amphetamine-induced efflux, and this was not remedied by aspartate at this position. Amphetamine-induced currents through SERTT81A were comparable with those through the wild type transporter. Both abundant Na+ entry and accumulation of SERTT81A in the inward facing conformation ought to favor amphetamine-induced efflux. Thus, we surmised that the N terminus must play a direct role in driving the transporter into a state that supports amphetamine-induced efflux. This hypothesis was verified by truncating the first 64 amino acids and by tethering the N terminus to an additional transmembrane helix. Either modification abolished amphetamine-induced efflux. We therefore conclude that the N terminus of monoamine transporters acts as a lever that sustains reverse transport.

Scientific competency questions as the basis for semantically enriched open pharmacological space development

Molecular information systems play an important part in modern data-driven drug discovery. They do not only support decision making but also enable new discoveries via association and inference. In this review, we outline the scientific requirements identified by the Innovative Medicines Initiative (IMI) Open PHACTS consortium for the design of an open pharmacological space (OPS) information system. The focus of this work is the integration of compound-target-pathway-disease/phenotype data for public and industrial drug discovery research. Typical scientific competency questions provided by the consortium members will be analyzed based on the underlying data concepts and associations needed to answer the questions. Publicly available data sources used to target these questions as well as the need for and potential of semantic web-based technology will be presented.

Annotating Human P‐Glycoprotein Bioassay Data

Huge amounts of small compound bioactivity data have been entering the public domain as a consequence of open innovation initiatives. It is now the time to carefully analyse existing bioassay data and give it a systematic structure. Our study aims to annotate prominent in vitro assays used for the determination of bioactivities of human P‐glycoprotein inhibitors and substrates as they are represented in the ChEMBL and TP‐search open source databases. Furthermore, the ability of data, determined in different assays, to be combined with each other is explored. As a result of this study, it is suggested that for inhibitors of human P‐glycoprotein it is possible to combine data coming from the same assay type, if the cell lines used are also identical and the fluorescent or radiolabeled substrate have overlapping binding sites. In addition, it demonstrates that there is a need for larger chemical diverse datasets that have been measured in a panel of different assays. This would certainly alleviate the search for other inter‐correlations between bioactivity data yielded by different assay setups.

Adverse outcome pathways: opportunities, limitations and open questions

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field.

The Application of the Open Pharmacological Concepts Triple Store (Open PHACTS) to Support Drug Discovery Research

Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery.

A Binding Mode Hypothesis of Tiagabine Confirms Liothyronine Effect on γ-Aminobutyric Acid Transporter 1 (GAT1)

Elevating GABA levels in the synaptic cleft by inhibiting its reuptake carrier GAT1 is an established approach for the treatment of CNS disorders like epilepsy. With the increasing availability of crystal structures of transmembrane transporters, structure-based approaches to elucidate the molecular basis of ligand–transporter interaction also become feasible. Experimental data guided docking of derivatives of the GAT1 inhibitor tiagabine into a protein homology model of GAT1 allowed derivation of a common binding mode for this class of inhibitors that is able to account for the distinct structure–activity relationship pattern of the data set. Translating essential binding features into a pharmacophore model followed by in silico screening of the DrugBank identified liothyronine as a drug potentially exerting a similar effect on GAT1. Experimental testing further confirmed the GAT1 inhibiting properties of this thyroid hormone.

Exploiting open data: a new era in pharmacoinformatics

Within the last decade open data concepts has been gaining increasing interest in the area of drug discovery. With the launch of ChEMBL and PubChem, an enormous amount of bioactivity data was made easily accessible to the public domain. In addition, platforms that semantically integrate those data, such as the Open PHACTS Discovery Platform, permit querying across different domains of open life science data beyond the concept of ligand-target-pharmacology. However, most public databases are compiled from literature sources and are thus heterogeneous in their coverage. In addition, assay descriptions are not uniform and most often lack relevant information in the primary literature and, consequently, in databases. This raises the question how useful large public data sources are for deriving computational models. In this perspective, we highlight selected open-source initiatives and outline the possibilities and also the limitations when exploiting this huge amount of bioactivity data.

Probing the Selectivity of Monoamine Transporter Substrates by Means of Molecular Modeling.

The structurally similar serotonin and dopamine transporter (resp. SERT and DAT) play an important role in neuronal transmission. Although the concept of their function, i.e. the re-uptake of neurotransmitters from the synaptic cleft, has been extensively studied,1–4 the exact mechanism for their substrate selectivity is still unknown. Phenylethylamines (PEAs) are ligands of SERT and DAT and many induce reverse transport (efflux) of the protein’s natural substrate (the neurotransmitters 5-hydroxytryptamine and dopamine) in varying degrees and with different kinetics.2,5–7 Thus, studying the interplay of bioactivity values and certain structural features of selected PEAs can lead to new insights about monoamine transporter selectivity. The broadest SAR data currently available for PEAs and their interaction with SERT and DAT has been measured in rat synaptosomes by Baumann and colleagues.8,9 Thus, we used this data set to figure out important features which contribute towards selectivity and to guide the selection of a probe compound for subsequent structure-based studies. Consequently, pEC50 values of 28 compounds for SERT and DAT (Table ​(Table1)1) were plotted against each other, providing a clear picture of the PEA′s selectivity profile (Figure ​(Figure1).1). Out of this, a couple of detailed SARs can be drawn: Table 1 Monoamine transporter substrate structure-activity relationships Figure 1 Selectivity plot with numbers corresponding to Table 1. Compounds with similar SERT/DAT affinity are located around the middle diagonal line, while compounds in the upper left corner and lower right corner are DAT and SERT-selective, respectively. Chirality of the α-methylene atom of amphetamines does not influence SERT/DAT selectivity. The (S)-enantiomer is the most active in both transporters. DAT selective substrates seem smaller in size and therefore, their conformational flexibility in the binding pocket is expected to be relatively high and interactions with the target less defined. N-Methyl substitution slightly increases activity in SERT (compare compounds 4, 8, 20 and 21), and is somewhat unchanged in DAT (compare compounds 16, 17, 18 and 19). The only exception is for the naphtylisopropylamine (NIPA, 23) which is not selective for both transporters and shows a slight decrease in SERT activity (24). N-Ethyl substitution is generally more favorable in SERT as compared to methyl substitution or no substitution, while it decreases activity in DAT (see compounds 19, 22 and 25). para-Chlorine, meta-CF3 or meta-methyl substitution dramatically increases SERT affinity (compare 9, 11, 12, 4, 17). β-Hydroxyl substitution (R4, Table ​Table1)1) decreases affinity in both SERT and DAT (compare 1, 3, 5, 7). para-Methyl substitution increases SERT affinity and slightly decreases DAT affinity (compare 4, 10, 26, 27). The highest SERT/DAT selectivity is shown by (S)-fenfluramine (SFF) and because of its relatively large size, docking studies with this ligand are expected to result in a more restricted amount of poses as compared to the smaller analogs. Subsequently, we used SFF as a probe compound in order to study the molecular basis of the high affinity and selectivity of this compound towards SERT by means of a structure-based approach. Conveniently, sequence identity between the human and rat transporters is very high (92 % with SERT; 93 % with DAT), and local alignment of the primary substrate binding site (S14) shows even 100 % sequence identity between both species.10 Thus, in order to build upon our already established protein homology models for human SERT11, we switched to human proteins for subsequent studies. To show that data derived from rat transporters indeed can be transferred to the human transporters, we confirmed the high selectivity of SFF for SERT employing an uptake inhibition assay on HEK cells expressing human SERT and DAT (IC50=5.89 µM in SERT and 118 µM in DAT, see Figure ​Figure22). Figure 2 Uptake inhibition by (S)-fenfluramine in HEK293 cells stably expressing YFP-tagged DAT and SERT. Uptake was inhibited by increasing concentrations of fenfluramine as indicated. The concentration of tritiated substrates was 0.15 µM in the case ... Docking of a set of diverse high-affinity SERT substrates (see Methods) into a homology model of hSERT followed by common scaffold clustering revealed a binding mode for SFF which is in accordance to previously published studies.12,13 In addition, SFF was docked into an analogously constructed homology model of hDAT. Results showed that this ligand fits nicely into the S1 site, meaning that steric hindrance caused by the trifluoromethyl or N-ethyl group could not serve as an explanation for its low DAT affinity (see Figure ​Figure3).3). In addition, scoring functions could not show a preference of SFF for SERT or DAT (see Table ​Table2)2) and hence are not able to capture the activity determining factors. Since SFF′s trifluoromethyl moiety seems to be driving the selectivity, we further analysed the pocket between the TM3 and TM8 helical domains where this moiety is located: local alignment of SERT and DAT showed that five of the seven residues within this pocket are different. In general, the SERT pocket has more lipophilic side chains in its binding site, except for Thr439 in SERT which is more hydrophilic than the corresponding Ala423 in DAT (see Table ​Table33). Figure 3 Overlay of the selected fenfluramine (SFF) poses in the substrate binding site of hSERT and hDAT with a T439(O)-F(SFF) distance of 3.5 A. Table 2 Average scoring values after docking and evaluation of (S)-fenfluramine in the substrate binding site of homology models of SERT and DAT Table 3 Local alignment of the helical domains TM3 and TM8 of hSERT and hDAT showing more lipophilic side chains in SERT, except that for Thr439 This indicates a potential role of the CF3 group and Thr439 for SERT selectivity. Furthermore, as shown in Table ​Table1,1, (S)-amphetamine and (S)-norfenfluramine only have a trifluoromethyl moiety dissimilar, and their Ki values for rSERT are 3830 nM and 214 nM, respectively.5,8 Since the ratio of these values should be similar to the KD ratio (and since Ki is comparable to KD14), the binding free energy formula can be applied: with T=298.15 K5 and Hence, from a ligand-based point of view, a more favorable binding energy of about 1.72 kcal/mol is calculated for (S)-norfenfluramine. Considering the inhibitory values of (S)-fenfluramine from our human DAT and SERT uptake inhibition assay, we obtain a binding free energy difference of about 1.75 kcal/mol:15 with T=293.15 K Both calculated energy values are close to each other, strengthening the evidence that the trifluoromethyl group is responsible for SERT/DAT selectivity and high SERT affinity. Moreover, these values are relatively close to the ΔG value of a sp3-fluorine hydrogen bond (−2.38 kcal/mol).16 It is thus tempting to speculate that an interaction of Thr439 with the CF3 group triggers both affinity and selectivity of SFF in SERT. In addition, lipophilic dispersion forces with the SERT specific side chains (Ala169, Ile172, Ala173) that surround the trifluoromethyl moiety might contribute. Further evidence for the potential role of the lipophilicity of this pocket can be deduced from the increase in activity of the more lipophilic meta-methyl-substituted compound 9 and a decrease in activity of the hydrophilic meta-hydroxy-substituted dopamine (1) and norepinephrine (3) in this protein. Finally, when comparing phentermine and chlorphentermine, the halide increases the SERT affinity 13 900/338=41 times,5 which corresponds to more favorable energy of about 2.21 kcal/mol. Whether this can be ascribed to an interaction between the chlorine and Thr439, or simply to lipophilic contributions, is a point of discussion. With this study we have shown that combining ligand- and structure based studies are a powerful tool to probe substrate selectivity of monoamine transporters leading to preliminary evidence for the potential role of halogen atoms and Thr439 in SERT. Synthesis of additional PEAs combined with biochemical studies in both wild type and T439A mutants are obvious further steps towards this direction.

Selectivity profiling of BCRP versus P-gp inhibition: from automated collection of polypharmacology data to multi-label learning

BackgroundThe human ATP binding cassette transporters Breast Cancer Resistance Protein (BCRP) and Multidrug Resistance Protein 1 (P-gp) are co-expressed in many tissues and barriers, especially at the blood–brain barrier and at the hepatocyte canalicular membrane. Understanding their interplay in affecting the pharmacokinetics of drugs is of prime interest. In silico tools to predict inhibition and substrate profiles towards BCRP and P-gp might serve as early filters in the drug discovery and development process. However, to build such models, pharmacological data must be collected for both targets, which is a tedious task, often involving manual and poorly reproducible steps.ResultsCompounds with inhibitory activity measured against BCRP and/or P-gp were retrieved by combining Open Data and manually curated data from literature using a KNIME workflow. After determination of compound overlap, machine learning approaches were used to establish multi-label classification models for BCRP/P-gp. Different ways of addressing multi-label problems are explored and compared: label-powerset, binary relevance and classifiers chain. Label-powerset revealed important molecular features for selective or polyspecific inhibitory activity. In our dataset, only two descriptors (the numbers of hydrophobic and aromatic atoms) were sufficient to separate selective BCRP inhibitors from selective P-gp inhibitors. Also, dual inhibitors share properties with both groups of selective inhibitors. Binary relevance and classifiers chain allow improving the predictivity of the models.ConclusionsThe KNIME workflow proved a useful tool to merge data from diverse sources. It could be used for building multi-label datasets of any set of pharmacological targets for which there is data available either in the open domain or in-house. By applying various multi-label learning algorithms, important molecular features driving transporter selectivity could be retrieved. Finally, using the dataset with missing annotations, predictive models can be derived in cases where no accurate dense dataset is available (not enough data overlap or no well balanced class distribution).Graphical abstract.

Similarity-based descriptors (SIBAR) – A tool for safe exchange of chemical information?

SummaryExchange of chemical information without disclosure of the respective structures would greatly increase the data sets available for model building. Within the framework of the ChemMask project we explored the principal applicability of SIBAR-descriptors to mask chemical structures. SIBAR is based on calculation of similarity values for each compound of the training set to a set of reference compounds. Although the SIBAR-approach per se does not allow to unambiguously trace back the chemical structure of a compound, similarity searching in a 1.5 million compound database spiked with compounds structurally analogous to the query structure lead to the retrieval of compounds structurally and pharmacologically highly analogous to the “hidden” query structure in all three examples investigated. Comparison to results obtained with the original descriptors used to calculate the SIBAR-values showed, that SIBAR indeed adds some fuzziness to the data matrix.