The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.

ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences.

Amino acids in transmembrane helix 1 confer major functional differences between human and mouse orthologs of the polyspecific membrane transporter OCT1.

Organic cation transporter 1 (OCT1) is a membrane transporter that affects hepatic uptake of cationic and weakly basic drugs. OCT1 transports structurally highly diverse substrates. The mechanisms conferring this polyspecificity are unknown. Here, we analyzed differences in transport kinetics between human and mouse OCT1 orthologs to identify amino acids that contribute to the polyspecificity of OCT1. Following stable transfection of HEK293 cells, we observed more than twofold differences in the transport kinetics of 22 out of 28 tested substrates. We found that the ß2-adrenergic drug fenoterol was transported with eightfold higher affinity but at ninefold lower capacity by human OCT1. In contrast, the anticholinergic drug trospium was transported with 11-fold higher affinity but at ninefold lower capacity by mouse Oct1. Using human-mouse chimeric constructs and site-directed mutagenesis, we identified nonconserved amino acids Cys36 and Phe32 as responsible for the species-specific differences in fenoterol and trospium uptake. Substitution of Cys36 (human) to Tyr36 (mouse) caused a reversal of the affinity and capacity of fenoterol but not trospium uptake. Substitution of Phe32 to Leu32 caused reversal of trospium but not fenoterol uptake kinetics. Comparison of the uptake of structurally similar ß2-adrenergics and molecular docking analyses indicated the second phenol ring, 3.3 to 4.8 Å from the protonated amino group, as essential for the affinity for fenoterol conferred by Cys36. This is the first study to report single amino acids as determinants of OCT1 polyspecificity. Our findings suggest that structure-function data of OCT1 is not directly transferrable between substrates or species.

Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening.

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 µM), three OATP1B1 inhibitors (2.69 to 10 µM), and five OATP1B3 inhibitors (1.53 to 10 µM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

Combining In Vivo Data with In Silico Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction.

Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of fatty acids in hepatocytes. In this study, we developed reliable in silico models for predicting hepatic steatosis on the basis of an in vivo data set of 1041 compounds measured in rodent studies with repeated oral exposure. The imbalanced nature of the data set (1:8, with the "steatotic" compounds belonging to the minority class) required the use of meta-classifiers-bagging with stratified under-sampling and Mondrian conformal prediction-on top of the base classifier random forest. One major goal was the investigation of the influence of different descriptor combinations on model performance (tested by predicting an external validation set): physicochemical descriptors (RDKit), ToxPrint features, as well as predictions from in silico nuclear receptor and transporter models. All models based upon descriptor combinations including physicochemical features led to reasonable balanced accuracies (BAs between 0.65 and 0.69 for the respective models). Combining physicochemical features with transporter predictions and further with ToxPrint features gave the best performing model (BAs up to 0.7 and efficiencies of 0.82). Whereas both meta-classifiers proved useful for this highly imbalanced toxicity data set, the conformal prediction framework also guarantees the error level and thus might be favored for future studies in the field of predictive toxicology.

Data-Driven Ensemble Docking to Map Molecular Interactions of Steroid Analogs with Hepatic Organic Anion Transporting Polypeptides.

Hepatic organic anion transporting polypeptides-OATP1B1, OATP1B3, and OATP2B1-are expressed at the basolateral membrane of hepatocytes, being responsible for the uptake of a wide range of natural substrates and structurally unrelated pharmaceuticals. Impaired function of hepatic OATPs has been linked to clinically relevant drug-drug interactions leading to altered pharmacokinetics of administered drugs. Therefore, understanding the commonalities and differences across the three transporters represents useful knowledge to guide the drug discovery process at an early stage. Unfortunately, such efforts remain challenging because of the lack of experimentally resolved protein structures for any member of the OATP family. In this study, we established a rigorous computational protocol to generate and validate structural models for hepatic OATPs. The multistep procedure is based on the systematic exploration of available protein structures with shared protein folding using normal-mode analysis, the calculation of multiple template backbones from elastic network models, the utilization of multiple template conformations to generate OATP structural models with various degrees of conformational flexibility, and the prioritization of models on the basis of enrichment docking. We employed the resulting OATP models of OATP1B1, OATP1B3, and OATP2B1 to elucidate binding modes of steroid analogs in the three transporters. Steroid conjugates have been recognized as endogenous substrates of these transporters. Thus, investigating this data set delivers insights into mechanisms of substrate recognition. In silico predictions were complemented with in vitro studies measuring the bioactivity of a compound set on OATP expressing cell lines. Important structural determinants conferring shared and distinct binding patterns of steroid analogs in the three transporters have been identified. Overall, this comparative study provides novel insights into hepatic OATP-ligand interactions and selectivity. Furthermore, the integrative computational workflow for structure-based modeling can be leveraged for other pharmaceutical targets of interest.

Differences in Metformin and Thiamine Uptake between Human and Mouse Organic Cation Transporter 1: Structural Determinants and Potential Consequences for Intrahepatic Concentrations.

The most commonly used oral antidiabetic drug, metformin, is a substrate of the hepatic uptake transporter OCT1 (gene name SLC22A1). However, OCT1 deficiency leads to more pronounced reductions of metformin concentrations in mouse than in human liver. Similarly, the effects of OCT1 deficiency on the pharmacokinetics of thiamine were reported to differ between human and mouse. Here, we compared the uptake characteristics of metformin and thiamine between human and mouse OCT1 using stably transfected human embryonic kidney 293 cells. The affinity for metformin was 4.9-fold lower in human than in mouse OCT1, resulting in a 6.5-fold lower intrinsic clearance. Therefore, the estimated liver-to-blood partition coefficient is only 3.34 in human compared with 14.4 in mouse and may contribute to higher intrahepatic concentrations in mice. Similarly, the affinity for thiamine was 9.5-fold lower in human than in mouse OCT1. Using human-mouse chimeric OCT1, we showed that simultaneous substitution of transmembrane helices TMH2 and TMH3 resulted in the reversal of affinity for metformin. Using homology modeling, we suggest several explanations, of which a different interaction of Leu155 (human TMH2) compared with Val156 (mouse TMH2) with residues in TMH3 had the strongest experimental support. In conclusion, the contribution of human OCT1 to the cellular uptake of thiamine and especially of metformin may be much lower than that of mouse OCT1. This may lead to an overestimation of the effects of OCT1 on hepatic concentrations in humans when using mouse as a model. In addition, comparative analyses of human and mouse orthologs may help reveal mechanisms of OCT1 transport. SIGNIFICANCE STATEMENT: OCT1 is a major hepatic uptake transporter of metformin and thiamine, but this study reports strong differences in the affinity for both compounds between human and mouse OCT1. Consequently, intrahepatic metformin concentrations could be much higher in mice than in humans, impacting metformin actions and representing a strong limitation of using rodent animal models for predictions of OCT1-related pharmacokinetics and efficacy in humans. Furthermore, OCT1 transmembrane helices TMH2 and TMH3 were identified to confer the observed species-specific differences in metformin affinity.

Integrative Data Mining, Scaffold Analysis, and Sequential Binary Classification Models for Exploring Ligand Profiles of Hepatic Organic Anion Transporting Polypeptides.

Hepatocellular organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are important for proper liver function and the regulation of the drug elimination process. Understanding their roles in different conditions of liver toxicity and cancer requires an in-depth investigation of hepatic OATP-ligand interactions and selectivity. However, such studies are impeded by the lack of crystal structures, the promiscuous nature of these transporters, and the limited availability of reliable bioactivity data, which are spread over different data sources in the open domain. To this end, we integrated ligand bioactivity data for hepatic OATPs from five open data sources (ChEMBL, the UCSF-FDA TransPortal database, DrugBank, Metrabase, and IUPHAR) in a semiautomatic KNIME workflow. Highly curated data sets were analyzed with respect to enriched scaffolds, and their activity profiles and interesting scaffold series providing indication for selective, dual-, or pan-inhibitory activity toward hepatic OATPs could be extracted. In addition, a sequential binary modeling approach revealed common and distinctive ligand features for inhibitory activity toward the individual transporters. The workflows designed for integrating data from open sources, data curation, and subsequent substructure analyses are freely available and fully adaptable. The new data sets for inhibitors and substrates of hepatic OATPs as well as the insights provided by the feature and substructure analyses will guide future structure-based studies on hepatic OATP-ligand interactions and selectivity.

Identification of anticancer OATP2B1 substrates by an in vitro triple-fluorescence-based cytotoxicity screen.

Membrane transporters play an important role in the absorption, distribution, metabolism and excretion of drugs. The cellular accumulation of many drugs is the result of the net function of efflux and influx transporters. Efflux transporters such as P-glycoprotein/ABCB1 have been shown to confer multidrug resistance in cancer. Although expression of uptake transporters has been confirmed in cancer cells, their role in chemotherapy response has not been systematically investigated. In the present study we have adapted a fluorescence-based cytotoxic assay to characterize the influence of key drug-transporters on the toxicity of approved anticancer drugs. Co-cultures of fluorescently labeled parental and transporter-expressing cells (expressing ABCB1, ABCG2 or OATP2B1) were screened against 101 FDA-approved anticancer drugs, using a novel, automated, triple fluorescence-based cytotoxicity assay. By measuring the survival of parental and transporter-expressing cells in co-cultures, we identify those FDA-approved anticancer drugs, whose toxicity is influenced by ABCB1, ABCG2 or OATP2B1. In addition to confirming known substrates of ABCB1 and ABCG2, the fluorescence-based cytotoxicity assays identified anticancer agents whose toxicity was increased in OATP2B1 expressing cells. Interaction of these compounds with OATP2B1 was verified in dedicated transport assays using cell-impermeant fluorescent substrates. Understanding drug-transporter interactions is needed to increase the efficacy of chemotherapeutic agents. Our results highlight the potential of the fluorescence-based HT screening system for identifying transporter substrates, opening the way for the design of therapeutic approaches based on the inhibition or even the exploitation of transporters in cancer cells.

Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project.

Read-across is one of the most frequently used alternative tools for hazard assessment, in particular for complex endpoints such as repeated dose or developmental and reproductive toxicity. Read-across extrapolates the outcome of a specific toxicological in vivo endpoint from tested (source) compounds to "similar" (target) compound(s). If appropriately applied, a read-across approach can be used instead of de novo animal testing. The read-across approach starts with structural/physicochemical similarity between target and source compounds, assuming that similar structural characteristics lead to similar human hazards. In addition, similarity also has to be shown for the toxicokinetic and toxicodynamic properties of the grouped compounds. To date, many read-across cases fail to demonstrate toxicokinetic and toxicodynamic similarities. New concepts, in vitro and in silico tools are needed to better characterise these properties, collectively called new approach methodologies (NAMs). This white paper outlines a general read-across assessment concept using NAMs to support hazard characterization of the grouped compounds by generating data on their dynamic and kinetic properties. Based on the overarching read-across hypothesis, the read-across workflow suggests targeted or untargeted NAM testing also outlining how mechanistic knowledge such as adverse outcome pathways (AOPs) can be utilized. Toxicokinetic models (biokinetic and PBPK), enriched by in vitro parameters such as plasma protein binding and hepatocellular clearance, are proposed to show (dis)similarity of target and source compound toxicokinetics. Furthermore, in vitro to in vivo extrapolation is proposed to predict a human equivalent dose, as potential point of departure for risk assessment. Finally, the generated NAM data are anchored to the existing in vivo data of source compounds to predict the hazard of the target compound in a qualitative and/or quantitative manner. To build this EU-ToxRisk read-across concept, case studies have been conducted and discussed with the regulatory community. These case studies are briefly outlined.

Empowering pharmacoinformatics by linked life science data.

With the public availability of large data sources such as ChEMBLdb and the Open PHACTS Discovery Platform, retrieval of data sets for certain protein targets of interest with consistent assay conditions is no longer a time consuming process. Especially the use of workflow engines such as KNIME or Pipeline Pilot allows complex queries and enables to simultaneously search for several targets. Data can then directly be used as input to various ligand- and structure-based studies. In this contribution, using in-house projects on P-gp inhibition, transporter selectivity, and TRPV1 modulation we outline how the incorporation of linked life science data in the daily execution of projects allowed to expand our approaches from conventional Hansch analysis to complex, integrated multilayer models.

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.

How Open Data Shapes In Silico Transporter Modeling.

Chemical compound bioactivity and related data are nowadays easily available from open data sources and the open medicinal chemistry literature for many transmembrane proteins. Computational ligand-based modeling of transporters has therefore experienced a shift from local (quantitative) models to more global, qualitative, predictive models. As the size and heterogeneity of the data set rises, careful data curation becomes even more important. This includes, for example, not only a tailored cutoff setting for the generation of binary classes, but also the proper assessment of the applicability domain. Powerful machine learning algorithms (such as multi-label classification) now allow the simultaneous prediction of multiple related targets. However, the more complex, the less interpretable these models will get. We emphasize that transmembrane transporters are very peculiar, some of which act as off-targets rather than as real drug targets. Thus, careful selection of the right modeling technique is important, as well as cautious interpretation of results. We hope that, as more and more data will become available, we will be able to ameliorate and specify our models, coming closer towards function elucidation and the development of safer medicine.

Transporter assays and assay ontologies: useful tools for drug discovery.

Transport proteins represent an eminent class of drug targets and ADMET (absorption, distribution, metabolism, excretion, toxicity) associated genes. There exists a large number of distinct activity assays for transport proteins, depending on not only the measurement needed (e.g. transport activity, strength of ligand­protein interaction), but also due to heterogeneous assay setups used by different research groups. Efforts to systematically organize this (divergent) bioassay data have large potential impact in Public-Private partnership and conventional commercial drug discovery. In this short review, we highlight some of the frequently used high-throughput assays for transport proteins, and we discuss emerging assay ontologies and their application to this field. Focusing on human P-glycoprotein (Multidrug resistance protein 1; gene name: ABCB1, MDR1), we exemplify how annotation of bioassay data per target class could improve and add to existing ontologies, and we propose to include an additional layer of metadata supporting data fusion across different bioassays.

Occurrence of "Natural Selection" in Successful Small Molecule Drug Discovery.

Published compounds from ChEMBL version 32 are used to seek evidence for the occurrence of "natural selection" in drug discovery. Three measures of natural product (NP) character were applied, to compare time- and target-matched compounds reaching the clinic (clinical compounds in phase 1-3 development and approved drugs) with background compounds (reference compounds). Pseudo-NPs (PNPs), containing NP fragments combined in ways inaccessible by nature, are increasing over time, reaching 67% of clinical compounds first disclosed since 2010. PNPs are 54% more likely to be found in post-2008 clinical versus reference compounds. The majority of target classes show increased clinical compound NP character versus their reference compounds. Only 176 NP fragments appear in >1000 clinical compounds published since 2008, yet these make up on average 63% of the clinical compound's core scaffolds. There is untapped potential awaiting exploitation, by applying nature's building blocks─"natural intelligence"─to drug design.

School of cheminformatics in Latin America.

We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24-25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for the meeting. As part of the meeting, advances in enumeration and visualization of chemical space, applications in natural product-based drug discovery, drug discovery for neglected diseases, toxicity prediction, and general guidelines for data analysis were discussed. Experts from ChEMBL presented a workshop on how to use the resources of this major compounds database used in cheminformatics. The school also included a round table with editors of cheminformatics journals. The full program of the meeting and the recordings of the sessions are publicly available at https://www.youtube.com/@SchoolChemInfLA/featured .

Artificial intelligence for natural product drug discovery.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action.

Read-across approaches often remain inconclusive as they do not provide sufficient evidence on a common mode of action across the category members. This read-across case study on thirteen, structurally similar, branched aliphatic carboxylic acids investigates the concept of using human-based new approach methods, such as in vitro and in silico models, to demonstrate biological similarity. Five out of the thirteen analogues have preclinical in vivo studies. Three out of them induced lipid accumulation or hypertrophy in preclinical studies with repeated exposure, which leads to the read-across hypothesis that the analogues can potentially induce hepatic steatosis. To confirm the selection of analogues, the expression patterns of the induced differentially expressed genes (DEGs) were analysed in a human liver model. With increasing dose, the expression pattern within the tested analogues got more similar, which serves as a first indication of a common mode of action and suggests differences in the potency of the analogues. Hepatic steatosis is a well-known adverse outcome, for which over 55 adverse outcome pathways have been identified. The resulting adverse outcome pathway (AOP) network, comprised a total 43 MIEs/KEs and enabled the design of an in vitro testing battery. From the AOP network, ten MIEs, early and late KEs were tested to systematically investigate a common mode of action among the grouped compounds. The targeted testing of AOP specific MIE/KEs shows that biological activity in the category decreases with side chain length. A similar trend was evident in measuring liver alterations in zebra fish embryos. However, activation of single MIEs or early KEs at in vivo relevant doses did not necessarily progress to the late KE "lipid accumulation". KEs not related to the read-across hypothesis, testing for example general mitochondrial stress responses in liver cells, showed no trend or biological similarity. Testing scope is a key issue in the design of in vitro test batteries. The Dempster-Shafer decision theory predicted those analogues with in vivo reference data correctly using one human liver model or the CALUX reporter assays. The case study shows that the read-across hypothesis is the key element to designing the testing strategy. In the case of a good mechanistic understanding, an AOP facilitates the selection of reliable human in vitro models to demonstrate a common mode of action. Testing DEGs, MIEs and early KEs served to show biological similarity, whereas the late KEs become important for confirmation, as progression from MIEs to AO is not always guaranteed.

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 SERT(T81A) and SERT(T81D), 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 SERT(T81A) in the inward conformation, its reduced turnover number, and a larger distance between its N and C termini) were verified. Most importantly, SERT(T81A) (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 SERT(T81A) were comparable with those through the wild type transporter. Both abundant Na(+) entry and accumulation of SERT(T81A) 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.

Molecular modelling studies of new potential human DNA polymerase α inhibitors.

The human polymerase α (pol α) is a promising target for the therapy of cancer e.g. of the skin. The authors recently built a homology model of the active site of human DNA pol α. This 3D model was now used for molecular modelling studies with eight novel analogues of 2-butylanilino-dATP, which is a highly selective nucleoside inhibitor of mammalian pol α. Our results suggest that a higher hydrophobicity of a carbohydrate side chain (pointing into a spacious hydrophobic cavity) may enhance the strength of the interaction with the target protein. Moreover, acyclic acyclovir-like derivatives outperformed those with a sugar-moiety, indicating that structural flexibility and higher conformational adaptability has a positive effect on the receptor affinity. Cytotoxicity tests confirmed our theoretical findings. Besides, one of our most promising compounds in the molecular modelling studies revealed high selectivity for the SCC-25 cell line derived from squamous cell carcinoma in man.