Advancing drug discovery through assay development: a survey of tool compounds within the human solute carrier superfamily.

With over 450 genes, solute carriers (SLCs) constitute the largest transporter superfamily responsible for the uptake and efflux of nutrients, metabolites, and xenobiotics in human cells. SLCs are associated with a wide variety of human diseases, including cancer, diabetes, and metabolic and neurological disorders. They represent an important therapeutic target class that remains only partly exploited as therapeutics that target SLCs are scarce. Additionally, many small molecules reported in the literature to target SLCs are poorly characterized. Both features may be due to the difficulty of developing SLC transport assays that fulfill the quality criteria for high-throughput screening. Here, we report one of the main limitations hampering assay development within the RESOLUTE consortium: the lack of a resource providing high-quality information on SLC tool compounds. To address this, we provide a systematic annotation of tool compounds targeting SLCs. We first provide an overview on RESOLUTE assays. Next, we present a list of SLC-targeting compounds collected from the literature and public databases; we found that most data sources lacked specificity data. Finally, we report on experimental tests of 19 selected compounds against a panel of 13 SLCs from seven different families. Except for a few inhibitors, which were active on unrelated SLCs, the tested inhibitors demonstrated high selectivity for their reported targets. To make this knowledge easily accessible to the scientific community, we created an interactive dashboard displaying the collected data in the RESOLUTE web portal (https://re-solute.eu). We anticipate that our open-access resources on assays and compounds will support the development of future drug discovery campaigns for SLCs.

Endovascular treatment of primary M3 occlusion stroke in clinical practice: analysis of the German Stroke Registry.

BACKGROUND: Endovascular treatment (ET) options for acute stroke due to distal middle cerebral artery occlusions are rapidly evolving, but data on outcome and safety are sparse. We therefore performed an analysis of patients undergoing ET for primary M3 occlusions in routine clinical practice in a nationwide registry. METHODS: Patients enrolled between 01/20 and 12/21 in the prospective, multicenter German Stroke Registry-Endovascular Treatment (GSR-ET) were screened for mechanical thrombectomy performed for primary M3 occlusion. We analyzed neurological deficit as measured by the National Institute of Health Stroke Scale (NIHSS), symptomatic intracranial hemorrhage (sICH), thrombectomy technique, successful reperfusion (modified Thrombolysis in Cerebral Infarction [mTICI] score of 2b-3) and functional outcome as measured by the modified Rankin Scale (mRS) at discharge and 90 days. RESULTS: Out of 5574 patients, 11 patients (0.2%, median age 80 years, 54.5% female) underwent ET for primary M3 occlusion. All patients had pre-admission mRS ≤ 1, median NIHSS on admission was 8, and successful reperfusion was achieved in 6/11 patients (54.5%). While no vasospasm, dissection or perforation was reported, symptomatic intracranial hemorrhage occurred in 2 patients (18.2%). Favorable outcome (mRS ≤ 2) was achieved in 6/11 patients (54.5%) at 90-day follow-up. CONCLUSIONS: ET for primary M3 occlusions is rarely performed. While technically feasible, the procedure's potential benefits must be carefully weighed against its associated risks, including clinically relevant complications. Caution and further research is needed to optimize patient selection for this intervention. TRIAL REGISTRATION: GSR-ET; ClinicalTrials.gov Identifier: NCT03356392; Trial Registration Date: 11/29/2017.

Defining the phylogenetics and resistome of the major Clostridioides difficile ribotypes circulating in Australia.

Clostridioides difficile infection (CDI) remains a significant public health threat globally. New interventions to treat CDI rely on an understanding of the evolution and epidemiology of circulating strains. Here we provide longitudinal genomic data on strain diversity, transmission dynamics and antimicrobial resistance (AMR) of C. difficile ribotypes (RTs) 014/020 (n=169), 002 (n=77) and 056 (n=36), the three most prominent C. difficile strains causing CDI in Australia. Genome scrutiny showed that AMR was uncommon in these lineages, with resistance-conferring alleles present in only 15/169 RT014/020 strains (8.9 %), 1/36 RT056 strains (2.78 %) and none of 77 RT002 strains. Notably, ~90 % of strains were resistant to MLSB agents in vitro, but only ~5.9 % harboured known resistance alleles, highlighting an incongruence between AMR genotype and phenotype. Core genome analyses revealed all three RTs contained genetically heterogeneous strain populations with limited evidence of clonal transmission between CDI cases. The average number of pairwise core genome SNP (cgSNP) differences within each RT group ranged from 23.3 (RT056, ST34, n=36) to 115.6 (RT002, ST8, n=77) and 315.9 (RT014/020, STs 2, 13, 14, 49, n=169). Just 19 clonal groups (encompassing 40 isolates), defined as isolates differing by ≤2 cgSNPs, were identified across all three RTs (RT014/020, n=14; RT002, n=3; RT056, n=2). Of these clonal groups, 63 % (12/19) comprised isolates from the same Australian State and 37 % (7/19) comprised isolates from different States. The low number of plausible transmission events found for these major RTs (and previously documented populations in animal and environmental sources/reservoirs) points to widespread and persistent community sources of diverse C. difficile strains as opposed to ongoing nationwide healthcare outbreaks dominated by a single clone. Together, these data provide new insights into the evolution of major lineages causing CDI in Australia and highlight the urgent need for enhanced surveillance, and for public health interventions to move beyond the healthcare setting and into a One Health paradigm to effectively combat this complex pathogen.

ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction.

The solute carrier transporter family 6 (SLC6) is of key interest for their critical role in the transport of small amino acids or amino acid-like molecules. Their dysfunction is strongly associated with human diseases such as including schizophrenia, depression, and Parkinson's disease. Linking single point mutations to disease may support insights into the structure-function relationship of these transporters. This work aimed to develop a computational model for predicting the potential pathogenic effect of single point mutations in the SLC6 family. Missense mutation data was retrieved from UniProt, LitVar, and ClinVar, covering multiple protein-coding transcripts. As encoding approach, amino acid descriptors were used to calculate the average sequence properties for both original and mutated sequences. In addition to the full-sequence calculation, the sequences were cut into twelve domains. The domains are defined according to the transmembrane domains of the SLC6 transporters to analyse the regions' contributions to the pathogenicity prediction. Subsequently, several classification models, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) with the hyperparameters optimized through grid search were built. For estimation of model performance, repeated stratified k-fold cross-validation was used. The accuracy values of the generated models are in the range of 0.72 to 0.80. Analysis of feature importance indicates that mutations in distinct regions of SLC6 transporters are associated with an increased risk for pathogenicity. When applying the model on an independent validation set, the performance in accuracy dropped to averagely 0.6 with high precision but low sensitivity scores.

Elucidating the catalytic mechanisms of O2 generation by [Mn2(µ-O)2(terpy)2(OH2)2]3+ using DFT calculations: a focus on ClO- as oxidant.

The experimentally reported Mn(IV)Mn(III) complex [Mn2(µ-O)2(terpy)2(OH2)2]3+ has been observed catalyzing O2 generation with oxidants like ClO- and HSO5-. Previous mechanistic studies primarily focused on O2 generation with HSO5-, concluding that Mn(IV)Mn(III) acts as a catalyst, generating a Mn(IV)Mn(IV)-oxyl species as a key intermediate responsible for O-O bond formation. This computational study employs DFT calculations to investigate whether the catalytic generation of O2 using ClO- follows the same mechanism previously identified with HSO5- as the oxidant, or if it proceeds through an alternate pathway. To this end, we explored multiple pathways using ClO- as the oxidant. Interestingly, our findings confirm that in the case of ClO- as the oxidant, similar to what was observed with HSO5-, the Mn(IV)Mn(IV)-oxyl species indeed plays a crucial role in driving the catalytic evolution of O2 with the potential formation of the binuclear complexes Mn(IV)Mn(IV)-oxy and Mn(IV)Mn(IV)-OH during the reaction. These complexes are reactive in producing O2, with activation free energies of 15.9 and 14.3 kcal mol-1, respectively. However, our calculations revealed that the Mn(IV)Mn(IV)-oxyl complex is significantly more reactive in producing O2 than Mn(IV)Mn(IV)-oxy and Mn(IV)Mn(IV)-OH, with a lower free energy barrier of 8.1 kcal mol-1. Consequently, even though Mn(IV)Mn(IV)-oxyl is predicted to be present in much lower concentrations than Mn(IV)Mn(IV)-oxy and Mn(IV)Mn(IV)-OH, it emerges as the species acting as the active catalyst for catalytic O2 generation. This study enhances our knowledge of high oxidation state (+3 and +4) manganese chemistry, highlighting its key role in catalysis and paving the way for more efficient Mn-based catalysts with broad applications.

Mechanisms of Mn(V)-Oxo to Mn(IV)-Oxyl Conversion: From Closed-Cubane Photosystem II to Mn(V) Catalysts and the Role of the Entering Ligands.

The low activation barrier for O-O coupling in the closed-cubane Oxygen-Evolving Centre (OEC) of Photosystem II (PSII) requires water coordination with the Mn4 'dangler' ion in the Mn(V)-oxo fragment. This coordination transforms the Mn(V)-oxo complex into a more reactive Mn4(IV)-oxyl species, enhancing O-O coupling. This study explains the mechanism behind the coordination and indicates that in the most stable form of the OEC, the Mn4 fragment adopts a trigonal bipyramidal geometry but needs to transition to a square pyramidal form to be activated for O-O coupling. This transition stabilizes the Mn4 dxy orbital, enabling electron transfer from the oxo ligand to the dxy orbital, converting the oxo ligand into an oxyl species. The role of the water is to coordinate with the square pyramidal structure, reducing the energy gap between the oxo and oxyl forms, thereby lowering the activation energy for O-O coupling. This mechanism applies not only to the OEC system but also to other Mn(V)-based catalysts. For other catalysts, ligands such as OH- stabilize the Mn(IV)-oxyl species better than water, improving catalyst activation for reactions like C-H bond activation. This study is the first to explain the Mn(V)-oxo to Mn(IV)-oxyl conversion, providing a new foundation for Mn-based catalyst design.

Telemedical stroke care significantly improves patient outcome in rural areas: Long-term analysis of the German NEVAS network.

BACKGROUND: Comprehensive stroke centers (CSC) offer state-of-the-art stroke care in metropolitan centers. However, in rural areas, sufficient stroke expertise is much scarcer. Recently, telemedical stroke networks have offered instant consultation by stroke experts, enabling immediate administration of intravenous thrombolysis (IVT) on-site and decision on thrombectomy. While these immediate decisions are made during the consult, the impact of the network structures on stroke care in spoke hospitals is still not well described. AIMS: This study was performed to determine if on-site performance in rural hospitals and patient outcome improve over time through participation and regular medical staff training within a telemedical stroke network. METHODS: In this retrospective study, we analyzed data from stroke patients treated in four regional hospitals within the telemedical Neurovascular Network of Southwest Bavaria (NEVAS) between 2014 and 2019. We only included those patients that were treated in the regional hospitals until discharge at home or to neurorehabilitation. Functional outcome (modified Rankin scale) at discharge, mortality rate and periprocedural intracranial hemorrhage served as primary outcome parameters. Door-to-imaging and door-to-needle times were secondary outcome parameters. RESULTS: In 2014-2019, 5,379 patients were treated for acute stroke with 477 receiving IVT. Most baseline characteristics were comparable over time. For all stroke patients, door-to-imaging times increased over the years, but significantly improved for potential IVT candidates and those finally treated with IVT. The percentage of patients with door-to-needle time <30 min increased from 10% to 25%. Clinical outcome at discharge improved for all stroke patients treated in the regional hospitals. Particularly for patients treated with IVT, good clinical outcome (modified Rankin scale 0-2) at discharge increased from 2014 to 2019 by 19% and mortality rates dropped from 13% to 5%. CONCLUSIONS: 24-h/7-day telemedical support and regular on-site medical staff training within a structured telemedicine stroke network such as NEVAS significantly improve on-site stroke care in rural areas, leading to a considerable benefit in clinical outcome. DATA ACCESS STATEMENT: The data that support the findings of this study are available upon reasonable request and in compliance with the local and international ethical guidelines.

The macrocycle inhibitor landscape of SLC-transporter.

In the past years the interest in Solute Carrier Transporters (SLC) has increased due to their potential as drug targets. At the same time, macrocycles demonstrated promising activities as therapeutic agents. However, the overall macrocycle/SLC-transporter interaction landscape has not been fully revealed yet. In this study, we present a statistical analysis of macrocycles with measured activity against SLC-transporter. Using a data mining pipeline based on KNIME retrieved in total 825 bioactivity data points of macrocycles interacting with SLC-transporter. For further analysis of the SLC inhibitor profiles we developed an interactive KNIME workflow as well as an interactive map of the chemical space coverage utilizing parametric t-SNE models. The parametric t-SNE models provide a good discrimination ability among several corresponding SLC subfamilies' targets. The KNIME workflow, the dataset, and the visualization tool are freely available to the community.

Experimental and Computational Analysis of Newly Identified Pathogenic Mutations in the Creatine Transporter SLC6A8.

Creatine is an essential metabolite for the storage and rapid supply of energy in muscle and nerve cells. In humans, impaired metabolism, transport, and distribution of creatine throughout tissues can cause varying forms of mental disability, also known as creatine deficiency syndrome (CDS). So far, 80 mutations in the creatine transporter (SLC6A8) have been associated to CDS. To better understand the effect of human genetic variants on the physiology of SLC6A8 and their possible impact on CDS, we studied 30 missense variants including 15 variants of unknown significance, two of which are reported here for the first time. We expressed these variants in HEK293 cells and explored their subcellular localization and transport activity. We also applied computational methods to predict variant effect and estimate site-specific changes in thermodynamic stability. To explore variants that might have a differential effect on the transporter's conformers along the transport cycle, we constructed homology models of the inward facing, and outward facing conformations. In addition, we used mass-spectrometry to study proteins that interact with wild type SLC6A8 and five selected variants in HEK293 cells. In silico models of the protein complexes revealed how two variants impact the interaction interface of SLC6A8 with other proteins and how pathogenic variants lead to an enrichment of ER protein partners. Overall, our integrated analysis disambiguates the pathogenicity of 15 variants of unknown significance revealing diverse mechanisms of pathogenicity, including two previously unreported variants obtained from patients suffering from the creatine deficiency syndrome.

Privacy-preserving techniques for decentralized and secure machine learning in drug discovery.

Data availability, data security, and privacy concerns often hamper optimal performance efficiency of machine learning (ML) techniques. Therefore, novel techniques for the utilization of private/sensitive data in the field of drug discovery have been proposed for ML model-building tasks. Some examples of the different techniques are secure multiparty computation, distributed deep learning, homomorphic encryption, blockchain-based peer-to-peer networking, differential privacy, and federated learning, as well as combinations of such techniques. In this paper, we present an overview of these techniques for decentralized ML to illustrate its benefits and drawbacks in the field of drug discovery.

Structural Insights into Neonicotinoids and N-Unsubstituted Metabolites on Human nAChRs by Molecular Docking, Dynamics Simulations, and Calcium Imaging.

Neonicotinoid pesticides were initially designed in order to achieve species selectivity on insect nicotinic acetylcholine receptors (nAChRs). However, concerns arose when agonistic effects were also detected in human cells expressing nAChRs. In the context of next-generation risk assessments (NGRAs), new approach methods (NAMs) should replace animal testing where appropriate. Herein, we present a combination of in silico and in vitro methodologies that are used to investigate the potentially toxic effects of neonicotinoids and nicotinoid metabolites on human neurons. First, an ensemble docking study was conducted on the nAChR isoforms α7 and α3ß4 to assess potential crucial molecular initiating event (MIE) interactions. Representative docking poses were further refined using molecular dynamics (MD) simulations and binding energy calculations using implicit solvent models. Finally, calcium imaging on LUHMES neurons confirmed a key event (KE) downstream of the MIE. This method was also used to confirm the predicted agonistic effect of the metabolite descyano-thiacloprid (DCNT).

Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets.

Each year, publicly available databases are updated with new compounds from different research institutions. Positive experimental outcomes are more likely to be reported; therefore, they account for a considerable fraction of these entries. Established publicly available databases such as ChEMBL allow researchers to use information without constrictions and create predictive tools for a broad spectrum of applications in the field of toxicology. Therefore, we investigated the distribution of positive and nonpositive entries within ChEMBL for a set of off-targets and its impact on the performance of classification models when applied to pharmaceutical industry data sets. Results indicate that models trained on publicly available data tend to overpredict positives, and models based on industry data sets predict negatives more often than those built using publicly available data sets. This is strengthened even further by the visualization of the prediction space for a set of 10,000 compounds, which makes it possible to identify regions in the chemical space where predictions converge. Finally, we highlight the utilization of these models for consensus modeling for potential adverse events prediction.

Intensive heart rhythm monitoring to decrease ischemic stroke and systemic embolism-the Find-AF 2 study-rationale and design.

BACKGROUND: Atrial fibrillation (AF) is one of the most frequent causes of stroke. Several randomized trials have shown that prolonged monitoring increases the detection of AF, but the effect on reducing recurrent cardioembolism, ie, ischemic stroke and systemic embolism, remains unknown. We aim to evaluate whether a risk-adapted, intensified heart rhythm monitoring with consequent guideline conform treatment, which implies initiation of oral anticoagulation (OAC), leads to a reduction of recurrent cardioembolism. METHODS: Find-AF 2 is a randomized, controlled, open-label parallel multicenter trial with blinded endpoint assessment. 5,200 patients ≥ 60 years of age with symptomatic ischemic stroke within the last 30 days and without known AF will be included at 52 study centers with a specialized stroke unit in Germany. Patients without AF in an additional 24-hour Holter ECG after the qualifying event will be randomized in a 1:1 fashion to either enhanced, prolonged and intensified ECG-monitoring (intervention arm) or standard of care monitoring (control arm). In the intervention arm, patients with a high risk of underlying AF will receive continuous rhythm monitoring using an implantable cardiac monitor (ICM) whereas those without high risk of underlying AF will receive repeated 7-day Holter ECGs. The duration of rhythm monitoring within the control arm is up to the discretion of the participating centers and is allowed for up to 7 days. Patients will be followed for at least 24 months. The primary efficacy endpoint is the time until recurrent ischemic stroke or systemic embolism occur. CONCLUSIONS: The Find-AF 2 trial aims to demonstrate that enhanced, prolonged and intensified rhythm monitoring results in a more effective prevention of recurrent ischemic stroke and systemic embolism compared to usual care.

The structured ambulatory post-stroke care program for outpatient aftercare in patients with ischaemic stroke in Germany (SANO): an open-label, cluster-randomised controlled trial.

BACKGROUND: Patients with ischaemic stroke are at risk of recurrent stroke. In this study, we aimed to compare the effect of a structured ambulatory post-stroke care programme versus usual care on recurrent vascular events and death and control of cardiovascular risk factors. METHODS: We did a prospective, open-label, cluster-randomised controlled trial (SANO) at stroke centres in regions of Germany. A cluster was defined as a region in which acute stroke care is provided by a participating stroke centre. Patients were eligible for participation if they were aged 18 years or older, had no severe disabilities before the index stroke (modified Rankin scale 0-1), had at least one modifiable cardiovascular risk factor, and presented within 14 days of symptom onset of their first ischaemic stroke. The participating regions were randomly assigned (1:1) to the intervention and control group (usual care) by the statistician using block randomisation (block sizes of six), stratified by rural and urban regions. In intervention regions, a cross-sectoral multidisciplinary network was established to provide a 1-year organisational and patient-centred intervention. Due to the type of intervention, masking of participants and study physicians was not possible. Endpoint adjudication was performed by an independent endpoint adjudication committee who were masked to cluster allocation. The primary endpoint was a composite of recurrent stroke, myocardial infarction, and all-cause death within 12 months after baseline assessment, assessed in the modified intention-to-treat (mITT) population, which included all patients who did not withdraw consent and completed the primary endpoint assessment at 12 months. This study was registered with the German Clinical Trials Register, DRKS00015322. FINDINGS: Between Jan 1, 2019 and Dec 22, 2020, 36 clusters were assessed for eligibility, of which 30 were randomly assigned to the intervention group (n=15 clusters) or control group (n=15 clusters). No clusters dropped out of the study. 1203 (86%) of 1396 enrolled patients in the intervention group and 1283 (92%) of 1395 enrolled patients in the control group were included in the mITT population. The primary endpoint was confirmed in 64 (5·3%) of 1203 patients in the intervention group and 80 (6·2%) of 1283 patients in the control group (unadjusted odds ratio [OR] 0·80 [95% CI 0·49-1·30]; adjusted OR [aOR] 0·95 [95% CI 0·54-1·67]). All-cause deaths occurred in 31 (2·4%) of 1203 patients in the intervention group and 12 (1·0%) of 1283 patients in the control group. The incidence of serious adverse events was higher in the intervention group (266 [23·1%] of 1151) than the control group (106 [9·2%] of 1152). Falls (134 [11·4%] of 1203 patients in the intervention group; 39 [3·3%] of 1152 patients in the control group), hypertensive crisis (55 [4·7%]; 34 [2·8%]), and diagnosis of depression (51 [4·3%]; 13 [1·1%]) were the most frequent adverse events in both groups. No differences were identified in the rate of readmission to hospital between groups. INTERPRETATION: No differences were identified between patients with ischaemic stroke in the intervention group and control group with regard to the incidence of vascular events 1 year after baseline assessment, despite positive effects with regard to the control of some cardiovascular risk factors. Longer-term effects and other potentially favourable effects on stroke-related sequelae and quality of life require further evaluation. FUNDING: Innovation Fund of the Federal Joint Committee.

Paralog-dependent isogenic cell assay cascade generates highly selective SLC16A3 inhibitors.

Despite being considered druggable and attractive therapeutic targets, most of the solute carrier (SLC) membrane transporters remain pharmacologically underexploited. One of the reasons for this is a lack of reliable chemical screening assays, made difficult by functional redundancies among SLCs. In this study we leveraged synthetic lethality between the lactate transporters SLC16A1 and SLC16A3 in a screening strategy that we call paralog-dependent isogenic cell assay (PARADISO). The system involves five isogenic cell lines, each dependent on various paralog genes for survival/fitness, arranged in a screening cascade tuned for the identification of SLC16A3 inhibitors. We screened a diversity-oriented library of ∼90,000 compounds and further developed our hits into slCeMM1, a paralog-selective and potent SLC16A3 inhibitor. By implementing chemoproteomics, we showed that slCeMM1 is selective also at the proteome-wide level, thus fulfilling an important criterion for chemical probes. This study represents a framework for the development of specific cell-based drug discovery assays.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.

The study of transporter proteins is key to understanding the mechanism behind multi-drug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

Systematic screening identifies ABCG2 as critical factor underlying synergy of kinase inhibitors with transcriptional CDK inhibitors.

BACKGROUND: Triple-negative breast cancer (TNBC) is a subtype of breast cancer with limited treatment options and poor clinical prognosis. Inhibitors of transcriptional CDKs are currently under thorough investigation for application in the treatment of multiple cancer types, including breast cancer. These studies have raised interest in combining these inhibitors, including CDK12/13 inhibitor THZ531, with a variety of other anti-cancer agents. However, the full scope of these potential synergistic interactions of transcriptional CDK inhibitors with kinase inhibitors has not been systematically investigated. Moreover, the mechanisms behind these previously described synergistic interactions remain largely elusive. METHODS: Kinase inhibitor combination screenings were performed to identify kinase inhibitors that synergize with CDK7 inhibitor THZ1 and CDK12/13 inhibitor THZ531 in TNBC cell lines. CRISPR-Cas9 knockout screening and transcriptomic evaluation of resistant versus sensitive cell lines were performed to identify genes critical for THZ531 resistance. RNA sequencing analysis after treatment with individual and combined synergistic treatments was performed to gain further insights into the mechanism of this synergy. Kinase inhibitor screening in combination with visualization of ABCG2-substrate pheophorbide A was used to identify kinase inhibitors that inhibit ABCG2. Multiple transcriptional CDK inhibitors were evaluated to extend the significance of the found mechanism to other transcriptional CDK inhibitors. RESULTS: We show that a very high number of tyrosine kinase inhibitors synergize with the CDK12/13 inhibitor THZ531. Yet, we identified the multidrug transporter ABCG2 as key determinant of THZ531 resistance in TNBC cells. Mechanistically, we demonstrate that most synergistic kinase inhibitors block ABCG2 function, thereby sensitizing cells to transcriptional CDK inhibitors, including THZ531. Accordingly, these kinase inhibitors potentiate the effects of THZ531, disrupting gene expression and increasing intronic polyadenylation. CONCLUSION: Overall, this study demonstrates the critical role of ABCG2 in limiting the efficacy of transcriptional CDK inhibitors and identifies multiple kinase inhibitors that disrupt ABCG2 transporter function and thereby synergize with these CDK inhibitors. These findings therefore further facilitate the development of new (combination) therapies targeting transcriptional CDKs and highlight the importance of evaluating the role of ABC transporters in synergistic drug-drug interactions in general.

Toxicity prediction using target, interactome, and pathway profiles as descriptors.

In silico methods are essential to the safety evaluation of chemicals. Computational risk assessment offers several approaches, with data science and knowledge-based methods becoming an increasingly important sub-group. One of the substantial attributes of data science is that it allows using existing data to find correlations, build strong hypotheses, and create new, valuable knowledge that may help to reduce the number of resource intensive experiments. In choosing a suitable method for toxicity prediction, the available data and desired toxicity endpoint are two essential factors to consider. The complexity of the endpoint can impact the success rate of the in silico models. For highly complex endpoints such as hepatotoxicity, it can be beneficial to decipher the toxic event from a more systemic point of view. We propose a data science-based modelling pipeline that uses compounds` connections to tissue-specific biological targets, interactome, and biological pathways as descriptors of compounds. Models trained on different combinations of the collected, compound-target, compound-interactor, and compound-pathway profiles, were used to predict the hepatotoxicity of drug-like compounds. Several tree-based models were trained, utilizing separate and combined target, interactome and pathway level variables. The model using combined descriptors of all levels and the random forest algorithm was further optimized. Descriptor importance for model performance was addressed and examined for a biological explanation to define which targets or pathways can have a crucial role in toxicity. Descriptors connected to cytochromes P450 enzymes, heme degradation and biological oxidation received high weights. Furthermore, the involvement of other, less discussed processes in connection with toxicity, such as the involvement of RHO GTPase effectors in hepatotoxicity, were marked as fundamental. The optimized combined model using only the selected descriptors yielded the best performance with an accuracy of 0.766. The same dataset using classical Morgan fingerprints for compound representation yielded models with similar performance measures, as well as the combination of systems biology-based descriptors and Morgan fingerprints. Consequently, adding the structural information of compounds did not enhance the predictive value of the models. The developed systems biology-based pipeline comprises a valuable tool in predicting toxicity, while providing novel insights about the possible mechanisms of the unwanted events.