Drug-target Relationships Research Articles

DTRE: A model for predicting drug-target interactions of endometrial cancer based on heterogeneous graph

Endometrial cancer is one of the most common gynecological malignancies affecting women worldwide, posing a serious threat to women’s health. Moreover, the identification of drug-target interactions (DTIs) is typically a time-consuming and costly critical step in drug discovery. In order to identify potential DTIs to enhance targeted therapy for endometrial cancer, we propose a deep learning model named DTRE (Drug-Target Relationship Enhanced) based on a heterogeneous graph to predict DTIs, which utilizes the relationships between drugs and targets to effectively capture their interactions. In the heterogeneous graph, nodes represent drugs and targets, and edges represent their interactions, then the representations of drugs and targets are learned through graph convolutional network, graph attention network and attention mechanism. Experimental results on the dataset proposed in this paper show that the AUC and AUPR of DTRE achieve 0.870 and 0.872 respectively, significantly outperforming comparative models and indicating that DTRE can effectively predict DTIs when applied to large-scale data. Additionally, DTRE also predicts the potential DTIs for endometrial cancer, providing new insights into targeted therapy for it.

ITRAQ-based proteomics reveals the mechanism of action of Yinlai decoction in treating pneumonia in mice consuming a high-calorie diet

ObjectiveTo uncover the underlying mechanisms of action of the Yinlai decoction on high-calorie diet-induced pneumonia through proteomics analysis. MethodsBased on the Gene Expression Omnibus (GEO) database, lung tissue samples from normal and high-fat diet (HFD) fed mice in the GSE16377 dataset were selected as test cohorts to identify differentially expressed genes and conduct bioinformatics analyses. In the animal experiments, mice were randomly divided into the control (N), high-calorie diet pneumonia (M), and Yinlai decoction treatment (Y) groups. Mice in the M group received high-calorie feed and a 0.5 mg/mL lipopolysaccharide solution spray for 30 min for 3 d. The mice in the Y group were intragastrically administered 2 mL/10 g Yinlai decoction twice daily for 3 d. Pathological evaluation of the lung tissue was performed. Differentially expressed proteins (DEPs) in the lung tissue were identified using quantitative proteomics and bioinformatics analyses. The drug-target relationships between Yinlai decoction and core DEPs in the lung tissue were verified using AutoDock Vina and Molecular Graphics Laboratory (MGL) Tools. DEPs were verified by western blot. ResultsGEO data mining showed that an HFD altered oxidative phosphorylation in mouse lung tissue. The Yinlai decoction alleviated pathological damage to lung tissue and pneumonia in mice that were fed a high-calorie diet. A total of 47 DEPs were identified between the Y and M groups. Enrichment analysis revealed their association with energy metabolism pathways such as the tricarboxylic acid cycle (TCA) and oxidative phosphorylation. The protein-protein interaction network revealed that Atp5a1, Pdha1, and Sdha were the target proteins mediating the therapeutic effects of Yinlai decoction. Molecular docking results suggested that the mechanism of the therapeutic effect of Yinlai decoction involves the binding of brassinolide, praeruptorin B, chrysoeriol, and other components in Yinlai decoction to Atp5a1. ConclusionThe Yinlai decoction alleviated lung tissue damage and pneumonia in mice that were fed a high-calorie diet by regulating the TCA and oxidative phosphorylation. Our study highlights the importance of a healthy diet for patients with pneumonia and provides a scientific basis for the prevention and treatment of pneumonia through dietary adjustments.

AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network.

Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship.

CAFrgDB: a database for cancer-associated fibroblasts related genes and their functions in cancer

As one of the most essential components of the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) interact extensively with cancer cells and other stromal cells to remodel TME and participate in the pathogenesis of cancer, which earmarked themselves as new promising targets for cancer therapy. Numerous studies have highlighted the heterogeneity and versatility of CAFs in most cancer types. Thus, the identification and appropriate use of CAF-related genes (CAFGenes) in the context of specific cancer types will provide critical insights into disease mechanisms and CAF-related therapeutic targets. In this study, we collected and curated 5421 CAFGenes identified from small- or large-scale experiments, encompassing 4982 responsors that directly or indirectly participate in cancer malignant behaviors managed by CAFs, 1069 secretions that are secreted by CAFs and 281 regulators that contribute in modulating CAFs in human and mouse, which covered 24 cancer types. For these human CAFGenes, we performed gene expression and prognostic marker-based analyses across 24 cancer types using TCGA data. Furthermore, we provided annotations for CAF-associated proteins by integrating the knowledge of protein-protein interaction(s), drug-target relations and basic annotations, from 9 public databases. CAFrgDB (CAF related Gene DataBase) is free for academic research at http://caf.zbiolab.cn and we anticipate CAFrgDB can be a useful resource for further study of CAFs.

AttentionDTA: Drug-Target Binding Affinity Prediction by Sequence-Based Deep Learning With Attention Mechanism.

The identification of drug-target relations (DTRs) is substantial in drug development. A large number of methods treat DTRs as drug-target interactions (DTIs), a binary classification problem. The main drawback of these methods are the lack of reliable negative samples and the absence of many important aspects of DTR, including their dose dependence and quantitative affinities. With increasing number of publications of drug-protein binding affinity data recently, DTRs prediction can be viewed as a regression problem of drug-target affinities (DTAs) which reflects how tightly the drug binds to the target and can present more detailed and specific information than DTIs. The growth of affinity data enables the use of deep learning architectures, which have been shown to be among the state-of-the-art methods in binding affinity prediction. Although relatively effective, due to the black-box nature of deep learning, these models are less biologically interpretable. In this study, we proposed a deep learning-based model, named AttentionDTA, which uses attention mechanism to predict DTAs. Different from the models using 3D structures of drug-target complexes or graph representation of drugs and proteins, the novelty of our work is to use attention mechanism to focus on key subsequences which are important in drug and protein sequences when predicting its affinity. We use two separate one-dimensional Convolution Neural Networks (1D-CNNs) to extract the semantic information of drug's SMILES string and protein's amino acid sequence. Furthermore, a two-side multi-head attention mechanism is developed and embedded to our model to explore the relationship between drug features and protein features. We evaluate our model on three established DTA benchmark datasets, Davis, Metz, and KIBA. AttentionDTA outperforms the state-of-the-art deep learning methods under different evaluation metrics. The results show that the attention-based model can effectively extract protein features related to drug information and drug features related to protein information to better predict drug target affinities. It is worth mentioning that we test our model on IC50 dataset, which provides the binding sites between drugs and proteins, to evaluate the ability of our model to locate binding sites. Finally, we visualize the attention weight to demonstrate the biological significance of the model. The source code of AttentionDTA can be downloaded from https://github.com/zhaoqichang/AttentionDTA_TCBB.

E-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature.

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

A brief introduction to chemical proteomics for target deconvolution.

Drug-target relationships provide the basis for network-based polypharmacology, and target deconvolution is a key step in phenotypic-screening based drug discovery. Due to the complexity of the mammalian proteomics and the often-limited affinity of the lead compound, it is challenging to identify the drug targets, especially when the goal is to identify all targets. This paper attempts to provide a brief and comprehensive introduction to the various methods in chemical proteomics for target deconvolution by categorizing them into two groups: the biochemical enrichment and the proteomics-screening methods. Moreover, a brief introduction of related Mass Spectrometry techniques is also provided, together with recent progress. The data for this review were queried from Web of Science and PubMed, the keywords used were Drug targets, Target deconvolution, and Chemical Proteomics. A total of over 500 relevant articles, with a time limit from 1953 to 2022, were identified according to search strategy. Duplicate records and review articles were excluded by their titles and abstracts. Finally, we found about 120 articles matching our inclusion criteria, which covered representative research and reviews of various target discovery methods. Existing target discovery methods can be grouped into either biochemical enrichment or the proteomics-screening methods, with the recent emergence of a hybrid method combining these two such as lysine reactivity profiling. The advantage of the biochemical enrichment method is the ease of operation and the comprehensive target coverage. However, most biochemical enrichment methods require a high-affinity binding of the drug to the target proteins and cannot differentiate direct/indirect targets. The proteomics-screening methods do not require drug modification but have limited protein coverage, and most of them cannot differentiate direct/indirect targets. Although existing target discovery methods have greatly facilitated pharmacological research, each of these methods has advantages and disadvantages. New strategies/methods are needed to further improve both the coverage of the proteosome and the specificity.

High‐throughput metabolomics predicts drug–target relationships for eukaryotic proteins

Chemical probes are important tools for understanding biological systems. However, because of the huge combinatorial space of targets and potential compounds, traditional chemical screens cannot be applied systematically to find probes for all possible druggable targets. Here, we demonstrate a novel concept for overcoming this challenge by leveraging high‐throughput metabolomics and overexpression to predict drug–target interactions. The metabolome profiles of yeast treated with 1,280 compounds from a chemical library were collected and compared with those of inducible yeast membrane protein overexpression strains. By matching metabolome profiles, we predicted which small molecules targeted which signaling systems and recovered known interactions. Drug–target predictions were generated across the 86 genes studied, including for difficult to study membrane proteins. A subset of those predictions were tested and validated, including the novel targeting of GPR1 signaling by ibuprofen. These results demonstrate the feasibility of predicting drug–target relationships for eukaryotic proteins using high‐throughput metabolomics.

Drug-target interactions prediction via deep collaborative filtering with multiembeddings.

Drug-target interactions (DTIs) prediction research presents important significance for promoting the development of modern medicine and pharmacology. Traditional biochemical experiments for DTIs prediction confront the challenges including long time period, high cost and high failure rate, and finally leading to a low-drug productivity. Chemogenomic-based computational methods can realize high-throughput prediction. In this study, we develop a deep collaborative filtering prediction model with multiembeddings, named DCFME (deep collaborative filtering prediction model with multiembeddings), which can jointly utilize multiple feature information from multiembeddings. Two different representation learning algorithms are first employed to extract heterogeneous network features. DCFME uses the generated low-dimensional dense vectors as input, and then simulates the drug-target relationship from the perspective of both couplings and heterogeneity. In addition, the model employs focal loss that concentrates the loss on sparse and hard samples in the training process. Comparative experiments with five baseline methods show that DCFME achieves more significant performance improvement on sparse datasets. Moreover, the model has better robustness and generalization capacity under several harder prediction scenarios.

COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19.

The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug–target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug–target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug–protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE.

Drug Repurposing for COVID-19 Treatment by Integrating Network Pharmacology and Transcriptomics.

Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug–disease pharmacological effect between drug–target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.

Drug-Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking.

The prediction of drug-target interaction (DTI) is a key step in drug repositioning. In recent years, many studies have tried to use matrix factorization to predict DTI, but they only use known DTIs and ignore the features of drug and target expression profiles, resulting in limited prediction performance. In this study, we propose a new DTI prediction model named AdvB-DTI. Within this model, the features of drug and target expression profiles are associated with Adversarial Bayesian Personalized Ranking through matrix factorization. Firstly, according to the known drug-target relationships, a set of ternary partial order relationships is generated. Next, these partial order relationships are used to train the latent factor matrix of drugs and targets using the Adversarial Bayesian Personalized Ranking method, and the matrix factorization is improved by the features of drug and target expression profiles. Finally, the scores of drug-target pairs are achieved by the inner product of latent factors, and the DTI prediction is performed based on the score ranking. The proposed model effectively takes advantage of the idea of learning to rank to overcome the problem of data sparsity, and perturbation factors are introduced to make the model more robust. Experimental results show that our model could achieve a better DTI prediction performance.

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction.

Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios, whose characteristics differ from a random split of conventional training data sets. We developed a pharmacological data set augmentation procedure, Stochastic Negative Addition (SNA), which randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, drug-screening benchmark performance increases from R2 = 0.1926 ± 0.0186 to 0.4269 ± 0.0272 (122%). This gain was accompanied by a modest decrease in the temporal benchmark (13%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed y-randomized controls. Our results highlight where data and feature uncertainty may be problematic and how leveraging uncertainty into training improves predictions of drug-target relationships.

A review on drug repurposing applicable to COVID-19.

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

A multi-scale systems pharmacology approach uncovers the anti-cancer molecular mechanism of Ixabepilone

It has been realized that FDA approved drugs may have more molecular targets than is commonly thought. Thus, to find the exact drug-target interactions (DTIs) is of great significance for exploring the new molecular mechanism of drugs. Here, we developed a multi-scale system pharmacology (MSSP) method for the large-scale prediction of DTIs. We used MSSP to integrate drug-related and target-related data from multiple levels, the network structural data formed by known drug-target relationships for predicting likely unknown DTIs. Prediction results revealed that Ixabepilone, an epothilone B analog for treating breast cancer patients, may target Bcl-2, an oncogene that contributes to tumor progression and therapy resistance by inhibiting apoptosis. Furthermore, we demonstrated that Ixabepilone could bind with Bcl-2 and decrease its protein expression in breast cancer cells. The down-regulation of Bcl-2 by Ixabepilone is resulted from promoting its degradation by affecting p-Bcl-2. We further found that Ixabepilone could induce autophagy by releasing Beclin1 from Beclin1/Bcl-2 complex. Inhibition of autophagy by knockdown of Beclin1 or pharmacological inhibitor augmented apoptosis, thus enhancing the antitumor efficacy of Ixabepilone against breast cancer cells in vitro and in vivo. In addition, Ixabepilone also decreases Bcl-2 protein expression and induces cytoprotective autophagy in human hepatic carcinoma and glioma cells. In conclusion, this study not only provides a feasible and alternative way exploring new molecular mechanisms of drugs by combing computation DTI prediction, but also reveals an effective strategy to reinforce the antitumor efficacy of Ixabepilone.

Computational approach for collection and prediction of molecular initiating events in developmental toxicity

Developmental toxicity is defined as the occurrence of adverse effects on the developing organism as a result from exposure to a toxic agent. These alterations can have long-term acute effects. Current in vitro models present important limitations and the evaluation of toxicity is not entirely objective. In silico methods have also shown limited success, in part due to complex and varied mechanisms of action that mediate developmental toxicity, which are sometimes poorly understood. In this article, we compiled a dataset of compounds with developmental toxicity categories and annotated mechanisms of action for both toxic and non-toxic compounds (DVTOX). With it, we selected a panel of protein targets that might be part of putative Molecular Initiating Events (MIEs) of Adverse Outcome Pathways of developmental toxicity. The validity of this list of candidate MIEs was studied through the evaluation of new drug-target relationships that include such proteins, but were not part of the original database. Finally, an orthology analysis of this protein panel was conducted to select an appropriate animal model to assess developmental toxicity. We tested our approach using the zebrafish embryo toxicity test, finding positive results.

Abstract C021: Validation of the interaction between a candidate compound and the intended drug target by a phenotypic rescue approach

Abstract New targets for cancer treatment frequently emerge in literature, but the thorough target validation required to consider these targets for a drug discovery program is often lacking. In pharmacological or genetic perturbation studies using complex biological assays, undesired off-target effects cannot be easily distinguished from the intended mode of action at the desired target. This is especially evident in cancer drug development where it is important to discriminate on-target effects on cell viability from off-target effects resulting in non-specific loss of cellular fitness. Neglecting the possibility of being deceived by off-target effects can have tremendous scientific and financial impact on a drug discovery program. Ideally, confidence in a preclinical drug target and a modulating compound is boosted in an early stage by more extensive analysis and validation of the actual drug-target relationship. Rescue of a disease-relevant phenotype by genetic restoration of a target mutation is a gold standard approach in drug discovery by which target validation can be achieved. We aim to follow this approach targeting the BRAF V600E mutation in a number of well described melanoma lines as well as the MAP2K1 Q56P mutation in the non-small cell lung cancer cell line H1437. Target validation for both BRAF and MAP2K1 will be addressed by assessing viability, phenotypic changes and sensitivity to compound modulation upon CRISPR/Cas9 repair of the target mutation or by exogenous re-expression of the wild type variant. Compounds tested will be Vemurafenib for BRAF and Trametinib for MAP2K1. Further investigation into target validity will be done using a physiologically relevant 3D spheroid based co-culture system. Mimicking the tumor microenvironment increases the knowledge about “drug-ability” of a target and sustainability of the target modulation at an early time point in the development process. Such early in-depth validation of the relationship between a compound and the drug target is vital to mitigate the risk of failure at later steps of drug development. Citation Format: Laure Grandmoursel, Lieke Geerts, Geraldine Servant, Miranda van der Ham, Armin Maier, Jamil Aarbiou, Marijn Vlaming, Jeroen DeGroot, Julia Schuler, Ian Waddell, Anne-Marie Zuurmond. Validation of the interaction between a candidate compound and the intended drug target by a phenotypic rescue approach [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C021. doi:10.1158/1535-7163.TARG-19-C021

Prediction of disease–drug relationships based on tissue specificity and direct neighbor similarity

The pathogenesis of complex diseases is a major problem in the field of human health. The development of new drugs through traditional methods requires considerable time and money, which has not met peoples actual requirements. Recently, identifying new therapeutic effects of known drugs via drug repositioning has become an effective way to treat numerous diseases. At present, tissue-specific research has achieved some success; however, traditional drug repositioning methods rarely consider the tissue specificity of the disease. To explore the influence of tissue specificity on drug repositioning studies, this study explores the development of tissue specificity and its characteristics and proposes using direct neighbor similarity in drug repositioning based on tissue-specific data. A total of 11405 known drug–target relationships were extracted from the database DrugBank, and five cancers and their disease-causing gene data were obtained from the human Mendelian genetic database. Through the direct neighbor method and using the tissue-specific interaction network as the background network, five tissue-specific drug–disease bipartite networks were constructed, which provided potential drug–disease associations. The results were verified by the CTD (comparative toxicogenomics database) standard. The experimental results show that the accuracy of drug repositioning studies based on tissue specificity and direct neighbor measurement will provide a reliable candidate set for in vivo and in vitro experiments of new drugs, which also provides new ideas for studying drug repositioning.

The effect of ATP-binding cassette (ABC) transporters in human cancers

Multi-drug resistance (MDR) can be defined as a negative effect on the drug-target relationships, decreasing in intracellular drug aggregation. ABC (ATP-binding cassette) are popular transporter proteins in the development of MDR. ABC proteins in the cell membrane structure reduce the therapeutic efficacy and cause drug resistance in cancer patients. Resistance to chemotherapy is a remarkable and important issue in the treatment of cancer patients. These drug resistance proteins interfere with the drugs administered to patients, resulting in a decrease in bioavailability of these drugs and a reduction in their passage through the blood-brain barrier. ABC transporters are the largest family of transmembrane proteins with 8 subfamilies assigned from A to G based on sequence and structural homology. In this review, general information about ABC proteins is mentioned and the role of ABC proteins causing MDR in cancers is also discussed.

Abstract 1265: Validation of the interaction between a candidate compound and the intended drug target by a phenotypic rescue approach

Abstract New targets for cancer treatment frequently emerge in literature, but the thorough target validation required to consider these targets for a drug discovery program is often lacking. In pharmacological or genetic perturbation studies using complex biological assays, undesired off-target effects cannot be easily distinguished from the intended mode of action at the desired target. This is especially evident in cancer drug development where it is important to discriminate on-target effects on cell viability from off-target effects resulting in non-specific loss of cellular fitness. Neglecting the possibility of being deceived by off-target effects can have tremendous scientific and financial impact on a drug discovery program. Ideally confidence in a preclinical drug target and a modulating compound is boosted in an early stage by more extensive analysis and validation of the actual drug-target relationship. Rescue of a disease-relevant phenotype by genetic restoration of a target mutation is a gold standard approach in drug discovery by which target validation can be achieved. We aim to follow this approach targeting the BRAF V600E mutation in a number of well described melanoma lines as well as the MAP2K1 Q56P mutation in non-small cell lung cancer cell line H1437. Target validation for both BRAF and MAP2K1 will be addressed by assessing viability, phenotypic changes and sensitivity to compound modulation upon CRISPR/Cas9 repair of the target mutation or by exogenous re-expression of the wildtype variant. Compounds tested will be Vemurafenib for BRAF and Trametinib for MAP2K1. Further investigation into target validity will be done using a physiologically relevant 3D spheroid based co-culture system. Mimicking the tumor microenvironment increases the knowledge about “drug-ability” of a target and sustainability of the target modulation at an early time point in the development process. Such early in-depth validation of the relationship between a compound and the drug target is vital to mitigate the risk of failure at later steps of drug development. Citation Format: Lieke Geerts, Laure Grandmoursel, Jamil Aarbiou, Jeroen DeGroot, Julia Schüler, Ian Waddell, Anne-Marie Zuurmond. Validation of the interaction between a candidate compound and the intended drug target by a phenotypic rescue approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1265.