New Drug Targets Research Articles

Abstract 1432: TORC2 dependent control of cell growth and size in cancer cells

Abstract Severe defects in cell growth and size are a nearly universal feature of cancer cells and have provided a basis for cancer pathology for over 100 years; however, the underlying mechanisms remain largely unknown. Recent work in budding yeast has shown that a highly conserved signaling network surrounding Tor complex 2 (TORC2) controls cell growth and size. A key component of the network is the yeast homolog of serum and glucocorticoid-regulated kinase (SGK), which controls production of ceramide signaling lipids that control cell growth and size. Here, we present evidence that a similar TORC2 network controls cell growth and size in mammalian cells. In addition, we show that expression of oncogenic drivers in NIH3T3 cells causes defects in cell size, as well as defects in TORC2-dependent control of cell size. Cancer cells also show severe defects in TORC2-dependent control of cell size. Our working hypothesis is that the TORC2 network is downstream of multiple oncogenes, and that aberrant TORC2 signaling is a major cause of the nearly universal size defects observed in cancer cells. A better understanding of the mechanisms that control cell growth and size in normal cells, and how they go awry in cancer, could identify new targets for drugs to improve cancer therapies. Citation Format: Jerry T. DeWitt, Douglas Kellogg. TORC2 dependent control of cell growth and size in cancer cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1432.

Drug target deconvolution by chemical proteomics

Drug target identification can help establish the association between drug activity and phenotypes, elucidate the mechanism of action, discover off-target effects and drug resistance mechanisms, discover new targets for therapeutic drugs, and predict possible side effects and toxicity at an early stage of drug discovery, thereby reducing the risk of failure in drug development. Although rapid progress has been made in scientific discoveries and technological advancements, the identification of drug targets is still a daunting task. Herein, the research progress in drug target identification during the past decade, especially novel technologies without chemical labeling, have been reviewed.

Another Breaker of the Wall: the Biological Function of the Usp45 Protein of Lactococcus lactis.

Lactococcus lactis is a Gram-positive bacterium that is widely used as a cell factory for the expression of heterologous proteins that are relevant in the pharmaceutical and nutraceutical fields. The signal peptide of the major secreted protein of L. lactis, Usp45, has been employed extensively in engineering strategies to secrete proteins of interest. However, the biological function of Usp45 has remained obscure despite more than 25 years of research. Studies on Usp45 homologs in other Gram-positive bacteria suggest that Usp45 may play a role in cell wall turnover processes. Here, we show the effect of inactivation and overexpression of the usp45 gene on L. lactis growth, phenotype, and cell division. Our results are in agreement with those obtained in streptococci and demonstrate that the L. lactis Usp45 protein is essential for proper cell division. We also show that the usp45 promoter is highly activated by galactose. Overall, our results indicate that Usp45 mediates cell separation, probably by acting as a peptidoglycan hydrolase.IMPORTANCE The cell wall, composed mainly of peptidoglycan, is key to maintaining the cell shape and protecting the cell from bursting. Peptidoglycan degradation by peptidoglycan hydrolysis and autolysins occurs during growth and cell division. Since peptidoglycan hydrolases are important for virulence, envelope integrity, and regulation of cell division, it is valuable to investigate their function and regulation. Notably, PcsB-like proteins such as Usp45 have been proposed as new targets for antimicrobial drugs and could also be target for the development of food-grade suicide systems. In addition, although various other expression and secretion systems have been developed for use in Lactococcus lactis, the most-used signal peptide for protein secretion in this bacterium is that of the Usp45 protein. Thus, elucidating the biological function of Usp45 and determining the factors affecting its expression would contribute to optimize several applications.

DNA topoisomerases as targets of anticancer drugs

Eukaryotic DNA topoisomerases are ubiquitous nuclear enzymes that alter DNA topology by breaking and rejoining DNA strands. There are two classes of DNA topoisomerases; DNA topoisomerase I introduces a transient single-strand DNA break, while topoisomerase II introduces transient double-strand DNA breaks for each catalytic reaction. Both are important for solving topological problems arising during DNA replication, transcription, recombination and other cellular functions. Recently, scientists have realized the importance of topoisomerases as new therapeutic targets for antibacterial, antifungal, antiparasitic, antiviral and anticancer drugs. The present review focuses on anticancer drugs targeting mammalian DNA topoisomerases, which are named DNA topoisomerase poison. Camptothecin and VM26 (teniposide) are representative DNA topoisomerase poisons that target DNA topoisomerase I and topoisomerase II, respectively. These drugs alter the breakage-reunion reactions of DNA topoisomerases by trapping topoisomerase-DNA cleavable complexes in both the purified system and cultured cells. However, resistance to various DNA topoisomerase poisons has been documented in cancer cells with repect to MDR1 overexpression, reduced topoisomerase levels, drug-resistant mutant topoisomerase, lengthened cell cycle time and altered DNA repair function. A better understanding of the molecular targets for anticancer drugs and the various drug-resistance mechanisms may help us to discover and tailor new drugs for particular drug-resistant tumors.

Molecular dynamic simulations of full-length human purinergic receptor subtype P2X7 bonded to potent inhibitors

Among the members of purinergic receptors, the family P2X of ionotropic proteins has the ion channel subtype P2X7 that show in studies to be an important molecular target for new drugs. The activity of human P2X7 receptor (hP2X7r) in the body, due to its pro-inflammatory function, can trigger physiological disorders related to chronic inflammatory processes, leading to neural degeneration, neuropathic pain and chronic pain. Recently, two series of promising new inhibitors of the hP2X7r ion channel have been reported. One series consisted of naphthoquinone derivatives and the other composed of triazole derivatives. The main objective of this study was to understand the binding mode differences between the hit compounds of each series and compare them to the native ligand ATP. The hP2X7r ion channel and membrane lipid models were prepared in order to allow study the appropriate protein molecular dynamics. Molecular modeling and molecular dynamics simulation approaches were applied in order to obtain atomistic and molecular details that are involved in intermolecular interactions. Both compounds AN-04 and 9d seem to have affinity to binding in the hP2X7r pore area according to molecular dynamics simulations results. The naphthoquinone derivative AN-04 demonstrated a binding free energy 7.68 fold larger than triazole derivative 9d and 3.8 fold lower than native ligand ATP. These results indicate that compound AN-04 might be a promising lead compound for the development of a novel selective hP2X7r inhibitor.

Design, synthesis and biological activity of novel substituted 3-benzoic acid derivatives as MtDHFR inhibitors

The enzyme dihydrofolate reductase from M.tuberculosis (MtDHFR) has a high unexploited potential to be a target for new drugs against tuberculosis (TB), due to its importance for pathogen survival. Preliminary studies have obtained fragment-like molecules with low affinity to MtDHFR which can potentially become lead compounds. Taking this into account, the fragment MB872 was used as a prototype for analogue development by bioisosterism/retro-bioisosterism, which resulted in 20 new substituted 3-benzoic acid derivatives. Compounds were active against MtDHFR, with IC50 values ranging from 7 to 40 μM, where compound 4e not only had the best inhibitory activity (IC50 = 7 μM), but also was 71-fold more active than the original fragment MB872. The 4e inhibition kinetics indicated an uncompetitive mechanism, which was supported by molecular modeling which suggested that the compounds can access an independent backpocket from the substrate and competitive inhibitors. Thus, based on these results, substituted 3-benzoic acid derivatives have strong potential to be developed as novel MtDHFR inhibitors and also anti-TB agents.

Characterization of HLA-G Regulation and HLA Expression in Breast Cancer and Malignant Melanoma Cell Lines upon IFN-γ Stimulation and Inhibition of DNA Methylation.

The potential role of human leukocyte antigen (HLA)-G as a target for new cancer immunotherapy drugs has increased the interest in the analysis of mechanisms by which HLA-G expression is regulated, and how the expression can be manipulated. We characterized HLA expression in breast cancer and malignant melanoma cell lines and investigated the induction of HLA-G expression by two distinct mechanisms: stimulation with interferon (IFN)-γ or inhibition of methylation by treatment with 5-aza-2’-deoxycytidine (5-aza-dC). The effect of IFN-γ and 5-aza-dC on HLA expression was dependent on the cancer cell lines studied. However, in general, surface expression of HLA class Ia was induced on all cell lines. Surface expression of HLA-G was inconclusive but induction of HLA-G mRNA was prevalent upon treatment with 5-aza-dC and a combination of IFN-γ and 5-aza-dC. IFN-γ alone failed to induce HLA-G expression in the HLA-G-negative cell lines. The results support that HLA-G expression is regulated partly by DNA methylation. Furthermore, IFN-γ may play a role in the maintenance of HLA-G expression rather than inducing expression. The study demonstrates the feasibility of manipulating HLA expression and contributes to the exploration of mechanisms that can be potential targets for immunotherapy in breast cancer and malignant melanoma.

The role of hexokinase in cancer

A thorough understanding of the processes occurring in cancer cells is necessary to make cancer treatment as effective as possible. Changes in cellular metabolism in relation to normal cells are considered particularly important. One of the most interesting and promising areas is glucose metabolism and the factors affecting this process, with special emphasis on the potential role of hexokinases, especially the isoform II of this enzyme. Hexokinases (HK) are transferase enzymes involved in the process of glycolysis. Hexokinase II (HK II) plays an important role in initiating and maintaining the glycolysis process at a high level of efficiency, which is crucial for the growth and proliferation of cancer cells. An increase in the number of copies of the HK II gene and increased transcription of this enzyme resulting in the suppression of apoptosis and the enhancement of cell proliferation have been found in tumor cells. Hexokinase II also participates in the Crabtree effect by affecting the amount of ATP and thus the efficiency of the Ca2+ removal process outside the cell membrane by Ca2+ ATPase. Overexpression of HK II has thus far been found in pancreatic cancer, gastric cancer, breast cancer, squamous cell carcinoma of the larynx, glioblastoma multiforme, ovarian cancer and biliary tract cancer, indicating the possible key role of this enzyme in their formation and progression and providing the basis for seeking potential benefits of cancer treatment using HK II as a target of new drugs.

Machine learning prediction of survival in diffuse large B-cell lymphoma based on gene-expression profiling.

8047 Background: The current clinical risk stratification of Diffuse Large B-cell Lymphoma (DLBCL) relies on the International Prognostic Index (IPI) comprising a limited number of clinical variables but is imperfect in the identification of high-risk disease. Our study aimed to: (1) develop a risk prediction model based on the genetic and clinical features; and (2) evaluate the model’s biological implications in association with the estimated profiles of immune infiltration. Methods: Gene-expression profiling was performed on 718 patients with DLBCL for which RNA sequencing data and clinical covariates were available by Reddy et al (2017). Unsupervised and supervised machine learning methods were used to discover and identify the best set of survival-associated gene signatures for prediction. A multivariate model of survival from these signatures was constructed in the training set and validated in an independent test set. The compositions of the tumor-infiltrating immune cells were enumerated using CIBERSORT for deconvolution analysis. Results: A four gene-signature-based score was developed that separated patients into high- and low-risk groups with a significant difference in survival in the training, validation and complete cohorts (p < 0.001), independently of the IPI. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The area-under-the-curve at 2 and 5 years increased from 0.71 and 0.69 to 0.75 and 0.74 in the validation set, respectively. Conclusions: By analyzing the gene-expression data with a systematic approach, we developed and validated a risk prediction model that outperforms existing risk assessment methods. Our study, which integrated the profiles of immune infiltration with prognostic prediction, unraveled important associations that have the potential to identify patients who could benefit from the various therapeutic interventions, as well as highlighting possible targets for new drugs.

Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach.

In silicodrug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug-gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug-target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes. Supplementary data are available at Bioinformatics online.

In silico evaluation and in vitro growth inhibition of Plasmodium falciparum by natural amides and synthetic analogs.

Malaria, caused by protozoa of the genus Plasmodium, is a disease that infects hundreds of millions of people annually, causing an enormous social burden in many developing countries. Since current antimalarial drugs are starting to face resistance by the parasite, the development of new therapeutic options has been prompted. The enzyme Plasmodium falciparum enoyl-ACP reductase (PfENR) has a determinant role in the fatty acid biosynthesis of this parasite and is absent in humans, making it an ideal target for new antimalarial drugs. In this sense, the present study aimed at evaluating the in silico binding affinity of natural and synthetic amides through molecular docking, in addition to their in vitro activity against P. falciparum by means of the SYBR Green Fluorescence Assay. The in vitro results revealed that the natural amide piplartine (1a) presented partial antiplasmodial activity (20.54μM), whereas its synthetic derivatives (1m-IC50 104.45μM), (1b, 1g, 1k, and 14f) and the natural amide piperine (18a) were shown to be inactive (IC50 > 200μM). The in silico physicochemical analyses demonstrated that compounds 1m and 14f violated the Lipinski's rule of five. The in silico analyses showed that 14f presented the best binding affinity (- 13.047kcal/mol) to PfENR and was also superior to the reference inhibitor triclosan (- 7.806kcal/mol). In conclusion, we found that the structural modifications in 1a caused a significant decrease in antiplasmodial activity. Therefore, new modifications are encouraged in order to improve the activity observed.

Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning.

Identifying interactions between drugs and target proteins is a critical step in the drug development process, as it helps identify new targets for drugs and accelerate drug development. The number of known drug-protein interactions (positive samples) is much lower than that of the unknown ones (negative samples), which forms a class imbalance. Most previous methods only utilised part of the negative samples to train the prediction model, so most of the information on negative samples was neglected. Therefore, a new method must be developed to predict candidate drug-related proteins and fully utilise negative samples to improve prediction performance. We present a method based on non-negative matrix factorisation and gradient boosting decision tree (GBDT), named NGDTP, to identify the candidate drug-protein interactions. NGDTP integrates multiple kinds of protein similarities, drugs-proteins interactions, and multiple kinds of drugs similarities at different levels, including target proteins of drugs, drug-related diseases, and side effects of drugs. We propose a network representation learning method based on matrix factorisation to learn low-dimensional vector representations of drug and protein nodes. On the basis of these low-dimensional node representations, a GBDT-based prediction model was constructed and it obtains the association scores through establishing multiple decision trees for a drug-protein pairs. NGDTP is an ensemble learning model that fully utilises all the negative samples to effectively alleviate the problem of class imbalance. NGDTP achieves superior prediction performance when it is compared with several state-of-the-art methods. The experimental results indicate that NGDTP also retrieves more actual drug-protein interactions in the top part of prediction result, which drew significant attention from the biologists. In addition, case studies on 10 drugs further confirmed the ability of the NGDTP to identify potential candidate proteins for drugs.

Protein kinases PknA and PknB independently and coordinately regulate essential Mycobacterium tuberculosis physiologies and antimicrobial susceptibility.

The Mycobacterium tuberculosis Ser/Thr protein kinases PknA and PknB are essential for growth and have been proposed as possible drug targets. We used a titratable conditional depletion system to investigate the functions of these kinases. Depletion of PknA or PknB or both kinases resulted in growth arrest, shortening of cells, and time-dependent loss of acid-fast staining with a concomitant decrease in mycolate synthesis and accumulation of trehalose monomycolate. Depletion of PknA and/or PknB resulted in markedly increased susceptibility to β-lactam antibiotics, and to the key tuberculosis drug rifampin. Phosphoproteomic analysis showed extensive changes in protein phosphorylation in response to PknA depletion and comparatively fewer changes with PknB depletion. These results identify candidate substrates of each kinase and suggest specific and coordinate roles for PknA and PknB in regulating multiple essential physiologies. These findings support these kinases as targets for new antituberculosis drugs and provide a valuable resource for targeted investigation of mechanisms by which protein phosphorylation regulates pathways required for growth and virulence in M. tuberculosis.

Childhood Cancer Drug Development: Why Alternative Designs for Drug Development are Required

In the US, the regulatory environment, through the Research to Accelerate Cures and Equity for Children Act (RACE for Children Act) requires FDA to develop a list of molecular targets and molecular targets of new drugs and biologics in development, that are determined to be substantially relevant to the growth and progression of pediatric cancer, and that may trigger the requirement for pediatric investigations. The intent is to engage Pharma in pediatric testing at an early stage in drug development. Under the RACE for Children Act, FDA may now require pediatric assessments when molecular targets under FDA review are substantially relevant to childhood cancer. This may require evaluation of up to 200 entities each year. However, it is clear that because a cancer has a particular ‘molecular driver’ it does not necessarily predict tumor response to a targeted agent. For example, some patients with low‐grade BRAF‐driven glioma have dramatic responses to MEK inhibitors, whereas others have some disease stabilization. Thus, factors other than the driver‐mutation appear important to tumor response. Consequently, to encompass clinical heterogeneity (genetic/epigenetic) that may determine response to treatment, a large number of models having similar molecular characteristics will have to be developed to allow adequate pre‐clinical evaluation of new targeted entities. Previously, the Pediatric Preclinical Testing Program (PPTP) conducted a retrospective analysis of over 2100 studies using 83 PDX and xenograft models and 67 drug entities undertaken using conventional testing (10 mice/group for solid tumor PDX models, 8 mice/group for leukemia PDX models). This analysis showed that use of one mouse per treatment group would accurately predict the group median response in 78% of experiments. Allowing a deviation of + or − one response category, the accuracy was 95% (Murphy B. et al, Cancer Res. 2016). Here we have prospectively tested the ‘single mouse’ experimental design to evaluate both small molecule and antibody‐drug conjugate drugs. We conclude that the ‘single mouse’ approach is highly accurate, and yields more information than traditional experimental approaches, particularly with respect to identifying biomarkers that associate with tumor response. Further, within the current resource constraints, this design allows up to 20 PDX models to be used for every one model used in the conventional testing design. Thus, the single mouse design potentially allows incorporation of disease heterogeneity that may be required for evaluating new agents under the RACE for Children Act.Support or Funding InformationNO1‐CM42216, UO1CA199297, CA165995 from NIH, and CPRIT RP160716

Mycobacterial Cell Wall: A Source of Successful Targets for Old and New Drugs

Eighty years after the introduction of the first antituberculosis (TB) drug, the treatment of drug-susceptible TB remains very cumbersome, requiring the use of four drugs (isoniazid, rifampicin, ethambutol and pyrazinamide) for two months followed by four months on isoniazid and rifampicin. Two of the drugs used in this “short”-course, six-month chemotherapy, isoniazid and ethambutol, target the mycobacterial cell wall. Disruption of the cell wall structure can enhance the entry of other TB drugs, resulting in a more potent chemotherapy. More importantly, inhibition of cell wall components can lead to mycobacterial cell death. The complexity of the mycobacterial cell wall offers numerous opportunities to develop drugs to eradicate Mycobacterium tuberculosis, the causative agent of TB. In the past 20 years, researchers from industrial and academic laboratories have tested new molecules to find the best candidates that will change the face of TB treatment: drugs that will shorten TB treatment and be efficacious against active and latent, as well as drug-resistant TB. Two of these new TB drugs block components of the mycobacterial cell wall and have reached phase 3 clinical trial. This article reviews TB drugs targeting the mycobacterial cell wall in use clinically and those in clinical development.

Parallel Interrogation of β-Arrestin2 Recruitment for Ligand Screening on a GPCR-Wide Scale using PRESTO-Tango Assay.

As the largest and most versatile gene superfamily and mediators of a gamut of cellular signaling pathways, G-protein-coupled receptors (GPCRs) represent one of the most promising targets for the pharmaceutical industry. Ergo, the design, implementation, and optimization of GPCR ligand screening assays is crucial, as they represent remote-control tools for drug discovery and for manipulating GPCR pharmacology and outcomes. In the past, G-protein dependent assays typified this area of research, detecting ligand-induced events and quantifying the generation of secondary messengers. However, since the advent of functional selectivity, as well as an increased awareness of several other G protein-independent pathways and the limitations associated with G-protein dependent assays, there is a greater push towards the creation of alternative GPCR ligand screening assays. Towards this endeavor, we describe the application of one such resource, the PRESTO-Tango platform, a luciferase reporter-based system that enables the parallel and simultaneous interrogation of the human GPCR-ome, a feat which was previously considered technically and economically unfeasible. Based on a G-protein independent β-arrestin2 recruitment assay, the universality of β-arrestin2-mediated trafficking and signaling at GPCRs makes PRESTO-TANGO an apt tool for studying approximately 300 non-olfactory human GPCRs, including approximately 100 orphan receptors. PRESTO-Tango's sensitivity and robustness make it suitable for primary high-throughput screens using compound libraries, employed to uncover new GPCR targets for known drugs or to discover new ligands for orphan receptors.

Synthesis, Evaluation and Proposed Binding Pose of Substituted Spiro-Oxindole Dihydroquinazolinones as IRAP Inhibitors.

Insulin‐regulated aminopeptidase (IRAP) is a new potential macromolecular target for drugs aimed for treatment of cognitive disorders. Inhibition of IRAP by angiotensin IV (Ang IV) improves the memory and learning in rats. The majority of the known IRAP inhibitors are peptidic in character and suffer from poor pharmacokinetic properties. Herein, we present a series of small non‐peptide IRAP inhibitors derived from a spiro‐oxindole dihydroquinazolinone screening hit (pIC50 5.8). The compounds were synthesized either by a simple microwave (MW)‐promoted three‐component reaction, or by a two‐step one‐pot procedure. For decoration of the oxindole ring system, rapid MW‐assisted Suzuki‐Miyaura cross‐couplings (1 min) were performed. A small improvement of potency (pIC50 6.6 for the most potent compound) and an increased solubility could be achieved. As deduced from computational modelling and MD simulations it is proposed that the S‐configuration of the spiro‐oxindole dihydroquinazolinones accounts for the inhibition of IRAP.

Bacterial biopolymers: from pathogenesis to advanced materials.

Bacteria are prime cell factories that can efficiently convert carbon and nitrogen sources into a large diversity of intracellular and extracellular biopolymers, such as polysaccharides, polyamides, polyesters, polyphosphates, extracellular DNA and proteinaceous components. Bacterial polymers have important roles in pathogenicity, and their varied chemical and material properties make them suitable for medical and industrial applications. The same biopolymers when produced by pathogenic bacteria function as major virulence factors, whereas when they are produced by non-pathogenic bacteria, they become food ingredients or biomaterials. Interdisciplinary research has shed light on the molecular mechanisms of bacterial polymer synthesis, identified new targets for antibacterial drugs and informed synthetic biology approaches to design and manufacture innovative materials. This Review summarizes the role of bacterial polymers in pathogenesis, their synthesis and their material properties as well as approaches to design cell factories for production of tailor-made bio-based materials suitable for high-value applications.

Extrapolating Antifungal Animal Data to Humans - Is it reliable?

This article aimed to review animal models of antifungals and identifies human literature to assess if the extrapolation of results is reliable. Animal studies have helped identify AUC/MIC targets for new drugs and formulations such as isavuconazole and delayed release posaconazole that have translated to successful outcomes in humans. Models have also been influential in the identification of possible combination therapies for the treatment of aspergillosis, such as voriconazole and echinocandins. However, challenges are endured with animal models when it comes to replicating the pharmacokinetics of humans which has been exemplified with the newest itraconazole formulation. Additionally, animal models have displayed a survival benefit with the use of iron chelators and amphotericin for mucormycosis which was not demonstrated in humans. Animal models have been a staple in the development and optimization of antifungal agents. They afford the ability to investigate uncommon diseases, such as invasive fungal infections, that would otherwise take years and many resources to complete. Although there are many benefits of animal models there are also shortcomings. This is why the reliability of extrapolating data from animal models to humans is often scrutinized.

Diethyldithiocarbamate-copper nanocomplex reinforces disulfiram chemotherapeutic efficacy through light-triggered nuclear targeting.

To circumvent the huge cost, long R&D time and the difficulty to identify the targets of new drugs, repurposing the ones that have been clinically approved has been considered as a viable strategy to treat different diseases. In the current study, we outlined the rationale for repurposing disulfiram (DSF, an old alcohol-aversion drug) to treat primary breast cancer and its metastases.Methods: To overcome a few shortcomings of the individual administration of DSF, such as the dependence on copper ions (Cu2+) and limited capability in selective targeting, we here artificially synthesized the active form of DSF, diethyldithiocarbamate (DTC)-Cu complex (CuET) for cancer therapeutics. To achieve a greater efficacy in vivo, smart nanomedicines were devised through a one-step self-assembly of three functional components including a chemically stable and biocompatible phase-change material (PCM), the robust anticancer drug (CuET) and a near-infrared (NIR) dye (DIR), namely CuET/DIR NPs. A number of in vitro assays were performed including the photothermal efficacy, light-triggered drug release behavior, nuclear localization, DNA damage and induction of apoptosis of CuET/DIR NPs and molecular mechanisms underlying CuET-induced repression on cancer metastatic behaviors. Meanwhile, the mice bearing 4T1-LG12-drived orthotopic tumors were employed to evaluate in vivo biodistribution and anti-tumor effect of CuET/DIR NPs. The intravenous injection model was employed to reflect the changes of the intrinsic metastatic propensity of 4T1-LG12 cells responding to CuET/DIR NPs.Results: The rationally designed nanomedicines have self-traceability for bioimaging, long blood circulation time for enhanced drug accumulation in the tumor site and photo-responsive release of the anticancer drugs. Moreover, our data unearthed that CuET/DIR nanomedicines behave like “Trojan horse” to transport CuET into the cytoplasm, realizing substantial intracellular accumulation. Upon NIR laser irradiation, massive CuET would be triggered to release from the nanomedicines and reach a high local concentration towards the nucleus, where the pro-apoptotic effects were conducted. Importantly, our CuET/DIR nanomedicines revealed a pronounced capability to leash breast cancer metastases through inhibition on EMT. Additionally, these nanomedicines showed great biocompatibility in animals.Conclusion: These combined data unearthed a remarkably enhanced tumor-killing efficacy of our CuET nanomedicines through nuclear targeting. This work may open a new research area of repurposing DSF as innovative therapeutic agents to treat breast cancer and its metastases.