Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.

Target identification and drug repurposing could benefit from network-based, rational deep learning prediction, and explore the relationship between drugs and targets in the heterogeneous drug–gene–disease network.

Recent advances in DNA/RNA sequencing have made it possible to identify new targets rapidly and to repurpose approved drugs for treating heterogeneous diseases by the ‘precise’ targeting of individualized disease modules. In this study, we develop a Genome-wide Positioning Systems network (GPSnet) algorithm for drug repurposing by specifically targeting disease modules derived from individual patient’s DNA and RNA sequencing profiles mapped to the human protein-protein interactome network. We investigate whole-exome sequencing and transcriptome profiles from ~5,000 patients across 15 cancer types from The Cancer Genome Atlas. We show that GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 approved drugs. Importantly, we experimentally validate that an approved cardiac arrhythmia and heart failure drug, ouabain, shows potential antitumor activities in lung adenocarcinoma by uniquely targeting a HIF1α/LEO1-mediated cell metabolism pathway. In summary, GPSnet offers a network-based, in silico drug repurposing framework for more efficacious therapeutic selections.

Deciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost. EXPERIMENTAL APPROACHIn this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays. KEY RESULTSHigh performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor α with IC 50 or EC 50 values ≤10 μM. Furthermore, two dual ligands with both agonistic and antagonistic activities ≤1 μM would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis. CONCLUSION AND IMPLICATIONSIn summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.Abbreviations bSDTNBI, balanced substructure-drug-target network-based inference; DTI, drug-target interaction; e P , precision enhancement; e R , recall enhancement; ERα, oestrogen receptor α; E2, estradiol; MoA, mechanism of action; NBI, network-based inference; P, precision; R, recall; ROC, receiver operating characteristic

No abstract

No effective targeted therapies exist for cancers with somatic KRAS mutations. Here we develop a synthetic lethal chemical screen in isogenic KRAS-mutant and wild-type cells to identify clinical drug pairs. Our results show that dual inhibition of polo-like kinase 1 and RhoA/Rho kinase (ROCK) leads to the synergistic effects in KRAS-mutant cancers. Microarray analysis reveals that this combinatory inhibition significantly increases transcription and activity of cyclin-dependent kinase inhibitor p21WAF1/CIP1, leading to specific G2/M phase blockade in KRAS-mutant cells. Overexpression of p21WAF1/CIP1, either by cDNA transfection or clinical drugs, preferentially impairs the growth of KRAS-mutant cells, suggesting a druggable synthetic lethal interaction between KRAS and p21WAF1/CIP1. Co-administration of BI-2536 and fasudil either in the LSL-KRASG12D mouse model or in a patient tumour explant mouse model of KRAS-mutant lung cancer suppresses tumour growth and significantly prolongs mouse survival, suggesting a strong synergy in vivo and a potential avenue for therapeutic treatment of KRAS-mutant cancers.

We described a prospective application of ligand-based virtual screening program SHAFTS to discover novel inhibitors for p90 ribosomal S6 protein kinase 2 (RSK2). Taking the putative 3D conformations of two weakly binding RSK2 NTKD inhibitors as query templates, SHAFTS was used to perform 3D similarity based virtual screening because of a lack of crystal structure of RSK2 protein, thus leading to the identification of several novel scaffolds that would have been missed by conventional 2D fingerprint methods. The most potent hit compounds show low micromolar inhibitory activities against RSK2. In particular, one of the hit compounds exhibits potent antimigration activity against the MDA-MB-231 tumor cell. The results exemplified SHAFTS' application in active enrichment and scaffold hopping, which is of general interest for lead identification in drug discovery endeavors and also provides novel scaffolds that lay the foundation for uncovering new RSK2 regulatory mechanisms.

Acute myeloid leukemia is a disorder characterized by abnormal differentiation of myeloid cells and a clonal proliferation derived from primitive hematopoietic stem cells. Interventions that overcome myeloid differentiation have been shown to be a promising therapeutic strategy for acute myeloid leukemia. In this study, we demonstrate that CRISPR/Cas9-mediated knockout of dihydroorotate dehydrogenase leads to apoptosis and normal differentiation of acute myeloid leukemia cells, indicating that dihydroorotate dehydrogenase is a potential differentiation regulator and a therapeutic target in acute myeloid leukemia. By screening a library of natural products, we identified a novel dihydroorotate dehydrogenase inhibitor, isobavachalcone, derived from the traditional Chinese medicine Psoralea corylifolia. Using enzymatic analysis, thermal shift assay, pull down, nuclear magnetic resonance, and isothermal titration calorimetry experiments, we demonstrate that isobavachalcone inhibits human dihydroorotate dehydrogenase directly, and triggers apoptosis and differentiation of acute myeloid leukemia cells. Oral administration of isobavachalcone suppresses subcutaneous HL60 xenograft tumor growth without obvious toxicity. Importantly, our results suggest that a combination of isobavachalcone and adriamycin prolonged survival in an intravenous HL60 leukemia model. In summary, this study demonstrates that isobavachalcone triggers apoptosis and differentiation of acute myeloid leukemia cells via pharmacological inhibition of human dihydroorotate dehydrogenase, offering a potential therapeutic strategy for acute myeloid leukemia.