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. Study of drug-target interaction is an important topic toward elucidation of protein functions and understanding of molecular mechanisms inside cells. Traditional methods to predict new targets for known drugs were based on small molecules, protein targets or phenotype features. Here, we proposed a network-based inference (NBI) method which only used drug-target bipartite network topology similarity to infer new targets for known drugs. The performance of NBI outperformed the drug-based similarity inference and target-based similarity inference methods as well as other published methods. Via the NBI method five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, were identified to have polypharmacological effects on human estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration from submicromolar to micromolar by in vitro assays. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that the drug-target bipartite network-based inference method could be a useful tool for fishing novel drug-target interactions in molecular polypharmacological space.

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.

Prostaglandin E2 (PGE2), a major cyclooxygenase-2 (COX-2) product, is highly secreted by the osteoblast lineage in the subchondral bone tissue of osteoarthritis (OA) patients. However, NSAIDs, including COX-2 inhibitors, have severe side effects during OA treatment. Therefore, the identification of novel drug targets of PGE2 signaling in OA progression is urgently needed. Osteoclasts play a critical role in subchondral bone homeostasis and OA-related pain. However, the mechanisms by which PGE2 regulates osteoclast function and subsequently subchondral bone homeostasis are largely unknown. Here, we show that PGE2 acts via EP4 receptors on osteoclasts during the progression of OA and OA-related pain. Our data show that while PGE2 mediates migration and osteoclastogenesis via its EP2 and EP4 receptors, tissue-specific knockout of only the EP4 receptor in osteoclasts (EP4 ^LysM ) reduced disease progression and osteophyte formation in a murine model of OA. Furthermore, OA-related pain was alleviated in the EP4 ^LysM mice, with reduced Netrin-1 secretion and CGRP-positive sensory innervation of the subchondral bone. The expression of platelet-derived growth factor-BB (PDGF-BB) was also lower in the EP4 ^LysM mice, which resulted in reduced type H blood vessel formation in subchondral bone. Importantly, we identified a novel potent EP4 antagonist, HL-43, which showed in vitro and in vivo effects consistent with those observed in the EP4 ^LysM mice. Finally, we showed that the Gαs/PI3K/AKT/MAPK signaling pathway is downstream of EP4 activation via PGE2 in osteoclasts. Together, our data demonstrate that PGE2/EP4 signaling in osteoclasts mediates angiogenesis and sensory neuron innervation in subchondral bone, promoting OA progression and pain, and that inhibition of EP4 with HL-43 has therapeutic potential in OA.

Metabolic rewiring is considered as a primary feature of cancer. Malignant cells reprogram metabolism pathway in response to various intrinsic and extrinsic drawback to fuel cell survival and growth. Among the complex metabolic pathways, pyrimidine biosynthesis is conserved in all living organism and is necessary to maintain cellular fundamental function (i.e. DNA and RNA biosynthesis). A wealth of evidence has demonstrated that dysfunction of pyrimidine metabolism is closely related to cancer progression and numerous drugs targeting pyrimidine metabolism have been approved for multiple types of cancer. However, the non-negligible side effects and limited efficacy warrants a better strategy for negating pyrimidine metabolism in cancer. In recent years, increased studies have evidenced the interplay of oncogenic signaling and pyrimidine synthesis in tumorigenesis. Here, we review the recent conceptual advances on pyrimidine metabolism, especially dihydroorotate dehydrogenase (DHODH), in the framework of precision oncology medicine and prospect how this would guide the development of new drug precisely targeting the pyrimidine metabolism in cancer.

Immune checkpoint blockade (ICB) has a limited effect on colorectal cancer, underlining the requirement of co‐targeting the complementary mechanisms. Here, we identified prostaglandin E2 (PGE ₂ ) receptor 4 (EP4) as the master regulator of immunosuppressive myeloid cells (IMCs), which are the major driver of resistance to ICB therapy. PGE ₂ ‐bound EP4 promotes the differentiation of immunosuppressive M2 macrophages and myeloid‐derived suppressor cells (MDSCs) and reduces the expansion of immunostimulated M1 macrophages. To explore the immunotherapeutic role of EP4 signaling, we developed a novel and selective EP4 antagonist TP‐16. TP‐16 effectively blocked the function of IMCs and enhanced cytotoxic T‐cell‐mediated tumor elimination in vivo . Cell co‐culture experiments revealed that TP‐16 promoted T‐cell proliferation, which was impaired by tumor‐derived CD11b ⁺ myeloid cells. Notably, TP‐16 and anti‐PD‐1 combination therapy significantly impeded tumor progression and prolonged mice survival. We further demonstrated that TP‐16 increased responsiveness to anti‐PD‐1 therapy in an IMC‐related spontaneous colorectal cancer mouse model. In summary, this study demonstrates that inhibition of EP4‐expressing IMCs may offer a potential strategy for enhancing the efficacy of immunotherapy for colorectal cancer.