Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play key roles in the discovery/development of drugs, pesticides, food additives, consumer products, and industrial chemicals. This information is especially useful when to conduct environmental and human hazard assessment. The most critical rate limiting step in the chemical safety assessment workflow is the availability of high quality data. This paper describes an ADMET structure-activity relationship database, abbreviated as admetSAR. It is an open source, text and structure searchable, and continually updated database that collects, curates, and manages available ADMET-associated properties data from the published literature. In admetSAR, over 210,000 ADMET annotated data points for more than 96,000 unique compounds with 45 kinds of ADMET-associated properties, proteins, species, or organisms have been carefully curated from a large number of diverse literatures. The database provides a user-friendly interface to query a specific chemical profile, using either CAS registry number, common name, or structure similarity. In addition, the database includes 22 qualitative classification and 5 quantitative regression models with highly predictive accuracy, allowing to estimate ecological/mammalian ADMET properties for novel chemicals. AdmetSAR is accessible free of charge at http://www.admetexp.org.

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.

SUMMARY The altered metabolism of tumor cells confers a selective advantage for survival and proliferation, and studies have shown that targeting such metabolic shifts may be a useful therapeutic strategy. We developed an intensely fluorescent, rapidly responsive, pH-resistant, genetically encoded sensor of wide dynamic range, denoted SoNar, for tracking cytosolic NAD+ and NADH redox states in living cells and in vivo. SoNar responds to subtle perturbations of various pathways of energy metabolism in real-time, and allowed high-throughput screening for new agents targeting tumor metabolism. Among > 5,500 unique compounds, we identified KP372-1 as a potent NQO1-mediated redox cycling agent that produced extreme oxidative stress, selectively induced cancer cell apoptosis and effectively decreased tumor growth in vivo. This study demonstrates that genetically encoded sensor-based metabolic screening could serve as a valuable approach for drug discovery.

Heat shock protein (hsp) 90 is an ATP-dependent molecular chaperone that maintains the active conformation of client oncoproteins in cancer cells. An isoform, hsp90A, promotes extracellular maturation of matrix metalloproteinase (MMP)-2, involved in tumor invasion and metastasis. Knockdown of histone deacetylase (HDAC) 6, which deacetylates lysine residues in hsp90, induces reversible hyperacetylation and attenuates ATP binding and chaperone function of hsp90. Here, using mass spectrometry, we identified seven lysine residues in hsp90A that are hyperacetylated after treatment of eukaryotic cells with a pan-HDAC inhibitor that also inhibits HDAC6. Depending on the specific lysine residue in the middle domain involved, although acetylation affects ATP, cochaperone, and client protein binding to hsp90A, acetylation of all seven lysines increased the binding of hsp90A to 17-allyl-amino-demethoxy geldanamycin. Notably, after treatment with the pan-HDAC inhibitor panobinostat (LBH589), the extracellular hsp90A was hyperacetylated and it bound to MMP-2, which was associated with increased in vitro tumor cell invasiveness. Treatment with antiacetylated hsp90A antibody inhibited in vitro invasion by tumor cells. Thus, reversible hyperacetylation modulates the intracellular and extracellular chaperone function of hsp90, and targeting extracellular hyperacetylated hsp90A may undermine tumor invasion and metastasis. [Cancer Res 2008;68(12):4833-42]