Screening for drugs potentially interfering with MCT8-mediated T3 transport in vitro identifies dexamethasone and some commonly used drugs as inhibitors of MCT8 activity.

BACKGROUND: Monocarboxylate transporter 8 (MCT8) is the first thyroid hormone transporter that has been linked to a human disease. Besides genetic alterations other factors might impair MCT8 activity. AIM: This study aimed at investigating whether some common drugs having a structural similarity with TH and/or whose treatment is associated with thyroid function test abnormalities, or which behave as antagonists of TH action can inhibit MCT8-mediated T3 transport. METHODS: [125I]T3 uptake and efflux were measured in COS-7 cells transiently transfected with hMCT8 before and after exposure to increasing concentrations of hydrocortisone, dexamethasone, prednisone, prednisolone, amiodarone, desethylamiodarone, dronedarone, buspirone, carbamazepine, valproic acid, and L-carnitine. The mode of inhibition was also determined. RESULTS: Dexamethasone significantly inhibited T3 uptake at 10 µM; hydrocortisone reduced T3 uptake only at high concentrations, i.e. at 500 and 1000 µM; prednisone and prednisolone were devoid of inhibitory potential. Amiodarone caused a reduction of T3 uptake by MCT8 only at the highest concentrations used (44% at 50 µM and 68% at 100 µM), and this effect was weaker than that produced by desethylamiodarone and dronedarone; buspirone resulted a potent inhibitor, reducing T3 uptake at 0.1-10 µM. L-Carnitine inhibited T3 uptake only at 500 mM and 1 M. Kinetic experiments revealed a noncompetitive mode of inhibition for all compounds. All drugs inhibiting T3 uptake did not affect T3 release. CONCLUSION: This study shows a novel effect of some common drugs, which is inhibition of T3 transport mediated by MCT8. Specifically, dexamethasone, buspirone, desethylamiodarone, and dronedarone behave as potent inhibitors of MCT8.

A Review of the Main Considerations for Formulation Development in Preclinical Toxicology Studies.

The main considerations for the development of a formulation for preclinical safety assessment testing are explored. Intravenous, inhalation, oral and dermal dosing are given focus and although different dose routes do present their own individual challenges there are common themes that emerge. In each case it is necessary to maximise exposure to achieve high doses to satisfy regulatory requirements for safety assessment testing. This often involves producing formulations that are at the limits of solubility and maximum volumes possible for administration to different test species by the chosen route. It is concluded that for all routes it is important to thoroughly explore the stability of the test item in the proposed formulation matrix well ahead of dosing any animals, giving careful consideration to which excipients are used and what their underlying toxicity profile may be for the relevant preclinical species. In addition, determining the maximum achievable concentrations and weighing that against the maximum volumes that can be given by the chosen route in all the test species at an early stage will also give a read on whether it would be theoretically possible to achieve suitably high enough doses to support clinical work. Not doing so can cause delays in the development programme and may have ethical repercussions.

LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network.

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.

Therapeutic effects of micro-RNAs in preclinical studies of acute kidney injury: a systematic review and meta-analysis.

AKI has a high mortality rate, may lead to chronic kidney disease, and effective therapies are lacking. Micro-RNAs (miRNAs) regulate biologic processes by potently inhibiting protein expression, and pre-clinical studies have explored their roles in AKI. We conducted a systematic review and meta-analysis of miRNAs as therapeutics in pre-clinical AKI. Study screening, data extraction, and quality assessments were performed by 2 independent reviewers. Seventy studies involving 42 miRNA species were included in the analysis. All studies demonstrated significant effects of the miRNA intervention on kidney function and/or histology, with most implicating apoptosis and phosphatase and tensin homolog (PTEN) signaling. Fourteen studies (20.0%) examined the effect of miRNA-21 in AKI, and meta-analysis demonstrated significant increases in serum creatinine and kidney injury scores with miR-21 antagonism and pre-conditioning. No studies reported on adverse effects of miRNA therapy. Limitations also included lack of model diversity (100% rodents, 61.4% ischemia-reperfusion injury), and predominance of male sex (78.6%). Most studies had an unclear risk of bias, and the majority of miRNA-21 studies were conducted by a single team of investigators. In summary, several miRNAs target kidney function and apoptosis in pre-clinical AKI models, with data suggesting that miRNA-21 may mediate protection and kidney repair.Systematic review registration ID: CRD42019128854.

Hallucinogenic/psychedelic 5HT2A receptor agonists as rapid antidepressant therapeutics: Evidence and mechanisms of action.

Major depressive disorder (MDD) is among the most prevalent mental health disorders worldwide, and it is associated with a reduced quality of life and enormous costs to health care systems. Available drug treatments show low-to-moderate response in most patients, with almost a third of patients being non-responders (treatment-resistant). Furthermore, most currently available medications need several weeks to achieve therapeutic effects, and the long-term use of these drugs is often associated with significant unwanted side effects and resultant reductions in treatment compliance. Therefore, more effective, safer, and faster-acting antidepressants with enduring effects are needed. Together with ketamine, psychedelics (or classic or serotoninergic hallucinogens) such as lysergic acid diethylamide (LSD), psilocybin, and ayahuasca are among the few compounds with recent human evidence of fast-acting antidepressant effects. Several studies in the 1950s to 1970s reported antidepressive and anxiolytic effects of these drugs, which are being confirmed by modern trials (LSD, one trial; psilocybin, five trials; ayahuasca, two trials). The effects of these drugs appear to be produced primarily by their agonism at serotonin (5-hydroxytryptamine, 5-HT) receptors, especially the 5-HT2A receptor. Considering the overall burden of MDD and the necessity of new therapeutic options, the promising (but currently limited) evidence of safety and efficacy of psychedelics has encouraged the scientific community to explore more fully their beneficial effects in MDD.

Computational prediction of drug response in short QT syndrome type 1 based on measurements of compound effect in stem cell-derived cardiomyocytes.

Short QT (SQT) syndrome is a genetic cardiac disorder characterized by an abbreviated QT interval of the patient's electrocardiogram. The syndrome is associated with increased risk of arrhythmia and sudden cardiac death and can arise from a number of ion channel mutations. Cardiomyocytes derived from induced pluripotent stem cells generated from SQT patients (SQT hiPSC-CMs) provide promising platforms for testing pharmacological treatments directly in human cardiac cells exhibiting mutations specific for the syndrome. However, a difficulty is posed by the relative immaturity of hiPSC-CMs, with the possibility that drug effects observed in SQT hiPSC-CMs could be very different from the corresponding drug effect in vivo. In this paper, we apply a multistep computational procedure for translating measured drug effects from these cells to human QT response. This process first detects drug effects on individual ion channels based on measurements of SQT hiPSC-CMs and then uses these results to estimate the drug effects on ventricular action potentials and QT intervals of adult SQT patients. We find that the procedure is able to identify IC50 values in line with measured values for the four drugs quinidine, ivabradine, ajmaline and mexiletine. In addition, the predicted effect of quinidine on the adult QT interval is in good agreement with measured effects of quinidine for adult patients. Consequently, the computational procedure appears to be a useful tool for helping predicting adult drug responses from pure in vitro measurements of patient derived cell lines.

SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19.

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine, hydroxychloroquine, tocilizumab, heparin), as well as a new combination therapy of 5 drugs (hydroxychloroquine, chloroquine, lopinavir, ritonavir, remdesivir), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1ß, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.

Curcumin and inflammatory bowel diseases: From in vitro studies to clinical trials.

Inflammatory bowel diseases (IBDs) may result from mutations in genes encoding for innate immunity, which can lead to exacerbated inflammatory response. Although some mono-targeted treatments have developed in recent years, IBDs are caused through several pathway perturbations. Therefore, targeting all these pathways is difficult to be achieved by a single agent. Moreover, those mono-targeted therapies are usually expensive and may cause side-effects. These limitations highlight the significance of an available, inexpensive and multi-targeted dietary agents or natural compounds for the treatment and prevention of IBDs. Curcumin is a multifunctional phenolic compound that is known for its anti-inflammatory and immunomodulatory properties. Over the past decades, mounting experimental investigations have revealed the therapeutic potential of curcumin against a broad spectrum of inflammatory diseases including IBDs. Furthermore, it has been reported that curcumin directly interacts with many signaling mediators implicated in the pathogenesis of IBDs. These preclinical findings have created a solid basis for the assessment of the efficacy of curcumin in clinical practice. In clinical trials, different dosages e.g., 550 mg /three times daily-1month, and 1 g /twice times daily-6month of curcumin were used for patients with IBDs. Taken together, these findings indicated that curcumin could be employed as a therapeutic candidate in the treatment of IBDs. Moreover, it seems that overcome to current limitations of curcumin i.e., poor oral bioavailability, and poor oral absorption with using nanotechnology and others, could improve the efficacy of curcumin both in pre-clinical and clinical studies.

Pre-clinical animal models are poor predictors of human toxicities in phase 1 oncology clinical trials.

BACKGROUND: Our objective was to determine the correlation between preclinical toxicity found in animal models (mouse, rat, dog and monkey) and clinical toxicity reported in patients participating in Phase 1 oncology clinical trials. METHODS: We obtained from two major early-Phase clinical trial centres, preclinical toxicities from investigational brochures and clinical toxicities from published Phase 1 trials for 108 drugs, including small molecules, biologics and conjugates. Toxicities were categorised according to Common Terminology Criteria for Adverse Events version 4.0. Human toxicities were also categorised based on their reported clinical grade (severity). Positive predictive values (PPV) and negative predictive values (NPV) were calculated to determine the probability that clinical studies would/would not show a particular toxicity category given that it was seen in preclinical toxicology analysis. Statistical analyses also included kappa statistics, and Matthews (MCC) and Spearman correlation coefficients. RESULTS: Overall, animal toxicity did not show strong correlation with human toxicity, with a median PPV of 0.65 and NPV of 0.50. Similar results were obtained based on kappa statistics and MCC. CONCLUSIONS: There is an urgent need to assess more novel approaches to the type and conduct of preclinical toxicity studies in an effort to provide better predictive value for human investigation.

High-Throughput Docking and Molecular Dynamics Simulations towards the Identification of Potential Inhibitors against Human Coagulation Factor XIIa.

Human coagulation factor XIIa (FXIIa) is a trypsin-like serine protease that is involved in pathologic thrombosis. As a potential target for designing safe anticoagulants, FXIIa has received a great deal of interest in recent years. In the present study, we employed virtual high-throughput screening of 500,064 compounds within Enamine database to acquire the most potential inhibitors of FXIIa. Subsequently, 18 compounds with significant binding energy (from -65.195 to -15.726 kcal/mol) were selected, and their ADMET properties were predicted to select representative inhibitors. Three compounds (Z1225120358, Z432246974, and Z146790068) exhibited excellent binding affinity and druggability. MD simulation for FXIIa-ligand complexes was carried out to reveal the stability and inhibition mechanism of these three compounds. Through the inhibition of activated factor XIIa assay, we tested the activity of five compounds Z1225120358, Z432246974, Z45287215, Z30974175, and Z146790068, with pIC50 values of 9.3∗10-7, 3.0∗10-5, 7.8∗10-7, 8.7∗10-7, and 1.3∗10-6 M, respectively; the AMDET properties of Z45287215 and Z30974175 show not well but have better inhibition activity. We also found that compounds Z1225120358, Z45287215, Z30974175, and Z146790068 could be more inhibition of FXIIa than Z432246974. Collectively, compounds Z1225120358, Z45287215, Z30974175, and Z146790068 were anticipated to be promising drug candidates for inhibition of FXIIa.

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov.

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, D-Sorbitol, D-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.

COMBSecretomics: A pragmatic methodological framework for higher-order drug combination analysis using secretomics.

Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions.

High Prevalence of Buprenorphine in Prenatal Drug Screens in an Appalachian City.

OBJECTIVES: To define the magnitude of buprenorphine presence in the urine drug screens of pregnant women and to assess the presence of illicit buprenorphine use versus the presence of prescribed buprenorphine use. METHODS: Initial prenatal drug screen results for all pregnant patients in our practice for a 1-year period were analyzed and tabulated. RESULTS: Buprenorphine was found in the urine drug screens of 16% of pregnant patients. The presence of buprenorphine was by far the highest for any substance associated with neonatal abstinence syndrome (NAS). We estimate that the exposure to buprenorphine of approximately one-third of individuals in our population is associated with illicit buprenorphine use. CONCLUSIONS: The high rate of NAS in our region is primarily associated with both illicit and prescribed buprenorphine rather than other substances. Buprenorphine usage at the time that prenatal care is initiated, rather than opiate use at the onset of prenatal care, is the underlying factor that must be addressed if our region is to successfully combat our high rates of NAS.

Machine learning decodes chemical features to identify novel agonists of a moth odorant receptor.

Odorant receptors expressed at the peripheral olfactory organs are key proteins for animal volatile sensing. Although they determine the odor space of a given species, their functional characterization is a long process and remains limited. To date, machine learning virtual screening has been used to predict new ligands for such receptors in both mammals and insects, using chemical features of known ligands. In insects, such approach is yet limited to Diptera, whereas insect odorant receptors are known to be highly divergent between orders. Here, we extend this strategy to a Lepidoptera receptor, SlitOR25, involved in the recognition of attractive odorants in the crop pest Spodoptera littoralis larvae. Virtual screening of 3 million molecules predicted 32 purchasable ones whose function has been systematically tested on SlitOR25, revealing 11 novel agonists with a success rate of 28%. Our results show that Support Vector Machine optimizes the discovery of novel agonists and expands the chemical space of a Lepidoptera OR. More, it opens up structure-function relationship analyses through a comparison of the agonist chemical structures. This proof-of-concept in a crop pest could ultimately enable the identification of OR agonists or antagonists, capable of modifying olfactory behaviors in a context of biocontrol.

In Vitro-to-In Vivo Extrapolation of Transporter Inhibition Data for Drugs Approved by the US Food and Drug Administration in 2018.

A systematic analysis of the inhibition transporter data available in New Drug Applications of drugs approved by the US Food and Drug Administration (FDA) in 2018 (N = 42) was performed. In vitro-to-in vivo predictions using basic models were available for the nine transporters currently recommended for evaluation. Overall, 29 parents and 16 metabolites showed in vitro inhibition of at least one transporter, with the largest number of drugs found to be inhibitors of P-gp followed by BCRP. The most represented therapeutic areas were oncology drugs and anti-infective agents, each comprising 31%. Among drugs with prediction values greater than the FDA recommended cutoffs and further evaluated in vivo, 56% showed positive clinical interactions (area under the concentration-time curve ratio (AUCRs) ≥ 1.25). Although all the observed or simulated inhibitions were weak (AUCRs < 2), seven of the nine interactions (involving five drugs) resulted in labeling recommendations. Interestingly, more than half of the drugs with predictions greater than the cutoffs had no further evaluations, highlighting that current basic models represent a useful, simple first step to evaluate the clinical relevance of in vitro findings, but that multiple other factors are considered when deciding the need for clinical studies. Four drugs had prediction values less than the cutoffs but had clinical evaluations or physiologically-based pharmacokinetic simulations available. Consistent with the predictions, all of them were confirmed not to inhibit these transporters in vivo (AUCRs of 0.94-1.09). Overall, based on the clinical evaluations available, drugs approved in 2018 were found to have a relatively limited impact on drug transporters, with all victim AUCRs < 2.

Antiinflammatory and anti-cancer activities of pomegranate and its constituent, ellagic acid: Evidence from cellular, animal, and clinical studies.

Inflammation is commonly characterized as a defensive and protective reaction of the body to various exogenous or endogenous stimuli, which aims to maintain the body health. Punica granatum (pomegranate) and its constituent ellagic acid (EA) are recently more taken into accounts since their promising pharmacological effects. Therefore, we aimed to obtain a comprehensive review regarding antiinflammatory, anticancer, and antioxidant activities of both pomegranate and EA and their possible involved mechanisms. In the procedure, scientific databases, including Web of Science, PubMed, Scopus, and Google Scholar, were searched in the English language, until the end of January 2019. Pomegranate belonging to the Punicaceae has been used for medical purposes from ancient and in different cultures. Several studies have also supported that EA is the major active compound of pomegranate and possesses antimutagenic, antiinflammatory, antifibrosis, anticancer, and antiaging properties. It has been suggested that pomegranate and EA possess promising immunomodulatory effects in preclinical models as well as human studies through regulation of the T-cell function and suppressing humoral immunity. Hopefully, we wish that this review and information could be helpful for designing further experiments to investigate the potential protective effects of pomegranate and EA.

Why Most Acute Stroke Studies Are Positive in Animals but Not in Patients: A Systematic Comparison of Preclinical, Early Phase, and Phase 3 Clinical Trials of Neuroprotective Agents.

OBJECTIVE: To analyze why numerous acute stroke treatments were successful in the laboratory but failed in large clinical trials. METHODS: We searched all phase 3 trials of medical treatments for acute ischemic stroke and corresponding early clinical and experimental studies. We compared the overall efficacy and assessed the impact of publication bias and study design on the efficacy. Furthermore, we estimated power and true report probability of experimental studies. RESULTS: We identified 50 phase 3 trials with 46,008 subjects, 75 early clinical trials with 12,391 subjects, and 209 experimental studies with >7,141 subjects. Three (6%) phase 3, 24 (32%) early clinical, and 143 (69.08%) experimental studies were positive. The mean treatment effect was 0.76 (95% confidence interval [CI] = 0.70-0.83) in experimental studies, 0.87 (95% CI = 0.71-1.06) in early clinical trials, and 1.00 (95% CI = 0.95-1.06) in phase 3 trials. Funnel plot asymmetry and trim-and-fill revealed a clear publication bias in experimental studies and early clinical trials. Study design and adherence to quality criteria had a considerable impact on estimated effect sizes. The mean power of experimental studies was 17%. Assuming a bias of 30% and pre-study odds of 0.5 to 0.7, this leads to a true report probability of <50%. INTERPRETATION: Pivotal study design differences between experimental studies and clinical trials, including different primary end points and time to treatment, publication bias, neglected quality criteria and low power, contribute to the stepwise efficacy decline of stroke treatments from experimental studies to phase 3 clinical trials. Even under conservative estimates, less than half of published positive experimental stroke studies are truly positive. ANN NEUROL 2020;87:40-51.

Predicting kinase inhibitors using bioactivity matrix derived informer sets.

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on three kinase targets not included in the PKIS.