Literature Based Discovery: models, methods, and trends.

J Biomed Inform. 2017 Aug 21;:

Authors: Sam Henry MS, McInnes BT

Abstract
OBJECTIVES: This paper provides an introduction and overview of literature based discovery (LBD) in the biomedical domain. It introduces the reader to modern and historical LBD models, key system components, evaluation methodologies, and current trends. After completion, the reader will be familiar with the challenges and methodologies of LBD. The reader will be capable of distinguishing between recent LBD systems and publications, and be capable of designing an LBD system for a specific application.
TARGET AUDIENCE: From biomedical researchers curious about LBD, to someone looking to design an LBD system, to an LBD expert trying to catch up on trends in the field. The reader need not be familiar with LBD, but knowledge of biomedical text processing tools is helpful.
SCOPE: This paper describes a unifying framework for LBD systems. Within this framework, different models and methods are presented to both distinguish and show overlap between systems. Topics include term and document representation, system components, and an overview of models including co-occurrence models, semantic models, and distributional models. Other topics include uninformative term filtering, term ranking, results display, system evaluation, an overview of the application areas of drug development, drug repurposing, and adverse drug event prediction, and challenges and future directions. A timeline showing contributions to LBD, and a table summarizing the works of several authors is provided. Topics are presented from a high level perspective. References are given if more detailed analysis is required.

PMID: 28838802 [PubMed - as supplied by publisher]

Repositioning approved drugs for the treatment of problematic cancers using a screening approach.

PLoS One. 2017;12(2):e0171052

Authors: Varbanov HP, Kuttler F, Banfi D, Turcatti G, Dyson PJ

Abstract
Advances in treatment strategies together with an earlier diagnosis have considerably increased the average survival of cancer patients over the last four decades. Nevertheless, despite the growing number of new antineoplastic agents introduced each year, there is still no adequate therapy for problematic malignancies such as pancreatic, lung and stomach cancers. Consequently, it is important to ensure that existing drugs used to treat other types of cancers, and potentially other diseases, are not overlooked when searching for new chemotherapy regimens for these problematic cancer types. We describe a screening approach that identifies chemotherapeutics for the treatment of lung and pancreatic cancers, based on drugs already approved for other applications. Initially, the 1280 chemically and pharmacologically diverse compounds from the Prestwick Chemical Library® (PCL) were screened against A549 (lung cancer) and PANC-1 (pancreatic carcinoma) cells using the PrestoBlue fluorescent-based cell viability assay. More than 100 compounds from the PCL were identified as hits in one or both cell lines (80 of them, being drugs used to treat diseases other than cancer). Selected PCL hits were further evaluated in a dose-response manner. Promising candidates for repositioning emanating from this study include antiparasitics, cardiac glycosides, as well as the anticancer drugs vorinostat and topotecan.

PMID: 28166232 [PubMed - indexed for MEDLINE]

A Screen of FDA-Approved Drugs for Inhibitors of Zika Virus Infection.

Cell Host Microbe. 2016 Aug 10;20(2):259-70

Authors: Barrows NJ, Campos RK, Powell ST, Prasanth KR, Schott-Lerner G, Soto-Acosta R, Galarza-Muñoz G, McGrath EL, Urrabaz-Garza R, Gao J, Wu P, Menon R, Saade G, Fernandez-Salas I, Rossi SL, Vasilakis N, Routh A, Bradrick SS, Garcia-Blanco MA

Abstract
Currently there are no approved vaccines or specific therapies to prevent or treat Zika virus (ZIKV) infection. We interrogated a library of FDA-approved drugs for their ability to block infection of human HuH-7 cells by a newly isolated ZIKV strain (ZIKV MEX_I_7). More than 20 out of 774 tested compounds decreased ZIKV infection in our in vitro screening assay. Selected compounds were further validated for inhibition of ZIKV infection in human cervical, placental, and neural stem cell lines, as well as primary human amnion cells. Established anti-flaviviral drugs (e.g., bortezomib and mycophenolic acid) and others that had no previously known antiviral activity (e.g., daptomycin) were identified as inhibitors of ZIKV infection. Several drugs reduced ZIKV infection across multiple cell types. This study identifies drugs that could be tested in clinical studies of ZIKV infection and provides a resource of small molecules to study ZIKV pathogenesis.

PMID: 27476412 [PubMed - indexed for MEDLINE]

In silico prediction of new inhibitors for the nucleotide pool sanitizing enzyme, MTH1, using drug repurposing.

J Biomol Struct Dyn. 2017 Aug 18;:1-19

Authors: Sohraby F, Bagheri M, Javaheri Moghadam M, Aryapour H

PMID: 28818011 [PubMed - as supplied by publisher]

N-Desmethylclozapine, Fluoxetine, and Salmeterol Inhibit Postentry Stages of the Dengue Virus Life Cycle.

Antimicrob Agents Chemother. 2016 Nov;60(11):6709-6718

Authors: Medigeshi GR, Kumar R, Dhamija E, Agrawal T, Kar M

Abstract
Around 10,000 people die each year due to severe dengue disease, and two-thirds of the world population lives in a region where dengue disease is endemic. There has been remarkable progress in dengue virus vaccine development; however, there are no licensed antivirals for dengue disease, and none appear to be in clinical trials. We took the approach of repositioning approved drugs for anti-dengue virus activity by screening a library of pharmacologically active compounds. We identified N-desmethylclozapine, fluoxetine hydrochloride, and salmeterol xinafoate as dengue virus inhibitors based on reductions in the numbers of infected cells and viral titers. Dengue virus RNA levels were diminished in inhibitor-treated cells, and this effect was specific to dengue virus, as other flaviviruses, such as Japanese encephalitis virus and West Nile virus, or other RNA viruses, such as respiratory syncytial virus and rotavirus, were not affected by these inhibitors. All three inhibitors specifically inhibited dengue virus replication with 50% inhibitory concentrations (IC50s) in the high-nanomolar range. Estimation of negative-strand RNA intermediates and time-of-addition experiments indicated that inhibition was occurring at a postentry stage, most probably at the initiation of viral RNA replication. Finally, we show that inhibition is most likely due to the modulation of the endolysosomal pathway and induction of autophagy.

PMID: 27572397 [PubMed - indexed for MEDLINE]

New approach to generating insights for aging research based on literature mining and knowledge integration.

PLoS One. 2017;12(8):e0183534

Authors: Kwon Y, Natori Y, Tanokura M

Abstract
The proportion of the elderly population in most countries worldwide is increasing dramatically. Therefore, social interest in the fields of health, longevity, and anti-aging has been increasing as well. However, the basic research results obtained from a reductionist approach in biology and a bioinformatic approach in genome science have limited usefulness for generating insights on future health, longevity, and anti-aging-related research on a case by case basis. We propose a new approach that uses our literature mining technique and bioinformatics, which lead to a better perspective on research trends by providing an expanded knowledge base to work from. We demonstrate that our approach provides useful information that deepens insights on future trends which differs from data obtained conventionally, and this methodology is already paving the way for a new field in aging-related research based on literature mining. One compelling example of this is how our new approach can be a useful tool in drug repositioning.

PMID: 28817730 [PubMed - in process]

A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications.

AMIA Jt Summits Transl Sci Proc. 2017;2017:166-174

Authors: Kuusisto F, Steill J, Kuang Z, Thomson J, Page D, Stewart R

Abstract
We present a simple text mining method that is easy to implement, requires minimal data collection and preparation, and is easy to use for proposing ranked associations between a list of target terms and a key phrase. We call this method KinderMiner, and apply it to two biomedical applications. The first application is to identify relevant transcription factors for cell reprogramming, and the second is to identify potential drugs for investigation in drug repositioning. We compare the results from our algorithm to existing data and state-of-the-art algorithms, demonstrating compelling results for both application areas. While we apply the algorithm here for biomedical applications, we argue that the method is generalizable to any available corpus of sufficient size.

PMID: 28815126 [PubMed]

Antihelminthic drug niclosamide inhibits CIP2A and reactivates tumor suppressor protein phosphatase 2A in non-small cell lung cancer cells.

Biochem Pharmacol. 2017 Aug 13;:

Authors: Kim MO, Choe MH, Yoon YN, Ahn J, Yoo M, Jung KY, An S, Hwang SG, Oh JS, Kim JS

Abstract
Protein phosphatase 2A (PP2A) is a critical tumor suppressor complex responsible for the inactivation of various oncogenes. Recently, PP2A reactivation has emerged as an anticancer strategy. Cancerous inhibitor of protein phosphatase 2A (CIP2A), an endogenous inhibitor of PP2A, is upregulated in many cancer cells, including non-small cell lung cancer (NSCLC) cells. We demonstrated that the antihelminthic drug niclosamide inhibited the expression of CIP2A and reactivated the tumor suppressor PP2A in NSCLC cells. We performed a drug-repurposing screen and identified niclosamide as a CIP2A suppressor in NSCLC cells. Niclosamide inhibited cell proliferation, colony formation, and tumor sphere formation, and induced mitochondrial dysfunction through increased mitochondrial ROS production in NSCLC cells; however, these effects were rescued by CIP2A overexpression, which indicated that the antitumor activity of niclosamide was dependent on CIP2A. We found that niclosamide increased PP2A activity through CIP2A inhibition, which reduced the phosphorylation of several oncogenic proteins. Moreover, we found that a niclosamide analog inhibited CIP2A expression and increased PP2A activity in several types of NSCLC cells. Finally, we showed that other well-known PP2A activators, including forskolin and FTY720, did not inhibit CIP2A and that their activities were not dependent on CIP2A. Collectively, our data suggested that niclosamide effectively suppressed CIP2A expression and subsequently activated PP2A in NSCLC cells. This provided strong evidence for the potential use of niclosamide as a PP2A-activating drug in the clinical treatment of NSCLC.

PMID: 28813646 [PubMed - as supplied by publisher]

In vivo loss-of-function screens identify KPNB1 as a new druggable oncogene in epithelial ovarian cancer.

Proc Natl Acad Sci U S A. 2017 Aug 15;:

Authors: Kodama M, Kodama T, Newberg JY, Katayama H, Kobayashi M, Hanash SM, Yoshihara K, Wei Z, Tien JC, Rangel R, Hashimoto K, Mabuchi S, Sawada K, Kimura T, Copeland NG, Jenkins NA

Abstract
Epithelial ovarian cancer (EOC) is a deadly cancer, and its prognosis has not been changed significantly during several decades. To seek new therapeutic targets for EOC, we performed an in vivo dropout screen in human tumor xenografts using a pooled shRNA library targeting thousands of druggable genes. Then, in follow-up studies, we performed a second screen using a genome-wide CRISPR/Cas9 library. These screens identified 10 high-confidence drug targets that included well-known oncogenes such as ERBB2 and RAF1, and novel oncogenes, notably KPNB1, which we investigated further. Genetic and pharmacological inhibition showed that KPNB1 exerts its antitumor effects through multiphase cell cycle arrest and apoptosis induction. Mechanistically, proteomic studies revealed that KPNB1 acts as a master regulator of cell cycle-related proteins, including p21, p27, and APC/C. Clinically, EOC patients with higher expression levels of KPNB1 showed earlier recurrence and worse prognosis than those with lower expression levels of KPNB1. Interestingly, ivermectin, a Food and Drug Administration-approved antiparasitic drug, showed KPNB1-dependent antitumor effects on EOC, serving as an alternative therapeutic toward EOC patients through drug repositioning. Last, we found that the combination of ivermectin and paclitaxel produces a stronger antitumor effect on EOC both in vitro and in vivo than either drug alone. Our studies have thus identified a combinatorial therapy for EOC, in addition to a plethora of potential drug targets.

PMID: 28811376 [PubMed - as supplied by publisher]

Biomolecular Network Controllability With Drug Binding Information.

IEEE Trans Nanobioscience. 2017 Jul;16(5):326-332

Authors: Wu L, Tang L, Li M, Wang J, Wu FX

Abstract
Complex networks are ubiquitous in nature. In biological systems, biomolecules interact with each other to form so-called biomolecular networks, which determine the cellular behaviors of living organisms. Controlling the cellular behaviors by regulating certain biomolecules in the network is one of the most concerned problems in systems biology. Recently, the connections between biological networks and structural control theory have been explored, uncovering some interesting biological phenomena. Some researchers have paid attentions to the structural controllability of networks in notion of the minimum steering sets (MSSs). However, because the MSSs for complex networks are not unique and the importance of different MSSs is diverse in real applications, MSSs with certain meanings should be studied. In this paper, we investigated the MSSs of biomolecular networks by considering the drug binding information. The biomolecules in the MSSs with binding preference are enriched with known drug targets and are likely to have more chemical-binding opportunities with existing drugs compared with randomly chosen MSSs, suggesting novel applications for drug target identification and drug repositioning.

PMID: 28809666 [PubMed - in process]

Precision medicine for suicidality: from universality to subtypes and personalization.

Mol Psychiatry. 2017 Aug 15;:

Authors: Niculescu AB, Le-Niculescu H, Levey DF, Phalen PL, Dainton HL, Roseberry K, Niculescu EM, Niezer JO, Williams A, Graham DL, Jones TJ, Venugopal V, Ballew A, Yard M, Gelbart T, Kurian SM, Shekhar A, Schork NJ, Sandusky GE, Salomon DR

Abstract
Suicide remains a clear, present and increasing public health problem, despite being a potentially preventable tragedy. Its incidence is particularly high in people with overt or un(der)diagnosed psychiatric disorders. Objective and precise identification of individuals at risk, ways of monitoring response to treatments and novel preventive therapeutics need to be discovered, employed and widely deployed. We sought to investigate whether blood gene expression biomarkers for suicide (that is, a 'liquid biopsy' approach) can be identified that are more universal in nature, working across psychiatric diagnoses and genders, using larger cohorts than in previous studies. Such markers may reflect and/or be a proxy for the core biology of suicide. We were successful in this endeavor, using a comprehensive stepwise approach, leading to a wealth of findings. Steps 1, 2 and 3 were discovery, prioritization and validation for tracking suicidality, resulting in a Top Dozen list of candidate biomarkers comprising the top biomarkers from each step, as well as a larger list of 148 candidate biomarkers that survived Bonferroni correction in the validation step. Step 4 was testing the Top Dozen list and Bonferroni biomarker list for predictive ability for suicidal ideation (SI) and for future hospitalizations for suicidality in independent cohorts, leading to the identification of completely novel predictive biomarkers (such as CLN5 and AK2), as well as reinforcement of ours and others previous findings in the field (such as SLC4A4 and SKA2). Additionally, we examined whether subtypes of suicidality can be identified based on mental state at the time of high SI and identified four potential subtypes: high anxiety, low mood, combined and non-affective (psychotic). Such subtypes may delineate groups of individuals that are more homogenous in terms of suicidality biology and behavior. We also studied a more personalized approach, by psychiatric diagnosis and gender, with a focus on bipolar males, the highest risk group. Such a personalized approach may be more sensitive to gender differences and to the impact of psychiatric co-morbidities and medications. We compared testing the universal biomarkers in everybody versus testing by subtypes versus personalized by gender and diagnosis, and show that the subtype and personalized approaches permit enhanced precision of predictions for different universal biomarkers. In particular, LHFP appears to be a strong predictor for suicidality in males with depression. We also directly examined whether biomarkers discovered using male bipolars only are better predictors in a male bipolar independent cohort than universal biomarkers and show evidence for a possible advantage of personalization. We identified completely novel biomarkers (such as SPTBN1 and C7orf73), and reinforced previously known biomarkers (such as PTEN and SAT1). For diagnostic ability testing purposes, we also examined as predictors phenotypic measures as apps (for suicide risk (CFI-S, Convergent Functional Information for Suicidality) and for anxiety and mood (SASS, Simplified Affective State Scale)) by themselves, as well as in combination with the top biomarkers (the combination being our a priori primary endpoint), to provide context and enhance precision of predictions. We obtained area under the curves of 90% for SI and 77% for future hospitalizations in independent cohorts. Step 5 was to look for mechanistic understanding, starting with examining evidence for the Top Dozen and Bonferroni biomarkers for involvement in other psychiatric and non-psychiatric disorders, as a mechanism for biological predisposition and vulnerability. The biomarkers we identified also provide a window towards understanding the biology of suicide, implicating biological pathways related to neurogenesis, programmed cell death and insulin signaling from the universal biomarkers, as well as mTOR signaling from the male bipolar biomarkers. In particular, HTR2A increase coupled with ARRB1 and GSK3B decreases in expression in suicidality may provide a synergistic mechanistical corrective target, as do SLC4A4 increase coupled with AHCYL1 and AHCYL2 decrease. Step 6 was to move beyond diagnostics and mechanistical risk assessment, towards providing a foundation for personalized therapeutics. Items scored positive in the CFI-S and subtypes identified by SASS in different individuals provide targets for personalized (psycho)therapy. Some individual biomarkers are targets of existing drugs used to treat mood disorders and suicidality (lithium, clozapine and omega-3 fatty acids), providing a means toward pharmacogenomics stratification of patients and monitoring of response to treatment. Such biomarkers merit evaluation in clinical trials. Bioinformatics drug repurposing analyses with the gene expression biosignatures of the Top Dozen and Bonferroni-validated universal biomarkers identified novel potential therapeutics for suicidality, such as ebselen (a lithium mimetic), piracetam (a nootropic), chlorogenic acid (a polyphenol) and metformin (an antidiabetic and possible longevity promoting drug). Finally, based on the totality of our data and of the evidence in the field to date, a convergent functional evidence score prioritizing biomarkers that have all around evidence (track suicidality, predict it, are reflective of biological predisposition and are potential drug targets) brought to the fore APOE and IL6 from among the universal biomarkers, suggesting an inflammatory/accelerated aging component that may be a targetable common denominator.Molecular Psychiatry advance online publication, 15 August 2017; doi:10.1038/mp.2017.128.

PMID: 28809398 [PubMed - as supplied by publisher]

Screening drug-target interactions with positive-unlabeled learning.

Sci Rep. 2017 Aug 14;7(1):8087

Authors: Peng L, Zhu W, Liao B, Duan Y, Chen M, Chen Y, Yang J

Abstract
Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usually randomly select negative samples from unlabeled drug-target pairs, which introduces a lot of false-positives. In this study, a negative sample extraction method named NDTISE is first developed to screen strong negative DTI examples based on positive-unlabeled learning. A novel DTI screening framework, PUDTI, is then designed to infer new drug repositioning candidates by integrating NDTISE, probabilities that remaining ambiguous samples belong to the positive and negative classes, and an SVM-based optimization model. We investigated the effectiveness of NDTISE on a DTI data provided by NCPIS. NDTISE is much better than random selection and slightly outperforms NCPIS. We then compared PUDTI with 6 state-of-the-art methods on 4 classes of DTI datasets from human enzymes, ion channels, GPCRs and nuclear receptors. PUDTI achieved the highest AUC among the 7 methods on all 4 datasets. Finally, we validated a few top predicted DTIs through mining independent drug databases and literatures. In conclusion, PUDTI provides an effective pre-filtering method for new drug design.

PMID: 28808275 [PubMed - in process]

Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry.

Nat Neurosci. 2017 Aug 14;:

Authors: So HC, Chau CK, Chiu WT, Ho KS, Lo CP, Yim SH, Sham PC

Abstract
Knowledge of psychiatric disease genetics has advanced rapidly during the past decade with the advent of genome-wide association studies (GWAS). However, less progress has been made in harnessing these data to reveal new therapies. Here we propose a framework for drug repositioning by comparing transcriptomes imputed from GWAS data with drug-induced gene expression profiles from the Connectivity Map database and apply this approach to seven psychiatric disorders. We found a number of repositioning candidates, many supported by preclinical or clinical evidence. Repositioning candidates for a number of disorders were also significantly enriched for known psychiatric medications or therapies considered in clinical trials. For example, candidates for schizophrenia were enriched for antipsychotics, while those for bipolar disorder were enriched for both antipsychotics and antidepressants. These findings provide support for the usefulness of GWAS data in guiding drug discovery.

PMID: 28805813 [PubMed - as supplied by publisher]

Repurposing as a strategy for the discovery of new anti-leishmanials: the-state-of-the-art.

Parasitology. 2017 Aug 14;:1-18

Authors: Charlton RL, Rossi-Bergmann B, Denny PW, Steel PG

Abstract
Leishmaniasis is a vector-borne neglected tropical disease caused by protozoan parasites of the genus Leishmania for which there is a paucity of effective viable non-toxic drugs. There are 1·3 million new cases each year causing considerable socio-economic hardship, best measured in 2·4 million disability adjusted life years, with greatest impact on the poorest communities, which means that desperately needed new antileishmanial treatments have to be both affordable and accessible. Established medicines with cheaper and faster development times may hold the cure for this neglected tropical disease. This concept of using old drugs for new diseases may not be novel but, with the ambitious target of controlling or eradicating tropical diseases by 2020, this strategy is still an important one. In this review, we will explore the current state-of-the-art of drug repurposing strategies in the search for new treatments for leishmaniasis.

PMID: 28805165 [PubMed - as supplied by publisher]

A chemical genomics approach to drug reprofiling in oncology: Antipsychotic drug risperidone as a potential adenocarcinoma treatment.

Cancer Lett. 2017 May 01;393:16-21

Authors: Dilly SJ, Clark AJ, Marsh A, Mitchell DA, Cain R, Fishwick CW, Taylor PC

Abstract
Drug reprofiling is emerging as an effective paradigm for discovery of cancer treatments. Herein, an antipsychotic drug is immobilised using the Magic Tag(®) chemical genomics tool and screened against a T7 bacteriophage displayed library of polypeptides from Drosophila melanogaster, as a whole genome model, to uncover an interaction with a section of 17-β-HSD10, a proposed prostate cancer target. A computational study and enzyme inhibition assay with full length human 17-β-HSD10 identifies risperidone as a drug reprofiling candidate. When formulated with rumenic acid, risperidone slows proliferation of PC3 prostate cancer cells in vitro and retards PC3 prostate cancer tumour growth in vivo in xenografts in mice, presenting an opportunity to reprofile risperidone as a cancer treatment.

PMID: 28188816 [PubMed - indexed for MEDLINE]

Process Pharmacology: A Pharmacological Data Science Approach to Drug Development and Therapy.

CPT Pharmacometrics Syst Pharmacol. 2016 Apr;5(4):192-200

Authors: Lötsch J, Ultsch A

Abstract
A novel functional-genomics based concept of pharmacology that uses artificial intelligence techniques for mining and knowledge discovery in "big data" providing comprehensive information about the drugs' targets and their functional genomics is proposed. In "process pharmacology", drugs are associated with biological processes. This puts the disease, regarded as alterations in the activity in one or several cellular processes, in the focus of drug therapy. In this setting, the molecular drug targets are merely intermediates. The identification of drugs for therapeutic or repurposing is based on similarities in the high-dimensional space of the biological processes that a drug influences. Applying this principle to data associated with lymphoblastic leukemia identified a short list of candidate drugs, including one that was recently proposed as novel rescue medication for lymphocytic leukemia. The pharmacological data science approach provides successful selections of drug candidates within development and repurposing tasks.

PMID: 27069773 [PubMed - indexed for MEDLINE]

Repurposing itraconazole as an anticancer agent.

Oncol Lett. 2017 Aug;14(2):1240-1246

Authors: Tsubamoto H, Ueda T, Inoue K, Sakata K, Shibahara H, Sonoda T

Abstract
Itraconazole, a common anti-fungal agent, has demonstrated potential anticancer activity, including reversing chemoresistance mediated by P-glycoprotein, modulating the signal transduction pathways of Hedgehog, mechanistic target of rapamycin and Wnt/β-catenin in cancer cells, inhibiting angiogenesis and lymphangiogenesis, and possibly interfering with cancer-stromal cell interactions. Clinical trials have suggested the clinical benefits of itraconazole monotherapy for prostate cancer and basal cell carcinoma, as well as the survival advantage of combination chemotherapy for relapsed non-small cell lung, ovarian, triple negative breast, pancreatic and biliary tract cancer. As drug repurposing is cost-effective and timesaving, a review was conducted of preclinical and clinical data focusing on the anticancer activity of itraconazole, and discusses the future directions for repurposing itraconazole as an anticancer agent.

PMID: 28789339 [PubMed]

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors.

PLoS Comput Biol. 2017 Aug 07;13(8):e1005678

Authors: Cichonska A, Ravikumar B, Parri E, Timonen S, Pahikkala T, Airola A, Wennerberg K, Rousu J, Aittokallio T

Abstract
Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications.

PMID: 28787438 [PubMed - as supplied by publisher]

Dual MET and SMO negative modulators overcome resistance to EGFR inhibitors in human non-small cell lung cancer.

J Med Chem. 2017 Aug 08;:

Authors: Morgillo F, Amendola G, Della Corte CM, Giacomelli C, Botta L, Di Maro S, Messere A, Ciaramella V, Taliani S, Marinelli L, Trincavelli ML, Martini C, Novellino E, Ciardiello F, Cosconati S

Abstract
Tyrosine kinase inhibitors (TKIs) of the EGF receptor (EGFR) have provided a significant improvement in the disease outcome of non-small cell lung cancer (NSCLC). Unfortunately, resistance to these agents frequently occurs, and it is often related to the activation of the Hedgehog (Hh) and MET signaling cascades driving the epithelial-to-mesenchymal transition (EMT). Since the concomitant inhibition of both Hh and MET pathways restores the sensitivity to anti-EGFR drugs, here we aimed at discovering the first compounds that block simultaneously MET and SMO. By using an "in silico drug repurposing" approach and by validating our predictions both in vitro and in vivo, we identified a set of compounds with the desired dual inhibitory activity and enhanced antiproliferative activity on EGFR TKI-resistant NSCLC. The identification of the known MET TKIs, glesatinib, and foretinib, as negative modulators of the Hh pathway, widens their application in the context of NSCLC.

PMID: 28787156 [PubMed - as supplied by publisher]

Clinical dosage of meclozine promotes longitudinal bone growth, bone volume, and trabecular bone quality in transgenic mice with achondroplasia.

Sci Rep. 2017 Aug 07;7(1):7371

Authors: Matsushita M, Esaki R, Mishima K, Ishiguro N, Ohno K, Kitoh H

Abstract
Achondroplasia (ACH) is the most common short-limbed skeletal dysplasia caused by gain-of-function mutations in the fibroblast growth factor receptor 3 (FGFR3). No effective FGFR3-targeted therapies for ACH are currently available. By drug repositioning strategies, we identified that meclozine, which has been used as an anti-motion-sickness, suppressed FGFR3 signaling in chondrocytes and rescued short-limbed phenotype in ACH mouse model. Here, we conducted various pharmacological tests for future clinical application in ACH. Pharmacokinetic analyses demonstrated that peak drug concentration (Cmax) and area under the concentration-time curve (AUC) of 2 mg/kg of meclozine to mice was lower than that of 25 mg/body to human, which is a clinical usage for anti-motion-sickness. Pharmacokinetic simulation studies showed that repeated dose of 2 mg/kg of meclozine showed no accumulation effects. Short stature phenotype in the transgenic mice was significantly rescued by twice-daily oral administration of 2 mg/kg/day of meclozine. In addition to stimulation of longitudinal bone growth, bone volume and metaphyseal trabecular bone quality were improved by meclozine treatment. We confirmed a preclinical proof of concept for applying meclozine for the treatment of short stature in ACH, although toxicity and adverse events associated with long-term administration of this drug should be examined.

PMID: 28785080 [PubMed - in process]