Exploration of the Anti-Inflammatory Drug Space Through Network Pharmacology: Applications for Drug Repurposing.

Front Physiol. 2018;9:151

Authors: de Anda-Jáuregui G, Guo K, McGregor BA, Hur J

Abstract
The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.

PMID: 29545755 [PubMed]

New opportunities for kinase drug repurposing and target discovery.

Br J Cancer. 2018 Mar 16;:

Authors: Knapp S

Abstract
Protein kinases are major drug targets for oncology. The large size of the kinome, active site conservation and the influence of activation states on drug binding complicates the analysis of their cellular mode of action. In a recent article in Science, Klaeger et al. analysed cellular targets of 243 drug candidates providing a large repository of data for drug repurposing.

PMID: 29545596 [PubMed - as supplied by publisher]

Therapeutic Effect of Quinacrine, an Anti-protozoan Drug, by Selective Suppression of p-CHK1/2 in p53-negative Malignant Cancers.

Mol Cancer Res. 2018 Mar 15;:

Authors: Park S, Oh AY, Cho JH, Yoon MH, Woo TG, Kang S, Lee HY, Jung Y, Park BJ

Abstract
Quinacrine (QNC), anti-protozoan drug commonly used against Malaria and Giardiasis, has been recently tried for rheumatics and prion diseases via drug repositioning. In addition, several reports suggest anti-tumor effects of QNC through suppression of NF-κB and activation of p53. This study, demonstrates the anti-cancer effect of QNC via a novel pathway through the elimination of check point kinase 1/2 (Chk1/2) under p53 inactivated conditions. Inhibition of p53, by PFT-α or siRNA, promotes QNC-induced apoptosis in normal fibroblast and p53-intact cancer cells. Considering that Chk1/2 kinases exert an essential role in the control of cell cycle, inhibition of Chk1/2 by QNC may induce cell death via uncontrolled cell cycle progression. Indeed, QNC reduces Chk1/2 expression under p53-impaired cancer cells and induces cell death in the G2/M phase. QNC increases the binding between p-Chk1/2 and β-TrCP and promotes proteasome-dependent degradation. Moreover, QNC treatment displayed anti-tumor effects in a Villin-Cre;p53+/LSL-R172H intestinal cancer mouse model system as well as HCT116 p53-/- xenografts.
IMPLICATIONS: Quinacrine has been used for the past over 70 years without obvious side-effects, as such it is a plausible drug candidate for relapsed cancers, small-cell lung cancer, breast cancer as well as various p53-inactivated human malignancies.

PMID: 29545477 [PubMed - as supplied by publisher]

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A novel computational approach for drug repurposing using systems biology.

Bioinformatics. 2018 Mar 09;:

Authors: Peyvandipour A, Saberian N, Shafi A, Donato M, Draghici S

Abstract
Motivation: Identification of novel therapeutic effects for existing U.S. Food and Drug Administration (FDA)-approved drugs, drug repurposing, is an approach aimed to dramatically shorten the drug discovery process, which is costly, slow and risky. Several computational approaches use transcriptional data to find potential repurposing candidates. The main hypothesis of such approaches is that if gene expression signature of a particular drug is opposite to the gene expression signature of a disease, that drug may have a potential therapeutic effect on the disease. However, this may not be optimal since it fails to consider the different roles of genes and their dependencies at the system level.
Results: We propose a systems biology approach to discover novel therapeutic roles for established drugs that addresses some of the issues in the current approaches. To do so, we use publicly available drug and disease data to build a drug-disease network (DDN) by considering all interactions between drug targets and disease-related genes in the context of all known signaling pathways. This network is integrated with gene-expression measurements to identify drugs with new desired therapeutic effects based on a system-level analysis method. We compare the proposed approach with the drug repurposing approach proposed by Sirota et al. on four human diseases: idiopathic pulmonary fibrosis (IPF), non-small cell lung cancer (NSCLC), prostate cancer, and breast cancer. We evaluate the proposed approach based on its ability to re-discover drugs that are already FDA-approved for a given disease.
Availability: The R package DrugDiseaseNet is under review for publication in Bioconductor and is available at https://github.com/azampvd/DrugDiseaseNet.
Contact: sorin@wayne.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.

PMID: 29534151 [PubMed - as supplied by publisher]

POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening.

Comput Biol Chem. 2018 Mar 01;74:39-48

Authors: Samdani A, Vetrivel U

Abstract
High throughput virtual screening plays a crucial role in hit identification during the drug discovery process. With the rapid increase in the chemical libraries, virtual screening process becomes computationally challenging, thereby posing a demand for efficiently parallelized software pipelines. Here we present a GNU Parallel based pipeline-POAP that is programmed to run Open Babel and AutoDock suite under highly optimized parallelization. The ligand preparation module is a unique feature in POAP, as it offers extensive options for geometry optimization, conformer generation, parallelization and also quarantines erroneous datasets for seamless operation. POAP also features multi receptor docking that can be utilized for comparative virtual screening and drug repurposing studies. As demonstrated using different structural datasets, POAP proves to be an efficient pipeline that enables high scalability, seamless operability, dynamic file handling and optimal utilization of CPU's for computationally demanding tasks. POAP is distributed freely under GNU GPL license and can be downloaded at https://github.com/inpacdb/POAP.

PMID: 29533817 [PubMed - as supplied by publisher]

CLC-Pred: A freely available web-service for in silico prediction of human cell line cytotoxicity for drug-like compounds.

PLoS One. 2018;13(1):e0191838

Authors: Lagunin AA, Dubovskaja VI, Rudik AV, Pogodin PV, Druzhilovskiy DS, Gloriozova TA, Filimonov DA, Sastry NG, Poroikov VV

Abstract
In silico methods of phenotypic screening are necessary to reduce the time and cost of the experimental in vivo screening of anticancer agents through dozens of millions of natural and synthetic chemical compounds. We used the previously developed PASS (Prediction of Activity Spectra for Substances) algorithm to create and validate the classification SAR models for predicting the cytotoxicity of chemicals against different types of human cell lines using ChEMBL experimental data. A training set from 59,882 structures of compounds was created based on the experimental data (IG50, IC50, and % inhibition values) from ChEMBL. The average accuracy of prediction (AUC) calculated by leave-one-out and a 20-fold cross-validation procedure during the training was 0.930 and 0.927 for 278 cancer cell lines, respectively, and 0.948 and 0.947 for cytotoxicity prediction for 27 normal cell lines, respectively. Using the given SAR models, we developed a freely available web-service for cell-line cytotoxicity profile prediction (CLC-Pred: Cell-Line Cytotoxicity Predictor) based on the following structural formula: http://way2drug.com/Cell-line/.

PMID: 29370280 [PubMed - indexed for MEDLINE]

Prediction of Novel Drugs for Hepatocellular Carcinoma Based on Multi-Source Random Walk.

IEEE/ACM Trans Comput Biol Bioinform. 2017 Jul-Aug;14(4):966-977

Authors: Yu L, Su R, Wang B, Zhang L, Zou Y, Zhang J, Gao L

Abstract
Computational approaches for predicting drug-disease associations by integrating gene expression and biological network provide great insights to the complex relationships among drugs, targets, disease genes, and diseases at a system level. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high rate of morbidity and mortality. We provide an integrative framework to predict novel d rugs for HCC based on multi-source random walk (PD-MRW). Firstly, based on gene expression and protein interaction network, we construct a gene-gene weighted i nteraction network (GWIN). Then, based on multi-source random walk in GWIN, we build a drug-drug similarity network. Finally, based on the known drugs for HCC, we score all drugs in the drug-drug similarity network. The robustness of our predictions, their overlap with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched KEGG pathway demonstrate our approach can effectively identify new drug indications. Specifically, regorafenib (Rank = 9 in top-20 list) is proven to be effective in Phase I and II clinical trials of HCC, and the Phase III trial is ongoing. And, it has 11 overlapping pathways with HCC with lower p-values. Focusing on a particular disease, we believe our approach is more accurate and possesses better scalability.

PMID: 27076463 [PubMed - indexed for MEDLINE]

FoxO3a mediates the inhibitory effects of the antiepileptic drug Lamotrigine on breast cancer growth.

Mol Cancer Res. 2018 Mar 09;:

Authors: Pellegrino M, Rizza P, Nigro A, Ceraldi R, Ricci E, Perrotta I, Aquila S, Lanzino M, Andò S, Morelli C, Sisci D

Abstract
Breast cancer (BC) is a complex and heterogeneous disease, with distinct histological features dictating the therapy. Although the clinical outcome of BC patients has been considerably improved, the occurrence of resistance to common endocrine and chemotherapy treatments remains the major cause of relapse and mortality. Thus, efforts in identifying new molecules to be employed in BC therapy are needed. As a "faster" alternative to reach this aim, we evaluated if Lamotrigine (LTG), a broadly used anticonvulsivant, could be "repurposed" as an antitumoral drug in BC. Our data show that LTG inhibits the proliferation, the anchorage-dependent and independent cell growth in BC cells (BCCs), including hormone-resistant cell models. These effects were associated with cell cycle arrest and modulation of related proteins (cyclin D1, cyclin E, p27Kip1 and p21Waf1/Cip1), all target genes of FoxO3a, an ubiquitous transcription factor negatively regulated by AKT. LTG also increases the expression of another FoxO3a target, PTEN, which, in turn, downregulates the PI3K/Akt signaling pathway, with consequent dephosphorylation, thus activation, of FoxO3a. Moreover, LTG induces FoxO3a expression by increasing its transcription through FoxO3a recruitment on specific FHRE located on its own promoter, in an autoregulatory fashion. Finally, LTG significantly reduced tumor growth in vivo, increasing FoxO3a expression.
IMPLICATIONS: The anticonvulsivant drug LTG shows strong antiproliferative activity on BC, both in vitro and in vivo, thus drug repurposing could represent a valuable option for a molecularly targeted therapy in BC patients.

PMID: 29523760 [PubMed - as supplied by publisher]

Comparative assessment of strategies to identify similar ligand-binding pockets in proteins.

BMC Bioinformatics. 2018 Mar 09;19(1):91

Authors: Govindaraj RG, Brylinski M

Abstract
BACKGROUND: Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges.
RESULTS: We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock.
CONCLUSIONS: Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5-10%. All data reported in this paper are freely available at https://osf.io/6ngbs/ .

PMID: 29523085 [PubMed - in process]

In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases.

Curr Med Chem. 2018 Mar 08;:

Authors: Andrade CH, Neves BJ, Melo-Filho CC, Rodrigues J, Silva DC, Braga RC, Cravo PVL

Abstract
Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs) have reached clinical trials in the last decades, underscoring the need for new, safe and effective treatments. In such context, drug repositioning, which allows finding novel indications for approved drugs whose pharmacokinetic and safety profiles are already known, is emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent of the typical drug discovery process that involves the systematic screening of chemical compounds against drug targets in high-throughput screening (HTS) efforts, for the identification of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics attempts to identify all potential ligands for all possible targets and diseases. In this review, we summarize current methodological development efforts in drug repositioning that use state-of-the-art computational ligand- and structure-based chemogenomics approaches. Furthermore, we highlighted the recent progress in computational drug repositioning for some NTDs, based on curation and modeling of genomic, biological, and chemical data. Additionally, we also present in-house and other successful examples and suggest possible solutions to existing pitfalls.

PMID: 29521204 [PubMed - as supplied by publisher]

In silico identification of potent small molecule inhibitors targeting epidermal growth factor receptor 1.

J Cancer Res Ther. 2018 Jan;14(1):18-23

Authors: Shi Z, Chen J, Guo X, Cheng L, Guo X, Yu T

Abstract
Background: The receptor tyrosine kinase of the epidermal growth factor receptor (EGFR, ErbB) family played an important role in multisignaling pathways, which controlled numerous biological activities including proliferation, differentiation, apoptosis, etc. EGFR abnormalities have been associated with a variety of human tumors, which was a well-characterized target for cancer treatment. It was known to all that drug repositioning has been considered as a useful tool to accelerate the process of drug development.
Materials and Methods: Herein, a total of 1408 small molecule drugs approved by the Food and Drug Administration (FDA) were employed to identify potential EGFR inhibitors by a series of bioinformatics approaches, including virtual screening and molecular dynamics (MD) simulations.
Results: According to the docking score, five small molecules were chosed for further MD simulations. Following the 5 ns MD simulations, ZINC03830276 (Benzonatate) were finally recognized as "new use" of FDA-approved EGFR-targeting drug.
Conclusions: Our findings suggested that the small molecule ZINC03830276 (Benzonatate) could be a promising EGFR inhibitor candidate and may also provide new ideas for designing more potent EGFR inhibitors for the future study.

PMID: 29516953 [PubMed - in process]

The multitargeted drug ivermectin: from an antiparasitic agent to a repositioned cancer drug.

Am J Cancer Res. 2018;8(2):317-331

Authors: Juarez M, Schcolnik-Cabrera A, Dueñas-Gonzalez A

Abstract
Drug repositioning is a highly studied alternative strategy to discover and develop anticancer drugs. This drug development approach identifies new indications for existing compounds. Ivermectin belongs to the group of avermectins (AVM), a series of 16-membered macrocyclic lactone compounds discovered in 1967, and FDA-approved for human use in 1987. It has been used by millions of people around the world exhibiting a wide margin of clinical safety. In this review, we summarize the in vitro and in vivo evidences demonstrating that ivermectin exerts antitumor effects in different types of cancer. Ivermectin interacts with several targets including the multidrug resistance protein (MDR), the Akt/mTOR and WNT-TCF pathways, the purinergic receptors, PAK-1 protein, certain cancer-related epigenetic deregulators such as SIN3A and SIN3B, RNA helicase, chloride channel receptors and preferentially target cancer stem-cell like population. Importantly, the in vitro and in vivo antitumor activities of ivermectin are achieved at concentrations that can be clinically reachable based on the human pharmacokinetic studies done in healthy and parasited patients. Thus, existing information on ivermectin could allow its rapid move into clinical trials for cancer patients.

PMID: 29511601 [PubMed]

Transcription Factor NRF2 as a Therapeutic Target for Chronic Diseases: A Systems Medicine Approach.

Pharmacol Rev. 2018 Apr;70(2):348-383

Authors: Cuadrado A, Manda G, Hassan A, Alcaraz MJ, Barbas C, Daiber A, Ghezzi P, León R, López MG, Oliva B, Pajares M, Rojo AI, Robledinos-Antón N, Valverde AM, Guney E, Schmidt HHHW

Abstract
Systems medicine has a mechanism-based rather than a symptom- or organ-based approach to disease and identifies therapeutic targets in a nonhypothesis-driven manner. In this work, we apply this to transcription factor nuclear factor (erythroid-derived 2)-like 2 (NRF2) by cross-validating its position in a protein-protein interaction network (the NRF2 interactome) functionally linked to cytoprotection in low-grade stress, chronic inflammation, metabolic alterations, and reactive oxygen species formation. Multiscale network analysis of these molecular profiles suggests alterations of NRF2 expression and activity as a common mechanism in a subnetwork of diseases (the NRF2 diseasome). This network joins apparently heterogeneous phenotypes such as autoimmune, respiratory, digestive, cardiovascular, metabolic, and neurodegenerative diseases, along with cancer. Importantly, this approach matches and confirms in silico several applications for NRF2-modulating drugs validated in vivo at different phases of clinical development. Pharmacologically, their profile is as diverse as electrophilic dimethyl fumarate, synthetic triterpenoids like bardoxolone methyl and sulforaphane, protein-protein or DNA-protein interaction inhibitors, and even registered drugs such as metformin and statins, which activate NRF2 and may be repurposed for indications within the NRF2 cluster of disease phenotypes. Thus, NRF2 represents one of the first targets fully embraced by classic and systems medicine approaches to facilitate both drug development and drug repurposing by focusing on a set of disease phenotypes that appear to be mechanistically linked. The resulting NRF2 drugome may therefore rapidly advance several surprising clinical options for this subset of chronic diseases.

PMID: 29507103 [PubMed - in process]

Statins in conditions other than hypocholesterolemic effects for chronic subdural hematoma therapy, old drug, new tricks?

Oncotarget. 2017 Apr 18;8(16):27541-27546

Authors: Zou H, Zhu XX, Ding YH, Zhang GB, Geng Y, Huang DS

Abstract
Chronic subdural hematoma (CSDH) is one of the most common intracranial hematomas worldwide with a high incidence in the general population. However, the optimum treatment for CSDH is Burr-hole drainage with or without rinse Considering the poor outcomes of CSDH in aged patients, and ambiguous prediction of recurrence in many sides of recurrent CSDHs who have been analyzed, new effective therapies are needed for those CSDHs who are predicated to have poor prognosis for surgery and/or have a higher risk of recurrence. Statins, which is the first-line treatment for patients with high cholesterol and coronary heart disease. However, statins are still not solely limited in the treatment of these diseases. It has been demonstrated that statins could improve CSDH due to its effect of regulation of angiogenesis and inflammation. In this review, in order to provide potential new treatment for CSDH we summarize the recent findings of statins in CSDH in order to try to clarify the mechanisms of this effect.

PMID: 28177914 [PubMed - indexed for MEDLINE]

New use of an old drug: inhibition of breast cancer stem cells by benztropine mesylate.

Oncotarget. 2017 Jan 03;8(1):1007-1022

Authors: Cui J, Hollmén M, Li L, Chen Y, Proulx ST, Reker D, Schneider G, Detmar M

Abstract
Cancer stem cells (CSCs) play major roles in cancer initiation, metastasis, recurrence and therapeutic resistance. Targeting CSCs represents a promising strategy for cancer treatment. The purpose of this study was to identify selective inhibitors of breast CSCs (BCSCs). We carried out a cell-based phenotypic screening with cell viability as a primary endpoint, using a collection of 2,546 FDA-approved drugs and drug-like molecules in spheres formed by malignant human breast gland-derived cells (HMLER-shEcad cells, representing BCSCs) and control immortalized non-tumorigenic human mammary cells (HMLE cells, representing normal stem cells). 19 compounds were identified from screening. The chemically related molecules benztropine mesylate and deptropine citrate were selected for further validation and both potently inhibited sphere formation and self-renewal of BCSCs in vitro. Benztropine mesylate treatment decreased cell subpopulations with high ALDH activity and with a CD44+/CD24- phenotype. In vivo, benztropine mesylate inhibited tumor-initiating potential in a 4T1 mouse model. Functional studies indicated that benztropine mesylate inhibits functions of CSCs via the acetylcholine receptors, dopamine transporters/receptors, and/or histamine receptors. In summary, our findings identify benztropine mesylate as an inhibitor of BCSCs in vitro and in vivo. This study also provides a screening platform for identification of additional anti-CSC agents.

PMID: 27894093 [PubMed - indexed for MEDLINE]

From gene networks to drugs: systems pharmacology approaches for AUD.

Psychopharmacology (Berl). 2018 Mar 01;:

Authors: Ferguson LB, Harris RA, Mayfield RD

Abstract
The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

PMID: 29497781 [PubMed - as supplied by publisher]

Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia.

Am J Psychiatry. 2018 Mar 02;:appiajp201717040410

Authors: Kauppi K, Rosenthal SB, Lo MT, Sanyal N, Jiang M, Abagyan R, McEvoy LK, Andreassen OA, Chen CH

Abstract
OBJECTIVE: Antipsychotic drugs were incidentally discovered in the 1950s, but their mechanisms of action are still not understood. Better understanding of schizophrenia pathogenesis could shed light on actions of current drugs and reveal novel "druggable" pathways for unmet therapeutic needs. Recent genome-wide association studies offer unprecedented opportunities to characterize disease gene networks and uncover drug-disease relationships. Polygenic overlap between schizophrenia risk genes and antipsychotic drug targets has been demonstrated, but specific genes and pathways constituting this overlap are undetermined. Risk genes of polygenic disorders do not operate in isolation but in combination with other genes through protein-protein interactions among gene product.
METHOD: The protein interactome was used to map antipsychotic drug targets (N=88) to networks of schizophrenia risk genes (N=328).
RESULTS: Schizophrenia risk genes were significantly localized in the interactome, forming a distinct disease module. Core genes of the module were enriched for genes involved in developmental biology and cognition, which may have a central role in schizophrenia etiology. Antipsychotic drug targets overlapped with the core disease module and comprised multiple pathways beyond dopamine. Some important risk genes like CHRN, PCDH, and HCN families were not connected to existing antipsychotics but may be suitable targets for novel drugs or drug repurposing opportunities to treat other aspects of schizophrenia, such as cognitive or negative symptoms.
CONCLUSIONS: The network medicine approach provides a platform to collate information of disease genetics and drug-gene interactions to shift focus from development of antipsychotics to multitarget antischizophrenia drugs. This approach is transferable to other diseases.

PMID: 29495895 [PubMed - as supplied by publisher]

Identification of cisapride as new inhibitor of putrescine uptake in Trypanosoma cruzi by combined ligand- and structure-based virtual screening.

Eur J Med Chem. 2018 Feb 13;149:22-29

Authors: Dietrich RC, Alberca LN, Ruiz MD, Palestro PH, Carrillo C, Talevi A, Gavernet L

Abstract
Nowadays, the pharmacological therapy for the treatment of Chagas disease is based on two old drugs, benznidazole and nifurtimox, which have restricted efficacy against the chronic phase of the illness. To overcome the lack of efficacy of the traditional drugs (and their considerable toxicity), new molecular targets have been studied as starting points to the discovery of new antichagasic compounds. Among them, polyamine transporter TcPAT12 (also known as TcPOT1.1) represents an interesting macromolecule, since polyamines are essential for Trypanosoma cruzi, the parasite that causes the illness, but it cannot synthesize them de novo. In this investigation we report the results of a combined ligand- and structure-based virtual screening for the discovery of new inhibitors of TcPAT12. Initially we filtered out ZINC and Drugbank databases with similarity and QSAR models and then we submitted the candidates to a validated docking based screening. Four structures were selected and tested in T. cruzi epimastigotes proliferation and two of them, Cisapride and [2-(cyclopentyloxy)phenyl]methanamine showed inhibitory effects. Additionally, we performed transport assays which demonstrated that Cisapride interferes with putrescine uptake in a specific mode.

PMID: 29494842 [PubMed - as supplied by publisher]

Challenges of using new and repurposed drugs for the treatment of multidrug-resistant tuberculosis in children.

Expert Rev Clin Pharmacol. 2018 Mar;11(3):233-244

Authors: Schaaf HS, Garcia-Prats AJ, McKenna L, Seddon JA

Abstract
INTRODUCTION: New and repurposed antituberculosis drugs are urgently needed to more safely and effectively treat multidrug-resistant (MDR) tuberculosis (TB) in children. Multiple challenges limit timely access to new MDR-TB treatments in children. Areas covered: Diagnosis of MDR-TB in children remains a barrier, with few children with MDR-TB diagnosed and treated. Other barriers to timely access to new and repurposed drugs are discussed, and include delayed initiation of paediatric trials, limited funding for paediatric drug development, fragmented regulatory systems and operational challenges. The status of access to current repurposed and novel drugs is presented. Expert commentary: More timely initiation of paediatric trials is needed and paediatric work should happen and be funded in parallel with each phase of adult trials. Better quality data, increased regulator resources and expertise, harmonization of regulatory requirements across borders/organisations and registration fee waivers would improve registration timelines. Improved diagnosis, recording and reporting will establish better demand. Improved systems for procurement and supply chain management would reduce in-country operational barriers to getting medications to children. The challenges must be addressed to ensure timely and equitable access to new drugs and regimens that are urgently needed for effective, safe and shorter treatment of children with MDR-TB.

PMID: 29280409 [PubMed - indexed for MEDLINE]