DeCoST: A New Approach in Drug Repurposing From Control System Theory.

Front Pharmacol. 2018;9:583

Authors: Nguyen TM, Muhammad SA, Ibrahim S, Ma L, Guo J, Bai B, Zeng B

Abstract
In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from "one: disease-gene-drug" to "multi: gene, dru" and from "lazy guilt-by-association" to "systematic model-based pattern matching," mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear-quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.).

PMID: 29922160 [PubMed]

Repurposing drugs to treat l-DOPA-induced dyskinesia in Parkinson's disease.

Neuropharmacology. 2018 Jun 01;:

Authors: Johnston TH, Lacoste AMB, Visanji NP, Lang AE, Fox SH, Brotchie JM

Abstract
In this review, we discuss the opportunity for repurposing drugs for use in l-DOPA-induced dyskinesia (LID) in Parkinson's disease. LID is a particularly suitable indication for drug repurposing given its pharmacological diversity, translatability of animal-models, availability of Phase II proof-of-concept (PoC) methodologies and the indication-specific regulatory environment. A compound fit for repurposing is defined as one with appropriate human safety-data as well as animal safety, toxicology and pharmacokinetic data as found in an Investigational New Drug (IND) package for another indication. We first focus on how such repurposing candidates can be identified and then discuss development strategies that might progress such a candidate towards a Phase II clinical PoC. We discuss traditional means for identifying repurposing candidates and contrast these with newer approaches, especially focussing on the use of computational and artificial intelligence (AI) platforms. We discuss strategies that can be categorised broadly as: in vivo phenotypic screening in a hypothesis-free manner; in vivo phenotypic screening based on analogy to a related disorder; hypothesis-driven evaluation of candidates in vivo and in silico screening with a hypothesis-agnostic component to the selection. To highlight the power of AI approaches, we describe a case study using IBM Watson where a training set of compounds, with demonstrated ability to reduce LID, were employed to identify novel repurposing candidates. Using the approaches discussed, many diverse candidates for repurposing in LID, originally envisaged for other indications, will be described that have already been evaluated for efficacy in non-human primate models of LID and/or clinically.

PMID: 29907424 [PubMed - as supplied by publisher]

Medical Concept Normalization in Social Media Posts with Recurrent Neural Networks.

J Biomed Inform. 2018 Jun 12;:

Authors: Tutubalina E, Miftahutdinov Z, Nikolenko S, Malykh V

Abstract
Text mining of scientific libraries and social media has already proven itself as a reliable tool for drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept in a controlled vocabulary, typically to the standard thesaurus in the Unified Medical Language System (UMLS), is known as medical concept normalization. This task is challenging due to the differences in the use of medical terminology between health care professionals and social media texts coming from the lay public. To bridge this gap, we use sequence learning with recurrent neural networks and semantic representation of one- or multi-word expressions: we develop end-to-end architectures directly tailored to the task, including bidirectional Long Short-Term Memory, Gated Recurrent Units with an attention mechanism, and additional semantic similarity features based on UMLS. Our evaluation against a standard benchmark shows that recurrent neural networks improve results over an effective baseline for classification based on convolutional neural networks. A qualitative examination of mentions discovered in a dataset of user reviews collected from popular online health information platforms as well as a quantitative evaluation both show improvements in the semantic representation of health-related expressions in social media.

PMID: 29906585 [PubMed - as supplied by publisher]

Simultaneous blockade of IL-6 and CCL5 signaling for synergistic inhibition of triple-negative breast cancer growth and metastasis.

Breast Cancer Res. 2018 Jun 14;20(1):54

Authors: Jin K, Pandey NB, Popel AS

Abstract
BACKGROUND: Metastatic triple-negative breast cancer (TNBC) is a heterogeneous and incurable disease. Numerous studies have been conducted to seek molecular targets to treat TNBC effectively, but chemotherapy is still the main choice for patients with TNBC. We have previously presented evidence of the important roles of interleukin-6 (IL-6) and chemokine (C-C motif) ligand 5 (CCL5) in TNBC tumor growth and metastasis. These experiments highlighted the importance of the crosstalk between cancer cells and stromal lymphatic endothelial cells (LECs) in tumor growth and metastasis.
METHODS: We examined the viability and migration of MDA-MB-231-LN, SUM149, and SUM159 cells co-cultured with LECs when treated with maraviroc (CCR5 inhibitor) and tocilizumab (anti-IL-6 receptor antibody). To assess the anti-tumor effects of the combination of these two drugs in an athymic nude mouse model, MDA-MB-231-LN cells were implanted in the mammary fat pad and maraviroc (8 mg/kg, orally daily) and cMR16-1 (murine surrogate of the anti-IL-6R antibody, 10 mg/kg, IP, 3 days a week) were administrated for 5 weeks and effects on tumor growth and thoracic metastasis were measured.
RESULTS: In this study, we used maraviroc and tocilizumab to confirm that IL-6 and CCL5 signaling are key pathways promoting TNBC cell proliferation and migration. Further, in a xenograft mouse model, we showed that tumor growth was dramatically inhibited by cMR16-1, the mouse version of the anti-IL6R antibody. The combination of maraviroc and cMR16-1 caused significant reduction of TNBC tumor growth compared to the single agents. Significantly, the combination of maraviroc and cMR16-1 abrogated thoracic metastasis.
CONCLUSION: Taken together, these findings show that IL-6 and CCL5 signaling, which promote crosstalk between TNBC and lymphatic vessels, are key enhancers of TNBC tumor growth and metastasis. Furthermore, these results demonstrate that a drug combination inhibiting these pathways may be a promising therapy for TNBC patients.

PMID: 29898755 [PubMed - in process]

Repositioning of Omarigliptin as a once-weekly intranasal Anti-parkinsonian Agent.

Sci Rep. 2018 Jun 12;8(1):8959

Authors: Ayoub BM, Mowaka S, Safar MM, Ashoush N, Arafa MG, Michel HE, Tadros MM, Elmazar MM, Mousa SA

Abstract
Drug repositioning is a revolution breakthrough of drug discovery that presents outstanding privilege with already safer agents by scanning the existing candidates as therapeutic switching or repurposing for marketed drugs. Sitagliptin, vildagliptin, saxagliptin & linagliptin showed antioxidant and neurorestorative effects in previous studies linked to DPP-4 inhibition. Literature showed that gliptins did not cross the blood brain barrier (BBB) while omarigliptin was the first gliptin that crossed it successfully in the present work. LC-MS/MS determination of once-weekly anti-diabetic DPP-4 inhibitors; omarigliptin & trelagliptin in plasma and brain tissue was employed after 2 h of oral administration to rats. The brain/plasma concentration ratio was used to deduce the penetration power through the BBB. Results showed that only omarigliptin crossed the BBB due to its low molecular weight & lipophilic properties suggesting its repositioning as antiparkinsonian agent. The results of BBB crossing will be of interest for researchers interested in Parkinson's disease. A novel intranasal formulation was developed using sodium lauryl sulphate surfactant to solubilize the lipophilic omarigliptin with penetration enhancing & antimicrobial properties. Intranasal administration showed enhanced brain/plasma ratio by 3.3 folds compared to the oral group accompanied with 2.6 folds increase in brain glucagon-like peptide-1 concentration compared to the control group.

PMID: 29895906 [PubMed - in process]

Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature.

Sci Rep. 2018 Jun 11;8(1):8857

Authors: Chang Y, Park H, Yang HJ, Lee S, Lee KY, Kim TS, Jung J, Shin JM

Abstract
In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a challenge. Herein, we report Cancer Drug Response profile scan (CDRscan) a novel deep learning model that predicts anticancer drug responsiveness based on a large-scale drug screening assay data encompassing genomic profiles of 787 human cancer cell lines and structural profiles of 244 drugs. CDRscan employs a two-step convolution architecture, where the genomic mutational fingerprints of cell lines and the molecular fingerprints of drugs are processed individually, then merged by 'virtual docking', an in silico modelling of drug treatment. Analysis of the goodness-of-fit between observed and predicted drug response revealed a high prediction accuracy of CDRscan (R2 > 0.84; AUROC > 0.98). We applied CDRscan to 1,487 approved drugs and identified 14 oncology and 23 non-oncology drugs having new potential cancer indications. This, to our knowledge, is the first-time application of a deep learning model in predicting the feasibility of drug repurposing. By further clinical validation, CDRscan is expected to allow selection of the most effective anticancer drugs for the genomic profile of the individual patient.

PMID: 29891981 [PubMed - in process]

Drug delivery for the treatment of endometriosis and uterine fibroids.

Drug Deliv Transl Res. 2017 Dec;7(6):829-839

Authors: Friend DR

Abstract
Endometriosis and uterine fibroids (also known as uterine leiomyomas) are serious medical conditions affecting large numbers of women worldwide. Many women are asymptomatic but those with symptoms require medical intervention to relieve chronic pain and dysmenorrhea and to address infertility. Drug delivery has played a role in reducing some of the symptoms associated with endometriosis and uterine fibroids. Use of drug delivery systems for both conditions can roughly be divided into two categories: (1) existing systems designed for other indications such as contraception for symptomatic relief and (2) development of novel systems aimed at addressing some of the underlying biochemical changes associated with endometriosis and uterine fibroids such as oxidative stress, angiogenesis, and matrix degradation. The latter drug delivery approaches rely heavily on nanotechnology. Existing systems that deliver estrogens and/or progestins include vaginal rings, transdermal patches, and intrauterine systems. Long-acting implantable contraceptives such as Implanon® and injectables such as Depo-Provera® have found use in treating endometriosis. Similarly, long-acting GnRH products (e.g., Lupron Depot®) are used to treat endometriosis. Other drugs formulated in long-acting formulations include intravaginal rings capable of delivering selective progesterone receptor modulators, androgens such as danazol, and aromatase inhibitors (e.g., anastrozole). Nanoparticles composed of silica, poly(lactic-co-glycolic acid), cerium oxide, dendrimers, and chitosan/polyethyleneamine have all been investigated to improve treatment of endometriosis and to a lesser extent, uterine fibroids.

PMID: 28828592 [PubMed - indexed for MEDLINE]

Targeting Hypoxia-Inducible Factors for Antiangiogenic Cancer Therapy.

Trends Cancer. 2017 Jul;3(7):529-541

Authors: Rey S, Schito L, Wouters BG, Eliasof S, Kerbel RS

Abstract
Hypoxia (low O2) is a pathobiological hallmark of solid cancers, resulting from the imbalance between cellular O2 consumption and availability. Hypoxic cancer cells (CCs) stimulate blood vessel sprouting (angiogenesis), aimed at restoring O2 delivery to the expanding tumor masses through the activation of a transcriptional program mediated by hypoxia-inducible factors (HIFs). Here, we review recent data suggesting that the efficacy of antiangiogenic (AA) therapies is limited in some circumstances by HIF-dependent compensatory responses to increased intratumoral hypoxia. In lieu of this evidence, we discuss the potential of targeting HIFs as a strategy to overcome these instances of AA therapy resistance.

PMID: 28718406 [PubMed - indexed for MEDLINE]

Cascade Ligand- and Structure-Based Virtual Screening to Identify New Trypanocidal Compounds Inhibiting Putrescine Uptake.

Front Cell Infect Microbiol. 2018;8:173

Authors: Alberca LN, Sbaraglini ML, Morales JF, Dietrich R, Ruiz MD, Pino Martínez AM, Miranda CG, Fraccaroli L, Alba Soto CD, Carrillo C, Palestro PH, Talevi A

Abstract
Chagas disease is a neglected tropical disease endemic to Latin America, though migratory movements have recently spread it to other regions. Here, we have applied a cascade virtual screening campaign combining ligand- and structure-based methods. In order to find novel inhibitors of putrescine uptake in Trypanosoma cruzi, an ensemble of linear ligand-based classifiers obtained by has been applied as initial screening filter, followed by docking into a homology model of the putrescine permease TcPAT12. 1,000 individual linear classifiers were inferred from a balanced dataset. Subsequently, different schemes were tested to combine the individual classifiers: MIN operator, average ranking, average score, average voting, with MIN operator leading to the best performance. The homology model was based on the arginine/agmatine antiporter (AdiC) from Escherichia coli as template. It showed 64% coverage of the entire query sequence and it was selected based on the normalized Discrete Optimized Protein Energy parameter and the GA341 score. The modeled structure had 96% in the allowed area of Ramachandran's plot, and none of the residues located in non-allowed regions were involved in the active site of the transporter. Positivity Predictive Value surfaces were applied to optimize the score thresholds to be used in the ligand-based virtual screening step: for that purpose Positivity Predictive Value was charted as a function of putative yields of active in the range 0.001-0.010 and the Se/Sp ratio. With a focus on drug repositioning opportunities, DrugBank and Sweetlead databases were subjected to screening. Among 8 hits, cinnarizine, a drug frequently prescribed for motion sickness and balance disorder, was tested against T. cruzi epimastigotes and amastigotes, confirming its trypanocidal effects and its inhibitory effects on putrescine uptake. Furthermore, clofazimine, an antibiotic with already proven trypanocidal effects, also displayed inhibitory effects on putrescine uptake. Two other hits, meclizine and butoconazole, also displayed trypanocidal effects (in the case of meclizine, against both epimastigotes and amastigotes), without inhibiting putrescine uptake.

PMID: 29888213 [PubMed - in process]

Learning Opportunities for Drug Repositioning via GWAS and PheWAS Findings.

AMIA Jt Summits Transl Sci Proc. 2018;2017:237-246

Authors: Yin W, Gao C, Xu Y, Li B, Ruderfer DM, Chen Y

Abstract
Drug repositioning for available medications can be preferred over traditional drug development, which requires substantially more effort to uncover new insights into medications and diseases. Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) are two complimentary methods for finding novel associations between genes and diseases. We hypothesize that discoveries from these studies could be leveraged to find new targets for existing drugs. Thus, we propose a framework to learn opportunities for inferring such relationships via overlapped genes between disease-associated genes (e.g. GWAS and PheWAS findings) and drugtargeted genes. We use drug indications found in Medication Indication Resource (MEDI) as a gold standard to evaluate if drug indications learned from GWAS and PheWAS findings have clinical indications. We examined 151,011 <drug, GWAS phenotype> pairs from 987 drugs across 153 diseases and 763 pairs were statistically significant. Out of these 763 pairs, 16 of them were found to have clinical indications.

PMID: 29888080 [PubMed]

A Network-Biology Informed Computational Drug Repositioning Strategy to Target Disease Risk Trajectories and Comorbidities of Peripheral Artery Disease.

AMIA Jt Summits Transl Sci Proc. 2018;2017:108-117

Authors: Shameer K, Dow G, Glicksberg BS, Johnson KW, Ze Y, Tomlinson MS, Readhead B, Dudley JT, Kullo IJ

Abstract
Currently, drug discovery approaches focus on the design of therapies that alleviate an index symptom by reengineering the underlying biological mechanism in agonistic or antagonistic fashion. For example, medicines are routinely developed to target an essential gene that drives the disease mechanism. Therapeutic overloading where patients get multiple medications to reduce the primary and secondary side effect burden is standard practice. This single-symptom based approach may not be scalable, as we understand that diseases are more connected than random and molecular interactions drive disease comorbidities. In this work, we present a proof-of-concept drug discovery strategy by combining network biology, disease comorbidity estimates, and computational drug repositioning, by targeting the risk factors and comorbidities of peripheral artery disease, a vascular disease associated with high morbidity and mortality. Individualized risk estimation and recommending disease sequelae based therapies may help to lower the mortality and morbidity of peripheral artery disease.

PMID: 29888052 [PubMed]

Incorporating Protein Dynamics Through Ensemble Docking in Machine Learning Models to Predict Drug Binding.

AMIA Jt Summits Transl Sci Proc. 2018;2017:26-34

Authors: Alghamedy F, Bopaiah J, Jones D, Zhang X, Weiss HL, Ellingson SR

Abstract
Drug discovery is an expensive, lengthy, and sometimes dangerous process. The ability to make accurate computational predictions of drug binding would greatly improve the cost-effectiveness and safety of drug discovery and development. This study incorporates ensemble docking, the use of multiple protein conformations extracted from a molecular dynamics trajectory to perform docking calculations, with additional biomedical data sources and machine learning algorithms to improve the prediction of drug binding. We found that we can greatly increase the classification accuracy of an active vs a decoy compound using these methods over docking scores alone. The best results seen here come from having an individual protein conformation that produces binding features that correlate well with the active vs. decoy classification, in which case we achieve over 99% accuracy. The ability to confidently make accurate predictions on drug binding would allow for computational polypharamacological networks with insights into side-effect prediction, drug-repurposing, and drug efficacy.

PMID: 29888034 [PubMed]

Connecting genetics and gene expression data for target prioritisation and drug repositioning.

BioData Min. 2018;11:7

Authors: Ferrero E, Agarwal P

Abstract
Developing new drugs continues to be a highly inefficient and costly business. By repurposing an existing compound for a different indication, drug repositioning offers an attractive alternative to traditional drug discovery. Most of these approaches work by matching transcriptional disease signatures to anti-correlated gene expression profiles of drug perturbations. Genome-wide association studies (GWASs) are of great interest to researchers in the pharmaceutical industry because drug programmes with supporting genetic evidence are more likely to successfully progress through the drug discovery pipeline. Here, we present a systematic approach to generate drug repositioning hypothesis based on disease genetics by mining public repositories of GWAS data and drug transcriptomic profiles. We find that genes genetically associated with a certain disease are more likely to be differentially expressed in the same disease (p-value = 1.54e-17 and AUC = 0.75) and that, in existing drug - disease combinations, genes significantly up- or down-regulated after drug treatment are enriched for genes genetically associated with that disease (p-value = 1.1e-79 and AUC = 0.64). Finally, we use this framework to generate and rank novel GWAS-driven drug repositioning predictions.

PMID: 29881461 [PubMed]

Overcoming the legal and regulatory barriers to drug repurposing.

Nat Rev Drug Discov. 2018 Jun 08;:

Authors: Breckenridge A, Jacob R

Abstract
Drug repurposing has been proposed as a strategy to develop new therapies that has fewer risks, lower costs and shorter timelines than developing completely new drugs. However, the potential of this strategy has not been as widely realized as hoped, in part owing to legal and regulatory barriers. Here, we highlight these barriers and consider how they could be overcome.

PMID: 29880920 [PubMed - as supplied by publisher]

Changing Trends in Computational Drug Repositioning.

Pharmaceuticals (Basel). 2018 Jun 05;11(2):

Authors: Yella JK, Yaddanapudi S, Wang Y, Jegga AG

Abstract
Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated-"The most fruitful basis for the discovery of a new drug is to start with an old drug". However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of "serendipity", discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules-pharmacologically relevant sub-networks of biomolecules and/or pathways-from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

PMID: 29874824 [PubMed]

Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network.

Methods. 2018 Jun 04;:

Authors: Zhang W, Yue X, Huang F, Liu R, Chen Y, Ruan C

Abstract
Drug-disease associations provide important information for drug discovery and drug repositioning. Drug-disease associations can induce different effects, and the therapeutic effect attractswidespreadinterest. Therefore, developing drug-disease association prediction methods is an important task, and differentiating therapeutic associations from other associations is also very important. In this paper, we formulate the known drug-disease associations as a bipartite network, and then present a novel representation for drugs and diseases based on the bipartite network and linear neighborhood similarity. Thus, we propose the network topological similarity-based inference method (NTSIM) to predict unobserved drug-disease associations. Further, we extend the work to the association classification, and propose the network topological similarity-based classification method (NTSIM-C) to differentiate therapeutic associations from others. Compared with existing drug-disease association prediction methods, NTSIM can produce superior performances in predicting drug-disease associations, and NTSIM-C can accurately classify drug-disease associations. Further, we analyze the capability of proposed methods by using several case studies. The studies show the usefulness of NTSIM and NTSIM-C in the real applications. In conclusion, NTSIM and NTSIM-C are promising for predicting drug-disease associations and their therapeutic functions.

PMID: 29879508 [PubMed - as supplied by publisher]

Topical delivery of ebselen encapsulated in biopolymeric nanocapsules: drug repurposing enhanced antifungal activity.

Nanomedicine (Lond). 2018 Jun 06;:

Authors: Jaromin A, Zarnowski R, Piętka-Ottlik M, Andes DR, Gubernator J

Abstract
AIM: Ebselen (Eb) is an example of a repurposed drug with poor aqueous solubility which requires sophisticated delivery system such as nanoencapsulation in nanocapsules for topical application.
MATERIALS & METHODS: Eb-nanocapsules were examined for morphology, activity against Candida spp., cytotoxicity and skin permeation.
RESULTS: Eb-nanocapsules were active against skin-infecting Candida tropicalis, Candida albicans and Candida parapsilosis yeasts (minimal inhibitory concentration values were about 4-, 2- and 1.25-times lower vs free Eb, respectively) and able to suppress induced lipid oxidation in the oil/water emulsion. Moreover, demonstrated minimal toxicity in normal human dermal fibroblast cell line, whereas ex vivo skin permeation studies showed no transdermal passage and strong interactions with stratum corneum.
CONCLUSION: Eb-nanocapsules represent a promising, safe and complementary alternative to the treatment of cutaneous candidiasis.

PMID: 29873597 [PubMed - as supplied by publisher]

Repurposing Valproate, Enteral Clonidine, and Phenobarbital for Comfort in Adult ICU Patients: A Literature Review with Practical Considerations.

Pharmacotherapy. 2017 Oct;37(10):1309-1321

Authors: Gagnon DJ, Fontaine GV, Riker RR, Fraser GL

Abstract
Provision of adequate sedation is a fundamental part of caring for critically ill patients. Propofol, dexmedetomidine, and benzodiazepines are the most commonly administered sedative medications for adult patients in the intensive care unit (ICU). These agents are limited by adverse effects, need for a monitored environment for safe administration, and lack of universal effectiveness. Increased interest has recently been expressed about repurposing older pharmacologic agents for patient comfort in the ICU. Valproate, enteral clonidine, and phenobarbital are three agents with increasing evidence supporting their use. Potential benefits associated with their utilization are cost minimization and safe administration after transition out of the ICU. This literature review describes the historical context, pharmacologic characteristics, supportive data, and practical considerations associated with the administration of these agents for comfort in critically ill adult patients.

PMID: 28833346 [PubMed - indexed for MEDLINE]

Bis-biguanide dihydrochloride inhibits intracellular replication of M. tuberculosis and controls infection in mice.

Sci Rep. 2016 09 07;6:32725

Authors: Shen H, Wang F, Zeng G, Shen L, Cheng H, Huang D, Wang R, Rong L, Chen ZW

Abstract
While there is an urgent need to develop new and effective drugs for treatment of tuberculosis (TB) and multi-drug resistant TB (MDR-TB), repurposing FDA (U.S. Food and Drug Administration) -approved drugs for development of anti-TB agents may decrease time and effort from bench to bedside. Here, we employed host cell-based high throughput screening (HTS) assay to screen and characterize FDA-approved, off-patent library drugs for anti-Mycobacterium tuberculosis (MTB) activities. The cell-based HTS allowed us to identify an anti-cancer drug of bis-biguanide dihydrochloride (BBD) as potent anti-mycobacteria agent. Further characterization showed that BBD could inhibit intracellular and extracellular growth of M. smegmatis and slow-growing M. bovis BCG. BBD also potently inhibited replication of clinically-isolated MTB and MDR-TB strains. The proof-of-concept study showed that BBD treatment of MTB-infected mice could significantly decrease CFU counts in the lung and spleen. Notably, comparative evaluation showed that MTB CFU counts in BBD-treated mice were lower than those in rifampicin-treated mice. No apparent BBD side effects were found in BBD-treated mice. Thus, our findings support further studies to develop BBD as a new and effective drug against TB and MDR-TB.

PMID: 27601302 [PubMed - indexed for MEDLINE]

Changes on the Horizon for Drug Repurposing, Rescue, and Repositioning at ASSAY.

Assay Drug Dev Technol. 2018 Jun 05;:

Authors: Melancon BJ, Mucke HAM

PMID: 29870272 [PubMed - as supplied by publisher]