DrugRep: an automatic virtual screening server for drug repurposing.

Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/ .

Innovative pharmaceutical strategies for niclosamide repositioning / Estratégias farmacêuticas inovadoras para o reposicionamento da niclosamida

The antiparasitic niclosamide has shown promising anticancer activity in preclinical studies against several types of cancer, such as colorectal and prostate. Thus, the objective of this work was to develop innovative formulations for the repositioning of niclosamide as an anticancer agent. In chapter I, a critical review of the literature on the physicochemical properties of the drug was carried out, in addition the results of clinical studies against colorectal and prostate cancer. Besides, a review was carried out on studies that developed formulations containing this drug, as well as hypotheses to improve the biopharmaceutical performance of this molecule. In chapter II, the development of solid amorphous dispersion containing niclosamide was carried out. Drug/polymer solutions were acoustic levitated and characterized by synchrotron X-ray light. This set allowed fast, high quality measurements, as well as the identification of niclosamide recrystallization. Plasdone® and Soluplus® demonstrated better properties to form amorphous dispersions, with the latter showing superior solubility enhancement. The study showed that the developed formulation increased the apparent saturation solubility of niclosamide in water by two times. In chapter III the objective was the development, physicochemical characterization and in vitro anticancer activity of a niclosamide nanoemulsion, having HCT-116 cells as a cellular model. Preliminary results indicated Capmul® MCM C8 as the best liquid lipid for the system, but the first nanoemulsions containing this lipid were not stable to justify its usage. On the other hand, Miglyol® 812 indicated to be a suitable liquid lipid for the system. The niclosamide nanoemulsion (~200 nm) with Miglyol® 812 and poloxamer 188 was stable for 56 days, with a monomodal particle size distribution. Cell viability assay against HCT-116 cells demonstrated that niclosamide cytotoxicity is time and concentration dependent. Results herein obtained encourage further research to understand and optimize niclosamide performance as an anticancer drug substance

O antiparasitário niclosamida tem apresentado promissora atividade anticâncer em estudos pré- clínicos contra diversos tipos de câncer, como coloretal e próstata. Assim, o objetivo deste trabalho foi desenvolver formulações inovadoras para o reposicionamento da niclosamida como agente anticâncer. No capítulo I foi realizada revisão crítica da literatura sobre as propriedades físico-químicas do fármaco, além de resultados de estudos clínicos da niclosamida contra câncer de coloretal e de próstata. Além disso, foi feita revisão sobre estudos que desenvolveram formulações contendo esse fármaco, bem como hipóteses para melhorar o desempenho biofarmacêutico dessa molécula. No capítulo II foi realizado o desenvolvimento de dispersão solida amorfa contendo niclosamida. Soluções de fármaco/polímero foram levitadas em levitador acústico e caracterizadas por raios-X de luz síncrotron. Este conjunto permitiu medições rápidas e de alta qualidade, bem como identificação de recristalização da niclosamida. Plasdone® e Soluplus® demonstraram melhores propriedades para formar as dispersões amorfas, com o último apresentando aumento de solubilidade superior. O estudo mostrou que a formulação desenvolvida aumentou em duas vezes a solubilidade aparente de saturação da niclosamida em água. No capítulo III o objetivo foi o desenvolvimento, a caracterização físicoquímica e atividade anticâncer in vitro de uma nanoemulsão de niclosamida, tendo células HCT-116 como modelo celular. Resultados preliminares indicaram o Capmul® MCM C8 como o melhor lipídio líquido para o sistema, mas as primeiras nanoemulsões contendo este lipídio não foram estáveis para justificar seu uso. Por outro lado, Miglyol® 812 indicou ser um lipídio líquido adequado para o sistema. A nanoemulsão de niclosamida (~200 nm) com Miglyol® 812 e poloxâmero 188 foi estável por 56 dias, com distribuição monomodal do tamanho de partícula. O ensaio de viabilidade celular contra células HCT-116 demonstrou que a citoxicidade da niclosamida é dependente do tempo e da concentração. Os resultados aqui obtidos encorajam mais pesquisas para entender e otimizar o desempenho da niclosamida como uma substância anticancerígena

How good are publicly available web services that predict bioactivity profiles for drug repurposing?

Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.

Link prediction in drug-target interactions network using similarity indices.

BACKGROUND: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning - methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem. RESULTS: We compare our method for DTI prediction against the well-known RBM approach. We show that when applied to the MATADOR database, our approach based on node neighborhoods yield higher precision for high-ranking predictions than RBM when no information regarding DTI types is available. CONCLUSION: This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions.

Network-based inference methods for drug repositioning.

Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods, ProbS and HeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies.

DRUGSURV: a resource for repositioning of approved and experimental drugs in oncology based on patient survival information.

The use of existing drugs for new therapeutic applications, commonly referred to as drug repositioning, is a way for fast and cost-efficient drug discovery. Drug repositioning in oncology is commonly initiated by in vitro experimental evidence that a drug exhibits anticancer cytotoxicity. Any independent verification that the observed effects in vitro may be valid in a clinical setting, and that the drug could potentially affect patient survival in vivo is of paramount importance. Despite considerable recent efforts in computational drug repositioning, none of the studies have considered patient survival information in modelling the potential of existing/new drugs in the management of cancer. Therefore, we have developed DRUGSURV; this is the first computational tool to estimate the potential effects of a drug using patient survival information derived from clinical cancer expression data sets. DRUGSURV provides statistical evidence that a drug can affect survival outcome in particular clinical conditions to justify further investigation of the drug anticancer potential and to guide clinical trial design. DRUGSURV covers both approved drugs (∼1700) as well as experimental drugs (∼5000) and is freely available at http://www.bioprofiling.de/drugsurv.

Drug repositioning framework by incorporating functional information.

As a shortcut for drug development, drug repositioning draws more and more attention in pharmaceutical industry to identify new indications for marketed drugs or drugs failed in late clinical trial phase. At the same time, the abundant high-throughput data pushes the computationally repositioning drugs a hot topic in the area of systems biology. Here, the authors propose a general framework for repositioning drug by incorporating various functional information. The framework starts with the identification of differentially expressed gene sets under disease state and drug treatment. Then the disease and drug are associated by the overlap of these two gene sets via biological function. The authors provide two strategies to assess the functional overlap. In the first strategy, functional relevance are evaluated by leveraging genes' lethality information to reveal drug's potential of curing diseases. In the second strategy, biological process perturbation profiles are identified by mapping differentially expressed genes into pathways and gene ontology (GO) terms. Their associations are assessed and used to rank drugs' potential of curing diseases. The preliminary results on prostate cancer demonstrate that our new framework improves the drug repositioning efficiency and various function information could complement each other. Importantly, the new framework will enhance the biological interpretation and rationale of drug repositioning and provide insights into drug action mechanisms.