Abstract: Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ultimately drug-disease associations.

Abstract: This study reports the stability of extemporaneously prepared gabapentin oral suspensions prepared at 100 mg/mL from bulk drug and capsules in either Oral Mix or Oral Mix SF suspending vehicles. Suspensions were packaged in amber plastic bottles and amber plastic syringes at 25 degrees C / 60%RH for up to 90 days. Throughout the study period, the following tests were performed to evaluate the stability of the preparations: organoleptic inspection to detect homogeneity, color or odor changes; pH measurements; and gabapentin assay using a stability-indicating HPLC-UV method. As crystallization was observed at 5 degrees C, storage at this temperature condition is not recommended. All preparations stored at 25 degrees C / 60%RH remained stable for the whole study duration of 90 days.

Abstract: Tumor necrosis factor-alpha (TNF-alpha) inhibitors are increasingly being used as treatment for rheumatoid arthritis (RA). However, the administration of these drugs carries the risk of inducing injection site reaction (ISR). ISR gives rise to patient stress, nervousness, and a decrease in quality of life (QoL). In order to alleviate pain and other symptoms, early countermeasures must be taken against this adverse event. In order to improve understanding of the risk factors contributing to the induction of ISR, we evaluated the association between TNF-alpha inhibitors and ISR by applying a logistic regression model to age-stratified data obtained from the Food and Drug Administration Adverse Event Reporting System (FAERS) database. The FAERS database contains 7,561,254 reports from January 2004 to December 2015. Adjusted reporting odds ratios (RORs) (95% Confidence Intervals) were obtained for interaction terms for age-stratified groups treated with etanercept (ETN) and adalimumab (ADA). The adjusted RORs for ETN* >/= 70 and ADA* >/= 70 groups were the lowest among the age-stratified groups undergoing the respective monotherapies. Furthermore, we found that crude RORs for ETN + methotrexate (MTX) combination therapy and ADA + MTX combination therapy were lower than those for the respective monotherapies. This study was the first to evaluate the relationship between aging and ISR using the FAERS database.

Abstract: The remarkable progress in engineering and clinical development of therapeutic antibodies in the last 40 years, after the seminal work by Kohler and Milstein, has led to the approval by the United States Food and Drug Administration (FDA) of 21 antibodies for cancer immunotherapy. We review here these approved antibodies, with emphasis on the methods used for their discovery, engineering, and optimization for therapeutic settings. These methods include antibody engineering via chimerization and humanization of non-human antibodies, as well as selection and further optimization of fully human antibodies isolated from human antibody phage-displayed libraries and immunization of transgenic mice capable of generating human antibodies. These technology platforms have progressively led to the development of therapeutic antibodies with higher human content and, thus, less immunogenicity. We also discuss the genetic engineering approaches that have allowed isotype switching and Fc modifications to modulate effector functions and bioavailability (half-life), which together with the technologies for engineering the Fv fragment, have been pivotal in generating more efficacious and better tolerated therapeutic antibodies to treat cancer.

Abstract: Malaria causes about half a million deaths annually, with Plasmodium falciparum being responsible for 90% of all the cases. Recent reports on artemisinin resistance in Southeast Asia warrant urgent discovery of novel drugs for the treatment of malaria. However, most bioactive compounds fail to progress to treatments due to safety concerns. Drug repositioning offers an alternative strategy where drugs that have already been approved as safe for other diseases could be used to treat malaria. This study screened approved drugs for antimalarial activity using an in silico chemogenomics approach prior to in vitro verification. All the P. falciparum proteins sequences available in NCBI RefSeq were mined and used to perform a similarity search against DrugBank, TTD and STITCH databases to identify similar putative drug targets. Druggability indices of the potential P. falciparum drug targets were obtained from TDR targets database. Functional amino acid residues of the drug targets were determined using ConSurf server which was used to fine tune the similarity search. This study predicted 133 approved drugs that could target 34 P. falciparum proteins. A literature search done at PubMed and Google Scholar showed 105 out of the 133 drugs to have been previously tested against malaria, with most showing activity. For further validation, drug susceptibility assays using SYBR Green I method were done on a representative group of 10 predicted drugs, eight of which did show activity against P. falciparum 3D7 clone. Seven had IC50 values ranging from 1 muM to 50 muM. This study also suggests drug-target association and hence possible mechanisms of action of drugs that did show antiplasmodial activity. The study results validate the use of proteome-wide target similarity approach in identifying approved drugs with activity against P. falciparum and could be adapted for other pathogens.

Abstract: Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Abstract: The explosive growth of literature and data in the life sciences challenges researchers to keep track of current advancements in their disciplines. Novel approaches in the life science like the One Health paradigm require integrated methodologies in order to link and connect heterogeneous information from databases and literature resources. Current publications in the life sciences are increasingly characterized by the employment of trans-disciplinary methodologies comprising molecular and cell biology, genetics, genomic, epigenomic, transcriptional and proteomic high throughput technologies with data from humans, plants, and animals. The literature search engine LIVIVO empowers retrieval functionality by incorporating various literature resources from medicine, health, environment, agriculture and nutrition. LIVIVO is developed in-house by ZB MED - Information Centre for Life Sciences. It provides a user-friendly and usability-tested search interface with a corpus of 55 Million citations derived from 50 databases. Standardized application programming interfaces are available for data export and high throughput retrieval. The search functions allow for semantic retrieval with filtering options based on life science entities. The service oriented architecture of LIVIVO uses four different implementation layers to deliver search services. A Knowledge Environment is developed by ZB MED to deal with the heterogeneity of data as an integrative approach to model, store, and link semantic concepts within literature resources and databases. Future work will focus on the exploitation of life science ontologies and on the employment of NLP technologies in order to improve query expansion, filters in faceted search, and concept based relevancy rankings in LIVIVO.

Abstract: Computational drug repositioning has been proved as an effective approach to develop new drug uses. However, currently existing strategies strongly rely on drug response gene signatures which scattered in separated or individual experimental data, and resulted in low efficient outputs. So, a fully drug response gene signatures database will be very helpful to these methods. We collected drug response microarray data and annotated related drug and targets information from public databases and scientific literature. By selecting top 500 up-regulated and down-regulated genes as drug signatures, we manually established the DrugSig database. Currently DrugSig contains more than 1300 drugs, 7000 microarray and 800 targets. Moreover, we developed the signature based and target based functions to aid drug repositioning. The constructed database can serve as a resource to quicken computational drug repositioning. Database URL: http://biotechlab.fudan.edu.cn/database/drugsig/.

Abstract: MicroRNAs (miRNAs) are a class of evolutionarily conserved small noncoding RNAs, ~22 nt in length, and found in diverse organisms and play important roles in the regulation of mRNA translation and degradation. It was shown that miRNAs were involved in many key biological processes through regulating the expression of targets. Genetic polymorphisms in miRNA target sites may alter miRNA regulation and therefore result in the alterations of the drug targets. Recent studies have demonstrated that SNPs in miRNA target sites can affect drug efficiency. However, there are still a large number of specific genetic variants related to drug efficiency that are yet to be discovered. We integrated large scale of genetic variations, drug targets, gene interaction networks, biological pathways, and seeds region of miRNA to identify miRNA polymorphisms affecting drug response. In addition, harnessing the abundant high quality biological network/pathways, we evaluated the cascade distribution of tarSNP impacts. We showed that the predictions can uncover most of the known experimentally supported cases as well as provide informative candidates complementary to existing methods/tools. Although there are several existing databases predicting the gain or loss of targeting function of miRNA mediated by SNPs, such as PolymiRTS, miRNASNP, MicroSNiPer, and MirSNP, none of them evaluated the influences of tarSNPs on drug response alterations. We developed a user-friendly online database of this approach named Mir2Drug.

Abstract: Diseases affecting skeletal muscle exhibit considerable heterogeneity in intensity, etiology, phenotypic manifestation and gene expression. Systems biology approaches using network theory, allows for a holistic understanding of functional similarities amongst diseases. Here we propose a co-expression based, network theoretic approach to extract functional similarities from 20 heterogeneous diseases comprising of dystrophinopathies, inflammatory myopathies, neuromuscular, and muscle metabolic diseases. Utilizing this framework we identified seven closely associated disease clusters with 20 disease pairs exhibiting significant correlation (p < 0.05). Mapping the diseases onto a human protein-protein interaction network enabled the inference of a common program of regulation underlying more than half the muscle diseases considered here and referred to as the "protein signature." Enrichment analysis of 17 protein modules identified as part of this signature revealed a statistically non-random dysregulation of muscle bioenergetic pathways and calcium homeostasis. Further, analysis of mechanistic similarities of less explored significant disease associations [such as between amyotrophic lateral sclerosis (ALS) and cerebral palsy (CP)] using a proposed "functional module" framework revealed adaptation of the calcium signaling machinery. Integrating drug-gene information into the quantitative framework highlighted the presence of therapeutic opportunities through drug repurposing for diseases affecting the skeletal muscle.