Prospects for productivity

Computational Advancements in Cancer Combination Therapy Prediction

Given the high attrition rate of de novo drug discovery and limited efficacy of single-agent therapies in cancer treatment, combination therapy prediction through in silico drug repurposing has risen as a time- and cost-effective alternative for identifying novel and potentially efficacious therapies for cancer. The purpose of this review is to provide an introduction to computational methods for cancer combination therapy prediction and to summarize recent studies that implement each of these methods. A systematic search of the PubMed database was performed, focusing on studies published within the past 10 years. Our search included reviews and articles of ongoing and retrospective studies. We prioritized articles with findings that suggest considerations for improving combination therapy prediction methods over providing a meta-analysis of all currently available cancer combination therapy prediction methods. Computational methods used for drug combination therapy prediction in cancer research include networks, regression-based machine learning, classifier machine learning models, and deep learning approaches. Each method class has its own advantages and disadvantages, so careful consideration is needed to determine the most suitable class when designing a combination therapy prediction method. Future directions to improve current combination therapy prediction technology include incorporation of disease pathobiology, drug characteristics, patient multiomics data, and drug-drug interactions to determine maximally efficacious and tolerable drug regimens for cancer. As computational methods improve in their capability to integrate patient, drug, and disease data, more comprehensive models can be developed to more accurately predict safe and efficacious combination drug therapies for cancer and other complex diseases.

Network-based drug repurposing for potential stroke therapy

Stroke is the leading cause of death and disability worldwide, with a growing number of incidences in developing countries. However, there are currently few medical therapies for this disease. Emerged as an effective drug discovery strategy, drug repurposing which owns lower cost and shorter time, is able to identify new indications from existing drugs. In this study, we aimed at identifying potential drug candidates for stroke via computationally repurposing approved drugs from Drugbank database. We first developed a drug-target network of approved drugs, employed network-based approach to repurpose these drugs, and altogether identified 185 drug candidates for stroke. To validate the prediction accuracy of our network-based approach, we next systematically searched for previous literature, and found 68 out of 185 drug candidates (36.8 %) exerted therapeutic effects on stroke. We further selected several potential drug candidates with confirmed neuroprotective effects for testing their anti-stroke activity. Six drugs, including cinnarizine, orphenadrine, phenelzine, ketotifen, diclofenac and omeprazole, have exhibited good activity on oxygen-glucose deprivation/reoxygenation (OGD/R) induced BV2 cells. Finally, we showcased the anti-stroke mechanism of actions of cinnarizine and phenelzine via western blot and Olink inflammation panel. Experimental results revealed that they both played anti-stroke effects in the OGD/R induced BV2 cells via inhibiting the expressions of IL-6 and COX-2. In summary, this study provides efficient network-based methodologies for in silico identification of drug candidates toward stroke.

Trends in small molecule drug properties: A developability molecule assessment perspective

Developability molecule assessment is a key interfacial capability across the biopharmaceutical industry, screening and staging molecules discovered by medicinal chemists for successful chemistry manufacturing controls (CMC) development and launch. The breadth of responsibility and expertise such teams possess puts them in a unique position to understand the impact of the physicochemical properties of a drug during its initial discovery and subsequent development. However, most of the publications describing trends in physicochemical properties are written from a medicinal chemistry perspective with the aim to identify molecules with better ADMET profiles that are either lead-like or drug-like, failing to describe the impact these properties have on CMC development. To systematically uncover knowledge obtained from recent trends in physicochemical properties and the corresponding impact on CMC development, a comprehensive analysis was conducted on molecules in the drug repurposing hub dataset. The only physicochemical property that seems to have been preserved in FDA-approved oral molecules over the decades (1900-2020) is a constant H-bond donor count, highlighting the importance this property has on cell permeability and lattice energy. Pharmaceutical attrition analysis suggests that partition-distribution coefficient, H-bond acceptors, polar surface area and the fraction of sp³ carbons are properties that are associated with compound attrition. Looking at pharmaceutical attrition asynchronously with the temporal analysis of FDA-approved oral molecules highlights the opposing trends, risks and diminishing effects some of these physiochemical properties (cLogP, cLogD and Fsp3) have on describing compound attrition during the past decade. Trellising the dataset by target class suggests that certain formulation and drug delivery strategies can be anticipated or put into place based on target class of a molecule. For example, molecules binding to nuclear hormone receptors are amenable to lipid-based drug delivery systems with proven commercial success. Although the poor solubility of kinase inhibitors is a combination of hydrophobicity (due to aromaticity) required to bind to its target and high lattice energy (melting point), they are a challenging target class to formulate. The influence of drug targets on physicochemical properties and the temporal nature of these properties is highlighted when comparing molecules in the drug repurposing dataset to those developed at Amgen. An improved understanding of the impact of molecular properties on performance attributes can accelerate decisions and facilitate risk assessments during candidate selection and development.

Distribution- and Metabolism-Based Drug Discovery: A Potassium-Competitive Acid Blocker as a Proof of Concept

Conventional methods of drug design require compromise in the form of side effects to achieve sufficient efficacy because targeting drugs to specific organs remains challenging. Thus, new strategies to design organ-specific drugs that induce little toxicity are needed. Based on characteristic tissue niche-mediated drug distribution (TNMDD) and patterns of drug metabolism into specific intermediates, we propose a strategy of distribution- and metabolism-based drug design (DMBDD); through a physicochemical property-driven distribution optimization cooperated with a well-designed metabolism pathway, SH-337, a candidate potassium-competitive acid blocker (P-CAB), was designed. SH-337 showed specific distribution in the stomach in the long term and was rapidly cleared from the systemic compartment. Therefore, SH-337 exerted a comparable pharmacological effect but a 3.3-fold higher no observed adverse effect level (NOAEL) compared with FDA-approved vonoprazan. This study contributes a proof-of-concept demonstration of DMBDD and provides a new perspective for the development of highly efficient, organ-specific drugs with low toxicity.

Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks

The understanding of therapeutic properties is important in drug repositioning and drug discovery. However, chemical or clinical trials are expensive and inefficient to characterize the therapeutic properties of drugs. Recently, artificial intelligence (AI)-assisted algorithms have received extensive attention for discovering the potential therapeutic properties of drugs and speeding up drug development. In this study, we propose a new method based on GraphSAGE and clustering constraints (DRGCC) to investigate the potential therapeutic properties of drugs for drug repositioning. First, the drug structure features and disease symptom features are extracted. Second, the drug–drug interaction network and disease similarity network are constructed according to the drug–gene and disease–gene relationships. Matrix factorization is adopted to extract the clustering features of networks. Then, all the features are fed to the GraphSAGE to predict new associations between existing drugs and diseases. Benchmark comparisons on two different datasets show that our method has reliable predictive performance and outperforms other six competing. We have also conducted case studies on existing drugs and diseases and aimed to predict drugs that may be effective for the novel coronavirus disease 2019 (COVID-19). Among the predicted anti-COVID-19 drug candidates, some drugs are being clinically studied by pharmacologists, and their binding sites to COVID-19-related protein receptors have been found via the molecular docking technology.

Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.

Recent advances in human respiratory epithelium models for drug discovery

The respiratory epithelium is intimately associated with the pathophysiologies of highly infectious viral contagions and chronic illnesses such as chronic obstructive pulmonary disorder, presently the third leading cause of death worldwide with a projected economic burden of £1.7 trillion by 2030. Preclinical studies of respiratory physiology have almost exclusively utilised non-humanised animal models, alongside reductionistic cell line-based models, and primary epithelial cell models cultured at an air-liquid interface (ALI). Despite their utility, these model systems have been limited by their poor correlation to the human condition. This has undermined the ability to identify novel therapeutics, evidenced by a 15% chance of success for medicinal respiratory compounds entering clinical trials in 2018. Consequently, preclinical studies require new translational efficacy models to address the problem of respiratory drug attrition. This review describes the utility of the current in vivo (rodent), ex vivo (isolated perfused lungs and precision cut lung slices), two-dimensional in vitro cell-line (A549, BEAS-2B, Calu-3) and three-dimensional in vitro ALI (gold-standard and co-culture) and organoid respiratory epithelium models. The limitations to the application of these model systems in drug discovery research are discussed, in addition to perspectives of the future innovations required to facilitate the next generation of human-relevant respiratory models.

Predicting drug approvals: The Novartis data science and artificial intelligence challenge

We describe a novel collaboration between academia and industry, an in-house data science and artificial intelligence challenge held by Novartis to develop machine-learning models for predicting drug-development outcomes, building upon research at MIT using data from Informa as the starting point. With over 50 cross-functional teams from 25 Novartis offices around the world participating in the challenge, the domain expertise of these Novartis researchers was leveraged to create predictive models with greater sophistication. Ultimately, two winning teams developed models that outperformed the baseline MIT model-areas under the curve of 0.88 and 0.84 versus 0.78, respectively-through state-of-the-art machine-learning algorithms and the use of newly incorporated features and data. In addition to validating the variables shown to be associated with drug approval in the earlier MIT study, the challenge also provided new insights into the drivers of drug-development success and failure.

A Convolutional Neural Network System to Discriminate Drug-Target Interactions

Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or functions. Exploring potential drug-target interactions (DTIs) are crucial for drug discovery and effective drug development. Computational methods were widely applied in drug-target interactions, since experimental methods are extremely time-consuming and resource-intensive. In this paper, we proposed a novel deep learning-based prediction system, with a new negative instance generation, to identify DTIs. As a result, our method achieved an accuracy of 0.9800 on our created dataset. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded a good performance with accuracy of 0.8814 and AUC value of 0.9527 on the dataset. The outcome of our experimental results indicated that the proposed method, involving the credible negative generation, can be employed to discriminate the interactions between drugs and targets. Website: http://www.dlearningapp.com/web/DrugCNN.htm.

Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening

Incorporating safety data early in the drug discovery pipeline is key to reducing costly lead candidate failures. For a single drug development project, we estimate that several thousand samples per day require screening (<10 s per acquisition). While chromatography-based metabolomics has proven value at predicting toxicity from metabolic biomarker profiles, it lacks sufficiently high sample throughput. Acoustic mist ionization mass spectrometry (AMI-MS) is an atmospheric pressure ionization approach that can measure metabolites directly from 384-well plates with unparalleled speed. We sought to implement a signal processing and data analysis workflow to produce high-quality AMI-MS metabolomics data and to demonstrate its application to drug safety screening. An existing direct infusion mass spectrometry workflow was adapted, extended, optimized, and tested, utilizing three AMI-MS data sets acquired from technical and biological replicates of metabolite standards and HepG2 cell lysates and a toxicity study. Driven by criteria to minimize variance and maximize feature counts, an algorithm to extract the pulsed scan data was designed; parameters for signal-to-noise-ratio, replicate filter, sample filter, missing value filter, and RSD filter were all optimized; normalization and batch correction strategies were adapted; and cell phenotype filtering was implemented to exclude high cytotoxicity samples. The workflow was demonstrated using a highly replicated HepG2 toxicity data set, comprising 2772 samples from exposures to 16 drugs across 9 concentrations and generated in under 5 h, revealing metabolic phenotypes and individual metabolite changes that characterize specific modes of action. This AMI-MS workflow opens the door to ultrahigh-throughput metabolomics screening, increasing the rate of sample analysis by approximately 2 orders of magnitude.

Computational drug repositioning for ischemic stroke: neuroprotective drug discovery

Background: A comprehensive approach to drug repositioning will be required to overcome translational hurdles and identify more neuroprotective drugs. Results & methods: Gene Set Enrichment Analysis was applied to identify related pathways and enriched genes. Candidate genes were optimized using ToppGene, ToppGenet and pBRIT. From the perspective of the local structures, gene-domain-substructure-drug relationships were constructed. Using the MCODE algorithm and K-means clustering, 31 functional subnetworks were obtained, and 252 drugs with proposed neuroprotective function were identified. Using computational analysis, 72 substructures with different scores were found to correspond to neuroprotective functions. The protective effects of benidipine and barnidipine were confirmed in vitro. Conclusion: The authors' research has great potential to discover more neuroprotective drugs and obtain more information regarding mechanisms of action and functional substructures.

In silico drug repositioning using deep learning and comprehensive similarity measures

BACKGROUND

Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized.

METHODS

In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions.

RESULTS

The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out.

CONCLUSION

The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.

Factors Affecting Outbound Open Innovation Performance in Bio-Pharmaceutical Industry-Focus on Out-Licensing Deals

Due to the high risk in development process, the bio-pharmaceutical industry has transformed itself into an open innovation framework in order to overcome economic risk. This study examines the relationship between outbound open innovation and financial performance in bio-pharmaceutical industry. Specifically, this study extends knowledge-based view to link the open innovation performance and licensor’s sustainability. In order to provide empirical evidence, this study uses econometric methodology with several databases including bio-pharmaceutical firms. The analysis shows firm’s desorptive capabilities have a significant effect on financial performance, confirming the application of knowledge capacity framework. The result of the study can suggest the way how the licensors can maintain the sustainability of competitiveness in bio-pharmaceutical industry.

Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways

Hepatocellular carcinoma (HCC) is a significant health problem worldwide with poor prognosis. Drug repositioning represents a profitable strategy to accelerate drug discovery in the treatment of HCC. In this study, we developed a new approach for predicting therapeutic drugs for HCC based on tissue-specific pathways and identified three newly predicted drugs that are likely to be therapeutic drugs for the treatment of HCC. We validated these predicted drugs by analyzing their overlapping drug indications reported in PubMed literature. By using the cancer cell line data in the database, we constructed a Connectivity Map (CMap) profile similarity analysis and KEGG enrichment analysis on their related genes. By experimental validation, we found securinine and ajmaline significantly inhibited cell viability of HCC cells and induced apoptosis. Among them, securinine has lower toxicity to normal liver cell line, which is worthy of further research. Our results suggested that the proposed approach was effective and accurate for discovering novel therapeutic options for HCC. This method also could be used to indicate unmarked drug-disease associations in the Comparative Toxicogenomics Database. Meanwhile, our method could also be applied to predict the potential drugs for other types of tumors by changing the database.

From Homology Modeling to the Hit Identification and Drug Repurposing: A Structure-Based Approach in the Discovery of Novel Potential Anti-Obesity Compounds

Although science and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In this scenario, drug repurposing has appeared as an alternative tool to accelerate the drug development process. Herein, we applied such an approach to the highly popular human Carbonic Anhydrase (hCA) VA drug target, that is involved in ureagenesis, gluconeogenesis, lipogenesis, and in the metabolism regulation. Albeit several hCA inhibitors have been designed and are currently in clinical use, serious drug interactions have been reported due to their poor selectivity. In this perspective, the drug repurposing approach could be a useful tool for investigating the drug promiscuity/polypharmacology profile. In this chapter, we describe a combination of virtual screening techniques and in vitro assays aimed to identify novel selective hCA VA inhibitors and to repurpose drugs known for other clinical indications.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

International Strategy for Sustainable Growth in Multinational Pharmaceutical Companies

Although it is generally agreed that the pharmaceutical industry is in a state of rapid internationalization, there is no clear comprehensive explanation of the current state and effectiveness of an international strategy. There are many studies about internationalization but they neither focus on the pharmaceutical industry nor evaluate the effectiveness of an international strategy with a control. Therefore, we investigate the current state of internationalization with a sample of the 30 multinational pharmaceutical companies, as well as the effectiveness of an international strategy by comparing two international strategies. In this study, we define an international strategy as a strategy that controls the geographic distribution of sales across regions to maximize total sales. The results show that 33% of the companies are still home-region-oriented, and we did not find any evidence of the effectiveness of an international strategy that pursues a balanced geographic distribution across regions in terms of total sales and adjusted total sales. The results provide the practical implication that pharmaceutical companies should weigh up the specific markets to secure higher sales through the advantage of adapting to customers’ needs. This paper contributes to the research on sustainable growth by empirically providing results of comparisons of different international strategies in the pharmaceutical industry.

Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets

Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.

Identification of novel analgesics through a drug repurposing strategy

The identification of new indications for approved or failed drugs is a process called drug repositioning or drug repurposing. The motivation includes overcoming the productivity gap that exists in drug development, which is a high-cost–high-risk process. Repositioning also includes rescuing drugs that have safely entered the market but have failed to demonstrate sufficient efficiency for the initial clinical indication. Considering the high prevalence of chronic pain, the lack of sufficient efficacy and the safety issues of current analgesics, repositioning seems to be an attractive approach. This review presents example of drugs that already have been repositioned and highlights new technologies that are available for the identification of additional compounds to stimulate the curiosity of readers for further exploration.

A review of computational drug repurposing

Although sciences and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In view of these circumstances, ‘drug repurposing’ (or ‘drug repositioning’) has appeared as an alternative tool to accelerate drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked drugs in the U.S. In the meantime, the explosive and large-scale growth of molecular, genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called computational drug repurposing. This review provides an overview of recent progress in the area of computational drug repurposing. First, it summarizes available repositioning strategies, followed by computational methods commonly used. Then, it describes validation techniques for repurposing studies. Finally, it concludes by discussing the remaining challenges in computational repurposing.

Response of safety pharmacologists to challenges arising from the rapidly evolving changes in the pharmaceutical industry

This commentary highlights and expands upon the thoughts conveyed in the lecture by Dr. Alan S. Bass, recipient of the 2017 Distinguished Service Award from the Safety Pharmacology Society, given on 27 September 2017 in Berlin, Germany. The lecture discussed the societal, scientific, technological, regulatory and economic events that dramatically impacted the pharmaceutical industry and ultimately led to significant changes in the strategic operations and practices of safety pharmacology. It focused on the emerging challenges and opportunities, and considered the lessons learned from drug failures and the influences of world events, including the financial crisis that ultimately led to a collapse of the world economies from which we are now recovering. Events such as these, which continue to today, challenge the assumptions that form the foundation of our discipline and dramatically affect the way that safety pharmacology is practiced. These include the latest scientific and technological developments contributing to the design and advancement of safe medicines. More broadly, they reflect the philosophical mission of safety pharmacology and the roles and responsibilities served by safety pharmacologists. As the discipline of Safety Pharmacology continues to evolve, develop and mature, the reader is invited to reflect on past experiences as a framework towards a vision of the future of the field.

Anti-Aβ drug candidates in clinical trials and plasmonic nanoparticle-based drug-screen for Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of neurodegenerative disorder in elderly people, and has become a social problem in aging societies globally. Amyloid-β (Aβ) aggregates (i.e., Aβ fibrils and plaques) present in the brains of AD patients are hallmarks of AD. Although various promising anti-Aβ drugs have been tested in pre-clinical and randomized controlled trials, the trial results have not yet been translated into clinical practice due to increasing time and cost of drug development. Recent investigations have addressed how the formation of Aβ aggregates is influenced by the surface of gold nanoparticles (AuNPs) to obtain a detailed understanding of the in vivo process of amyloid formation. Particularly, AuNPs catalytically provide nucleation sites to accelerate the formation of Aβ aggregates. Moreover, AuNPs have great potential as a sensing tool due to their optical property. Employing this dual function (i.e., catalytic and optical property), AuNP-based colorimetry is highlighted as a simple and innovative method for monitoring the efficacy of anti-Aβ reagents. In this review, we briefly survey important developments and designs of anti-Aβ drugs. The significance and perspectives of AuNP-based drug-screening in pharmacologic research are also discussed.

Repurposing of Drugs as Novel Influenza Inhibitors From Clinical Gene Expression Infection Signatures

Influenza virus infections remain a major and recurrent public health burden. The intrinsic ever-evolving nature of this virus, the suboptimal efficacy of current influenza inactivated vaccines, as well as the emergence of resistance against a limited antiviral arsenal, highlight the critical need for novel therapeutic approaches. In this context, the aim of this study was to develop and validate an innovative strategy for drug repurposing as host-targeted inhibitors of influenza viruses and the rapid evaluation of the most promising candidates in Phase II clinical trials. We exploited in vivo global transcriptomic signatures of infection directly obtained from a patient cohort to determine a shortlist of already marketed drugs with newly identified, host-targeted inhibitory properties against influenza virus. The antiviral potential of selected repurposing candidates was further evaluated in vitro, in vivo, and ex vivo. Our strategy allowed the selection of a shortlist of 35 high potential candidates out of a rationalized computational screening of 1,309 FDA-approved bioactive molecules, 31 of which were validated for their significant in vitro antiviral activity. Our in vivo and ex vivo results highlight diltiazem, a calcium channel blocker currently used in the treatment of hypertension, as a promising option for the treatment of influenza infections. Additionally, transcriptomic signature analysis further revealed the so far undescribed capacity of diltiazem to modulate the expression of specific genes related to the host antiviral response and cholesterol metabolism. Finally, combination treatment with diltiazem and virus-targeted oseltamivir neuraminidase inhibitor further increased antiviral efficacy, prompting rapid authorization for the initiation of a Phase II clinical trial. This original, host-targeted, drug repurposing strategy constitutes an effective and highly reactive process for the rapid identification of novel anti-infectious drugs, with potential major implications for the management of antimicrobial resistance and the rapid response to future epidemic or pandemic (re)emerging diseases for which we are still disarmed.

Precision medicine review: rare driver mutations and their biophysical classification

How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.

Making the Case for Functional Proteomics

"Making the Case for Functional Proteomics" first differentiates the Functional Proteome from the products of genetic protein expression. Qualitatively, the prevalence of posttranslational modifications (PTMs) virtually insures that individual, functional proteins do not equate to their genetic expression counterparts. Quantitatively, considering the frequency of PTMs and a conservative estimate of the number of functional entities arising from protein interactions, the size of the Functional Proteome exceeds that of the human genome by at least two orders of magnitude. The human genome does not, cannot, map the Functional Proteome. Further, the collective genome of the human microbiome dwarfs the human genome. With these facts established, "Making the Case…" proceeds to examine Functional Proteomics (of which both "gene expression" and "epigenetics" are but parts of a larger whole) within the context of Systems Biology, concluding that functionally related networks comprise the dominant motif for biological activity. Creating just such a network focus is essential in not only expanding basic knowledge but also in applying that knowledge in the pragmatic efforts of drug and biomarker development. Outlines for development of drugs and biomarkers, as well as the realization of precision medicine, within a functional proteomics-based, network motif are provided. The chapter proceeds to asses both the knowledge base and the tools to fully embrace Functional Proteomics. Given the decades-long infatuation with the reductionism of genomics, it is not surprising that both the proteomics knowledge base and tools are assessed as poor to fair. However, even a minor shift in research funding and a renewed challenge to methods developers will rapidly improve the current situation. Adoption of the included "Roadmap" will realistically make the twenty-first century the century of a long-awaited revolution in biology.

Implementation of a Pipeline Using Disease-Disease Associations for Computational Drug Repurposing

Drug repurposing is a powerful technique which has been recently employed in both industry and academia to discover and validate previously approved drugs for new indications. It provides the quickest possible transition from bench to bedside. In essence, computational strategies are appealing because they putatively nominate the most encouraging candidate for a given indication. A wide range of computational methods exist for repositioning. In this chapter we present the guidelines for performing integrated drug repurposing strategy by combining disease-disease association and molecular simulation analysis.

Development of Human Target Validation Classification that Predicts Future Clinical Efficacy

Fewer new medicines have become available to patients during the last decades. Clinical efficacy failures in late-phase development have been identified as a common cause of this decline. Improved ways to ensure early selection of the right drug targets when it comes to efficacy is therefore a highly desirable goal. The aim of this work was to develop a strategy to facilitate selection of novel targets already in the discovery phase that later on in clinical development would demonstrate efficacy. A cross-functional team at AstraZeneca with extensive experience in drug discovery and development participated in several workshops to identify the critical elements that contribute to building human target validation [(HTV); the relevance of the target from a human perspective]. The elements were consolidated into a 10-point HTV classification system that was ranked from lowest to highest in terms of perceived impact on future clinical efficacy. Using 50 years of legacy research and development data, the ability of the 10-point HTV classification to predict future clinical efficacy was evaluated. Drug targets were classified as having low, medium, or high HTV at the time of candidate drug selection. Comparing this HTV classification with later clinical development efficacy data showed that HTV classification was highly predictive of future clinical efficacy success. This new strategy for HTV assessment provides a novel approach to early prediction of clinical efficacy and a better understanding of portfolio risk.

PISTON: Predicting drug indications and side effects using topic modeling and natural language processing

The process of discovering novel drugs to treat diseases requires a long time and high cost. It is important to understand side effects of drugs as well as their therapeutic effects, because these can seriously damage the patients due to unexpected actions of the derived candidate drugs. In order to overcome these limitations, computational methods for predicting the therapeutic effects and side effects have been proposed. In particular, text mining is a widely used technique in the field of systems biology, because it can discover hidden relationships between drugs, genes and diseases from a large amount of literature data. Compared with in vivo/in vitro experiments, text mining derives meaningful results with less time and cost. In this study, we propose an algorithm for predicting novel drug-phenotype associations and drug-side effect associations using topic modeling and natural language processing (NLP). We extract sentences in which drugs and genes co-occur from the abstracts of the literature and identify words that describe the relationship between them using NLP. Considering the characteristics of the identified words, we determine if the drug has an up-regulation effect or a down-regulation effect on the gene. Based on genes that affect drugs and their regulatory relationships, we group the frequently occurring genes and regulatory relationships into topics, and build a drug-topic probability matrix by calculating the score that the drug will have a topic using topic modeling. Using the matrix, a classifier is constructed for predicting the novel indications and side effects of drugs considering the characteristics of known drug-phenotype associations or drug-side effect associations. The proposed method predicts both indications and side effects with a single algorithm, and it can exclude drugs with serious side effects or side effects that patients do not want to experience from among the candidate drugs provided for the treatment of the phenotype. Furthermore, lists of novel candidate drugs for phenotypes and side effects can be continuously updated with our algorithm every time a document is added. More than a thousand documents are produced per day, and it is possible for our algorithm to efficiently derive candidate drugs because it requires less cost than the existing drug repositioning methods. The resource of PISTON is available at databio.gachon.ac.kr/tools/PISTON.

A novel computational approach for drug repurposing using systems biology

Motivation

Identification of novel therapeutic effects for existing US 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 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, non-small cell lung cancer, 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 and implementation

The R package DrugDiseaseNet is under review for publication in Bioconductor and is available at https://github.com/azampvd/DrugDiseaseNet.

Supplementary information

Supplementary data are available at Bioinformatics online.

Changing Trends in Computational Drug Repositioning

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.

The Current Landscape of 3D In Vitro Tumor Models: What Cancer Hallmarks Are Accessible for Drug Discovery?

Cancer prognosis remains a lottery dependent on cancer type, disease stage at diagnosis, and personal genetics. While investment in research is at an all-time high, new drugs are more likely to fail in clinical trials today than in the 1970s. In this review, a summary of current survival statistics in North America is provided, followed by an overview of the modern drug discovery process, classes of models used throughout different stages, and challenges associated with drug development efficiency are highlighted. Then, an overview of the cancer hallmarks that drive clinical progression is provided, and the range of available clinical therapies within the context of these hallmarks is categorized. Specifically, it is found that historically, the development of therapies is limited to a subset of possible targets. This provides evidence for the opportunities offered by novel disease-relevant in vitro models that enable identification of novel targets that facilitate interactions between the tumor cells and their surrounding microenvironment. Next, an overview of the models currently reported in literature is provided, and the cancer biology they have been used to explore is highlighted. Finally, four priority areas are suggested for the field to accelerate adoption of in vitro tumour models for cancer drug discovery.

Inhibitors of LexA Autoproteolysis and the Bacterial SOS Response Discovered by an Academic-Industry Partnership

The RecA/LexA axis of the bacterial DNA damage (SOS) response is a promising, yet nontraditional, drug target. The SOS response is initiated upon genotoxic stress, when RecA, a DNA damage sensor, induces LexA, the SOS repressor, to undergo autoproteolysis, thereby derepressing downstream genes that can mediate DNA repair and accelerate mutagenesis. As genetic inhibition of the SOS response sensitizes bacteria to DNA damaging antibiotics and decreases acquired resistance, inhibitors of the RecA/LexA axis could potentiate our current antibiotic arsenal. Compounds targeting RecA, which has many mammalian homologues, have been reported; however, small-molecules targeting LexA autoproteolysis, a reaction unique to the prokaryotic SOS response, have remained elusive. Here, we describe the logistics and accomplishments of an academic-industry partnership formed to pursue inhibitors against the RecA/LexA axis. A novel fluorescence polarization assay reporting on RecA-induced self-cleavage of LexA enabled the screening of 1.8 million compounds. Follow-up studies on select leads show distinct activity patterns in orthogonal assays, including several with activity in cell-based assays reporting on SOS activation. Mechanistic assays demonstrate that we have identified first-in-class small molecules that specifically target the LexA autoproteolysis step in SOS activation. Our efforts establish a realistic example for navigating academic-industry partnerships in pursuit of anti-infective drugs and offer starting points for dedicated lead optimization of SOS inhibitors that could act as adjuvants for current antibiotics.

Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration

Background

Prediction of drug-disease interactions is promising for either drug repositioning or disease treatment fields. The discovery of novel drug-disease interactions, on one hand can help to find novel indictions for the approved drugs; on the other hand can provide new therapeutic approaches for the diseases. Recently, computational methods for finding drug-disease interactions have attracted lots of attention because of their far more higher efficiency and lower cost than the traditional wet experiment methods. However, they still face several challenges, such as the organization of the heterogeneous data, the performance of the model, and so on.

Methods

In this work, we present to hierarchically integrate the heterogeneous data into three layers. The drug-drug and disease-disease similarities are first calculated separately in each layer, and then the similarities from three layers are linearly fused into comprehensive drug similarities and disease similarities, which can then be used to measure the similarities between two drug-disease pairs. We construct a novel weighted drug-disease pair network, where a node is a drug-disease pair with known or unknown treatment relation, an edge represents the node-node relation which is weighted with the similarity score between two pairs. Now that similar drug-disease pairs are supposed to show similar treatment patterns, we can find the optimal graph cut of the network. The drug-disease pair with unknown relation can then be considered to have similar treatment relation with that within the same cut. Therefore, we develop a semi-supervised graph cut algorithm, SSGC, to find the optimal graph cut, based on which we can identify the potential drug-disease treatment interactions.

Results

By comparing with three representative network-based methods, SSGC achieves the highest performances, in terms of both AUC score and the identification rates of true drug-disease pairs. The experiments with different integration strategies also demonstrate that considering several sources of data can improve the performances of the predictors. Further case studies on four diseases, the top-ranked drug-disease associations have been confirmed by KEGG, CTD database and the literature, illustrating the usefulness of SSGC.

Conclusions

The proposed comprehensive similarity scores from multi-views and multiple layers and the graph-cut based algorithm can greatly improve the prediction performances of drug-disease associations.

Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues

A relatively larger dataset consisting of 970 compounds was collected. Classification RF models were established based on different training sets and different descriptors. model validation and evaluation.

Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor

Design of ligands that provide receptor selectivity has emerged as a new paradigm for drug discovery of G protein-coupled receptors, and may, for certain families of receptors, only be achieved via identification of chemically diverse allosteric modulators. Here, the extracellular vestibule of the M2 muscarinic acetylcholine receptor (mAChR) is targeted for structure-based design of allosteric modulators. Accelerated molecular dynamics (aMD) simulations were performed to construct structural ensembles that account for the receptor flexibility. Compounds obtained from the National Cancer Institute (NCI) were docked to the receptor ensembles. Retrospective docking of known ligands showed that combining aMD simulations with Glide induced fit docking (IFD) provided much-improved enrichment factors, compared with the Glide virtual screening workflow. Glide IFD was thus applied in receptor ensemble docking, and 38 top-ranked NCI compounds were selected for experimental testing. In [(3)H]N-methylscopolamine radioligand dissociation assays, approximately half of the 38 lead compounds altered the radioligand dissociation rate, a hallmark of allosteric behavior. In further competition binding experiments, we identified 12 compounds with affinity of ≤30 μM. With final functional experiments on six selected compounds, we confirmed four of them as new negative allosteric modulators (NAMs) and one as positive allosteric modulator of agonist-mediated response at the M2 mAChR. Two of the NAMs showed subtype selectivity without significant effect at the M1 and M3 mAChRs. This study demonstrates an unprecedented successful structure-based approach to identify chemically diverse and selective GPCR allosteric modulators with outstanding potential for further structure-activity relationship studies.

Adaptive Clinical Trial Design: An Overview and Potential Applications in Dermatology

The challenges of drug development, including increasing costs, late-stage drug failures, and the decline in the number of drugs being approved by the US Food and Drug Administration over time, have generated interest in adaptive study designs that have the potential to address these problems. Adaptive trial designs use interim data analysis to amend trials, and have been recognized for more than a decade as a way to increase trial efficiency, partly by the increased probability of demonstrating a drug effect if one exists. In this article, we define adaptive trials; give examples of the most common types; highlight the pros, cons, and ethical considerations of these designs; and illustrate how these tools can be applied to drug development in dermatology.

Pooled screening for antiproliferative inhibitors of protein-protein interactions

Protein-protein interactions (PPIs) are emerging as a promising new class of drug targets. Here, we present a novel high-throughput approach to screen inhibitors of PPIs in cells. We designed a library of 50,000 human peptide binding motifs and used a pooled lentiviral system to express them intracellularly and screen for their effects on cell proliferation. We thereby identified inhibitors that drastically reduced the viability of a pancreas cancer line (RWP1) while leaving a control line virtually unaffected. We identified their target interactions computationally, and validated a subset in experiments. We also discovered their potential mechanisms of action including apoptosis and cell cycle arrest. Finally, we confirmed that synthetic lipopeptide versions of our inhibitors have similarly specific and dosage dependent effects on cancer cell growth. Our screen reveals new drug targets and peptide drug leads and it provides a rich dataset covering phenotypes for inhibition of thousands of interactions.

When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis

A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models. We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially "interesting" screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.

Drug discovery in ophthalmology: past success, present challenges, and future opportunities

BACKGROUND

Drug discovery has undergone major transformations in the last century, progressing from the recognition and refinement of natural products with therapeutic benefit, to the systematic screening of molecular libraries on whole organisms or cell lines and more recently to a more target-based approach driven by greater knowledge of the physiological and pathological pathways involved. Despite this evolution increasing challenges within the drug discovery industry are causing escalating rates of failure of development pipelines.

DISCUSSION

We review the challenges facing the drug discovery industry, and discuss what attempts are being made to increase the productivity of drug development, including a refocusing on the study of the basic biology of the disease, and an embracing of the concept of 'translational research'. We consider what ophthalmic drug discovery can learn from the sector in general and discuss strategies to overcome the present limitations. This includes advances in the understanding of the pathogenesis of disease; improvements in animal models of human disease; improvements in ophthalmic drug delivery and attempts at patient stratification within clinical trials. As we look to the future, we argue that investment in ophthalmic drug development must continue to cover the whole translational spectrum (from 'bench to bedside and back again') with recognition that both biological discovery and clinical understanding will drive drug discovery, providing safe and effective therapies for ocular disease.

Drug Signature-based Finding of Additional Clinical Use of LC28-0126 for Neutrophilic Bronchial Asthma

In recent decades, global pharmaceutical companies have suffered from an R&D innovation gap between the increased cost of a new drug’s development and the decreased number of approvals. Drug repositioning offers another opportunity to fill the gap because the approved drugs have a known safety profile for human use, allowing for a reduction of the overall cost of drug development by eliminating rigorous safety assessment. In this study, we compared the transcriptional profile of LC28-0126, an investigational drug for acute myocardial infarction (MI) at clinical trial, obtained from healthy male subjects with molecular activity profiles in the Connectivity Map. We identified dyphilline, an FDA-approved drug for bronchial asthma, as a top ranked connection with LC28-0126. Subsequently, we demonstrated that LC28-0126 effectively ameliorates the pathophysiology of neutrophilic bronchial asthma in OVA_LPS-OVA mice accompanied with a reduction of inflammatory cell counts in the bronchoalveolar lavage fluid (BALF), inhibition of the release of proinflammatory cytokines, relief of airway hyperactivity, and improvement of histopathological changes in the lung. Taken together, we suggest that LC28-0126 could be a potential therapeutic for bronchial asthma. In addition, this study demonstrated the potential general utility of computational drug repositioning using clinical profiles of the investigational drug.

Drug-target interaction prediction: databases, web servers and computational models

Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.

Synthesis of Diaryliodonium Salts Having Pentafluorosulfanylarenes and Their Application to Electrophilic Pentafluorosulfanylarylation of C-, O-, N-, and S-Nucleophiles

Novel reagents for the electrophilic introduction of pentafluorosulfanyl (SF5) arenes into target molecules are disclosed. Unsymmetrical diaryliodonium salts 1 having SF5-arenes were synthesized by a one-pot process from iodo-SF5-benzenes 2 in good yields. The SF5-aryliodonium salts 1 were found to be efficient for the electrophilic SF5-arylation of carbon and heterocentered nucleophiles to furnish the corresponding substituted SF5-arenes in good to high yields.

Novel methods and technologies for 21st-century clinical trials: a review

IMPORTANCE

New technologies are rapidly reshaping health care. However, their effect on drug development to date generally has been limited.

OBJECTIVES

To evaluate disease modeling and simulation, alternative study design, novel objective measures, virtual research visits, and enhanced participant engagement and to examine their potential effects as methods and tools on clinical trials.

EVIDENCE REVIEW

We conducted a systematic search of relevant terms on PubMed (disease modeling and clinical trials; adaptive design, clinical trials, and neurology; Internet, clinical trials, and neurology; and telemedicine, clinical trials, and neurology), references of previous publications, and our files. The search encompassed articles published from January 1, 2000, through November 30, 2014, and produced 7976 articles, of which 22 were determined to be relevant and are included in this review.

FINDINGS

Few of these new methods and technologies have been applied to neurology clinical trials. Clinical outcomes, including cognitive and stroke outcomes, increasingly are captured remotely. Other therapeutic areas have successfully implemented many of these tools and technologies, including web-enabled clinical trials.

CONCLUSIONS AND RELEVANCE

Increased use of new tools and approaches in future clinical trials can enhance the design, improve the assessment, and engage participants in the evaluation of novel therapies for neurologic disorders.

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.

A survey of current trends in computational drug repositioning

Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.