On the Integration of In Silico Drug Design Methods for Drug Repurposing

March-Vila, Eric; Pinzi, Luca; Sturm, Noé; Tinivella, Annachiara; Engkvist, Ola; Chen, Hongming; Rastelli, Giulio

doi:10.3389/fphar.2017.00298

Cited by 181 publications

(133 citation statements)

References 45 publications

(56 reference statements)

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“…We acknowledge that the underlying mechanisms are largely unknown, which require further studies to explore. During the past decade, various algorithms for integration of omics data and metabolic models have been developed to predict the anti‐tumor effect of non‐oncological drugs . The algorithm of transcriptional regulated flux balance analysis aimed to construct cancer cell‐specific models has successfully identified terbinafine as a potential anti‐tumor drug .…”

Section: Discussionmentioning

confidence: 99%

Use of terbinafine and risk of death in patients with prostate cancer: A population‐based cohort study

Sundquist

2018

Intl Journal of Cancer

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Terbinafine is used for the treatment of several superficial fungal infections. In silico analyses and animal models suggest that terbinafine might have an anti‐tumor effect. We aimed to explore whether subsequent administration of terbinafine might be associated with a lower mortality rate in patients with prostate cancer. We identified patients diagnosed with prostate cancer between July 2005 and December 2014 from the Swedish Cancer Registry, and linked them to the Swedish Prescribed Drug Register to ascertain subsequent use of terbinafine. A total of 799 patients received oral treatment of terbinafine during the study period with a mortality rate of 18.6 per 1,000 person‐years. Compared to patients who did not use terbinafine and adjusting for a range of confounding factors, patients that received oral treatment of terbinafine had a decreased risk of death from prostate cancer (HR, 0.53; 95% CI, 0.38–0.73) and a decreased risk of death overall (HR, 0.64; 95% CI, 0.52–0.77). To account for indication bias, we further identified 907 patients who received topical use of terbinafine. However, the risk of death from prostate cancer in patients with topical use of terbinafine was 0.92 (95% CI 0.74–1.12) and the risk of death overall was 1.03 (95% CI 0.91–1.17) as compared to the controls, which suggests that there was no association between risk of death in patients with prostate cancer with topical use of terbinafine. These findings suggest that this drug's potential anti‐tumor effect needs to be explored further.

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Section: Discussionmentioning

confidence: 99%

Use of terbinafine and risk of death in patients with prostate cancer: A population‐based cohort study

Sundquist

2018

Intl Journal of Cancer

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“…Drug repurposing has been the subject of many computationally-driven efforts (March-Vila et al, 2017). Science and pharmaceutical research, are not new to computer aided research, and they are not new to hyped confidence in computational methods and failures.…”

Section: Big Data For Systems Pharmacology and Drug Repurposingmentioning

confidence: 99%

Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare

Rossi

Grifantini

2018

Front. Digit. Humanit.

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Research and innovation are constant imperatives for the healthcare sector: medicine, biology and biotechnology support it, and more recently computational and data-driven disciplines gained relevance to handle the massive amount of data this sector is and will be generating. To be effective in translational and healthcare industrial research, big data in the life science domain need to be organized, well annotated, catalogued, correlated and integrated: the biggest the data silos at hand, the stronger the need for organization and tidiness. The degree of such organization marks the transition from data to knowledge for strategic decision making. Medicine is supported by observations and data and for certain aspects medicine is becoming a data science supported by clinicians. While medicine defines itself as personalized, quantified (precision med) or in high-definition, clinicians should be prepared to deal with a world in which Internet of People paraphrases the Internet of Things paradigm. Integrated use of electronic health records (EHRs) and quantitative data (both clinical and molecular) is a key process to develop precision medicine. Health records collection was originally designed for patient care and billing and/or insurance purposes. The digitization of health records facilitates and opens up new possibilities for science and research and they should be now collected and managed with this aim in mind. More data and the ability to efficiently handle them is a significant advantage not only for clinicians and life science researchers, but for drugs producers too. In an industrial sector spending increasing efforts on drug repurposing, attention to efficient methods to unwind the intricacies of the hugely complex reality of human physiology, such as network based methods and physical chemistry computational methods, became of paramount importance. Finally, the main pillars of industrial R&D processes for vaccines, include initial discovery, early-late pre clinics, pre-industrialization, clinical phases and finally registration-commercialization. The passage from one step to another is regulated by stringent pass/fail criteria. Bottlenecks of the R&D process are often represented by animal and human studies, which could be rationalized by surrogate in vitro assays as well as by predictive molecular and cellular signatures and models.

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“…24 Ideally, disparate sources or types of data may yield orthogonality or complementarity in results, i.e., different top compounds are captured and reported as putative therapeutics for different reasons. 27,28 For example, Tan et al obtained an increased recall rate in a virtual screening experiment using ligand-based two dimensional fingerprint data fused with structure-based molecular docking energies. 29 Ligand-and structure-based methods have been combined for use in virtual screening pipelines and platforms, with successes reported in the use of sequential, parallel, and hybrid techniques for data integration.…”

Section: Introductionmentioning

confidence: 99%

Fingerprinting CANDO: Increased Accuracy with Structure and Ligand Based Shotgun Drug Repurposing

Schuler

Samudrala

2019

Preprint

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1We have upgraded our Computational Analysis of Novel Drug Opportunities (CANDO) 2 platform for shotgun drug repurposing to include ligand-based, data fusion, and decision tree 3 pipelines. The first version of CANDO implemented a structure-based pipeline that mod-4 eled interactions between compounds and proteins on a large scale, generating compound-5 proteome interaction signatures used to infer similarity of drug behavior; the new pipelines 6 accomplish this by incorporating molecular fingerprints and the Tanimoto coefficient. We 7 obtain improved benchmarking performance with the new pipelines across all three evalua-8 tion metrics used: average indication accuracy, pairwise accuracy, and coverage. The best 9 performing pipeline achieves an average indication accuracy of 19.0% at the top10 cutoff, 10 compared to 11.7% for v1, and 2.2% for a random control. Our results demonstrate that the 11 CANDO drug recovery accuracy is substantially improved by integrating multiple pipelines, 12 thereby enhancing our ability to generate putative therapeutic repurposing candidates, and 13 increasing drug discovery efficiency. 14 Introduction 15 Drug repurposing 16 Bringing a new drug to the market may costs hundreds of millions of dollars and takes years 17 of work. 1 Drug repurposing is the process of discovering a new use for an existing drug. 2,3 18This process may take advantage of existing data on safety and pharmacokinetic properties 19 from previous trials and clinical use to reduce costs and time associated with traditional drug 20 discovery. Classic examples of drug repurposing include sildenafil (Viagra) and thalidomide, 21 which initially were developed to treat chest pain and morning sickness, but were repurposed 22 to treat erectile dysfunction and erythema nodosum leprosum respectively. 2,4,5 Drugs which 23 have already been repurposed once are being researched for even more novel uses. For exam-24 ple, raloxifene was originally indicated for prevention of osteoporosis and was subsequently 25 approved for risk reduction in the development of breast cancer. 6 More recently, raloxifene 26 has been suggested as a possible treatment for Ebola virus disease 7-9 '. These examples of 27 putative and/or successful drug repurposing underlies the diverse mechanisms through which 28 a single compound may treat a variety of disease types. 10,11 High throughput, target-based, 29 and phenotypic screening of compounds can be used to generate putative candidates for re-30 purposing. 12 For example, potential treatments for Zika virus infection were identified using 31 a phenotypic screen. 13 32 Computational drug discovery and repurposing 33 Finding new drugs or new uses for existing drugs computationally takes advantage of the 34 growing amount of data generated from wet lab experiments accessible on the Internet, 35 increased computational power, and higher fidelity of computational models to reality. Ap-36 proaches to computational drug discovery and repurposing have been classified as structure-37 b...

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On the Integration of In Silico Drug Design Methods for Drug Repurposing

Cited by 181 publications

References 45 publications

Use of terbinafine and risk of death in patients with prostate cancer: A population‐based cohort study

Use of terbinafine and risk of death in patients with prostate cancer: A population‐based cohort study

Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare

Fingerprinting CANDO: Increased Accuracy with Structure and Ligand Based Shotgun Drug Repurposing

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