ArticlePLoS computational biology2016
A Drug-Centric View of Drug Development: How Drugs Spread from Disease to Disease.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A review of validation strategies for computational drug repositioning.Briefings in bioinformatics · 2018Pooled it
- The speed of information propagation in the scientific network distorts biomedical research.PeerJ · 2022Article
- A computational multi-targeting approach for drug repositioning for psoriasis treatment.BMC complementary medicine and therapies · 2021Article
- Drug target gene-based analyses of drug repositionability in rare and intractable diseases.Scientific reports · 2021Article
- Potential for Drug Repositioning of Midazolam for Dentin Regeneration.International journal of molecular sciences · 2019Article
- DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.Bioinformatics (Oxford, England) · 2018Article
- Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning.CPT: pharmacometrics & systems pharmacology · 2018Observational
- How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors.Frontiers in chemistry · 2018Article
- A standard database for drug repositioning.Scientific data · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drugs are often seen as ancillary to the purpose of fighting diseases. Here an alternative view is proposed in which they occupy a spearheading role. In this view, drugs are technologies with an inherent therapeutic potential. Once created, they can spread from disease to disease independently of the drug creator's original intentions. Through the analysis of extensive literature and clinical trial records, it can be observed that successful drugs follow a life cycle in which they are studied at an increasing rate, and for the treatment of an increasing number of diseases, leading to clinical advancement. Such initial growth, following a power law on average, has a degree of momentum, but eventually decelerates, leading to stagnation and decay. A network model can describe the propagation of drugs from disease to disease in which diseases communicate with each other by receiving and sending drugs. Within this model, some diseases appear more prone to influence other diseases than be influenced, and vice versa. Diseases can also be organized into a drug-centric disease taxonomy based on the drugs that each adopts. This taxonomy reflects not only biological similarities across diseases, but also the level of differentiation of existing therapies. In sum, this study shows that drugs can become contagious technologies playing a driving role in the fight against disease. By better understanding such dynamics, pharmaceutical developers may be able to manage drug projects more effectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.