ArticleScientific reports2017
From malaria to cancer: Computational drug repositioning of amodiaquine using PLIP interaction patterns.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 54 citations in OpenAlex.
- Computational drug repurposing: approaches, evaluation of in silico resources and case studies.Nature reviews. Drug discovery · 2025Review
- Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint.ACS omega · 2024Article
- New Theobromine Apoptotic Analogue with Anticancer Potential Targeting the EGFR Protein: Computational andACS omega · 2024Article
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- Molecular docking and simulation studies of Chloroquine, Rimantadine and CAP-1 as potential repurposed antivirals for decapod iridescent virus 1 (DIV1).Fish and shellfish immunology reports · 2023Article
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- Repurposing of neprilysin inhibitor 'sacubitrilat' as an anti-cancer drug by modulating epigenetic and apoptotic regulators.Scientific reports · 2023Article
- An interaction-based drug discovery screen explains known SARS-CoV-2 inhibitors and predicts new compound scaffolds.Scientific reports · 2023Article
- Exploring Amodiaquine's Repurposing Potential in Breast Cancer Treatment-Assessment of In-Vitro Efficacy & Mechanism of Action.International journal of molecular sciences · 2022Article
- Decomposing compounds enables reconstruction of interaction fingerprints for structure-based drug screening.Journal of cheminformatics · 2022Article
- Simulating drug concentrations in PDMS microfluidic organ chips.Lab on a chip · 2021Article
- A Systems Approach to Brain Tumor Treatment.Cancers · 2021Review
- Small Hsps as Therapeutic Targets of Cystic Fibrosis Transmembrane Conductance Regulator Protein.International journal of molecular sciences · 2021Review
- Structural binding site comparisons reveal Crizotinib as a novel LRRK2 inhibitor.Computational and structural biotechnology journal · 2021Article
- Comprehensive of N1-Methyladenosine Modifications Patterns and Immunological Characteristics in Ovarian Cancer.Frontiers in immunology · 2021Article
- Repositioned Drugs for Chagas Disease Unveiled via Structure-Based Drug Repositioning.International journal of molecular sciences · 2020Article
- Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches.Molecules (Basel, Switzerland) · 2020Review
- Development of pharmaceutically scalable inhaled anti-cancer nanotherapy - Repurposing amodiaquine for non-small cell lung cancer (NSCLC).Materials science & engineering. C, Materials for biological applications · 2020Article
- Overcoming cancer therapeutic bottleneck by drug repurposing.Signal transduction and targeted therapy · 2020Review
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug repositioning identifies new indications for known drugs. Here we report repositioning of the malaria drug amodiaquine as a potential anti-cancer agent. While most repositioning efforts emerge through serendipity, we have devised a computational approach, which exploits interaction patterns shared between compounds. As a test case, we took the anti-viral drug brivudine (BVDU), which also has anti-cancer activity, and defined ten interaction patterns using our tool PLIP. These patterns characterise BVDU's interaction with its target s. Using PLIP we performed an in silico screen of all structural data currently available and identified the FDA approved malaria drug amodiaquine as a promising repositioning candidate. We validated our prediction by showing that amodiaquine suppresses chemoresistance in a multiple myeloma cancer cell line by inhibiting the chaperone function of the cancer target Hsp27. This work proves that PLIP interaction patterns are viable tools for computational repositioning and can provide search query information from a given drug and its target to identify structurally unrelated candidates, including drugs approved by the FDA, with a known safety and pharmacology profile. This approach has the potential to reduce costs and risks in drug development by predicting novel indications for known drugs and drug candidates.
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