ArticlePLoS computational biology2008
Search algorithms as a framework for the optimization of drug combinations.
Article in PLoS computational biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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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.
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Who cites it
42 citing papers in PubMed.
- Advances in artificial intelligence-based technologies for increasing the quality of medical products.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Review
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
- Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.Nature biomedical engineering · 2023Article
- Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.International journal of molecular sciences · 2023Review
- Artificial intelligence in drug discovery and development.Drug discovery today · 2021Review
- Predicting the Effects of Drug Combinations Using Probabilistic Matrix Factorization.Frontiers in bioinformatics · 2021Article
- Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy.Frontiers in genetics · 2021Article
- Prediction of ultra-high-order antibiotic combinations based on pairwise interactions.PLoS computational biology · 2019Article
- On-demand serum-free media formulations for human hematopoietic cell expansion using a high dimensional search algorithm.Communications biology · 2019Article
- Searching Synergistic Dose Combinations for Anticancer Drugs.Frontiers in pharmacology · 2018Review
- Article
- Combination therapy design for maximizing sensitivity and minimizing toxicity.BMC bioinformatics · 2017Article
- Prediction of multidimensional drug dose responses based on measurements of drug pairs.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Optimization of drug combinations using Feedback System Control.Nature protocols · 2016Article
- In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index.Scientific reports · 2015Article
- Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer.Angiogenesis · 2015Article
- Preclinical optimization of a broad-spectrum anti-bladder cancer tri-drug regimen via the Feedback System Control (FSC) platform.Scientific reports · 2015Article
- A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose-Response Curve.CPT: pharmacometrics & systems pharmacology · 2015Article
- A Network-Based Target Overlap Score for Characterizing Drug Combinations: High Correlation with Cancer Clinical Trial Results.PloS one · 2015Article
- Computational analysis of image-based drug profiling predicts synergistic drug combinations: applications in triple-negative breast cancer.Molecular oncology · 2014Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms -- originally developed for digital communication -- modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs using only one-third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.
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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.