A data-driven predictive approach for drug delivery using machine learning techniques.
In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3285649?pdf=render |
| _version_ | 1828910312167833600 |
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| author | Yuanyuan Li Scott C Lenaghan Mingjun Zhang |
| author_facet | Yuanyuan Li Scott C Lenaghan Mingjun Zhang |
| author_sort | Yuanyuan Li |
| collection | DOAJ |
| description | In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro. |
| first_indexed | 2024-12-13T18:41:42Z |
| format | Article |
| id | doaj.art-8ed87f3eea634fd2bcfaf5ed585e0bb8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T18:41:42Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-8ed87f3eea634fd2bcfaf5ed585e0bb82022-12-21T23:35:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0172e3172410.1371/journal.pone.0031724A data-driven predictive approach for drug delivery using machine learning techniques.Yuanyuan LiScott C LenaghanMingjun ZhangIn drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.http://europepmc.org/articles/PMC3285649?pdf=render |
| spellingShingle | Yuanyuan Li Scott C Lenaghan Mingjun Zhang A data-driven predictive approach for drug delivery using machine learning techniques. PLoS ONE |
| title | A data-driven predictive approach for drug delivery using machine learning techniques. |
| title_full | A data-driven predictive approach for drug delivery using machine learning techniques. |
| title_fullStr | A data-driven predictive approach for drug delivery using machine learning techniques. |
| title_full_unstemmed | A data-driven predictive approach for drug delivery using machine learning techniques. |
| title_short | A data-driven predictive approach for drug delivery using machine learning techniques. |
| title_sort | data driven predictive approach for drug delivery using machine learning techniques |
| url | http://europepmc.org/articles/PMC3285649?pdf=render |
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