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A data-driven predictive approach for drug delivery using machine learning techniques.

Li Y, Lenaghan SC, Zhang M - PLoS ONE (2012)

Bottom Line: In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment.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 dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy.

View Article: PubMed Central - PubMed

Affiliation: Mechanical, Aerospace and Biomedical Engineering Department, University of Tennessee, Knoxville, Tennessee, United States of America.

ABSTRACT
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.

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Related in: MedlinePlus

Example of second order PSA built from the state sequence, .This state transition diagram shows the probability of transitioning between states. The sum of all possible states transitions equals one, indicating that a state transition must occur.
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pone-0031724-g002: Example of second order PSA built from the state sequence, .This state transition diagram shows the probability of transitioning between states. The sum of all possible states transitions equals one, indicating that a state transition must occur.

Mentions: Note that the function and must satisfy the following conditions for every and . These equations maintain that the probability of transitioning between all possible states is 1, as graphically represented in Figure 2. This means that there is a 100% probability that some transition will occur.


A data-driven predictive approach for drug delivery using machine learning techniques.

Li Y, Lenaghan SC, Zhang M - PLoS ONE (2012)

Example of second order PSA built from the state sequence, .This state transition diagram shows the probability of transitioning between states. The sum of all possible states transitions equals one, indicating that a state transition must occur.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3285649&req=5

pone-0031724-g002: Example of second order PSA built from the state sequence, .This state transition diagram shows the probability of transitioning between states. The sum of all possible states transitions equals one, indicating that a state transition must occur.
Mentions: Note that the function and must satisfy the following conditions for every and . These equations maintain that the probability of transitioning between all possible states is 1, as graphically represented in Figure 2. This means that there is a 100% probability that some transition will occur.

Bottom Line: In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment.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 dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy.

View Article: PubMed Central - PubMed

Affiliation: Mechanical, Aerospace and Biomedical Engineering Department, University of Tennessee, Knoxville, Tennessee, United States of America.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus