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Predicting New Target Conditions for Drug Retesting Using Temporal Patterns in Clinical Trials: A Proof of Concept.

He Z, Weng C - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Efficient drug repurposing promises to accelerate drug discovery with reduced cost.However, most successful repurposing cases so far have been achieved by serendipity.There is a need for more efficient computational methods for predicting new indications for existing drugs.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University, New York, NY.

ABSTRACT
Drug discovery is costly and time-consuming. Efficient drug repurposing promises to accelerate drug discovery with reduced cost. However, most successful repurposing cases so far have been achieved by serendipity. There is a need for more efficient computational methods for predicting new indications for existing drugs. This paper conducts a retrospective analysis of the temporal patterns of drug intervention trials for every drug in a pair of different conditions in ClinicalTrials.gov, including 550 drugs used for 451 conditions between 2003 and 2013. We found that drugs are often targeted towards conditions that are related by similar or identical eligibility criteria. We demonstrated the preliminary feasibility of predicting new target conditions for drug retesting among conditions with similar aggregated clinical trial eligibility criteria and confirmed this hypothesis using evidence from the literature.

No MeSH data available.


Number of drugs and number of retested conditions predicted for various thresholds.
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f4-2085703: Number of drugs and number of retested conditions predicted for various thresholds.

Mentions: Leveraging the observed drug retesting patterns, we designed a basis for predicting drugs for retesting on different conditions given a threshold value for the minimum number of shared CEFs between the initial condition and the possible different condition. Each prediction consists of a drug and a possible different condition. A prediction was made if (1) a drug has been tested for the initial condition but has never been tested for the possible different condition, (2) there exists another drug that has been tested for both conditions, and (3) the number of shared CEFs between two conditions is above a threshold. Figure 4 shows the number of drug predicted and the number of different conditions for threshold values between 20 and 200. Higher thresholds yielded fewer predictions, which may also be more clinically relevant. The number of drugs is consistently greater than the number of different conditions, showing that a drug may be predicted for multiple conditions.


Predicting New Target Conditions for Drug Retesting Using Temporal Patterns in Clinical Trials: A Proof of Concept.

He Z, Weng C - AMIA Jt Summits Transl Sci Proc (2015)

Number of drugs and number of retested conditions predicted for various thresholds.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525223&req=5

f4-2085703: Number of drugs and number of retested conditions predicted for various thresholds.
Mentions: Leveraging the observed drug retesting patterns, we designed a basis for predicting drugs for retesting on different conditions given a threshold value for the minimum number of shared CEFs between the initial condition and the possible different condition. Each prediction consists of a drug and a possible different condition. A prediction was made if (1) a drug has been tested for the initial condition but has never been tested for the possible different condition, (2) there exists another drug that has been tested for both conditions, and (3) the number of shared CEFs between two conditions is above a threshold. Figure 4 shows the number of drug predicted and the number of different conditions for threshold values between 20 and 200. Higher thresholds yielded fewer predictions, which may also be more clinically relevant. The number of drugs is consistently greater than the number of different conditions, showing that a drug may be predicted for multiple conditions.

Bottom Line: Efficient drug repurposing promises to accelerate drug discovery with reduced cost.However, most successful repurposing cases so far have been achieved by serendipity.There is a need for more efficient computational methods for predicting new indications for existing drugs.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University, New York, NY.

ABSTRACT
Drug discovery is costly and time-consuming. Efficient drug repurposing promises to accelerate drug discovery with reduced cost. However, most successful repurposing cases so far have been achieved by serendipity. There is a need for more efficient computational methods for predicting new indications for existing drugs. This paper conducts a retrospective analysis of the temporal patterns of drug intervention trials for every drug in a pair of different conditions in ClinicalTrials.gov, including 550 drugs used for 451 conditions between 2003 and 2013. We found that drugs are often targeted towards conditions that are related by similar or identical eligibility criteria. We demonstrated the preliminary feasibility of predicting new target conditions for drug retesting among conditions with similar aggregated clinical trial eligibility criteria and confirmed this hypothesis using evidence from the literature.

No MeSH data available.