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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 condition pairs and average number of shared CEFs for pairs of conditions over counts of retested drugs.
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f3-2085703: Number of condition pairs and average number of shared CEFs for pairs of conditions over counts of retested drugs.

Mentions: Figure 3 illustrates the number of condition pairs and the average number of shared CEFs for pairs of conditions with the same number of retested drugs. The x-axis shows the number of drugs used for a condition pair. The left y-axis shows the number of condition pairs. The right y-axis shows the average number of shared CEFs between two conditions in a pair. On average, each condition has 172 CEFs. The average number of CEFs shared by any two conditions is 52, whereas the average number of CEFs shared by condition pairs involving drug retesting is 139. 64.6% of these condition pairs have 100–200 shared CEFs, while only 2.9% condition pairs have fewer than 50 shared CEFs, indicating that drug retesting often occurred between conditions with a large number of shared CEFs. Previously, Boland et al. used CEFs shared among diseases to identify disease relatedness10. The average number of shared CEFs increases with the number of retested drugs, which indicates that conditions with more shared CEFs, implying the research on these two conditions tend to use similar criteria for patient recruitment, are more likely to use the same drug as an intervention on these conditions. For example, 15 drugs (e.g., Bendamustine, Bortezomib, brentuximab vedotin) that were tested for lymphoproliferative disorders were later retested for leukemia. Lymphoproliferative disorders and leukemia share 199 CEFs (e.g., electrocorticogram, alanine transaminase, creatinine clearance). However, some successful repurposed drugs also occurred in non-similar diseases. For example, metformin was initially tested for diabetes mellitus and later tested for treating breast neoplasm. These two conditions shared only 61 CEFs.


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 condition pairs and average number of shared CEFs for pairs of conditions over counts of retested drugs.
© Copyright Policy
Related In: Results  -  Collection

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

f3-2085703: Number of condition pairs and average number of shared CEFs for pairs of conditions over counts of retested drugs.
Mentions: Figure 3 illustrates the number of condition pairs and the average number of shared CEFs for pairs of conditions with the same number of retested drugs. The x-axis shows the number of drugs used for a condition pair. The left y-axis shows the number of condition pairs. The right y-axis shows the average number of shared CEFs between two conditions in a pair. On average, each condition has 172 CEFs. The average number of CEFs shared by any two conditions is 52, whereas the average number of CEFs shared by condition pairs involving drug retesting is 139. 64.6% of these condition pairs have 100–200 shared CEFs, while only 2.9% condition pairs have fewer than 50 shared CEFs, indicating that drug retesting often occurred between conditions with a large number of shared CEFs. Previously, Boland et al. used CEFs shared among diseases to identify disease relatedness10. The average number of shared CEFs increases with the number of retested drugs, which indicates that conditions with more shared CEFs, implying the research on these two conditions tend to use similar criteria for patient recruitment, are more likely to use the same drug as an intervention on these conditions. For example, 15 drugs (e.g., Bendamustine, Bortezomib, brentuximab vedotin) that were tested for lymphoproliferative disorders were later retested for leukemia. Lymphoproliferative disorders and leukemia share 199 CEFs (e.g., electrocorticogram, alanine transaminase, creatinine clearance). However, some successful repurposed drugs also occurred in non-similar diseases. For example, metformin was initially tested for diabetes mellitus and later tested for treating breast neoplasm. These two conditions shared only 61 CEFs.

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.