Lee Yoon Hyeok, Choi Hojae, Park Seongyong, Lee Boah, Yi Gwan-Su
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea.
BMC Bioinformatics. 2017 May 31;18(Suppl 7):226. doi: 10.1186/s12859-017-1637-5.
Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs.
We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness.
In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence.
This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.
最近，药物空间的代谢物相似性已出现，并为在药物发现中探索类人类代谢物候选物开辟了新的可能性。然而，代谢物相似性在药物发现中的适用性在很大程度上尚未得到探索。此外，尽管可以从酶的代谢物与药物之间的相似关系直接推断酶 - 药物关系，但尚无关于其在将药物重新定位为可能的酶调节剂方面应用的报道。
我们构建了一个药物 - 代谢物结构相似性矩阵，其中包含1861种FDA批准的药物和1110种人类中间代谢物，并使用Tanimoto相似性进行评分。为了验证用于药物重新定位的代谢物相似性度量，我们分析了17种已知的抗代谢药物，这些药物与其11种靶酶的天然代谢物相似，作为金标准阳性对照。得分高的药物因其相应的代谢物而被选为可能的酶调节剂。然后，我们通过接受者操作特征分析评估了代谢物相似性的性能，并将其与其他药物 - 靶标预测方法进行了比较。我们基于约登指数的最大化设定了新酶调节剂的药物重新定位候选物的相似性阈值。我们还进行了文献调查，以支持基于代谢物相似性的药物重新定位结果。
在本文中，我们应用代谢物相似性将FDA批准的药物重新定位为类似于人类天然代谢物的疾病相关酶调节剂。所有抗代谢药物都与其已知的11种靶酶进行了映射，与相应代谢物具有统计学上显著的相似性值。与其他药物 - 靶标预测方法的比较表明，代谢物相似性在预测酶调节剂方面具有更高的性能。之后，得分高于相似性分数0.654的药物因其相应的代谢物而被选为可能的酶调节剂。此外，我们表明10种酶的药物重新定位结果与文献证据一致。
本研究介绍了一种使用人类代谢物相似性将已知药物重新定位为疾病相关酶可能调节剂的方法。我们证明了这种方法对已知抗代谢药物有效，并且表明所提出的方法在酶调节剂预测方面比其他药物靶标预测方法具有更好的性能。本研究作为概念验证，展示了如何将代谢物相似性应用于药物重新定位，以及随着我们获得更多疾病相关代谢物 - 靶标蛋白关系在进一步扩展方面的潜力。
