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. 2021 Apr 21:19:2318-2328.
doi: 10.1016/j.csbj.2021.04.035. eCollection 2021.

Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease

Jiayi Yin  1 , Xiaoxu Li  1 , Fengcheng Li  1 , Yinjing Lu  1 , Su Zeng  1   2 , Feng Zhu  1   2   3
Affiliations

Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease

Jiayi Yin et al. Comput Struct Biotechnol J. .

Abstract

An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein-protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.

Keywords: Artificial intelligence; Cancer; Cardiovascular disease; Drug mechanism; Narrow therapeutic index.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Violin plot of the 13 features identified in cancer. For each feature, dark blue represents the targets of NTI drugs for cancer, and light blue represents the targets of NNTI drugs for all indications. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
Violin plot of the 11 features identified in cardiovascular disease. For each feature, dark orange represents the targets of NTI drugs for cardiovascular disease, and light orange represents the targets of NNTI drugs for all indications. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3
Fig. 3
Classification of the key features of cancer and cardiovascular disease determined in this study into three feature groups.
See this image and copyright information in PMC

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