Abstract
Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for these diseases. For this reason, deliberately introducing anti-fibrosis characteristics into predictive modeling may lead to more success in drug repositioning. In this study, anti-fibrosis knowledge base was first built by collecting data from multiple resources. Both structural and biological profiles were then derived from the knowledge base and used for constructing machine learning models including Structural Profile Prediction Model (SPPM) and Biological Profile Prediction Model (BPPM). Three external public data sets were employed for validation purpose and further exploration of potential repositioning drugs in wider chemical space. The resulting SPPM and BPPM models achieve area under the receiver operating characteristic curve (area under the curve) of 0.879 and 0.972 in the training set, and 0.814 and 0.874 in the testing set. Additionally, our results also demonstrate that substantial amount of multi-targeting natural products possess notable anti-fibrosis characteristics and might serve as encouraging candidates in fibrosis treatment and drug repositioning. To leverage our methodology and findings, we developed repositioning prediction platform, drug repositioning based on anti-fibrosis characteristic that is freely accessible via https://www.biosino.org/drafc.
Highlights
Fibrosis is defined as the process of excessive accumulation of fibrous connective tissue in most tissues or organs, where normal cells are replaced by the extracellular matrix (ECM), resulting in disrupted tissue function
CMap, respectively and used for model construction. 2640 approved drugs in DrugBank and 1223 compounds in the anti-fibrosis knowledge base served as the raw data for Structural Profile Prediction Model (SPPM) construction. 6100 biological profiles of 1309 small molecules in cMap served as the raw data for Biological Profile Prediction Model (BPPM)
[24] and 6100 biological profiles were labeled as positive candidates and negative candidates based on their anti-fibrosis characteristic in the anti-fibrosis knowledge base
Summary
Fibrosis is defined as the process of excessive accumulation of fibrous connective tissue in most tissues or organs, where normal cells are replaced by the extracellular matrix (ECM), resulting in disrupted tissue function. In the new era of 21st century, the morbidity and mortality rates of various fibrotic diseases have increased progressively, bringing a huge global health burden. Fibroproliferative diseases are responsible for nearly 45% of deaths[1]. One of the well-known fibrotic diseases, idiopathic pulmonary fibrosis(IPF), has a poor prognosis with the 5 year survival rate less than 30% and median survival ranging from 3 to 5 years[2]. The outcomes of IPF patients are even worse than those with many types of cancers [3]. As data obtained by Clinical Practice Research
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