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Drug Target Identification and Drug Repurposing in Psoriasis through Systems Biology Approach, DNN-Based DTI Model

Yu-Ping Zhan¹, Bor-Sen Chen¹

¹Laboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.

International Journal of Molecular Sciences

|June 28, 2023

Summary

This summary is machine-generated.

This study used systems biology to uncover psoriasis mechanisms, identifying STAT3, CEBPB, NF-κB, and FOXO1 as key biomarkers and drug targets. Researchers developed a deep neural network to find potential multi-molecule drugs like Naringin, Butein, and Betulinic acid for psoriasis treatment.

Keywords:

DNN-based DTI model DTI databases bid data mining core signaling pathways drug design specifications multi-molecule drug pathogenic mechanism of psoriasis systems biology method

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Area of Science:

Genomics and Systems Biology
Dermatology
Pharmacology

Background:

Psoriasis is a chronic, non-communicable skin disease affecting millions globally.
The World Health Organization (WHO) recognized psoriasis as a serious health concern in 2014.
Understanding psoriasis pathogenesis is crucial for developing effective therapeutic strategies.

Purpose of the Study:

To investigate the pathogenic mechanisms of psoriasis using a systems biology approach.
To identify potential drug targets for psoriasis treatment.
To discover novel multi-molecule drugs for psoriasis therapy.

Main Methods:

Construction of genome-wide genetic and epigenetic networks (GWGENs) via big data mining.
Identification of real GWGENs using system identification and order detection.
Extraction of core GWGENs using Principal Network Projection (PNP) and KEGG pathway annotation.
Development of a deep neural network (DNN)-based drug-target interaction (DTI) model.
Selection of candidate drugs based on regulatory ability, toxicity, and sensitivity.

Main Results:

STAT3, CEBPB, NF-κB, and FOXO1 were identified as significant biomarkers and potential drug targets.
A DNN-based DTI model successfully predicted candidate molecular drugs.
Naringin, Butein, and Betulinic acid were selected as promising compounds for multi-molecule drug development.

Conclusions:

Systems biology provides a powerful framework for understanding complex diseases like psoriasis.
Targeting identified biomarkers (STAT3, CEBPB, NF-κB, FOXO1) offers therapeutic potential.
Naringin, Butein, and Betulinic acid represent promising candidates for novel psoriasis treatments.