Multiple-Molecule Drug Design Based on Systems Biology Approaches and Deep Neural Network to Mitigate Human Skin Aging

Abstract

Human skin aging is affected by various biological signaling pathways, microenvironment factors and epigenetic regulations. With the increasing demand for cosmetics and pharmaceuticals to prevent or reverse skin aging year by year, designing multiple-molecule drugs for mitigating skin aging is indispensable. In this study, we developed strategies for systems medicine design based on systems biology methods and deep neural networks. We constructed the candidate genomewide genetic and epigenetic network (GWGEN) via big database mining. After doing systems modeling and applying system identification, system order detection and principle network projection methods with real time-profile microarray data, we could obtain core signaling pathways and identify essential biomarkers based on the skin aging molecular progression mechanisms. Afterwards, we trained a deep neural network of drug–target interaction in advance and applied it to predict the potential candidate drugs based on our identified biomarkers. To narrow down the candidate drugs, we designed two filters considering drug regulation ability and drug sensitivity. With the proposed systems medicine design procedure, we not only shed the light on the skin aging molecular progression mechanisms but also suggested two multiple-molecule drugs for mitigating human skin aging from young adulthood to middle age and middle age to old age, respectively.

Pathophysiological and Genetic Aspects of the Brain–Skin Axis: The Role of Stress and Inflammation in Skin Aging

“…Various studies suggest the influence of genetic polymorphisms of the TNFA gene (responsible for TNF- expression) in various chronic diseases [28,29,30]. In another study, it was suggested that IL6 (the gene responsible for IL6 expression) is a biomarker of health status in the elderly [31]. A number of studies have confirmed the association of the IL6 gene with longevity and diseases associated with premature aging [32,33].…”

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

“…The nodes of the candidate GWGEN are divided into several groups: proteins, receptors, transcription factors (TFs), genes, miRNAs, and lncRNAs. Then, candidate GWGENs are pruned to real GWGENs of psoriasis and non-psoriasis in Figure 2 using their corresponding microarray data by system identification in Equations ( 1)- (20) to trim off false positives in candidate GWGEN. Here, we utilize the Akaike information criteria (AIC) [21] in Equations ( 21)-( 28) to perform the system order detection method to trim off the false-positive interactions and obtain real GWGENs of psoriasis and non-psoriasis.…”

“…The constraints in Equations ( 18)-( 20) ensure that the estimated post-transcriptional regulatory ability of miRNAs on genes, lncRNAs, and miRNAs is negative. Therefore, we can estimate the optimal vectors Θa , Θb , Θc , Θd of the protein interaction, gene, lncRNA, and miRNA regulation by solving the constrained least squares parameter estimation problems in Equations ( 17)- (20) with the MATLAB Optimization Toolbox, respectively.…”

Use of Artificial Intelligence in Skin Aging

“…Combinations of these factors vary from individual to individual, warranting the exploration of AI in this area as a means for regaining homeostasis of the skin with specific interventions and therapeutics. For example, using a neural network system with a large database, Yeh et al processed information regarding protein-protein interaction, gene regulation, and human skin gene expression data to identify possible pathways and biomarkers of skin aging [46]. Using drug-target interaction data, they also proposed two drug treatments based on the identified biomarkers, one for young adulthood to middle age and another for middle age to old age [46].…”