Title

Bridging Molecular and Digital Therapeutics for Brain Disorders: From EGFR-Targeted Neuroinflammation Modulation to Data-Driven Cognitive Interventions

Alternative Title

뇌 질환 치료의 분자 및 디지털 패러다임 통합 : EGFR 표적 신경염증 조절에서 데이터 기반 인지 중재 전략까지

DGIST Authors

Yoo Joo Jeong ; Jung Ho Hyun ; Hyang-Sook Hoe

Advisor

현정호

Co-Advisor(s)

Hyang-Sook Hoe

Issued Date

2026

Awarded Date

2026-08-01

Type

Thesis

Description

Neurodegeneration, neurodevelopmental disorder, EGFR tyrosine kinase inhibitors, neuroinflammation, drug repurposing, ADHD, cognitive assessments, digital therapeutics （DTx）, machine learning

Table Of Contents

1. General Introduction 1
2. Molecular Therapeutics 4
2.1. Background 4
2.2 Material and Methods 5
2.2.1. Olmutinib 5
2.2.2. Osimertinib 6
2.2.3. BV2 microglial cells 6
2.2.4. C57BL6/N mice 6
2.2.5. MTT assay 6
2.2.6. Real-time quantitative PCR in BV2 microglial cells, and C57BL6/N mice 7
2.2.7. Western blotting 9
2.2.8. Nuclear Fractionation 10
2.2.9. Immunofluorescence staining (IF) 10
2.2.10. NLRP3 siRNA transfection 11
2.2.11. Statistical analysis 11
2.3. Molecular Targeting of Neuroinflammation: EGFR Inhibition as a Therapeutic Strategy for Alzheimer’s Disease 12
2.3.1. Biological features of cancer and Alzheimer’s disease 12
2.3.2. Potent druggable targets and therapeutic strategies for AD 19
2.3.3. Repurposing of anticancer drugs as AD medications 20
2.3.4. Clinical practice: from cancer to AD 29
2.4. EGFR Inhibitor Olmutinib Regulates LPS-Mediated Neuroinflammatory Responses by Regulating AKT/STAT3 Signaling 30
2.4.1. Olmutinib significantly downregulates LPS-induced neuroinflammatory responses and AKT/STAT3 signaling in BV2 microglial cells 30
2.4.2. Olmutinib administration reduces EGFR expression, microgliosis, and astrogliosis in vivo 32
2.4.3. Olmutinib decreases LPS-induced proinflammatory cytokine levels in vivo 34
2.4.4. Olmutinib administration downregulates LPS-evoked microglial and astroglial-associated neuroinflammatory dynamics in vivo 36
2.4.5. Olmutinib treatment diminishes LPS-induced STAT3 phosphorylation levels in vivo 36
2.4.6. Olmutinib treatment suppresses LPS-mediated NLRP3 inflammasome in vitro and in vivo 38
2.4.7. Olmutinib attenuates LPS-evoked proinflammatory responses in NLRP3-dependent manner in BV2 microglial cells 39
2.5. EGFR inhibitor Osimertinib attenuates LPS-evoked neuroinflammatory responses through NLRP3 in wild-type mice 41
2.5.1. Osimertinib regulates LPS-induced proinflammatory cytokine levels in a route-dependent manner in vivo 41
2.5.2. Osimertinib administration regulates LPS-evoked microglial and astroglial-associated neuroinflammatory dynamics in vivo 41
2.5.3. Osimertinib treatment diminishes LPS-induced EGFR and STAT3 phosphorylation levels in a route-dependent manner in vivo 43
2.5.4. Osimertinib suppresses LPS-mediated NLRP3 inflammasome activation in a route-dependent manner in vivo 45
2.6. Discussion 47
3. Digital Therapeutics 52
3.1. Background 52
3.2. Material and Methods 53
3.2.1. Participants 53
3.2.2. Procedure 53
3.2.3. K-WISC-V: Processing Speed Index (PSI) 54
3.2.4. Serious Game Contents for Processing Speed Measurement 55
3.2.5. Behavioral Feature Extraction from the Serious Game 56
3.2.6. Data Preprocessing 57
3.2.7. Machine Learning Models Used for Predicting PSI 58
3.2.8. Machine Learning Model Training and Hyperparameter Optimization for PSI Prediction 59
3.3. Digital Therapeutics for Alzheimer's and Parkinson's Diseases: Current Trends and Future Perspectives 60
3.3.1. Development status of therapeutics for AD and PD 60
3.3.2. Therapeutic Features of DTx for AD and PD 61
3.3.3. Limitations of DTx for AD and PD 67
3.3.4. Future Perspectives for NDD Targeting DTx Development 68
3.4. Current Status and Future Directions in the Development of Digital Therapeutic Interventions for Neurodevelopmental Disorders 73
3.4.1. Neurodevelopmental diseases 73
3.4.2. Current Status of Digital Therapeutics for Neurodevelopmental Diseases 73
3.4.3. Challenges for Developing Digital Therapeutics for Neurodevelopmental Diseases 74
3.4.4. Implications for Revitalizing the Development of Digital Therapeutics for Neurodevelopmental Diseases 78
3.5. A Novel Approach Using Serious Game Data to Predict the WISC-V Processing Speed Index in Children With Attention-Deficit/Hyperactivity Disorder: Machine Learning Study 79
3.5.1. The SVR machine learning model effectively predicts PSI scores from the serious game performance outcomes of children diagnosed with ADHD 79
3.5.2. The ensemble of AdaBoost and Elastic Net Model exhibits high training performance for predicting PSI scores of children with ADHD 81
3.5.3. The ensemble of AdaBoost, Elastic Net, and SVR exhibits high testing performance for predicting PSI scores of children with ADHD 81
3.5.4. Comparison of the PSI score predictions of the ensemble model of AdaBoost, Elastic Net, and SVR with actual PSI scores 82
3.6. Discussion 84
4. Conclusion 89
5. Reference 91

URI

https://scholar.dgist.ac.kr/handle/20.500.11750/60720
http://dgist.dcollection.net/common/orgView/200001006957

DOI

10.22677/THESIS.200001006957

Degree

Doctor

Department

Department of Brain Sciences

Publisher

DGIST

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