NLM IRP Seminar Schedule

Systems-based approaches to coding & noncoding RNA drug & target development.

The complexity of Alzheimer’s Disease (AD) means that approaches for effective therapeutic target identification and drug development need to be multifaceted. Genetically identified target genes have yet to be shown as clinically effective as drug targets. Data driven approaches to discovery are far more successful when tightly linked to predictive assessment in biological systems. Using a comparative systems approach, we have focused upon the activity of individual pathways to map dysregulated function across human and model systems. Integrated Pathway Activity Analysis (IPAA) compares human brains with 3D Alzheimer’s disease (AD) neural cell culture models, ensuring selection of the most accurate model. This approach identifies crucial pathways and new drug candidates, validated in neural cell culture models, accelerating the development of AD interventions. Precise alignment of cellular model functional recapitulation with human AD pathology streamlines drug discovery and minimizes the risk of clinical trial failures. The P38 MAPK pathway is identified as a key dysregulated pathway, consistently activated in both AD brains and 3D AD neural cell culture models. We validated the impact of this pathway by therapeutic intervention with known clinical p38 MAPK inhibitors. We are now exploring the potential modulation of pathogenic pathways using microRNAs (miRNAs). Utilizing a miRNA-Pathway prediction framework, PanomiR, we systematically analyze the role of miRNAs in regulating the multi-pathway activity events we have discovered related to AD. This approach has led to the identification of key miRNAs that target coordinated groups of disease pathways, offering novel insights into the regulatory mechanisms in AD and highlighting potential therapeutic candidates.

How do Clinicians Feel? A Trajectory Analysis of Clinical Notes using Large Language Models

Clinical notes can provide insight into caregiver attitudes and how they impact patient care and satisfaction. However, detecting clinician attitudes from the language used in clinical notes is a challenging task, given the concise and standardized format of clinical notes and other contextual factors. In this study, we leverage multiple large language models to identify clinician attitudes from the linguistic features in clinical notes. This approach promises to provide a reliable means of improving patient care, clinician well-being, and communication by identifying specific clinicians' attitude trajectories from clinical notes.

TBD