Preclinical Core Research Facility Services

What We Do

The EDGE Preclinical Core provides expert support and infrastructure for translational cancer research related to drug development. Our platform is organized around two major stages of early drug development:

  1. Discovery and Target Validation

    We help to identify promising compounds—ranging from small molecules and antibody-drug conjugates (ADCs) to natural products—and assess their therapeutic potential in biologically relevant models. Our team also supports biomarker discovery and studies on drug mechanisms.

  2. Preclinical Evaluation

    We rigorously test therapeutic candidates using state-of-the-art in vitro (cell-based) and in vivo (mouse-based) models. These models are developed from patient-derived samples whenever possible, ensuring that research results are relevant to Hawaiʻi’s diverse patient population. Our services include:
    1. High-throughput drug screening (e.g., ADCs, small molecules, immunotherapies, and natural compounds)
    2. Tissue imaging & biomarker validation using digital slide scanning and multiplex staining
    3. Cancer model development using cell lines, patient-derived xenografts (PDX), and organoids
    4. Preclinical data generation to support IND-enabling studies and grant submissions

  3. Computational Modeling and AI-based Drug Discovery

    (led by Dr. Shuxing Zhang via Chemical Library Facility)
    Dr. Shuxing Zhang is an expert in computational biology/chemistry, with intensive and extensive experience of big data, artificial intelligence and machine learning for molecular structural studies and rational drug design. In collaboration with the Preclinical Core, our goal is to help investigators who have identified promising drug targets identify new therapeutic agents, elucidate molecular mechanisms of action, perform automated AI-guided lead optimization, and conduct predictive ADMET modeling through their digital twin technologies.

How We Assist

Target Optimization

We work with you to refine your molecular target using AI tools—predicting how drug molecules will bind, how they might be improved, and how to anticipate off-target effects.

Compound Prioritization

When multiple candidate molecules are available, we use AI models to rank them based on predicted potency, safety, and drug-like properties, saving lab time and resources.

Designing New Molecules

For targets without good existing inhibitors, Dr. Zhang’s team can help design novel compounds (or suggest modifications) that AI predicts will have better activity or selectivity.

Integration with Preclinical Testing

Once AI narrows down candidates, the Preclinical Core helps test them in vitro (cell-based assays) and in vivo (animal models), and supports biomarker validation to see which compounds show promise in realistic settings.