Alex (Shu) Liu, PhD

- Led cross-functional drug discovery programs advancing CDK8 inhibitors (e.g., MK-256), integrating biology, chemistry, and translational studies to support IND development in AML, ALL, and solid tumors (e.g., colorectal cancer) - Built and managed external collaborations with CROs and academic partners, coordinating synthesis, screening, and in vivo studies to accelerate timelines and expand experimental capacity - Developed deep expertise in cancer biology and oncogenic signaling, focusing on actionable dependencies across pathways such as Hippo/YAP, STAT, and kinase-driven transcriptional regulation, linking molecular mechanisms to therapeutic strategy - Built end-to-end drug discovery experience, spanning: target identification and validation hit discovery and lead optimization ADMET profiling, PK/PD modeling, and in vivo efficacy studies enabling progression from early concept to preclinical candidates - Applied computational chemistry and modeling methods across the pipeline, including: homology modeling and structure-based design virtual screening and molecular docking/scoring QSAR and pharmacophore modeling molecular dynamics simulations to drive rational hit identification and lead optimization - Designed and executed compound screening campaigns in collaboration with the UCSF Small Molecule Discovery Center, enabling identification of novel hits and expansion of the therapeutic pipeline - Conducted and interpreted in vitro functional assays (proliferation, apoptosis, signaling pathway modulation) and in vivo models (xenografts, pharmacodynamic readouts) to validate targets and refine candidate compounds - Scaled research productivity by integrating computational design, experimental validation, and external partnerships into a unified workflow, enabling faster iteration cycles and more efficient decision-making across programs

MK-256 (CDK8 inhibitor) - Characterized MK-256 as a potent, selective, orally bioavailable CDK8 inhibitor for acute myeloid leukemia (AML) - Demonstrated anti-cancer activity across AML cell lines - Showed that MK-256 targets leukemia stem cell populations (CD34⁺/CD38⁻) by inducing differentiation and maturation - Established mechanism of action via downregulation of STAT signaling, including reduced phosphorylation of STAT1 (S727) and STAT5 (S726) - Positioned MK-256 as a clinically relevant candidate targeting transcriptional dependencies in AML YAP1 → ABCG2 — Cancer Stemness & Drug Resistance - Identified YAP1 as a direct transcriptional regulator of ABCG2, a key drug efflux transporter driving chemoresistance - Showed that YAP1 controls cancer stem-like side population (SP) cells, affecting tumorsphere formation and stemness - Demonstrated that YAP1 inhibition (siRNA/verteporfin) reduces ABCG2 and sensitizes cells to chemotherapy (doxorubicin) - Established the YAP1–ABCG2 axis as a mechanism linking Hippo signaling to drug resistance and relapse Coumestrol (Natural Product → Targeted Therapy Insight) - Identified coumestrol from the NCI natural product library as a novel CK2 inhibitor using high-throughput kinase screening - Demonstrated potent, selective, ATP-competitive inhibition of CK2 (IC₅₀ ~228 nM) with defined binding via molecular docking - Showed that coumestrol suppresses CK2-mediated Akt phosphorylation, linking target inhibition to downstream signaling effects - Validated anti-proliferative activity across cancer cell lines, establishing coumestrol as a potential therapeutic lead

Drug Discovery & Design - Designed small-molecule inhibitors targeting protein–protein interaction (PPI) interfaces in CDK/cyclin complexes, moving beyond conserved ATP-binding sites - Developed non-ATP competitive kinase inhibitors by disrupting substrate recruitment and regulatory interactions rather than catalytic activity - Targeted challenging PPI surfaces by translating peptide binding motifs into tractable small-molecule scaffolds Fragment-Based & REPLACE Strategy - Applied REPLACE (fragment replacement) methodology to convert bioactive peptides into drug-like small molecules - Identified key binding residues (e.g., arginine hot spots) and replaced them with chemically stable isosteres preserving electrostatic and H-bond interactions - Performed iterative fragment optimization, balancing affinity, molecular weight, and drug-like properties Structure-Based Modeling - Conducted structure-based drug design (SBDD) using molecular docking, scoring, and interaction mapping - Analyzed CDK2/cyclin A crystal structures to identify non-ATP and allosteric binding pockets - Guided rational design through binding mode analysis and interaction fingerprints Mechanistic Insights & Validation - Characterized non-ATP competitive inhibition mechanisms, including disruption of cyclin binding and substrate docking - Demonstrated that arginine-mediated interactions in PPIs can be mimicked by small molecules - Integrated computational predictions with biochemical assays and SAR analysis to iteratively refine compounds Conceptual Contributions - Established a framework for converting peptide-based inhibitors into small molecules targeting PPIs - Contributed to early strategies for selective kinase inhibition via allosteric and interface targeting