We utilize patient biology, biomarker-based screening, and advanced data analytics to de-risk clinical translation.
Patient-inspired drug discovery and development involves actively engaging and incorporating patient perspectives throughout the process, aiming to create more effective and patient-centric treatments that address real-world needs and challenges.
AI/ML powered drug discovery and development involves using artificial intelligence and machine learning to analyze vast amounts of data, predict drug efficacy, identify potential candidates, and optimize clinical trials, ultimately accelerating the process of finding new and effective treatments for improved patient outcomes.
Currently focused on drug discovery for Parkinson’s disease (PD)
Human PD patient derived midbrain organoids recapitulate key elements of PD pathophysiology
including dopamine neuron degeneration and synaptic dysfunction.
Genetic Entry Points
Utilize familial PD genetic entry points into dysfunctional autophagy/lysosomal and
mitochondrial/oxidative stress pathways for expansion into the sporadic PD population.
Underlying Disease Processes
Phenotypic screening of targeted small molecule libraries with clinically translatable endpoints to
identify compounds that ameliorate the underlying disease processes.
Design better clinical trials
Identify and define key molecular signatures to refine clinical trial endpoints and patient selection.