Research Programs
The AIEDD CRC proposes three interconnected and synergistic research programs (RPs) designed to drive innovation. The RPs will adopt a comprehensive patient-centric framework to drug discovery, integrating patient data and outcomes to guide more informed and clinically relevant decision making throughout the development process.
Underpinning, supporting and enabling the 3 RPs are three cross-cutting CRC Pillars, bringing together expertise and resources in (Pillar A) education and training; (Pillar B) data, technology and computing infrastructure; and (Pillar C) commercialisation and clinical translation.
The AIEDD CRC's vision is to fundamentally strengthen Australia’s drug discovery ecosystem and mitigate clinical failure by driving the national adoption and uptake of AI-enhanced analysis of human-derived data and new approach methodologies. By exclusively utilising patient-centric human data coupled with new AI/ML tools, the CRC will tackle the primary cause of trial failure—lack of efficacy—to better inform clinically-relevant decision-making.
This strategic focus on accelerating the high-cost discovery and preclinical phases will build the critical capacity, networks, and infrastructure needed for Australia's biotech, AI, and pharmaceutical SMEs to secure a meaningful share of the rapidly expanding market for advanced human-relevant models and capitalise on our foundational research excellence by developing new, Australian-identified drug candidates, molecular probes and biomarkers.
RP1 will develop and use AI/ML tools to integrate and interrogate human -omics and other biological datasets, to discover and validate drug targets and corresponding biomarkers for patient selection and pharmacodynamic endpoints, better understand disease heterogeneity, develop novel molecular probes, and stratify patient cohorts for optimal therapeutic targeting.
RP2 will develop and employ human-derived, disease-relevant in vitro laboratory models and platforms (‘new approach methodologies,’ NAMs) to interrogate and validate therapeutic responses to novel drugs and targets, using outputs to then create and test predictive AI-driven digital models of disease. RP2 will also design and test ex vivo assays for therapeutic biomarkers, and develop methods to apply these biomarkers for in vivo trials.
RP3 will facilitate design and development of lead molecules and drug candidates against newly identified targets, combining AI modelling with laboratory-based medicinal chemistry and systems biology in highly efficient predict–make-test cycles that predict multi-system effects and interactions of these potential therapeutics, enabling optimisation for improved pharmacology and toxicity profiles. RP3 will also probe clinical datasets for intelligent drug repurposing opportunities that can be tested and optimised against RP1 targets using RP2 models, and development of new chemical entities using RP3 workflows for improved patient suitability and IP position.