Differential, Disease Modifying, Tractable: Identifying Optimal Drug Targets with Discovery Proteomics and AI

Drug development is centered on interaction with endogenous host proteins to modify their activity and achieve therapeutic efficacy. Target discovery aims to identify optimal protein targets that are 1) mechanistically involved in disease pathogenesis, 2) uniquely or differentially expressed in diseased cells, and 3) tractable, with properties that allow it to be modulated by the drug. The number of tractable targets is rapidly expanding thanks to new modalities such as protein degraders, radioligands, ADCs, and T cell engagers opening the “druggable” space from a few hundred to several thousand proteins. The challenge now is comprehensively characterizing the expanded breadth of potential protein targets to identify the best target for a given therapy.

Target identification to date has largely focused on genetics and RNA-sequencing to identify differentially expressed genes; however, these surrogate measures are often poorly correlated with quantitative protein measures, particularly in disease or treated states.

In this webinar hosted by AAPS, we will discuss how mass spectrometry-based discovery proteomics combined with AI-based target validation enables direct, deep interrogation of the proteome, across thousands of proteins in thousands of biosamples, for robust target discovery to identify and match differentially expressed, disease modifying, tractable protein targets with a drug to optimize its probability of success.

You’ll learn about:

• How mass spectrometry-based discovery proteomics can be used to rapidly explore, identify, and comprehensively characterize novel drug targets in the expanding druggable space – including quantification of protein copy numbers
• How deep biocomputational analysis can be applied to contextualize biology and downstream pathways for protein targets – from signaling cascades to subcellular localization and normal tissue expression and enrichment
• How AI is being applied to assess the therapeutic tractability of key targets to better predict therapeutic efficacy and mitigate off-target effects