Development of a Rule-Based Method for the Assessment of Protein Druggability

Abstract

Target selection is a critical step in the majority of modern drug discovery programs. The viability of a drug target depends on two components: biological relevance and chemical tractability. The concept of druggability was introduced to describe the second component, and it is defined as the ability of a target to bind a drug-like molecule with a therapeutically useful level of affinity. To investigate the rules that govern druggability, we developed an algorithm to isolate and characterize the binding pockets of protein targets. Using this algorithm, we performed a comparative analysis between the relevant pockets of 60 targets of approved drugs and a diverse set of 440 ligand-binding pockets. As a result, we defined a preferred property space for druggable pockets based on five key properties (volume, depth, enclosure, percentage of charged residues and hydrophobicity), and we represented it with a set of simple rules. These rules may be applicable in the future to evaluate the chemical tractability of prospective targets.

New Frontiers in Druggability

“…5,10–12,14,16 For method validation, Schmidtke and Barril compiled a large set of protein targets with associated degrees of druggability. 11 Krasowski et al 12 added further proteins to the training set, and develop the DrugPred druggability score.…”

Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening

“…Starting with a diverse set of proteins that are of interest to a particular therapy area, the strategy rapidly identifies tractable targets and simultaneously defines novel starting points for therapeutic programme teams to explore. To our knowledge, no other report provides an experimental approach to tractability assessment that can evaluate a large set of proteins de novo in such a short period of time1517.…”

“…Edfeldt’s more recent feature highlights the use of fragment technology to probe the ligandability of 36 targets in the AstraZeneca portfolio over an 8-year period15. Computational methods and docking approaches have been reported and are highly valueable1617. These techniques have helped guide target selection for the past decade but are often limited by target throughput, experimental efficiency and the prerequisite that structural information is available.…”

Mitigating risk in academic preclinical drug discovery

“…Druggable also implies the target is amenable to ligand binding from a structural (thermodynamic) perspective. Therefore, having reliable structural models for the target and closely homologous targets is advantageous, as it can facilitate later structure-based design methods and target engagement studies 30–32 . However, judging target druggability is not a purely scientific endeavour.…”