Case Studies
Breaking Barriers in Challenging Targets: DNMT1 Drug Discovery with Kuano
See how Kuano’s quantum drug discovery platform generated new hits for DNMT1, a historically challenging epigenetic target.
The Challenge: Selectivity hurdles block DNMT1 drug discovery
DNMT1 is a member of the DNA methyltransferase family and is strongly implicated in various cancers, both solid and hematologic.
DNMT1 shares an endogenous substrate with paralogues DNMT3a and DNMT3b. This leads to selectivity challenges with targeting the catalytic site and has led to therapeutics targeting DNA (allosteric) - an approach which leads to significant side effects.
The existing inhibitor landscape includes nucleosides (mimetics) and allosteric inhibitors interacting with DNA. The limited work towards orthosteric inhibition has been impeded by selectivity difficulties. Non-nucleoside inhibitors mostly target allosteric/DNA as their MoA.
DNMT1 Transition State - Methyl Transfer from cofactor to DNA
The Solution: Selective binders for DNMT1
Kuano used its state of the art simulation engine to produce a transition state structure of the reaction between endogenous substrate and DNA. Understanding what drives selectivity in this family of targets.
How?
The transition state of the DNMT1 was simulated.
Outputs of the simulation were used for AI generative drug design.
Top 40 molecules being selected for synthesis and biological evaluation.
The Results: Lab Verified Selective binders for DNMT1 in 2 months
Remarkably, that by screening 40 compounds we established 3 novel chemotypes for DNMT1. These chemotypes all exhibit selectivity over DNMT3a/b paralogues and 2 of which are equipotent to expanded hit series in the literature.
Pinpointing Selectivity Through Quantum Energy Profiling: Explaining Phosphatase Selectivity
See how Kuano’s quantum drug discovery platform decodes selectivity beyond sequence similarity in SHP1/2
The Challenge: Unexplained SHP1/2 selectivity in phosphatases
Researchers identified a natural product derived series of compounds which were found to exhibit selectivity in a subfamily of phosphatases. The selectivity was rationalised by differentiated residues except for the observed SHP1/2 selectivity where there was no differentiation of residues observed. Understanding and rationalising this selectivity is key to designing selective inhibitors in the future and we believed Kuano’s Quantum Lens technology would reveal.
The Solution: Application of Kuano’s Quantum Interaction Analysis Tool
Quantitative measurement of energy contributions of the catalytic and peripheral residues, led to the investigation of potential selectivity being likely within the peripheral residues due to the higher relative energy contributions observed.
The Result: Revealed peripheral residue energies drive selectivity.
Kuano’s Quantum Lens revealed that the residues identified by the researchers weren’t sufficient to explain the selectivity observed between SHP1/2. A comparison of the peripheral residue energies showed a LYS contributing a large repulsive energy only in the SHP1 complex. This identification of an unintuitive residue interaction can enable enhanced optimisation of mildly selective compounds.
PlaceHolder: Covalent inhibitor design
See how Kuano’s quantum drug discovery platform rationalises covalent ligand reactivity in JAK3
The Challenge: Warhead selectivity mechanisms remained poorly understood.
Designing covalent inhibitors often relies on trial-and-error because the mechanisms driving warhead selectivity are not well understood. Subtle differences in reactivity between warhead classes, such as aryl versus aliphatic groups, are difficult to rationalize using conventional methods.
These systems are important in drug discovery and can be only successfully modelled at the quantum level, to be revealed by the Quantum Lens.
The Solution: Application of Kuano’s Quantum information Fingerprints
We applied quantum information fingerprints to Pfizer’s post-rationalised JAK3 covalent inhibitors, providing a new lens to probe the molecular drivers of selectivity. This approach allowed us to capture subtle energetic and electronic features missed by traditional analysis.
How
Measured entanglement and correlation energy differences.
The Result: Predicted reactivity and selectivity of warheads.
This analysis demonstrated the ability to discern and predict the reactivity of different chemical species. By linking quantum descriptors directly to experimental outcomes, we provide a path to more rational and selective design of covalent inhibitors.
Pipeline
Kuano’s innovative quantum approach to drug discovery enables unique targets to be explored to provide innovative therapies.
“Apart from checkpoint inhibitors for a few, there have been few advances in colorectal cancer in the last 20 years. Kuano's novel approach exploiting quantum mechanics has unlocked a unique target that permits an exciting biologically effective novel therapy for a much larger proportion of cancer sufferers.”
John Bridgewater, Clinical Professor, UCL