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Newington, J.

Publications and source records attributed to Newington, J..

2 recordsLinked to original sources

Novel Target Combinations in Lung Squamous Cell Carcinoma proposed by the Emet AI Research Environment and supported by discovery stage experimentation

Early drug discovery is frequently bottlenecked by target identification, a challenge that becomes particularly difficult in complex diseases driven by overlapping, redundant pathways rather than a single dominant driver. Lung squamous cell carcinoma (LUSC) is one such disease: mutationally complex, lacking a dominant actionable target, and marked by a long series of failed single-agent targeted trials. We hypothesized a research environment, capable of reasoning across interconnected datasets, would be well-suited to generate novel, mechanistically grounded therapeutic hypotheses for LUSC. Emet, an AI research environment utilizing a biomedical knowledge graph of more than 1.5 billion triples connected to over 100 specialised biological databases, was paired with an Agentic Research Director that pursues each hypothesis through a graph-of-thoughts search, invoking scoped link-prediction and retrieval tools and committing every round of findings to persistent memory. The top-ranked hypotheses were two-target combinations rather than a single target. The four highest-ranked combinations were advanced to dose-matrix testing in NCI-H520 and SK-MES-1 cells, and two produced combination effects exceeding either single agent; to our knowledge neither of these two pairings had previously been evaluated in LUSC. Dual inhibition of CDC7 (TAK-931) and PKMYT1 (RP-6306) produced statistically significant synergy in both lines that strengthened from day 5 to day 7 (HSA 19.24 to 24.19 in NCI-H520; 11.07 to 13.73 in SK-MES-1). Dual inhibition of USP13 (spautin-1) and PI3K (alpelisib) produced an additive, cell-line-dependent effect, and immunoblotting confirmed the predicted mechanism: time-dependent depletion of MCL-1, c-Myc and SOX-2 driven by the USP13 arm. Agentic reasoning over multi-domain biomedical evidence can therefore nominate testable, mechanism-bearing combination hypotheses that survive experimental scrutiny.

bioinformatics↗

Photochemical probe identification of the small-molecule binding site in a mammalian membrane-bound O-acyltransferase

The mammalian membrane-bound O-acyltransferase (MBOAT) superfamily is involved in biological processes including growth, development and appetite sensing. MBOATs are attractive drug targets in cancer and obesity; however, information on the binding site and molecular mechanisms underlying small-molecule inhibition is elusive. This study reports development of a photochemical probe to interrogate the small-molecule binding site in the human MBOAT Hedgehog acyltransferase (HHAT) based on HHAT inhibitor RUSKI-201. Structure-activity relationship investigation identified the improved enantiomeric inhibitor IMP-1575, which is the most potent HHAT inhibitor reported to-date, and guided rational design of a photocrosslinking probe that maintained HHAT-inhibitory potency. Photocrosslinking and proteomic sequencing of HHAT delivered identification of the first small-molecule binding site in a mammalian MBOAT. Topology and homology data suggested a potential mechanism for HHAT inhibition which was confirmed via kinetic analysis. Our results provide an optimal HHAT inhibitor IMP-1575 (Ki = 38 nM) and a strategy for mapping of interaction sites in MBOATs.

biochemistry↗