Lorente-Macias, A. | bioRxiv Science

bioRxiv2025

The c-Src inhibitor eCF506 diminishes opioid tolerance creating bias against β-arrestin2 recruitment

Opioids reduce severe pain, but persistent use is compromised by tolerance, attenuated by either {beta}-arrestin2 depletion, prompting development of biased opioids limited by partial efficacy, or c-Src kinase inhibitors, potentially acting through off-target effects. We tested eCF506, a conformationally selective c-Src inhibitor, on morphine antinociception and examined its effect on receptor signaling and {beta}-arrestin2 recruitment. Oral eCF506 inhibited morphine tolerance in C57BL/6J mice. Exposure of PathHunter CHO cells to eCF506 did not affect inhibition of cAMP accumulation by the agonist, DAMGO, but reduced {beta}-arrestin2 recruitment. This effect, mimicked by targeted degradation of c-Src, occurred through inhibition of c-Src catalytic function as evidenced by its diminution by the catalytically inactive Src250-536(K298M) construct. This mutant also restricted the effect of c-Src inhibitors on {beta}-arrestin2 recruitment. eCF506 additionally increased surface expression of receptors and limited their internalization by endomorphin-2 but did not alter DAMGO-evoked GRK-mediated receptor phosphorylation. These findings suggest that eCF506 prolongs opioid antinociception by inducing signalling bias, diminishing {beta}-arrestin2-mediated receptor regulation.

neuroscience↗

bioRxiv2025

Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models

Glioblastoma multiforme (GBM) is an aggressive primary brain tumour that presents significant treatment challenges due to its complex pathology and heterogeneity. The lack of validated molecular targets is a major obstacle for discovering new therapeutic candidates, with no new effective GBM therapies delivered to patients in over two decades. Here, we report the identification of compounds that target the GBM stem cell survival phenotype. Our approach employs machine learning (ML) predictors of cell survival trained on high-throughput, image-based, phenotypic screening data for 3,561 compounds, at multiple concentrations, across a panel of six heterogeneous, patient-derived, GBM stem cell lines. We computationally screened more than 12,000 compounds spanning various chemical classes. Experimental validation of ML-identified candidates across the GBM stem cell lines led to the identification of three compounds with activity against the GBM phenotype. Notably, one of our validated hits, the Hsp90 inhibitor XL888, displayed targeted elimination of all six GBM stem cell lines with IC50 in the nanomolar range. The other two compounds, which displayed broad activity across multiple GBM cell lines with distinct cell line sensitivities, offer routes for future personalised medicine campaigns. Our work demonstrates the use of phenotypic screening in tandem with ML can effectively identify therapeutic leads for personalised treatments in highly heterogeneous indications with few known molecular targets.

cancer biology↗