Galapagos NV

Background:Idiopathic pulmonary fibrosis (IPF) is characterised by progressive loss of pulmonary function and poor survival. Although biomarkers for disease progression and mortality exist, their reliability in large studies remains unproven. This study investigates prognostic biomarkers from the ISABELA trials, the largest IPF cohort to date, to identify those predicting worse clinical outcomes. Methods:Plasma from 1280 IPF patients in ISABELA 1 and 2 (NCT03711162, NCT03733444) was analysed for 17 circulating soluble disease-related biomarkers at multiple time-points and for the MUC5B (rs35705950_T) genotype. Statistical learning algorithms investigated biomarker levels/status with disease progression (≥10% decline in forced vital capacity (FVC) or mortality within 1 year) and pharmacotherapy. Results:Patients with ≥10% annual decline in FVC had higher median baseline of matrix metalloproteinase-7 (MMP-7) versus those with <10% decline (5.5 versus 4.2 µg·L-1; p<0.005). Patients with baseline MMP-7 ≥5.2 μg·L-1 and/or C-C motif chemokine ligand 18 (CCL18) ≥75.2 μg·L-1 had increased risk of mortality (p<0.0001); with patients having both elevated biomarkers at an even greater risk. Machine learning identified CCL18 changes by week 26 as a predictor of disease progression. The rs35705950_T genotype predicted neither mortality nor disease progression. Conclusions:We provide new insights into the prognostic value of MMP-7 and CCL18 in identifying high-risk IPF patients in the largest cohort to date. The combination of high baseline MMP-7 and CCL18 levels, along with longitudinal changes in CCL18, has the potential to enhance risk stratification and support efficacy assessment and monitoring in clinical trials.

Hepatitis B virus (HBV) is the most common and severe liver infection, and approximately 260 million people suffer from chronic hepatitis B infection, which can lead to life-threatening conditions such as cirrhosis and hepatocellular carcinoma. Treatment of the infection resulting in sustained clearance of hepatitis B surface antigen (HBsAg) is considered as "functional cure". Although current treatments effectively reduce the viral load, few agents have achieved HBsAg reduction. In this context, we focused on PAPD5/7 inhibitors as effective HBsAg suppressors. Leveraging a 2-oxo-5H-chromeno[4,3-b]pyridine template, we exploited a chemical enablement strategy for broad combinatorial explorations. This rapidly led to the identification of optimal groups not seen in other PAPD5/7 inhibitors. The effort culminated with the extremely potent preclinical candidate GLPG3808, which was active in an animal model of HBV infection. Progression was halted after neurological findings in a 13-week toxicological study in rat.

Computational blind challenges offer critical, unbiased opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the past decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform Discovery Consortium's pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay end points, using previously undisclosed comprehensive experimental drug discovery data sets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top-performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.

Abstract The tumor suppressor p53 is inactivated in approximately 50% of human cancers. In about 1% of solid tumors, a specific Y220C point mutation induces p53 misfolding, leading to its inactivation. This mutation also creates a unique, druggable pocket on the protein's surface, making it a promising target for cancer therapy. The first-generation small molecule reactivator (PC14586, Rezatapopt), designed to bind to this pocket, to refold p53 and to restore its tumor-suppressive functions, has shown clinical efficacy in patients harboring the Y220C mutations. However, this first-generation compound is limited by modest potency, necessitating high dosing in patients. This high-dose requirement increases the risk of adverse events and narrows the therapeutic window, as monotherapy and even more so in combination regimens. As a result, this compound fails to deliver the drug pressure needed to reach full reactivation of this critical tumor suppressor. Thus, there is a clear need to discover and develop a highly potent p53 small molecule reactivator to fully unlock the therapeutic potential of this promising target. At Onco3R Therapeutics, our patient centric approach, integrating deep translational science with rational, structure-based and AI-augmented drug design, has led to the identification of best-in-class series of p53 Y220C reactivators. Our lead series exhibits more than 200-fold superior potency compared to the clinical compound PC14586 (Rezatapopt) across diverse preclinical assays, including biochemical p53 refolding, cellular p53 refolding, and cellular p21 induction. Importantly, our lead series, characterized by strong potency and long residence times in vitro, significantly sustains target engagement and P53 reactivation in cells. This profile enables robust activation of the pro-apoptotic protein PUMA and induces potent, selective cytotoxicity in p53 Y220C mutant cells and in an isogenic cell model. In line with their superior cellular activity and in combination with optimized key ADME, safety parameters, and favorable in vivo PK profiles across species, our leads reach similar efficacy in vivo as PC145586 at much lower exposure. In conclusion, we have identified unique p53 Y220C small molecule reactivators with clear best-in-class cellular potency and favorable drug-like properties. We are currently further profiling these leads as potential drug candidates to achieve superior efficacy at substantially lower doses, maximizing the safety window to ultimately deliver better outcomes to cancer patients with p53 Y220C mutations. Citation Format: Ruben Boon, Jo Alen, Nihed Draoui, Koen Vandyck, Elke Behaeghel, Godelieve Lammens, Sandrine Vendeville, Lijs Beke, Pieter Peeters, Stéphane De Cesco, Bart Stoops, Pierre Raboisson, Francois Gonzalvez, Carla De Legher. Discovery of a Best-in-Class small molecule p53 Y220C reactivator: Breaking through the potency ceiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4578.

Background and Aims Translational attrition remains a major obstacle in inflammatory bowel disease (IBD) drug development due to limited concordance between conventional preclinical models and patient responses. In this study we aimed to evaluate the activity of the selective salt-inducible kinase 2/3 (SIK2/3) inhibitor GLPG3970 across complementary experimental platforms, including murine colitis, patient-derived intestinal organoids, and ex vivo mucosal biopsies, and compared these findings with transcriptional data from a UC-phase 2 study.Methods Therapeutic efficacy of GLPG3970 (20 mg/kg) was assessed in the murine CD4 + CD25- T-cell transfer colitis model. Epithelial-intrinsic responses were investigated in ulcerative colitis (UC) patient-derived intestinal organoids (n = 5). In parallel, mucosal biopsies from UC (n = 10) and Crohn's disease (CD; n = 10) patients were treated ex vivo with GLPG3970 (60 & micro;M) or tofacitinib (10 & micro;M) for 24 h. Treatment-induced transcriptional signatures were compared with bulk RNA sequencing data from patients enrolled in a short 6-week UC-phase 2 study (GLPG3970 n = 21, placebo n = 10; NCT04577794).Results GLPG3970 reduced disease activity and restored goblet cell-associated mucus production in the T-cell transfer model. In patient-derived intestinal organoids, SIK2/SIK3 inhibition enhanced epithelial mucus secretion. Ex vivo exposure of IBD biopsies elicited anti-inflammatory transcriptional and proteomic responses, revealing disease- and treatment-specific signatures. Notably, GLPG3970-induced signatures in ex vivo biopsies closely aligned with treatment-responsive gene expression changes observed in the UC-phase 2 study.Conclusions Integrated analyses across murine, patient-derived, and clinical trial datasets demonstrate conserved immune-epithelial responses to SIK2/SIK3 inhibition by GLPG3970. These findings support the value of patient-relevant experimental platforms to assess target engagement and therapeutic activity prior to clinical testing, strengthening translational alignment in IBD drug development.