Pliant (United States)

Abstract BACKGROUND: Bexotegrast is a dual inhibitor of TGF-β-activating integrins αVβ6 and αVβ1 currently in late-stage clinical development for the treatment of idiopathic pulmonary fibrosis (IPF). Preclinical evaluation of bexotegrast in precision-cut lung slices (PCLS) from IPF patient explants showed reduced fibrotic gene expression in specific pathologic cell populations. In this study, we used single-nuclei RNA sequencing (snRNA-seq) to evaluate the antifibrotic activity of bexotegrast in fibrotic PCLS generated from non-IPF interstitial lung disease (ILD) patient lung explants. METHODS: PCLS were generated from lung explants obtained at the time of transplant from patients with non-IPF fibrosing ILD, including hypersensitivity pneumonitis and rheumatoid arthritis-associated ILD. After 7 days of treatment with bexotegrast or vehicle, single nuclei were isolated and processed for single nuclear barcoding using 10x Chromium Next GEM 3’ HT kits. Resulting libraries were sequenced, processed using CellRanger, and analyzed using Seurat. A custom reference based on a published fibrotic lung single-cell RNAseq dataset was used to annotate identified cell clusters. Differential gene expression (│log2FC│ > 0.5, FDR < 0.05) and pathway enrichment analyses were performed on annotated cell populations. RESULTS: snRNA-seq analysis of non-IPF ILD PCLS identified target cell populations including integrin β6 (ITGB6)-expressing epithelial cell populations (e.g. ATI and ATII) and multiple fibroblast populations (e.g. myofibroblasts). Within fibroblasts, we identified a distinct subpopulation of COL1A1-high/CTHRC1-high profibrotic fibroblasts like that described in lungs of patients with IPF and systemic scleroderma.1 Additional genes that marked this population included COL5A1, POSTN, and VCAN. In profibrotic fibroblasts, bexotegrast significantly reduced expression of collagen-containing extracellular matrix genes (GO 0062023) including COL1A1, COL5A1, and FN1. Similar reductions in fibrogenic gene expression were observed in myofibroblasts and proliferating fibroblasts. In ATI cells, bexotegrast significantly reduced the expression of ITGB6, ITGAV, and SMAD7, consistent with its previously described mechanism of action of reducing TGF-β signaling, as well as with clinical data demonstrating reduction in circulating ITGB6 in patients with IPF following bexotegrast treatment.2 CONCLUSIONS: In PCLS from fibrotic non-IPF ILD explants, inhibition of integrins αVβ6 and αVβ1 with bexotegrast reduced fibrogenic gene expression across multiple fibroblast subpopulations. These data are consistent with our previous observations in IPF explants and support further investigation of the antifibrotic activity of bexotegrast in ILD-associated progressive pulmonary fibrosis.1 Tsukui, T. et al. Nat Comm 2020 2 Lancaster, L. et al. AJRCCM 2024

The urgent need for effective treatments for acute and chronic lung diseases underscores the significance of developing innovative preclinical human research tools. The 2023 American Thoracic Society Workshop on Precision-Cut Lung Slices (PCLSs) brought together 35 experts to discuss and address the role of human tissue–derived PCLSs as a unique tool for target and drug discovery and validation in pulmonary medicine. With increasing interest and usage, together with advancements in methods and technology, there is a growing need for consensus on PCLS methodology and readouts. The current document recommends standard reporting criteria and emphasizes the requirement for careful collection and integration of clinical metadata. We further discuss current clinically relevant readouts that can be applied to PCLSs and highlight recent developments and future steps for implementing novel technologies for PCLS modeling and analysis. The collection and correlation of clinical metadata and multiomic analysis will further advance the integration of this preclinical platform into patient endotyping and the development of tailored therapies for patients with lung disease.

<bold>Rationale:</bold> Biomarkers of early therapeutic response are needed to inform drug development in idiopathic pulmonary fibrosis (IPF). Here we used proteomic screening to assess longitudinal changes in plasma protein levels in participants with IPF receiving bexotegrast, a dual-selective inhibitor of TGF-β-activating integrins α_Vβ₆ and α_Vβ₁ shown to reduce forced vital capacity (FVC) decline in participants with IPF over 12 weeks (Wuyts, W. et al. ERS Int Conf 2023, #42463), to identify candidate biomarkers of therapeutic response. <bold>Methods:</bold> Plasma samples from INTEGRIS-IPF (<ext-link>NCT04396756</ext-link>) collected at baseline, Week 4 and Week 12 were analyzed by Olink Explore 384 Inflammation panel. Pairwise comparisons and mixed effects model for repeated measures (MMRM) were used to assess differences in protein levels at baseline, between treatment groups across time, and to correlate protein levels with % predicted FVC (FVCpp). <bold>Results:</bold> Pairwise comparison of plasma proteins showed minimal differences between bexotegrast and placebo groups at baseline, with a downward shift in most proteins observed with bexotegrast relative to placebo at Weeks 4 and 12. MMRM analysis showed a significant difference in change from baseline for 13 proteins for bexotegrast (320 mg QD) vs placebo (FDR<0.05), with approximate dose-dependent differences observed for ITGB6, MMP10, PLAUR, and SPON1. Nine proteins were also found to significantly associate with longitudinal changes in FVCpp (FDR<0.05). <bold>Conclusions:</bold> Thirteen candidate biomarkers of early therapeutic response were identified in participants with IPF receiving bexotegrast. Further biomarker analyses are planned in the enrolling Phase 2b BEACON-IPF study (<ext-link>NCT06097260</ext-link>).

Technologies currently employed to find and identify drug metabolites in complex biological matrices generally yield results that offer a comprehensive picture of the drug metabolite profile. However, drug metabolites can be missed or are captured only late in the drug development process. This could be due to a variety of factors, such as metabolism that results in partial loss of the molecule, covalent bonding to macromolecules, the drug being metabolized in specific human tissues, or poor ionization in a mass spectrometer. These scenarios often draw a great deal of attention from chemistry, safety assessment, and pharmacology. This review will summarize scenarios of missing metabolites, why they are missing, and associated uncovering strategies from deeper investigations. Uncovering previously missed metabolites can have ramifications in drug development with toxicological and pharmacological consequences, and knowledge of these can help in the design of new drugs.

All atom molecular dynamics (MD) simulations offer a powerful tool for molecular modeling, but the short time steps required for numerical stability of the integrator place many interesting molecular events out of reach of unbiased simulations. The popular and powerful Markov state modeling (MSM) approach can extend these time scales by stitching together multiple short discontinuous trajectories into a single long-time kinetic model but necessitates a configurational coarse-graining of the phase space that entails a loss of spatial and temporal resolution and an exponential increase in complexity for multimolecular systems. Latent space simulators (LSS) present an alternative formalism that employs a dynamical, as opposed to configurational, coarse graining comprising three back-to-back learning problems to (i) identify the molecular system's slowest dynamical processes, (ii) propagate the microscopic system dynamics within this slow subspace, and (iii) generatively reconstruct the trajectory of the system within the molecular phase space. A trained LSS model can generate temporally and spatially continuous synthetic molecular trajectories at orders of magnitude lower cost than MD to improve sampling of rare transition events and metastable states to reduce statistical uncertainties in thermodynamic and kinetic observables. In this work, we extend the LSS formalism to short discontinuous training trajectories generated by distributed computing and to multimolecular systems without incurring exponential scaling in computational cost. First, we develop a distributed LSS model over thousands of short simulations of a 264-residue proteolysis-targeting chimera (PROTAC) complex to generate ultralong continuous trajectories that identify metastable states and collective variables to inform PROTAC therapeutic design and optimization. Second, we develop a multimolecular LSS architecture to generate physically realistic ultralong trajectories of DNA oligomers that can undergo both duplex hybridization and hairpin folding. These trajectories retain thermodynamic and kinetic characteristics of the training data while providing increased precision of folding populations and time scales across simulation temperature and ion concentration.

Abstract Prostate cancer is the most common non-cutaneous malignancy in men, accounting for approximately 268,000 cases and 35,000 deaths in the United States each year. This disease is a heterogeneous entity with a wide spectrum of clinical behaviors and outcomes - while a majority of men will be cured of early-stage prostate cancer, approximately 20% will develop metastatic disease, which is incurable and ultimately fatal. Despite extensive investigation, the factors promoting aggressive prostate cancer are incompletely understood. Using recently reported microarray data representing a comprehensive analysis of lncRNA prevalence across thousands of patients with prostate cancer, we now report the identification of a novel lncRNA, Prostate Locus of Uncharacterized Transcript Outlier 201 (PLUTO-201), which is strongly associated with metastasis and poor overall survival in men with prostate cancer. We find that in vitro manipulation of PLUTO-201 expression levels in prostate cancer cell lines modulates proliferation rates and markers of aggressive phenotype. Further investigation shows that PLUTO-201 localizes to the nucleus and regulates transcription of multiple genes including those responsible for steroid biosynthesis and the MHC class 1 complex, driving increased growth in androgen-depleted conditions and decreased susceptibility to T cell-mediated cytotoxicity. Overall, our findings nominate PLUTO-201 as a driver of aggressive prostate cancer phenotypes and poor clinical outcomes and suggest possible mechanisms accounting for this activity. Citation Format: Haolong Li, Noah Younger, Hyun Jin Shin, Bhavna Malik, Shuang G. Zhao, Sethuramasundaram Pitchiaya, Marsha Calvert, Meng Zhang, Laura Chang, Lisa Chesner, Junjie Hua, Kathy Li, Vishal Kothari, Emily Egusa, Jonathan Chou, Rajdeep Das, Alma Burlingame, Arul M. Chinnaiyan, Hui Li, Felix Y. Feng. Identification and characterization of PLUTO-201, a novel lncRNA associated with prostate cancer metastasis [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A027.