Biology subjects

Nguyen, B. T.

Publications and source records attributed to Nguyen, B. T..

5 recordsLinked to original sources

bioRxiv2026

Structural engineering of dual-targeting antisense oligonucleotides enhances exon 44 skipping for treating Duchenne muscular dystrophy

Antisense oligonucleotides (ASOs) inducing exon-skipping during pre-mRNA splicing offer promising treatment for diseases such as Duchenne muscular dystrophy. Recently, dual-targeting ASOs that simultaneously bind two splicing regulatory sequences have been shown to improve exon-skipping efficiency. Current ASOs are designed to interact with target RNAs solely based on Watson-Crick base pairing. Here we report the development of dual-targeting three-dimensional bridge (3D-bridge) ASOs in which tertiary interactions are engineered to increase target-binding affinity and the number of target residues occupied. We designed a 3D-bridge ASO for skipping exon 44 of dystrophin mRNA and biochemically validated its enhanced binding affinity for the target RNA over a similar ASO with the tertiary interactions disrupted. We subsequently optimized the 3D-bridge ASO by varying its ends and ribose chemical modifications using patient-derived induced directly reprogrammed myotubes and identified an LNA/DNA mixmer as the most potent. We further demonstrate enhanced exon-skipping efficacy and potency of the 3D-bridge ASO compared to previously reported ASOs in myotubes differentiated from patient-derived primary myoblasts and MyoD-reprogrammed fibroblasts harboring mutations amenable to reframing through exon 44 skipping. In conclusion, the 3D-bridge ASO technology improves the current dual-targeting design, demonstrating the utility and power of structure-based ASO design methods.

pharmacology and toxicology↗

bioRxiv2026

A novel workflow integrating whole-body PET microdosing data and therapeutic-dose pharmacokinetics across species to inform first-in-human dose selection

Introduction: First-in-human studies require dose extrapolation from pharmacokinetic animal studies combined with safety assessments. Subtherapeutic doses of radiolabelled drugs can be administered in preclinical and early clinical development to gain a dynamic pharmacokinetic understanding, potentially informing pharmacologically-based pharmacokinetics (PBPK) models through whole-body PET data. Aims: to develop a PET-informed modelling framework for preclinical-to-human extrapolation, with dolutegravir as a case-study. Methods: We developed a structured workflow integrating micro- and conventional-dose data for interspecies and dose extrapolation. We built a whole-body PBPK model in PK-Sim/MoBi (v12.1) using non-human primate (NHP) data in 5 key organs incorporating dolutegravir physico-chemical properties, protein binding, metabolism and efflux. Sensitivity analyses and parameter estimation were performed sequentially first with PET microdosing organ data over 3h, then with fluid and tissue concentrations following a 2.5 mg/kg IV injection. Finally, 100 Caucasian healthy adults (50% male, 20-80 years) receiving 50 mg qd po after high-fat meals were simulated using the two sets of estimated parameters and physiology-related parameter distributions provided by PK-Sim. Results: Dolutegravir blood data were well described in NHPs over 3 hours, with parameters adjusted to handle the macrodose-related changes. While microdose-based parameter estimates systematically underpredicted exposure, combining NHP micro- and conventional dose data predicted steady-state geometric mean AUC0-24 and Cmax closely matching human reported profiles, although slightly underpredicting Ctrough. Conclusions: PET-PBPK modelling combining micro- and conventional doses in NHPs successfully predicted dolutegravir concentrations in healthy volunteers, additionally informing tissue distribution. This proof of concept study supports early PET data acquisition to build robust priors for first-in-human studies.

pharmacology and toxicology↗

bioRxiv2026

Multi-level, multi-body atomic interaction graphs for machine learning-based prediction of protein-ligand binding energies

Accurate prediction of binding affinity is crucial for rational drug design and discovery. Traditional computational methods often rely on complex scoring functions that incorporate a multitude of physical and chemical descriptors, leading to high computational demands and sometimes limited generalizability. In this work, we propose a novel scoring function that models multi-level, multi-body atomic interactions using graph-based representations. Our method constructs comprehensive interaction graphs that incorporate both pairwise and triplet-wise atomic features that help capture cooperative spatial patterns essential for binding affinity prediction. By employing a feature fusion strategy, GMI-Score maintains model simplicity while enhancing accuracy. Extensive evaluation across multiple datasets, such as PDBbind v2013, PDBbind v2016, PDBbind v2020, CSAR-NRC-HiQ, and PDBbind-Redocked, demonstrates that our model consistently outperforms state-of-the-art scoring functions, achieving Pearson correlation coefficients up to 0.877. Furthermore, it retains strong predictive power under strict data leakage controls and realistic docking conditions to high-light its robustness and generalizability. Scientific ContributionIn this study, we present a scoring methodology that systematically captures higher-order atomic interactions within a unified graph framework, making a conceptual shift in cheminformatics scoring functions. Its consistent outperformances of existing methods and strong validity under redocked and withheld atascenarios demonstrate its utility for broad-scale molecular modeling applications and open heminformaticsworkflows.

bioinformatics↗

bioRxiv2024

Visualization of DNA Methylation in Cell Cycle Genes during iPSC Cardiac Differentiation

BackgroundEpigenetic DNA methylation is an essential mechanism controlling gene expression and cellular function. Existing analyses with conventional assays have generated significant insights into static states of DNA methylation, but were unable to visualize the dynamics of epigenetic regulation. AimWe utilized a genomic DNA methylation reporter (GMR) system to track changes in DNA methylation during cardiac differentiation. Methods and ResultsThe promoter region of Cdk1 (Cyclin-dependent kinase 1) or Sox2 (SRY-Box Transcription Factor 2) gene was cloned upstream of the small nuclear ribonucleoprotein polypeptide N (Snrpn) minimal promoter followed by a fluorescent reporter gene. Mouse induced pluripotent stem cells (iPSCs) carrying Sox2 GMR rapidly lost fluorescent reporter signal upon the induction of differentiation. Cdk1 GMR reporter signal was strong in undifferentiated iPSCs, and gradually decreased during directed cardiomyocyte (CM) differentiation. RT-qPCR and pyrosequencing demonstrated that the reduction of Sox2 and Cdk1 was regulated by hypermethylation of their CpG regions during cardiac differentiation. The present study demonstrated the dynamic DNA methylation along the course of cell cycle withdrawal during CM differentiation. ConclusionThe GMR reporter system can be a useful tool to monitor real-time epigenetic DNA modification at single-cell resolution.

cell biology↗

bioRxiv2020

The effect of device configuration and patient body composition on image artifact and RF heating of deep brain stimulation devices during MRI at 1.5T and 3T

BACKGROUNDPatients with deep brain stimulation (DBS) implants have limited access to MRI due to safety concerns associated with RF-induced heating. Currently, MRI in these patients is allowed only in 1.5T horizontal scanners and with pulse sequences with reduced power. Nevertheless, off-label use of MRI at 3T is increasingly reported based on limited safety assessments. Here we present results of systematic RF heating measurements for two commercially available DBS systems during MRI at 1.5T and 3T. PURPOSETo assess the effect of imaging landmark, DBS lead configuration, and patient body composition on RF heating of DBS leads during MRI at 1.5 T and 3T. STUDY TYPEPhantom study. POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODELGel phantoms and cadaver brain. FIELD STRENGTH/SEQUENCE1.5T and 3T, T1-weighted turbo spin echo. ASSESSMENTRF heating was measured at tips of DBS leads implanted in brain-mimicking gel. STATISTICAL TESTSNone. RESULTSWe observed substantial fluctuation in RF heating mainly affected by phantom composition and DBS lead configuration, ranging from 0.14{degrees}C to 23.73{degrees}C at 1.5 T, and from 0.10{degrees}C to 7.39{degrees}C at 3T. The presence of subcutaneous fat substantially altered RF heating at electrode tips (-3.06{degrees}C < {Delta}T < 19.05{degrees}C). Introducing concentric loops in the extracranial portion of the lead at the surgical burr hole reduced RF heating by up to 89% at 1.5T and up to 98% at 3T compared to worst case heating scenarios. DATA CONCLUSIONDevice configuration and patient body composition significantly altered the RF heating of DBS leads during MRI at 1.5T and 3T. Interestingly, certain lead trajectories consistently reduced RF heating and image artifact over different imaging landmarks, RF frequencies, and phantom compositions. Such trajectories could be implemented in patients with minimal disruption to the surgical workflow.

bioengineering↗