Standing, J. F. | Search bioRxiv

bioRxiv2026

Antiviral drug synergy and mutational signatures in different epithelial cell models of RSV and hPIV infection

Despite the huge global health burden presented by respiratory viruses, effective broad-spectrum antiviral therapeutic options remain limited. Here we evaluated the antiviral activity of four RNA-dependent RNA polymerase (RdRp) inhibitors, remdesivir, ribavirin, favipiravir, and molnupiravir, against respiratory syncytial virus (RSV) and human parainfluenza (hPIV) using monotherapy or dual-drug combinations with epithelial cell lines and primary human airway culture models. Remdesivir showed the greatest potency across both viruses, while ribavirin and favipiravir also demonstrated inhibition. Molnupiravir was active against RSVA but not hPIV3. Several dual-drug combinations, including remdesivir-favipiravir, remdesivir-molnupiravir and favipiravir-molnupiravir, produced marked synergy against RSVA, and more limited synergy for hPIV3. Antiviral efficacy was validated in primary airway epithelial cultures, where effective concentrations preserved epithelial integrity and attenuated viral disruption of ciliary function. Across both viruses, increasing antiviral exposure was associated with dose-dependent signature mutagenesis. Importantly, antivirals induced significantly higher RSVA mutation burden in the primary airway model. These findings highlight the therapeutic potential of RdRp inhibitor combinations for RSVA and hPIV3, provide mechanistic insight through antiviral-related mutational signatures and demonstrate advantages of the primary human airway culture model for development of effective multi-drug regimens and broad-spectrum antiviral preparedness. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/699296v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1eb00dforg.highwire.dtl.DTLVardef@186358forg.highwire.dtl.DTLVardef@265181org.highwire.dtl.DTLVardef@1b10d0e_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Named Entity Recognition of Pharmacokinetic parameters in the scientific literature

The development of accurate predictions for a new drugs absorption, distribution, metabolism, and excretion profiles in the early stages of drug development is crucial due to high candidate failure rates. The absence of comprehensive, standardised, and updated pharmacokinetic (PK) repositories limits pre-clinical predictions and often requires searching through the scientific literature for PK parameter estimates from similar compounds. While text mining offers promising advancements in automatic PK parameter extraction, accurate Named Entity Recognition (NER) of PK terms remains a bottleneck due to limited resources. This work addresses this gap by introducing novel corpora and language models specifically designed for effective NER of PK parameters. Leveraging active learning approaches, we developed an annotated corpus containing over 4,000 entity mentions found across the PK literature on PubMed. To identify the most effective model for PK NER, we fine-tuned and evaluated different NER architectures on our corpus. Fine-tuning BioBERT exhibited the best results, achieving a strict F1 score of 90.37% in recognising PK parameter mentions, significantly outperforming heuristic approaches and models trained on existing corpora. To accelerate the development of end-to-end PK information extraction pipelines and improve pre-clinical PK predictions, the PK NER models and the labelled corpus were released open source at https://github.com/PKPDAI/PKNER.

pharmacology and toxicology↗