Simplified scalable synthesis of a water-soluble toll-like receptor 2 agonistic lipopeptide adjuvant for use with protein-based viral vaccines.

Bioorg Chem

Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh 160014, India; National Interdisciplinary Centre of Vaccine Immunotherapeutics and Antimicrobials, Panjab University, Chandigarh 160014, India. Electronic address:

Published: December 2024

AI Article Synopsis

  • Toll-like receptors (TLRs) are crucial for linking the innate and adaptive immune systems, with TLR2 agonists like PamCSK showing potential as vaccine adjuvants, but facing issues like poor water solubility and complicated synthesis.
  • The new compound PamCS-DMAPA (13) was developed to be water-soluble and effectively enhance the immune response to SARS-CoV2 and hepatitis B vaccines in mice, outperforming earlier options in manufacturability.
  • Combining PamCS-DMAPA with 2% aluminum hydroxide gel significantly boosted vaccine effectiveness, marking it as a promising TLR2-targeted adjuvant for future development.

Article Abstract

Toll-like receptors (TLRs) form a key bridge between the innate and adaptive immune systems. The lipopeptide based TLR2 agonists such as PamCSK are promising vaccine adjuvants but drawbacks include its surfactant like nature and cumbersome synthesis. Although the TLR2 activity of PamCS-OMe is commensurate with PamCSK, its water solubility is much less, rendering it ineffective for clinical use. In the present investigation, we designed a synthesis pathway for a novel water-soluble TLR2-active analogue, PamCS-DMAPA (13), which enhanced the immunogenicity of recombinant SARS-CoV2 and hepatitis B antigens in mice. Co-formulation of compound 13 with 2 % aluminium hydroxide gel led to a further significant improvement in vaccine immunogenicity. This synthetically simpler compound 13 was water soluble and equally potent to PamCSK adjuvant, but was superior in terms of manufacturing simplicity and scalability. This makes compound 13 a promising TLR2 targeted adjuvant for further development.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11614683PMC
http://dx.doi.org/10.1016/j.bioorg.2024.107835DOI Listing

Publication Analysis

Top Keywords

simplified scalable
4
scalable synthesis
4
synthesis water-soluble
4
water-soluble toll-like
4
toll-like receptor
4
receptor agonistic
4
agonistic lipopeptide
4
lipopeptide adjuvant
4
adjuvant protein-based
4
protein-based viral
4

Similar Publications

Human brain organoids (HBOs) derived from pluripotent stem cells hold great potential for disease modeling and high-throughput compound screening, given their structural and functional resemblance to fetal brain tissues. These organoids can mimic early stages of brain development, offering a valuable in vitro model to study both normal and disordered neurodevelopment. However, current methods of generating HBOs are often low throughput and variable in organoid differentiation and involve lengthy, labor-intensive processes, limiting their broader application in both academic and industrial research.

View Article and Find Full Text PDF

Traditional magneto-optical traps are often bulky and complex, which limits their application in portable and scalable technologies. In this study, we propose a method for generating cold atoms using a transmission-grating-based magneto-optical trap (TGMOT). This approach addresses the limitations of traditional magneto-optical traps using a transmission-grating design that simplifies the optical configuration, allowing for efficient atom capture with a single incident beam.

View Article and Find Full Text PDF

Light manipulation and control are essential in various contemporary technologies, and as these technologies evolve, the demand for miniaturized optical components increases. Planar-lens technologies, such as metasurfaces and diffractive optical elements, have gained attention in recent years for their potential to dramatically reduce the thickness of traditional refractive optical systems. However, their fabrication, particularly for visible wavelengths, involves complex and costly processes, such as high-resolution lithography and dry-etching, which has limited their availability.

View Article and Find Full Text PDF

The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.

View Article and Find Full Text PDF

AIScholar: An OpenFaaS-enhanced cloud platform for intelligent medical data analytics.

Comput Biol Med

January 2025

Machine Intelligence Lab, College of Computer Science, Sichuan University, Chengdu, 610065, China.

This paper presents AIScholar, an intelligent research cloud platform developed based on artificial intelligence analysis methods and the OpenFaaS serverless framework, designed for intelligent analysis of clinical medical data with high scalability. AIScholar simplifies the complex analysis process by encapsulating a wide range of medical data analytics methods into a series of customizable cloud tools that emphasize ease of use and expandability, within OpenFaaS's serverless computing framework. As a multifaceted auxiliary tool in medical scientific exploration, AIScholar accelerates the deployment of computational resources, enabling clinicians and scientific personnel to derive new insights from clinical medical data with unprecedented efficiency.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!