Biology subjects

Antov, D.

Publications and source records attributed to Antov, D..

3 recordsLinked to original sources

bioRxiv2026

Accelerated discovery of thermostable vaccines using data-efficient AI

The inherent instability of mRNA- lipid nanoparticles (LNPs) necessitates ultra-cold storage, creating significant barriers for global distribution and limiting their broader application in advanced delivery systems. Solid-state, water-free formulations offer a promising solution by enhancing thermostability and enabling integration into emerging delivery modalities such as microneedle (MN) patches. Prior efforts to stabilize mRNA-LNPs have been constrained by narrow formulation scope and low-throughput screening methods. Here, we introduce AGENT (Algorithm-Guided Experimental design for lipid Nanoparticle Thermostabilization), an AI-driven framework that couples high-throughput experimentation with Bayesian optimization to rapidly identify thermostable mRNA-LNP formulations. Manual exploration of the formulation space required months of screening and yielded suboptimal candidates. In contrast, AGENT extracted maximal information from sparse experimental datasets, enabling efficient formulation optimization in only six iterations completed within one month. Using AGENT, we stabilized mRNA vaccines with diverse LNPs, including those in clinical use, into solid state formulations that retained 100% bioactivity after storage at 37 degree C for over two months. The thermostable vaccines induced antigen-specific IgG and germinal center B cell responses that were non-inferior to those elicited by freshly prepared soluble vaccines. The solid-state formulations were further incorporated into dissolvable MN patches and administered to rodents and nonhuman primates, yielding comparable neutralizing antibody titers compared to conventional intramuscular delivery of fresh vaccines. To our knowledge, this study presents the first demonstration of AI-driven design of thermostable RNA vaccines, offering a scalable, cold-chain-free solution for global immunization. By addressing both stability and delivery challenges, AGENT provides a potentially transformative platform for developing accessible next-generation therapeutics.

bioengineering↗

bioRxiv2026

FALCON: Closed-Loop Multi-Objective Optimization of Lipid Nanoparticles for Cell-Selective mRNA Delivery

Efficient, cell type-selective delivery of genetic payloads remains a central challenge in the development of gene and cell therapies. Lipid nanoparticles (LNPs) offer a versatile delivery platform, but their optimization is hindered by reliance on brute-force screening methods that are laborious, resource-intensive, and focus on single targets. Here, we present FALCON (Framework for Active Learning-driven Compositional Optimization of Nanoparticles), a closed-loop pipeline that leverages iterative screening, surrogate modeling, and multi-objective optimization to accelerate LNP compositional design. In B cell-targeted validation experiments, FALCON-optimized LNPs achieved a 1.8-fold increase in splenic B cell transfection in vivo compared with reference compositions. When optimized for selectivity, FALCON LNPs displayed an 84-fold improvement in selective transfection of splenic B cells over off-target liver populations and enabled spleen-tropic behavior across factorial panels of varying ionizable and helper lipid chemistries. In vaccine studies, these LNPs induced higher IgG2c antibody titers and a more Th1-biased immune profile. FALCON was also deployed to optimize LNPs for myeloid cell-selective delivery, achieving enhanced in vivo selectivity following systemic administration both across and within spleen and liver compartments. Our results establish FALCON as a useful tool for data-driven design of LNP compositions for precision gene delivery.

bioengineering↗

bioRxiv2025

Continuous Production of Recombinant Adeno-Associated Virus in the Insect Cell/Baculovirus Expression Vector System

Continuous production processes may offer significant advantages for biotherapeutic manufacturing, including increased productivity, consistent product quality, reduced facility footprint, and decreased process turnaround time. Despite these benefits, the in-situ formation of defective recombinant baculovirus expression vectors (BEVs) hinders the continuous manufacturing of recombinant adeno-associated viruses (rAAV) in the baculovirus expression vector system. This study investigates an approach of reducing defective viruses through the infusion of standard recombinant baculovirus (rBV) and the compartmentalization of early- and late-stage infected cells, resulting in stable rAAV production. In this study, rAAVs were continuously produced in a series of cascading reactors, comprising a feeder reactor, an infection reactor, and a production reactor. Residence times and transfer rates across the three reactors were optimized based on the production kinetics of rBV and rAAV derived from our mechanistic model. The majority of rBV was produced within the production reactor, thereby reducing the impact of defective viruses in the infection reactor, enabling continuous rAAV production. This study demonstrates the successful implementation of a continuous rAAV production process, yielding over 5x1010 vg/mL per day for 4 weeks. This work represents the first reported continuous rAAV production process utilizing the Sf9/BEVS platform and establishes engineering know-how for overcoming manufacturing challenges associated with rAAV-based gene therapies.

bioengineering↗