Biology subjects

Tomaras, G.

Publications and source records attributed to Tomaras, G..

4 recordsLinked to original sources

bioRxiv2025

Engineering Sustained-Release Broadly Neutralizing Antibody Formulations

Sustained serum levels of broadly neutralizing antibodies (bnAbs) are crucial for effective passive immunization against infectious diseases as protection persists only while these bnAbs remain at adequate concentrations within the body. Current obstacles, such as poor pharmacokinetics (PK) and burdensome administration, must be overcome to make bnAbs a viable option for pre- and post-exposure prophylaxis. In this work, we explore how a polymer-nanoparticle (PNP) hydrogel depot technology can be engineered to prolong protein delivery. In-vivo studies in mice and rats demonstrate prolonged protein release, and modeling efforts predict the impact of both the elimination half-life of the active pharmaceutical ingredient and hydrogel depot volume on overall pharmacokinetics. Moreover, flow cytometry characterization reveals that immune cell infiltration into the hydrogel depot can result in faster-than-expected release of antibody cargo on account of active transport via cellular uptake. We then demonstrate that co-formulation of antibodies with an anti-inflammatory agent reduces cellular infiltration and resulting active transport, further extending delivery and pharmacokinetics. Finally, multicompartmental modeling predicts the human PK profiles of clinically relevant HIV bnAbs delivered via subcutaneous hydrogel injection. These findings aid in the development of next generation hydrogel materials that stabilize and slowly release bnAbs for long-term pre-exposure immunoprophylaxis.

bioengineering↗

bioRxiv2024

Somatic hypermutation-mediated paratope flexibility improves the cross-reactivity of human malaria antibodies

The protective capacity of antibodies targeting circumsporozoite protein on sporozoites of the malaria parasite Plasmodium falciparum (PfCSP) is linked to high affinity and cross-reactivity with the PfCSP central repeat domain and N-terminal junction. However, the role of somatic hypermutation (SHM) in the development of such antibodies remains unclear. Here we define the contributions of SHM to the high affinity and strong repeat and N-junction cross-reactivity of the potent anti-PfCSP monoclonal antibody (mAb) 4493 and of similar antibodies with shared SHM and affinity maturation trajectories. Molecular dynamics simulations reveal that SHM reduces the flexibility of the unbound mAb 4493 but increases the flexibility of the antigen-bound complex, thereby lowering the entropic cost for antigen binding. Furthermore, we identify an inverse relation between antibody affinity and serum stability, which limits the protective capacity of these antibodies. Our study provides molecular level evidence for the different roles that the SHM process plays in increasing VH3-49+V{kappa}3-20 antibody affinity and cross-reactivity and demonstrates how antibody affinity maturation can negatively impact antibody stability and thereby function.

immunology↗

bioRxiv2022

Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding

1Discovery research for therapeutic antibodies and vaccine development requires an in-depth understanding of antibody-antigen interactions. Label-free techniques such as Surface Plasmon Resonance (SPR) enable the characterization of biomolecular interactions through kinetics measurements, typically by binding antigens in solution to monoclonal antibodies immobilized on a SPR chip. 1:1 Langmuir binding model is commonly used to fit the kinetics data and derive rate constants. However, in certain contexts it is necessary to immobilize the antigen to the chip and flow the antibodies in solution. One such scenario is the screening of monoclonal antibodies (mAbs) for breadth against a range of antigens, where a bivalent analyte binding model is required to adequately describe the kinetics data unless antigen immobilizaion density is optimized to eliminate avidity effects. A bivalent analyte model is offered in several existing software packages intended for standard throughput SPR instruments, but lacking for high throughput SPR instruments. Existing methods also do not explore multiple local minima and parameter identifiability, issues common in non-linear optimization. Here, we have developed a method for analyzing bivalent analyte binding kinetics directly applicable to high throughput SPR data collected in a non-regenerative fashion, and have included a grid search on initial parameter values and a profile likelihood method to determine parameter identifiability. We fit the data of a broadly neutralizing HIV-1 mAb binding to HIV-1 envelope glycoprotein gp120 to a system of ordinary differential equations modeling bivalent binding. Our identifiability analysis discovered a non-identifiable parameter when data is collected under the standard experimental design for monitoring the association and dissociation phases. We used simulations to determine an improved experimental design, which when executed, resulted in the reliable estimation of all rate constants. These methods will be valuable tools in analyzing the binding of mAbs to an array of antigens to expedite therapeutic antibody discovery research. 2 Author summaryWhile commercial software programs for the analysis of bivalent analyte binding kinetics are available for low-throughput instruments, they cannot be easily applied to data generated by high-throughput instruments, particularly when the chip surface is not regenerated between titration cycles. Further, existing software does not address common issues in fitting non-linear systems of ordinary differential equations (ODEs) such as optimizations getting trapped in local minima or parameters that are not identifiable. In this work, we introduce a pipeline for analysis of bivalent analyte binding kinetics that 1) allows for the use of high-throughput, non-regenerative experimental designs, 2) optimizes using several sets of initial parameter values to ensure that the algorithm is able to reach the lowest minimum error and 3) applies a profile likelihood method to explore parameter identifiability. In our experimental application of the method, we found that one of the kinetics parameters (kd2) cannot be reliably estimated with the standard length of the dissociation phase. Using simulation and identifiability analysis we determined the optimal length of dissociation so that the parameter can be reliably estimated, saving time and reagents. These methodologies offer robust determination of the kinetics parameters for high-throughput bivalent analyte SPR experiments.

biophysics↗

bioRxiv2020

SIMON: open-source knowledge discovery platform

Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of the biological datasets, but necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software SIMON to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data. With an easy to use graphical user interface, standardized pipelines, automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

systems biology↗