Biology subjects

Kevrekidis, Y.

Publications and source records attributed to Kevrekidis, Y..

3 recordsLinked to original sources

bioRxiv2025

A Digital Twin to Optimize Treatment Efficacy of Targeted Alpha-particle Therapies by Antibody-Radioconjugate Cocktails Against Solid Tumors

Advanced solid tumors are incurable. Antibody-delivered targeted alpha-particle (-particle) radionuclide therapies (TAT) comprise a tumor-agnostic treatment type, due to the unparalleled killing efficacy of, and irradiation precision (4-5 cell lengths) by, -particles, as well as the selectivity in tumor cell targeting by antibody technologies. However, cells not being directly hit by -particles will likely not be killed. METHODSTo address the limited solid tumor penetration by highly specific and strongly binding antibody-radioconjugates, an experimentally informed digital twin, based on transport (diffusion/advection) first principles, was developed to describe an approach where a fraction of the administered (radio)activity is delivered by a separate type of a model -particle antibody-radioconjugate of low(er)/no affinity for the same marker. The latter was chosen because it can irradiate cells residing in the deep regions of solid tumors away from vasculature. RESULTSThe digital twin that was trained and validated on spheroids that were employed as surrogates of the avascular tumor regions, demonstrated that the investigated cocktails of antibody-radioconjugates with controlled affinities exhibited better inhibition of spheroid growth compared to the extent of growth inhibition by the high-affinity antibody-radioconjugate alone, for the same total (incubated) activity concentrations; this prediction was independent of spheroid size and/or level of expression of the targeted markers. CONCLUSIONThe findings of this study suggest that antibody-delivered TAT (that is already in the clinic) can be augmented by delivering a fraction of the same total activity by low(er) affinity antibody-radioconjugates. This combination of separate antibody-radioconjugates with variable affinities (for the same targeted marker) is a promising approach to possibly even more delay recurrence and further prolong survival of patients with advanced solid tumors.

bioengineering↗

bioRxiv2025

Comparing Kinetic versus Stoichiometric Priorities in Hybrid Models of CHO Metabolism

Understanding Chinese hamster ovary (CHO) cell metabolism through mathematical models is essential for optimizing culture media and biomanufacturing processes. Current mechanistic models rely primarily on either flux balance analysis (FBA), estimating intracellular fluxes while assuming steady state, or kinetic modeling, capturing dynamic behavior but typically for a limited number of reactions. Dynamic FBA (dFBA) integrates both approaches in a hybrid framework, but challenges remain in integrating the two formats to describe bioprocesses. In this study, we first enhanced an existing dynamic CHO-metabolism model by incorporating 13C-labeled data to refine kinetic expressions and stoichiometric constraints of amino acid pathways, including the asparagine-aspartate network and serine biosynthesis. We next evaluated the impact of prioritizing either stoichiometry, through the pseudo steady state assumption (PSSA), or the kinetic expressions of fluxes. Comparing error and predictive performance for both models for two industrially relevant fed-batch CHO culture conditions involving varying initial concentrations of nutrients and three feed streams, demonstrated that the kinetic-oriented model (KOM) yielded superior predictions for viable cell density (VCD), antibody production, and a range of amino acids and metabolites compared to the stoichiometric oriented model (SOM). Indeed, the KOM was able to predict production-to-consumption shifts of lactate and alanine, fluctuating levels of ammonia based on reversible kinetic expressions, and amino acids like asparagine and the serine-glycine pool. The KOM also provided better predictions for a third case including lactate-supplemented (LS) feed; however, slight parameter adjustments helped to improve model fidelity, likely due to the impact of high lactate on kinetic expressions of antibody (directly) and VCD (indirectly). In summary, our findings demonstrate that hybrid models emphasizing empirical kinetics over strict pseudo-steady-state constraints capture biologically realistic dynamics such as transient shifts for key metabolites like lactate, alanine, and ammonia, and also produce parameters useful across varying conditions, making them a practical and powerful tool for characterizing CHO cell culture performance in the future.

systems biology↗

bioRxiv2024

Transport Cocktails for Cancer Therapeutics

Beyond biological cell heterogeneity, evidenced by different resistances to therapeutics, "delivery heterogeneity" crucially limits treatment efficacy for advanced solid tumors: variations in therapeutic drug delivery to different tumor areas (perivascular, perinecrotic) leading to nonuniform drug concentrations/doses and to unsuccessful treatment (cancer cell kill). Short-range (40-80 {micro}m), high energy (1-5 MeV) alpha-particles successfully address the biological heterogeneity: the double-strand DNA breaks they cause make them impervious to cell resistance mechanisms. Multiresponsive nanocarriers and/or engineered antibody-drug-conjugates are elegant approaches to delivering such alpha-particle emitters. Delivery heterogeneity, however, remains a challenge in established (i.e. large, vascularized) tumors. Remarkably, delivery properties enabling efficacy at the cell scale (targeting selectivity, affinity, cell drug uptake) may act against spatial delivery uniformity at the tumor scale (binding-site barrier effect1). We have previously demonstrated, in different mouse models, that spatial delivery uniformity, key to the effective killing of solid tumors, can be achieved utilizing combinations of different, distinct delivery carriers of the same emitter, but with different, complementary delivery properties, "leaving no cancer cell behind". We build first principles reaction-transport models (quantitatively informed by experiments) that explain the "geographically complementary" behaviors of such carrier cocktails, and help optimally design these cocktails and their delivery protocols.

cancer biology↗