Biology subjects

Lazebnik, T.

Publications and source records attributed to Lazebnik, T..

5 recordsLinked to original sources

bioRxiv2022

Graph-Based Pharmacokinetic-Pharmadynamic Modeling for Large Scale Systems: Nanoparticles Case

Pharmaceutical nanoparticles (NPs) carrying molecular payloads are used for medical purposes such as diagnosis and medical treatment. They are designed to modify the pharmacokinetics-pharmacodynamics (PKPD) of their associated payloads, to obtain better clinical results. Currently, the research process of discovering the PKPD properties of new candidates for efficient clinical treatment is complicated and time-consuming. In silico experiments are known to be powerful tools for studying biological and clinical processes and therefore can significantly improve the process of developing new and optimizing current NPs-based drugs. However, the current PKPD models are limited by the number of parameters they can take into consideration and the ability to solve large-scale in vivo settings, thus providing relatively large errors in predicting treatment outcomes. In this study, we present a novel mathematical graph-based model for PKPD of NPs-based drugs. The proposed model is based on a population of NPs performing a directed walk on a graph describing the blood vessels and organs, taking into consideration the interactions between the NPs and their environment. In addition, we define a mechanism to perform different prediction queries on the proposed model to analyze two in vivo experiments with eight different NPs, done on mice, obtaining a fitting of 0.84 {+/-} 0.01 and 0.66 {+/-} 0.01 (mean {+/-} standard deviation), respectively, comparing the in vivo values and the in silico results.

bioinformatics↗

bioRxiv2022

Generic Purpose Pharmacokinetics-Pharmacodynamics Mathematical Model For Nanomedicine Targeted Drug Delivery: Mouse Model

Pharmaceutical nanoparticles (NPs) carrying molecular payloads are used for medical purposes such as diagnosis and medical treatment. Currently, the research process of discovering a new applicative candidate for efficient clinical treatment is a time- and resource-consuming process due to the uncertainty of how NP behaves which requires a large number of experiments to study the properties of NPs-based drugs for clinical usage. In silico experiments are known to be powerful tools for studying biological and clinical systems and evaluating a drugs efficiency, which can significantly reduce the number of in vivo experiments required. To this extent, in this study, we present a novel spatio-temporal pharmacokinetics-pharmacodynamics (PKPD) model of NPs based drugs. The proposed model takes into consideration the blood flow in the cardiovascular system as well as PKPD dynamics taking place during the drugs flow and in the target sites. We show that the proposed model has a better fidelity compared to previous models on five in vivo experiments with 13 different NPs, done on mice.

bioinformatics↗

bioRxiv2022

Collective Evolution Learning Model for Vision-Based Collective Motion with Collision Avoidance

Collective motion (CM) takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during CM the individuals of the group avoid collisions. These CM and collision avoidance (CA) behaviors are based on input from the environment such as smell, air pressure, and vision, all of which are processed by the individual and defined action. In this work, a novel vision-based CM with CA model (i.e., VCMCA) simulating the collective evolution learning process is proposed. In this setting, a learning agent obtains a visual signal about its environment, and throughout trial-and-error over multiple attempts, the individual learns to perform a local CM with CA which emerges into a global CM with CA dynamics. The proposed algorithm was evaluated in the case of locusts swarms, showing the evolution of these behaviors in a swarm from the learning process of the individual in the swarm. Thus, this work proposes a biologically-inspired learning process to obtain multi-agent multi-objective dynamics. Author summaryMulti-agent multi-objective tasks are common in nature with examples as collective movement in birds and economic management in humans. These problems are famous for being convoluted to efficiently solve. Nonetheless, nature has been successfully solving it for millennials using an evolution strategy. A prominent example is a task of flocking performed by multiple species, which involves both collective motion and collision avoidance. In our work, we simulate agents that are able to learn behaviors on the individual level, that in turn translate into the desired group (multi-agent) behavior. Using nature-inspired genetic algorithms and reinforcement-learning methods, the agents are successfully implementing a collective behavior similar to the one encountered in nature.

bioinformatics↗

bioRxiv2021

Generic Approach For Mathematical Model of Multi-Strain Pandemics

Pandemics with multi-strain have become a major concern. We introduce a new model for assessing the connection between multi-strain pandemic and the mortality rate, basic reproduction number, and the maximum of infected individuals. The proposed model provides a general mathematical approach for representing multi-strain pandemics, generalizing for an arbitrary number of strains. We show the proposed model fits well with epidemiological historical data world health over a long period. From a theoretical point of view, we show that the increasing number of strains increases logarithmically the maximum number of infected individuals and the mean mortality rate. Moreover, the mean basic reproduction number is statistically identical to the single, most aggressive strain pandemic for multi-strain pandemics.

bioinformatics↗

bioRxiv2021

Clinically Relevant Mathematical Model for the BCG-based Treatment Of Type 1 Diabetes

This work introduces a model of immunotherapy treatment, namely the Bacillus Calmette-Guerin (BCG) vaccine, of type 1 diabetes (T1D). The model takes into consideration a clinically relevant interaction network between multiple immune cells and compartments. A set of ordinary differential equations (ODEs) is introduced to capture the connectivity between these variables and clinical presentation of the disease. Four subsets of the T1D patients and healthy controls that exhibit normal and high-level glucose consumption are evaluated. The results that obtained for mice, suggest that BCG treatment of the T1D patients that follow healthy eating habits normalizes glucose to levels observed in non-diabetic controls. Furthermore, glucose consumption profoundly influences disease progression. The stable equilibrium state with constant glucose levels is not attainable without repeated BCG treatment. This outcome suggests that immunotherapy may modulate molecular and cellular manifestations of the disease but it does not eliminate T1D. Of note, our data indicate that the BCG immunotherapy treatment may benefit healthy controls on a high-glucose diet. One may speculate the preventive BCG treatment to provide long-term health benefits in this specific cohort. Author summaryWe proposed a clinically relevant mathematical model of Bacillus Calmette-Guerin (BCG) based immunotherapy for type 1 diabetes (T1D) treatment. The model shows that BCG treatment is able to delay the T1D effects and to provide long-term health benefits while it may modulate molecular and cellular manifestations of the disease but it does not eliminate T1D. The main advantage of the proposed model is the ability to personalize the treatment protocol according to the patients metabolism and diet.

bioinformatics↗