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In the framework of systems biology a lot of different modeling methods have been developed. All these methods allow to take into model account both description of different processes functioning at all levels of biological systems organization and simulation of various types of experimental and clinical data.
In connection with this fact the main positive property of model-based approach is a possibility to integrate into united account different types of data characterizing studied problem from all sides. In such case any model becomes a depositary of biological of clinical information collected at the stage of model development. However, in contrast to data bases, which functioning only as static data storage, model could be used for receipt of predictions which can became a stimulus for further studies of the problem or minimize costs of experiments or clinical trials.
So, based on foregoing statements existent models could be divided into following categories. In the Institute for Systems Biology SPb we could accumulate an experience in all of these modeling techniques. Furthermore, part of these methods is our innovation (e. g. systemic models of transfer systems of organism – blood circulation, lymphatic and digestive systems).
Compartmental PK models
- Development and application of 1-compartment, 2-compartment, multi-compartment models to clinical datasets and analysis of inter- and intra- individual variability of PK parameters and covariates.
Basic PK/PD models
Indirect response models
- Development and application of indirect response models to both preclinical and clinical datasets. Inter- and intra- individual variability of PD parameters, analysis of covariates for PD effects. Model of drug-target interaction, synergism of drugs, adverse effects, drug metabolism, NLME, optimization of treatment regimens, simulation (virtual patients).
Effect compartment models
- Development and application of effect compartment models to both preclinical and clinical datasets. Inter- and intra- individual variability of PD parameters, analysis of covariates for PD effects. Model of drug-target interaction, synergism of drugs, adverse effects, drug metabolism, NLME, optimization of treatment regimens, simulation (virtual patients).
Receptor binding models
- Development and application of receptor binding models to both preclinical and clinical datasets. Inter- and intra- individual variability of PD parameters, analysis of covariates for PD effects. Model of drug-target interaction, synergism of drugs, adverse effects, drug metabolism, NLME, optimization of treatment regimens, simulation (virtual patients).
Target-mediated disposition models
- Development and application of target-mediated disposition models to both preclinical and clinical datasets. Inter- and intra- individual variability of PD parameters, analysis of covariates for PD effects. Model of drug-target interaction, synergism of drugs, adverse effects, drug metabolism, NLME, optimization of treatment regimens, simulation (virtual patients).
PBPK models
- Development and application of PBPK models to both preclinical and clinical datasets. PBPK modeling using knowledge of partition coefficients and tissue distribution; Predictions of drug PK on the base of tissue partition coefficients; Theoretical calculations of tissue partition coefficients on the base of physical-chemical parameters.
Systems biological models
Biologic pathway and/or signal transduction models
- Development and application of biological pathway and signal transduction models to both preclinical and clinical datasets. Modeling of intracellular biochemical processes: metabolic, signaling and gene regulatory pathways and interplay between them. Integration of in vitro and in vivo experimental data (cell free extract, metabolomics, metabonomics, proteomics, transcriptomics, etc.) into the models. Sensitivity analysis. Prediction of contribution of each enzyme/process to control of any flux of the pathways. Modeling and simulation of response of the pathway(s) to inhibition/activation of any enzyme/process resulted from drug (drug candidate) administration. Integration of the experimental data resulted from high throughput techniques.
- Reconstruction and modeling of catalytic cycles of individual enzymes. Modeling of enzymes with complex regulation: membrane potential, allosteric enzymes, non-michaelis enzymes. Kinetic modeling of enzyme-inhibitor interactions. Optimization of enzyme assay for high throughput screening. Modeling of intracellular biophysical and transport processes: generation of electric potential differences and ion gradients; oxidative phosphorylation in mitochondria; generation of receptor potential and action potential in neurons; transfer of proteins and low weight molecules from one intracellular compartment to other and dependence of the transport processes on electric membrane potential and ion gradients.
Disease progression models
- Development and application of disease progression models to both preclinical and clinical datasets. Modeling of disease at intracellular, cellular, organ or whole organism level. Integration of preclinical and clinical data on biomarker(s) response to disease progression and treatment with different drug (drug candidates).
Biomarker identification aided by modeling
- Development and application of intracellular, physiological and combined models to biomarker identification and validation to both preclinical and clinical datasets. Organ level modeling; whole organism modeling; modeling of blood circulation system, lymphatic system, digestive system; integration of models of intracellular pathways into physiological models.
Gene regulatory network models
- Development and application of gene regulatory, metabolic and signaling models as well as their combinations to both preclinical and clinical datasets.
Cell dynamics models
Cell growth and death (e. g. bacteria, viruses, tumors)
- Development and application of conventional cell growth and death models to both preclinical and clinical datasets. These models can be applied to describe cell turnover models, dynamics of cell infection with virus, bacteria growth and death.
- We have experience in modeling of cross regulations between different cells in cell culture and in organism in vivo. Integration of models of intracellular pathways into cellular dynamics models: multi-compartmental models taking into account (i) intracellular compartments of different cell types and pathways specified for each of them, (ii) extracellular compartment and processes/reactions of transformation, transport, degradation of signaling molecules excreted by each cell type as well as interaction of the molecules with corresponding receptors.
Phases of the cell cycle and/or circadian rhythm
- Development and application of cell cycle and circadium rhythm models to both preclinical and clinical datasets.
Systems pharmacological/mechanistic PK/PD models
Biologic target turnover models
- Development and application of biologic target turnover models to both preclinical and clinical datasets. Detailed or semi-empiric modeling of target-receptor synthesis and degradation in different cell types involved in disease progression and response to drug action. Influence of biologic-receptor binding on intracellular signaling and gene regulatory pathways.
Target-mediated disposition models
- Development and application of target-mediated disposition models to both preclinical and clinical datasets. Binding biologic/drug to the receptor, internalization of the complex and control of the target-mediated disposition with intracellular signaling and gene regulatory pathways in different cell types involved in disease progression and response to drug action.
Models that explicitly distinguish between drug and system determinants of a pharmacological response.
- These models enable us to estimate contribution of
both drug and system determinants in such features of pharmacological Response as Maximum Effect, Potency, Duration;
- These models are based on combination of various modeling approaches (intracellular pathway modeling, PK/PD modeling and cell dynamics modeling) and may include description of intracellular signaling, metabolic and gene regulatory pathways, drug-target interactions, pharmacokinetics of the drug and translation of state of intracellular pathways to PD response. These models can be applied to both preclinical and clinical datasets.
system determinants (blood and lymph flows, biological fluids volume, intracellular protein expression and degradation, proliferation, differentiation and death of cells, and parameters of biomarker interaction) in pharmacological response;
- These models take into account (1) architecture as a whole and key properties of blood circulation and lymphatic systems, (2) different cell types belonging to different phases of the systems (mobile and immobile cells) which are involved in response to the drug, (3) different properties of intracellular pathways (protein expression level, ability to excrete various signaling molecules etc) and (4) interaction between cells, excreted signaling molecules and biomarkers. These models can be applied to both preclinical and clinical datasets.
drug properties (affinity, intrinsic efficacy, degradation rate, lipophilicity etc) in pharmacological response.
- These models are based on detailed description of drug-target interactions, pharmacokinetics of the drug and translation of effect of the drug to state of intracellular pathways to PD response. These models can be applied to both preclinical and clinical datasets.
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