# US 2007/0054331 A1 · System and method of evaluation of stochastic interactions of a soluble ligand with a target cell population for optimization of drug design and delivery > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2007/0054331 A1 - Document ID: US20070054331A1 - Status: Published application - Kind: A1 - Application number: 11/449,648 - Filed: 2006-06-09 - Published: 2007-03-08 - Earliest claimed: 2005-06-13 - Assignees: Optimata Ltd. - Inventors: Oleg Kirnasovsky; Vladimir Vainstein; Yuri Kogan; Zvia Agur - Web page: https://patentgrep.com/patent/US20070054331A1 ## CPC - G01N 33/5011: {for testing antineoplastic activity} - G16B 5/20: Probabilistic models - G16B 5/30: Dynamic-time models - G16C 20/30: Prediction of properties of chemical compounds, compositions or mixtures - G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients - G16H 50/50: for simulation or modelling of medical disorders - G16B 5/00: ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - G16C: COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A computer system for recommending an optimal treatment protocol comprising a model of biological processes related to a disease. A treatment protocol generator generates a plurality of treatment protocols for treating a disease using drugs. A selector selects an optimal treatment protocol from said plurality of treatment protocols based on model. The model further comprises a pharmacokinetics macro module adapted to analyze interactions between a ligand and a population of target cells at a tissue level. The model further comprises a pharmacokinetics micro module adapted to analyze interactions between the ligand and a cell at an individual cell level. The pharmacokinetics micro module is adapted to model behavior of the ligand and receptors related to single cell level of ligand-cell interactions, as a stochastic process. ## Claims ### Claim 1 A computer system for recommending an optimal treatment protocol comprising: a model of biological processes related to a disease; a treatment protocol generator for generating a plurality of treatment protocols for treating a disease using drugs; and a selector adapted to select an optimal treatment protocol from said plurality of treatment protocols based on the model the model further comprising: a pharmacokinetics macro module adapted to analyze interactions between a ligand and a population of target cells at a tissue level; a pharmacokinetics micro module adapted to analyze interactions between the ligand and a cell at an individual cell level; the micro module adapted to model behavior of the ligand and receptors related to single cell level of ligand-cell interactions, as a stochastic process. ### Claim 2 (depends on 1) The system of claim 1 wherein the model further comprises a pharmacodynamics module that is adapted to model actual effects of ligands on the population of target cells using pharmacokinetics modules' outcome. ### Claim 3 (depends on 2) The system of claim 2, wherein the pharmacokinetics micro module is operable to compute a distribution of cells according to a number of bound receptors and a number of free receptors in a cell. ### Claim 4 (depends on 3) The system of claim 3, wherein the distribution of cells is provided to the pharmacodynamics module. ### Claim 5 (depends on 1) The system of claim 1, wherein the stochastic process is a Markov chain. ### Claim 6 (depends on 1) The system of claim 1, wherein the ligand is a monoclonal antibody. ### Claim 7 (depends on 1) The system of claim 1, wherein the receptor is an antigen on cell surface of said target cells. ### Claim 8 (depends on 1) The system of claim 1, wherein the target cell population is cancerous. ### Claim 9 (depends on 1) The system of claim 1, wherein, the pharmacokinetics macro module is a system of differential equations, each of said differential equation describing behavior of a separate parameter. ### Claim 10 (depends on 5) The system of claim 5, wherein the Markov chain is countable and continuous in time. ### Claim 11 (depends on 9) The system of claim 9, wherein the differential equations include at least one selected from an equation denoting molar concentrations of free antibodies as a function of time, equation denoting molar concentrations of free antigens as a function of time, equation denoting molar concentrations of bound antigens as a function of time, equation denoting molar concentrations of internalized conjugates. ### Claim 12 (depends on 10) The system of claim 10, wherein a base set of the Markov chain is chosen to form an infinite grid of states wherein each of said states is a cell that includes a set of free antigens and a set of bound antigens. ### Claim 13 (depends on 12) The system of claim 12, wherein the model is adapted to consider at least one of the next moves selected from: a free antigen binding with an antibody; a free antigen undergoing internalizations, a bound antigen undergoing internalization, a bound antigen disassociating and a new free antigen being produced, wherein each of the next moves proceeds with a predetermined probability density. ### Claim 14 (depends on 1) The system of claim 1, wherein the pharmacokinetics macro module is assumed to arrive at steady state prior to implementing the micro module. ### Claim 15 (depends on 2) The system of claim 2, wherein the pharmacodynamics module includes an effector function that computes ligand effects. ### Claim 16 (depends on 1) The system of claim 1 further comprising: a model modifier operable to modify the model based on parameters specific to an individual. ### Claim 17 A computer-implemented method for recommending an optimal treatment protocol for treating a disease comprising: modeling biological processes related to the disease; enumerating a plurality of treatment protocols for treating the disease using drugs; selecting an optimal treatment protocol from said plurality of treatment protocols based on the modeling; and recommending said optimal treatment, wherein the step of modeling further includes: analyzing interactions between a ligand and a population of target cells at a tissue level using a pharmacokinetics macro module; analyzing interactions between the ligand and a cell at an individual cell level using a pharmacokinetics micro module; the pharmacokinetics micro module models behavior of the ligand and receptors related to single cell level of ligand-cell interactions, as a stochastic process. ### Claim 18 (depends on 17) The computer-implemented method of claim 17 further comprising: modeling actual effects of ligands on the population of target cells using pharmacokinetics modules' outcome using a pharmacodynamics module. ### Claim 19 (depends on 18) The computer-implemented method of claim 18, further comprising: computing a distribution of cells according to a number of bound receptors and a number of free receptors in a cell using the pharmacokinetics micro module. ### Claim 20 (depends on 19) The computer-implemented method of claim 19, further comprising: providing the distribution of cells to the pharmacodynamics module. ### Claim 21 (depends on 17) The computer-implemented method of claim 17, wherein the stochastic process is a Markov chain. ### Claim 22 (depends on 17) The computer-implemented method of claim 17, wherein the ligand is a monoclonal antibody. ### Claim 23 (depends on 17) The computer-implemented method of claim 17, wherein the receptor is an antigen on cell surface of said target cells. ### Claim 24 (depends on 17) The computer-implemented method of claim 17, wherein the target cell population is cancerous. ### Claim 25 (depends on 17) The computer-implemented method of claim 17, wherein, the pharmacokinetics macro module is a system of differential equations, each of said differential equation describing behavior of a separate parameter. ### Claim 26 (depends on 21) The computer-implemented method of claim 21, wherein the Markov chain is countable and continuous in time. ### Claim 27 (depends on 25) The computer-implemented method of claim 25, wherein the differential equations include at least one selected from an equation denoting molar concentrations of free antibodies as a function of time, equation denoting molar concentrations of free antigens as a function of time, equation denoting molar concentrations of bound antigens as a function of time, equation denoting molar concentrations of internalized conjugates. ### Claim 28 (depends on 26) The computer-implemented method of claim 26, wherein a base set of the Markov chain is chosen to form an infinite grid of states wherein each of said states is a cell that includes a set of free antigens and a set of bound antigens. ### Claim 29 (depends on 28) The computer-implemented method of claim 28, wherein the model is adapted to consider at least one of the next moves selected from: a free antigen binding with an antibody; a free antigen undergoing internalizations, a bound antigen undergoing internalization, a bound antigen disassociating and a new free antigen being produced, wherein each of the next moves proceeds with a predetermined probability density. ### Claim 30 (depends on 17) The computer-implemented method of claim 17, wherein the pharmacokinetics macro module is assumed to arrive at steady state prior to implementing the micro module. ### Claim 31 (depends on 18) The computer-implemented method of claim 18, wherein the pharmacodynamics module includes an effector function that computes ligand effects. ### Claim 32 (depends on 17) The computer-implemented method of claim 17 further comprising: modifying the model based on parameters specific to an individual.