Optimata Ltd.

Institute for Medical Biomathematics

Zvia Agur

Kirill Skomorovski

Sarel Fleishmann

Moshe Vardi

Levon Arakelyan

Yuri Kogan

Oleg U Kirnasovsky

Vera Selitser

Vladimir Vainstein

Tali Eilam Tzoreff

Yuri Kheifetz

Itamar Sela

Lilach Tencer

A computerized system and method for planning a medical treatment for an individual under specific medical condition comprises a data input utility configured for receiving input data, and a data processor. The input data includes first input data comprising medical data of a specific individual, and second input data comprising data indicative of at least one endpoint of treatment. The data processor is configured for utilizing the medical data of the specific individual and the data indicative of the at least one endpoint and processing data indicative of disease progression models, each disease progression model corresponding to a treatment plan comprising one or more predetermined treatment protocols for treating the specific medical condition. The data processor generates output data indicative of a treatment effect on the individual with respect to each of the treatment plans and at least one endpoint to evaluate the treatment plans.

Method and system for prediction of medical treatment effect

A method of determining a therapeutic regimen for the treatment of cancer with Dickkopf (Dkk) protein in which a mathematical model of differential equations describing the major signaling pathways involved in stem cell regulation is created and used to simulate signals from the cancer stem cell environment based upon administration of at least a single dose of Dkk in vitro and/or in vivo. A method of modulating cancer stem cells is also provided in which the stem cell computer model is simulates the effect of Dkk and performs a calibration test to determine a threshold value above which Dkk induces cell differentiation and a threshold value below which Dkk induces stem cell proliferation.

Therapeutic implications of dickkopf affecting cancer stem cell fate

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 the 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.

System and method of evaluation of stochastic interactions of a soluble ligand with a target cell population for optimization of drug design and delivery

System for recommending an optimal treatment protocol for a specific individual are disclosed. The systems comprise generally a system model, a plurality of treatment protocols, a system model modifier, wherein said system model is modified by the system model modifier based on parameters specific to the individual; and a selector to select an optimal treatment protocol from said plurality of treatment protocols based on the modified system model. Systems embodying the above techniques but for a general patient are also disclosed. Systems for a general patient and an individual for various specific diseases are disclosed. Methods and computer program products embodying the above techniques are also disclosed.

System and methods for optimized drug delivery and progression of diseased and normal cells

A method for repurposing a pharmaceutical compound. The method includes identifying a pharmaceutical compound, the pharmaceutical compound corresponding to a drug that has failed in clinical development or an approved drug. A mathematical model describing the physiological processes related to at least one disease and the effects of the pharmaceutical compound on the disease is created. The model is adjusted based upon information from preclinical or clinical trials. A new treatment protocol is suggested to salvage the failed drug or a new way to use an approved drug. The suggested treatment protocol is displayed. Systems and computer program products encompassing the above techniques are also disclosed.

Techniques for purposing a new compound and for re-purposing a drug

Systems for recommending an optimal treatment protocol for a specific individual are disclosed. The systems comprise generally a system model, a plurality of treatment protocols, a system model modifier, wherein said system model is modified by the system model modifier based on parameters specific to the individual; and a selector to select an optimal treatment protocol from said plurality of treatment protocols based on the modified system model. Systems embodying the above techniques but for a general patient are also disclosed. Systems for a general patient and an individual for various specific diseases are disclosed. Methods and computer program products embodying the above techniques are also disclosed.

A method of performing interactive clinical trials for testing a new drug. A pre-clinical phase is performed in which a computer model for pharmacokinetics and pharmacodynamics of the drug is created and adjusted based on in vitro studies and in vivo studies in animals. A phase I clinical research is performed in which a clinical trial on at least a single dose is performed in parallel with performing computer simulation studies using the computer model. An optimal protocol is determined for the most responsive patient populations and indications for a phase II clinical trial. Phase II clinical trial is performed where a number of small scale clinical trials are performed in parallel based on results of the above. Phase III clinical research is performed for chosen indications by chosen protocols. Phase IV studies are performed for post-marketing subpopulation analysis and long term product safety assessment.

Interactive technique for optimizing drug development from the pre-clinical phases through phase-IV

A computer-implemented method for determining an optimal treatment protocol for a disease related to angiogenesis, comprising creating an angiogenesis model including pro-angiogenic and anti-angiogenic factors. Effective vessel density (EVD) is incorporated as a factor regulating switching on and switching off of at least one component in the angiogenesis model. Effects of vasculature maturation and mature vessel destabilization are incorporated. Pro-angiogenic and anti-angiogenic factors, which can influence changes in state of a tissue, are selected. Effects of drugs in the pro-angiogenic and anti-angiogenic factors are incorporated. A plurality of treatment protocols in a protocol space is generated. A best treatment protocol based on a pre-determined criteria is selected.

Treatment protocol generation for diseases related to angiogenesis

A system for modeling thrombopoietic lineage in an individual is presented. The system comprises a thrombopoiesis system model including a process progression model, for cells involved in thrombopoiesis, said progression model including multiplication and differentiation. It further comprises a system model modifier, wherein said thrombopoiesis system model is modified by the system model modifier based on parameters specific to the individual. In addition to modifying an individual's thrombopoietic process, the system can be modified to portray different pathological scenarios as well as different scenarios of response to treatment.

Growing community of inventors

Tel Aviv, Israel

Zvia Agur