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In Silico Biosciences, Inc.

Mechanistic modelling experts for CNS diseases

In Silico Biosciences, Inc.
405 Waltham Street
Lexington, Massachusetts 02421
USA
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Team Leader

Robert Carr

CEO at ISB & QSPainRelief IIB Chair and Innovation Manager
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Team Staff

Dr. Petri Takkala

Principal Scientist
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Dr. Hugo Geerts

Senior QSP Consultant
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Dr. Athan Spiros

Project Manager
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Institute Presentation

In Silico Biosciences has a proven track record of developing validated mechanistic mathematical QSP models of the pathophysiology of CNS diseases such as schizophrenia, Alzheimer’s, and Parkinson’s disease. The integration of current knowledge from many sources is applied to better understand the systemic physiological disease processes at the neuronal circuit level. Our purpose is to significantly aid in the discovery, development, and clinical delivery of medications by creating and using the latest tools available for quantitative systems pharmacology.

For the QSPainRelief initiative, In Silico Biosciences will contribute to the development of a validated QSPainRelief modelling platform that is able to adequately predict analgesic efficacy and side effect potential for new combinations of existing CNS drugs to treat chronic pain. The QSPainRelief model platform will be based on integration of CNS drug distribution (ULEI), and interaction with the site of action (ULEI), and subsequent neuronal circuit activations for pain relief. This QSPainRelief model platform will be validated using preclinical and clinical data to enable greater generalizability and quantitative predictability for actual outcomes in clinical pain patient populations. This platform will be utilized for high-throughput systematic in silico screening of combinations of opioids with other CNS active medications that are approved by the European Union, to identify promising combination pharmacotherapies for better treatment of chronic pain. Our aim is to systematically optimize analgesic efficacy over side-effects by modelling biophysically realistic neuronal circuits to determine poly-pharmaceutical dose-response profiles in diverse patient populations.