In Silico Biosciences has been building mechanistic mathematical models of physiological systems of CNS diseases such as schizophrenia, Alzheimer's and Parkinson's diseases since 1999. The integration of current knowledge from many sources is applied so as to better understand the systemic behavior of the disease physiology in the human vs. the animal brain. A multiplicity of validation methodologies are utilized for the models.

      Computational neuropharmacology as knowledge support for CNS R&D projects:

• Today, only 8% of CNS drugs starting in clinical development are successfully approved, suggesting a low predictiveness of preclinical animal models.

• We have identified a number of underappreciated roadblocks in preclinical models and developed a radically new approach addressing some of these issues with a focus on psychiatric and neurological diseases and cognitive deficits.

• The computational platform is based on integrating biophysically realistic physiology from preclinical rodent and primate models and is parametrized with specific human pathology, pharmacology and genotypic diversity in patients.

• Unlike traditional animal models, the platform is validated using correlations between the outcome of an unbiased set of different existing marketed drugs at their appropriate doses in the computer model and their publicly reported clinical outcome.

• Using the mechanistic disease model in reverse we have identified a profile of 3 non-dopaminergic receptors that, when combined with a lower dose of an existing antipsychotic, are predicted to have a substantially better effect on PANSS-total compared to the standard-of-care. There is additional limited clinical, genetic and preclinical evidence that affecting these targets in the right way would substantially enhance PANSS total outcome by improving mostly negative and cognitive outcomes.
  • We have founded a company Sanomentis to pursue this drug discovery operation.