In Silico Biosciences, Inc.

Background: Possible solutions for the low success rate in CNS Drug discovery and development in CNS diseases include drug repurposing. Objectives: As a possible alternative to prohibitively expensive systematic testing in animal models, we propose to use a humanized quantitative systems pharmacology (QSP) platform as an example of a well-validated phenotypic assay in Parkinson's disease (PD) tremor for filtering out possible interesting molecules that then can be tested in preclinical animal models. This will significantly reduce discovery time and costs, while at the same time providing a better predictability to the human clinical outcome. The method will be applied to the Prestwick library, a library of FDA approved and off-patent medications. Methods: The platform contains 30 CNS physiologically implemented targets and simulates biophysically realistic neuronal network interactions between supplemental motor cortex and motor striatum based on preclinical neurophysiology and human electrophysiology data. Importantly, the platform is further calibrated using retrospective clinical data on Parkinsonian side-effects with antipsychotics. Results: We use this QSP platform to screen pharmacological profiles of serotonergic drugs in the Prestwick library. We identified five interesting multi-pharmacology agents, including trazodone that in a previously reported study improved clinical PD scales as augmentation strategy. Conclusion: The Quantitative Systems Pharmacology platform is a powerful modeling and simulation tool with a relevant human clinical scale as output, where multi-target drugs effect can be simulated and promising candidates for further study in pharmacological profiling or animal models can be identified.

Quantitative systems pharmacology (QSP) is a recent addition to the modeling and simulation toolbox for drug discovery and development and is based upon mathematical modeling of biophysical realistic biological processes in the disease area of interest. The combination of preclinical neurophysiology information with clinical data on pathology, imaging and clinical scales makes it a real translational tool. We will discuss the specific characteristics of QSP and where it differs from PK/PD modeling, such as the ability to provide support in target validation, clinical candidate selection and multi-target MedChem projects. In clinical development the approach can provide additional and unique evaluation of the effect of comedications, genotypes and disease states (patient populations) even before the initiation of actual trials. A powerful property is the ability to perform failure analysis. By giving examples from the CNS R&D field in schizophrenia and Alzheimer's disease, we will illustrate how this approach can make a difference for CNS R&D projects.

A review.The field of Alzheimer's disease (AD) research has been quite fortunate - in contrast to some other neurodegenerative psychiatric diseases - in that a number of animal models have been developed based on genetic and neuropathol. information.These animal models have been "validated" based on the fact that they reflect one or a few of the neuropathol. features found in postmortem brain of AD patients.However, the predictive value of these models for drug discovery has been far from spectacular.Limitations of these models include the failure to capture the dynamics of the ongoing pathol. in a clin. setting and to display the total neuropathol.However, animal models - because of the species barrier - have a number of addnl. limitations for a successful drug development program, which are not always fully appreciated.This chapter discusses (1) differences in drug affinities between human and rodent targets, (2) the absence of key human functional genotypes in rodent models, (3) the intrinsic difference in some neurotransmitter circuits, and (4) the difficulty of simulating the same amount of drug exposure as in the clin. situation.In addition, the problems associated with extrapolating cognitive tests in animal studies with actual performance of treated AD patients on clin. scales are explored.Possible solutions to this dilemma include (1) developing multitarget directed ligands where cholinesterase inhibition is combined with disease modification, (2) a better translation of clin. endophenotypes, (3) capitalizing on drug discovery efforts for cognition in other disease areas, (4) the introduction of realistic polypharmacy in early stages of preclin. tests, and (5) the systematic testing of the face-value of a specific preclin. model/readout combination using marketed drugs with documented clin. effects.Finally, Computational Neuropharmacol., a novel and highly innovative computer modeling approach of interacting brain circuits is introduced.When added to the toolbox of preclin. drug discovery, this approach intends to bridge the difference between preclin. animal models and the clin. situation and reduce the rate of attrition.