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Simulated antipsychotic drugs breakthrough

Litmus Molecular Design has created two antipsychotic drug candidates, which are the first to have been designed, synthesised and tested based upon knowledge obtained from spectral analysis of known antipsychotics. 

LMD's Spectral Modelling technology employs a methodology of computer modelling and mathematical techniques based on spectral analysis to predict the biological properties of molecules on crucial parameters relevant to drug action in humans, such as receptor affinities.  

LMD, in collaboration with Dr Herbert Y Meltzer, an expert in schizophrenia, has been using in silico Spectral Modelling techniques to create a bank of antipsychotic drug candidates designed to have similar or better effectiveness than available antipsychotic drugs, but without certain antipsychotic-associated harmful side effects. These side effects include weight gain and agranulocytosis (an acute blood disorder characterised by a reduction in white blood cells, which is a serious side effect of antipsychotics as well as other drug classes).   

The goal of the spectral analysis was to produce molecules that are more potent serotonin 2A than dopamine D2 receptor antagonists (the profile of the most widely used antipsychotics: quetiapine, risperidone, olanzapine and ziprasidone). Two of LMD's antipsychotic drug candidates were synthesised and tested for these affinities and they were not only shown to have the expected ratio of activities, but they were almost exactly the absolute affinities predicted by spectral analysis. These agents are now being tested in vivo to confirm their antipsychotic activity, but as this profile has great predictive validity for antipsychotic action, it is expected that the results in tests such as blockade of amphetamine-induced locomotor activity or conditioned avoidance response will be positive. 

The complete bank of antipsychotic drug candidates, including the two confirmed antipsychotic candidates, is available to be out-licensed to pharmaceutical companies for further development.

In addition to receptor-binding affinities, Spectral Modelling can be used to predict chemical reactivity, carcinogenicity, biological activity, toxicity, metabolism, absorption, and chemical and physical properties.


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