PhD ceremony: Mr. V. Pilla Reddy, 11.50 uur, Academiegebouw, Broerstraat 5, Groningen
Dissertation: Translational PKPD modelling in schizophrenia. Linking receptor occupancy of antipsychotics to efficacy and safety
Promotor(s): prof. G.M.M. Groothuis, prof. M. Danhof
Faculty: Mathematics and Natural Sciences
The work of Venkatesh Pilla Reddy demonstrates how data from in vitro, preclinical and clinical studies can be combined to predict the optimal human dose for new antipsychotic drugs.
Schizophrenia is a severe psychiatric illness, which affects approximately 1% of the population and usually becomes manifest between the 15th and 30th year of life. The symptoms such as hallucinations and hearing voices can be reduced by antipsychotic drugs. These drugs are not always sufficiently effective against the various symptoms of schizophrenia. In the development of new medicines, it is difficult to predict whether they are effective, among others, because the efficacy is dependent on the amount of the drug that reaches the brain and binds to the dopamine receptors.
The research described in the thesis of Pilla Reddy aims to develop a mathematical model to predict the effectiveness of a new drug using in vitro data, data from animal experiments and PET scans in volunteers or patients, based on data for a number of existing drugs, accounting for the placebo effect. The model of Pilla Reddy shows that 50-70% of the receptors in the brain should be occupied by the drug to be sufficiently effective. If more than 80% of the receptors are occupied, the risk of side effects rises sharply. These results support the use of receptor occupancy as a biomarker in drug development.
The model was also applied to investigate whether the questionnaire (e.g., Positive and Negative Syndrome Scale), used by the psychiatrist to assess the efficacy of a drug, could be improved. The model demonstrates how data from in vitro, preclinical and clinical studies can be combined to understand drug effects in human. The model can be applied in drug development to characterize and predict the time courses of clinical effects.
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