SB-773812: Correlation between in-silico and in-vivo metabolism
Pinky Dua(1), Nicoletta Pons(2), Luigina Bertolotti(2), Clare Burgess(3), Roberto Gomeni(1)
(1)Clinical Pharmacology Modelling and Simulation, (2)PCD-DMPK, (3)Discovery Medicine, GlaxoSmithKline
Background: Schizophrenia is a severe, debilitating, and usually chronic condition that affects about 1% of the adult population worldwide. SB-773812 is a molecule in development for schizophrenia and has been specifically designed to target antagonism at those receptors believed to be associated with antipsychotic efficacy (D2, D3, 5-HT2A, 5-HT2C, 5-HT6) while designing out affinity at receptors suggested to be linked to the side effects (H1, muscarinic M1-4, D1, adrenergic 1B, adrenergic 1-3) of current antipsychotics. Ideally an antipsychotic drug, used for chronic treatment, should have minimal drug-drug interaction (DDI) liability. The in-vitro data indicated that SB-773812 is metabolised predominantly by CYP3A4 enzyme. To determine the extent to which the inhibition of the 3A4 metabolic pathway could affect the metabolism of SB-773812, Ketoconazole a potent CYP3A4 inhibitor, was co-administered with SB-773812.
Objective: The aim of this work was to predict the extent of DDI by developing in-silico model in SimCYPTM and then using the model predictions to guide the study design.
Methods: An in-silico model for co-administration of SB-773812 and Ketoconazole was developed in SimCYPTM and model predictions were compared with the clinical data. The model was used to further guide and amend the clinical study part-way through in order to more accurately assess the maximum effect of Ketoconazole. Two groups of Ketaconazole dosing durations were examined, a total of 20 subjects in group 1 (original study) and 16 subjects in group 2 (amended study) were included in the study. Model predictions from SimCYPTM for the amended study were also matched against the clinical data.
Results: SimCYPTM model predictions matched well with the in-vivo data from the original study (group 1). An interim PK check was conducted to understand the extent of interactions of SB-773812 and Ketoconazole. The interim analysis indicated that extending the co-administration of Ketoconazole was necessary for assessing the extent of interaction. The in-vivo results from the amended study (group 2) were also in good agreement with in-silico predictions from SimCYPTM.
Conclusions: This study was carried out to analyze the interaction of Ketoconazole co-administration on the pharmacokinetics of SB-773812. In-silico modelling tools such as SimCYPTM are widely and increasingly being used to explore and characterize DDIs. This work illustrates the importance of using in-silico modelling for reliably predicting in-vivo metabolism and DDIs.
 Rostami-Hodjegan A and Tucker GT, Nat Rev Drug Discov. 6 (2007) 140-148.