You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

PK/PD assessment in CNS drug discovery: Prediction of CSF concentration in rodents for P-glycoprotein substrates and application to in vivo potency estimation. The unbound drug concentration in brain parenchyma is considered to be the relevant driver for interaction with central nervous system (CNS) biological targets. Drug levels in cerebrospinal fluid (C_CSF) are frequently used surrogates for the unbound concentrations in brain. For drugs actively transported across the blood-brain barrier (BBB), C_CSF differs from unbound plasma concentration (Cu_p) to an extent that is commonly unknown. In this study, the relationship between CSF-to-unbound plasma drug partitioning in rats and the mouse Pgp (Mdr1a) efflux ratio (ER) obtained from in vitro transcellular studies has been investigated for a set of 61 CNS compounds exhibiting substantial diversity in chemical structure and physico-chemical properties. In order to understand the in vitro-in vivo extrapolation of Pgp efflux, a mechanistic model was derived relating in vivo CNS distribution kinetics to in vitro active transport. The model was applied to predict C_CSF from Cu_p and ER data for 19 proprietary Roche CNS drug candidates. The calculated CSF concentrations were correlated with CNS pharmacodynamic responses observed in rodent models. The correlation between in vitro and in vivo potency for different pharmacological endpoints indicated that the predicted C_CSF is a valuable surrogate of the concentration at the target site. Overall, C_CSF proved superior description of PK/PD data than unbound plasma or total brain concentration for Mdr1a substrates. Predicted C_CSF can be used as a default approach to understand the PK/PD relationships in CNS efficacy models and can support the extrapolation of efficacious brain exposure for new drug candidates from rodent to man.
{"Chemical": []}