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:

Allometric scaling and prediction of concentration-time profiles of coagulation factors in humans from animals. Abstract 1. Allometric scaling is a useful tool in early drug development and can be used for the prediction of human pharmacokinetic (PK) parameters from animal PK parameters. The main objective of this work was to predict concentration-time profiles of coagulation factors in humans in a multi-compartment system using animal PK parameters. 2. The prediction of concentration-time profiles in humans in a multi-compartment system was based on the predicted values of clearance and volumes of distribution (V(c), V(ss) and V(β)) from animals. Five coagulation factors from the literature were chosen that were described by two-compartment model in both humans and animals. Clearance and volumes of distribution from animals were allometrically scaled to humans and then were used to predict concentration-time profiles in humans. 3. The predicted concentration-time profile for a given coagulation factor was accurate for most of the time points. Percent prediction error range varied across coagulation factors. The prediction error >50% was observed either at 1 or a maximum of two time points for a given drug. 4. The study indicated that the allometric scaling can be useful in the prediction of concentration-time profiles of coagulation factors in humans from animals and may be helpful in designing a first-in-human study.
{"Chemical": []}