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:

Development of PVP/PEG mixtures as appropriate carriers for the preparation of drug solid dispersions by melt mixing technique and optimization of dissolution using artificial neural networks. The effect of plasticizer's (PEG) molecular weight (MW) on PVP based solid dispersions (SDs), prepared by melt mixing, was evaluated in the present study using Tibolone as a poorly water soluble model drug. PEGs with MW of 400, 600, and 2000g/mol were tested, and the effect of drug content, time and temperature of melt mixing on the physical state of Tibolone, and the dissolution characteristics from SDs was investigated. PVP blends with PEG400 and PEG600 were completely miscible, while blends were heterogeneous. Furthermore, a single Tg recorded in all samples, indicating that Tibolone was dispersed in a molecular lever (or in the form of nanodispersions), varied with varying PEG's molecular weight, melt mixing temperature, and drug content, while FTIR analysis indicated significant interactions between Tibolone and PVP/PEG matrices. All prepared solid dispersion showed long-term physical stability (18months in room temperature). The extent of interaction between mixture components was verified using Fox and Gordon-Taylor equations. Artificial neural networks, used to correlate the studied factors with selected dissolution characteristics, showed good prediction ability.
{"Chemical": ["PEGs", "PEG600", "PEG", "PVP", "Tibolone", "PEG400"]}