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:

Contrasting toxicokinetic evaluations and interspecies pharmacokinetic scaling approaches for small molecules and biologics: applicability to biosimilar development. 1. A 2-fold threshold typically used for prediction accuracy of interspecies scaling of clearance (CL) may be too liberal when using pharmacokinetic similarity in animals to advance biosimilar candidate selection for clinical testing. The purpose of this review is to identify interspecies scaling methods for use in de-risking biosimilar development prior to clinical testing. 2. Scaling approaches for predicting macromolecule CL were identified through literature review. Reports that evaluated predicted and observed human CLs for ≥5 individual compounds were considered. Absolute average fold-error (AAFE) was calculated for each method along with the proportion of compounds with individual fold-error values within a tighter threshold of 0.7-1.3. 3. Traditional simple allometry with a minimum of three species and the rule of exponents performed inconsistently with some groups of compounds resulting in a greater than 2-fold error (i.e. AAFE > 2). For monoclonal antibodies (mAbs), simplified allometric approaches employing a single species (monkey) with a fixed exponent of 0.85 consistently resulted in lower AAFEs and a higher proportion of compounds within the tighter range of 0.7-1.3. 4. For macromolecules, and particularly mAbs, employing single-species monkey "simplified" allometric approaches with a fixed exponent of 0.85 may be more appropriate than traditional allometric approaches.
{"Chemical": []}