2847775-89-5
2847775-89-5
Chemical Structure
Valerylcarnitine-d9
Synonym(s): C5:0 L-carnitine-d9
- CAS No.: 2847775-89-5
- Formula:C12H14D9NO4
- Molecular Weight:254.37
InChIKey: VSNFQQXVMPSASB-YEELJIISSA-N
SMILES: CCCCC(O[C@H](CC([O-])=O)C[N+](C([2H])([2H])[2H])(C([2H])([2H])[2H])C([2H])([2H])[2H])=O
Biological Activity: Valerylcarnitine-d9 (C5:0 L-carnitine-d9) is the deuterium labeled Valerylcarnitine (HY-113266). Valerylcarnitine is an endogenous metabolite, belonging to the short-chain acylcarnitines. Valerylcarnitine acts as a metabolomic biomarker for ionizing radiation exposure in nonhuman primates. Valerylcarnitine can be used for the research of type 1 diabetes[1][2].
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Valerylcarnitine-d9 | Valerylcarnitine-d9 (C5:0 L-carnitine-d9) is the deuterium labeled Valerylcarnitine (HY-113266). Valerylcarnitine is an endogenous metabolite, belonging to the short-chain acylcarnitines. Valerylcarnitine acts as a metabolomic biomarker for ionizing radiation exposure in nonhuman primates. Valerylcarnitine can be used for the research of type 1 diabetes. | |||||||||||||||||||||
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Valerylcarnitine (Standard) | ≥98% | Valerylcarnitine (Standard) is the analytical standard of Valerylcarnitine. Valerylcarnitine is an endogenous metabolite, belonging to the short-chain acylcarnitines. Valerylcarnitine acts as a metabolomic biomarker for ionizing radiation exposure in nonhuman primates. Valerylcarnitine can be used for the research of type 1 diabetes. | ||||||||||||||||||||
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Valerylcarnitine | 95.0% | Valerylcarnitine is an endogenous metabolite, belonging to the short-chain acylcarnitines. Valerylcarnitine acts as a metabolomic biomarker for ionizing radiation exposure in nonhuman primates. Valerylcarnitine can be used for the research of type 1 diabetes. | ||||||||||||||||||||
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References
- [1]. Pannkuk EL, et al. Targeted Metabolomics of Nonhuman Primate Serum after Exposure to Ionizing Radiation: Potential Tools for High-throughput Biodosimetry. RSC Adv. 2016;6(56):51192-51202. [Content Brief]
- [2]. Tuerxunyiming M, et al. LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetes. Front Endocrinol (Lausanne). 2025;16:1588718. Published 2025 Aug 7. [Content Brief]
