Literature DB >> 11377012

Development and validation of a cholate binding capacity method for DMP 504, a bile acid sequestrant.

M A Schreiber1, K L Moyer, B J Mueller, M A Ramos, J S Green, L White, W Hedgepeth, K Juliano, J R Scull, P K Hovsepian.   

Abstract

DMP 504, a highly cross-linked insoluble polymer, is a bile acid sequestrant developed by the DuPont Pharmaceuticals Company for serum cholesterol reduction. Since DMP 504 is insoluble, it was necessary to develop unique specific analytical methods to measure and control the quality of different lots of the drug. Since the mechanism of action of DMP 504 is believed to be by sequestration of bile acids, the in-vitro binding capacity of the polymer for cholic acid was chosen as a surrogate of in-vivo performance and used to assess potency of the compound. In this method, individual aliquots of DMP 504 at three different levels were incubated with a cholate solution of known concentration. The residual cholate solution was filtered and analyzed by a reversed-phase HPLC method using refractive index detection. When the bound cholate was plotted versus the mass of DMP 504, the resulting curve was linear. The slope of this curve is the cholate binding capacity of DMP 504. This method has been shown to be precise and robust. Precision of the method was shown to have an RSD of 2.0% with injection precision of 0.4% and stability of cholate solutions up to 73 h. It is also a unique binding capacity method due to its multi-point determination, and it has been shown to be a suitable quality control method for ensuring lot-to-lot consistency of drug substance.

Entities:  

Mesh:

Substances:

Year:  2001        PMID: 11377012     DOI: 10.1016/s0731-7085(00)00521-5

Source DB:  PubMed          Journal:  J Pharm Biomed Anal        ISSN: 0731-7085            Impact factor:   3.935


  2 in total

1.  Evaluation of the in vivo disintegration of solid dosage forms of a bile acid sequestrant in dogs using gamma-scintigraphy and correlation to in vitro disintegration.

Authors:  Munir A Hussain; Rong-Kun Chang; Erik Sandefer; Richard C Page; George A Digenis
Journal:  Pharm Res       Date:  2003-03       Impact factor: 4.200

2.  Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors.

Authors:  Akshaya Kumar Aliyana; S K Naveen Kumar; Pradeep Marimuthu; Aiswarya Baburaj; Michael Adetunji; Terrance Frederick; Praveen Sekhar; Renny Edwin Fernandez
Journal:  Sci Rep       Date:  2021-12-21       Impact factor: 4.379

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.