메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
D. Narendar (Kakatiya University India) N. Arjun (Kakatiya University India) K. Someshwar (Kakatiya University India) Y. Madhusudan Rao (Kakatiya University India)
저널정보
한국약제학회 Journal of Pharmaceutical Investigation Journal of Pharmaceutical Investigation 제46권 제3호
발행연도
2016.6
수록면
253 - 263 (11page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
The aim of the investigation was to develop and optimize the effervescent floating matrix tablets of Quetiapine Fumarate (QF) by using 23 factorial design. Amount of hydroxyl propyl methyl cellulose K4M (HPMC K4M (A1)), amount of hydroxyl propyl methyl cellulose K15M (HPMC K15M (A2)) and amount of sodium bicarbonate (A3) (as gas-generating agent) were considered as independent variables and floating lag time (FLT, B1) (sec), percent drug release in 2 h (B2, Q2) and 6 h (B3, Q6) as dependent variables, respectively. Floating tablets of QF were prepared by effervescent technique using direct compression method. Drug-excipient compatibility studies were conducted by using DSC and FTIR techniques. The floating tablets were evaluated for physical characteristics, drug content, swelling index, in vitro buoyancy and in vitro release studies. Optimized formulation contains 25 mg of A1, 12.5 mg of A2 and 25 mg of A3 which resulted in 32 s of B1, 32.89 ± 3.1 % of B2, and 73.61 ± 1.8 % of B3, respectively. DSC and FTIR studies revealed that no interaction between the drug and excipients in the developed formulation. The drug release followed Higuchi model and the Fickian transport. Physico-chemical stability studies revealed that the optimized formulation was stable for 90 days. Based on the physical evaluation and in vitro drug release characteristics, it was concluded that QF was suitable for incorporation into a floating drug delivery system.

목차

등록된 정보가 없습니다.

참고문헌 (26)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0