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네트워크분석을 통한 바이오 및 합성 의약품 안전평가기술 국가연구개발 동향
장석찬,이상원,권순홍 대한약학회 2020 약학회지 Vol.64 No.4
It is important to compare the differences in safety evaluation trends of chemical drugs, which have beenmainstream in the existing pharmaceutical field, and emerging biologic drugs. Therefore, this study conducted networkanalyses on government-granted Research and Development (R&D) projects across biopharmaceuticals and chemicalsyntheticdrugs. Information on research projects conducted between 2002 and 2017 was extracted from the NationalScience and Technology Information Service. The projects were categorized into four groups (2002-2006, 2007-2011,2012-2016, and 2017). We visualized networks using NetMiner ver.4 (Cyram Inc). The research on biologics increasedfrom 593 projects in 2002-2006 to 2,001 projects in 2012-2016; those on synthetic drugs increased from 3,376 projectsto 4,741. The network for biologics becomes complicated by not only focusing on evaluation technology but connectingwith safety and use while the network for synthetic drugs gets simple with centered knots of safety and use. For both,researches related to evaluation technology have been increasing. To establish appropriate regulatory policies for biologics,a blueprint of R&D covering evaluation technology, safety, and use needs to be planned.
잠재지문 검출제로서 Ninhydrin 유도체들과 Glycine 과의 반응성에 관한 분자 홀로그래픽적인 QSPR분석
성낙도,김세곤,장석찬,조윤기,황태연,박성우 한국분석과학회 2007 분석과학 Vol.20 No.4
To search the ninhydrin derivatives that have high chromogenic and fluorogenic properties, molecularninhydrin analogues as latent fingerprint detector were derived and investigated quantitatively. The εLUMO(e.v.) energy of ninhydrin molecule was an important factor to reactivity of ninhydrin. And, it is suggestedthat the nucleophilic reaction by orbital-controlled reaction from the frontier molecular orbital (FMO) interactionbetween glycine and ninhydrin derivatives was more superior than that of electrophilic reaction by chargedcontroled reaction. The analytical results in atomic contribution maps also shows that the reactivity of ninhydrinsugested by HQSPR and QSPR model that the 5,6-dinitroninhydrin molecule would increase the reactivity asmuch as three times as compared to none substituted ninhydrin molecule.
성낙도,박창식,장석찬,최경섭,Sung, Nack-Do,Park, Chang-Sik,Jang, Seok-Chan,Choi, Kyung-Seob 한국동물번식학회 2006 Reproductive & developmental biology Vol.30 No.3
돼지 페르몬성 분자를 탐색하기 위하여 일련의 green odorant로서 기질 분자인 2-(Cyclohexyloxy)tetrahydrofurane 유도체들의 정량적인 구조와 수용체인 porcine odorant binding protein(pOBP) 사이의 결합 친화력 상수($p(Od)_{50}$)에 대한 비교 분자 유사성 지수 분석(CoMSIA)을 실행하였다. 가장 양호한 CoMSIA 모델(I-AI)은 기질 분자내 입체 중심의 절대 배열이 $I:\;C_{1'}(R),\;C_2(S)$인 분자를 atom based fit 정렬하였을 경우의 입체장 조건에서 유도되었으며 PLS 분석 결과, 예측성이 ${r^2}_{cv.}(q^2)=0.856)$ 그리고 적합성이 ${r^2}_{ncv.}=0.964)$ 이었다. 모델의 CoMSIA 등고도 상, pOBP와 냄새 분자 사이의 상호작용으로부터 가장 높은 결합 친화력을 나타내는 분자의 구조적 특정들을 이해할 수 있었다. To search of a new porcine pheromonal odorants, the comparative molecular similarity indices analysis(CoMSIA) between porcine odorant binding protein(pOBP) as receptor and ligands of green odorants 2-(Cyclohexyloxy)tetrahydrofurane derivatives as substrate molecule were conducted and disscused quantitatively. In the optimized CoMSIA model(I-AI) with chirality($I:\;C_{1'}(R),\;C_2(S)$) in substrate molecules and atom based fit alignment(AE) of the odorants the statistical PLS results showed the best predictability of the binding affinities based on the LOO cross-validated value ${r^2}_{cv.}\;(q^2=0.856)$ and non cross-validated conventional coefficient(${r^2}_{ncv.}=0.964)$). The structural distinctions of the highest active molecules were able to understand from the interaction between pOBP and green odorants in the contour maps with CoMSIA model.
성낙도,박창식,장석찬,최경섭 충남대학교 형질전환복제돼지연구센터 2007 논문집 Vol. No.10
돼지 페르몬성 분자를 탐색하기 위하여 일련의 green odorant로서 기질 분자인 2-(cyclohexyloxy)tetrahydrofurane 유도체들의 정량적인 구조와 수용체인 porcine odorant binding protein (pOBP) 사이의 결합 친화력 상수(p(Od)_(50))에 대한 비교 분자 유사성 지수 분석(CoMSLA)을 실행하였다. 가장 양호한 CoMSLA 모델(I-AI)은 기질 분자내 입체 중심의 절대 배열이 I:C₁(R),C₂(S)인 분자를 atom based fit 정렬하였을 경우의 입체장 조건에서 유도되었으며 PLS 분석 결과, 예측성이 r²_(cv.)(q²)=0.856 그리고 적합성이 r²_(ncv.)=0.964이었다. 모델의 CoMSIA 등고도 상, pOBP와 냄새 분자 사이의 상호작용으로부터 가장 높은 결합 친화력을 나타내는 분자의 구조적 특징들을 이해할 수 있었다. To search of a new porcine pheromonal odorants, the comparative molecular similarity indices analysis (CoMSIA) between porcine odorant binding protein (pOBP) as receptor and ligands of green odorants 2-(cyclohexyloxy)tetra-hydrofurane derivatives as substrate molecule were conducted and disscused quantitatively. In the optimized. CoMSIA model (I-AI) with chirality (I: C₁(R), C₂(S)) in substrate molecules and atom based fit alignment (AF) of the odorants, the statistical PLS results showed the best predictability of the binding affinities based on the LOO cross-validated value r²_(cv.) (q²=0.856) and non cross-validated conventional coefficient (r²_(ncv.)= 0.964). The structural distinctions of the highest active molecules were able to understand from the interaction between poBP and green odorants in the contour maps with CoMSIA model.
성낙도,박창식,장석찬,최경섭 한국동물생명공학회(구 한국동물번식학회) 2006 Reproductive & developmental biology Vol.30 No.3
돼지 페르몬성 분자를 탐색하기 위하여 일련의 green odorant로서 기질 분자인 2-(cyclohexyloxy)tetrahydrofurane 유도체들의 정량적인 구조와 수용체인 porcine odorant binding protein (pOBP) 사이의 결합 친화력 상수(p(Od)50)에 대한 비교 분자 유사성 지수 분석(CoMSIA)을 실행하였다. 가장 양호한 CoMSIA 모델(I-AI)은 기질 분자내 입체 중심의 절대 배열이 I: C1'(R),C2(S)인 분자를 atom based fit 정렬하였을 경우의 입체장 조건에서 유도되었으며 PLS 분석 결과, 예측성이 r2cv.(q2)=0.856 그리고 적합성이 r2ncv.=0.964이었다. 모델의 CoMSIA 등고도 상, pOBP와 냄새 분자 사이의 상호작용으로부터 가장 높은 결합 친화력을 나타내는 분자의 구조적 특징들을 이해할 수 있었다. To search of a new porcine pheromonal odorants, the comparative molecular similarity indices analysis (CoMSIA) between porcine odorant binding protein (pOBP) as receptor and ligands of green odorants 2-(cyclohexyloxy)tetrahydrofurane derivatives as substrate molecule were conducted and disscused quantitatively. In the optimized CoMSIA model (I-AI) with chirality (I: C1'(R), C2(S)) in substrate molecules and atom based fit alignment (AF) of the odorants, the statistical PLS results showed the best predictability of the binding affinities based on the LOO cross-validated value r2cv. (q2=0.856) and non cross-validated conventional coefficient (r2ncv.= 0.964). The structural distinctions of the highest active molecules were able to understand from the interaction between pOBP and green odorants in the contour maps with CoMSIA model.