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    ์ž์—ฐํ•˜์ฒœ์—์„œ ๋ฌผ์งˆ ํ˜ผํ•ฉํ•ด์„์„ ์œ„ํ•œ ์ €์žฅ๋Œ€์—์„œ์˜ ์ •์ฒด์‹œ๊ฐ„๋ถ„ํฌ ์‚ฐ์ •

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2022.2. ์„œ์ผ์›.์ž์—ฐํ•˜์ฒœ์—์„œ ์šฉ์กด๋ฌผ์งˆ์˜ ๊ฑฐ๋™์€ ํ•˜์ฒœ์˜ ์ง€ํ˜•ํ•™์ ์ธ ์š”์ธ์œผ๋กœ ํ˜•์„ฑ๋œ ์ €์žฅ๋Œ€์˜ ์˜ํ–ฅ์— ์˜ํ•ด ํ๋ฆ„ ์˜์—ญ์˜ ํŠน์„ฑ๋งŒ์œผ๋กœ ํ•ด์„๋  ์ˆ˜ ์—†๋‹ค. ์ด๋Ÿฌํ•œ ์ €์žฅ๋Œ€ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ์ง€๋‚œ ์ˆ˜์‹ญ๋…„๋™์•ˆ ๋‹ค์–‘ํ•œ ๊ตฌ์กฐ์˜ ์ €์žฅ๋Œ€ ๋ชจํ˜•์ด ์ œ์‹œ๋˜์–ด ์™”๋‹ค. ์šฉ์กด๋ฌผ์งˆ์˜ ํ•˜๋ฅ˜์ด์†ก์„ ์ง€์ฒด์‹œํ‚ค๋Š” ์ด๋Ÿฌํ•œ ์ €์žฅ๋Œ€์˜ ์˜ํ–ฅ์„ ๊ณ ๋ คํ•˜๊ธฐ ์œ„ํ•ด, ๊ธฐ์กด์˜ 1์ฐจ์› ์ด์†ก-๋ถ„์‚ฐ ๋ฐฉ์ •์‹์„ ๋ฐ”ํƒ•์œผ๋กœ ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ์ €์žฅ๋Œ€ ๋ชจํ˜•์ด ๊ฐœ๋…์ ์œผ๋กœ ์ œ์‹œ๋˜์–ด ์™”๋‹ค. ์ด๋Ÿฌํ•œ ๋ชจํ˜•์˜ ํƒ€๋‹น์„ฑ์€ ๋Œ€๋ถ€๋ถ„ ํ๋ฆ„์˜์—ญ์—์„œ ์ธก์ •ํ•œ ์ถ”์ ์ž์˜ ๋†๋„-์‹œ๊ฐ„ ๊ณก์„ ์˜ ์‹ค์ธก๊ฐ’์œผ๋กœ๋ถ€ํ„ฐ ์ฆ๋ช…๋˜์–ด ์™”๋‹ค. ํ•˜์ง€๋งŒ, ํ๋ฆ„์˜์—ญ์—์„œ์˜ ์ถ”์ ์ž ๊ฑฐ๋™์€ ์ €์žฅ๋Œ€์˜ ์˜ํ–ฅ๋ณด๋‹ค ์ด์†ก๊ณผ ๋ถ„์‚ฐ์˜ ์˜ํ–ฅ์— ๋”์šฑ ๋ฏผ๊ฐํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ด๋Š” ์ €์žฅ๋Œ€์˜ ์˜ํ–ฅ์„ ๋Œ€ํ‘œํ•˜๊ธฐ ์–ด๋ ค์šฐ๋ฉฐ, ์ €์žฅ๋Œ€ ๋ชจ๋ธ๋ง์€ ์ €์žฅ๋Œ€์˜ ์˜ํ–ฅ์˜ ์‹ค์ธก๊ฐ’์œผ๋กœ๋ถ€ํ„ฐ ๊ฒ€์ฆ๋˜์–ด์•ผ ํ•œ๋‹ค. ํ•˜์ง€๋งŒ, ์ž์—ฐํ•˜์ฒœ์˜ ์ €์žฅ๋Œ€๋Š” ๊ทธ ํ˜•ํƒœ๊ฐ€ ๋‹ค์–‘ํ•˜๋ฉฐ ๊ฒฝ๊ณ„๊ฐ€ ๋ชจํ˜ธํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์‹ค์ธก๊ฐ’์„ ์–ป๊ธฐ ํž˜๋“  ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด์†ก, ๋ถ„์‚ฐ์˜ ์˜ํ–ฅ๊ณผ ์ €์žฅ๋Œ€์˜ ์˜ํ–ฅ์„ ๋ช…์‹œ์ ์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจํ˜•์„ ์ œ์‹œํ•˜๊ณ , ์—ญํ•ฉ์„ฑ๊ณฑ ๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ํ๋ฆ„์˜์—ญ์—์„œ ์ธก์ •ํ•œ ์ถ”์ ์ž์˜ ๊ฑฐ๋™์œผ๋กœ๋ถ€ํ„ฐ ์ด์†ก๊ณผ ๋ถ„์‚ฐ์˜ ์˜ํ–ฅ์„ ์ œ์™ธํ•˜์—ฌ ์ €์žฅ๋Œ€์˜ ์˜ํ–ฅ๋งŒ์„ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ธก์ •ํ•œ ์ €์žฅ๋Œ€์˜ ์˜ํ–ฅ์€ ๊ฐ€์žฅ ๋Œ€ํ‘œ์ ์ธ 1์ฐจ์› ์ €์žฅ๋Œ€ ๋ชจํ˜•์ธ Transient Storage Model (TSM)์˜ ๋ชจ์˜ ๊ฒฐ๊ณผ์™€ ๊ฒฐ์ •๋œ ๋งค๊ฐœ๋ณ€์ˆ˜์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜๋Š”๋ฐ ํ™œ์šฉ๋˜์—ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ TSM์˜ ๋ชจ์˜๋Š” ์‹ค์ œ ํ•˜์ฒœ์˜ ์ €์žฅ๋Œ€์˜ ์˜ํ–ฅ์„ 44%๊นŒ์ง€ ๊ณผ์†Œํ‰๊ฐ€ํ•˜๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ๋˜ํ•œ, ์ž์—ฐํ•˜์ฒœ์—์„œ ์ €์žฅ๋Œ€๊ฐ€ ์ˆ˜๊ณ„ ์ƒ๋ฌผํ™”ํ•™์  ๋ฐ˜์‘์˜ ์ฃผ์š” ์˜์—ญ์ด๋ผ๋Š” ์ ์„ ๊ณ ๋ คํ•˜์—ฌ, ํ‰๊ฐ€๋œ ์ •์ฒด์‹œ๊ฐ„๋ถ„ํฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ์—ฌ๋Ÿฌ ์œ ๊ธฐํ™”ํ•™๋ฌผ์งˆ๋ณ„ ์ƒํ™”ํ•™์  ๋ฐ˜์‘์— ์˜ํ•œ ๊ฐ์‡ ์ •๋„๋ฅผ ํ‰๊ฐ€ํ•˜๋Š”๋ฐ ํ™œ์šฉ๋˜์—ˆ๋‹ค.The solute propagation along stream flow cannot be interpreted only by hydrodynamic properties of surface flow due to the influence from surrounding storage zones of the stream. To analyze this unidentified storage effect, various transient storage models have been proposed for recent decades. The time dependent behavior of solute within the storage zone was often modeled a conceptualized retention time function added to conventional advection-dispersion equation. The validity of these models has been predominantly demonstrated with tracer breakthrough curves measured in surface flow. However, the storage effect is less responsible for the breakthrough curve behavior than in-stream flow dynamics. For model validation purpose, tracer behavior only within storage zones should be investigated. The present study is aimed at quantifying the time-dependent storage effect, herein termed the net retention time distribution (NRTD), from tracer measurements at the flow zone using a deconvolution technique with filtering in the Fourier domain. The results showed that the deconvolved NRTDs successively represented the temporal behavior of the tracer in the storage zones without significant distortion in the observed breakthrough curves. Using the estimates of NRTD, we evaluated the validity of first-order mass transfer and its parameters of the transient storage model (TSM), which is the most widely-used storage zone model. The simulation results of the parameter-optimized TSM underestimated the inherent storage effect by as much as an average 44 %. It is also noteworthy that the larger net retention time scale the channel has, the larger discrepancy the TSMโ€™s exponential retention time function could yield.LIST OF FIGURES LIST OF TABLES LIST OF SYMBOLS LIST OF ABBREVIATIONS CHAPTER I. INTRODUCTION 1 1.1 Motivation 1 1.2 Problem Statement 3 II. THEORETICAL BACKGROUNDS 8 2.1 One-dimensional solute transport modeling 8 2.2 Conceptualization of storage mechanism 13 2.3 Determination of TSM parameters 23 2.4 Summary of literatural review 26 III. MATERIALS AND METHODS 27 3.1 Tracer experiments in a stream 27 3.1.1 Site description 27 3.1.2 Tracer Measurement 30 3.1.3 Preprocessing for Breakthrough Curves 31 3.2 Development of algorithm for storage effect quantification 32 3.2.1 Concept of residence time distribution 33 3.2.2 Convolutional Decomposition Equation (CDE) 34 3.2.3 Deconvolution technique with BTCs 39 3.2.4 Data stabilization for deconvolution 43 3.2.5 Parameter estimation 47 3.3 Net retention time distribution in TSM 52 3.4 Biodegradation of chemicals in streams 56 IV. RESULTS AND DISCUSSIONS 59 4.1 Tracer behavior in a stream 59 4.2 Net retention time distribution 66 V. APPLICATION 70 5.1 Evaluation of TSM simulation 70 5.2 Prediction of biodegradation of chemicals 78 IV. CONCLUSIONS 82 REFERENCES 85์„

    ์•ฝ๋™ํ•™/์•ฝ๋ ฅํ•™ ๋ชจ๋ธ์„ ์ด์šฉํ•œ ์š”๋…์ฆ์˜ evogliptin ์•ฝ๋™ํ•™์— ๋Œ€ํ•œ ์˜ํ–ฅ ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ž„์ƒ์•ฝ๋ฆฌํ•™์ „๊ณต, 2023. 8. ์กฐ์ฃผ์—ฐ.์„œ๋ก : ์š”๋…์ฆ(uremia) ๋˜๋Š” ์š”๋…์ฆํ›„๊ตฐ(uremic syndrome)์€ ์‹ ๊ธฐ๋Šฅ ์ €ํ•˜๋กœ ์ธํ•ด ํ˜ˆ์•ก ์ค‘ ๋…ธํ๋ฌผ(์š”๋…์†Œ)์ด ์ถ•์ ๋˜๋Š” ๋ณ‘๋ฆฌํ•™์  ์ƒํƒœ์ด๋‹ค. ์š”๋…์†Œ๋Š” ๋ชธ์— ์ถ•์ ๋˜์–ด ์‚ฌ์ดํ† ํฌ๋กฌ P450 ํšจ์†Œ(CYP3A4 ๋“ฑ)๋ฅผ ํ†ตํ•ด ์ด๋ฃจ์–ด์ง€๋Š” ์•ฝ๋ฌผ ๋Œ€์‚ฌ ๋ฐ ๋ฐฐ์„ค๊ณผ ๊ฐ™์€ ์—ฌ๋Ÿฌ ์ƒ๋ฆฌ ๊ณผ์ •์— ์˜ํ–ฅ์„ ์ค€๋‹ค. ์—๋ณด๊ธ€๋ฆฝํ‹ด(evogliptin)์€ 2ํ˜• ๋‹น๋‡จ๋ณ‘ ์น˜๋ฃŒ์— ์‚ฌ์šฉ๋˜๋Š” ๋””ํŽฉํ‹ฐ๋”œ ํŽฉํ‹ฐ๋‹ค์ œ-4(DPP-4) ์–ต์ œ์ œ๋กœ, ์ฃผ๋กœ ๊ฐ„์—์„œ CYP3A4 ํšจ์†Œ์— ์˜ํ•ด ๋Œ€์‚ฌ๋œ๋‹ค. ์š”๋…์ฆ์€ CYP3A4์˜ ๊ธฐ๋Šฅ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ์—๋ณด๊ธ€๋ฆฝํ‹ด์˜ ๋Œ€์‚ฌ ๋ฐ ๋ฐฐ์„ค์— ์ค‘์š”ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค. ์‹ ์žฅ ์†์ƒ ํ™˜์ž์—์„œ CYP3A4๋ฅผ ์ฃผ๋กœ ๋Œ€์‚ฌ์‹œํ‚ค๋Š” ์—๋ณด๊ธ€๋ฆฝํ‹ด๊ณผ ๊ฐ™์€ ์•ฝ๋ฌผ์— ๋Œ€ํ•œ ์ธ๊ตฌ ์•ฝ๋™ํ•™(PK) ๋ฐ ์•ฝ๋ ฅํ•™(PD) ๋ชจ๋ธ๋ง์„ ์ˆ˜ํ–‰ํ•˜๋ฉด, CYP3A4๋ฅผ ์ฃผ๋กœ ๋Œ€์‚ฌ์‹œํ‚ค๋Š” ์•ฝ๋ฌผ์˜ ์•ฝ๋™ํ•™์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์–‘ํ•œ ์ •๋„ ์‹ ์žฅ ์งˆํ™˜์„ ๊ฐ€์ง„ ํ™˜์ž์—์„œ ์—๋ณด๊ธ€๋ฆฝํ‹ด์˜ ์ธ๊ตฌ PK ๋ฐ PD ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋‹ค. ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์—๋ณด๊ธ€๋ฆฝํ‹ด์˜ ๋‘ ๊ฐ€์ง€ ์ž„์ƒ ์—ฐ๊ตฌ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ํ•˜๋‚˜๋Š” ๋‹ค์–‘ํ•œ ์ •๋„์˜ ์‹ ์žฅ ์†์ƒ ํ™˜์ž์™€ ์ •์ƒ ์‹ ์žฅ ๊ธฐ๋Šฅ์„ ๊ฐ€์ง„ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์ž„์ƒ ์—ฐ๊ตฌ(NCT02214693)์ด๊ณ , ๋‹ค๋ฅธ ํ•˜๋‚˜๋Š” ๋ง๊ธฐ ์‹ ์žฅ ์งˆํ™˜(ESRD) ํ™˜์ž์™€ ์ •์ƒ ์‹ ์žฅ ๊ธฐ๋Šฅ์„ ๊ฐ€์ง„ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๋‹จํšŒ ํˆฌ์—ฌ ์—ฐ๊ตฌ(NCT04195919)์ด๋‹ค. ๋‘ ์—ฐ๊ตฌ์—์„œ ๋Œ€์ƒ์ž๋“ค์€ ๊ณต๋ณต ์ƒํƒœ์—์„œ 5mg์˜ ์—๋ณด๊ธ€๋ฆฝํ‹ด์„ ํˆฌ์—ฌ ๋ฐ›์•˜๋‹ค. ์ด 46๋ช…์˜ ๋Œ€์ƒ์ž๋กœ๋ถ€ํ„ฐ ์ทจ๋“ํ•œ 688๊ฑด์˜ ์—๋ณด๊ธ€๋ฆฝํ‹ด ๋†๋„ ๋ฐ 598๊ฑด์˜ DPP-4 ํ™œ์„ฑ ๋ฐ์ดํ„ฐ๊ฐ€ ๋ถ„์„์— ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์—๋ณด๊ธ€๋ฆฝํ‹ด์˜ PK/PD ๋ฐ์ดํ„ฐ์™€ ํ˜ˆ์•กํ•™, ํ˜ˆ์•กํ™”ํ•™, ์ธ๊ตฌํ†ต๊ณ„ํ•™ ๋ฐ์ดํ„ฐ ๋“ฑ์˜ ์ž ์žฌ์  ๊ณต๋ณ€๋Ÿ‰ ์ •๋ณด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ธ๊ตฌ PK/PD ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ๋ชจ๋ธ ๊ตฌ์ถ•์—๋Š” ๋น„์„ ํ˜• ํ˜ผํ•ฉํšจ๊ณผ ๋ชจ๋ธ๋ง ์†Œํ”„ํŠธ์›จ์–ด(NONMEMยฎ ๋ฒ„์ „ 7.4)๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ์ผ์ฐจ ์กฐ๊ฑด๋ถ€ ์ถ”์ •๊ณผ ์ƒํ˜ธ ์ž‘์šฉ(FOCE-I)์„ ์ด์šฉํ•˜์˜€๋‹ค. ๊ฐ ๋งค๊ฐœ๋ณ€์ˆ˜๋Š” ์ „์ง„ ์„ ํƒ ๋ฐ ํ›„์ง„ ์ œ๊ฑฐ ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ตฌ์กฐ ๋ชจ๋ธ์— ์ˆœ์ฐจ์ ์œผ๋กœ ์ถ”๊ฐ€๋˜์—ˆ์œผ๋ฉฐ, ๊ฐ๊ฐ 0.01๊ณผ 0.001์˜ ์œ ์˜ ์ˆ˜์ค€์„ ์ ์šฉํ•˜์˜€๋‹ค. ๋น„๋ชจ์ˆ˜์  ๋ถ€ํŠธ์ŠคํŠธ๋žฉ ์žฌํ‘œ๋ณธ ์ถ”์ถœ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ชจ๋ธ์˜ ์•ˆ์ •์„ฑ๊ณผ ๋ชจ๋ธ ๋งค๊ฐœ๋ณ€์ˆ˜์˜ ์‹ ๋ขฐ๊ตฌ๊ฐ„(CI)์„ ํ‰๊ฐ€ํ•˜์˜€์œผ๋ฉฐ, ๋ฐ์ดํ„ฐ ์„ธํŠธ์˜ ๋ถ€ํŠธ์ŠคํŠธ๋žฉ ๋ณต์ œ๋ณธ(n=1000)์— ๋Œ€ํ•ด ์ตœ์ข… ๋ชจ๋ธ์„ ๋ฐ˜๋ณต์ ์œผ๋กœ ์ ์šฉํ•˜์˜€๋‹ค. ์˜ˆ์ธก ์ˆ˜์ • ์‹œ๊ฐ ์˜ˆ์ธก ๊ฒ€์ฆ(pcVPCs; 500ํšŒ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ณต์ œ๋ณธ)์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ตœ์ข… ๋ชจ๋ธ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ตœ์ข… PK ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ 5 mg ๋‹จํšŒ ํˆฌ์—ฌ๋ฅผ ๊ฐ€์ •ํ•œ ๋†๋„-์‹œ๊ฐ„ ๊ณก์„  ํ•˜ ๋ฉด์ (AUC) ๋ฐ ์ตœ๋Œ€ ํ˜ˆ์žฅ ๋†๋„(Cmax)๋ฅผ ๊ณ„์‚ฐํ•˜์˜€์œผ๋ฉฐ, ๋‹ค์–‘ํ•œ ์ •๋„์˜ ์‹ ์žฅ ๊ธฐ๋Šฅ์„ ๊ฐ€์ง„ ํ™˜์ž๋“ค์˜ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: ๋‹ค์–‘ํ•œ ์ •๋„์˜ ์‹ ์žฅ ์†์ƒ์„ ๊ฐ€์ง„ ํ™˜์ž์™€ ๊ฑด๊ฐ•ํ•œ ๋Œ€์ƒ์ž๋“ค๋กœ ๊ตฌ์„ฑ๋œ ์ด 46๋ช…์˜ ์ฐธ์—ฌ์ž๊ฐ€ ์—ฐ๊ตฌ์— ์ฐธ์—ฌํ•˜์˜€๋‹ค. ๊ฐ ์—ฐ๊ตฌ์˜ ํ™˜์ž ๊ทธ๋ฃน์€ ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ์ด ๋น„์Šทํ–ˆ์œผ๋ฉฐ, ํ™˜์ž๊ตฐ์˜ ์‹ ์žฅ ์†์ƒ์˜ ์ •๋„๋Š” ๋‹ฌ๋ž๋‹ค. ์ด 688๊ฐœ์˜ ํ˜ˆ์žฅ PK ์ƒ˜ํ”Œ์„ ์‚ฌ์šฉํ•˜์—ฌ ์—๋ณด๊ธ€๋ฆฝํ‹ด์˜ ์ธ๊ตฌ ์•ฝ๋™ํ•™(PK)์„ ์„ค๋ช…ํ•˜๋Š” ๋น„์„ ํ˜• ํ˜ผํ•ฉํšจ๊ณผ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์•„์นด์ด์ผ€ ์ •๋ณด ๊ธฐ์ค€(AIC), ๊ฐ์ข… ์ง„๋‹จ ํ”Œ๋กฏ ๋ฐ ๋ชฉํ‘œ ํ•จ์ˆ˜ ๊ฐ’(OFV)์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ 2-๊ตฌํš ๋ชจ๋ธ๊ณผ ์ผ์ฐจ ํก์ˆ˜๊ฐ€ ์„ ํƒ๋œ ๊ธฐ๋ณธ PK ๋ชจ๋ธ์ด ์„ ํƒ๋˜์—ˆ๋‹ค. ๊ธฐ๋ณธ PK ๋ชจ๋ธ์€ ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ๋งค๊ฐœ๋ณ€์ˆ˜ ์ถ”์ • ๋ฐ ๊ด€์ฐฐ๋œ ๋ฐ์ดํ„ฐ์™€ ์˜ˆ์ธก๋œ ๋ฐ์ดํ„ฐ ๊ฐ„์˜ ๊ฐ•ํ•œ ์ผ์น˜์„ฑ์„ ๋ณด์˜€๋‹ค. ์ตœ์ข… ๋ชจ๋ธ์— ์œ ์ง€๋œ ์ค‘์š”ํ•œ ๊ณต๋ณ€๋Ÿ‰์€ ํ˜ˆ์ค‘ chloride ๋ฐ amylase ์ˆ˜์น˜๊ฐ€ Fr(์ƒ๋Œ€์  ์ƒ์ฒด์ด์šฉ๋ฅ )์—, ๋‚˜์ด๊ฐ€ CL/F (์™ธ์  ์ฒญ์†Œ์œจ)์—, ๊ทธ๋ฆฌ๊ณ  ์ฒด์ค‘์ด V3/F (๋ง์ดˆ ๋ถ„ํฌ๋Ÿ‰)์— ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. pcVPC๋Š” ์‹œ๋ฎฌ๋ ˆ์ด์…˜๋œ ์—๋ณด๊ธ€๋ฆฝํ‹ด ๋†๋„์™€ ๊ด€์ฐฐ๋œ ๋†๋„ ๊ฐ„์˜ ์ค‘์ฒฉ์„ ๋ณด์—ฌ์ฃผ์—ˆ์œผ๋ฉฐ, ๋ถ€ํŠธ์ŠคํŠธ๋žฉ์€ 1000ํšŒ ๋ณต์ œ๋ณธ ์ค‘ 93.1%์˜ ์„ฑ๊ณต๋ฅ ์„ ๋ณด์˜€๋‹ค. ์—๋ณด๊ธ€๋ฆฝํ‹ด์˜ ์•ฝ๋™ํ•™์— ๊ด€๋ จ๋œ ๊ณต๋ณ€๋Ÿ‰์˜ ์ž ์žฌ์  ์˜ํ–ฅ์„ ๋ชฌํ…Œ์นด๋ฅผ๋กœ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ์™€ ์ด์ „์— ๋ณด๊ณ ๋œ evogliptin์˜ PK ๋ฐ์ดํ„ฐ๋ฅผ ์ข…ํ•ฉํ•˜์˜€์„ ๋•Œ, ์ค‘์ฆ ์‹ ์žฅ ๊ธฐ๋Šฅ ์ €ํ•˜ ํ™˜์ž์—์„œ ์ดˆํšŒํ†ต๊ณผํšจ๊ณผ ๋Œ€์‚ฌ ์–ต์ œ๊ฐ€ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์ด ์˜ˆ์ธก๋˜์—ˆ๋‹ค. Direct link sigmoidal Emax ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ํ˜ˆ์žฅ evogliptin ๋†๋„์™€ DPP-4 ์–ต์ œ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์„ค๋ช…ํ–ˆ๋‹ค. Evogliptin์˜ PK/PD ๋ชจ๋ธ์€ ์ตœ๋Œ€ ํšจ๊ณผ ์‹œ์— DPP-4์˜ ๊ฑฐ์˜ ์™„์ „ํ•œ ์–ต์ œ๋ฅผ ์˜ˆ์ธกํ•˜์˜€์œผ๋ฉฐ (Emax: 95.7%), ๋‚ฎ์€ EC50 ๊ฐ’ (0.837 ฮผg/L)์„ ๋ณด์—ฌ์ฃผ์–ด evogliptin์˜ ๋†’์€ ํšจ๋ ฅ๊ณผ ํšจ๋Šฅ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ๊ฒฐ๋ก : ๊ฐœ๋ฐœ๋œ ์—๋ณด๊ธ€๋ฆฝํ‹ด์˜ PK/PD ๋ชจ๋ธ์€ ์‹ ์žฅ ๊ธฐ๋Šฅ ์ €ํ•˜๊ฐ€ ์žˆ๋Š” ๊ฐœ์ฒด์—์„œ ํก์ˆ˜, ์ฒด๋‚ด ๋…ธ์ถœ, ๋ฐฐ์„ค ๋ณ€๋™์„ฑ์„ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์‹ ์žฅ ์†์ƒ์˜ ์ •๋„๊ฐ€ CYP3A4๋ฅผ ํ†ตํ•ด ๋Œ€์‚ฌ๋˜๋Š” ์•ฝ๋ฌผ์˜ ์ƒ๋Œ€ ์ƒ์ฒด์ด์šฉ๋ฅ ์— ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ด ๋ชจ๋ธ์€ ์•ž์œผ๋กœ ์‹ ์žฅ ๊ธฐ๋Šฅ ์ €ํ•˜ ํ™˜์ž์—์„œ ๋น„ ์‹ ์žฅ ์•ฝ๋ฌผ ์ฒญ์†Œ์— ๋Œ€ํ•œ ์š”๋…์ฆ์˜ ์˜ํ–ฅ์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ทผ๊ฑฐ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์‹ ์žฅ ๊ธฐ๋Šฅ ์ €ํ•˜ ํ™˜์ž๋ฅผ ์œ„ํ•œ ์šฉ๋Ÿ‰ ์กฐ์ • ๋ฐฉ์•ˆ์„ ์ตœ์ ํ™”ํ•˜๋Š” ๋ฐ ๋„์›€์„ ์ฃผ๋ฆฌ๋ผ ํŒ๋‹จ๋œ๋‹ค.Introduction: Uremia, also known as uremic syndrome, is a pathological condition characterized by the retention of waste products (uremic toxins) in the blood due to inadequate kidney function. Uremic toxins can accumulate in the body and affect various physiological processes, including drug metabolism and elimination mediated by cytochrome P450 enzymes such as CYP3A4. Evogliptin is a dipeptidyl peptidase-4 (DPP-4) inhibitor used to treat type 2 diabetes and is primarily metabolized by the liver enzyme CYP3A4. Uremia may affect the function of CYP3A4, which may have significant implications for the metabolism and elimination of evogliptin. By conducting population pharmacokinetics (PK) and pharmacodynamics (PD) modeling on evogliptin in patients with renal impairment, it is possible to predict the PK of drugs that are mainly metabolized by CYP3A4 in renal impairment conditions. This study aimed to construct a population PK and PD model of evogliptin in patients with varying degrees of kidney disease. Methods: This study implemented data from two clinical studies of evogliptin: an open-label, parallel-group clinical study conducted in patients with varying degrees of renal impairment and normal renal function (NCT02214693) and a single-dose, open-label, parallel-group study conducted in patients with end-stage renal disease (ESRD) and normal renal function (NCT04195919). In both studies, subjects were administered 5 mg evogliptin in a fasting state. A total of 46 subjects with 688 evogliptin concentration measurements and 598 DPP-4 activity measurements were available for analysis. PK/PD data for evogliptin, as well as potential covariate information including hematology, blood chemistry, and demographic data, were used to construct a population PK/PD model. The model construction used nonlinear mixed-effects modeling software (NONMEMยฎ version 7.4) with first-order conditional estimation with interaction (FOCE-I). Each parameter was added to the structural model in a stepwise approach with forward and backward elimination, employing significance levels of 0.01 and 0.001, respectively. Nonparametric bootstrap resampling was used to evaluate model stability and to estimate confidence intervals (CIs) for the model parameters by repeatedly fitting the final model to bootstrap replicates (n = 1000) of the dataset. Prediction-corrected visual predictive checks (pcVPCs; 500 simulation replicates) were conducted to validate the final model. The final PK model was used to simulate concentration-time profiles, and the area under the concentrationโ€“time curve (AUC) from time zero to 120 h was derived, and the maximum plasma concentration (Cmax) was calculated, assuming a single dose of 5 mg in various covariate conditions. Results: A total of 46 participants with varying degrees of renal impairment and healthy subjects were enrolled. All subject groups had comparable demographic characteristics but different levels of renal impairment. A nonlinear mixed-effects model was developed to describe the population PK of evogliptin using 688 plasma PK samples. A two-compartment model with first-order absorption was selected as the base PK model on the basis of the Akaike information criterion (AIC), diagnostic plots, and objective function values (OFVs). The base PK model demonstrated reliable parameter estimation and a strong agreement between observed and predicted data without systematic bias. The significant covariates retained in the final model included chloride and amylase on Fr (relative bioavailability), age on CL/F (apparent clearance) and body weight on V3/F (peripheral volume of distribution). Varying chloride and amylase levels contributed to increasing the bioavailability of evogliptin. Lower clearance was observed in older patients, and body weight correlated with increasing V3/F. The goodness-of-fit plots indicated an adequate model structure for predicting evogliptin concentrations. The pcVPC showed an overlap between simulated and observed evogliptin concentrations, and bootstrapping resulted in 93.1% successful replication among 1000 replicates. The potential effects of relevant covariates on CYP3A4-mediated evogliptin PK were evaluated using Monte Carlo simulation. The simulation findings, in conjunction with previously reported PK data of evogliptin, provided evidence of a significant inhibition of first-pass metabolism in severe renal impairment conditions. A direct-link sigmoidal Emax model was developed to describe the relationship between plasma evogliptin concentration and DPP-4 inhibition. The final model robustly estimated PD parameters. The PK/PD model of evogliptin predicted near complete inhibition of DPP-4 at the maximum effect (Emax: 95.7%) and exhibited a low EC50 value (0.837 ฮผg/L), suggesting the high potency and efficacy of evogliptin. Conclusion: The developed PK/PD model of evogliptin accurately predicted absorption, systemic exposure, and elimination variability in individuals with renal impairment. This study indicates that renal impairment and the resulting biochemical changes may impact the relative bioavailability of CYP3A4-metabolized drugs. This model serves as a basis for future evaluations of uremia's effect on nonrenal drug clearance and aids in optimizing dosing regimens for patients with renal impairment.ABSTRACT i Table of Contents vi List of Tables viii List of Figures ix List of Abbreviations xi Chapter 1. Introduction ๏ผ‘ 1.1. Study Background ๏ผ‘ 1.2. Purpose of research ๏ผ” Chapter 2. Methods 6 2.1. Clinical Study and Data Collection 6 2.1.1. Study Design and Population 6 2.1.2. PK Sample Collection and Bioanalytical Assay 10 2.1.3. PD Sample Collection and Bioanalytical Assay 13 2.2. Development of the Population PK Model 17 2.2.1. Base PK model development 17 2.2.2. Covariate PK model 18 2.2.3. Model validation 22 2.2.4. PK simulation in renal impairment patients 22 2.2.5. PD model of evogliptin 23 Chapter 3. Results 25 3.1. Clinical Study and Data Collection 25 3.1.1. Clinical study results 25 3.1.2. Pharmacokinetic results 28 3.1.3. Pharmacodynamic results 30 3.2. Development of the Population PK Model 32 2.2.1. Base model 32 2.2.2. Covariate model 37 2.2.3. Model validation 41 2.2.4. PK simulation 45 2.2.5. PD model of evogliptin 50 Chapter 4. Discussion 55 Chapter 5. Conclusion 65 Bibliography 66 Abstract in Korean 69๋ฐ•

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    ์—ญํ•™๋ฐ๊ฑด๊ฐ•์ฆ์ง„ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์•”ํ™˜์ž์˜ ๋ณด์™„๋Œ€์ฒด์š”๋ฒ•์˜ ์ด์šฉ์ˆ˜์ค€์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด 2๊ฐœ ์ข…ํ•ฉ๋ณ‘์›์˜ ์™ธ๋ž˜ ๋ฐ ์ž…์› ์•” ํ™˜์ž ์ค‘ ์ง„๋‹จ์ดํ›„ 3๊ฐœ์›”์ด ์ง€๋‚œ ํ™˜์ž ๋ฐ ๊ฐ€์กฑ์„ ๋Œ€์ƒ์œผ๋กœ ๊ตฌ์กฐํ™”๋œ ์„ค๋ฌธ์ง€๋ฅผ ์ด์šฉํ•˜์—ฌ ์กฐ์‚ฌ์›์— ์˜ํ•œ ์ง์ ‘๋ฉด์ ‘๋ฒ•์œผ๋กœ ์กฐ์‚ฌํ•˜์˜€๋‹ค. 2004๋…„ 2์›” 1์ฃผ์ผ๊ฐ„์˜ ์˜ˆ๋น„์กฐ์‚ฌ๋กœ ์ถ”๊ฐ€ํ•ญ๋ชฉ์— ๋Œ€ํ•œ ๊ฒ€ํ†  ๋ฐ ํ•ญ๋ชฉ์กฐ์ •๊ณผ ์ˆ˜์ •๊ณผ์ •์„ ๊ฑฐ์ณ 4์›”๋ถ€ํ„ฐ ๋ณธ ์กฐ์‚ฌ๋ฅผ ์‹œ์ž‘ํ•˜์—ฌ 5์›” 30์ผ๊นŒ์ง€ 6์ฃผ๊ฐ„ ์‹ค์‹œํ•˜์—ฌ, ์ตœ์ข… ๋ถ„์„๋Œ€์ƒ์ž๋Š” ์ด 736๋ช…์ด์—ˆ๋‹ค. ์—ฐ๊ตฌ์˜ ์ค‘์š”ํ•œ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์—ฐ๊ตฌ๋Œ€์ƒ์ž์˜ ์ง€๋‚œ 3๊ฐœ์›”๊ฐ„ ๋ณด์™„๋Œ€์ฒด์š”๋ฒ•์˜ ์ด์šฉ์œจ์€ ์ „์ฒด๋Œ€์ƒ์ž์˜ 50.1% ์˜€์œผ๋ฉฐ, ์‹์ด์š”๋ฒ•์ด 39.8%, ํ•œ๋ฐฉ๋ฏผ๊ฐ„์š”๋ฒ• 13.2%, ์•ฝ๋ฌผ์š”๋ฒ• 10.1%, ํ•œ์˜ํ•™์š”๋ฒ• 2.9% ์ด์šฉํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด์šฉ๋ชฉ์ ์€ ์น˜๋ฃŒ๋ณด์กฐ 64.8%๋กœ, ์ฒด๋ ฅ๋ณด๊ฐ• 39.8%, ํ™˜์ž์˜ ์‹ฌ๋ฆฌ์ ยท์ •์„œ์  ์•ˆ์ • 13.3%, ์งˆํ™˜์˜ ์น˜๋ฃŒ ๋ชฉ์  10.0% ์ด์—ˆ์œผ๋ฉฐ, ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ์ž์˜ 50.8%๋Š” ๋‹ด๋‹น์˜์‚ฌ์˜ ํ™•์ธ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ์‹œ ๋ถ€์ž‘์šฉ์ˆ˜์ค€์€ ์ „์ฒด(ํ‰๊ท  1.1ยฑ0.5์ )์ ์œผ๋กœ ๋ถ€์ž‘์šฉ์ด ์—†๋Š” ํŽธ์ž„์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋‘˜์งธ, ๋ณด์™„๋Œ€์ฒด์š”๋ฒ•์˜ ์ด์šฉ๋น„์šฉ ๋น„๊ต๋Š” ์ „์ฒด๋Œ€์ƒ์ž ์ค‘์—์„œ ์—ฌ์ž๋ณด๋‹ค๋Š” ๋‚จ์ž๊ฐ€, ๊ต์œก์ˆ˜์ค€์ด ๋†’์„์ˆ˜๋ก, ๊ทธ๋ฆฌ๊ณ  ์™ธ๋ž˜๋ณด๋‹ค๋Š” ์ž…์›์‹œ์— ๋ณด๋‹ค ๋†’์€ ๋ณด์™„๋Œ€์ฒด์š”๋ฒ•๋น„์šฉ์„ ์ง€๋ถˆํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์™ธ๋ž˜๋ณด๋‹ค๋Š” ์ž…์›์‹œ์— ๋ณด๋‹ค ๋†’์€ ๋ณด์™„๋Œ€์ฒด์š”๋ฒ•๋น„์šฉ์„ ์ง€๋ถˆํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ์ด์˜๋ฃŒ ๋น„์šฉ ๋Œ€๋น„ ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ๋น„์šฉ๋ถ„์œจ ๋น„๊ต๋Š” ์ „์ฒด๋Œ€์ƒ์ž ์ค‘์—์„œ ์—ฌ์ž๋ณด๋‹ค๋Š” ๋‚จ์ž๊ฐ€, ๋‹ค๋ฅธ ์•”๋ณด๋‹ค๋Š” ์œ ๋ฐฉ์•”๊ณผ ์œ„์•”์ด, ๊ทธ๋ฆฌ๊ณ  ์•”์ง„๋‹จํ›„ ๊ธฐ๊ฐ„์ด ์˜ค๋ž˜ ๊ฒฝ๊ณผํ• ์ˆ˜๋ก ๋ณด๋‹ค ๋†’์€ ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ๋น„์šฉ๋ถ„์œจ์„ ๋ณด์ด๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ์ž์—์„œ๋Š” ๋‚จ์ž๋ณด๋‹ค๋Š” ์—ฌ์ž๊ฐ€, ์ž…์›๋ณด๋‹ค๋Š” ์™ธ๋ž˜๊ฐ€, ๋‹ค๋ฅธ ์•”๋ณด๋‹ค๋Š” ์œ„์•”๊ณผ ์œ ๋ฐฉ์•”์ด, ๊ทธ๋ฆฌ๊ณ  ์•”์ง„๋‹จํ›„ ๊ฒฝ๊ณผ๊ธฐ๊ฐ„์ด ๊ธธ์ˆ˜๋ก ๋ณด๋‹ค ๋†’์€ ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ๋น„์šฉ๋ถ„์œจ์„ ๋ณด์ด๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋„ท์งธ, ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ๋น„์šฉ ๊ด€๋ จ์š”์ธ์„ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„ ํ–ˆ์„ ๋•Œ ์ „์ฒด๋Œ€์ƒ์ž์—์„œ ํ•™๋ ฅ์ˆ˜์ค€์˜ ๊ฒฝ์šฐ์—๋Š” ๊ณ ์กธ(p=0.033)๊ณผ ๋Œ€ํ•™์žฌํ•™์ด์ƒ(p=0.002), ์žฌ์‚ฐ์ˆ˜์ค€์˜ ๊ฒฝ์šฐ์—๋Š” 5์ฒœ๋งŒ์› ์ด์ƒ ~ 1์–ต์› ๋ฏธ๋งŒ(p=0.015), ์•”์ข…๋ฅ˜์˜ ๊ฒฝ์šฐ์—๋Š” ๋Œ€์žฅ์•”(p=0.050), ๊ฐ„์•”(p=0.042), ๊ทธ๋ฆฌ๊ณ  ์œ ๋ฐฉ์•”(p=0.044) ๋“ฑ์ด ๋ณด์™„๋Œ€์ฒด์š”๋ฒ•๋น„์šฉ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ์ž์—์„œ๋Š” ํ•™๋ ฅ์ˆ˜์ค€์˜ ๊ฒฝ์šฐ์—๋Š” ๊ณ ์กธ(p=0.036), ๋Œ€ํ•™์žฌํ•™์ด์ƒ(p=0.002), ์žฌ์‚ฐ์ˆ˜์ค€์˜ ๊ฒฝ์šฐ์—๋Š” 5์ฒœ๋งŒ์› ์ด์ƒ ~ 1์–ต์› ๋ฏธ๋งŒ(p=0.019), ์•”์ข…๋ฅ˜์˜ ๊ฒฝ์šฐ์—๋Š” ๋Œ€์žฅ์•”(p=0.049)๊ณผ ๊ฐ„์•”(p=0.049) ๋“ฑ์ด ๋ณด์™„๋Œ€์ฒด์š”๋ฒ•๋น„์šฉ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ์šฐ๋ฆฌ๋‚˜๋ผ์—์„œ๋Š” ๋ณด์™„๋Œ€์ฒด์š”๋ฒ•์ด ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์ด์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๊ทธ๋ฆฌ ๋งŽ์ง€ ์•Š๋‹ค. ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ๊ณผ ๊ด€๋ จํ•œ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ์œจ์ด๋‚˜ ๋น„์šฉ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ ๋ฟ ์•„๋‹ˆ๋ผ, ๋ณด์™„๋Œ€์ฒด์š”๋ฒ• ์ด์šฉ์˜ ์ ์ •์„ฑ, ๊ณต์ค‘๋ณด๊ฑด๊ณผ ์ „์ฒด์˜๋ฃŒ์ฒด๊ณ„์—์„œ ๋ณด์™„๋Œ€์ฒด์š”๋ฒ•๊ณผ ๊ทธ ์ด์šฉ์˜ ์˜๋ฏธ์™€ ์˜ํ–ฅ์— ๋Œ€ํ•ด ์ข€ ๋” ๊นŠ์€ ์ง€์†์  ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. [์˜๋ฌธ]We have researched, man to man, among those patients who have been cancers for 3months (both inpatients and outpatients) and their families from 2major hospitals about using level of Complementary and Alternative Medicine(CAM) with systemized questionnaires. It had been through processes which are adding items, mediate items and modify items since Feb. 2004 for a week and finally we started main research from April to May, 30th for 6weeks. And there were 736 Below is the result. First, for last 3 months, 50.1% of subject - CAM, 39.8% of subject - Dietetic Treatment, 13.2% of subject - An herbal folk remedy, 10.1% of subject - medical therapy, 2.9% of subject - Chinese herb treatment. And the purposes of using those therapies are supporting cure - 64.8%, strengthen stamina - 39.8%, mentally, emotionally stabled - 13.3%, cure for diseases - 10.0%, 50.8% of the users of CAM have been confirmed by the doctor. Theres was no side effects of CAM as a whole(average 1.1ยฑ0.5 point). Secondly, Female patient and more educated person spent more for CAM. And patients who were hospitalized spent more than outpatients. Thirdly, in the rate of CAM cost for total medical expenses among all patients, male patient''s rate was higher than female patient''s. And the rate of CAM for breast cancer and stomach cancer had been higher than any other cancer''s since cancer was found. As for the rate of CAM cost among the users of CAM, female patient, outpatient, stomach cancer and breast cancer had been higher than male, hospitalized patient, and other cancers since cancer was found. Lastly, when done by regression analysis over the entire group of people to figure out the key cast affecting factors in terms of their educational background, wealth, and cancer types each, high-school graduates(p=0.033) and college graduate and above(p=0.002), patients with colon cancer(p=0.050), liver cancer(p=0.042), and breast cancer(p=0.044) were found to be the factors respectively. Among only the users of this method, high-school graduates(p=0.036) and college graduates and above(p=0.002) are found to be key factors determining the cost of the method, while patients with wealth of 50 to 100 million won(p=0.019) and those who suffer from colon cancer(p=0.049) and liver cancer(p=0.049) also affect its cost. In conclusion, CAM has been widely used throughout the country but not many studies about its use haven''t been conducted, Therefore, more profound studies need to be continued about the propriety of this method as well as the percentage of its use and the cost determining factors. A study of its meaning and influences on public health and the entire medical system is also demanded.ope

    Noise Simulation and Interference Pattern Analysis for Submarine Passive Sonar

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์กฐ์„ ํ•ด์–‘๊ณตํ•™๊ณผ, 2013. 2. ์„ฑ์šฐ์ œ.์ž ์ˆ˜ํ•จ์—์„œ ์  ์ˆ˜์ƒํ•จ์ด๋‚˜ ์ž ์ˆ˜ํ•จ์„ ์›๊ฑฐ๋ฆฌ์—์„œ ํƒ์ง€ํ•˜๊ณ  ๊ณต๊ฒฉ๊นŒ์ง€ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•œ ์žฅ๋น„๋กœ ์ˆ˜๋™์†Œ๋‚˜๋ฅผ ๋“ค ์ˆ˜ ์žˆ๋‹ค. ์ˆ˜๋™์†Œ๋‚˜์— ์ˆ˜์‹ ๋˜๋Š” ์†Œ์Œ์›์€ ์  ์ˆ˜์ƒํ•จ์ด๋‚˜ ์ž ์ˆ˜ํ•จ์˜ ๋ฐฉ์‚ฌ์†Œ์Œ๊ณผ ํ•ด์–‘ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ํ•ด์–‘๋ฐฐ๊ฒฝ์†Œ์Œ, ์ž ์ˆ˜ํ•จ์˜ ์†๋ ฅ์— ๋”ฐ๋ผ ์ž ์ˆ˜ํ•จ ์„ ์ฒด๋ฅผ ํ๋ฅด๋ฉด์„œ ์†Œ๋‚˜์— ์ˆ˜์‹ ๋˜๋Š” ์ž์ฒด์†Œ์Œ ๋“ฑ์ด ์žˆ๋‹ค. ๊ตญ๋‚ด์˜ ๊ธฐ์กด ์ž ์ˆ˜ํ•จ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋“ค์€ ์ด๋Ÿฐ ์†Œ์Œ์›๋“ค์„ ๋ชจ์˜ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์•„์ฃผ ๊ฐ„๋‹จํ•œ ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ์‚ฌ์šฉํ•˜๊ฑฐ๋‚˜ ์‹ค์ œ์›์Œ์„ ๋ถˆ๋Ÿฌ์˜ค๋Š” ๋ฐฉ์‹์œผ๋กœ ๋ชจ์˜๋ฅผ ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๋ฐฉ์‹์€ ์‹ค์ œ ํ•ด์–‘์—์„œ ํ™˜๊ฒฝ๊ณผ ๊ธฐ๋™์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ผ ์ˆ˜์‹ ์Œ์˜ ๋ ˆ๋ฒจ๊ณผ ํŒจํ„ด์ด ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ์ ์„ ๊ฐ„๊ณผํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐ ์†Œ์Œ์›๋“ค์„ ์‹ค์ œ์™€ ๊ฐ€๊น๊ฒŒ ๋ชจ์˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ์ œ์‹œํ•˜๋ฉฐ, ๊ทธ๋Ÿฌํ•œ ์†Œ์Œ์›๋“ค์ด ์ „๋‹ฌ๋ชจ๋ธ์„ ํ†ตํ•ด ์ž ์ˆ˜ํ•จ์— ์ „๋‹ฌ๋˜๋Š” ๊ณผ์ •์„ ๋ชจ์˜ํ•˜์—ฌ ์‹ค์ œ์™€ ๊ฐ€๊นŒ์šด ์†Œ์Œ์›์„ ๋ชจ์˜ํ•˜๋Š”๋ฐ ์ค‘์ ์„ ๋‘์—ˆ๋‹ค. ์ด๋ ‡๊ฒŒ ํ•จ์œผ๋กœ์จ ์ž ์ˆ˜ํ•จ ์ˆ˜๋™์†Œ๋‚˜์— ์†Œ์Œ์› ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ์ ์šฉํ•˜์—ฌ ์ž ์ˆ˜ํ•จ ์Šน์กฐ์›๋“ค์ด ํ‰์‹œ์— ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋ฅผ ํ†ตํ•˜์—ฌ ์‹ค์ œ์™€ ๊ฐ€๊นŒ์šด ํ•ด์–‘ํ™˜๊ฒฝ์—์„œ ์ž ์ˆ˜ํ•จ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•จ์œผ๋กœ์จ ์ž ์ˆ˜ํ•จ ์Šน์กฐ์›๋“ค์˜ ํ›ˆ๋ จํšจ๊ณผ๋ฅผ ๊ทน๋Œ€ํ™”ํ•˜๋Š”๋ฐ ๋„์›€์ด ๋˜๋ฆฌ๋ผ ๋ณธ๋‹ค.๊ตญ๋ฌธ์ดˆ๋ก โ…ฐ ๊ทธ๋ฆผ๋ชฉ์ฐจ โ…ณ ํ‘œ ๋ชฉ์ฐจ โ…ถ 1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ˜„ํ™ฉ 1 1.2 ์—ฐ๊ตฌ ๊ฐœ์š” 2 2. ์†Œ๋‚˜ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ ์†Œ์Œ์› ๋ชจ์˜ ๋ฐ ๊ฒ€์ฆ 4 2.1. ์ˆ˜๋™ ์†Œ๋‚˜ ๋ชจ๋ธ๋ง 5 2.2. ์„ ๋ฐ• ํ”„๋กœํŽ ๋Ÿฌ ์†Œ์Œ ๋ชจ๋ธ๋ง 6 2.3. ์„ ๋ฐ• ๋ฐฉ์‚ฌ์†Œ์Œ์ค€์œ„ ๋ชจ๋ธ๋ง 10 2.4. ์ž ์ˆ˜ํ•จ ์ž์ฒด ์†Œ์Œ์› ๋ชจ๋ธ๋ง 16 2.5. ํ•ด์–‘ ๋ฐฐ๊ฒฝ์†Œ์Œ์› ๋ชจ๋ธ๋ง 26 3. ์†Œ์Œ์› ์ „๋‹ฌ๋ชจ๋ธ ๋ฐ ์ขŒํ‘œ๊ณ„ ์„ค์ • 34 3.1. ์†Œ์Œ์› ์ „๋‹ฌ ๋ชจ๋ธ 35 3.2. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ขŒํ‘œ๊ณ„ 35 3.2.1. 3์ฐจ์› ๊ธฐ๋™์— ๋”ฐ๋ฅธ ๋„ํ”Œ๋Ÿฌํšจ๊ณผ ์‚ฐ์ถœ 35 3.2.2. ๋ณ€์นจ๊ณผ ๋ณ€์นจ์œจ ์ ์šฉ 40 3.2.3 ์žํ•จ์ค‘์‹ฌ ์ขŒํ‘œ๊ณ„ 43 4. ํ•ด์–‘ํ™˜๊ฒฝ๊ณผ ๊ธฐ๋™์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ฅธ ๊ฐ„์„ญํŒจํ„ด ๋ถ„์„ 44 5. ์†Œ๋‚˜ ์œ„์น˜๋ณ„ ์ˆ˜์‹ ์‹ ํ˜ธ ๊ทผ์‚ฌ 48 6. ๊ฒฐ๋ก  61 ์ฐธ๊ณ ๋ฌธํ—Œ 63 Abstract 72Maste

    ์ธํ„ฐ๋„ท ์‡ผํ•‘๋ชฐ๋‚ด์˜ ๋””์ง€ํ„ธ ์ƒํ’ˆ๊ณผ ๋ฌผ๋ฆฌ์  ์ƒํ’ˆ๋ณ„ ๋น„์šฉ์šฐ์œ„ ํšจ๊ณผ ๋ถ„์„ ์—ฐ๊ตฌ : ์ „ํ†ต์‹œ์žฅ๊ณผ์˜ ๋น„๊ต๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ฒฝ์˜ํ•™๊ณผ ๊ฒฝ์˜ํ•™์ „๊ณต,2001.Maste
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