• Proceedings of the Korean Society of Applied Pharmacology
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• 1994.04a
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• pp.41-49
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• 1994

The most common conventional method of discovering a drug involves a massive screening of a large number of compounds in chemical libraries or in the extracts from natural sources such as plants or microbial broths followed by chemical modification of one or more active compounds to improve their properties as a drug. When the three-dimensional structure of the target molecule for which the drug is searched is known the drug discovery process can be significantly simplified, This is especially true when the three-dimensional structure of a complex between the target and a lead compound is known. In this lecture our experience on the structure-based drug design for human CDK2(cyclin-dependent protein kinase 2) will be discussed with special emphasis on the strength and weakness of this approach of drug discovery. The regulation of the activity of CDK2 plays an important role in the cell proliferation of normal and cancer cells.

• Bulletin of the Korean Chemical Society
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• v.35 no.11
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• pp.3156-3157
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• 2014

• Bulletin of the Korean Chemical Society
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• v.35 no.7
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• pp.1929-1930
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• 2014

• Biomolecules & Therapeutics
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• v.25 no.1
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• pp.57-68
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• 2017

Seven transmembrane receptors (7TMRs), also known as G protein-coupled receptors, are popular targets of drug development, particularly 7TMR systems that are activated by peptide ligands. Although many pharmaceutical drugs have been discovered via conventional bulk analysis techniques the increasing availability of structural and evolutionary data are facilitating change to rational, targeted drug design. This article discusses the appeal of neuropeptide-7TMR systems as drug targets and provides an overview of concepts in the evolution of vertebrate genomes and gene families. Subsequently, methods that use evolutionary concepts and comparative analysis techniques to aid in gene discovery, gene function identification, and novel drug design are provided along with case study examples.

• Journal of Integrative Natural Science
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• v.7 no.2
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• pp.75-78
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• 2014

Computer-aided drug design uses computational chemistry to discover, enhance, or study drugs and related biologically active molecules. It is now proved to be effective in reducing costs and speeding up drug discovery. In this short review, we discussed on the importance of combining molecular docking and molecular dynamics simulation methodologies. We also reviewed the importance of protein flexibility, refinement of docked complexes using molecular dynamics and the use of free energy calculations for the calculation of accurate binding energies has been reviewed.

• Journal of Korean Academy of Nursing
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• v.37 no.5
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• pp.645-654
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• 2007

Purpose: This research was conducted to compare the effects of drug therapy, physical therapy, and exercise on pain, disability, and depression in patients with chronic low back pain. Methods: The research design of this study was a nonequivalent control group pretest-posttest design. The subjects of this study were 28 patients for the drug therapy & physical therapy, 24 patients for the drug therapy & exercise, and 22 patients for the physical therapy & exercise. Data was collected by MVAS, Oswestry disability questionnaires, and questionnaires of depression. It was analyzed by paired t-test for effectiveness, ANOVA, and Scheffe for comparison of the effects of the 3 experimental treatments, using SPSS/WIN 12.0. Results: There were no effects of drug therapy & physical therapy on pain, disability, and depression. However, there were effects of drug therapy & exercise and the physical therapy & exercise on pain, disability, and depression. The effects of physical therapy & exercise on pain, disability, and depression were the greatest, but there was no statistically significant differences between the drug therapy & exercise and the physical therapy & exercise. Conclusions: Exercise is regarded as a more effective and easily accessible nursing intervention to apply alone than drug therapy or physical therapy simultaneously in reducing pain, disability and depression.

• Journal of Integrative Natural Science
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• v.5 no.4
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• pp.216-219
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• 2012

Structure-based drug design possibly benefit from in silico methods that precisely predict the binding affinity of small molecules to target macromolecules. There are many limitations arise from the difficulty of predicting the binding affinity of a small molecule to a biological target with the current scoring functions. There is thus a strong interest in novel methodologies based on MD simulations that claim predictions of greater accuracy than current scoring functions, helpful for a regular use designed for drug discovery in the pharmaceutical industry. Herein, we report a short review on free energy calculations using MMPBSA method a useful method in structure based drug discovery.

• Journal of Pharmaceutical Investigation
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• v.40 no.5
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• pp.263-267
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• 2010

Nanoemulsions have been widely investigated for many years because of their attractive and unique characteristics. Nanoemulsions are composed of oil, surfactant, co-surfactant and water. Especially, cosurfactant plays a critical role in formation of nanoemulsions. In pharmaceutical area, a pre-concentrate form of nanoemulsions which is known as self-nanoemulsifying drug delivery systems (SNEDDS) was available for some water-insoluble drugs. In this study, we investigated the functional behaviors of cosurfactant in design of SNEDDS and nanoemulsions. Cremophor RH 40$^{(R)}$, Propylene carbonate and medium chain triglyceride were selected for surfactant, cosurfactant and oil, respectively. Cyclosporine was employed as a drug. Phase diagrams showed the area of isotropic o/w region which forms o/w nanoemulsions was not significantly affected by the compositional ratio of cosurfactant. But, drug solubilization capacity, droplet size of nanoemulsions and drug release rate were greatly affected by the cosurfactant.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.443-452
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• 2001

The 2x3 cross-over design is proposed for the bioequivalence of two test drug formulations with a reference drug formulation. Oh et al.(1999) and Park et al.(1998) derived 3x2 cross-over design and discussed its benefits, since the 3x3 cross-over design may not be of practical design. We discuss the statistical issues for2x3 cross-over design and show its statistical properties. Bioequivalence problem in 2x3 cross-over design is considered statistically and an illustrated example is given.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.373-386
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• 2022

Purpose: This study proposes the application of Six Sigma management innovation method for more systematically enhanced execution of Quality by Design (QbD) activities. QbD requires a deeper understanding of the product and process at the design and development stage of the drug, and it is very important to ensure that no fault is fundamentally generated through thorough process control. Methods: Analyzing the background and specific procedures of quality improvement based on the drug design basis, and analyzing the key contents of each step, we have differentated and common points from the 6 Sigma methodology. We propose a new model of Six Sigma management innovation method suitable for pharmaceutical industry. Results: Regulatory agencies are demanding results from statistical analysis as a scientific basis in developing medicines to treat human life through quality improvement activities based on drug design. By utilizing the education system to improve the statistical analysis capacity in the Six Sigma activities and operating the 6 Sigma Belt system in conjunction, it helped systematically strengthen the execution power of quality improvement activities based on pharmaceutical design based on the members of the pharmaceutical industry. Conclusion: By using QbD Six Sigma, which combines quality enhancement based on pharmaceutical design basis and Six Sigma methodology suitable for pharmaceutical industry, it is possible to obtain satisfactory results both by pharmaceutical companies and regulators by using appropriate statistical analysis methods for preparing scientific evidence data required by regulatory.