• Journal of the Korea Convergence Society
• /
• v.10 no.3
• /
• pp.23-30
• /
• 2019

The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.10b
• /
• pp.17-21
• /
• 2007

Drug discovery is a long process with a low rate of successful new therapeutic discovery regardless of the advances in information technologies. Identification of candidate proteins is an essential step for the drug discovery and it usually requires considerable time and efforts in the drug discovery. The drug discovery is not a logical, but a fortuitous process. Nevertheless, considerable amount of information on drugs are accumulated in UniProt, NCBI, or DrugBank. As a result, it has become possible to try to devise new computational methods classifying drug target candidates extracting the common features of known drug target proteins. In this paper, we devise a method for drug target protein classification by using weighted feature summation and Support Vector Machine. According to our evaluation, the method is revealed to show moderate accuracy $85{\sim}90%$. This indicates that if the devised method is used appropriately, it can contribute in reducing the time and cost of the drug discovery process, particularly in identifying new drug target proteins.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.3
• /
• pp.131-138
• /
• 2017

New drug development is time-consuming and costly. Hence, it is necessary to repurpose old drugs for finding new indication. We suggest the way that repurposing old drug using massive literature data and biological network. We supposed a disease-drug relationship can be available if signal pathways of the relationship include significant genes identified in literature data. This research is composed of three steps-identifying significant gene using co-occurrence in literature; analyzing the shortest path on biological network; and scoring a relationship with comparison between the significant genes and the shortest paths. Based on literatures, we identify significant genes based on the co-occurrence frequency between a gene and disease. With the network that include weight as possibility of interaction between genes, we use shortest paths on the network as signal pathways. We perform comparing genes that identified as significant gene and included on signal pathways, calculating the scores and then identifying the candidate drugs. With this processes, we show the drugs having new possibility of drug repurposing and the use of our method as the new method of drug repurposing.

• Toxicological Research
• /
• v.26 no.1
• /
• pp.1-8
• /
• 2010

The process of new drug development consists of several stages; after identifying potential candidate compounds, preclinical studies using animal models link the laboratory and human clinical trials. Among many steps in preclinical studies, toxicology and safety assessments contribute to identify potential adverse events and provide rationale for setting the initial doses in clinical trials. Gene modulation is one of the important tools of modern biology, and is commonly employed to examine the function of genes of interest. Advances in new drug development have been achieved by exploding information on target selection and validation using genetically modified animal models as well as those of cells. In this review, a recent trend of genetically modified methods is discussed with reference to safety assessments, and the exemplary applications of gene-modulating tools to the tests in new drug development were summarized.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.1
• /
• pp.11-18
• /
• 2022

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively.

• Journal of Pharmaceutical Investigation
• /
• v.36 no.4
• /
• pp.263-269
• /
• 2006

Deficiencies in the early ADMET(absorption, distribution, metabolism, elimination and toxicity) information on drug candidate extract a significant economic penalty on pharmaceutical firms. Microscale cell culture analogue-microscale gastrointestinal(${\mu}CCA-{\mu}GI$) device using Caco 2, L2 and HEp G2/C3A cells, which mimic metabolic process after absorption occurring in humans was used to investigate the toxicity of the model chemical, acetaminophen(AAP). The toxicity of acetaminophen determined after induction of CYP 1A1/2 in Caco 2 cells was not significant. In a coculture system, although no significant reduction in viability of HEp G2/C3A and L2 cells was found, approximately 5 fold increase in the CYP 1A1/2 activity was observed. These results appear to be related to organ-organ interaction. The oral administration of a drug requires addition of the absorption process through small intestine to the current ${\mu}CCA$ device. Therefore, a perfusion coculture system was employed for the evaluation of the absolution across the small intestine and resulting toxicity in the liver and lung. This system give comprehensive and physiologic information on oral uptake and resulting toxicity as in the body. The current ${\mu}CCA$ device can be used to demonstrate the toxic effect due to organ to organ interaction after oral administration,

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2001.08a
• /
• pp.139-149
• /
• 2001

Since 1990, the National Cancer Institute(NCI) has screened more than 60.000 compounds against a panel of 60 human cancer cell lines. The 50-percent growth-inhibitory concentration (GI$_{50}$) values encode unexpectedly rich, detailed information on mechanisms of drug action and drug resistance. Each compound's pattern is like a fingerprint, essentially unique among the many billions of distinguishable possibilities. These activity patterns are being used in conjunction with molecular structural features of the tested agents to explore the NCI's database of more than 460, 000 compounds, and they are providing insight into potential target molecules and modulators of activity in the 60 cell lines. For example, the information is being used to search for candidate anticancer drugs that are not dependent on intact p53 suppressor gene function for their activity. It remains to be seen how effective this information-intensive strategy will be at generating new clinically active agents.s.

• Proceedings of the Korean Society of Applied Pharmacology
• /
• 2007.11a
• /
• pp.19-27
• /
• 2007

An important potential of metabolomics-based approach is the possibility to develop fingerprints of diseases or cellular responses to classes of compounds with known common biological effect. Such fingerprints have the potential to allow classification of disease states or compounds, to provide mechanistic information on cellular perturbations and pathways and to identify biomarkers specific for disease severity and drug efficacy. Metabolic profiles of biological fluids contain a vast array of endogenous metabolites. Changes in those profiles resulting from perturbations of the system can be observed using analytical techniques, such as NMR and MS. $^1H$ NMR was used to generate a molecular fingerprint of serum or urinary sample, and then pattern recognition technique was applied to identity molecular signatures associated with the specific diseases or drug efficiency. Several metabolites that differentiate disease samples from the control were thoroughly characterized by NMR spectroscopy. We investigated the metabolic changes in human normal and clinical samples using $^1H$ NMR. Spectral data were applied to targeted profiling and spectral binning method, and then multivariate statistical data analysis (MVDA) was used to examine in detail the modulation of small molecule candidate biomarkers. We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences between healthy and disease population. Such metabolic signatures could provide diagnostic markers for a disease state or biomarkers for drug response phenotypes.

• Archives of Pharmacal Research
• /
• v.26 no.12
• /
• pp.1002-1008
• /
• 2003

Ferulic acid (3-methoxy, 4-hydroxy cinnamic acid) is a flavoid component possessing antioxidant property. The compound is currently under development as a new drug candidate for the treatment of the dementia. The objective of this preformulation study was to determine the physicochemical properties of ferulic acid. The n-octanol to water partition coefficients of ferulic acid were 0.375 and 0.489 at the pHs of 3 and 10, respectively. Accelerated stability study for ferulic acid indicated that the t 90 value for the drug was estimated to be 459 days at $25^{\circ}C$. Ferulic acid was also found to be unstable under the relative humidity of more than 76%, probably because of the hygroscopic nature of the drug. In order to study compatibility of ferulic acid with typical excipients, potential change in differential scanning calorimetry spectrum was studied in 1: 1 binary mixtures of ferulic acid and typical pharmaceutical excipients (e.g., Aerosil, Avicel, CMC, Eudragit, lactose, PEG, PVP, starch and talc). Avicel, CMC, PVP and starch were found to be incompatible with ferulic acid, indicating the addition of these excipients may complicate the manufacturing of the formulation for the drug. Particle size distribution of ferulic acid powder was in the size range of 10-190 $\mu$m with the mean particle size of 61 $\mu$m. The flowability of ferulic acid was apparently inadequate, indicating the granulation may be necessary for the processing of the drug to solid dosage forms. Two polymorphic forms were obtained by recrystallization from various solvents used in formulation. New polymorphic form of ferulic acid, Form II, was obtained by recrystallization from 1,4-dioxane. The equilibrium solubility for Form I was approximately twice of that for Form II. The dissolution rate of Form II was higher than that of Form I in the early phase (<6 min). Therefore, these physicochemical information has to be taken in the consideration for the formulation of ferulic acid.

• Mass Spectrometry Letters
• /
• v.6 no.2
• /
• pp.48-51
• /
• 2015

Kinsenoside is a principle bioactive compound of Anoectochilus formosanus. It exhibits various pharmacological effects such as antihyperglycemic, antioxidant, anti-inflammatory, immunostimulating, and hepatoprotective activities and has recently been developed as an antidiabetic drug candidate. In this study, as part of an in vitro pharmacokinetic study, the stability of kinsenoside in rat and human liver microsomes was evaluated. Kinsenoside was found to have good metabolic stability in both rat and human liver microsomes. These results will provide useful information for further in vivo pharmacokinetic and metabolism studies.