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변진주,박민호,신석호,신영근 대한화학회 2018 Bulletin of the Korean Chemical Society Vol.39 No.12
Bispecific antibodies are generally prepared by co-expression of mixtures of recombinant monoclonal antibodies. Because of increased molecular complexity, characterization of a desired bispecific antibody or a mixture of monoclonal antibodies is more challenging than characterization of a conventional single monoclonal antibody. The purpose of this study is to develop a fast and simple method for qualitative/semi-quantitative analysis of antibody mixtures using liquid chromatography?electrospray triple time-of-flight mass spectrometry (LC-ESI-TOF/MS) to complement the enzyme-linked immunosorbent assay. To demonstrate the proof of concept for the analysis of antibody mixtures, three different tool monoclonal antibodies (trastuzumab, rituximab, and cetuximab) with various mixture ratios were treated by either PNGase or Fabricator to simplify the antibody structures without glycans. After deglycosylation, the mixtures of antibodies were analyzed by LC-ESI-TOF/MS in the positive ion mode. The m/z scan range of 2000?4000 was used for the deconvolution of each peak from antibodies. Because each antibody could show different ionization efficiency in TOF MS, the peak intensities obtained from various mixture antibodies (1:6:3 or 3:1:6 or 6:3:1) were normalized by the peak intensities of 1:1:1 mixture of three antibodies. Overall, two different methods treated by either PNGase or Fabricator were comparable in estimating the mixture ratios; however, the accuracy and precision data from the Fabricator group were slightly better than PNGase group possibly due to the generation of smaller fragments by Fabricator.
정량적 구조-활성 상관 관계와 생리학 기반 약물동태를 사용한 새로운 선도물질 최적화 전략
변진주(Jin-Ju Byeon),박민호(Min-Ho Park),신석호(Seok-Ho Shin),신영근(Young Geun Shin) 대한약학회 2015 약학회지 Vol.59 No.4
The purpose of this study is to demonstrate how lead compounds are best optimized with the application of in silico QSAR and PBPK modeling at the early drug discovery stage. Several predictive QSAR models such as IC50 potency model, intrinsic clearance model and brain penetration model were built and applied to a set of virtually synthesized library of the BACE1 inhibitors. Selected candidate compounds were also applied to the PBPK modeling for comparison between the predicted animal pharmacokinetic parameters and the observed ones in vivo. This novel lead optimization strategy using QSAR and PBPK modelings could be helpful to expedite the drug discovery process.
박민호,신석호,변진주,이관호,유병용,Young G. Shin 대한약리학회 2017 The Korean Journal of Physiology & Pharmacology Vol.21 No.1
Over the last decade, physiologically based pharmacokinetics (PBPK) application has been extended significantly not only to predicting preclinical/ human PK but also to evaluating the drug-drug interaction (DDI) liability at the drug discovery or development stage. Herein, we describe a case study to illustrate the use of PBPK approach in predicting human PK as well as DDI using in silico, in vivo and in vitro derived parameters. This case was composed of five steps such as: simulation, verification, understanding of parameter sensitivity, optimization of the parameter and final evaluation. Caffeine and ciprofloxacin were used as tool compounds to demonstrate the “fit for purpose” application of PBPK modeling and simulation for this study. Compared to caffeine, the PBPK modeling for ciprofloxacin was challenging due to several factors including solubility, permeability, clearance and tissue distribution etc. Therefore, intensive parameter sensitivity analysis (PSA) was conducted to optimize the PBPK model for ciprofloxacin. Overall, the increase in Cmax of caffeine by ciprofloxacin was not significant. However, the increase in AUC was observed and was proportional to the administered dose of ciprofloxacin. The predicted DDI and PK results were comparable to observed clinical data published in the literatures. This approach would be helpful in identifying potential key factors that could lead to significant impact on PBPK modeling and simulation for challenging compounds.
신석호(Seok Ho Shin),박민호(Min Ho Park),변진주(Jin Ju Byeon),이병일(Byeong ill Lee),박유리(Yuri Park),최장미(Jangmi Choi),김나혜(Nahye Kim),신영근(Young Geun Shin) 대한약학회 2018 약학회지 Vol.62 No.3
The antibody-drug conjugate (ADC) is one of the most rapidly growing next generation antibody therapies used in oncology, autoimmunity and chronic inflammatory diseases. ADC is consisted of monoclonal antibody conjugated via chemical linker with highly cytotoxic small molecules, called payloads. Due to the rising prevalence of cancer and population aging, there is a consistent unmet needs on novel therapeutics for patients where the traditional antibody therapy does not work. With the successful market settlement of FDA-approved Adcetris?? and Kadcyla??, global pharmaceutical companies have put their resources for development of this innovative drug class. There are still some drawbacks in this class of drug including linker stability and heterogenous drug antibody ratio (DAR) profile. However new platform technologies are making progress to overcome these problems and further investment will drive growth in the next generation antibody therapies market. In this manuscript, we review the market drivers and restraints and introduce four approved ADCs. We then mainly discuss the important aspects considered while developing ADCs (cytotoxic drug, linker, DAR) and ADC platforms widely used in the industries.