A simple and selective HPLC method, using a monolithic column, was developed for the simultaneous determination of the histamine H2-receptor antagonists: famotidine, cimetidine and nizatidine, in the presence of sulfadimethoxine as internal standard. The separation was performed on a Chromolith Performance RP-18 column (100 mm x 4.6 mm i.d.) with an isocratic mobile phase consisting of 0.05 mol/L acetate buffer (adjusted to pH 6.5 with triethylamine)/methanol/ acetonitrile (85:10:5, v/v/v). The wavelength was set at 230 nm. Linearity was obtained for concentrations between 0.2 to 50 μg/mL and limits of detection were in the range 0.07-0.17 μg/mL. Full validation with respect to linearity, selectivity, detection and quantification limits, accuracy, precision and robustness, the latter using the Youden’s test, was carried out. The method was successfully applied to the determination of the drugs in human serum and urine following solid phase extraction. Average recoveries between 88.0 to 104.4% and 88.0 to 108.0% in serum and urine samples, respectively, were obtained.
{"title":"Use of a monolithic column for the development and validation of a HPLC method for the determination of famotidine, cimetidine and nizatidine in biological fluids","authors":"M. Kontou, A. Zotou","doi":"10.17145/JAB.17.013","DOIUrl":"https://doi.org/10.17145/JAB.17.013","url":null,"abstract":"A simple and selective HPLC method, using a monolithic column, was developed for the simultaneous determination of the histamine H2-receptor antagonists: famotidine, cimetidine and nizatidine, in the presence of sulfadimethoxine as internal standard. The separation was performed on a Chromolith Performance RP-18 column (100 mm x 4.6 mm i.d.) with an isocratic mobile phase consisting of 0.05 mol/L acetate buffer (adjusted to pH 6.5 with triethylamine)/methanol/ acetonitrile (85:10:5, v/v/v). The wavelength was set at 230 nm. Linearity was obtained for concentrations between 0.2 to 50 μg/mL and limits of detection were in the range 0.07-0.17 μg/mL. Full validation with respect to linearity, selectivity, detection and quantification limits, accuracy, precision and robustness, the latter using the Youden’s test, was carried out. The method was successfully applied to the determination of the drugs in human serum and urine following solid phase extraction. Average recoveries between 88.0 to 104.4% and 88.0 to 108.0% in serum and urine samples, respectively, were obtained.","PeriodicalId":15014,"journal":{"name":"Journal of Applied Bioanalysis","volume":"48 1","pages":"1856"},"PeriodicalIF":0.0,"publicationDate":"2017-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79063919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Panderi, K. Perez, Lulu Cao, Lelia C Noble, Kara A Lombardo, T. Walsh, Dionysios P. Pantazatos
1Brown University, Warren Alpert Medical School, COBRE Center for Cancer Research, Rhode Island Hospital, Providence, RI, USA. 2National and Kapodistrian University of Athens, Department of Pharmacy, Laboratory of Pharmaceutical Analysis, Athens, Greece. 3Brown University, Warren Alpert Medical School, Department of Hematology/Oncology, Providence, RI, USA. 4Stanford University, School of Medicine, Department of Genetics, Stanford, CA, USA. 5Brown University, Warren Alpert Medical School, Department of Pathology, Rhode Island Hospital, Providence, RI, USA. 6Weill Cornell Medicine, Cornell University, Division of Infectious Diseases, Transplantation-Oncology Infectious Disease Program, New York, NY, USA. 7Weill Cornell Medicine, Cornell University, Departments of Medicine, Pediatrics, and Microbiology & Immunology, New York, NY, USA.
{"title":"Assessment of molecular differentiation in FFPE colon adenocarcinoma tissues using PCA analysis of MALDI IMS spectral data","authors":"I. Panderi, K. Perez, Lulu Cao, Lelia C Noble, Kara A Lombardo, T. Walsh, Dionysios P. Pantazatos","doi":"10.17145/jab.17.012","DOIUrl":"https://doi.org/10.17145/jab.17.012","url":null,"abstract":"1Brown University, Warren Alpert Medical School, COBRE Center for Cancer Research, Rhode Island Hospital, Providence, RI, USA. 2National and Kapodistrian University of Athens, Department of Pharmacy, Laboratory of Pharmaceutical Analysis, Athens, Greece. 3Brown University, Warren Alpert Medical School, Department of Hematology/Oncology, Providence, RI, USA. 4Stanford University, School of Medicine, Department of Genetics, Stanford, CA, USA. 5Brown University, Warren Alpert Medical School, Department of Pathology, Rhode Island Hospital, Providence, RI, USA. 6Weill Cornell Medicine, Cornell University, Division of Infectious Diseases, Transplantation-Oncology Infectious Disease Program, New York, NY, USA. 7Weill Cornell Medicine, Cornell University, Departments of Medicine, Pediatrics, and Microbiology & Immunology, New York, NY, USA.","PeriodicalId":15014,"journal":{"name":"Journal of Applied Bioanalysis","volume":"40 1","pages":"81-97"},"PeriodicalIF":0.0,"publicationDate":"2017-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88209333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maria Petrocheilou, V. Samanidou, L. Kovatsi, M. Tsolaki, I. Papadoyannis
Galantamine (GAL), donepezil (DON) and rivastigmine (RIV) are cholinesterase inhibitors administered to patients who suffer from Alzheimer’s disease (AD). We have currently developed and validated an HPLC-DAD method for the determination of GAL, DON, RIV in cerebrospinal fluid (CSF), blood serum and urine. The retention times of the drugs were 1.5, 2.2 and 3.1 min, respectively and the total time of analysis was 5 min. Validation was performed in terms of linearity, selectivity, accuracy, precision and stability. Following validation, the method was applied to clinical CSF, blood serum and urine samples. The currently developed method is a reliable tool for monitoring CSF, serum and urine galantamine, donepezil and rivastigmine levels in patients under treatment with these drugs.
{"title":"A simple and direct HPLC-DAD method for the simultaneous determination of galantamine, donepezil and rivastigmine in cerebrospinal fluid, blood serum and urine","authors":"Maria Petrocheilou, V. Samanidou, L. Kovatsi, M. Tsolaki, I. Papadoyannis","doi":"10.17145/JAB.17.010","DOIUrl":"https://doi.org/10.17145/JAB.17.010","url":null,"abstract":"Galantamine (GAL), donepezil (DON) and rivastigmine (RIV) are cholinesterase inhibitors administered to patients who suffer from Alzheimer’s disease (AD). We have currently developed and validated an HPLC-DAD method for the determination of GAL, DON, RIV in cerebrospinal fluid (CSF), blood serum and urine. The retention times of the drugs were 1.5, 2.2 and 3.1 min, respectively and the total time of analysis was 5 min. Validation was performed in terms of linearity, selectivity, accuracy, precision and stability. Following validation, the method was applied to clinical CSF, blood serum and urine samples. The currently developed method is a reliable tool for monitoring CSF, serum and urine galantamine, donepezil and rivastigmine levels in patients under treatment with these drugs.","PeriodicalId":15014,"journal":{"name":"Journal of Applied Bioanalysis","volume":"118 1","pages":"59-69"},"PeriodicalIF":0.0,"publicationDate":"2017-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84951952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Venessa Jim, Corinne LaViolette, Margaret M Briehl, Jani C Ingram
The aim of the study is to better understand where uranium deposits in mice kidneys. The spatial distribution of uranium was examined in the kidneys of C57BL/6 mice using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Mice were exposed to varying levels of uranyl nitrate in their drinking water. Calibration standards were developed to allow for semi-quantitative measurement of uranium in the cortical and medullary regions of mice kidney by LA-ICP-MS. Scanning electron microscopy was used to image the ablation patterns on the kidney. Uranium levels were observed to increase in kidney tissue as uranyl nitrate treatment exposure levels increased. A trend towards a higher uranium concentration in the medullary versus cortical region of the kidneys was observed. These results show the usefulness of LA-ICP-MS in toxicity studies by providing a quantitative, spatial assessment of uranium deposition in a target organ.
{"title":"Spatial distribution of uranium in mice kidneys detected by laser ablation inductively coupled plasma mass spectrometry.","authors":"Venessa Jim, Corinne LaViolette, Margaret M Briehl, Jani C Ingram","doi":"10.17145/jab.17.007","DOIUrl":"10.17145/jab.17.007","url":null,"abstract":"<p><p>The aim of the study is to better understand where uranium deposits in mice kidneys. The spatial distribution of uranium was examined in the kidneys of C57BL/6 mice using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Mice were exposed to varying levels of uranyl nitrate in their drinking water. Calibration standards were developed to allow for semi-quantitative measurement of uranium in the cortical and medullary regions of mice kidney by LA-ICP-MS. Scanning electron microscopy was used to image the ablation patterns on the kidney. Uranium levels were observed to increase in kidney tissue as uranyl nitrate treatment exposure levels increased. A trend towards a higher uranium concentration in the medullary versus cortical region of the kidneys was observed. These results show the usefulness of LA-ICP-MS in toxicity studies by providing a quantitative, spatial assessment of uranium deposition in a target organ.</p>","PeriodicalId":15014,"journal":{"name":"Journal of Applied Bioanalysis","volume":"3 3","pages":"43-48"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5699501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35586721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sjoukje Postma-Kunnen, J. Yska, Gerard Hommema, Sikke Koopmans, B. Wilffert, E. Roon
To determine metoprolol and its metabolite α-hydroxymetoprolol in human serum we validated a method on an LC system with an Exactive® Orbitrap mass spectrometer (Thermo Scientific) as detector and isotope-labelled metoprolol-d7 as internal standard. A simple sample preparation was used with water-acetonitrile (15:85, v/v) as precipitation reagent. This method has a chromatographic run time of 15 min and linear calibration curves in the range of 5.0-250 µg/L for both metoprolol and α-hydroxymetoprolol. Validation showed the method to be accurate, with a good precision, selective and with a lower limit of quantitation of 2.0 µg/L for metoprolol and 1.0 µg/L for α-hydroxymetoprolol, respectively. This validated LC-Orbitrap MS analysis for metoprolol and α-hydroxymetoprolol can be used for application in human pharmacokinetics.
{"title":"A validated high-resolution accurate mass LC-MS assay for quantitative determination of metoprolol and α-hydroxymetoprolol in human serum for application in pharmacokinetics.","authors":"Sjoukje Postma-Kunnen, J. Yska, Gerard Hommema, Sikke Koopmans, B. Wilffert, E. Roon","doi":"10.17145/JAB.17.008","DOIUrl":"https://doi.org/10.17145/JAB.17.008","url":null,"abstract":"To determine metoprolol and its metabolite α-hydroxymetoprolol in human serum we validated a method on an LC system with an Exactive® Orbitrap mass spectrometer (Thermo Scientific) as detector and isotope-labelled metoprolol-d7 as internal standard. A simple sample preparation was used with water-acetonitrile (15:85, v/v) as precipitation reagent. This method has a chromatographic run time of 15 min and linear calibration curves in the range of 5.0-250 µg/L for both metoprolol and α-hydroxymetoprolol. Validation showed the method to be accurate, with a good precision, selective and with a lower limit of quantitation of 2.0 µg/L for metoprolol and 1.0 µg/L for α-hydroxymetoprolol, respectively. This validated LC-Orbitrap MS analysis for metoprolol and α-hydroxymetoprolol can be used for application in human pharmacokinetics.","PeriodicalId":15014,"journal":{"name":"Journal of Applied Bioanalysis","volume":"22 1","pages":"49-57"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76523124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The acyl glucuronide (AG) of ASP3258 was abundant in incurred monkey samples, and because an AG generally back-converts to its aglycone, accurate quantification of ASP3258 in monkey plasma was a challenge. To prevent the back-conversion of ASP3258-AG, cooling and acidification were incorporated during sample collection, storage, and extraction. To demonstrate that the AG did not affect the determination of ASP3258, the present study used incurred samples to examine whole blood stability, short-term stability, freeze-thaw stability, frozen stability, and stability during extraction. The concentration changes were within −11.4% to 15.0% compared with a reference value and were therefore judged acceptable. The present study presents a detailed account of test items, a reference value, sample numbers, sample selection, and an equation for assessment in the incurred sample stability tests. This bioanalytical method was applied successfully to a study of the toxicokinetics of ASP3258 in monkeys.
{"title":"Incurred sample stability of ASP3258 in the presence of its acyl glucuronide","authors":"Yoshiaki Ohtsu","doi":"10.17145/JAB.17.006","DOIUrl":"https://doi.org/10.17145/JAB.17.006","url":null,"abstract":"The acyl glucuronide (AG) of ASP3258 was abundant in incurred monkey samples, and because an AG generally back-converts to its aglycone, accurate quantification of ASP3258 in monkey plasma was a challenge. To prevent the back-conversion of ASP3258-AG, cooling and acidification were incorporated during sample collection, storage, and extraction. To demonstrate that the AG did not affect the determination of ASP3258, the present study used incurred samples to examine whole blood stability, short-term stability, freeze-thaw stability, frozen stability, and stability during extraction. The concentration changes were within −11.4% to 15.0% compared with a reference value and were therefore judged acceptable. The present study presents a detailed account of test items, a reference value, sample numbers, sample selection, and an equation for assessment in the incurred sample stability tests. This bioanalytical method was applied successfully to a study of the toxicokinetics of ASP3258 in monkeys.","PeriodicalId":15014,"journal":{"name":"Journal of Applied Bioanalysis","volume":"69 1","pages":"34-42"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73349580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
search organization (CRO) in the US, we inquired about the CRO’s use of lab automation to help improve wet lab capacity and data quality and integrity for one of our LBA-LC-MS/MS assays placed there, and learned that they only use 96 well aspirator/dispenser type of automation devices to automate a few 96 well plate-wide liquid handling steps. When asked why they don’t use lab automation to automate majority of wet lab work, they answered: we don’t have the resources for that.
{"title":"Automation of LBA-LC-MS/MS assays","authors":"M. Ma, Ming Li","doi":"10.17145/jab.17.005","DOIUrl":"https://doi.org/10.17145/jab.17.005","url":null,"abstract":"search organization (CRO) in the US, we inquired about the CRO’s use of lab automation to help improve wet lab capacity and data quality and integrity for one of our LBA-LC-MS/MS assays placed there, and learned that they only use 96 well aspirator/dispenser type of automation devices to automate a few 96 well plate-wide liquid handling steps. When asked why they don’t use lab automation to automate majority of wet lab work, they answered: we don’t have the resources for that.","PeriodicalId":15014,"journal":{"name":"Journal of Applied Bioanalysis","volume":"1 4","pages":"31-33"},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91504873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
(mAbs), are complex protein molecules produced from mammalian tissue culture cells through recombinant DNA technology. As a result of naturally-occurring molecular heterogeneity as well as chemical and enzymatic modifications during manufacture, process, and storage, there are many product quality attributes (PQAs) presenting in therapeutic proteins. These PQAs can potentially include: product-related structural heterogeneity related to glycosylation profile, disulfide bond pattern, and higher order structure; product-related degradants and impurities, such as deamidation, oxidation, sequence variants; and process-related impurities and residuals, such as host cell protein (HCP), host cell DNA, and residual protein A [1]. Regulatory agencies have recently recommended a Quality by Design (QbD) approach for the manufacturing of therapeutic molecules [2-5], which requires in-depth understanding of these PQAs at the molecular level to ensure that the drug products meet the desired safety and efficacy profiles [6]. The QbD guidelines require development of a quality target product profile (QTPP) that identifies critical quality attributes (CQAs) and implementation of control strategies to ensure that the QTPP is achieved. QTPP is a prospective summary of the quality characteristics of a drug product to be achieved to ensure the desired quality, safety and efficacy [2]. QTPP describes the design criteria for the product and forms the basis for determination of the CQAs, critical process parameters (CPPs), and control strategy. A CQA is a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality [2]. A CQA is identified based on the severity of harm to a patient resulting from failure to meet that quality attribute. Analytical methods to identify and quantify these PQAs, especially CQAs, are essential for the development of QTPP and implementation of control strategies. Conventionally, a panel of analytical techniques such as size-exclusion chromatography (SEC), ion-exchange chromatography (IEX), hydrophobic-interaction chromatography (HIC), or capillary electrophoresis (CE) is typically used to monitor product quality consistency as well as product variants and impurities at the intact protein level [7-9]. Although these chromatographic and electrophoretic methods widely are used as release assays for biologics [10], they cannot directly monitor biologically relevant PQAs at the molecular level, which does not align with QbD principles. The complexity of biologics attributes and the implementation of QbD strategies demand a multi-attribute method (MAM) that can monitor multiple biologics PQAs at the molecular level in a single assay. Coupling liquid chromatography (LC) to high resolution and high accuracy mass spectrometry (MS) techniques, LC-MS based peptide mapping has become a MAM approach that can identify and quantify multip
单克隆抗体(mab)是通过重组DNA技术从哺乳动物组织培养细胞中产生的复杂蛋白质分子。由于天然存在的分子异质性以及在制造、加工和储存过程中的化学和酶修饰，治疗蛋白中存在许多产品质量属性(pqa)。这些pqa可能包括:与糖基化谱、二硫键模式和高阶结构相关的与产物相关的结构异质性;与产品相关的降解物和杂质，如脱酰胺、氧化、序列变异;以及过程相关的杂质和残留物，如宿主细胞蛋白(HCP)、宿主细胞DNA和残留蛋白A[1]。监管机构最近推荐了一种治疗性分子制造的质量设计(QbD)方法[2-5]，该方法需要在分子水平上深入了解这些pqa，以确保药物产品满足所需的安全性和有效性[6]。QbD指南要求开发质量目标产品概要(QTPP)，确定关键质量属性(cqa)并实施控制策略，以确保实现QTPP。QTPP是对药品所要达到的质量特征进行前瞻性总结，以保证其达到预期的质量、安全性和有效性[2]。QTPP描述了产品的设计标准，并构成了确定cqa、关键工艺参数(CPPs)和控制策略的基础。CQA是一种物理、化学、生物或微生物性质或特性，应在适当的限制、范围或分布范围内，以确保所需的产品质量[2]。CQA是根据未能满足该质量属性对患者造成伤害的严重程度来确定的。识别和量化这些pqa，特别是cqa的分析方法对于QTPP的发展和控制策略的实施至关重要。传统上，通常使用一组分析技术，如尺寸排除色谱(SEC)、离子交换色谱(IEX)、疏水相互作用色谱(HIC)或毛细管电泳(CE)来监测产品质量一致性以及完整蛋白质水平上的产品变异和杂质[7-9]。虽然这些色谱和电泳方法被广泛用于生物制剂的释放分析[10]，但它们不能在分子水平上直接监测生物学相关的pqa，这与QbD原则不一致。生物制剂属性的复杂性和QbD策略的实施需要一种多属性方法(MAM)，可以在单次分析中在分子水平上监测多种生物制剂pqa。将液相色谱(LC)与高分辨率和高精度质谱(MS)技术相结合，基于LC-MS的肽图谱已经成为一种可以识别和量化多种属性的MAM方法。JOURNAL OF APPLIED BIOANALYSIS, 2017, p. 21-25。http://dx.doi.org/10.17145/jab.17.003 (ISSN 2405-710X)第三卷，第2期
{"title":"LC-MS multi-attribute method for characterization of biologics","authors":"Xiaobin Xu, Haibo Qiu, Ning Li","doi":"10.17145/JAB.17.003","DOIUrl":"https://doi.org/10.17145/JAB.17.003","url":null,"abstract":"(mAbs), are complex protein molecules produced from mammalian tissue culture cells through recombinant DNA technology. As a result of naturally-occurring molecular heterogeneity as well as chemical and enzymatic modifications during manufacture, process, and storage, there are many product quality attributes (PQAs) presenting in therapeutic proteins. These PQAs can potentially include: product-related structural heterogeneity related to glycosylation profile, disulfide bond pattern, and higher order structure; product-related degradants and impurities, such as deamidation, oxidation, sequence variants; and process-related impurities and residuals, such as host cell protein (HCP), host cell DNA, and residual protein A [1]. Regulatory agencies have recently recommended a Quality by Design (QbD) approach for the manufacturing of therapeutic molecules [2-5], which requires in-depth understanding of these PQAs at the molecular level to ensure that the drug products meet the desired safety and efficacy profiles [6]. The QbD guidelines require development of a quality target product profile (QTPP) that identifies critical quality attributes (CQAs) and implementation of control strategies to ensure that the QTPP is achieved. QTPP is a prospective summary of the quality characteristics of a drug product to be achieved to ensure the desired quality, safety and efficacy [2]. QTPP describes the design criteria for the product and forms the basis for determination of the CQAs, critical process parameters (CPPs), and control strategy. A CQA is a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality [2]. A CQA is identified based on the severity of harm to a patient resulting from failure to meet that quality attribute. Analytical methods to identify and quantify these PQAs, especially CQAs, are essential for the development of QTPP and implementation of control strategies. Conventionally, a panel of analytical techniques such as size-exclusion chromatography (SEC), ion-exchange chromatography (IEX), hydrophobic-interaction chromatography (HIC), or capillary electrophoresis (CE) is typically used to monitor product quality consistency as well as product variants and impurities at the intact protein level [7-9]. Although these chromatographic and electrophoretic methods widely are used as release assays for biologics [10], they cannot directly monitor biologically relevant PQAs at the molecular level, which does not align with QbD principles. The complexity of biologics attributes and the implementation of QbD strategies demand a multi-attribute method (MAM) that can monitor multiple biologics PQAs at the molecular level in a single assay. Coupling liquid chromatography (LC) to high resolution and high accuracy mass spectrometry (MS) techniques, LC-MS based peptide mapping has become a MAM approach that can identify and quantify multip","PeriodicalId":15014,"journal":{"name":"Journal of Applied Bioanalysis","volume":"1994 1","pages":"21-25"},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82429264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ADC bioanalytical strategies With the US FDA approval of Adcetris® (brentuximab vedotin) in 2011 and Kadcyla® (ado-transtuzumab emtansine) in 2013, antibody-drug conjugate (ADC) has been a hot topic in industry. Because of the complexity of an ADC, combining monoclonal antibody and small molecule toxin, its bioanalysis has seen unprecedented amount of discussion compared to other drug modalities. Two review articles, Stephan et al. [1] and Kaur et al. [2], and an AAPS Drug Conjugate Working Group position paper [3] best describe the challenges and strategies of ADC bioanalysis. There are three key points from these milestone publications on ADC bioanalysis: 1. These articles outline the bioanalytical strategies to measure three PK analytes for non-clinical and clinical studies: total antibody, conjugated-antibody or antibody conjugated-drug, and free drug and its metabolites using ligand-binding, LC-MS or hybrid ligand-binding LC-MS assays [1,2]. They also point out that the analytes measured for a particular ADC could vary and the number of analytes could possibly be reduced late in clinical development. 2. Drug-to-antibody ratio (DAR) in vivo may change due to deconjugation and/or different clearance rates. The total-antibody and conjugated-antibody assay should measure different DAR species equally without DAR bias. DAR bias or DAR sensitivity has been the most challenging and debated topic in ADC bioanalytical assays. 3. Affinity capture LC-MS measurement of intact ADCs to characterize DAR distribution change in vitro and in vivo is important to understanding ADC biotransformation in developing ADCs. JOURNAL OF APPLIED BIOANALYSIS, April 2017, p. 26-30. http://dx.doi.org/10.17145/jab.17.004 (ISSN 2405-710X) Vol. 3, No. 2
随着美国FDA于2011年批准Adcetris®(brentuximab vedotin)和2013年批准Kadcyla®(ado-transtuzumab emtansine)，抗体-药物偶联物(ADC)已成为业界的热门话题。由于ADC的复杂性，结合了单克隆抗体和小分子毒素，与其他药物模式相比，其生物分析已经看到了前所未有的大量讨论。Stephan et al.[1]和Kaur et al.[1]两篇综述文章以及AAPS药物偶联物工作组的立场文件[3]最好地描述了ADC生物分析的挑战和策略。这些具有里程碑意义的ADC生物分析出版物中有三个关键点:1。这些文章概述了用于非临床和临床研究的三种PK分析物的生物分析策略:总抗体、偶联抗体或抗体偶联药物，以及使用配体结合、LC-MS或杂交配体结合LC-MS测定的游离药物及其代谢物[1,2]。他们还指出，针对特定ADC测量的分析物可能会有所不同，并且在临床开发后期可能会减少分析物的数量。2. 体内药物抗体比(DAR)可能因解偶联和/或不同的清除率而改变。总抗体和偶联抗体试验应平等地测量不同的DAR种类，而不存在DAR偏差。在ADC生物分析分析中，DAR偏倚或DAR敏感性一直是最具挑战性和争议的话题。3.通过亲和捕获LC-MS测量完整ADC来表征DAR在体外和体内的分布变化，对于了解ADC在发育过程中的生物转化非常重要。应用生物分析学报，2017年4月，p. 26-30。http://dx.doi.org/10.17145/jab.17.004 (ISSN 2405-710X)第三卷，第2期
{"title":"Current status of antibody-drug conjugate bioanalysis","authors":"Jian Wang","doi":"10.17145/JAB.17.004","DOIUrl":"https://doi.org/10.17145/JAB.17.004","url":null,"abstract":"ADC bioanalytical strategies With the US FDA approval of Adcetris® (brentuximab vedotin) in 2011 and Kadcyla® (ado-transtuzumab emtansine) in 2013, antibody-drug conjugate (ADC) has been a hot topic in industry. Because of the complexity of an ADC, combining monoclonal antibody and small molecule toxin, its bioanalysis has seen unprecedented amount of discussion compared to other drug modalities. Two review articles, Stephan et al. [1] and Kaur et al. [2], and an AAPS Drug Conjugate Working Group position paper [3] best describe the challenges and strategies of ADC bioanalysis. There are three key points from these milestone publications on ADC bioanalysis: 1. These articles outline the bioanalytical strategies to measure three PK analytes for non-clinical and clinical studies: total antibody, conjugated-antibody or antibody conjugated-drug, and free drug and its metabolites using ligand-binding, LC-MS or hybrid ligand-binding LC-MS assays [1,2]. They also point out that the analytes measured for a particular ADC could vary and the number of analytes could possibly be reduced late in clinical development. 2. Drug-to-antibody ratio (DAR) in vivo may change due to deconjugation and/or different clearance rates. The total-antibody and conjugated-antibody assay should measure different DAR species equally without DAR bias. DAR bias or DAR sensitivity has been the most challenging and debated topic in ADC bioanalytical assays. 3. Affinity capture LC-MS measurement of intact ADCs to characterize DAR distribution change in vitro and in vivo is important to understanding ADC biotransformation in developing ADCs. JOURNAL OF APPLIED BIOANALYSIS, April 2017, p. 26-30. http://dx.doi.org/10.17145/jab.17.004 (ISSN 2405-710X) Vol. 3, No. 2","PeriodicalId":15014,"journal":{"name":"Journal of Applied Bioanalysis","volume":"3 1","pages":"26-30"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78718176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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