Analysis Of Biomolecular And Cellular Systems 2011 Problem Set 1: Fill & Download for Free

New mass spectrometry (MS) methods, collectively known as data independent analysis and hyper reaction monitoring, have recently emerged. These methods hold promises to address the shortcomings of data-dependent analysis and selected reaction monitoring (SRM) employed in shotgun and targeted proteomics, respectively.Proteomic strategies can be divided into two approaches according to their purpose (Fig. 1). One is peptide mass fingerprinting (PMF) by MALDI-MS, which is used for detailed protein identification in a high-throughput manner (Lam, Li, Lo, Guggenheim, & To, 2007). In this method, it is important to isolate proteins as much as possible using two-dimensional electrophoresis (2D-PAGE). The other is shotgun proteomics, which is used for global peptide identification by hybrid MS (precursor ion selection, MS1, and then product ion detection, MS2). In the shotgun method, it is necessary to first digest the protein mixture, and then accurately separate the large number of generated peptides using high-performance LC (Stephanowitz, Lange, Lang, Freund, & Krause, 2012). From the viewpoint of protein separation, 2D-PAGE is one of the best protein separation protocols. However, this method has issues such as a complicated procedure and poor reproducibility. Immobilized pH gradient gels and automated 2D-PAGE systems have been developed, but these still have not lead to solutions for intrinsic recovery. Currently, the first choice for proteomic approaches is usually LC-ESI-MS, because of improvements in nanoliter-flow LC, column resins, probe performance, automated MS and MS/MS analysis, and the basic potential for MS and its peripheral devices (Howells et al., 2005, Iwasaki et al., 2010, Sun et al., 2014). In addition, MS is a platform that analyzes molecules after they are ionized into the gas phase. Therefore, the molecules must be ionized in some way, and the ionization technologies, fast atom bombardment (FAB) (Morris, Panico, Karplus, Lloyd, & Riniker, 1982), electron ionization (EI), chemical ionization (CI) (Oka et al., 1991), and specific chemical modification are available (Calderon-Celis, Encinar, & Sanz-Medel, 2017). MALDI and ESI are widely used, especially for biomolecules (Hommerson, Khan, de Jong, & Somsen, 2011). These ionization reactions are called “soft ionization” methods. Since the precursor ions of the biomolecule are usually not cleaved, structure-derived product ions from the biomolecule are easily detected (Standing, 2003).Fig. 1Fig. 1. Two proteomic approaches by shotgun LC-MS/MS analysis and 2D-PAGE/PMF.Proteins are extracted correctly from biological samples. 1) Proteins are first digested by trypsin protease after denaturation and reductive alkylation. And generated peptides are separated by reverse-phase HPLC, and directly induced into ESI interface. MS and MS/MS spectra are obtained by data-dependent acquisition. 2) Proteins are denatured in reductive chaotropic solution with non-ionic detergent, and separated by isoelectric point and molecular weight. Each protein spots are digested by trypsin after reductive alkylation. PMF spectra are obtained after purification and mixing with ionization matrix. Protein identification is performed by proteome database server by using processed MS/MS and PMF spectra for the determination of post-translational modification or molecular annotation.Additionally, the amount of detected ions can also be measured. Considering the feature of global MS analysis, a dataset of containing ion amounts with molecular information has great significance when studying a biological system. One popular area that uses proteomics is the development of clinical biomarkers (Jingushi et al., 2017, Nirmalan et al., 2011). Biomarker signatures are defined as an index that can be measured in the body fluids and tissues and used to evaluate objectively the pharmacological responses during normal biological, pathological processes, and practical treatments (Rifai et al., 2006, Vasan, 2006). Moreover, these biomarkers are verified as indicators for the prediction of disease, prognosis, and individual differences. Biomarker rules are roughly classified into three categories: reference limits that statistically derive cut-off points based on the reference samples, discrimination limits that separate patients with a disease from those without it, and threshold follow-ups that identify the level of disease. As a therapeutic index, the blood level monitoring of therapeutic drugs can be visualized, and the treatment responses can be quantified to support practical decisions. Biomarkers can be distinctly subdivided in accordance with their purpose. Starting from broad categories like diagnostic markers or companion diagnosis, biomarkers suitable for a situation have more clinical significance, such as prognostic, predictive, surrogate, monitoring, toxicity, pharmacodynamics (PD), autoantigen, and stratification markers (Poste et al., 2012). The strategies for biomarker discovery differ largely according to the target for the therapeutic or diagnostic phase. A biological system exerts its functions by controlling the expression of many molecules in response to constantly changing phenomena. Even though phenotypic surfaces are silently homeostatic, systems are predicted to be based on various controls in order to maintain the biological process (Kurachi et al., 2009). Taking this situation into consideration, a disease profile and mechanism can be elucidated in detail using an identical biological sample set from the initiation of disease with inflammatory symptoms, pathogenic events to regenerative recovery. Integrated proteomic strategies with protein expression and time-dependent profile changes leads to the discovery of more in-depth biomarkers (Shimada et al., 2010). Quantitative proteomic views and approaches are indispensable for analyzing these systems biology.In this review, we discuss the principles and applications of MS in the context of the trend of quantitative proteomics based on biomolecular-specific structures, and the analytical advances in MS and their contributions to the life science, medical, and pharmaceutical fields. We also describe our recent advances in MS-based bioanalysis and how they have contributed to the remarkable development of antibody drugs and their therapeutic applications.2. Structure-indicated quantitation by mass spectrometry2.1. MS/MS fragmentation, accuracy, and resolution for peptide identificationCurrently, in the proteomic field with MS, accurate protein identification is generally performed using a dataset of precursor and product ions generated by collision-induced dissociation (CID) in hybrid MS function. Peptide fragmentation patterns depend on several factors such as amino acid sequence, covalent potential of amide or other covalent bonds, and ion valence. In a peptide bond dissociation reaction, product ions derived from amide bonds are preferentially cleaved and detected because of the larger dissociation constant of the amide compared to that of residues to α‑carbon, and high probability of charge localization on the amide by weak conjugation formation. Consequently, the detection probability derived from the b- and y-ion series increases. These ion series are most commonly used for structure assignment because they are reflected in peptide-specific amino acid sequences (Guthals & Bandeira, 2012).The atomic content of peptides includes a naturally constant ratio of stable isotopes. For the exact mass, the lowest m/z peak in no isotope distribution, the monoisotopic mass, is selected (Valkenborg, Jansen, & Burzykowski, 2008). The selection of the monoisotopic peak should be carried out as accurately as possible. Peak processing errors lead to a mass difference of 1 Da or more. Proteomic analysis involves a huge number of peptides as analytical targets, and several precursor ions are frequently detected in the near m/z. Therefore, peak processing errors definitely prevent protein identification (DeFelice et al., 2017). On the other hand, high-resolution MS greatly aids in identification and structure assignment. Isoleucine and leucine are isobaric and cannot be separated, but it is possible to distinguish between glutamate and leucine (mass δ = 0.036 Da). Moreover, neutral loss ions of deamidation (δ = 17 Da) and dehydration (δ = 18 Da) can be also reliably separated. As proteomic strategies are designed to be more accurate, high-resolution MS becomes more essential.2.2. Protein identification by mass spectrometry data processingProteomic analysis is assisted seamlessly by excellent MS data processing algorithms. The exact monoisotopic peaks of peptide ions from the protein digestion procedure are acquired, and a peak list of the combination of the precursor and generated product ion information with ion valence is produced. Then, this peak list is entered into the database analysis server, which returns with the ion scores of each peptide. The ion scores of the peptides are calculated using an appropriate scoring algorithm with a theoretical peptide fragment dataset generated from submitted sequence databases. The closest match or matches for a peptide can be identified using the identification criteria of the reliability scores. Currently, Mascot (Matrix Science, UK) (Eng et al., 1994, Pappin et al., 1993), ProteinProspector (UCSF, US) (Chalkley et al., 2005), ProteinPilot (Sciex, US), Proteome Discoverer (Thermo Fisher, US), ProteinScape (Bruker, DE), and Expressionist (Genedata, Switzerland) (Ueda et al., 2011) are the main active well-known proteome servers, and are indispensable engines for large-scale proteomics. For the functional annotation and statistical analysis of identified proteins, Gene Ontology (Gene Ontology Resource) (Camon et al., 2004, Zeeberg et al., 2003) and DABID Bioinformatics (http://david.ncifcrf.gov/) (Huang da, Sherman, & Lempicki, 2009) provide web-accessible engines to visualize biological investigation from several omics dataset. Furthermore, ProteinAtras (http://www.proteinatlas.org/) (Uhlen et al., 2010, Uhlen et al., 2015) and ExSPASy (https://www.expasy.org/) (Artimo et al., 2012) were built as unified protein knowledgebases.The process of data analysis and annotation of large scale dataset from MS is extremely significant for the proteomics-based sciences, which is why, MS is also referred to as an information science.2.3. Comprehensive quantitation for overall systems biologyA quantitative proteomic dataset with high reproducibility provides valuable biological impacts. Specifically, when elucidating a biological landscape as a system, unified strategies combined with multi-omics technologies are necessary. To quantitate the proteins comprehensively, individual ion peaks derived from identified proteins are determined. For more effective and successive quantitation, a targeted quantitation method is currently carried out. The hybrid triple quadrupole (TQ) MS function of selecting a precursor ion from tryptic peptides by the 1st quadrupole (Q1), CID reaction with Argon or a rare gas in the 2nd quadrupole (Q2), and selection of a structure-specific product ion by the 3rd quadrupole (Q3) is optimal for the accurate and successive quantitation of the proteome. The combination of the precursor (Q1) and product ion (Q3) m/z is named the transition of multiple reaction monitoring (MRM) or selected reaction monitoring (SRM). Global quantitative analysis becomes possible by switching this transition at high speed. However, the transition switching speed has an upper limit, and there are some cases in which the MRM method alone cannot perform a satisfactory quantitation of large-scale proteomics due to the prevalence of high-performance LC. Additionally, in recent years, the MRM method has been improved to the parallel reaction monitoring (PRM) (Kreimer et al., 2017). In PRM, a specific product ion is not selected by Q3, but all fragment ions are detected and quantitated after data acquisition. Compared with MRM, the features of PRM encompass a wider range of conditions for quantitative use. Moreover, PRM can be used to clarify the quantitative structure indexes and transition interference from a biological matrix. The database resources PeptideAtras (PeptideAtlas) (Desiere et al., 2006), Skyline (http://skyline.ms/project/home/software/Skyline/begin.view) (MacLean et al., 2010), MaxQuant (http://www.biochem.mpg.de/5111795/maxquant) (Cox & Mann, 2008), and iMPAQT (http://www.bioreg.kyushu-u.ac.jp/saibou/index_en.html) (Matsumoto et al., 2017) are currently being advanced and contributed to the accurate quantitative proteomics.Matsumoto M and Nakayama K et al. have performed excellent study to elucidate the landscape of metabolic systems by identifying and quantifying the global metabolic enzymes on the pathway under the assumption that the expression levels of proteins are the rate-limiting factors in metabolic regulation (Matsumoto et al., 2017). In order to quantitate an individual protein within the genome-wide proteome, they first developed the proteotypic peptide (PTP) (Michalski et al., 2011, Razavi et al., 2012, Selevsek et al., 2015), which can be used to perform the specific quantitation of an individual protein. PTPs are selected from among the tryptic peptides of recombinant human proteins from full-length human cDNA libraries (Goshima et al., 2008). By using actual MS data for the selection of PTPs, optimal PTP selection has become possible through the overall consideration of the efficiency of digestion, peptide release, and ionization. Moreover, the copy number of expressed protein can be determined using the stable isotope-labeled (SIL) PTP as an internal standard (Howden et al., 2013, Ong et al., 2002). Using this strategy, the quantitation of a low-abundance proteome is possible with a wide dynamic range.3. Bioanalytical technologies for protein biopharmaceuticals3.1. Recent advancements in antibody drugs and the necessity of clinical indicators for drug efficacySince the 1980s, specific molecules and pathways related to individual diseases have been identified, and the research to elucidate the molecular mechanism has advanced dramatically. These achievements accelerated the development of molecular target drugs that inhibit a specific molecule and signaling pathway. The approval of anti-CD20 Rituximab for lymphoma (Maloney et al., 1997), anti-ErbB2 Trastuzumab for breast cancer (Baselga, Norton, Albanell, Kim, & Mendelsohn, 1998), and anti-TNFα Infliximab for rheumatoid arthritis (Targan et al., 1997) contributed to the acceleration of the development of antibody drugs in the late 1990s. Recently, antibody drugs against CD antigens, receptors, growth factors, cytokines, immune checkpoints, infectious factors, and amyloid beta have been developed, and about more 60 drugs have been approved as molecular targeted antibody drugs (Mullard, 2017a, Mullard, 2017b). Furthermore, more than 500 items and their biosimilars have been developed, and further improvements are expected. In fact, the combined administration of low-molecular-weight and antibody drugs against a specific disease has become a standard treatment (Avallone et al., 2016). On the other hand, drug efficacy, toxicity, and side effects are correlated with individual differences (Ito, Kondo, Tada, & Kitano, 2015). Thus, treatment has evolved into individualized and optimized medicine in which a patient group that is expected to receive medicinal treatment is selected scientifically before the practical treatment. In the realization of individualized medicine, additional information such as on the expression level of the target molecules, genetic background, indicators of drug efficacy, toxicity biomarkers, immunoreactivity, and metabolism are extremely important.The drug efficacy of small-molecular target drugs typically has a narrow therapeutic window, and blood concentration is one of a key surrogate efficacy index for the appropriate dosage for an individual. Currently, for the anti-cancer agents methotrexate (Przybylski, Preiss, Dennebaum, & Fischer, 1982) and imatinib (Beumer, 2013, Hamada et al., 2003), and pharmaceuticals such as immunosuppressive agents cyclosporine and tacrolimus, it is possible to perform effective treatment maintenance by carrying out therapeutic drug monitoring (Wong, 2001). On the other hand, the metabolic pathway and drug efficacy index of antibody drugs remain unclear, and as a result, the dosage level is not determined enough. However, a positive correlation between the blood concentration and survival was reported about in recent clinical trials of Trastuzumab (Kang et al., 2014) and Ramucirumab (Tabernero et al., 2017) for progressive gastric cancer. Moreover, in a clinical study of Infliximab and Adalimumab against rheumatoid arthritis (Mulleman et al., 2009, Pouw et al., 2015), the blood concentration and inflammation level were inversely correlated. Furthermore, the overproduction of anti-drug antibodies as one of the causes has been addressed (G. P. Eng et al., 2015, Marinari et al., 2014). In 2013, cancer immunotherapy was selected as the breakthrough of the year (Couzin-Frankel, 2013, Ishida et al., 1992): by inhibiting the binding of PD-1/PD-L1, it activates cytotoxic T cells, and lead to the attack to cancer cells that originally escaped from detection by immune system. The antibody drug will most likely be applied to many cancers as the notable treatments of the fourth option, immunotherapy, following surgery, chemotherapy, and radiation. In addition, for the novel therapeutic strategy of controlling the delicate balance of the immune system, the phenotypic response, which had not been anticipated, has been confirmed, and the development of an index that can be used to accurately evaluate efficacy, toxicity, and other factors, has become an urgent issue (Davies and Duffield, 2017, Gao et al., 2012). In the near future, the development of therapeutic methods incorporating scientific indicators for treatment using antibody drugs is required.3.2. Monitoring of protein biopharmaceuticals using current proteomic approachesThe ligand binding assay (LBA) is generally used as a bioanalytical method for monitoring antibody drugs. The LBA method, consists of a specific antibody against an antigen, the purification and concentration of the antigen from the biological matrix, and then a sandwich immunoassay using the detected antibody. Although this detection method mainly involves colorimetric and chemiluminescence produced by an enzymatic reaction such as horseradish peroxidase, an analysis with higher selectivity is possible using MS. This method is called hybrid LBA/LC-MS or the mass spectrometric immunoassay (Trenchevska, Nelson, & Nedelkov, 2016).In bioanalysis using hybrid LBA/LC-MS, in addition to the evaluation of selectivity and the recovery rate from the matrix used in the usual validation test, it is also necessary to validate the affinity capture efficiency of the enzyme and the digestion efficiency. Moreover, in the LBA, it is very important to assess the lot-to-lot variability of the capturing antibody or ligand. In consideration of these points, to establish the internal standard material, the SIL of authentic samples of the full length of the antibody to be analyzed is an ideal procedure (Ismaiel, Mylott, & Jenkins, 2017). Furthermore, the accelerated production of endogenous antibodies against antibody drugs is considered to interfere with precise measurements (Bader et al., 2017). In particular, it is possible to directly affect the affinity capture efficiency. To address this issue, the whole digest samples from the serum are used, and the signature peptide to be selected from the LC-MS is quantified based on the technology of conventional shotgun proteomics. With this technique, it is possible to perform a bioanalysis dependent only on the selectivity potential of high performance MS.3.3. Novel approach of Fab-selective proteolysis nSMOLThe method of LBA or hybrid LBA/LC-MS requires an affinity antibody against each antibody drug and it has become difficult to maintain the analytical the quality in the recent clinical advancement. We confirmed that the influence on the MS quantified value and its behavior differ depending on the type of idiotypic antibody against each antibody drug (Iwamoto, Hamada, & Shimada, 2017). This suggests that it is difficult to prepare an appropriate affinity antibody clones or specific ligands for hybrid LBA/LC-MS, and confirmation of the actual value is essential for the individual clinical difference. And by the whole digest approaches, it is difficult to maintain the ESI interface clearance because complex biological samples have a large excess of peptide analytes (Liu et al., 2012). In the protein bioanalysis using MS, there are several principle issues to overcome, such as the problem of separation ability and carryover in the chromatograph portion, the ion suppression effect and desolvation efficiency of the target molecule due to coexisting ions in the MS, and the handling of multiple sample processing. Accordingly, the development of bioanalytical methods, which make use of the features of MS such as high selectivity, completeness, and continuity, is needed involving the overall optimization of sample processing, biochemistry, structure, enzyme reaction and release efficiency, material sciences, column separation, ionization efficiency, and throughput ability, to contribute to clinical chemistry and pharmaceutical science.We proposed a novel analytical method based on the structure specificity of an antibody independent of a variety of monoclonal antibodies, and this basic technology has been widely applicable to the development and clinical use of antibody drugs. Immunoglobulin G (IgG) is the main antibody drug that has been analyzed. IgG has a 15 nm molecular diameter and is tetramer protein that has two heavy and light chains. IgG is the second major proteome in blood protein content. Since the structural features representing the antigen specificity of antibodies are determined by amino acid substitution through somatic hypermutation on the variable fragment Fv (Novotny and Bruccoleri, 1987, Wu and Kabat, 1970), universal antibody analysis becomes possible by selectively quantitating the complementarity-determining regions (CDRs). Based on these considerations, we developed a novel method, nano-surface and molecular-orientation limited (nSMOL) proteolysis (Iwamoto et al., 2014), for the bioanalysis of antibody drugs (Fig. 2). The nSMOL method consists of orientating the fragment antigen-binding (Fab) towards the reaction solution side by binding IgG in a porous body with a pore diameter of 100 nm via the constant region Fc, and immobilizing the protease on the surface of the nanoparticles with a diameter of 200 nm, resulting in physicochemically limiting the access of the protease to the immobilizing IgG substrate. In this solid-solid reaction, Fab-selective proteolysis becomes possible, and consequently, it is possible to greatly reduce the complexity of the peptide to be analyzed by holding the structural specificity of IgG and eliminating the contamination of the protease. The peptides that include CDRs are candidate signature peptides for antibody drug determination and quantitation, and it is possible to provide high sensitivity, high selectivity, and universal use of analytical technology with appropriately selecting MRM transitions that have antibody structure specificity.Fig. 2 .Fig. 2. Concept representation of nSMOL proteolysis.IgG molecules are collected in the porous resin with 100 nm diameter via fragment, crystallizable (Fc), so that fragment, antigen-binding (Fab) is oriented towards reaction solution. Modified trypsin (shown in yellow picture) is immobilized on the surface of nanoparticle with 200 nm diameter. In this solid-solid reaction, trypsin access to IgG substrate is physically limited, and Fab-selective proteolysis successfully continues. Consequently, it is possible to greatly reduce the complexity of the peptide analyte by holding the structural specificity of IgG on CDR and eliminating contamination of protease, and providing the high-sensitivity and high-selectivity quantitation of universal antibody bioanalysis. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)Even the good antibody with high-specificity and high-affinity to the ligand, bioanalytical validation using the common LBA-related methods surely require the one antibody substrate for one ligand. This one-to-one approach is not only difficult to apply into the broad and rapid clinical studies, but also the cost for preparation and quality control of biological components become a critical issue. However, using Fab-selective reaction nSMOL coupled with mass-based resolution LC-MS/MS, bioanalytical validation for many monoclonal antibodies become possible on the identical platform. And the nSMOL can be configured the condition setting independent of animal matrices. This feature means that comparative data analysis may be addressed from the initial stage of antibody development in animal model to the first-in-human and late-phase clinical trials, and the contribution to antibody drug discovery. Moreover, this strategy can accelerate the cross-sectional trials on rare diseases, and drug-drug interaction assessment for the new combination therapy.The analytical reliability of antibody drug analysis using nSMOL is based on the “Enforcement Regulations of the Law Concerning the Quality, Effectiveness and Security of Securing Drugs, Medical Equipment, etc.” and “Guideline on Bioanalytical Method Validation in Pharmaceutical Development”. In order to verify that nSMOL proteolysis accompanied by TQ-LC-MS analysis meets the criteria of these guideline, the authors have already carried out and reported on the full validation analysis against various antibody drugs (Trastuzumab (Iwamoto, Yamane, Umino, Hamada, & Shimada, 2015), Bevacizumab (Iwamoto, Umino, et al., 2016), Cetuximab (Iwamoto, Takanashi, et al., 2016), Rituximab (Iwamoto, Takanashi, Hamada, & Shimada, 2016b), Nivolumab (Iwamoto, Shimada, Terakado, and Hamada, 2016), Brentuximab vedotin (Iwamoto, Takanashi, Hamada, & Shimada, 2016a), and Trastuzumab emtansine (Iwamoto, Shimomura, Tamura, Hamada, & Shimada, 2017)) using nSMOL. Moreover, since the nSMOL reacts to whole IgG molecules in the blood in principle, multiplex quantitation is possible for the application of combination cancer therapy and therapeutic drug monitoring for inflammatory immune diseases (Iwamoto et al., 2016d, Messenheimer et al., 2017, Steenholdt et al., 2014). nSMOL results have demonstrated that blood concentrations in the range of about 0.1 to 300 μg/mL fulfill all validation criteria for antibody drugs, and it has both analytical reliability and multipurpose universal use (Table 1). This quantifiable range will fulfill without the requirements of any additional operations such as dilution and covers clinical dosage levels and pharmacokinetic (PK) studies, which can be expected to be widely applicable to antibody drug monitoring.Table 1. Fully validated antibodies by nSMOL coupled with LC-MS/MS bioanalysis. Quantitative information in human plasma for individual signature peptide sequence, MRM transition, and linear dynamic range are summarized.Antibodiesa Signature peptide sequences MRM transition Quantitation range in plasma[μg/mL]Precursor ionQ1 [m/z] Product ionQ3 [m/z]Trastuzumab IYPTNGYTR 542.8 404.7 0.06–250Bevacizumab FTFSLDTSK 523.3 797.4 0.15–300Cetuximab SQVFFK 378.2 540.3 0.58–300Rituximab ASGYTFTSYNMHWVK 598.1 817.5 0.58–300Nivolumab ASGITFSNSGMHWVR 550.8 661.5 0.15–250Brentuximab vedotin VLIYAASNLESGIPAR 837.5 343.1 0.58–300Trastuzumab emtansine IYPTNGYTR 542.8 404.7 0.06–250aDetail bioanalytical conditions for each antibody are described in: Trastuzumab (Iwamoto et al., 2015); Bevacizumab (Iwamoto, Umino, et al., 2016); Cetuximab (Iwamoto, Takanashi, et al., 2016); Rituximab (Iwamoto et al., 2016b); Nivolumab (Iwamoto, Shimada, et al., 2016); Brentuximab vedotin (Iwamoto et al., 2016a); Trastuzumab emtansine (Iwamoto et al., 2017).4. Summary and future aspectsThe analytical data obtained from MS include the structure-derived ions of measured molecules, but it can be developed into a wide variety of research applications by correctly understanding and applying the MS data. Proteomics, which is a necessary MS technology, is an optimal strategy for understanding the complicated systems biology (Bakalarski & Kirkpatrick, 2016). Although a part of the actual approach has not been generally used and applied, proteomics has become a cutting-edge scientific field, which opens new avenues for life sciences on the premise of coverage, by combining various aspects of biochemistry, analytical chemistry, physical chemistry, material science, and informatics with the structure specificity of biomolecules as an indicator. Specifically, in addition to not only structural information and molecular identification information, but also quantitative information can be obtained, which makes it possible to integrate with superior database, and it has led to great progress in understanding biology. The accumulation of quantitative proteome analysis technology is informative, and due to improvements in liquid chromatography technologies, the range of applications has dramatically expanded. The antibody drug monitoring technology nSMOL, which we recently developed, is also a technology conceived as a result of addressing the fundamental technologies of proteomics and clinical and pharmaceutical issues.Although one medicinal efficacy indicators for antibody drugs, blood concentration, is important, it is not enough. We consider that the technologies related to the estimation of lesion distribution, quantification of anti-drug antibodies and search for metabolic pathways, have the potential to open doors in research fields on the PK of antibody drugs, for which there remain many unknowns.In the future, the development of new indicators and surrogate markers is required to advance to personalized medicine and the dosing strategy of highly efficient pharmaceuticals. For the development of indicators in new fields, such as the quantification of major histocompatibility complex (MHC) peptide complex, the high efficiency identification of short chain peptides, development of exosome markers, immune cell monitoring, MS technology, and improvement of ionization reaction efficiency, will become significant areas (Caron et al., 2015, Chen and Mellman, 2013). We hope that the analytical technology of MS will expand in parallel with these advances.Conflict of interest statementAll the authors are employees of SHIMADZU Corporation, and declare no conflicts of interest associated with this manuscript.AcknowledgmentsThis review includes a part of results from research collaborations. The authors sincerely appreciate the valuable discussions with Dr. Koichi Tanaka of SHIMADZU Corporation, Dr. Masaya Ikegawa of Doshisha University, Dr. Akinobu Hamada and Dr. Hitoshi Nakagama of the National Cancer Center, and Dr. Atsushi Yonezawa of Kyoto University.ReferencesAebersold and Mann, 2016R. Aebersold, M. MannMass-spectrometric exploration of proteome structure and functionNature, 537 (2016), pp. 347-355CrossRefView Record in ScopusGoogle ScholarAnderson and Anderson, 2002N.L. Anderson, N.G. 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