Review Article, J Clin Exp Oncol Vol: 7 Issue: 4
Screening of Monoclonal Antibodies for Cancer Treatment
*Corresponding Author : Shijie Liu
Department of Paper and Bioprocess Engineering, State University of New York-College of Environmental Science and Forestry, Syracuse, New York, USA
Tel: (315) 470-6885/470-6501
E-mail: [email protected]; [email protected]
Received: August 01, 2017 Accepted: January 23, 2018 Published: January 31, 2018
Citation: Tang P, Liang S, Xu J, Wang S, Wang L, et al. (2018) Screening of Monoclonal Antibodies for Cancer Treatment. J Clin Exp Oncol 7:4. doi: 10.4172/2324-9110.1000225
With the rapid development of cancer treatment using monoclonal antibodies (mAbs), the screening process of suitable biologics and indications attracts much attention. A general definition of ‘screening’ in the biopharmaceutical industry includes three aspects: the appropriate biologics for the specific cancers, the appropriate indications for the specific biologics and the promising biologic candidates from the pool at the pre-clinical drug discovery stage. Effective screening strategies in the biopharmaceutical industry are crucial to accelerate the drug commercialization process and select the effective biologics for patients. The current status of commercial mAbs and the global pharmaceutical market was briefly reviewed. The mechanism of commercial mAbs and the indications, as well as the current technologies for mAbs screening in the new drug discovery and cell line development stages were systematically reviewed, with an aim as a beneficial reference for screening highquality mAbs, appropriate indications with efficient technologies.
Keywords: Screening; Cancer treatment; Biomarker; Monoclonal antibodies
ADC: Antibody Drug Conjugate; BLA: Biologic License Application; CMC: Chemistry Manufacturing and Control; ELISA: Enzyme-Linked Immunosorbent Assay; FMAT: Fluorometric Microvolume Assay; FACS: Fluorescence-Activated Cell Sorting; MAb: Monoclonal Antibody; M-M: Michaelis-Menten; MWC: Monod-Wyman-Changeux; QbD: Quality by Design
Cancer is the global leading cause of death . It is featured as unregulated cell division and growth . Caused by genic mutation or gene expression disorder, abnormal metabolism can be observed within cells . While gene therapy is still away from well accepted by FDA, monoclonal antibodies (mAbs), as one of the major parts of biologics, are currently widely recognized drugs for conservative cancer treatment. The mAbs are antibodies made by identical cells, which are all derived from a unique parent immune cell [4,5]. The history of mAbs can be traced back to 1975 when recombinant DNA technology was applied to antibody design . The first mAb approved by FDA was OKT3 in 1986  though it took almost three decades to the current ‘golden age’ of cancer therapies using mAbs . Currently, around one hundred commercial mAbs are available in the global market.
The biopharmaceutical industry is regarded, as a matter of fact, a high risk and high revenue industry. On average, it takes $1.2 to $4 billion and 10 to 12 years for a biologic candidate to be approved and enter the market from the discovery stage. From the risk point of view, less than 0.1 % of the biologic candidates before CMC stages are able to enter into Phase I. Among those biologics, 60 % fail to pass Phase Ⅱ, while there’s another 50% failure risk at Phase Ⅲ the clinical stage. In addition, there are significantly higher risks at earlier new drug discovery stages. Thus, the efficient and successful screening of mAb candidates and corresponding indications is crucial. The word “screening” in the biopharmaceutical industry refers to three aspects:
Screening of drug or biologic candidates for specific diseases;
Screening the potential indications for specific drugs or biologics; and
Screening of promising drug or biologic candidates from the pool in the pre-clinical stage.
Undoubtedly, all the aspects are definitive for the destiny of one biologic candidate.
In this work, the current commercial mAbs and the recognized biomarkers were systematically reviewed. The principles, criteria, modeling and detection methods of biologics screening were presented and compared. This review aims at providing comprehensive screening information for cancer treatment, which is potentially beneficial for research institutes, pharmaceutical companies and patients.
It is known that one distinctive characteristics of cancer from other diseases is that immune cells have difficulties to distinguish tumor cells from normal cells . Therefore, a process that can either assist the immune cells to identify the tumor cells, or stimulate the immune cells to be more active should exhibit potential for cancer treatment. MAbs, which are designed for distinguishing the biomarkers abnormally expressed on tumor cells or specifically expressed by immune cells, are recognized as promising biologics to annihilate tumor cells. Though the exact metabolic details of how mAbs work is awaiting better understanding, the general mechanism typically falls into two categories:
mAbs distinguish and bind the biomarkers abnormally expressed by the tumor cells, helping the immune cells to target these cells. For example, trastuzumab, which was designed to target the biomarkers HER2, is a representative commercial mAb for breast cancer.
The immune cells are activated by mAbs to attack the tumor cells. Successful commercialized examples, such as nivolumab and pembrolizumab which target PD-1 and PD-L1 biomarkers; respectively, were designed based on such mechanism.
Owing to limited understanding of mammalian cell metabolism, limited biomarkers have been detected and only with parts of them have been used for mAbs design. Table 1 listed 43 recognized biomarkers that have been successfully used for commercial mAb design. Indications that exhibited abnormal expression of these biomarkers have been well studied. The one-to-one correspondence shown in the table aims to help narrow down the screening scope of the mAbs and indications, as well as predict the clinical results and control the quality of the designed protein therapeutics, which is in compliance with the Quality by Design (QbD) principles. This information may help biopharmaceutical industry to make decisions on biologics design at early discovery stage or on indication selection at clinical stages
|α-4 integrin||Multiple sclerosis|||
|BLyS||Systemic lupus erythematosus|||
|CCR4||Relapsed or refractory adult T-cell leukemia/lymphoma|||
|CD3||Transplant rejection, organ|||
|CD6||Psoriasis, Arthritis, rheumatoid|||
|CD19||Precursor B-cell acute lymphoblastic leukemia|||
|CD20||Relapsed or refractory low-grade, follicular, or transformed B-cell non-Hodgkin's lymphoma, chronic lymphocytic leukemia, and multiple sclerosis|||
|CD30||Hodgkin lymphoma, and anaplastic large-cell lymphoma||[46,47]|
|CD52||B-cell chronic lymphocytic leukemia|||
|Clostridium difficile toxin B||Prevent recurrence of Clostridium difficile infection|||
|Complement component 5||Paroxysmal nocturnal hemoglobinuria|||
|Dabigatran||Emergency reversal of anticoagulant dabigatran|||
|EGFR||Metastatic colorectal carcinoma, and metastatic squamous non-small cell lung carcinoma||[54,55]|
|EpCAM||Malignant ascites, multiple cancers|||
|F protein of RSV||Respiratory syncytial virus|||
|Ganglionside P3||Multiple cancers|||
|GD2||Pediatric high-risk neuroblastoma|||
|GPIIb/IIIa||Percutaneous coronary intervention|||
|HER2||Metastatic breast cancer|||
|IgE||Moderate to severe persistent asthma|||
|IL23||Psoriatic arthritis, plaque psoriasis, and crohn's disease||[63,64]|
|IL17A / IL17RA||Plaque psoriasis|||
|IL1B||Cryopyrin-associated periodic syndrome|||
|IL2RA||Prophylaxis of acute organ rejection in renal transplant|||
|IL6||Multicentric Castleman's disease|||
|IL6R||Rheumatoid arthritis, and systemic juvenile idiopathic arthritis|||
|integrin receptor||Ulcerative colitis, crohn's disease|||
|PCSK9||Heterozygous familial hypercholesterolemia, and refractory hypercholesterolemia|||
|PD-1||Metastatic melanoma, and metastatic squamous non-small cell lung carcinoma||[76-78]|
|PD-L1||Urothelial carcinoma, metastatic non-small cell lung cancer, and metastatic Merkel cell carcinoma||[79,80]|
|PDGFRA||Soft tissue sarcoma|||
|Protective antigen of Bacillus anthracis / Anthrax toxin||Inhalational anthrax|||
|PSMA||Diagnostic imaging agent in newly diagnosed prostate cancer or post-prostatectomy|||
|RANKL||Postmenopausal women with osteoporosis|||
|TNF||Rheumatoid arthritis, juvenile idiopathic arthritis, psoriatic arthritis, ankylosing spondylitis, crohn's disease, ulcerative colitis, and plaque psoriasis||[86,87]|
|TNF α||Crohn's disease|||
|VEGF||Metastatic colorectal cancer||[88,89]|
|VEGFR1||Wet age-related macular degeneration|||
|VEGFR2||Wet age-related macular degeneration, and gastric cancer||[89,91]|
Table 1: The recognized biomarkers for biologics and the approved indications.
Design Technologies Selection
Unlike small molecule drugs that can directly enter the cells and interrupt the metabolism with poor capacity of discernment, mAbs are mild and impact indirectly on the metabolic pathways with good targeting ability on tumor cells. To improve the efficacy as well as avoid patent dispute, several advanced design technologies have been applied to improve mAb performance. The characteristics of different biologic design technologies are briefly summarized in Table 2, which were further discussed as follows:
|ADC||**||0.4-5||50-70||Small and Large Molecule|
|Bi-specific||**||0.5-3||<20||Tumor and Immune Cells (Synergistic)|
|Combined Medication||***||Depends||Depends||Synergistic Effect|
Table 2: Different biologic design technologies with their characteristics.
Antibody drug conjugate
Antibody drug conjugate (ADC) technology is one improvement strategy, which allows small molecules conjugated on an antibody molecule . Via the excellent targeting ability of the protein therapeutics, the small molecule is able to directly interrupt the metabolic pathways of tumor cells with significantly improved capacity of discernment . Nevertheless, challenges that ADC faces in manufacturing include:
Unstable expression level due to the variety of the link structure, especially, when the link consists of unnatural amino acids.
Low downstream yield due to the added purification steps after the conjugation of the small molecule drug to the protein molecule.
Bi-specific antibody technology is another option for better biologics design. It allows one biologic molecule to recognize two biomarkers simultaneously. Theoretically, the combination of the two biomarkers should fall into one of the three types: both on tumor cells, both on immune cells and one on tumor cell and one on immune cell. Nevertheless, the third design is the major preference in the industry. By this design, the immune cells can be effectively activated, then rapidly and adequately attack the tumor cells in situ . This makes the protein therapeutics exhibit synergistic effect compared to using two or more independent mAbs. This technology has the potential to enhance the efficacy while reducing the biologics dosage and side effects. However, bi-specific molecules also have their drawbacks in manufacturing, such as:
Low expression level during cell culture due to the high risks of chain mispairing and protein aggregation
Low downstream separation efficiency because of the similar physicochemical properties of the mis-pairing molecules.
Combined medication technology is an alternative choice to improve the efficacy. The joint usage of two or more biologics or biologics with small molecule drugs has the potential to exhibit synergistic effect on tumor cells, because different biologics and/or drugs may impact on different metabolic pathways. This technology provides one strategy to screen new indications for existing mAbs avoiding huge investment for new drug design and application. This strategy, however, has some disadvantages for commercialization, including:
Significantly high cost in manufacturing, factory operation, storage and supply chain management for different products/ molecules;
Difficulties in maintaining acceptable stability of different biologics and/or drugs if in one formulation;
Inconvenience and high cost in drug delivery if different formulations were used for different biologics/drugs.
With increasing attention focused on mAbs, large amounts of investment have been attracted into the biopharmaceutical industry aiming at biologics commercialization. The study of the current commercial mAbs can benefit the biopharmaceutical industry, especially those start-up companies focusing on biosimilars, to rapidly follow up the recent trends and successfully screen the promising molecules. Figure 1 lists the top 10 best-selling mAbs in 2016 and their global annual sales in the recent three years. The data were obtained from the annual reports of these enterprises. Little ranking change was observed in Figure 1, except Opdivo was regarded as a dark-horse in the recent years due to the excellent performance of this anti-PD-1 mAb. Humira, Remicade and Rituxan kept the top three best-selling for the recent three years (Figure 1), bringing considerable revenue to Abbvie, Johnson & Johnson and Roche, respectively. In 2016, the total sales of these 10 mAbs were $ 61.2 billion, which is almost 70% of the whole global antibody market. The global oligopolistic market landscape may not be broken in the next few years due to limited number of validated biomarkers and long period of time for one biologic product to be commercialized. A list of biomarkers with corresponding commercial mAbs and patent holders were shown in Table 3. The mAbs and patent information was filtered manually from an open source tool called ‘Citeline Service’ before July 16th, 2017. Most of the biosimilars currently in research were designed based on these listed mAbs. This table also includes the major biomarkers for ADC and bi-specific antibodies, which is a trend for biologic design in the next few years.
|Antigen Biomarkers||Biologics||Patent Holders||BLA Approved|
|α-4 integrin||Tysabri||Biogen Idec||11/23/2004|
|BLyS||Benlysta||Human Genome Sciences||03/09/2011|
|CD6||Alzumab||Center of Molecular Immunology||01/07/2013, India|
|Clostridium difficile toxin B||Zinplava||Merck||10/21/2016|
|Complement component 5||Soliris||Alexion||03/16/2007|
|BIOMAB EGFR||Biocon||11/12/2007, India|
|CIMAher||Center of Molecular Immunology||11/18/1994, Cuba|
|CD3||Ior-t3a||Center of Molecular Immunology||05/15/1996, Cuba|
|Removab||Fresenius, Swedish Orphan Biovitrum, Neovii Biotech||01/27/2011, EMEA|
|F protein of RSV||Synagis||Med-Immune||06/19/1998|
|Ganglionside P3||Vaxira||Recombio, Laboratorio Elea, Innogene Kalbiotech||12/31/2012, Cuba|
|IL8||ABCreama||Yes Biotech||07/13/2004, China|
|Protective antigen of Bacillus anthracis||Raxibacumab||Human Genome Sciences||12/24/2012|
|Protective antigen of the Anthrax toxin||Anthem||Elusys Therapeutics||03/18/2016|
|Simponi Aria||Janssen Biotech||07/18/2013|
If the BLA was not approved by FDA, the biologics was specified with its approved location or organization
Table 3: Commercial biologics and the corresponding antigen biomarkers, patent holders and the biologic license application (BLA) approved dates.
mAbs Screening Methods
After the designing of the mAbs based on the mechanisms discussed above, there could still be thousands of candidates available. This will be followed by two major screening processes to obtain best performed antigen-specific antibodies from the pool, which are:
Functional screening including cell growth, proliferation, apoptosis, endothelial tube formation, etc. 
While the functional assays are based on different disease models, the binding screening assays are universal in biopharmaceutical industry.
Specificity is the ability of the antibody binding to its cognate antigen and not to other targets. Affinity is the characteristic of antibody-antigen binding strength. These two criteria are crucial to ensure the efficacy, while good specificity can minimize the side-effects and good affinity is well preferred to reduce the drug dosage.
Functional activities are often the most significant characteristics of an antibody, including ability to deliver a toxin, antagonist activity, partial and full agonist activity, etc. These activities are often related to the protein allostery via the antigen-antibody specific binding [15,16].
To quantitatively evaluate the above criteria, kinetic modeling strategies are usually applied . Known models include Michaelis– Menten (M-M) model , Hill Equation [19-22], different types of Binding Models [23-25], Morpheein Model, Monod–Wyman– Changeux (MWC) model , Mechanistic kinetic description strategy  and empirical models derived from software such as JMP . Nevertheless, different models have their advantages and drawbacks and none is appropriate in all situations.
M-M Equation has been the preferred modeling strategy in many enzyme kinetic studies due to its convenience for calculation [20,29]. M-M equation is only applicable for single domain enzymes or noninteractive oligomeric enzymes. However, most of the enzymes involved in metabolism are oligomeric. By introducing the Hill Coefficient, better simulation results can usually be generated than those using the M-M Equation . However, kinetic parameters lose their mechanistic information due to the forcible introduction of the empirical Hill Coefficient. This shortcoming makes the Hill Equation more appropriate for empirical data manipulation in industry instead of mechanism research.
The Binding Models are extensions of the M-M and Hill Equations when there are more than two, typically three, molecules involved in one reaction. They were derived based on the ordered/ random molecule collision process and the second order elementary reaction mechanism . These models considered multiple reaction processes simultaneously. In addition, the substrate-enzyme binding during the subsequent coenzyme and substrate binding processes for oligomeric enzymes with more than two active subunits can be important [24,31].
Morpheein Model, MWC model and Mechanistic kinetic description strategy are three modeling methods to mechanistically illustrate the molecular kinetic process by taking the interactive nature of one molecule with substrate and/or inhibitors into consideration [27,32]. More parameters are involved in the modeling which typically requires much more experimental data to support. Thus, these time- and cost-consuming methods are not the first choice in most commercial activities.
Table 4 presented a summary of the above models with their typical mathematic formula and applicable scopes. In the current biopharmaceutical researches, M-M Model and Hill Equation are the favored modeling strategies for biologics screening, due to their simplicity. While Morpheein Model, MWC Model and Mechanistic kinetic description strategy are able to well describe the kinetic properties of the molecular interactions, if the kinetic mechanism is critical to understand the biologics. Different types of Binding Models can be applied for multiple molecules involved reactions, such as bi-specific antibody involved reactions. Different strategies are selectively used based on the study purposes and research limitations.
|Type||Formula||Parameter number||Applicable Scope||References|
|M-M Model||2||non-interactive oligomeric or mono- molecular interactions||[18,23]|
|Hill Equation||3||Data manipulation for all molecular interactions||[22,92]|
|MWC model||5||Oligomeric molecular interactions||[32,93]|
|Morpheein Model||5||Oligomeric molecular interactions|||
|Random Binding Model||3||Three molecule involved interactions||[25,95]|
|Ordered Binding Model||3||Three molecule involved interactions||[25,96]|
|Mechanistic Kinetic Description||5||Oligomeric molecular interactions||[27,94]|
Table 4: Reported kinetic modeling strategies for molecular interaction study.
Binding screening assays
Enzyme-linked Immunosorbent assay (ELISA) is one of the most popular platform technologies to identify antigen-antibody complex and both qualitatively and quantitatively evaluate the binding strength. The basic principle of ELISA based on radioimmunoassay techniques dates back top 1941  and the exact method was created in 1971 . Currently, it is a major detection method for biologics screening, because it is simple, quick, sensitive, specific and highthroughput . Another screening technology is surface Plasmon resonance (SPR) biosensor . As a gold standard for real-time and label-free monitoring technology of bimolecular interactions, it is able to determine the thermodynamic and kinetic properties of specific molecular interactions .
While ELISA and SPR are the common techniques for extracellular or cell-free antigen-antibody binding detection, fluorometric micro volume assay (FMAT) and fluorescence-activated cell sorting (FACS) are well-developed methods for on-cell or native binding screening . The working principle based on antibodies binding to the antigen expressed on cell surface and the immunoglobulin constant region of the antibodies is detected by a fluorescently conjugated secondary antibody. As a high-throughput cell-based assay in the hybridoma screening, FMAT and FACS based technologies has significantly improved the screening efficiency and success probability.
Though cancers are not incurable disease due to the rapid technology development, they are still a leading threat for human health. In this paper, the recent trends and technologies of mAb development are comprehensively reviewed. The information of biomarkers, indications, commercial mAbs and the pattern status were systematically reviewed, which is beneficial for biopharmaceutical industry, research institutes and patients to make decisions. This review aims at providing a comprehensive understanding of the biomarker, indication and mAb screening strategies, which may promote further advancements in new drug discovery, novel indications of exiting drugs, as well as joint usage of mAbs and other cancer treatment methods.
The authors thank Mr. Boyuan Yin from General Electric Company, Mr. Pan Tian, Ms. Jing Zhao and Mr. Qing Dai from Mab-Venture Biopharm Co. Ltd. for their kind comments on this work.
Declaration of Interest
The authors declare no financial or commercial conflict of interest.
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