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Hybridoma technology: is it still useful?

Jane zveiter moraes.

a Universidade Federal de São Paulo, São Paulo, Brazil

Bárbara Hamaguchi

Camila braggion, enzo reina speciale, fernanda beatriz viana cesar.

b Centro Universitário São Camilo, São Paulo, Brazil

Gabriela de Fátima da Silva Soares

Juliana harumi osaki, tauane mathias pereira, rodrigo barbosa aguiar.

The isolation of single monoclonal antibodies (mAbs) against a given antigen was only possible with the introduction of the hybridoma technology, which is based on the fusion of specific B lymphocytes with myeloma cells. Since then, several mAbs were described for therapeutic, diagnostic, and research purposes. Despite being an old technique with low complexity, hybridoma-based strategies have limitations that include the low efficiency on B lymphocyte-myeloma cell fusion step, and the need to use experimental animals. In face of that, several methods have been developed to improve mAb generation, ranging from changes in hybridoma technique to the advent of completely new technologies, such as the antibody phage display and the single B cell antibody ones. In this review, we discuss the hybridoma technology along with emerging mAb isolation approaches, taking into account their advantages and limitations. Finally, we explore the usefulness of the hybridoma technology nowadays.

  • • Hybridoma technology is the most popular technique to obtain monoclonal antibodies.
  • • Hybridoma technology variants include B cell and stereospecific targeting protocols.
  • • Phage display and single B cell methods are hybridoma technology alternatives.

1. Introduction

Monoclonal antibodies (mAbs) are universal highly specific binding proteins that were envisioned for a long time as “magic bullets” in the fight against diseases, and also important tools for other biological uses, including diagnosis and research ( Parray et al., 2020 ). These applications were only possible with the advent of methodologies that allow the isolation of individual antibodies. The hybridoma technology was the pioneer on that. Indeed, this technique revolutionized the therapeutic and research scenario, which was further recognized by the 1984 Nobel Prize in physiology or medicine ( Leavy, 2016 ). Other strategies have been developed for the same purpose. In this review, we explore the relevance of the hybridoma technology nowadays, how it has evolved with time, and its advantages and limitations compared with other methods that further come out.

2. Hybridoma technology

The hybridoma technology, described by Georges Köhler and Cesar Milstein in 1975 ( Fig. 1 ), is based on the immunization of animals with the desired antigen, followed by the fusion of specific B lymphocytes with “immortal” myeloma cells. The generated hybrid cells, called hybridomas, are then cloned to obtain stable monoclonal cell lines ( Köhler, and Milstein, 1975 ). After selecting the antibody-secreting clones of interest, the cells are transferred to large-scale culture setups to produce the antibody in the desired amounts ( Holzlöhner and Hanack, 2017 ). B lymphocyte-myeloma cell fusion is often obtained by using the chemical compound polyethylene glycol (PEG). However, this agent can be cytotoxic at some level, and non-specific membrane fusion may occur ( Tomita and Tsumoto, 2011 ; Smith and Crowe, 2015 ). Fusogenic viruses, such as the Sendai and the vesicular stomatitis viruses, are alternatives that bypass the cytotoxic effects of PEG ( Smith and Crowe, 2015 ). Another possibility is the pearly chain method, through which the fusion occurs with the aid of an electric field and laser radiation. In this case, the contact cell surface is irradiated with pulsed laser beams to make a small perforation in the cell membrane, which enhances the chance to promote cell fusion ( Ohkohchi et al., 2000 ; Tomita and Tsumoto, 2011 ). Although the pearly chain method has advantages over the PEG-mediated strategy, it still cannot selectively control the fusion of a specific B lymphocyte with myeloma cell ( Tomita and Tsumoto, 2011 ).

Fig. 1

Timeline of important events in the generation of monoclonal antibodies. (A) Milestones related to hybridoma technology (boxes in green) and the obtainment of mAbs similar to those produced by humans (boxes in gray). (B) Landmarks related to hybridoma technology alternatives: display library techniques (boxes in orange) and single B cell antibody technology (box in purple).

Since the introduction of the hybridoma technology, mAbs have had a profound impact on medicine, providing an almost limitless source of therapeutic, diagnostic, and research reagents ( Nissim and Chernajovsky, 2008 ; Ribatti, 2014 ). Given the universality and usefulness of mAbs, many discoveries came as a result of hybridoma technology, allowing the generation of antibodies directed against an antigen or even different antibodies against the same antigen ( Parray et al., 2020 ). Among the advantages of this technique, we can list the highly reproducible mAb obtainment, once the hybridoma clones are established, the preservation of the native pairing of the combination of genes of the antibody variable and constant regions, and the in vivo antibody affinity maturation ( Zaroff and Tan, 2019 ) ( Table 1 ).

Table 1

Advantages and disadvantages of technologies used to generate monoclonal antibodies.

Muromonab-CD3, also called orthoclone (OKT3), was the first mAb approved by the Food and Drug Administration (FDA), in 1986, for therapeutic use in humans ( Fig. 1 ) ( Ecker et al., 2015 ). That is a murine hybridoma-derived mAb targeting CD3 on mature peripheral T cells to avoid organ allograft rejection ( Colvin and Preffer, 1991 ). However, the occurrence of a human anti-mouse immune response has limited the clinical applicability of murine mAbs in humans ( Gonzales et al., 2005 ). The most appropriate strategy for obtaining therapeutic mAbs would come with the use of human hybridomas, but attempts to obtain these hybrid cells failed, mostly due to their genetic instability ( Smith and Crowe, 2015 ). On the other hand, technological advances allowed the structural modification of these molecules, and the first achievements on that made feasible the removal of antibody murine markers, giving rise to chimeric mAbs containing fragments of variable regions of the murine antibody light and heavy chains linked to human immunoglobulin constant regions. The chimeric mAbs are originated from mouse myeloma cells transfected with chimeric genes, producing antibodies with human features and the same antigen specificity of the antibody originally generated in mice ( Morrison et al., 1984 ). Abciximab (c7E3 Fab) was the first chimeric antibody approved by the FDA, in 1994, to inhibit platelet aggregation in high-risk angioplasty cases ( Fig. 1 ) ( Lefkovits and Topol, 1995 ). Following studies led to a process known as antibody humanization, which grafts non-human antibodies complementarity determining regions (CDR) into human antibody scaffolds. That is obtained using non-human antibody framework regions as CDR graft acceptors ( Jones et al., 1986 ; Safdari et al., 2013 ). In 1997, the FDA approved the first humanized antibody, called daclizumab ( Fig. 1 ), which is indicated for prophylaxis of acute organ rejection in patients who received a kidney transplant and, subsequently, it was also allowed for the treatment of adults with recurrent forms of multiple sclerosis ( Kim and Baker, 2016 ; Baldassari and Rose, 2017 ). In the next decade, a great advance happened with the obtainment of appropriate transgenic animals for generating fully human mAbs ( Lonberg et al., 1994 ). This achievement was possible due to several methodological advances that allowed the integration of the human immunoglobulin gene loci into the mouse genome in a stable way, along with the inactivation of the endogenous murine immunoglobulin genes ( Osborn et al., 2013 ; Murphy et al., 2014 ). Other transgenic animals, such as cattle, rabbits, and rats, can also be exploited for the biological production of human antibodies ( Flisikowska et al., 2011 ; Osborn et al., 2013 ; Matsushita et al., 2014 ). The genetic manipulation of the genome was made such that the transgenic animal immunization with the antigen of interest turns possible the generation of murine hybridomas secreting human mAbs. The first hybridoma-derived human mAb isolated from transgenic animals – panitumumab – was approved for therapeutic use in 2006 ( Jakobovits et al., 2007 ) ( Fig. 1 ).

The hybridoma technology has remained at the forefront of the mAb generation field ( Zaroff and Tan 2019 ). Currently, more than 90% of the antibodies approved for therapeutic use were generated by this technology, most of them in chimeric or humanized versions ( Parray et al., 2020 ). However, the dominance of this method is accompanied by its low efficiency. Hybridoma-based mAb generation is marked by long screening processes, suboptimal selection of specific mAb-secreting cells, a mAb validation that is rarely possible at an early stage, not to mention that the availability of the purified antigen target is needed ( Harlow and Lane, 1988 ). To optimize antibody generation, several variants of this technology have been developed over the years. Examples are the B Cell Targeting and the Stereospecific Targeting techniques, which are described below.

2.1. B Cell Targeting (BCT)

The B Cell Targeting (BCT) method, also known as Pulsed Electric Field (PEF), was described by Lo et al., in 1984 ( Fig. 1 ) ( Lo et al., 1984 ). It is based on two central points: the preselection of B lymphocytes recognizing the antigen of interest, and the further B lymphocyte fusion with myeloma cells by using direct current electrical pulses ( Tomita and Tsumoto, 2011 ). Briefly, specific biotin-labeled antigen binds to the corresponding B lymphocytes, which are subsequently recovered by using streptavidin, giving rise to a B lymphocyte-antigen-biotin-streptavidin complex ( Tomita and Tsumoto, 2011 ; Greenfield, 2019 ). Then, such B lymphocyte complexes are co-cultured with biotin-labeled myeloma cells and the resulting mixture is exposed to PEF to promote cell fusion ( Lo et al., 1984 ).

This last step, the most critical one, is characterized by the cell membrane destabilization after electrostatic field exposure, which eases the occurrence of fusion between cell membranes ( Greenfield, 2019 ). For that, a strong electric field is formed vertically between electrodes arranged in parallel and guides the alignment of the B lymphocyte-myeloma cell complexes along with it, favoring the fusion of the membranes close to each other. No electrical fusion occurs in complexes arranged in any other direction ( Tomita and Tsumoto, 2011 ). Different research groups have explored the application of electrostatic pulses for generating hybridomas ( Wojchowski and Sytkowski, 1986 ; Werkmeister et al., 1991 ; Hewish and Werkmeister, 1989 ). In general, the cell fusion mediated by electric field was found more efficient than the achieved with PEG, a cytotoxic agent ( Tomita and Tsong, 1990 ; Awsiuk et al., 2019 ), with improvements not only in the number of fused cells but also in the hybridoma growth rate. The BCT technique demonstrated five-to-ten times greater efficiency in the formation of hybridoma cells secreting the antibodies of interest, in comparison with the PEG-mediated method. However, based on the reported data, such fusion efficiency does not seem to go far beyond 20% ( Tomita et al., 2006 ), and the BCT protocol is more complex than the original hybridoma one. Another point to note is that the electrofusion yields are low when the fusion partner cells have different sizes, although this is a limitation that can be overcome with the use of nanosecond pulse electroporation ( Rems et al., 2013 ).

The BCT method can also be used for the simultaneous generation of at least three to five mAbs against different antigens, using a single mouse ( Awsiuk et al., 2019 ), which reduces not only the laboratory work but also the number of animals needed for isolating mAbs. This procedure, known as multitargeting, is based on mouse immunization with multiple antigens, followed by the immunoglobulin B-cell receptor-guided selection of B lymphocytes sensitized by each of the desired antigens. As a disadvantage, immunosuppression caused by immunization with several antigens may occur ( Table 1 ) ( Tomita and Tsumoto, 2011 ; Awsiuk et al., 2019 ).

2.2. Stereospecific targeting (SST)

Early descriptions of conformation-specific mAbs were published in the 1960s ( Janeway and Sela, 1967 ), highlighting the characteristic of these antibodies in specifically recognizing only one type of stereoisomer of a given chemical compound. It is known that stereospecific mAbs have high specificity for their ligands, which is helpful for diagnostic and therapeutic approaches. However, the generation of these mAbs is technically challenging, particularly in the case of highly structured and well-preserved targets. Examples are extracellular loops or domains of multi-transmembrane proteins, such as membrane-bound receptors ( Hazen et al., 2014 ). The Stereospecific Targeting (SST) method was proposed to address this problem ( Tomita et al., 2007 ) ( Fig. 1 ) and consists of four phases.

A modification in the original hybridoma technology was performed already in the first step, the animal immunization. The immunogen is administered intramuscularly in the DNA form ( Tomita et al., 2007 ), which guides the expression of the antigen in its native form. Thereby, the chances of inducing the production of functional mAbs are greater, even against the most challenging targets ( Liu et al., 2016 ). Compared to protein inoculation, gene immunization allows the efficient testing of different designs of immunogens, does not require purification of proteins from a pathogen, circumvents the difficulty of expressing and purifying antigens in large quantities, and can also be used to obtain antibodies against several proteins at the same time through immunization with several nucleic acid sequences that encode different proteins or different subunits of the same protein ( Liu et al., 2016 ), which are relevant advantages for generating high-quality mAbs. Although the DNA immunizations can be considered not very immunogenic in some cases, the use of immunomodulators, if necessary, does not interfere negatively in the conformation of the antigen. Also, among the options of entry pathways for DNA immunization, the intrasplenic administration may be still more efficient, since a single dose of DNA is sufficient to generate the desired antibody responses, with reduced immunization period and technique cost, compared to the traditional protein administration ( Parray et al., 2020 ). On the other hand, the antigen glycosylation pattern, that differs from the occurring in humans, as well as the possibility of inducing immune tolerance and generating anti-DNA antibodies may be problems when using this approach ( Khan, 2013 ). The transduction of myeloma cells to express the antigen is a limitation that sums to those described for BCT. In a recent update, an additional intraperitoneal injection containing cells that express the target antigen has been proposed to increase the humoral response and ensure the recognition of antigenic structures. The idea is to promote a further stage in the B cell maturation. Indeed, an increase in serum antibody titers, when compared to the results of gene immunization only, could be observed ( Table 1 ) ( Yamasaki et al., 2020 ). The second step involves the preselection of conformational epitope-recognizing B cells. For this, isolated splenic cells are incubated for a short period with myeloma cells transduced with a vector carrying the antigen gene for the formation of B lymphocyte-myeloma cell complexes ( Shabani et al., 2010 ). The third step is the cell fusion itself, which occurs by using electrical pulses as described for the BCT method. The screening of hybridomas secreting the desired mAbs, the fourth step, makes use of the native antigen targets expressed on a cell surface. The clone selection may include an additional step to discard the undesirable clones by using recombinant protein, which may contain partially denatured structures ( Yamasaki et al., 2020 ). The SST method provides more than 50% positivity for B lymphocyte-myeloma cell fusion, and more than 24% of the generated clones were found to secrete the desired mAbs ( Yamasaki et al., 2020 ).

3. Antibody phage display technology

The antibody phage display technology, initially reported in 1990 ( McCafferty et al., 1990 ), is considered a powerful tool to generate mAbs ( Fig. 1 ). The methodology, based on the phage display concept described by George Smith in 1985 ( Smith, 1985 ), consists in the development of a combinatorial antibody phage library – that is, a huge collection of phages displaying antibody fragments – and the subsequent screening of the antibodies that recognize the antigen of interest.

To generate an antibody phage library, firstly it is necessary to clone antibody gene fragments into vectors. Both filamentous M13 phage and phagemid, which combines the characteristics of plasmids and phages ( Tikunova and Morozova, 2009 ), can be used as vectors. Comparatively, while the first one has all the ability to produce phage particles and display antibody, the phagemid needs to infect bacteria with a helper phage, that is required to package the phagemid as single-strand DNA into virion particle ( Barbas et al., 1991 ; Lowman, 2013 ; Almagro et al., 2019 ). In both cases, vectors are used to transform E. coli by electroporation. After obtaining the phage display library, the antibodies displayed on the vector surface are screened through a process called biopanning ( Wu et al., 2016 ). It should be noted that the antibodies are most often displayed in single-chain variable fragment (scFv) or antigen-binding fragment (Fab) forms.

There are four types of antibody display libraries: immune, naïve, semisynthetic, and synthetic. The immune libraries are obtained from immunized animals or humans and are mostly used to discover antibodies against infectious pathogens ( Trott et al., 2014 ) or antigenic targets in cancer patients ( Thie et al., 2011 ; Frenzel et al., 2016 ). This library contains a restricted antibody repertoire that underwent antigen-driven in vivo selection ( Barbas et al., 1991 ; Orum et al., 1993 ; Frenzel et al., 2016 ), which differs from the other phage display libraries, known as “universal”, that theoretically provide binders for all possible antigen structures ( Frenzel et al., 2016 ). The naïve antibody libraries are generated from a pool of B lymphocytes of non-immunized donors, and one successful example is the scFv library licensed from Cambridge Antibody Technology (CAT; now part of MedImmune/AstraZeneca) ( Javle et al., 2014 ; Almagro et al., 2019 ). While the naïve libraries are derived from natural antibody gene repertoires, the synthetic ones are entirely based on in silico design to obtain individual antibody amino acid sequences ( Fuh, 2007 ), bypassing the need to isolate antibody genes. The semisynthetic libraries, on the other hand, are created using both naturally and synthetically ( in silico ) randomized CDRs. In this library type, it is possible to redesign natural CDRs to improve the chance of finding antibodies with high specificity and affinity ( Orum et al., 1993 ; Fuh, 2007 ).

Building the phage display library is the most important step of this technology. There is a directly proportional relationship between the size of the antibody library and the probability of finding a particular antibody ( Burioni et al., 1997 ; Almagro et al., 2019 ). The Next-Generation Sequencing (NGS) is an important tool to analyze the variability, the sequence composition, and the size of antibody phage display libraries ( Rouet et al., 2018 ). The construction of a phage display library is more expensive than generating hybridomas after animal immunization. However, the antibody screening step of the phage display method is faster and cheaper ( Hentrich et al., 2018 ).

The first antibody discovered by phage display (CAT library) as well as the first human antibody approved for therapy was adalimumab (Humira®) ( Fig. 1 ) ( Burmester et al., 2013 ). It is an IgG1 mAb that binds tumor necrosis factor-alpha (TNF-α) and prevents the interaction of this inflammatory cytokine with the corresponding receptor. Having been discovered from an scFv phage library, gene manipulation was needed to obtain the final IgG format ( Machold and Smolen, 2003 ). This antibody has been used for the treatment of patients with moderate to severe rheumatoid arthritis, among other autoimmune diseases.

Although the phage display library is a promising technology for the development of antibodies, it has limitations. The diversity of the phage library depends on the bacterial transformation efficiency and is limited to the 10 10 -10 11 variant antibody maximum repertoire of the phage display library. This restriction can be overcome by mRNA and ribosome display strategies, which are in vitro cell-free methods having a bigger library size and a higher displayed antibody diversity (10 14 variants) ( Hudson and Souriau, 2003 ; Kunamneni et al., 2020 ). It should be also considered that phage display-selected mAbs are generated in E. coli and therefore are not glycosylated; the use of eukaryotic display platforms, like yeast ( Doerner et al., 2014 ) and mammalian expression systems ( Zhu and Hatton, 2017 ), is a possibility to circumvent that. Other antibody phage display methodology disadvantages are the propensity to generate biased repertoires and the loss of information of antibody natural pairing ( Saggy et al., 2012 ) ( Table 1 ).

4. Single B cell antibody technology

Several technological platforms have been proposed to generate mAbs from hybridomas. An inherent characteristic of these methods is the need to fuse B lymphocytes with myeloma cells ( Köhler, and Milstein, 1975 ) and this was, for a long time, a required step to isolate single antibodies of known specificity. In the last few decades, technical advances have allowed the detection and isolation of single functional B lymphocytes from heterogeneous primary cell populations, as well as the antibody gene amplification and cloning without the need to immortalize the selected antibody-secreting cell (ASC). These single B lymphocyte approaches, collectively known as “single B cell antibody technology” ( Fig. 1 ) ( Babcook et al., 1996 ), revealed attractive and useful to generate neutralizing mAbs in a rapid way for several applications ( Tiller et al., 2008 ), including the management of emerging pathologies. Indeed, an increasing number of mAbs against infections caused by viral agents, such as HIV ( Scheid et al., 2009a ), Dengue ( Durham et al., 2019 ), MERS-CoV ( Wang et al., 2018 ), and SARS-Cov-2 ( Cao et al., 2020 ), were obtained with such technology. The following items briefly describe the basic concepts and benefits of the single B cell antibody technology.

4.1. Identification and isolation of single B cells

The screening and isolation of ASC can occur in a random or antigen-specific manner, from peripheral blood or lymphoid tissue samples. For random selection, B cells can be recovered by flow cytometry ( Smith et al., 2009 ) or can be picked from tissues by micromanipulation ( Küppers et al., 1993 ). For antigen-specific selection, multi-parameter flow cytometry or other fluid-based approaches are generally used ( Clargo et al., 2014 ; Meng et al., 2015 ; Rajan et al., 2018 ). Flow cytometry systems are efficient to recover single cells ( Battye et al., 2000 ) and an example is their successful use to isolate IgG ​+ ​memory B lymphocytes reactive to gp140 from donors with HIV ( Scheid et al., 2009a , 2009b ). In this case, anti-CD19 and anti-IgG antibodies, along with biotinylated gp140, were used to select the desired cell subset. Such methodology led to the generation of anti-gp140 mAbs with different antigen neutralization activities ( Scheid et al., 2009b ).

It should be noted that antigen-specific IgG ​+ ​B cells comprise just a small percentage of circulating cells and, to identify and isolate them, reagents targeting B cell surface markers are desirable. A variety of antibodies are available to detect human B lymphocytes, which makes it even possible to distinguish cells at different stages of development and differentiation. This is an advantage of the single B cell technology over the original hybridoma technique. On the other side, the scenario is not the same when it comes to isolating non-human subsets. Indeed, we do have antibodies against mouse B lymphocyte markers ( Starkie et al., 2016 ), such as CD45R and CD19, but the sorting of B cells from most of the other species (rabbit and guinea pig, for example), although feasible ( Starkie et al., 2016 ; Lei et al., 2019 ), becomes challenging due to the low or absent repertoire of appropriate B cell-targeting antibodies. Another point that should be considered is related to cost: the use of expensive sorting devices integrates an important part of the procedures to isolate antigen-specific single B lymphocytes from a polyclonal mixture. Alternatively, other strategies can be used, including antigen-coated magnetic beads ( Adler et al., 2017 ), cell-based microarrays ( Jin et al., 2011 ), and soft lithographic methods for micro engraving ( Love et al., 2006 ). The downside? These techniques are also costly or require extensive knowledge.

4.2. Single-cell immunoglobulin gene transcript amplification, cloning, and expression

Having isolated single B cells, the next step is the immunoglobulin gene amplification. The cells are lysed, the cDNA is synthesized by reverse transcription of total mRNA, and the full-length immunoglobulin genes for the variable and constant regions of the light and heavy chains are amplified by PCR ( Tiller et al., 2008 ). The obtained fragments are cloned into linear expression cassettes to further generate the immunoglobulin domains in cell-based expression systems (mammalian or bacterial cells). In scenarios without the cultivation of the recovered B cells, the cDNA is synthesized from single-cell material. The antibodies are typically expressed in Fab form ( Clargo et al., 2014 ), but it is also possible to express them in other formats, including full-length IgG and single-chain variable fragment (scFv) ( Meng et al., 2015 ; Rajan et al., 2018 ).

These procedures summarize a common protocol route for protein expression. However, more robust and sophisticated systems are also available. That is the case of the “single-cell RT-PCR-linked in vitro expression” (SICREX) platform, through which the antibodies are expressed outside a cell unit ( Jiang et al., 2006 ; Ojima-Kato et al., 2015 ). In this system, the protein synthesis occurs in a mixture containing the transcription/translation machinery from E. coli , and therefore the gene-cloning, transformation, and cultivation procedures are not needed. As a consequence, the time to generate the antibodies is greatly reduced to just a few days. Here we also have a drawback: incorrect folding of the antibody domains sometimes occurs.

From a broad perspective, the single B cell antibody technology, just like the other methods discussed in this review, has its advantages balanced by downsides, revealing a singular panel that characterizes it. Compared with the current hybridoma technology, though, the single B cell approaches have some positive points that stand out and even exceed those exposed above. It can be included here the potential to (a) isolate mAbs reactive to conformational determinants that are difficult to emulate in vitro ; and (b) in experimental studies, collect multiple samples after the immunization period without the need to euthanize the animals ( Tiller et al., 2008 ; Starkie et al., 2016 ; Rajan et al., 2018 ). But the biggest advantage of single B cell approaches is the possibility to isolate neutralizing mAbs from vaccinated or naturally immunized human subjects, as well as from those with autoimmune diseases. The high-throughput screening of individual ASC repertoire based on phenotypic and genotypic features allows the analysis of the human immune response to pathogens ( Shi et al., 2019 ), accelerates the search for neutralizing mAbs of therapeutic relevance, and also provides insights for a rational vaccine design strategy ( Scheid et al., 2009b ).

Overall, the recent advances in the single B cell field trace a path that was out of reach when César Milstein and Georges Köhler found on the hybridoma creation the magic solution to isolate mAbs ( Köhler, and Milstein, 1975 ). Table 1 summarizes some of the advantages and drawbacks of the single B cell antibody technology, in comparison with the hybridoma and phage display techniques.

5. Discussion

Given the foregoing, we can consider that the choice of the method to be used for obtaining an antibody must be guided by the purpose of the demand. The first demonstration that mAbs could be isolated came with the hybridoma technology, which made feasible the use of these molecules for a variety of biological applications. The task was revealed to be not as practical as it might seem, though. Hybridoma-derived immunoglobulins are of animal origin and, to be used as therapeutic tools, need to be converted into human mAbs. Such protein structural change can be currently achieved with established antibody chimerization and humanization protocols or the use of appropriate transgenic animals, in strategies that were crucial for the obtainment of the therapeutic mAb repertoire available today but are known to be costly, time-consuming, and technically challenging ( Safdari et al., 2013 ). The limitations are not restricted to that. The low efficiency of the B lymphocyte-myeloma cell fusion and the further hybridoma cell isolation are important bottlenecks of this technology, not to mention the constant risk of cell culture contamination and the genetic instability of the generated hybridoma cell lines ( Harlow and Lane, 1988 ).

Since the mid-1980s, several methods have been developed to work around these limitations, starting with changes in hybridoma technology. Examples are the proposed BCT and SST protocols, that brought relative improvements in the B lymphocyte-myeloma cell fusion efficiency, but instead turned the hybridoma technique more complex and hardworking, compared with the original methodology. Alterations in the other steps of this technology, such as the selection of the desired antibody-secreting cells, have been also described ( Manz et al., 1995 ; Hanack et al., 2016 ; Listek et al., 2020 ); however, despite indeed accelerating the mAb identification process, the need to generate hybridomas remains. Based on different principles, the antibody phage display method emerged as the first alternative to the hybridoma technology. It brings important advantages, such as the potential to isolate mAbs against toxic and non-immunogenic antigens, and the possibility to generate, for the first time, antibodies without using experimental animals. On the other hand, an important limitation is the need to have an available and previously identified target antigen, which is also valid for the hybridoma technology.

Despite improvements in the hybridoma technology, and the development of antibody display ( Winter et al., 1994 ), chimerization and humanization strategies ( Winter and Milstein, 1991 ), a major advance came with the discovery of tools to isolate mAbs directly from single B cells. Besides not strictly depending on B cell culture and the use of experimental animals, the single B cell antibody technology allows a simple and rapid generation of mAbs with therapeutic potential without the need to previously know the target and have it available. This is a promising technique with the potential for even isolating functional mAbs against conformation determinants that are difficult to emulate in vitro but, currently, it still has low accessibility, particularly compared to the hybridoma methodology.

Overall, all the technologies discussed above revealed useful for obtaining therapeutic antibodies against several disorders, including infectious diseases. More than a hundred mAbs described against the Ebola virus illustrate that ( Saphire et al., 2018 ) and, among them, some hybridoma-derived antibodies were used to develop therapeutic cocktails, such as ZMapp, composed of three chimeric mAbs ( Qiu et al., 2011 , 2014 ; Pettit et al., 2016 ), and REGN-EB3, comprising three human mAbs generated by using appropriate transgenic mice ( Pascal et al., 2018 ). Other examples of antibodies generated toward the Ebola virus are the phage display-derived mAb KZ52 ( Maruyama et al., 1999 ), and the single B cell-isolated antibody Mab114, obtained from a human survivor ( Corti et al., 2016 ). But when considering emerging diseases, the hybridoma technology does not seem to be the most appropriate, particularly taking into account the need to obtain therapeutics in a short time. In this situation, the single B cell antibody technology seems to better respond to the urgent demand for functional mAbs, which is illustrated by the experience in the recent COVID-19 pandemic. In a period less than one year, at least 14 single B cell-derived human mAbs or mAb cocktails were obtained against SARS-CoV-2, the causative agent of this disease, and five of them entered Phase 2/3 clinical trials ( Tuccori et al., 2020 ). Another positive point of the single B cell antibody technology is the possibility to isolate the desired mAbs without previously knowing the antigen target, which could be particularly helpful in infectious disease cases. However, all that does not exclude the potential application of other methodologies in the fight against emerging pathogens. Indeed, a panel of neutralizing mAbs elicited against SARS-CoV-2 was obtained from phage display libraries ( Noy-Porat et al., 2020 ), and even hybridoma-based strategies have been explored for that purpose ( Wang et al., 2020 ).

So, is the hybridoma technology still useful? The reported data so far indicate yes. Beyond being a pioneer, this methodology is very popular. Several of the most recently generated mAbs were discovered on murine hybridomas ( de Aguiar et al., 2016 ; Sanches et al., 2016 ; Parray et al., 2020 ), including some of the most successful FDA-approved antibodies, such as the immune checkpoint inhibitors nivolumab (anti-programmed cell death protein 1; anti-PD-1) ( Robert et al., 2014 ) and atezolizumab (anti-programmed cell death protein ligand 1; PD-L1) ( Fehrenbacher et al., 2016 ), used in the management of non-small cell lung carcinomas, head and neck cancers, melanomas, renal cell carcinomas, and several other tumors ( Parray et al., 2020 ). Despite the emergence of new promising technologies for generating mAbs, it seems that none of them was able to provoke a technological shift up to now, remaining the hybridoma-based strategies in a leadership position.

Credit author statement

T.M.P. and F.B.V.C. wrote the “Hybridoma technology” section. C.B. and G.S. wrote the “B Cell Targeting (BCT)” section. B.H. and E.R.S. wrote the “Stereospecific Targeting (SST)” section. J.H.O., J.Z.M., and R.B.A. wrote the “Antibody phage display technology” section. R.B.A. wrote the “Single B cell antibody technology” section and prepared the timeline figure. All authors contributed critically to the review preparation, discussed the covered topics, and approved the final text. R.B.A. and J.Z.M wrote the discussion section, revised all the text, and answered the reviewer.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors acknowledge the support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil), and Fundação de Apoio à Pesquisa do Estado de São Paulo (grant no. 16/14358-2; FAPESP, Brazil).

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Hybridoma technology a versatile method for isolation of monoclonal antibodies, its applicability across species, limitations, advancement and future perspectives

Affiliations.

  • 1 Translational Health Science & Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana 121001, India.
  • 2 Translational Health Science & Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana 121001, India. Electronic address: [email protected].
  • 3 Translational Health Science & Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana 121001, India. Electronic address: [email protected].
  • PMID: 32473573
  • PMCID: PMC7255167
  • DOI: 10.1016/j.intimp.2020.106639

The advancements in technology and manufacturing processes have allowed the development of new derivatives, biosimilar or advanced improved versions for approved antibodies each year for treatment regimen. There are more than 700 antibody-based molecules that are in different stages of phase I/II/ III clinical trials targeting new unique targets. To date, approximately more than 80 monoclonal antibodies (mAbs) have been approved. A total of 7 novel antibody therapeutics had been granted the first approval either in the United States or European Union in the year 2019, representing approximately 20% of the total number of approved drugs. Most of these licenced mAbs or their derivatives are either of hybridoma origin or their improvised engineered versions. Even with the recent development of high throughput mAb generation technologies, hybridoma is the most favoured method due to its indigenous nature to preserve natural cognate antibody pairing information and preserves innate functions of immune cells. The recent advent of antibody engineering technology has superseded the species level barriers and has shown success in isolation of hybridoma across phylogenetically distinct species. This has led to the isolation of monoclonal antibodies against human targets that are conserved and non-immunogenic in the rodent. In this review, we have discussed in detail about hybridoma technology, its expansion towards different animal species, the importance of antibodies isolated from different animal sources that are useful in biological applications, advantages, and limitations. This review also summarizes the challenges and recent progress associated with hybridoma development, and how it has been overcome in these years to provide new insights for the isolation of mAbs.

Keywords: Antibody engineering; Biosimilar; Clinical trials; Hybridoma; Monoclonal antibodies; Therapeutics.

Copyright © 2020 Elsevier B.V. All rights reserved.

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  • Antibodies, Monoclonal* / isolation & purification
  • Antibodies, Monoclonal* / therapeutic use
  • Hybridomas*
  • Antibodies, Monoclonal

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Hybridoma Technology

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Series: Methods In Molecular Biology > Book: ELISA

Protocol | DOI: 10.1007/978-1-4939-2742-5_2

  • Produce Safety and Microbiology Unit (PSM), Western Regional Research Center (WRRC), Pacific West Area (PWA), Agricultural Research Service (ARS), United States Department of Agriculture (USDA), Albany, CA, USA
  • Foodborne Toxin Detection and Prevention Unit (FTDP), Western Regional Research Center (WRRC), Pacific West Area (PWA), Agricultural Research Service (ARS), United States Department of Agriculture (USDA), Albany, CA, USA

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The generation of hybridoma cell lines by the fusion of splenocytes from immunized mice with immortal myeloma cells is a well-established method for the production of monoclonal antibodies. Although other methods have emerged as an effective

The generation of hybridoma cell lines by the fusion of splenocytes from immunized mice with immortal myeloma cells is a well-established method for the production of monoclonal antibodies. Although other methods have emerged as an effective alternative for the generation of monoclonal antibodies, the use of hybridoma technology remains a viable technique that is accessible to a wide number of laboratories that perform basic cell biological research. Hybridoma technology represents a relatively simple procedure at minimal cost for the continuous production of native whole immunoglobulins. This chapter will describe the materials and methodologies needed for the successful generation of monoclonal antibody (mAb)-producing hybridoma cell lines against target antigens.

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hybridoma technology research paper

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Related articles, production of monoclonal antibody, production and screening of monoclonal peptide antibodies, monoclonal antibodies, hybridoma technology for the generation of monoclonal antibodies, production of human antibodies from transgenic mice, characterization of sars-cov-2 glycoprotein using a quantitative cell–cell fusion system, reporter systems to study htlv-1 transmission, a double-sandwich elisa for identification of monoclonal antibodies suitable for sandwich immunoassays, hybridoma technology for the generation of rodent mabs via classical fusion, production and purification of monoclonal antibodies.

  • Kohler G, Milstein C (1975) Continuous cultures of fused cells secreting antibody of predefined specificity. Nature 256:495–497
  • Kohler G, Howe SC, Milstein C (1976) Fusion between immunoglobulin-secreting and nonsecreting myeloma cell lines. Eur J Immunol 6:292–295
  • Galfre G, Howe SC, Milstein C et al (1977) Antibodies to major histocompatibility antigens produced by hybrid cell lines. Nature 266:550–552
  • Shulman M, Wilde CD, Kohler G (1978) A better cell line for making hybridomas secreting specific antibodies. Nature 276:269–270
  • Galfre G, Milstein C (1981) Preparation of monolconal antibodies: strategies and procedures. Methods Enzymol 73B:3–46
  • Hurrell JGR (1982) Monoclonal hybridoma antibodies: techniques and applications. CRC Press, Boca Raton, FL
  • Schreier M, Kohler G, Hengartner H et al (1980) Hybridoma Techniques: EMBO, SKMB Course 1980, Basel. Cold Spring Harbor, New York
  • Golde WT, Gollobin P, Rodriguez LL (2005) A rapid, simple, and humane method for submandibular bleeding of mice using a lancet. Lab Anim 9:39–43

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Non-animal-derived monoclonal antibodies are not ready to substitute current hybridoma technology

  • África González-Fernández   ORCID: orcid.org/0000-0002-9226-4825 1 ,
  • Francisco J. Bermúdez Silva   ORCID: orcid.org/0000-0003-3133-9691 2 ,
  • Marcos López-Hoyos   ORCID: orcid.org/0000-0003-0562-427X 3 ,
  • César Cobaleda   ORCID: orcid.org/0000-0003-3807-9204 4 ,
  • Lluís Montoliu   ORCID: orcid.org/0000-0003-3941-1176 5 ,
  • Margarita Del Val   ORCID: orcid.org/0000-0001-6769-4279 6 &
  • Kirk Leech 7  

Nature Methods volume  17 ,  pages 1069–1070 ( 2020 ) Cite this article

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To the Editor — We write on behalf of the COSCE (Confederation of Spanish Scientific Societies) Transparency Agreement on Animal Research, supported by the EARA (European Animal Research Association). In May 2020, the European Commission’s Joint Research Centre (EC-JRC) released a recommendation on the development of non-animal-derived antibodies, urging government authorities, funding agencies and publishers to endorse the use of these antibodies to improve scientific reproducibility 1 . These recommendations were based on the work done by the Scientific Advisory Committee (ESAC) of the European Union Reference Laboratory for alternatives to animal testing (EURL ECVAM). Recent correspondence to Nature 2 and Nature Methods 3 claims that non-animal antibodies are ready to replace animal-derived ones in all known applications. In our view, however, both the EC-JRC document and the published correspondence contain distorted perceptions of the current possibilities for non-animal-derived antibodies. While we are all committed to replacing animal experimentation with alternative methods, these methods need further scientific validation to justify replacing the use of animals without affecting the desired outcome of the experiment.

In our opinion, there are still major concerns barring the substitution of hybridoma technology with animal-free methods, such as insufficient technological development, inconsistent efficiency depending on the application, and difficulty in implementation at a global scale.

There are no universal (phage, yeast, ribosome or mRNA) display technologies yet, and in many cases animal immunization is still needed. In addition, display technologies have important limitations when generating antibodies against native structures 4 — for example, viruses. In vivo somatic hypermutation allows the development of monoclonal antibodies with much higher affinity and specificity than those generated by phage technology 5 . While efforts have been made to improve the quality of display-based antibodies, further development requiring substantial experimentation, time and resources is still needed. By contrast, hybridoma technology is well established and allows the isolation of native antibodies generated in the context of an immune response against a given antigen. During vaccine development, this method provides valuable information on how the immune system reacts to that antigen, including the immunoglobulin genes involved, secondary reordering, insertions and deletions, affinity maturation, the clonal relationships of different antibodies, isotypes 6 , 7 etc. All this crucial information is lost when using display technology.

It is important to highlight that hybridoma generation only requires animals during the immunization phase. The next steps — for example, cellular fusion, clonal selection and antibody production — are animal-free, and large quantities of monoclonal antibodies for therapeutic use are currently being produced by genetically modified cells at an industrial scale. Thus, the antibody production field is mostly animal-free already, and protocols for immunization have been improved to maximize animal welfare. This includes the use of better adjuvants with fewer secondary effects, reduced number of immunizations, and improved routes of immunization.

Display technologies can have advantages in some fields (for example, toxicology, antivenom research) but do not work equally well for other applications, such as therapeutic antibody development, and for analytical purposes 8 . The two techniques are complementary 9 , and the transition toward non-animal-derived antibodies needs to take into account these differences in applications 8 , 10 .

Finally, the implementation of new technologies in a laboratory requires staff training and an adaptation period. There are as yet very few laboratories in the world with access to display technologies or other animal-free systems. ESAC members are aware of these limitations, as they state in their recommendation 1 , implicitly acknowledging the enormous difficulty in implementing display-based technology. To improve affinity and specificity, antibody-producing companies have the resources to use large, diverse libraries, high-throughput selection methods to pick ideal binders, and careful engineering strategies to further improve them if necessary. By contrast, the scenario is very different for small research laboratories without experience in display techniques, or without powerful libraries and selection platforms.

Unfortunately, display technologies are still not a real alternative to hybridoma technology for all applications, and therefore we believe that the endorsement promoted by the EC-JRC is not scientifically justified. The ideal scenario is to have both and to choose the best method in every case according to the particular application. Any change should be supported by scientific evidence and monitored by regulatory bodies, independent of third-party interests. Use of animal-free methods should be limited to those fields where it has been demonstrated to be a real alternative to traditional technology, while developing a roadmap that allows a successful transition to these new technologies and thus really benefiting society.

Viegas Barroso, J., Halder, M. E. & Whelan, M. EURL ECVAM recommendation on non-animal-derived antibodies, EUR 30185 EN (European Union, 2020); https://doi.org/10.2760/80554

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Acknowledgements

We thank the COSCE societies for their support ( https://cosce.org/ ).

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CINBIO, Centro de Investigaciones Biomédicas, Universidade de Vigo, Vigo, Spain

África González-Fernández

UGC Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA, Hospital Regional Universitario de Málaga, Málaga, Spain

Francisco J. Bermúdez Silva

Immunology Service, Hospital Universitario Marqués de Valdecilla-IDIVAL, Santander, Spain

Marcos López-Hoyos

Immune System Development and Function Unit. Department of Cell Biology and Immunology, Centro de Biología Molecular Severo Ochoa (Consejo Superior de Investigaciones Científicas – Universidad Autónoma de Madrid) (CBMSO-CSIC/UAM), Madrid, Spain

César Cobaleda

Animal Models by Genetic Manipulation Unit, National Centre for Biotechnology (Consejo Superior de Investigaciones Científicas) (CNB- CSIC) and Biomedical Research Networking Center on Rare Diseases (Instituto de Salud Carlos III) (CIBERER-ISCIII), Madrid, Spain

Lluís Montoliu

Viral Immunology, Centro de Biología Molecular Severo Ochoa (Consejo Superior de Investigaciones Científicas – Universidad Autónoma de Madrid) (CBMSO, CSIC-UAM), Madrid, Spain

Margarita Del Val

European Animal Research Association (EARA), London, UK

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ELISA pp 15–28 Cite as

Hybridoma Technology

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The generation of hybridoma cell lines by the fusion of splenocytes from immunized mice with immortal myeloma cells is a well-established method for the production of monoclonal antibodies. Although other methods have emerged as an effective alternative for the generation of monoclonal antibodies, the use of hybridoma technology remains a viable technique that is accessible to a wide number of laboratories that perform basic cell biological research. Hybridoma technology represents a relatively simple procedure at minimal cost for the continuous production of native whole immunoglobulins. This chapter will describe the materials and methodologies needed for the successful generation of monoclonal antibody (mAb)-producing hybridoma cell lines against target antigens.

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Kohler G, Milstein C (1975) Continuous cultures of fused cells secreting antibody of predefined specificity. Nature 256:495–497

Article   CAS   PubMed   Google Scholar  

Kohler G, Howe SC, Milstein C (1976) Fusion between immunoglobulin-secreting and nonsecreting myeloma cell lines. Eur J Immunol 6:292–295

Galfre G, Howe SC, Milstein C et al (1977) Antibodies to major histocompatibility antigens produced by hybrid cell lines. Nature 266:550–552

Shulman M, Wilde CD, Kohler G (1978) A better cell line for making hybridomas secreting specific antibodies. Nature 276:269–270

Galfre G, Milstein C (1981) Preparation of monolconal antibodies: strategies and procedures. Methods Enzymol 73B:3–46

Article   Google Scholar  

Hurrell JGR (1982) Monoclonal hybridoma antibodies: techniques and applications. CRC Press, Boca Raton, FL

Google Scholar  

Schreier M, Kohler G, Hengartner H et al (1980) Hybridoma Techniques: EMBO, SKMB Course 1980, Basel. Cold Spring Harbor, New York

Golde WT, Gollobin P, Rodriguez LL (2005) A rapid, simple, and humane method for submandibular bleeding of mice using a lancet. Lab Anim 9:39–43

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Produce Safety and Microbiology Unit (PSM), Western Regional Research Center (WRRC), Pacific West Area (PWA), Agricultural Research Service (ARS), United States Department of Agriculture (USDA), 800 Buchanan St., Albany, CA, 94710, USA

Robert M. Hnasko

Foodborne Toxin Detection and Prevention Unit (FTDP), Western Regional Research Center (WRRC), Pacific West Area (PWA), Agricultural Research Service (ARS), United States Department of Agriculture (USDA), 800 Buchanan St., Albany, CA, 94710, USA

Larry H. Stanker

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Hnasko, R.M., Stanker, L.H. (2015). Hybridoma Technology. In: Hnasko, R. (eds) ELISA. Methods in Molecular Biology, vol 1318. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2742-5_2

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Understanding Hybridoma Technology for Monoclonal Antibody Production

By fusing antibody-producing cells with immortal myeloma cells, researchers produce reliable supplies of highly specific antibodies..

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What Is Hybridoma Technology?

Hybridoma technology involves fusing short-lived antibody-producing B cells with immortal myeloma cells, creating cell lines that produce a never-ending supply of a specific monoclonal antibody .   The technique was created in 1975 by Nobel prize-winning scientists Georges Kohler and Cesar Milstein. 1

Monoclonal Antibodies

Scientists create monoclonal antibodies by cloning a single antibody-producing cell line. In contrast to polyclonal antibodies, monoclonals are highly specific to an antigen. By fusing myeloma cells that can divide indefinitely with B cells that produce specific antibodies against the target of interest, researchers obtain a nearly unlimited source of identical monoclonal antibodies. 2

Since 1986, over 117 monoclonal antibodies have been FDA-approved, beginning with the mouse monoclonal antibody Muromonab-CD3 for kidney transplant rejection. 3 In additional to antibodies from mice and humans, researcher have produced chimeric and humanized monoclonal antibodies that are composed of sequences from both species. By replacing mouse-derived protein sequences with human ones, these monoclonal antibodies reduce the risk of triggering an immune response in humans. 4    Using various methods, scientists have also produced monoclonal antibodies from other mammals for diverse purposes.

Hybridoma Technology Step by Step

Step 1: Immunization

Researchers inject a mammal, typically a mouse, with a target antigen, stimulating an immune response. Antigen injection may occur in a series over the course of several weeks. Then, researchers harvest the mouse's spleen to obtain B cells that produce the desired antibody. 1

Step 2: Cell Fusion

Researchers fuse antibody-producing B cells with myeloma cells in cell culture. Polyethylene glycol (PEG) facilitates fusion of both cells’ plasma membranes, forming a single hybridoma cell with two or more nuclei. Alternatively, electrofusion can merge the cells using a pulsed electrical field. 5

Step 3: Hybridoma Cell Growth

Less than 1 percent of the initial cells fuse to form hybridoma cells. Unused B cells in the culture stop dividing naturally, while chemotherapy destroys the unfused myeloma cells. Researchers use HAT (hypoxanthine-aminopterin-thymidine) medium to allow the selective proliferation of immortal monoclonal antibody-producing cell lines. The aminopterin in the HAT medium stops nucleotide synthesis, while hypoxanthine and thymidine can be used by cells, such as B cells, carrying the HGPRT (hypoxanthine-guanine phosphoribosyl transferase) enzyme. Hybridoma cells with functional HGPRT enzyme can survive and grow, while myeloma cells lacking it eventually die. 1

Step 4: Screening

Researchers often screen hybridoma cells for the monoclonal antibody of interest using an enzyme-linked immunosorbent assay (ELISA). Indirect ELISAs identify antibodies with the appropriate specificity by immobilizing the antigen on a surface and incubating it with a hybridoma cell supernatant. Researchers also use techniques including western blot, flow cytometry, and immunoprecipitation-mass spectrometry to screen their hybridoma cultures. 1

Step 5: Hybridoma Expansion 

The final step involves cloning desired hybridoma cells to obtain a stable cell population and growing the culture to collect large amounts of monoclonal antibodies. This can be achieved through one of two methods. 1

  • In vitro growth of hybrid cells in tissue culture 
  • In vivo growth following inoculation of hybridoma cells into a mouse’s abdomen

An illustration of the hybridoma technique starting with inoculation of a mouse with an antigen, followed by cell fusion to create hybridoma cells, which are then identified and replicated to produce monoclonal antibodies.

Hybridoma Benefits and Limitations

This technology offers numerous advantages, namely 1 , 6

  • Precise antigen targeting
  • A never-ending supply of consistent antibodies 
  • High sensitivity and specificity for use in biological assays
  • Elimination of the need for animal models (in vitro method)
  • Utilization in therapeutic and diagnostic treatments, vaccine creation, and chemotherapy

Nevertheless, the technology also has a few limitations. 1 , 7

  • Long production time
  • Resource-intensive and expensive workflow
  • Not suitable for generating short peptides and fragment antigens
  • Susceptibility to contamination and poor cell viability
  • Risk of virus contamination and disease transmission
  • Absence of stable myeloma cells for human antibody production

Hybridoma Applications

Diagnostic applications

Owing to their high specificity, the antibodies produced by hybridoma technology have a wide range of diagnostic applications, including the following.

  • Enzyme-linked immunosorbent (ELISA): detecting HIV antibodies, hepatitis B surface antigen, and pregnancy hormone 8
  • Immunofluorescence assay (IFA): detecting autoimmune disorders, influenza virus, and Chlamydia trachomatis 9
  • Western blot: analyzing cancer biomarkers 8
  • Flow cytometry: assessing immune cells in HIV, leukemia, and lymphoma 8
  • Immunohistochemistry (IHC): analyzing cancer biomarkers 8
  • Rapid antigen tests: detecting malaria, dengue, Zika virus, and COVID-19 10 , 11  

Immunotherapy

There are various FDA-approved indications of monoclonal antibodies (see table below). 3 Common indications include the following.

  • Cancer treatment: anticancer immunotherapy against prostate, breast, lung, bladder, liver, gastric, colorectal, and endometrial cancer
  • Autoimmune disorders: management of rheumatoid arthritis, Crohn's disease, lupus, psoriasis
  • Infectious diseases: prevention and treatment of respiratory syncytial virus and COVID-19
  • Organ transplant: rejection prevention of kidney, liver, lung, and heart transplants

Noteworthy FDA-approved monoclonal antibodies 3

hybridoma technology research paper

2. Mitra S, Tomar PC. Hybridoma technology; advancements, clinical significance, and future aspects. J Genet Eng Biotechnol . 2021;19(1):159. doi:10.1186/s43141-021-00264-6

3. Antibody therapeutics approved or in regulatory review in the EU or US.  Antibody Society. Accessed March, 2023.

4. Castelli MS, McGonigle P, Hornby PJ. The pharmacology and therapeutic applications of monoclonal antibodies. Pharmacol Res Perspect . 2019;7(6):e00535. doi:10.1002/prp2.535

5. Tabll A, Abbas AT, El-Kafrawy S, Wahid A. Monoclonal antibodies: Principles and applications of immmunodiagnosis and immunotherapy for hepatitis C virus. World J Hepatol . 2015;7(22):2369-2383.  doi:10.4254/wjh.v7.i22.2369

6. Kohler G, Milstein C. Continuous cultures of fused cells secreting antibody of predefined specificity. Nature . 1975;256:495–497. doi: 10.1038/256495a0

7. Moraes JZ, Hamaguchi B, Braggion C, et al. Hybridoma technology: is it still useful? Curr Res Immunol . 2021;2:32-40. doi:10.1016/j.crimmu.2021.03.002

8. Sundarraj S, Rajagopal G, Sundaramahalingam B, et al. Methods of Protein Detection in Cancer for Diagnosis, Prognosis and Therapy. Protein Detection . IntechOpen; 2022. doi.org/10.5772/intechopen.101050

9. Mazzulli T. Laboratory Diagnosis of Infection Due to Viruses, Chlamydia, Chlamydophila, and Mycoplasma. Principles and Practice of Pediatric Infectious Disease . 2008;1352-1368. doi:10.1016/B978-0-7020-3468-8.50293-5

10. Centers for Disease Control and Prevention. CDC Yellow Book 2020: Health Information for International Travel. New York: Oxford University Press; 2017.

11. Drain PK. Rapid Diagnostic Testing for SARS-CoV-2. N Engl J Med . 2022;386(3):264-272. doi:10.1056/NEJMcp2117115

Related immunology Research Resources

Building Broader B Cell Diversity for Better Monoclonal Antibody Discovery<br ><br>

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Beyond Cytotoxicity: The Importance of T Cell Memory

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Science sleuths are using technology to find fakery and plagiarism in published research

FILE - A sign hangs from the Dana-Farber Cancer Institute, Aug. 18, 2022, in Boston. Dana-Farber Cancer Institute announced it’s requesting six retractions and 31 corrections of scientific papers after a British blogger flagged problems in early January 2024. (AP Photo/Charles Krupa, File)

FILE - A sign hangs from the Dana-Farber Cancer Institute, Aug. 18, 2022, in Boston. Dana-Farber Cancer Institute announced it’s requesting six retractions and 31 corrections of scientific papers after a British blogger flagged problems in early January 2024. (AP Photo/Charles Krupa, File)

This photo provided by Sholto David shows David at his home in Pontypridd, Wales, Friday, Jan. 26, 2024. David is a scientist-sleuth who detects image manipulation in published scientific papers. Dana-Farber Cancer Institute announced it is requesting six retractions and 31 corrections of scientific papers after he flagged problems in a recent blog post. (Sholto David via AP)

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Allegations of research fakery at a leading cancer center have turned a spotlight on scientific integrity and the amateur sleuths uncovering image manipulation in published research.

Dana-Farber Cancer Institute, a Harvard Medical School affiliate, announced Jan. 22 it’s requesting retractions and corrections of scientific papers after a British blogger flagged problems in early January.

The blogger, 32-year-old Sholto David, of Pontypridd, Wales, is a scientist-sleuth who detects cut-and-paste image manipulation in published scientific papers.

He’s not the only hobbyist poking through pixels. Other champions of scientific integrity are keeping researchers and science journals on their toes. They use special software, oversize computer monitors and their eagle eyes to find flipped, duplicated and stretched images, along with potential plagiarism.

Defense Secretary Lloyd Austin speaks during a Pentagon press briefing at the Pentagon on Thursday, Feb. 1, 2024 in Washington. (AP Photo/Kevin Wolf)

A look at the situation at Dana-Farber and the sleuths hunting sloppy errors and outright fabrications:

WHAT HAPPENED AT DANA-FARBER?

In a Jan. 2 blog post , Sholto David presented suspicious images from more than 30 published papers by four Dana-Farber scientists, including CEO Laurie Glimcher and COO William Hahn.

Many images appeared to have duplicated segments that would make the scientists’ results look stronger. The papers under scrutiny involve lab research on the workings of cells. One involved samples from bone marrow from human volunteers.

The blog post included problems spotted by David and others previously exposed by sleuths on PubPeer , a site that allows anonymous comments on scientific papers.

Student journalists at The Harvard Crimson covered the story on Jan. 12, followed by reports in other news media. Sharpening the attention was the recent plagiarism investigation involving former Harvard president Claudine Gay, who resigned early this year .

HOW DID DANA-FARBER RESPOND?

Dana-Farber said it already had been looking into some of the problems before the blog post. By Jan. 22, the institution said it was in the process of requesting six retractions of published research and that another 31 papers warranted corrections.

Retractions are serious. When a journal retracts an article that usually means the research is so severely flawed that the findings are no longer reliable.

Dr. Barrett Rollins, research integrity officer at Dana-Farber, said in a statement: “Following the usual practice at Dana-Farber to review any potential data error and make corrections when warranted, the institution and its scientists already have taken prompt and decisive action in 97 percent of the cases that had been flagged by blogger Sholto David.”

WHO ARE THE SLEUTHS?

California microbiologist Elisabeth Bik, 57, has been sleuthing for a decade. Based on her work, scientific journals have retracted 1,133 articles, corrected 1,017 others and printed 153 expressions of concern, according to a spreadsheet where she tracks what happens after she reports problems.

She has found doctored images of bacteria, cell cultures and western blots, a lab technique for detecting proteins.

“Science should be about finding the truth,” Bik told The Associated Press. She published an analysis in the American Society for Microbiology in 2016: Of more than 20,000 peer-reviewed papers, nearly 4% had image problems, about half where the manipulation seemed intentional.

Bik’s work brings donations from Patreon subscribers of about $2,300 per month and occasional honoraria from speaking engagements. David told AP his Patreon income recently picked up to $216 per month.

Technology has made it easier to root out image manipulation and plagiarism, said Ivan Oransky, who teaches medical journalism at New York University and co-founded the Retraction Watch blog. The sleuths download scientific papers and use software tools to help find problems.

Others doing the investigative work remain anonymous and post their findings under pseudonyms. Together, they have “changed the equation” in scientific publication, Oransky said.

“They want science to be and do better,” Oransky said. “And they are frustrated by how uninterested most people in academia — and certainly in publishing — are in correcting the record.” They’re also concerned about the erosion of public trust in science.

WHAT MOTIVATES MISCONDUCT?

Bik said some mistakes could be sloppy errors where images were mislabeled or “somebody just grabbed the wrong photo.”

But some images are obviously altered with sections duplicated or rotated or flipped. Scientists building their careers or seeking tenure face pressure to get published. Some may intentionally falsify data, knowing that the process of peer review — when a journal sends a manuscript to experts for comments — is unlikely to catch fakery.

“At the end of the day, the motivation is to get published,” Oransky said. “When the images don’t match the story you’re trying to tell, you beautify them.”

WHAT HAPPENS NEXT?

Scientific journals investigate errors brought to their attention but usually keep their processes confidential until they take action with a retraction or correction.

Some journals told the AP they are aware of the concerns raised by David’s blog post and were looking into the matter.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

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Our next-generation model: Gemini 1.5

Feb 15, 2024

The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.

SundarPichai_2x.jpg

A note from Google and Alphabet CEO Sundar Pichai:

Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced . Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI .

Our teams continue pushing the frontiers of our latest models with safety at the core. They are making rapid progress. In fact, we’re ready to introduce the next generation: Gemini 1.5. It shows dramatic improvements across a number of dimensions and 1.5 Pro achieves comparable quality to 1.0 Ultra, while using less compute.

This new generation also delivers a breakthrough in long-context understanding. We’ve been able to significantly increase the amount of information our models can process — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model yet.

Longer context windows show us the promise of what is possible. They will enable entirely new capabilities and help developers build much more useful models and applications. We’re excited to offer a limited preview of this experimental feature to developers and enterprise customers. Demis shares more on capabilities, safety and availability below.

Introducing Gemini 1.5

By Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team

This is an exciting time for AI. New advances in the field have the potential to make AI more helpful for billions of people over the coming years. Since introducing Gemini 1.0 , we’ve been testing, refining and enhancing its capabilities.

Today, we’re announcing our next-generation model: Gemini 1.5.

Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.

The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and performs at a similar level to 1.0 Ultra , our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.

Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via AI Studio and Vertex AI in private preview.

As we roll out the full 1 million token context window, we’re actively working on optimizations to improve latency, reduce computational requirements and enhance the user experience. We’re excited for people to try this breakthrough capability, and we share more details on future availability below.

These continued advances in our next-generation models will open up new possibilities for people, developers and enterprises to create, discover and build using AI.

Context lengths of leading foundation models

Highly efficient architecture

Gemini 1.5 is built upon our leading research on Transformer and MoE architecture. While a traditional Transformer functions as one large neural network, MoE models are divided into smaller "expert” neural networks.

Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialization massively enhances the model’s efficiency. Google has been an early adopter and pioneer of the MoE technique for deep learning through research such as Sparsely-Gated MoE , GShard-Transformer , Switch-Transformer, M4 and more.

Our latest innovations in model architecture allow Gemini 1.5 to learn complex tasks more quickly and maintain quality, while being more efficient to train and serve. These efficiencies are helping our teams iterate, train and deliver more advanced versions of Gemini faster than ever before, and we’re working on further optimizations.

Greater context, more helpful capabilities

An AI model’s “context window” is made up of tokens, which are the building blocks used for processing information. Tokens can be entire parts or subsections of words, images, videos, audio or code. The bigger a model’s context window, the more information it can take in and process in a given prompt — making its output more consistent, relevant and useful.

Through a series of machine learning innovations, we’ve increased 1.5 Pro’s context window capacity far beyond the original 32,000 tokens for Gemini 1.0. We can now run up to 1 million tokens in production.

This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we’ve also successfully tested up to 10 million tokens.

Complex reasoning about vast amounts of information

1.5 Pro can seamlessly analyze, classify and summarize large amounts of content within a given prompt. For example, when given the 402-page transcripts from Apollo 11’s mission to the moon, it can reason about conversations, events and details found across the document.

Reasoning across a 402-page transcript: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can understand, reason about and identify curious details in the 402-page transcripts from Apollo 11’s mission to the moon.

Better understanding and reasoning across modalities

1.5 Pro can perform highly-sophisticated understanding and reasoning tasks for different modalities, including video. For instance, when given a 44-minute silent Buster Keaton movie , the model can accurately analyze various plot points and events, and even reason about small details in the movie that could easily be missed.

Multimodal prompting with a 44-minute movie: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can identify a scene in a 44-minute silent Buster Keaton movie when given a simple line drawing as reference material for a real-life object.

Relevant problem-solving with longer blocks of code

1.5 Pro can perform more relevant problem-solving tasks across longer blocks of code. When given a prompt with more than 100,000 lines of code, it can better reason across examples, suggest helpful modifications and give explanations about how different parts of the code works.

Problem solving across 100,633 lines of code | Gemini 1.5 Pro Demo

Gemini 1.5 Pro can reason across 100,000 lines of code giving helpful solutions, modifications and explanations.

Enhanced performance

When tested on a comprehensive panel of text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing our large language models (LLMs). And when compared to 1.0 Ultra on the same benchmarks, it performs at a broadly similar level.

Gemini 1.5 Pro maintains high levels of performance even as its context window increases. In the Needle In A Haystack (NIAH) evaluation, where a small piece of text containing a particular fact or statement is purposely placed within a long block of text, 1.5 Pro found the embedded text 99% of the time, in blocks of data as long as 1 million tokens.

Gemini 1.5 Pro also shows impressive “in-context learning” skills, meaning that it can learn a new skill from information given in a long prompt, without needing additional fine-tuning. We tested this skill on the Machine Translation from One Book (MTOB) benchmark, which shows how well the model learns from information it’s never seen before. When given a grammar manual for Kalamang , a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.

As 1.5 Pro’s long context window is the first of its kind among large-scale models, we’re continuously developing new evaluations and benchmarks for testing its novel capabilities.

For more details, see our Gemini 1.5 Pro technical report .

Extensive ethics and safety testing

In line with our AI Principles and robust safety policies, we’re ensuring our models undergo extensive ethics and safety tests. We then integrate these research learnings into our governance processes and model development and evaluations to continuously improve our AI systems.

Since introducing 1.0 Ultra in December, our teams have continued refining the model, making it safer for a wider release. We’ve also conducted novel research on safety risks and developed red-teaming techniques to test for a range of potential harms.

In advance of releasing 1.5 Pro, we've taken the same approach to responsible deployment as we did for our Gemini 1.0 models, conducting extensive evaluations across areas including content safety and representational harms, and will continue to expand this testing. Beyond this, we’re developing further tests that account for the novel long-context capabilities of 1.5 Pro.

Build and experiment with Gemini models

We’re committed to bringing each new generation of Gemini models to billions of people, developers and enterprises around the world responsibly.

Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI . Read more about this on our Google for Developers blog and Google Cloud blog .

We’ll introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model.

Early testers can try the 1 million token context window at no cost during the testing period, though they should expect longer latency times with this experimental feature. Significant improvements in speed are also on the horizon.

Developers interested in testing 1.5 Pro can sign up now in AI Studio, while enterprise customers can reach out to their Vertex AI account team.

Learn more about Gemini’s capabilities and see how it works .

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UK signals step change for regulators to strengthen AI leadership

The UK is on course for more agile AI regulation, as the government publishes its response to the AI Regulation White Paper consultation today.

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  • Over £100 million to support regulators and advance research and innovation on AI , including Hubs in healthcare and chemical discovery
  • Key regulators asked to publish plans by end of April for how they are responding to AI risks and opportunities
  • UK government makes case for introducing future targeted, binding requirements for most advanced general-purpose AI systems

The UK is on course for more agile AI regulation, backing regulators with the skills and tools they need to address the risks and opportunities of AI , as part of the government’s response to the AI Regulation White Paper consultation today (6 February).

It comes as £10 million is announced to prepare and upskill regulators to address the risks and harness the opportunities of this defining technology. The fund will help regulators develop cutting-edge research and practical tools to monitor and address risks and opportunities in their sectors, from telecoms and healthcare to finance and education. For example, this might include new technical tools for examining AI systems.

Many regulators have already taken action. For example, the Information Commissioner’s Office has updated guidance on how our strong data protection laws apply to AI systems that process personal data to include fairness and has continued to hold organisations to account, such as through the issuing of enforcement notices. However, the UK government wants to build on this by further equipping them for the age of AI as use of the technology ramps up. The UK’s agile regulatory system will simultaneously allow regulators to respond rapidly to emerging risks, while giving developers room to innovate and grow in the UK.

In a drive to boost transparency and provide confidence to British businesses and citizens, key regulators, including Ofcom and the Competition and Markets Authority, have been asked to publish their approach to managing the technology by 30 April. It will see them set out AI -related risks in their areas, detail their current skillset and expertise to address them, and a plan for how they will regulate AI over the coming year.

This forms part of the AI regulation white paper consultation response, published today, which carves out the UK’s own approach to regulation and which will ensure it can quickly adapt to emerging issues and avoid placing burdens on business which could stifle innovation. This approach to AI regulation will mean the UK can be more agile than competitor nations, while also leading on AI safety research and evaluation, charting a bold course for the UK to become a leader in safe, responsible AI innovation.

The technology is rapidly developing, and the risks and most appropriate mitigations, are still not fully understood. The UK government will not rush to legislate, or risk implementing ‘quick-fix’ rules that would soon become outdated or ineffective. Instead, the government’s context-based approach means existing regulators are empowered to address AI risks in a targeted way.

The UK government has for the first time, however, set out its initial thinking for future binding requirements which could be introduced for developers building the most advanced AI systems - to ensure they are accountable for making these technologies sufficiently safe.

Secretary of State for Science, Innovation, and Technology, Michelle Donelan said: 

The UK’s innovative approach to AI regulation has made us a world leader in both AI safety and AI development. I am personally driven by AI ’s potential to transform our public services and the economy for the better – leading to new treatments for cruel diseases like cancer and dementia, and opening the door to advanced skills and technology that will power the British economy of the future. AI is moving fast, but we have shown that humans can move just as fast. By taking an agile, sector-specific approach, we have begun to grip the risks immediately, which in turn is paving the way for the UK to become one of the first countries in the world to reap the benefits of AI safely.

Meanwhile, nearly £90 million will go towards launching nine new research hubs across the UK and a partnership with the US on responsible AI . The hubs will support British AI expertise in harnessing the technology across areas including healthcare, chemistry, and mathematics.

£2 million of Arts and Humanities Research Council ( AHRC ) funding is also being announced today, which will support new research projects that will help to define what responsible AI looks like across sectors such as education, policing and the creative industries. These projects are part of the AHRC ’s Bridging Responsible AI Divides ( BRAID ) programme.

£19 million will also go towards 21 projects to develop innovative trusted and responsible AI and machine learning solutions to accelerate deployment of these technologies and drive productivity. This will be funded through the Accelerating Trustworthy AI Phase 2 competition, supported through the UKRI Technology Missions Fund, and delivered by the Innovate UK BridgeAI programme.

The government will also be launching a steering committee in spring to support and guide the activities of a formal regulator coordination structure within government in the spring. 

These measures sit alongside the £100 million invested by the government in the world’s first AI Safety Institute to evaluate the risks of new AI models, and the global leadership shown by hosting the world’s first major summit on AI safety at Bletchley Park in November.

The groundbreaking International Scientific Report on Advanced AI Safety which was unveiled at the summit will also help to build a shared evidence-based understanding of frontier AI ,  while the work of the AI Safety Institute will see the UK working closely with international partners to boost our ability to evaluate and research AI models.

The UK further commits to this approach today with an investment of £9 million through the government’s International Science Partnerships Fund, bringing together researchers and innovators in the UK and the United States to focus on developing safe, responsible, and trustworthy AI .

The government’s response lays out a pro-innovation case for further targeted binding requirements on the small number of organisations that are currently developing highly capable general-purpose AI systems, to ensure that they are accountable for making these technologies sufficiently safe. This would build on steps the UK’s expert regulators are already taking to respond to AI risks and opportunities in their domains.

Hugh Milward, Vice-President, External Affairs Microsoft UK said:

The decisions we take now will determine AI ’s potential to grow our economy, revolutionise public services and tackle major societal challenges and we welcome the government’s response to the AI White Paper. Seizing this opportunity will require responsible and flexible regulation that supports the UK’s global leadership in the era of AI ”.

Aidan Gomez, Co-Founder and CEO of Cohere, said:

By reaffirming its commitment to an agile, principles-and-context based, regulatory approach to keep pace with a rapidly advancing technology the UK government is emerging as a global leader in AI policy. The UK is building an AI -governance framework that both embraces the transformative benefits of AI , while being able to address emerging risks.

Lila Ibrahim, Chief Operating Officer, Google DeepMind:

I welcome the UK government’s statement on the next steps for AI regulation, and the balance it strikes between supporting innovation and ensuring AI is used safely and responsibly. The hub and spoke model will help the UK benefit from the domain expertise of regulators, as well as provide clarity to the AI ecosystem - and I’m particularly supportive of the commitment to support regulators with further resources. AI represents an opportunity to drive progress for humanity, and we look forward to working with the government to ensure that the UK can continue to be a global leader in AI research and set the standard for good regulation.

Tommy Shaffer Shane, AI Policy Advisor at the Centre for Long-Term Resilience, said:

We’re pleased to see this update to the government’s thinking on AI regulation, and especially the firm recognition that new legislation will be needed to address the risks posed by rapid developments in highly-capable general purpose systems. Moving quickly here while thinking carefully about the details will be crucial to balancing innovation and risk mitigation, and to the UK’s international leadership in AI governance more broadly. We look forward to seeing the government work through this challenge at pace, and to further updates on the approach to new legislation in the coming weeks and months.

Julian David, CEO at techUK said:  

techUK welcomes the government’s commitment to the pro-innovation and pro-safety approach set out in the AI Whitepaper. We now need to move forward at speed, delivering the additional funding for regulators and getting the Central Function up and running. Our next steps must also include bringing a range of expertise into government, identifying the gaps in our regulatory system and assessing the immediate risks. If we achieve this the Whitepaper is well placed to provide the regulatory clarity needed to support innovation, and the adoption of AI technologies, that promises such vast potential for the UK.”  

Kate Jones, Chief Executive of the Digital Regulation Cooperation Forum (DRCF), said:

The DRCF member regulators are all keen to maximise the benefits of AI for individuals, society and the economy, while managing its risks effectively and proportionately. To that end, we are taking significant steps to implement the White Paper principles, and are collaborating closely on areas of shared interest including our forthcoming AI and Digital Hub pilot service for innovators.

John Boumphrey, UK Country Manager of Amazon said:

Amazon supports the UK’s efforts to establish guardrails for AI , while also allowing for continued innovation. As one of the world’s leading developers and deployers of AI tools and services, trust in our products is one of our core tenets and we welcome the overarching goal of the white paper. We encourage policymakers to continue pursuing an innovation-friendly and internationally coordinated approach, and we are committed to collaborating with government and industry to support the safe, secure, and responsible development of AI technology.

Markus Anderljung, Head of Policy, Centre for the Governance of AI said:

The UK’s approach to AI regulation is evolving in a positive direction: it heavily relies on existing regulators, takes concrete steps to support them, while also investing in identifying and addressing gaps in the regulatory ecosystem. I am particularly pleased that the response acknowledges the need to address one such gap that has become more apparent since the white paper’s publication: how the most impactful and compute-intensive AI systems are developed and deployed onto the market.

The consultation has highlighted the strong support for the five cross-sectoral principles which are the foundation of the UK’s approach and include safety, transparency, fairness, and accountability.

The publication of the AI Regulation White Paper last March laid the foundations for the UK’s approach to regulating AI by driving safe, responsible innovation. This common sense, pragmatic approach will now be further strengthened by robust regulator expertise, allowing people across the country to safely harness the benefits of AI for years to come.

Notes to editors

Read the full government response to the AI White Paper consultation .

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IMAGES

  1. (PDF) Hybridoma Technology

    hybridoma technology research paper

  2. Hybridoma Technology for the Win

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  3. Hybridoma Technology

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  4. Advancement in the field of hybridoma technology

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  5. Hybridoma Technology AND Cytokines

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  6. (PDF) Hybridoma technology: the preferred method for monoclonal

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VIDEO

  1. Hybridoma technology: production, purification and application

  2. In hybridoma technology:

  3. Hybridoma Technology

  4. Hybridoma Technology

  5. HYBRIDOMA TECHNOLOGY PART2 PRODUCTION OF MONOCLONAL ANTIBODIES

  6. Hybridoma technology has been successfully used in:

COMMENTS

  1. Hybridoma technology; advancements, clinical significance, and future aspects

    Hybridoma technology is one of the most common methods used to produce monoclonal antibodies. In this process, antibody-producing B lymphocytes are isolated from mice after immunizing the mice with specific antigen and are fused with immortal myeloma cell lines to form hybrid cells, called hybridoma cell lines.

  2. Hybridoma technology a versatile method for isolation of monoclonal

    PMID: 32473573 Hybridoma technology a versatile method for isolation of monoclonal antibodies, its applicability across species, limitations, advancement and future perspectives Hilal Ahmed Parray, 1 Shivangi Shukla, 1 Sweety Samal, Tripti Shrivastava, Shubbir Ahmed, Chandresh Sharma, ⁎⁎ and Rajesh Kumar ⁎

  3. Hybridoma technology: is it still useful?

    Hybridoma technology is the most popular technique to obtain monoclonal antibodies. • Hybridoma technology variants include B cell and stereospecific targeting protocols. • Phage display and single B cell methods are hybridoma technology alternatives. Go to: 1. Introduction

  4. Hybridoma technology: is it still useful?

    Hybridoma technology is the most popular technique to obtain monoclonal antibodies. • Hybridoma technology variants include B cell and stereospecific targeting protocols. • Phage display and single B cell methods are hybridoma technology alternatives. Abstract

  5. Hybridoma technology; advancements, clinical significance, and future

    Background: Hybridoma technology is one of the most common methods used to produce monoclonal antibodies. In this process, antibody-producing B lymphocytes are isolated from mice after immunizing the mice with specific antigen and are fused with immortal myeloma cell lines to form hybrid cells, called hybridoma cell lines.

  6. Hybridoma technology; advancements, clinical significance, and future

    Hybridoma technology is one of the most common methods used to produce monoclonal antibodies. In this process, antibody-producing B lymphocytes are isolated from mice after immunizing the mice with specific antigen and are fused with immortal myeloma cell lines to form hybrid cells, called hybridoma cell lines.

  7. Highly efficient hybridoma generation and screening strategy ...

    We carried out hybridoma generation using cell electrofusion technology, viz., a simple, fast, controllable, and reproducible technique that can obtain higher fusion efficiency than the ...

  8. Hybridoma Technology

    The generation of hybridoma cell lines by the fusion of splenocytes from immunized mice with immortal myeloma cells is a well-established method for the production of monoclonal antibodies. Although other methods have emerged as an effective alternative for the generation of monoclonal antibodies, t …

  9. A novel selection strategy for antibody producing hybridoma ...

    The field of monoclonal antibody generation via hybridoma technology is still ... Bradbury, A. & Plückthun, A. Standardize antibodies used in research. Nat ... K.H. wrote the paper, analyzed the ...

  10. Hybridoma technology a versatile method for isolation of monoclonal

    Hybridoma technology used to produce mAbs: ... Academic and industrial research groups with expertise in the field of antibody isolation, hybridoma methodology continues being the methodology of the first choice, particularly if the goal is to obtain antibodies for analytical purposes. ... RK wrote the paper and finalized with the help of HAP ...

  11. Hybridoma technology a versatile method for isolation of monoclonal

    Hybridoma technology a versatile method for isolation of monoclonal antibodies, its applicability across species, limitations, advancement and future perspectives Int Immunopharmacol. 2020 Aug:85:106639. doi: 10.1016/j.intimp.2020.106639. Epub 2020 May 27. Authors

  12. Hybridoma Technology

    Nature 266:550-552. Shulman M, Wilde CD, Kohler G (1978) A better cell line for making hybridomas secreting specific antibodies. Nature 276:269-270. Galfre G, Milstein C (1981) Preparation of monolconal antibodies: strategies and procedures. Methods Enzymol 73B:3-46. Hurrell JGR (1982) Monoclonal hybridoma antibodies: techniques and ...

  13. (PDF) Hybridoma technology; advancements, clinical significance, and

    Sanchita Mitra All India Institute of Medical Sciences Pushpa Chaudhary Tomar Abstract and Figures Background Hybridoma technology is one of the most common methods used to produce monoclonal...

  14. Hybridoma technology: is it still useful?

    Hybridoma technology: is it still useful? J. Z. Moraes, Bárbara Hamaguchi, +6 authors R. Aguiar Published in Current Research in… 22 March 2021 Medicine, Biology View on PubMed doi.org Save to Library Create Alert Cite Figures and Tables from this paper figure 1 table 1 14 Citations Citation Type More Filters

  15. Hybridoma technology a versatile method for isolation of monoclonal

    @article{Parray2020HybridomaTA, title={Hybridoma technology a versatile method for isolation of monoclonal antibodies, its applicability across species, limitations, advancement and future perspectives}, author={Hilal Ahmed Parray and Shivangi Shukla and Sweety Samal and Tripti Shrivastava and Shubbir Ahmed and Chandresh Sharma and Rajesh Kumar ...

  16. Non-animal-derived monoclonal antibodies are not ready to ...

    In May 2020, the European Commission's Joint Research Centre (EC ... hybridoma technology is well established and allows the isolation of native antibodies generated in the context of an immune ...

  17. (PDF) Hybridoma technology: a brief review on its diagnostic and

    ... To prepare myeloma cells a few weeks before the cell fusion takes place, metastatic tumor cells are incubated in 8-azaguanine to urge non-functional hypoxanthine-guanine...

  18. [PDF] Hybridoma technology; advancements, clinical significance, and

    The advantages and challenges associated with monoclonal antibody production are discussed, and the advancement, clinical significance, and future aspects of this technique are enlightened. Hybridoma technology is one of the most common methods used to produce monoclonal antibodies. In this process, antibody-producing B lymphocytes are isolated from mice after immunizing the mice with specific ...

  19. Some Recent Aspect on Hybridoma Technology

    Hybridoma technology, i.e. the fusion of an antigen-specific B-lymphocyte with an appropriate myeloma cell line, has resulted in a vast number of different reagents (monoclonal antibodies, MAb) with specificity for such molecules as enzymes and hormones, external and internal structures of bacteria, viruses and eucaryotic cells.

  20. PDF Hybridoma technology; advancements, clinical significance, and future

    Hybridoma technology is one of the most common methods used to produce monoclonal antibod-ies. In this process, antibody-producing B lymphocytes are isolated from mice after immunizing the mice with specific antigen and are fused with immortal myeloma cell lines to form hybrid cells, called hybridoma cell lines.

  21. Hybridoma Technology

    Schematic of hybridoma technology illustrating steps involved in the generation of a monoclonal antibody producing hybridoma cell line. Step 1 is mouse immunization with defined antigen.Step 2 spleen is harvested and splenocytes isolated.Step 3 is splenocytes are fused with myeloma cells to generate hybrids.Step 4 fused cells are cultured and hybrids selected by growth in HAT medium.

  22. (PDF) Hybridoma technology: an overview

    Volume: I Authors: Subha Ganguly Government of West Bengal Abstract The term hybridoma was coined by Leonard Herzenberg in the laboratory of César Milstein's in 1976-1977. Hybridoma...

  23. Understanding Hybridoma Technology for Monoclonal Antibody Production

    What Is Hybridoma Technology? Hybridoma technology involves fusing short-lived antibody-producing B cells with immortal myeloma cells, creating cell lines that produce a never-ending supply of a specific monoclonal antibody. The technique was created in 1975 by Nobel prize-winning scientists Georges Kohler and Cesar Milstein. 1. Monoclonal ...

  24. Hybridoma Technology Research Papers

    Academia.edu Publishing We're Hiring! Help Center Find new research papers in: Physics View Hybridoma Technology Research Papers on Academia.edu for free.

  25. Science sleuths are using technology to find fakery and plagiarism in

    The papers under scrutiny involve lab research on the workings of cells. One involved samples from bone marrow from human volunteers. ... Technology has made it easier to root out image manipulation and plagiarism, said Ivan Oransky, who teaches medical journalism at New York University and co-founded the Retraction Watch blog. The sleuths ...

  26. Introducing Gemini 1.5, Google's next-generation AI model

    Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.

  27. Gartner Emerging Technologies and Trends Impact Radar for 2024

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  28. Hybridoma technology: a brief review on its diagnostic and clinical

    Hybridoma technology is the method in which large quantity of identical antibodies are produced which are also known as monoclonal antibodies by the administration of antigen in mouse which produces an immune response. The term hybridoma was coined by Leonard Herzenberg in the laboratory of Cesar Milstein's in 1976-1977. Hybridoma technology is the method in which large quantity of identical ...

  29. UK signals step change for regulators to strengthen AI leadership

    The fund will help regulators develop cutting-edge research and practical tools to monitor and address risks and opportunities in their sectors, from telecoms and healthcare to finance and education.