Monoclonal antibody humanness score and its applications
© Gao et al.; licensee BioMed Central Ltd. 2013
Received: 31 January 2013
Accepted: 20 June 2013
Published: 5 July 2013
Monoclonal antibody therapeutics are rapidly gaining in popularity for the treatment of a myriad of diseases, ranging from cancer to autoimmune diseases and neurological diseases. Multiple forms of antibody therapeutics are in use today that differ in the amount of human sequence present in both the constant and variable regions, where antibodies that are more human-like usually have reduced immunogenicity in clinical trials.
Here we present a method to quantify the humanness of the variable region of monoclonal antibodies and show that this method is able to clearly distinguish human and non-human antibodies with excellent specificity. After creating and analyzing a database of human antibody sequences, we conducted an in-depth analysis of the humanness of therapeutic antibodies, and found that increased humanness score is correlated with decreased immunogenicity of antibodies. We further discovered a surprisingly similarity in the immunogenicity of fully human antibodies and humanized antibodies that are more human-like based on their humanness score.
Our results reveal that in most cases humanizing an antibody and confirming the humanness of the final form may be sufficient to eliminate immunogenicity issues to the same extent as using fully human antibodies. We created a public website to calculate the humanness score of any input antibody sequence based on our human antibody database. This tool will be of great value during the preclinical drug development process for new monoclonal antibody therapeutics.
KeywordsTherapeutic antibody Humanization Immunogenicity
The first monoclonal antibody therapeutic was approved for use in the United States in 1986, the mouse anti-CD3 molecule muromonab that reduces organ transplant rejection . Since then, nearly 30 full-length therapeutic antibodies and 10 fragment antigen-binding (Fab) antibodies have been approved in the United States or Europe, with hundreds more in preclinical and clinical development [2–7]. Therapeutic antibodies are used in a myriad of disease settings, including oncology, autoimmune, and neurological disorders. There are four major antibody types used as therapeutics: fully non-human (most commonly mouse), chimeric (non-human variable region, human constant) , humanized (human and non-human variable region sequence, human constant) [9, 10], and fully human . Therapeutics have evolved to include more human sequences, since in general the more human sequence that is present in the antibody, the less immunogenic the antibody will be once introduced to humans . Immunogenicity refers to the ability of a therapeutic antibody to induce the formation of anti-drug antibodies (ADAs) when administrated into a human. ADAs are immune system generated antibodies against the therapeutic that can reduce the efficacy of the drug, and more importantly they can also cause adverse effects ranging from a rash at the site of injection to a systemic inflammatory reaction that can be fatal [12, 13]. Therefore it would be extremely valuable to know whether an antibody may be immunogenic prior to clinical trials are initiated.
The complementarity determining regions (CDRs) of an antibody are the most variable segments of the variable domain that are essential for the antibody-antigen binding specificity and affinity . The CDRs and the surrounding framework segments comprise the full variable sequence of an antibody. Several attempts have previously been made to calculate a humanness score of the variable region sequences of antibodies, since a vital use for such a tool is to aid in identifying antibody sequences that are likely to have reduced immunogenicity when introduced to humans. First, Abhinandan and Martin  developed the H-score, which calculates the average sequence identity of a given antibody sequence as compared to a small database of human variable region sequences. Using these data, however, the authors were unable to find a clear correlation between H-score and immunogenicity of a small set of therapeutic antibodies. Second, Pelat et al.  defined a germinality index that is the proportion of framework residues that are identical between a given antibody variable region sequence and the closest related human germline sequence. The score was used to analyze humanized forms of a macaque anthrax toxin antibody. Third, Thullier et al.  developed the G-score, which is similar to the H-score expect that the score attempts to classify which germline framework sequence the input antibody was originally derived from. The authors used this tool to study the humanness of multiple macaque antibody sequences.
The germinality index only compares sequences to a single germline sequence, and the databases for the H-score and G-score were composed of a small set of ~3,500 antibody sequences. Further, after assigning H-scores to a set of mouse antibody sequences, nearly all mouse heavy chain sequences scored in the same range as human heavy chain sequences, and more than half of the mouse light chains scored in the same range as human light chain sequences . If an antibody humanness scoring method is unable to clearly distinguish a human and mouse sequence, it is not possible to definitely state how human a given sequence really is by these scoring methods. Here we have developed a new tool to determine the humanness of antibody variable region sequences, termed the T20 score analyzer. After refining the input antibody databases, we found that the T20 score can clearly separate human sequences from mouse sequences and many other species as well. The T20 score was used to conduct a thorough analysis of a large set of therapeutic antibodies, revealing surprising similarities between human-like humanized antibodies and fully human antibodies. The T20 score analyzer is available for free use online: http://abAnalyzer.lakepharma.com.
T20 score analyzer development
An antibody humanness score should possess the ability to distinguish human antibody sequences from other species' antibody sequences, such as mouse, with high specificity. To determine the ability of the T20 score to differentiate human antibody sequences from mouse, we scored thousands of mouse antibody sequences with the T20 score analyzer using the All Human Databases (Figure 2A, Additional file 1: Figure S1A). Most of the scores for human and mouse sequences had clear separation; however some amount of overlap remained between human and mouse VH and VK sequences. One potential reason to explain why many of the mouse sequences are scoring higher T20 scores is due to their similarity with the human sequences that had the lowest T20 scores, i.e. the mouse sequences could be similar to the human sequences that looked the least human-like. If this was correct, we predicted that if the least human-like sequences were removed from the database and the mouse sequences were re-scored, this would create a clearer separation between mouse and human sequences. To test this, we removed the bottom 15% of human sequences from each database to obtain T20 Cutoff Human Databases (Figure 1C). This created a T20 score cutoff, which is the T20 score where sequences above this cutoff are considered human-like. We then re-scored the mouse sequences using the T20 Cutoff Human Databases and found a striking reduction in the T20 scores for the full-length and framework only mouse sequences, with minimal overlap between human and mouse sequences (Figure 2B, Additional file 1: Figure S1B). Importantly, despite removing many human sequences from the databases, re-scoring all human sequences with the refined databases lead to very little change in their T20 scores (Additional file 2: Figure S2). Therefore by developing a humanness analysis tool that focused on the top 85% of human antibody sequences, we were able to clearly separate human and mouse antibody sequences with excellent specificity, and defined human-like sequences as those scoring above the T20 score cutoff. From here on, the T20 score only refers to sequences scored with the T20 Cutoff Human Databases.
T20 score analysis of a collection of antibody sequences
We next analyzed the variable regions of human and mouse germline sequences, and while there was some variability depending on the chain type being analyzed, in general the T20 score accurately classified the human germline sequences as having high T20 scores with the mouse germline sequences having scores mostly below the human-like T20 score cutoff; similar trends were seen for both full-length and framework only germline sequences (Figure 3). We further found that human antibodies produced in mice that were transgenic for human IgG genes [20–22] produced human antibodies with high T20 scores that were similar to germline sequences. By this metric these human antibodies from mice were indistinguishable from fully human antibodies. Overall, these data further validate the T20 score as a means to consistently and accurately classify antibody sequences as human-like or not.
To determine the humanness of antibody sequences from other species, T20 scores were calculated for full-length and framework only sequences for 12 additional species ranging from fish to non-human primates (Figure 3). Generally, the non-human primate antibody sequences had high T20 scores and thus many of them are difficult to distinguish from human sequences, with the Rhesus macaque and Chimpanzee having the highest scores. Fish sequences showed the lowest T20 scores, while mammals and amphibians were somewhere in between. No antibody sequences from non-primates besides mice and rats looked human-like (Figure 3).
The humanness of therapeutic antibodies
We next calculated the average T20 score for each type of therapeutic antibody. As anticipated the humanness of mouse and chimeric antibodies were nearly identical for both full-length and framework only sequences (Figure 4B-E). For full-length sequences, the humanness of humanized heavy and light chain sequences increased strongly, and the T20 score analyzer was sensitive enough to detect a further significant increase in the humanness of fully human antibodies (Figure 4B-C). In contrast for framework only sequences, the humanness of both humanized and human sequences were very similar (Figure 4D-E).
To compare the effectiveness of the T20 score to classify the humanness of antibodies as compared to other antibody humanness scoring tools, we determined the H-score  and G-score  for the 98 therapeutic antibodies. When the T20 score and H-score or G-score were directly compared for the same antibody sequences, the scores were positively correlated for both heavy and light chain sequences (P<10-20 for each comparison; Additional file 3: Figure S3). Despite the positive correlation with the T20 score, the H-score and G-score were unable to separate the humanness of the therapeutic antibody groups as well as the T20 score (Figure 4B-C). In particular the differences between mouse/chimeric and humanized or human antibodies, and the differences between humanized and human sequences, were much less significant with the H-score or G-score as compared to the T20 score. And although the average H-score and G-score for human antibodies were trending higher than humanized antibodies, when individual sequences were analyzed separate from their group, these scoring methods were not able to clearly distinguish most of the humanized antibodies from fully human antibodies to the same extent as the T20 score (Figure 4B-C). These data reveal that the T20 score is more reliable at classifying antibodies as human-like with better specificity than alternative antibody humanness analysis tools.
Correlation of humanness and immunogenicity of therapeutic antibodies
A critical issue with therapeutic antibodies in the clinic is reducing the potential immunogenicity of the final therapeutic form . One of the more recent tactics to avoid possible immunogenicity issues is by using fully human antibodies, either produced in transgenic mice  or using phage display technology , which are often difficult processes compared to traditional monoclonal antibody development . Data presented here revealed that despite a significant difference in the humanness of humanized and fully human antibody sequences, with minor exceptions the immunogenicity of the antibodies was nearly indistinguishable. This was shown in detail for three antibodies that started with high levels of immunogenicity as mouse antibodies, and after their humanization they became non-immunogenic antibodies; the decreased immunogenicity was correlated with an increase in the humanness score of both the heavy and light chains of each antibody. Therefore an additional mechanism to decrease the chance of a humanized antibody being immunogenic in humans is verifying that both the heavy and light chains of the humanized antibody have increased T20 scores over the parental sequence and are thus more human-like. While it is possible that humanized antibodies with lower T20 score will not be immunogenic in the clinic, in general non-human-like antibodies were associated with increased immunogenicity (Figure 5A).
The unique ability of the T20 score analyzer to specifically determine the humanness of the framework regions of an antibody sequence is especially useful when performing antibody humanization, where the CDR sequences are purposely left as parental sequences and only the framework region is transformed into a human-like sequence. For example, the humanness of an antibody framework sequence can be followed throughout the process of performing humanization, where each subsequent change to the framework region can be scored and tracked. Our own antibody humanization efforts revealed that despite the high T20 scores for most donor human germline framework sequences (Figure 3D), re-introducing specific parental framework sequences into a human germline sequence is sometimes sufficient to revert the T20 score of the humanized framework sequence to the same score as the initial parental framework sequence (Additional file 6: Figure S6). Since the T20 score analyzer would be simple to implement during the therapeutic antibody development process, it can be used to prevent antibody sequences with low humanness scores from progressing along the pipeline.
Other methods have been developed to predict antibody immunogenicity, such as experimentally screening molecules for antigen specific activation of T-cells or experimentally searching for T-cell epitopes followed by their removal through targeted substations [23, 26]. However these methods require a combination of in vitro experimentation in addition to in silico prediction tools. We propose that these complicated and time-consuming methods are not necessary since it appears that increasing the humanness of the variable region sequence is sufficient to remove most cases of immunogenicity, which can be directly monitored with the T20 score analyzer.
Researchers have utilized phage display technologies to express synthetic repertoires of antibodies that can be screened for binding to specific antigens [27–29]. During the screening process it would be useful to predict whether these synthetic antibodies would be immunogenic in humans or not, for example by utilizing the T20 score to analyze the sequences. We used the T20 score analyzer to determine the humanness score of a small set of synthetic antibodies and compared these to human antibodies. Surprisingly, the average and range of T20 scores observed for the variable regions of synthetic antibodies was very similar to human antibodies (Additional file 7: Figure S7). Due to the absence of immunogenicity data from synthetic antibodies, we were unable to directly correlate the T20 score of synthetic antibodies with immunogenicity. However since the T20 scores of synthetic and human antibodies are similar, we suggest that the T20 score may be able to predict the immunogenicity of synthetic antibodies to a similar extent as human antibodies.
Here we have developed the T20 score analyzer to calculate the humanness of variable region sequences of monoclonal antibodies with high specificity and reproducibility. In addition to providing a score for the full-length antibody sequence of heavy, kappa light, and lambda light chains, the tool can exclude the CDR regions to calculate a separate score focusing only on the framework regions. We used this tool to study therapeutic antibodies that have been approved for clinical use or are currently in clinical development. Of note we observed consistent decreases in the immunogenicity of antibodies that underwent humanization that resulted in increased T20 scores, suggesting that the T20 score may be used as a metric to determine whether an antibody has been truly humanized. We further found that the T20 score analyzer was better at assessing the differences in the humanness of therapeutic antibodies compared to previously published humanness scoring methods. This tool will be a valuable asset to accurately measure the humanness of the variable region of new therapeutic antibodies during their preclinical development.
Antibody variable region sequence curation
For the All Human Databases, antibody variable region protein sequences were obtained from NCBI IgBLAST (http://www.ncbi.nlm.nih.gov/igblast/retrieveig.html). Sequences were obtained and processed in high-throughput using scripts written in Python. Variable heavy chain (VH), kappa light chain (VK), and lambda light chain (VL) sequences were downloaded separately. Synthetic Ig molecules were excluded, and the minimum sequence length was set to 90 amino acids. Sequences were assigned Kabat numbering using the Abnum tool  and CDR residues were identified following the guidelines put forth by Kabat . Sequences that Abnum was unable to assign the numbering scheme to were excluded from further analysis. Duplicate sequences were removed prior to forming the final databases, and sequences mislabeled as ‘human’ humanized antibodies or human antibodies obtained from transgenic mice were also excluded. In total 29,958 heavy chain sequences, 5,042 kappa light chain sequences, and 3,708 lambda light chain sequences were curated for the All Human Databases.
The mouse protein sequences used to validate the human databases were also obtained from NCBI IgBLAST and processed in the same way as the human sequences. In total 11,781 heavy chain sequences, 3,652 kappa light chain sequences, and 357 lambda light chain sequences were curated.
The human and mouse germline sequences were obtained from NCBI IgBLAST (http://www.ncbi.nlm.nih.gov/igblast/showGermline.cgi). Human antibodies from transgenic mice were selected from the human sequences downloaded from IgBLST described above based on the descriptions found in the NCBI protein database (http://www.ncbi.nlm.nih.gov/protein). The independent set of human and mouse antibody sequences were obtained from Abysis database (http://www.bioinf.org.uk/abysis); these were compared to the sequences in the All Human Databases to remove any overlapping sequences. The antibody sequences for the remaining species and synthetic antibodies were obtained from Abysis and/or NCBI protein database. Sequences for the therapeutic antibodies were obtained from United States patent applications (patft.uspto.gov) and the KEGG drug database (http://www.genome.jp/kegg/drug).
T20 score analyzer
An input VH, VK, or VL variable region protein sequence is first assigned Kabat numbering and CDR residues are identified. The full-length sequence or the framework only sequence (with CDR residues removed) is compared to every sequence in the respective antibody database using the blastp protein-protein BLAST algorithm . The sequence identity between each pairwise comparison is isolated, and after every sequence in the database has been analyzed, the sequences are sorted from high to low based on the sequence identity to the input sequence. The percent identity of the Top 20 matched sequences is averaged to obtain the T20 score. The T20 score analyzer was coded entirely using Python.
Formation of T20 cutoff human databases
For each chain type (VH, VK, VL) and sequence length (full-length or framework only) in the All Human Databases, each antibody sequence was scored with its respective database using the T20 score analyzer. The T20 score was obtained for the top 20 matched sequences after the input sequence itself was excluded (the percent identity of sequences 2 through 21 were averaged since sequence 1 was always the input antibody itself). The T20 scores for each group were sorted from high to low. The decrease in score was roughly linear for most of the sequences; however the T20 scores for the bottom ~15% of antibodies started decreasing sharply. Therefore the bottom 15 percent of sequences were removed and the remaining sequences formed the T20 Cutoff Human Databases, where the T20 score cutoff indicates the lowest T20 score of a sequence in the new database. The exact number of antibody sequences in each database for full-length and framework only is provided in Figure 1C; note that the numbers are slightly different for full-length and framework only sequences since the T20 score cutoff was rounded to the nearest whole number closest to the bottom 15th percentile sequence. A web-based tool is provided to calculate the T20 score of antibody sequences using the T20 Cutoff Human Databases: http://abAnalyzer.lakepharma.com.
We thank Brian Zabel for comments on the manuscript. All authors were funded internally by LakePharma, Inc., which is a private corporation that receives no outside financing.
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