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Drop it and run

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Therapeutic antibodies have been successfully used for decades to treat various diseases. For antibodies targeting soluble antigens, however, a so-called “antibody buffering” effect, which can prolong the persistence of the target in the blood instead of clearing it, was observed. When a conventional IgG is injected into the body and binds to its corresponding antigen, the immune complexes are taken up into the cell where a certain amount of the antigen dissociates from the antibody in the endosomal compartments. The dissociated antigen is directed to the lysosomes for degradation, but the remaining amount of antigen (still bound to the IgG molecules) is recycled out of the cell by the neonatal Fc receptor (FcRn), and this can lead to an extension rather than a decrease of the antigen half-life in the bloodstream (1-5).

To overcome this buffering effect, antibodies with pH-dependent antigen binding characteristics were developed. These IgGs bind the soluble target molecules at physiological pH, but release antigen at the acidic pH in the endosomes. Antigen will then be directed into lysosomes for degradation and free antibodies will be recycled out of the cell, available for consecutive rounds of antigen binding and intracellular delivery. This method has been successfully applied to target different soluble antigens, demonstrating enhanced antigen clearance from the bloodstream compared to a conventional IgG with no pH-dependent antigen binding characteristics (6-9). Furthermore, to facilitate even more efficient antigen elimination, pH-dependent antibodies with additional modifications were generated. By increasing the antibody affinity for FcRn or FcyRIIb, soluble antigen bound to the engineered antibodies will enter the cell much more efficiently than by fluid-phase uptake. The combined effects of increased uptake and pH-dependent antigen dissociation resulted in a remarkable decrease of antigen levels following injection of engineered “sweeping” antibodies, opening possibilities for improved therapeutic applications in the future (10-13). We look forward to receiving further news about pH-dependent antibodies already in development (9, 14-15).

References:
1, Finkelman et al, J Immunol. 1993 Aug 1;151(3):1235-44.
2, O’Hear and Foote, Proc Natl Acad Sci U S A. 2005 Jan 4;102(1):40-4.
3, Phelan et al, J Immunol. 2008 Jan 1;180(1):44-8.
4, Davda and Hansen, MAbs. 2010 Sep-Oct;2(5):576-88. doi: 10.4161/mabs.2.5.12833.
5, Xiao et al, AAPS J. 2010 Dec;12(4):646-57. doi: 10.1208/s12248-010-9222-0.
6, Igawa et al, Nat Biotechnol. 2010 Nov;28(11):1203-7. doi: 10.1038/nbt.1691.
7, Chaparro-Riggers et al, J Biol Chem. 2012 Mar 30;287(14):11090-7. doi: 10.1074/jbc.M111.319764.
8, Devanaboyina et al, MAbs. 2013 Nov-Dec;5(6):851-9. doi: 10.4161/mabs.26389.
9, Fukuzawa et al, Sci Rep. 2017 Apr 24;7(1):1080. doi: 10.1038/s41598-017-01087-7.
10, Igawa et al, PLoS One. 2013 May 7;8(5):e63236. doi: 10.1371/journal.pone.0063236.
11, Iwayanagi et al, J Immunol. 2015 Oct 1;195(7):3198-205. doi: 10.4049/jimmunol.1401470.
12, Igawa et al, Immunol Rev. 2016 Mar;270(1):132-51. doi: 10.1111/imr.12392.
13, Yang et al, accepted manuscript, MAbs. 2017 Aug 8:0. doi: 10.1080/19420862.2017.1359455.
14, ALXN1210, https://clinicaltrials.gov/ct2/show/NCT02946463
15, SA237, https://clinicaltrials.gov/ct2/show/NCT02028884

Anna Tramontano – In Memoriam

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Written by Dr. Andrew Martin, Institute of Structural and Molecular Biology, University College London

Anna Tramontano (14 July 1957 – 10 March 2017), Professor of Biochemistry at the “Sapienza” University in Rome, was an outstanding scientist who made hugely important contributions to our understanding of antibody structure.

She obtained her PhD in physics from the University of Naples in 1980, but then moved to a post doc at the University of California, San Francisco. At a time when molecular graphics meant using specialized hardware – some the size of a small refrigerator – she developed a software package which was later commercialized as InsightII. In 1988, she moved to the European Molecular Biology Laboratory (EMBL) in Heidelberg to work with Arthur Lesk on analysis and modelling of antibodies. Between 1989 and 1990, together with Cyrus Chothia in Cambridge, Anna and Arthur published three key papers on the conformation of antibody complementarity-determining regions (CDRs) [1-3] – a development of the idea of canonical conformations of CDRs first published by Cyrus, Arthur and others in 1986 [4]. At this time, I was doing my D.Phil. in Oxford with Tony Rees on modelling antibodies and, to be frank, we didn’t believe this work!  They were claiming that five of the six hypervariable antibody CDRs adopted only a small number of conformations defined by the length of the loop and the presence of a small number of key residues both within, and outside, the CDRs.  However, this was based on analysis of only about eight available structures from the billions and billions of possible antibodies.  Indeed, when the third of those papers came out [5], our view seemed to be reinforced – it appeared to be an exception to the rules – and we expected that there would be lots of these exceptions in future.  However, with over 1000 antibody structures now available, some enhancements to the simple rules in those papers are still able to predict the conformations of five of the six CDRs with high accuracy.

In the early 1990s, Anna returned to a research institute in Rome that was then acquired by Merck & Co. She was involved in developing methods for designing both inhibitors and proteins and in protein modelling and docking.  She continued to publish in the area of antibodies throughout the 1990s including work on phage display libraries [6-9], modelling [10-13], docking [14] and epitope prediction [15].

In 2001, she became a full professor at the Sapienza University in Rome where she continued her interest in protein structure, but also started to look at RNA molecules and their structure, but hardly published on antibodies again until 2008 when she developed the PIGS server for the automatic prediction of antibody structure [16, 17]. She then revived her interest in antibodies, among other things looking at the structural repertoire of immunoglobulin lambda light chains [18], the association of light and heavy variable domains [19], and mutations in paired light and heavy chains in chronic lymphocytic leukemia B cells [20]. In 2012, she created a database of antibodies together with a set of integrated tools known as DIGIT [21], a complement to servers such as IMGT, SAbDab and our own abYsis. Since then she has developed the proABC server for predicting interactions in antibody-antigen complexes [22], improved the modelling of CDR-H3 [23]; developed tools for antibody humanization [24]; developed methods for profiling antigen regions recognized by serum antibodies from NextGen sequencing [25] and methods for comparison and clustering of antibody binding sites [26].

Anna was a Fellow and Vice President of the International Society of Computational Biology (ISCB), which is responsible for the largest international bioinformatics conference (Intelligent Systems for Molecular Biology (ISMB)). She was also an organizer of the European Conference on Computational Biology (ECCB) and, with John Moult, of the Critical Assessment of Structure Prediction (CASP) experiment held every other year to assess progress on protein modelling.  She was passionate about training in Bioinformatics and about encouraging young scientists.

Here I have focussed on her work on antibodies, but she was hugely influential in many areas of Bioinformatics. She will be greatly missed by the scientific community, and by me personally. She was always very supportive of my research and nominated me as an expert on the nomenclature of antibody-based drugs to the WHO-INN. Her science and the scientists that she has mentored and trained will remain as her lasting legacy.

1.      Tramontano A, Chothia C, Lesk AM. Structural determinants of the conformations of medium-sized loops in proteins. Proteins. 1989;6(4):382-94. PMID: 2622909.
2.      Chothia C, Lesk AM, Tramontano A, Levitt M, Smith-Gill SJ, Air G, Sheriff S, Padlan EA, Davies D, Tulip WR, et al. Conformations of immunoglobulin hypervariable regions. Nature. 1989 Dec 21-28;342(6252):877-83. PMID: 2687698.
3.      Tramontano A, Chothia C, Lesk AM. Framework residue 71 is a major determinant of the position and conformation of the second hypervariable region in the VH domains of immunoglobulins. J Mol Biol. 1990 Sep 5;215(1):175-82. PMID: 2118959.
4.      Chothia C, Lesk AM, Levitt M, Amit AG, Mariuzza RA, Phillips SE, Poljak RJ.The predicted structure of immunoglobulin D1.3 and its comparison with the crystal structure. Science. 1986 Aug 15;233(4765):755-8. PMID: 3090684.
5.      Tramontano A, Chothia C, Lesk AM. Framework residue 71 is a major determinant of the position and conformation of the second hypervariable region in the VH domains of immunoglobulins. J Mol Biol. 1990 Sep 5;215(1):175-82. PMID: 2118959.
6.      Leplae R, Tramontano A. PLANET: a phage library analysis expert tool.Physiol Chem Phys Med NMR. 1995;27(4):331-8. PMID: 8768788.
7.      Cortese R, Monaci P, Nicosia A, Luzzago A, Felici F, Galfré G, Pessi A, Tramontano A, Sollazzo M. Identification of biologically active peptides using random libraries displayed on phage. Curr Opin Biotechnol. 1995 Feb;6(1):73-80. PMID: 7534506.
8.      Luzzago A, Felici F, Tramontano A, Pessi A, Cortese R. Mimicking of discontinuous epitopes by phage-displayed peptides, I. Epitope mapping of human H ferritin using a phage library of constrained peptides. Gene. 1993 Jun 15;128(1):51-7. PMID: 7685301.
9.      Tramontano A, Pizzi E, Felici F, Luzzago A, Nicosia A, Cortese R. A database system for handling phage library-derived sequences. Gene. 1993 Jun 15;128(1):143-4. PMID: 8508956.
10.  Tramontano A, Lesk AM. Proteins. 1992 Jul;13(3):231-45. Common features of the conformations of antigen-binding loops in immunoglobulins and application to modeling loop conformations. PMID: 1603812.
11.  Morea V, Tramontano A, Rustici M, Chothia C, Lesk AM. Conformations of the third hypervariable region in the VH domain of immunoglobulins. J Mol Biol. 1998 Jan 16;275(2):269-94. PMID: 9466909.
12.  Morea V, Tramontano A, Rustici M, Chothia C, Lesk AM .Antibody structure, prediction and redesign. Biophys Chem. 1997 Oct;68(1-3):9-16. PMID: 9468606
13.  Morea V, Lesk AM, Tramontano A. Antibody modeling: implications for engineering and design. Methods. 2000 Mar;20(3):267-79. PMID: 10694450.
14.  Helmer-Citterich M, Rovida E, Luzzago A, Tramontano A. Modelling antibody-antigen interactions: ferritin as a case study. Mol Immunol. 1995 Sep;32(13):1001-10. PMID: 7476997.
15.  Pizzi E, Cortese R, Tramontano A. Mapping epitopes on protein surfaces. Biopolymers. 1995 Nov;36(5):675-80. PMID: 7578958.
16.  Marcatili P1, Rosi A, Tramontano A. PIGS: automatic prediction of antibody structures. Bioinformatics. 2008 Sep 1;24(17):1953-4. doi: 10.1093/bioinformatics/btn341. PMID: 18641403.
17.  Lepore R, Olimpieri PP, Messih MA, Tramontano A. PIGSPro: prediction of immunoGlobulin structures v2. Nucleic Acids Res. 2017 May 4. doi: 10.1093/nar/gkx334. PMID: 28472367.
18.  Chailyan A, Marcatili P, Cirillo D, Tramontano A. Structural repertoire of immunoglobulin λ light chains. Proteins. 2011 May;79(5):1513-24. doi: 10.1002/prot.22979. PMID: 21365679.
19.  Chailyan A, Marcatili P, Tramontano A. The association of heavy and light chain variable domains in antibodies: implications for antigen specificity. FEBS J. 2011 Aug;278(16):2858-66. doi: 10.1111/j.1742-4658.2011.08207.x. PMID: 21651726.
20.  Ghiotto F, Marcatili P, Tenca C, Calevo MG, Yan XJ, Albesiano E, Bagnara D, Colombo M, Cutrona G, Chu CC, Morabito F, Bruno S, Ferrarini M, Tramontano A, Fais F, Chiorazzi N. Mutation pattern of paired immunoglobulin heavy and light variable domains in chronic lymphocytic leukemia B cells. Mol Med. 2011;17(11-12):1188-95. doi: 10.2119/molmed.2011.00104. PMID: 21785810.
21.  Chailyan A, Tramontano A, Marcatili P. A database of immunoglobulins with integrated tools: DIGIT. Nucleic Acids Res. 2012 Jan;40(Database issue):D1230-4. doi: 10.1093/nar/gkr806. PMID: 22080506.
22.  Olimpieri PP, Chailyan A, Tramontano A, Marcatili P. Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server. Bioinformatics. 2013 Sep 15;29(18):2285-91. doi: 10.1093/bioinformatics/btt369. PMID: 23803466.
23.  Messih MA, Lepore R, Marcatili P, Tramontano A. Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies. Bioinformatics. 2014 Oct;30(19):2733-40. doi: 10.1093/bioinformatics/btu194. PMID: 24930144
24.  Olimpieri PP, Marcatili P, Tramontano A. Tabhu: tools for antibody humanization. Bioinformatics. 2015 Feb 1;31(3):434-5. doi: 10.1093/bioinformatics/btu667. PMID: 25304777.
25.  Domina M, Lanza Cariccio V, Benfatto S, D’Aliberti D, Venza M, Borgogni E, Castellino F, Biondo C, D’Andrea D, Grassi L, Tramontano A, Teti G, Felici F, Beninati C. Rapid profiling of the antigen regions recognized by serum antibodies using massively parallel sequencing of antigen-specific libraries. PLoS One. 2014 Dec 4;9(12):e114159. doi: 10.1371/journal.pone.0114159. PMID: 25473968.
26. Di Rienzo L, Milanetti E, Lepore R, Olimpieri PP, Tramontano A. Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen. Scientific Reports 2017;7:45053.

How antibody USAN and INN sing a different tune

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We recently reported about the new World Health Organization (WHO) naming scheme for antibody therapeutics which resulted in deleting the source infix in international nonproprietary names (INN) (Parren, Carter & Plückthun, MAbs 2017 9:898-906). The changes implemented remove critical ambiguities in antibody naming going forward.

The WHO has now released the full executive summary of the 64th Consultation on International Nonproprietary Names for Pharmaceutical Substances at which the decision to remove the source infix was taken. The summary is in full agreement with the information previously provided in our perspective.  Notably, the new scheme will change the sounds of antibody names that have become so familiar to us (see Table 1). This diversification is in fact quite important as distinct names are thought to further reduce the potential for medication errors as more therapeutic antibodies make it to the market.

Alignment of INNs with United States Adopted Names (USAN) is essential since (approved) antibody therapeutics will usually carry both an INN and a USAN. We are therefore very pleased to report that the USAN Council has agreed to implement the same naming scheme. Eliminating the source infix also ends an important disagreement between the USAN Council and WHO on how the source infix should be defined and assigned (also see Parren et al. Mabs 2017 for more information). Importantly, it is noted that no changes to previously assigned USAN names are contemplated. Similarly, WHO notes that substituting previously assigned INNs requires extraordinary circumstances such as medication, prescription or distribution errors that occur due to name similarities, also indicating that existing INN will unlikely be changed.

Notably, the WHO stresses the importance of careful dissemination of information on the new scheme and highlights the role of The Antibody Society. Consequently we will continue to update you via this channel as new information becomes available.

Like this post, but not a member? Please join!

Membership is free for students and employees of the Society’s corporate sponsors.

Mouse vs Pan

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By William R (Bill) Strohl, BiStro Biotech Consulting LLC,  7-25-17

This is the first of several blogs that I will be writing for The Antibody Society on topics in the field of therapeutic antibodies and proteins that I find of interest.  The intention of these blogs is to stimulate thought and discussion about various aspects of therapeutic antibodies and related molecules, something that anyone reading on this website ought to appreciate.  For my first blog, I thought I would tackle an age-old (at least since the 1990s) discussion point concerning the use of transgenic mice producing human antibodies vs panning human libraries as preferred sources for fully human antibodies.  Virtually everyone with whom I have had the opportunity to discuss this topic has an opinion on it, some of which are rather strong.  That should make this all the more fun!  Moreover, 2017 appears, at least thus far, to be the year of the fully human antibody.  Thus far (to 7/23/17), 23 innovative drugs have been approved by the US Food and Drug Administration (FDA), seven of which are monoclonal antibodies.  Of those, six are fully human antibodies!

I’ll start out by reminiscing about antibody meetings in the timeframe of around 2000-2001, before any fully human antibodies were approved for marketing and commercial use.  In those days, scientists from the big four, i.e., Medarex and Abgenix on the transgenic mouse side, and Cambridge Antibody Technology (CaT) and Morphosys on the human antibody library side, would give talks at meetings, often back to back, on their platforms.  Of course, as would be expected, each group would expound on the positives for their particular approach, while finding negatives, or minimally providing neutral comments, for the others.  Coffee breaks and lunches at those meetings were typically filled with discussion about which approach, transgenic mouse or human antibody library panning, was better.  It was a fun time, watching, listening to, and participating in those discussions.  I’m sure that in many venues, vigorous discussions along the line of “Mouse vs Pan” still continue.

I will say up front that I don’t particularly have a favorite, because I see the potential use of both approaches.  Anecdotally, over my career I have been involved with many programs that were sourced from Balb/c mice and then humanized, transgenic mice producing human antibodies, and human antibody libraries, and I can’t say categorically that I’ve seen that a trend for “better antibodies” coming from one source or another.  It turns out that one of the worst behaved antibodies we made actually came from a Balb/c mouse, and it took intense phage-based engineering to improve the antibody into one that could potentially be developed.

First, though, to the statistics:  we all know that the first “fully human” antibody to be approved for commercial use, and the most valuable antibody on the market today, is adalimumab, marketed by Abbvie under the name of Humira®.  Approved by the US FDA in December, 2002, Humira® was used to treat nearly a million patients in 2016 and generated over US $16 billion in gross revenues.  Adalimumab was sourced from the CaT human antibody library using a technology called guided selection.

With the very recent approval of Janssen R&D’s guselkumab, there are now 75 antibodies and Fc fusion proteins approved by major regulatory agencies, according to my database.  Of these, 25 (one-third!) are fully human antibodies, 18 of which were derived from transgenic mice producing human antibodies and seven derived from human antibody libraries.  Additionally, there are currently 69 antibodies and Fc fusion proteins in Phase III clinical trials, 20 of which are fully human antibodies.  Of these 20 late clinical stage human antibody candidates, 10 are from transgenic mice, six were derived from human antibody libraries, and four are sourced directly from human B cells (i.e., the new kid on the block).  Combining the approved and Phase III numbers leaves us with 28 approved or late stage human antibodies sourced from transgenic mice, 13 sourced from human antibody libraries, and another four from human B cells.

From the data above, it is clear that transgenic mice have played a much larger role to date than antibody libraries in sourcing successful candidates that have either reached approval stage or late stage clinical trials.  That may or may not tell the whole story, however, as target selection, financing, collaborations, and clinical expertise of the four innovative companies mentioned above, dating back to the pre-2005 timeframe also would have a huge impact on those programs that ultimately were successful and represented by these data.  I do believe, however, that the overall numbers don’t lie, and that for many targets, including soluble cytokines or other serum proteins and single pass receptors with prominent exodomains, the combination of which make up the vast majority of antibody targets, immunization of a transgenic animal should lead rapidly to high quality, high affinity antibodies.  When it comes to isolating epitopes, certain infectious disease targets, and multi-pass membrane proteins, however, often times human antibody library display approaches may have an edge to obtain the right antibody to develop.

Historically, there has been a preconception, largely supported by various data, that antibodies derived from animals or mammalian cells were generally “better behaved” than antibodies derived from human antibody libraries (Jain et al., 2017).  Many papers have been written supporting various aspects of this argument, including the case against hydrophobic patches found in library-based antibodies, the case for phage-based sequence bias, the lack of mammalian cell “editing”, the need for in vitro affinity maturation, and so forth.  Some of these arguments may apply to some library-sourced antibodies, but likely not to all of them.  Many human antibody library antibodies in fact are derived from human antibody genes using PCR-based approaches.  Depending on the maturation level of the recovered variable sequences, one would expect that many of these structures might be more “animal like”, even if they have not gone through substantial in vivo editing.  Moreover, more and more library-based approaches are using eukaryotic cells (e.g., yeast, mammalian cells) for expression and display of their antibodies, which should improve the selection for antibodies with improved expression, folding, and solubility characteristics.

There are now more than a dozen transgenic animal platforms available today, ranging from the original Medarex HuMab mouse and the Abgenix Xenomouse, to transgenic rats, rabbits, chickens, and even cows producing human antibodies, so there is no shortage of potential transgenic animal platforms from which to derive human antibodies (Strohl, 2017).  Moreover, most companies using these approaches have moved away from low yield hybridomas to high yield and throughput B cell cloning approaches, substantially increasing the ability to obtain high quality clones from animals.

Likewise, for displayed antibodies, the field has progressed significantly from phage displayed scFv or Fab human antibody libraries from CaT and Morphosys, respectively, to libraries generated and panned in yeast, mammalian cells, and most recently, in recombinant mammalian cells in which the antibodies are matured “in cell”.  Thus, the range of display options, combined with FACS sorting and analysis, next generation sequencing, and tagging technologies, have significantly increased the ability to obtain large numbers of high quality antibodies from human antibody libraries.

The critical quality attributes of a human antibody panel are: (i) the ability to bind and neutralize key “functional” epitopes; (ii) high affinity and selectivity; (iii) proper biophysical properties (e.g., “well-behaved”, soluble, not aggregation-prone, biochemical stability); (iv) excellent expression as full-length antibodies in manufacturing cell lines; and (v) lack of immunogenicity when dosed in humans.  While virtually any of the approaches discussed above can ultimately end up with excellent attributes in each category, it is likely that, for most targets, antibodies sourced from transgenic animals will be more likely to achieve all the attributes more easily and quickly than those sourced from traditional human antibody libraries.  This may evolve, however, as improved cell-based maturation and panning approaches, such the HuTARG™ technology developed by Innovative Targeting Solutions in Vancouver, which couples mammalian display with cell-based V(D)J recombination and FACS sorting, may revolutionize how fully human antibody variable sequences are sourced in the future.

References:

Strohl WR.  2017.  Chapter 5.  Human antibody discovery platforms, pp. 115-160.  Protein Therapeutics, 2 Volume Set.  T. Vaughan, J. Osbourn, B. Jallal, R. Mannhold, G. Folkers, H. Buschmann, eds.  Wiley.  ISBN: 978-3-527-34086-6.

Jain T, Sun T, Durand S, Hall A, Houston NR, Nett JH, Sharkley B, Bobrowitz B, Caffry I, et al.  2017.  Biophysical properties of the clinical-stage antibody landscape.  Proc. Nat’l. Acad. Sci. USA 114:944-949.

Happy antibody hunting…

Bill Strohl, www.bistrobiotech.com