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Influence of Antibody Attributes on Clinical Success - A Technology and Coporate Benchmark Analysis

Published By :

La Merie Publishing

Published Date : Mar 2013

Category :

Biotechnology

No. of Pages : 88 Pages

The analytical report „Influence of Antibody Attributes on Clinical Success – A Technology and Coporate Benchmark Analysis“ evaluates the impact of a number of antibody attributes on the success rate of antibodies in clinical development. Among the antibody attributes studied  in this investigation are antibody generation technology, animal species of the parental antibody, antibody format, immunoglobulin class and isotype, target, and therapeutic area. Information from more than 500 naked recombinant monoclonal antibodies was used for this research. Antibodies which failed in clinical development were further analyzed for the reason of failure and phase in which they were discontinued. Biotech and pharmaceutical companies with a significant R&D portfolio of therapeutic antibodies were benchmarked for their antibody success rate in development and the underlying antibody attributes contributing to success.

Specific antibody attributes evaluated for their influence on success in clinical development in this research study are:

  • In vitro antibody generation technologies: display technologies from CAT, Dyax, Morphosys, BioInvent, Domantis, Genentech, others;
  • In vivo antibody generation technologies: chimeric, primatized, nanobodies, deimmunized, human engineered, humaneered, humanized, XenoMouse, HuMab mouse, KM mouse, VelocImmune mouse, human B-cell derived;
  • Animal species of parental wild-type antibody: mouse, rat, rabbit, hamster, cynomolgus monkey, camelid;
  • Target;
  • Immunoglobulin (Ig) class;
  • IgG isotype;
Therapeutic area of lead indication.

When discovering a new monoclonal antibody, researchers have a number of choices to make regarding antibody generation technologies as well as antibody format and immunoglobulin isotpye among other attributes. One of the basic controversies in selecting the antibody generation technolgoy is the question whether antibodies generated in vitro by display technologies are really equivalent to those generated in vivo by a competent immune system.

Typical questions in antibody R&D are:

  • When selecting an in vivo system for antibody generation, are conventional in vivo systems with an animal immune system and subsequent chimerization or humanization creating the same as „modern“ transgenic animals? 
  • Are full length antibodies more successful than modern engineered nanobodies, scFv molecules or even domain antibodies?
  • Do companies have different success rates in their antibody development portfolio?
  • And, if yes, are they using different technologies than their peers which could explain the difference?
  • Is there a different success rate of antibodies against the same target based on generation technologies or other attributes?
  • Are there therapeutic areas with higher likelihood of successful development of antibodies than others?
  • What are the main reasons for antibody failure in clinical development?
  • In which phase do antibodies typically fail?

This analytical report will give you answers for many of these questions. The results of the analysis show

  • whether and how antibody generation technologies differently impact clinical success;
  • why and when antibodies fail;
  • how target selection influences clinical success;
  • if antibody format, class and isotype is relevant for development success;
  • the antibody success rate of therapeutic areas;
  • which companies are the most successful and which antibody attributes they prefer.
Table of Contents

1 Executive Summary and Discussion  

2 Introduction  

3 Methodology 

4 Results  
4.1 Use of antibody technologies  
4.2 Attrition rates   
4.3 Reasons for failure  
4.4 Antibody generation technologies and targets 
4.5 Antibody technologies and antibody formats  
4.6 Parental animal species of in vivo generated antibodies 
4.7 Immunoglobulin class and isotype vs. antibody technology  
4.8 Antibody technology and therapeutic areas   
4.9 Attrition rates of antibodies in therapeutic areas 
4.10  Benchmark analysis: big pharma and biotech antibody technology preferences and attrition rates 

List of Table


Table 1 Overall attrition rate of in vitro generated antibodies
Table 2 Overall attrition rate of in vivo generated antibodies
Table 3 Highest phase of active antibodies generated by in vitro technologies
Table 4 Highest phase of active antibodies generated by in vivo technologies
Table 5 Year of antibody failure for in vitro generated antibodies
Table 6 Year of antibody failure for in vivo generated antibodies
Table 7 Attrition rate of in vitro generated antibodies in the period 2006-2013
Table 8 Attrition rate of in vivo generated antibodies in the period 2006-2013
Table 9 Highest phase of failed antibodies generated by in vitro technologies
Table 10 Highest phase of failed antibodies generated by in vivo technologies
Tables 11 Reasons for failure of antibodies generated by in vitro technologies
Tables 12 Reasons for failure of antibodies generated by in vivo technologies
Table 13 Reasons for failure of humanized antibodies per phase
Tables 14 Targets of failed in vitro generated antibodies per technology
Tables 15 Targets vs. in vitro and in vivo antibody generation technologies
Tables 16 Transgenic mouse antibodies and targets
Table 17 Antibody technologies and antibody formats
Table 18 Parental animal species of in vivo generated antibodies
Table 19 Immunoglobulin class and isotype vs antibody technology
Table 20 In vitro antibody technology and therapeutic areas
Table 21 In vivo antibody technology and therapeutic areas
Table 22 Failed antibodies from in vitro technologies vs therapeutic areas
Table 23 Failed antibodies from in vivo technologies vs therapeutic areas
Table 24 Roche (Genentech(Chugai) use of antibody technologies vs attrition rates
Table 25 AstraZeneca (MedImmune/CAT) use of antibody technologies vs attrition rates
Table 26 Amgen use of antibody technologies vs attrition rates
Table 27 Lilly (ImClone) use of antibody technologies vs attrition rates
Table 28 Pfizer (Wyeth) use of antibody technologies vs attrition rates
Table 29 Novartis use of antibody technologies vs attrition rates
Table 30 GlaxoSmithKline (HGS) use of antibody technologies vs attrition rates
Table 31 Sanofi (Genzyme) use of antibody technologies vs attrition rates
Table 32 Bristol-Myers Squibb (Medarex) use of antibody technologies vs attrition rates
Table 33 Biogen Idec use of antibody technologies vs attrition rates
Table 34 Janssen (Centocor/J&J) use of antibody technologies vs attrition rates
Table 35 AbbVie (Abbott) use of antibody technologies vs attrition rates
Table 36 Kyowa Hakko Kirin Pharma use of antibody technologies vs attrition rates
Table 37 Merck (Schering-Plough) use of antibody technologies vs attrition rates
Table 38 UCB (Celltech) use of antibody technologies vs attrition rates
Table 39 Eisai (Morphotek) use of antibody technologies vs attrition rates
Table 40 Novo Nordisk use of antibody technologies vs attrition rates
Table 41 Ranking list of Big Pharma & Biotech companies and overall antibody attrition rates
Table 42 Ranking list of Big Pharma & Biotech companies and in vitro antibody attrition rates
Table 43 Ranking list of Big Pharma & Biotech companies and in vivo antibody attrition rates
Table 44 Ranking list of Big Pharma & Biotech companies and in vivo antibody preference rate
Table 45 Big Pharma & Biotech companies and preferred in vivo antibody technologies: humanization vs. transgenic mice

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