4.5 Review

Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

期刊

MABS
卷 13, 期 1, 页码 -

出版社

TAYLOR & FRANCIS INC
DOI: 10.1080/19420862.2021.1895540

关键词

mAb; monoclonal antibody; therapeutic; developability; viscosity; aggregation; solubility; polyspecificity; pharmacokinetics; immunogenicity; humanization; affinity; specificity; high throughput; prediction; design; computational modeling

资金

  1. National Institutes of Health [T32-GM008353, R01GM104130, R01AG050598, RF1AG059723, R35GM136300]
  2. T32 fellowship [T32-GM008353]
  3. National Science Foundation [CBET 1813963, CBET 1605266, CBET 1804313]
  4. Albert M. Mattocks Chair

向作者/读者索取更多资源

Developing monoclonal antibodies as therapeutic agents for diverse human disorders has sparked intense interest. Key recent advances in high-throughput methods for identifying antibodies with desirable properties have shown great promise in rational antibody design and prediction of drug-like behaviors, but outstanding challenges remain in fully realizing their potential to minimize development times and improve success rates in the clinic.
There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.

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