Mark Trautwein

Evaluation of human pancreatic RNase as effector molecule in a therapeutic antibody platform

Human antibody-ribonuclease (RNase) fusion proteins, referred to as immunoRNases, have been proposed as an alternative to heterologous immunotoxins, without their immunogenicity and unspecific toxicity issues. In this study, we investigated if human pancreatic RNase will be suitable as effector component in a therapeutic antibody development platform. We generated several fusion proteins consisting of tumor-specific human immunoglobulins (IgGs) and human pancreatic RNase. Transient mammalian cell production was efficient and IgG-RNases were purified to homogeneity. Antigen binding was comparable to the parental antibodies and RNase catalytic activity was retained even in the presence of 50-fold molar excess of human cytosolic RNase inhibitor (RI). Serum stability, cell binding and internalization of IgG-RNases were comparable to the parental IgGs. Despite these promising properties, none of the IgG-RNases revealed significant inhibition of tumor cell growth in vitro even when targeting different antigens putatively employing different endocytotic pathways. The introduction of different linkers containing endosomal protease cleavage sites into the IgG-RNase did not enhance cytotoxicity. Similarly, RI evasive human pancreatic RNase variants mediated only small inhibiting effects on tumor cell growth at high concentrations, potentially reflecting inefficient cytosolic translocation. Taken together, human pancreatic RNase and variants did not prove to be generally suitable as effector component for a therapeutic antibody drug development platform.

Modular Protein Expression Toolbox (MoPET), a standardized assembly system for defined expression constructs and expression optimization libraries

The design and generation of an optimal expression construct is the first and essential step in in the characterization of a protein of interest. Besides evaluation and optimization of process parameters (e.g. selection of the best expression host or cell line and optimal induction conditions and time points), the design of the expression construct itself has a major impact. However, the path to this final expression construct is often not straight forward and includes multiple learning cycles accompanied by design variations and retesting of construct variants, since multiple, functional DNA sequences of the expression vector backbone, either coding or non-coding, can have a major impact on expression yields. To streamline the generation of defined expression constructs of otherwise difficult to express proteins, the Modular Protein Expression Toolbox (MoPET) has been developed. This cloning platform allows highly efficient DNA assembly of pre-defined, standardized functional DNA modules with a minimal cloning burden. Combining these features with a standardized cloning strategy facilitates the identification of optimized DNA expression constructs in shorter time. The MoPET system currently consists of 53 defined DNA modules divided into eight functional classes and can be flexibly expanded. However, already with the initial set of modules, 792,000 different constructs can be rationally designed and assembled. Furthermore, this starting set was used to generate small and mid-sized combinatorial expression optimization libraries. Applying this screening approach, variants with up to 60-fold expression improvement have been identified by MoPET variant library screening.