Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes.

The production of recombinant therapeutic proteins from animal or human cell lines entails the risk of endogenous viral contamination from cell substrates and adventitious agents from raw materials and environment. One of the approaches to control such potential viral contamination is to ensure the manufacturing process can adequately clear the potential viral contaminants. Viral clearance for production of human monoclonal antibodies is achieved by dedicated unit operations, such as low pH inactivation, viral filtration, and chromatographic separation. The process development of each viral clearance step for a new antibody production requires significant effort and resources invested in wet laboratory experiments for process characterization studies. Machine learning methods have the potential to help streamline the development and optimization of viral clearance unit operations for new therapeutic antibodies. The current work focuses on evaluating the usefulness of machine learning methods for process understanding and predictive modeling for viral clearance via a case study on low pH viral inactivation.

Femtomolar Fab binding affinities to a protein target by alternative CDR residue co-optimization strategies without phage or cell surface display.

In therapeutic or diagnostic antibody discovery, affinity maturation is frequently required to optimize binding properties. In some cases, achieving very high affinity is challenging using the display-based optimization technologies. Here we present an approach that begins with the creation and clonal, quantitative analysis of soluble Fab libraries with complete diversification in adjacent residue pairs encompassing every complementarity-determining region position. This was followed by alternative recombination approaches and high throughput screening to co-optimize large sets of the found improving mutations. We applied this approach to the affinity maturation of the anti-tumor necrosis factor antibody adalimumab and achieved ~500-fold affinity improvement, resulting in femtomolar binding. To our knowledge, this is the first report of the in vitro engineering of a femtomolar affinity antibody against a protein target without display screening. We compare our findings to a previous report that employed extensive mutagenesis and recombination libraries with yeast display screening. The present approach is widely applicable to the most challenging of affinity maturation efforts.

A micro-scale process for high-throughput expression of cDNAs in the yeast Saccharomyces cerevisiae.

Methods have been developed aimed at applying at high-throughput technology for expression of cloned cDNAs in yeast. Yeast is a eukaryotic host, which produces soluble recombinant proteins and is capable of introducing post-translational modifications of protein. It is, thus, an appropriate expression system both for the routine expression of various cDNAs or protein domains and for the expression of proteins, which are not correctly expressed in Escherichia coli. Here, we describe a standard system in Saccharomyces cerevisiae, based on a vector for intracellular protein expression, where the gene products are fused to specific peptide sequences (tags). These epitope tags, the N-terminal His(6) tag and the C-terminal StrepII tag, allow subsequent immunological identification and purification of the gene products by a two-step affinity chromatography. This method of dual-tagged recombinant protein purification eliminates contamination by degraded protein products. A miniaturization of the procedures for cloning, expression, and detection was performed to allow all steps to be carried out in 96-well microtiter plates. The system is, thus, suitable for automation. We were able to analyze the simultaneous protein expression of a large number of cDNA clones due to the highly parallel approach of protein production and purification. The microtiter plate technology format was extended to quantitative analysis. An ELISA-based assay was developed that detects StrepII-tagged proteins. The application of this high-throughput expression system for protein production will be a useful tool for functional and structural analyses of novel genes, identified by the Human Genome Project and other large-scale sequencing projects.