Improving protein therapeutic development through cloud-based data integration.

Pharma 4.0 is a digital evolution of the pharmaceutical industry that automates scientists' traditional workflows with the implementation of modern technologies like cloud pipelines, artificial intelligence, robotic platforms, and augmented reality. Lab data capture (LDC) is an essential strategy for initiating Pharma 4.0 that aggregates and harmonizes siloed lab data from analytical instruments, reporting systems, and operational platforms. This publication describes the execution of LDC within a quantitative PCR (qPCR) workflow using the Tetra Data Platform (TDP). We selected this workflow because the qPCR instrument, the ViiA7, generates discrete file-based data that documents execution of individual assays for quantifying residual DNA throughout biologics process development and product profiling. TDP executes LDC through the deployment of file scanning software agents, scanning and ingestion processes, and a cloud-based parsing pipeline that harmonizes source data. Web applications were developed to query, visualize, and interpret harmonized qPCR data for automated experiment data processing and process control charting from the TDP platform. Our implementation of LDC enables analytical researchers to harness FAIR (Findable, Accessible, Interoperable, Reproducible) data practices across the organization and establishes a "compliance-by-code" culture in development labs.

Novel peptide ligands for antibody purification provide superior clearance of host cell protein impurities.

The quest for ligands alternative to Protein A for the purification of monoclonal antibodies (mAbs) has been pursued for almost three decades. Yet, the IgG-binding peptides known to date still fall short of the host cell protein (HCP) logarithmic removal value (LRV) set by Protein A media (2.5-3.1). In this study, we present an integrated computational-experimental approach leading to the discovery of peptide ligands that provide HCP LRVs on par with Protein A. First, the screening of 60,000 peptide variants was performed using a high-throughput search algorithm to identify sequences that ensure IgG affinity binding. Select sequences WQRHGI, MWRGWQ, RHLGWF, and GWLHQR were then negatively screened in silico against a panel of model HCPs to ensure the selection of peptides with high binding selectivity. Candidate ligands WQRHGI and MWRGWQ were conjugated to chromatographic resins and characterized by isothermal binding and breakthrough assays to quantify static and dynamic binding capacity (Qmax and DBC10%), respectively. The resulting Qmax were 52.6 mg of IgG per mL of adsorbent for WQRHGI and 57.48 mg/mL for MWRGWQ, while the DBC10% (2 minutes residence time) were 30.1 mg/mL for WQRHGI and 36.4 mg/mL for MWRGWQ. Evaluation of the peptides by isothermal titration calorimetry (ITC) confirmed the binding energy predicted in silico, and an amino acid scanning study corroborated the affinity-like binding activity of the peptides. WQRHGI-WorkBeads resin was finally characterized by purification of a monoclonal antibody from a Chinese Hamster Ovary (CHO) cell culture harvest, affording a remarkable HCP LRV of 2.7, and consistent product yield and purity over 100 chromatographic cycles. These results demonstrate the potential of WQRHGI as an effective alternative to Protein A for antibody purification.