The body's immune system exhibits a remarkable ability to scrutinize a cell's interior, a function of central importance. This scrutiny enables the detection of changes indicative of the transformation from a normal cell to a rapidly multiplying cancer cell. The human immune system has developed an intricate mechanism where every intracellular protein is presented on the cell's surface for immune surveillance. Proteins are broken down into peptides within the cell, binding to human leukocyte antigen (HLA) molecules and forming peptide HLA complexes (pHLA). If the amount of presented peptide is altered or unusual peptides are presented, cytotoxic T-cells recognize it through their T-cell receptors, selectively eliminating the transformed tumor cell.
Despite being the most abundant surface markers on cancer cells, pHLA complexes present challenges for targeted cancer immunotherapies due to variable expression levels and marginal distinctions from those found in normal cells. This complicates the development of pHLA-targeting therapeutics, especially bi-specific T-cell engagers (TCEs) designed to redirect cytotoxic T-cells towards tumor sites. (Figure 1).
First, TCEs must precisely target cancer-specific pHLA complexes with the utmost specificity to avoid off-target effects, preventing severe toxicity in the patient. Second, the pHLA-targeting moiety needs to be combined with a T-cell targeting moiety to form a bi-specific modality. This is commonly achieved by engineering two distinct monoclonal antibodies — one directed at the cancer-specific pHLA complex and the other at a T-cell-specific protein like CD3 — into a single bi-specific antibody. Since bi-specific T-cell engaging antibodies do not naturally occur, their technical development requires extensive protein engineering efforts. The goal is to achieve a manufacturable, stable, and soluble compound which can be produced in substantial quantities for medical applications. The optimization of bi-specific T-cell engagers for pre-clinical and clinical development is a process that typically needs several years.
This combination of technologies may well be a transformative shift in the progression of next-generation pHLA-targeting T-cell engagers, aiming for superior safety and increased efficiency. This commitment is driven by the utilization of modern screening technology for ultra-high throughput pHLA and the automated protein engineering platform for unparalleled speed and efficiency in drug optimization. Both technologies are seamlessly integrated within a closed loop that incorporates artificial intelligence, enhancing the predictive capability for the design of pHLA-targeting TCEs with optimal developability and highest safety standards.
Precision Screening
Achieving precise discrimination of cancer cells without affecting healthy tissue is crucial for the development of T-cell engagers (TCEs). This requires accurate characterization of the pHLA-targeting antibody, considering its binding properties not only to the target cancer pHLA but also to other pHLAs resembling the target structure. Specialized technology is essential to meet these demands.
Precision screening is key for evaluating antibody binding to pHLA complexes, leveraging advanced microsystems technology and cutting-edge microarray chips. These technologies ensure exceptional accuracy in characterizing the binding properties of antibodies to pHLA complexes. The screening chips embed thousands of pHLA complexes, facilitating rapid and precise measurement of antibody binding to all relevant pHLA complexes in the body, enhanced by highly specialized bioinformatic workflows.
To initiate the process, in silico predictions are generated to identify pHLA complexes closely resembling the target pHLA. Those predicted pHLAs, together with rationally designed pHLA complexes, undergo precise measurement. The gathered results are augmented with additional experimental and omics data, allowing a precise bioinformatic prediction for the toxicity profile of a pHLA-targeting TCE. This risk assessment serves as a basis for informed decision making in the drug development process regarding safety and effectiveness.
Data-driven Drug Discovery Process
This technology serves as a cornerstone of developing next generation TCEs. The measurement data enables AI-driven predictions, establishing a closed loop that facilitates comprehensive antibody characterization and optimization across multiple cycles, all powered by the automated platform.
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