Antibody markup language (AbML) - a notation language for antibody-based drug formats and software for creating and rendering AbML (abYdraw)

Immunotargeting of Cancer Stem Cells

The generally accepted view is that CSCs hijack the signaling pathways attributed to normal stem cells that regulate the self-renewal and differentiation processes. Therefore, the development of selective targeting strategies for CSC, although clinically meaningful, is associated with significant challenges because CSC and normal stem cells share many important signaling mechanisms for their maintenance and survival. Furthermore, the efficacy of this therapy is opposed by tumor heterogeneity and CSC plasticity. While there have been considerable efforts to target CSC populations by the chemical inhibition of the developmental pathways such as Notch, Hedgehog (Hh), and Wnt/β-catenin, noticeably fewer attempts were focused on the stimulation of the immune response by CSC-specific antigens, including cell-surface targets. Cancer immunotherapies are based on triggering the anti-tumor immune response by specific activation and targeted redirecting of immune cells toward tumor cells. This review is focused on CSC-directed immunotherapeutic approaches such as bispecific antibodies and antibody-drug candidates, CSC-targeted cellular immunotherapies, and immune-based vaccines. We discuss the strategies to improve the safety and efficacy of the different immunotherapeutic approaches and describe the current state of their clinical development.

Development of human-machine language interfaces for the visual analysis of complex biologics and RNA modalities and associated experimental data

The advent of recombinant protein-based therapeutic agents in the 1980s and subsequent waves of innovation in molecular biology and engineering of biologics has permitted the production of an increasingly broad array of complex, high molecular weight constructs. While this has opened a powerful new toolbox of molecular scaffolds with which to probe and interdict biological processes, it also makes deciphering the architectural nuances between individual constructs intuitively difficult. Key to downstream data processes for the detection of data trends is the ability to unambiguously identify, compare, and communicate the nature of molecular compositions. Existing small molecule orientated software tools are not intended for structures such as peptides, proteins, antibodies, and RNA, and do not contain adequate atomistic or domain-level detail to appropriately convey their higher structural complexity. Similarly, there is a paucity of large molecule-focused data analysis and visualization tools. This article will describe four new approaches we developed for the graphical representation and analysis of complex large molecules and experimental data. These tools help fulfill key needs in scientific communication and structure-property analysis of complex biologics and modified oligonucleotide-based drug candidates.

VERITAS: Harnessing the power of nomenclature in biologic discovery

We are entering an era in which therapeutic proteins are assembled using building block-like strategies, with no standardized schema to discuss these formats. Existing nomenclatures, like AbML, sacrifice human readability for precision. Therefore, considering even a dozen such formats, in combination with hundreds of possible targets, can create confusion and increase the complexity of drug discovery. To address this challenge, we introduce Verified Taxonomy for Antibodies (VERITAS). This classification and nomenclature scheme is extensible to multispecific therapeutic formats and beyond. VERITAS names are easy to understand while drawing direct connections to the structure of a given format, with or without specific target information, making these names useful to adopt in scientific discourse and as inputs to machine learning algorithms for drug development.