Model-Informed Drug Development of the Masked Anti-PD-L1 Antibody CX-072.

CX-072 is an anti-PD-L1 (programmed death ligand 1) Probody therapeutic (Pb-Tx) designed to be preferentially activated by proteases in the tumor microenvironment and not in healthy tissue. Here, we report the model-informed drug development of CX-072. A quantitative systems pharmacology (QSP) model that captured known mechanisms of Pb-Tx activation, biodistribution, elimination, and target engagement was used to inform clinical translation.

Quantitative Systems Pharmacology Model of a Masked, Tumor-Activated Antibody.

PROBODY therapeutics (Pb-Tx) are protease-activatable prodrugs of monoclonal antibodies (mAbs) designed to target tumors where protease activity is elevated while avoiding normal tissue. They are composed of a parental mAb, a mask that inhibits antibody binding to target, and a protease-cleavable substrate between the mask and the mAb. We report a quantitative systems pharmacology model for the rational design and clinical translation of Pb-Tx.

Quantitative Systems Pharmacology Model of hUGT1A1-modRNA Encoding for the UGT1A1 Enzyme to Treat Crigler-Najjar Syndrome Type 1.

Crigler-Najjar syndrome type 1 (CN1) is an autosomal recessive disease caused by a marked decrease in uridine-diphosphate-glucuronosyltransferase (UGT1A1) enzyme activity. Delivery of hUGT1A1-modRNA (a modified messenger RNA encoding for UGT1A1) as a lipid nanoparticle is anticipated to restore hepatic expression of UGT1A1, allowing normal glucuronidation and clearance of bilirubin in patients. To support translation from preclinical to clinical studies, and first-in-human studies, a quantitative systems pharmacology (QSP) model was developed.

Quantitative systems toxicology.

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks.

Adaptive informatics for multifactorial and high-content biological data.

Whereas genomic data are universally machine-readable, data from imaging, multiplex biochemistry, flow cytometry and other cell- and tissue-based assays usually reside in loosely organized files of poorly documented provenance. This arises because the relational databases used in genomic research are difficult to adapt to rapidly evolving experimental designs, data formats and analytic algorithms. Here we describe an adaptive approach to managing experimental data based on semantically typed data hypercubes (SDCubes) that combine hierarchical data format 5 (HDF5) and extensible markup language (XML) file types.