Viral Kinetics Model of SARS-CoV-2 Infection Informs Drug Discovery, Clinical Dose, and Regimen Selection.

Quantitative systems pharmacology (QSP) has been an important tool to project safety and efficacy of novel or repurposed therapies for the SARS-CoV-2 virus. Here, we present a QSP modeling framework to predict response to antiviral therapeutics with three mechanisms of action (MoA): cell entry inhibitors, anti-replicatives, and neutralizing biologics. We parameterized three distinct model structures describing virus-host interaction by fitting to published viral kinetics data of untreated COVID-19 patients. The models were used to test theoretical behaviors and map therapeutic design criteria of the different MoAs, identifying the most rapid and robust antiviral activity from neutralizing biologic and anti-replicative MoAs. We found good agreement between model predictions and clinical viral load reduction observed with anti-replicative nirmatrelvir/ritonavir (Paxlovid®) and neutralizing biologics bamlanivimab and casirivimab/imdevimab (REGEN-COV®), building confidence in the modeling framework to inform a dose selection. Finally, the model was applied to predict antiviral response with ensovibep, a novel DARPin therapeutic designed as a neutralizing biologic. We developed a new in silico measure of antiviral activity, area under the curve (AUC) of free spike protein concentration, as a metric with larger dynamic range than viral load reduction. By benchmarking to bamlanivimab predictions, we justified dose levels of 75, 225, and 600 mg ensovibep to be administered intravenously in a Phase 2 clinical investigation. Upon trial completion, we found model predictions to be in good agreement with the observed patient data. These results demonstrate the utility of this modeling framework to guide the development of novel antiviral therapeutics.

Systems Modeling Identifies Divergent Receptor Tyrosine Kinase Reprogramming to MAPK Pathway Inhibition.

INTRODUCTION: Targeted cancer therapeutics have demonstrated more limited clinical efficacy than anticipated, due to both intrinsic and acquired drug resistance. Underlying mechanisms have been largely attributed to genetic changes, but a substantial proportion of resistance observations remain unexplained by genomic properties. Emerging evidence shows that receptor tyrosine kinase (RTK) reprogramming is a major alternative process causing targeted drug resistance, separate from genetic alterations. Hence, the contributions of mechanisms leading to this process need to be more rigorously assessed. METHODS: To parse contributions of multiple mechanisms to RTK reprogramming, we have developed a quantitative multi-receptor and multi-mechanistic experimental framework and kinetic model. RESULTS: We find that RTK reprogramming mechanisms are disparate among RTKs and nodes of intervention in the MAPK pathway. Mek inhibition induces increased Axl and Her2 levels in triple negative breast cancer (TNBC) cells while Met and EGFR levels remain unchanged, with Axl and Her2 sharing re-wiring through increased synthesis and differing secondary contributing mechanisms. While three Mek inhibitors exhibited mechanistic similarity, three Erk inhibitors elicited effects different from the Mek inhibitors and from each other, with MAPK pathway target-specific effects correlating with Erk subcellular localization. Furthermore, we find that Mek inhibitor-induced RTK reprogramming occurs through both BET bromodomain dependent and independent mechanisms, motivating combination treatment with BET and Axl inhibition to overcome RTK reprogramming. CONCLUSIONS: Our findings suggest that RTK reprogramming occurs through multiple mechanisms in a MAPK pathway target-specific manner, highlighting the need for comprehensive resistance mechanism profiling strategies during pharmacological development.

High-throughput protease activity cytometry reveals dose-dependent heterogeneity in PMA-mediated ADAM17 activation.

As key components of autocrine signaling, pericellular proteases, a disintegrin and metalloproteinases (ADAMs) in particular, are known to impact the microenvironment of individual cells and have significant implications in various pathological situations including cancer, inflammatory and vascular diseases. There is great incentive to develop a high-throughput platform for single-cell measurement of pericellular protease activity, as it is essential for studying the heterogeneity of protease response and the corresponding cell behavioral consequences. In this work, we developed a microfluidic platform to simultaneously monitor protease activity of many single cells in a time-dependent manner. This platform isolates individual microwells rapidly on demand and thus allows single-cell activity measurement of both cell-surface and secreted proteases by confining individual cells with diffusive FRET-based substrates. With this platform, we observed dose-dependent heterogeneous protease activation of HepG2 cells treated with phorbol 12-myristate 13-acetate (PMA). To study the temporal behavior of PMA-induced protease response, we monitored the pericellular protease activity of the same single cells during three different time periods and revealed the diversity in the dynamic patterns of single-cell protease activity profile upon PMA stimulation. The unique temporal information of single-cell protease response can help unveil the complicated functional role of pericellular proteases.