In 2022, the average cost for a single drugs development increased by 15%, reaching $2.3 billion. In drug development, oncology has the highest attrition rate with 95% of new drugs failing Phase 2 clinical trials alone. Consequently, innovative approaches are needed to assess clinical utility prior to proceeding with human trials. Current drug development follows the theory that best potency is equal to the best drug, which is a concentration-centric paradigm focused on optimizing drug-target interaction (Kd) to improve the concentration of drug necessary to elicit its therapeutic effect (IC50). However, both drug concentration and exposure time contribute to clinical efficacy, yet most laboratory studies focus on IC50 alone. This manuscript characterizes drug potency and kinetics of ten oncology drugs in three different cancer cell lines and integrates in vivo pharmacokinetic-pharmacodynamic models to identify how these parameters relate to real-world clinical outcomes. Our analyses revealed that Cmax normalization is necessary for concentration response data as IC50 alone does not account for drugs that are studied at sub-therapeutic concentrations. Additionally, the temporal effect of drug efficacy varies between cell lines and dose, where some drugs are unable to overcome the proliferation rate of the cells to induce a decrease in disease progression. This work aims to enhance the design and implementation of drug regimens by understanding the time-dependence of clinical efficacy and cytotoxicity.
Fast Approximation Algorithm for Non-Monotone DR-submodular Maximization under Size Constraint
arXiv:2511.02254v1 Announce Type: cross Abstract: This work studies the non-monotone DR-submodular Maximization over a ground set of $n$ subject to a size constraint $k$. We


