For healthcare and drug discovery, particularly during the earlier stages of target identification and hit finding, recent advancements in data availability, computational power, and methods (machine learning (ML) and artificial intelligence (AI)) have shown great promise to further rationalized the drug development pipeline initiating a transition from a โdata generation and triagingโ to a...
In traditional pharmacology, dose-response relationships have long been the gold standard for selecting optimal therapeutic doses. Typically, these relationships are monotonic. However, in recent years hormetic, bell-shaped relationships have become more common. This is the case of, for example, bispecific T-cell engager (TCE) drugs, treatments where anti-drug antibodies (ADAs) may act as...
Drug development in oncology faces high failure rates and significant costs due to methodological and operational challenges. Modeling and simulation approaches increasingly support decision-making by integrating biological knowledge and existing data throughout development programs. Nonlinear joint modeling of longitudinal and survival data characterizes the dynamic relationship between...
The development of T cell engagers (TCEs) in immuno-oncology can leverage fit-for-purpose modeling strategy that aligns quantitative tools with the key decisions guiding the development pipeline. In preclinical and first-in-human stages, mechanistic PKPD models translate in vitro and in vivo data into predictions of safe, biologically active starting doses by characterizing trimolecular...
In drug development, mathematical models and quantitative tools are essential to driving efficiency and innovation. Such tools include quantitative systems pharmacology (QSP) models, disease progression models, pharmacokinetics and pharmacodynamics (PKPD) models, virtual populations, digital twins, and in silico clinical trials. These are used to assist in decision making across all stages of...
