Design, modelling, and pharmacokinetic prediction of novel BRD4-targeting PROTACs

Proteolysis Targeting Chimeras (PROTACs) represent an emerging modality for targeted protein degradation, yet their development is often constrained by suboptimal physicochemical and pharmacokinetic properties. BRD4 degraders require careful optimization of linker architecture to balance ternary complex formation with in vivo developability. This study presents an integrated computational framework combining ternary complex modelling, molecular dynamics (MD) simulations, and physiologically based pharmacokinetic (PBPK) modelling to rationally design and predict the performance of novel BRD4-targeting PROTACs. Four novel PROTACs were designed with systematically varied linker chemotypes while keeping established BRD4 and VHL warheads. Ternary complex formations were evaluated using 100 ns all-atom MD simulations, assessing interface stability, hydrogen-bond persistence, π-π stacking, and protein-protein contact networks. PBPK simulations using PK-Sim were conducted to predict intravenous pharmacokinetics, including half-life, AUC, clearance, and mean residence time. Novel_1 and Novel_2 showed superior ternary complex stability, with persistent BRD4-VHL contacts, reduced conformational dispersion, and enriched interaction networks compared to Novel_3 and Novel_4. PBPK simulations predicted extended half-lives (∼47 h), high systemic exposure (AUC up to 13,055 µmolimen/L for Novel_2), and prolonged residence times, showing potential for sustained pharmacodynamic activity. In contrast, Novel_4 showed poor structural stability and negligible plasma exposure due to high predicted clearance. Benchmarking against known degraders (MZ1, Pred-1797) reproduced expected stability and potency trends, supporting the predictive relevance of the integrated workflow. This integrative modelling strategy effectively differentiates high- and low-performing PROTAC candidates and highlights Novel_1 and Novel_2 as promising BRD4 degraders with favourable structural and pharmacokinetic profiles. The study shows the value of combining MD-derived mechanistic insights with PBPK-based translational predictions to accelerate rational PROTAC optimization and prioritization.