Chrysin, a naturally occurring flavonoid, exhibits a broad spectrum of biological activities, including showing anticancer properties. However, its clinical application is limited by poor bioavailability and low solubility. The introduction of an amine, amide, ester, or alkoxy group to a flavone skeleton influences the biological activity. This review also discusses hybrid compounds, such as the chrysin–porphyrin hybrid, which are characterized by higher biological activity and better bioavailability properties than single molecules. This review concentrates on the anticancer activity of chrysin and its derivatives against the most popular cancers, such as breast, lung, prostate, and gastrointestinal tumors.

(1) Background: Caffeic acid phenethyl ester (CAPE) exhibits anticancer activity; however, its time-dependent effects on interconnected signalling pathways remain incompletely characterised. (2) Methods: We combined wet-lab experiments (MTT viability assay and ELISA measurements of total $NF-\kappa B$ p65 and p53) with a Bayesian digital twin framework to quantify signalling dynamics in prostate cancer cells following CAPE exposure. p53-deficient PC3 and p53-competent LNCaP cell lines were treated for 24 h and 48 h across multiple CAPE concentrations. Experimental data were integrated into a mechanistic Bayesian model using robust likelihoods, enabling uncertainty-aware parameter inference and posterior predictive validation via leave-one-dose-out analysis. (3) Results: In PC3 cells, CAPE induced dose-dependent inhibition of $NF-\kappa B$ p65 that was consistently associated with reduced cell viability at both time points, consistent with a p53-independent regulatory regime. In contrast, LNCaP cells exhibited a transient $NF-\kappa B$–p53 coupling at 24 h, characterised by delayed $NF-\kappa B$ suppression and pronounced p53 activation, followed by a more stable and weakly coupled signalling state at 48 h. These temporal patterns were supported by posterior parameter estimates and predictive performance under leave-one-dose-out validation. (4) Conclusions: This study demonstrates that Bayesian digital twins enable quantitative, uncertainty-aware analysis of time-dependent drug responses, extending beyond conventional dose–response assessments and supporting mechanistic hypothesis generation in cancer pharmacology.