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Background and Importance Ibrutinib is a covalent Bruton’s tyrosine kinase (BTK) inhibitor widely used in the treatment of B-cell malignancies. Its pharmacokinetics display marked inter- and intra-individual variability, influenced by multiple factors that may compromise therapeutic efficacy and safety. Currently, no accessible clinical tools allow individualised prediction of these parameters. The development of a pharmacokinetic simulator could support prediction of responses and toxicities, optimising therapy.Aim and Objectives To develop a pharmacokinetic simulation tool for ibrutinib integrating cumulative modifiers of drug exposure, aiming to optimise therapeutic outcomes and support individualised treatment decisions.Material and Methods A comprehensive literature review on ibrutinib population pharmacokinetics was conducted. The validated models by Marostica et al. and Al-Ghazawi et al. were selected as the basis for simulation. Using published parameters, a population pharmacokinetic model was implemented in R, incorporating clinical/lifestyle covariates (dose, hepatic impairment, CYP3A4 inhibitors/inducers, food intake, and smoking status). Multiple simulation scenarios were planned by combining these covariates. Simulations were deterministic and based entirely on published population parameters. Simulations generated key pharmacokinetic and pharmacodynamic outputs such us Cmax, AUC0–2 4h, BTK occupancy, and relative platelet count as a surrogate of haematological toxicity.Results Simulations were performed across multiple virtual patient clinical scenarios (n=144) combining the main covariates influencing ibrutinib exposure. Under standard conditions (420 mg/day, fasting), predicted Cmax and AUC 0–2 4h were 23.6 ng/mL and 265.2 ng·h/mL, respectively, with BTK occupancy >95% and platelets reduced to 89.7% of baseline. Strong and moderate CYP3A4 inhibitors increased Cmax by 172% and 65%, reducing platelet levels by 18% and 7%. Moderate hepatic impairment raised Cmax by 45%. Food intake enhanced bioavailability by 49%, with a 5.7% platelet decrease, while smoking reduced Cmax by 30%, compromising exposure. BTK occupancy remained >95%, indicating maintenance of the efficacy threshold. Cumulative effects were assessed to derive dose adjustment recommendations by clinical context.Conclusion and Relevance This population-based simulation tool reliably reproduced published pharmacokinetic profiles and quantitatively illustrated the impact of clinical modifiers on ibrutinib exposure, efficacy, and safety. The model provides a practical instrument to support hospital pharmacists and clinicians in individualised ibrutinib dosing, enhancing therapeutic precision and minimising risk.References and/or Acknowledgements 1. Marostica E, et al. Cancer Chemother Pharmacol. 2015.2. Al-Ghazawi M, et al. Eur J Drug Metab Pharmacokinet. 2021.Conflict of Interest No conflict of interest