Clinical Use of Bayesian Model-Informed Precision Dosing in Routine Practice: A Focused Systematic Review

Background: Bayesian model-informed precision dosing (MIPD) is increasingly used to individualize drug therapy; therefore, this review aimed to identify and characterize its implementation in routine clinical practice. Methods: A focused systematic review was conducted. Web of Science Core Collection and PubMed were searched from inception to February 2026. Eligible studies were original research articles evaluating Bayesian MIPD in routine clinical practice using software platforms that supported dosing decisions. Data were synthesized descriptively. No formal risk-of-bias assessment was performed due to heterogeneity in study design. Results: Fifteen studies met the inclusion criteria. Anti-infective therapy predominated, particularly vancomycin (n = 11), with additional studies involving busulfan, mycophenolate mofetil, amikacin, and tobramycin. Commonly reported software platforms included InsightRx (n = 6) and DoseMeRx (n = 4), along with Abbottbase, NextDose, and ISBA. MIPD was mainly applied with therapeutic drug monitoring, reflecting predominant a posteriori use in routine care. Across studies, implementation was associated with improved pharmacokinetic target attainment, while a subset reported clinical benefits, including reduced nephrotoxicity and favorable effectiveness-related outcomes. Pharmacist involvement was commonly described. Conclusions: Published evidence indicates that Bayesian MIPD is being implemented in routine clinical settings, but current published experience is dominated by vancomycin-focused studies. Although the evidence base remains limited, it has grown since 2020 and suggests that software-supported Bayesian dosing can improve pharmacokinetic target attainment and may support better clinical outcomes.