Therapeutic drug monitoring (TDM) is a critical tool for optimising antimicrobial therapy, yet its uptake in clinical practice varies widely across institutions and drug classes. Understanding and monitoring patterns of TDM uptake not only supports local quality improvement but also informs the national research agenda by identifying gaps in evidence and practice. Large digital drug concentration datasets, generated through routine TDM, offer unique opportunities to address important clinical questions that are otherwise difficult to study, including rare drug interactions, population-specific pharmacokinetics, and exposure-response relationships. Successful examples of embedding research within digital electronic health records (EHR) systems, such as pragmatic clinical trials and real-world data studies, highlight the potential of integrating research into routine care. Emerging applications of machine learning further enhance this potential, with models being developed to provide patient-specific, data-driven drug dosing recommendations based on TDM results. Together, these approaches demonstrate the power of digital health infrastructure and big data to advance precision antimicrobial therapy and improve patient outcomes.

The utilization of real-world big data (RWD) from adverse event report databases and clinical healthcare records is advancing therapeutic drug monitoring (TDM) and clinical toxicology. This presentation introduces case studies demonstrating key applications of big data analytics in evaluating drug safety, optimizing dosing, and improving patient outcomes.
Adverse event report databases are widely used to detect safety signals and generate hypotheses regarding drug-adverse reaction associations. Building on this, we have explored the increased frequency of adverse events due to drug-drug interactions and validated hypotheses derived from preclinical studies using RWD. A major focus is the assessment of daptomycin-statin interactions. We examined how statin co-administration influences daptomycin-related muscle toxicity, underscoring the role of pharmacovigilance in detecting interactions and guiding clinical decisions. For non-steroidal anti-androgens, we investigated liver injury mechanisms through basic research and confirmed the findings with RWD. This integrative approach bridges experimental toxicity models with real-world patient data, offering insights into drug-induced liver injury and the potential hepatoprotective effects of steroids. We also analyzed hospital-based clinical databases to evaluate follow-up TDM following initial vancomycin monitoring. Our findings indicate that ongoing TDM assessments contribute to reducing toxicity risks.
The presentation highlights RWD-based approaches for TDM and clinical toxicology, ultimately advancing safer and more effective pharmacotherapy.

At the end of this session participants will be able to:
 Explain the mechanism of action of Biologics and sources of variability in exposure of biologics.
 Cite examples of several Biologics in hemato(oncology) where the dose can be optimized using model informed precision dosing and their specific challenges.
 Discuss which model informed precision dosing strategies can be used to personalize the dose in order to reduce financial and clinical toxicity.

Biologics are more and more used in hemato oncology. Evidence is growing that a more personalized dose individualization is beneficial for clinical outcome as well as reduction of clinical and financial toxicity. Combining therapeutic drug monitoring with PK-PD modelling has opened a new way of dose and therapy optimization called model-informed precision dosing. Model-informed precision dosing (MIPD) is an more advanced quantitative approach focusing on individualized dosage optimization, integrating complex mathematical and statistical models of drugs and disease combined with individual demographic and clinical patient characteristics. In this session challenges and opportunities of MIPD of Biologics in (hemato)oncology will be discussed. Several traditional biologics will be discussed, Alemtuzumab, Daratumab, Pembrolizumab, Nivolumab, as well as more complex biologics such as AT(L)G and Antibody drug conjugates (ADCs)

Optimizing medication dosing for children presents unique challenges due to rapid developmental changes in body size, physiology, and organ function, significantly impacting drug disposition and responses. This presentation discusses the application of model-informed approaches to personalizing medication dosing tailored to individual needs. Specifically, the talk will explore clinical implementations of model-informed precision dosing (MIPD) and highlight how machine learning approaches can be used to enhance the MIPD framework. Additionally, the presentation introduces a decision support dashboard as an innovative bedside tool designed to assist clinicians in integrating MIPD effectively into routine pediatric care.