The Competency Framework for Therapeutic Drug Monitoring (TDM) Pharmacists, developed collaboratively by the Division of Therapeutic Drug Monitoring of the Chinese Pharmacological Society and the Division of Hospital Pharmacy of the Chinese Pharmaceutical Association, establishes standardized competencies for pharmacists and laboratory personnel engaged in TDM practices. Responding to national health policy drivers emphasizing clinical pharmacy integration, rational drug use, and patient-centered care, this framework addresses the absence of TDM-specific competency standards both domestically and internationally. Developed using the Modified Delphi Method, it defines dual-track competencies for testing and interpretation roles, structured across three proficiency levels and detailed through a hierarchical system of 6 first-level, 23 second-level, and 90 third-level indicators. Officially published on November 30, 2023, in the Chinese Journal of Hospital Pharmacy, the framework provides a systematic foundation for training, professional evaluation, and quality enhancement in TDM services, aiming to elevate individualized pharmacotherapy standards nationwide.

The application of targeted anticancer drugs has significantly improved the precision and efficacy of cancer treatment. However, the associated hepatotoxicity has become an increasing concern. Tyrosine kinase inhibitors (TKIs), as a crucial class of molecularly targeted drugs, inhibit specific signaling pathways to block tumor cell proliferation and are widely used in the treatment of various malignancies, including lung cancer and leukemia. However, TKIs can induce varying degrees of liver injury through complex mechanisms involving oxidative stress, mitochondrial dysfunction, inflammation, and cholestasis. This study focuses on elucidating the molecular mechanisms of hepatotoxicity induced by crizotinib and sunitinib, while also highlighting the potential of licorice in mitigating TKI-induced liver toxicity. These findings have significant implications for optimizing clinical drug regimens, reducing adverse effects, and ultimately improving patient survival outcomes.

Liver injury severely limits the clinical use of voriconazole. Clarifying the mechanism and markers of voriconazole–induced liver injury is of great significance. Our retrospective study results shown that the median time to hepatotoxicity was 3 days (range 1–24 days), and 83.2% of cases of hepatotoxicity occurred within 7 days of voriconazole initiation. Voriconazole trough concentration was significantly associated with hepatotoxicity. Significant predictors of grade ≥3 hepatotoxicity was trough concentration >6.87 mg/L, concomitant use of at least three hepatotoxic drugs, and septic shock. Furthermore, we constructed a quantitative systems toxicology model of voriconazole–induced liver injury through integrating the mechanism–based hepatoxic parameters generated from in vitro assays into a physiologically based pharmacokinetic model. The hepatotoxic substances, main mechanism, dose correlation and markers of voriconazole–induced liver injury were determined according to liver injury incidence of simulated populations. The voriconazole–treated mice, voriconazole and voriconazole N–oxide (VNO)–treated HepG2 cell were used to validate the relationship of liver injury with oxidative stress and VNO. The results demonstrated that the incidence of voriconazole–induced liver injury was 17.9%, which was dose–dependent. VNO–induced oxidative stress contributed most to liver injury, which was manifested by reactive oxygen species (ROS) accumulation and antioxidant enzymes inhibition. Liver ROS/reactive nitrogen species (RNS) baseline clearance Vmax was negatively correlated to plasma alanine aminotransferase elevation and loss of liver adenosine triphosphate. We concluded that VNO–induced oxidative stress was the main cause of voriconazole–induced liver injury, and liver RNS/ROS baseline clearance Vmax might be a potential marker. This study may provide new insights for mechanism understanding and early warning of voriconazole–induced liver injury.

The safety management of antineoplastic drugs is still one of the most important challenges in oncology. This report explores how AI-driven approaches are transforming the safety landscape of both classical chemotherapeutic agents and novel targeted therapies. By synthesizing robust evidence from clinical guidelines with innovative AI methods, we have developed precision tools to enhance early identification and proactive management of adverse events.
For high-dose methotrexate (HDMTX), recent guideline emphasizes the role of individualized dosing regimens in mitigating toxicity. AI models were developed to predict HDMTX-related toxicities with enhanced accuracy, offering actionable insights for optimized patient care. Similarly, in the field of novel therapies, Bruton’s tyrosine kinase (BTK) inhibitors have shown remarkable efficacy in B-cell malignancies but pose challenges in managing hematological toxicity. By combining real-world data and machine learning algorithms, an XGBoost model was employed to predict severe hematological events, providing support for risk stratification and personalized care. In the context of immune checkpoint inhibitors (ICIs), immune-related thyroid dysfunction (ICI-TD) has emerged as a prevalent adverse event. Based on risk factors drawn from systematic reviews, AI-powered prediction models have been developed to facilitate early detection and tailored management strategies. These models, validated through rigorous methodologies, demonstrate the potential to improve clinical outcomes by enabling timely interventions.
We are advancing toward a new era of precision oncology, the integration of AI with evidence-based medicine represents a significant advancement in the prediction and management of antineoplastic drug toxicity. This collaborative approach helps to pave the way for enhanced safety management of antineoplastic drugs.