# Proactive Therapeutic Drug Monitoring of Dalbavancin in the Long-Term Treatment of Chronic Infections: A Narrative Review

**Authors:** Dario Cattaneo, Jessica Cusato

PMC · DOI: 10.3390/antibiotics15030253 · Antibiotics · 2026-03-01

## TL;DR

This review discusses how monitoring dalbavancin drug levels can help tailor treatment for chronic infections, ensuring effective and safe long-term use.

## Contribution

The paper introduces proactive therapeutic drug monitoring and model-informed dosing as novel strategies to optimize long-term dalbavancin therapy.

## Key findings

- Dalbavancin's pharmacokinetics vary significantly between individuals, affecting drug exposure during long-term treatment.
- Proactive therapeutic drug monitoring can help adjust dosing to maintain optimal drug concentrations.
- Bayesian forecasting and machine learning tools show promise in improving dosing precision.

## Abstract

Dalbavancin is a long-acting lipoglycopeptide antibiotic increasingly off-label used for the management of complex and chronic Gram-positive infections, including osteoarticular, prosthetic, and cardiovascular device-related infections. While its prolonged half-life enables infrequent dosing, marked inter-individual pharmacokinetic variability has been documented during extended treatment courses, potentially resulting in suboptimal exposure. This narrative review explores the role of proactive therapeutic drug monitoring (TDM) as a strategy to individualize dalbavancin dosing in patients requiring long-term therapy. We summarized current evidence on pharmacokinetic determinants of dalbavancin exposure, including renal function, body weight, and hypoalbuminemia, and discussed proposed pharmacokinetic/pharmacodynamic targets to support TDM implementation. Available analytical methods for dalbavancin quantification and clinical experiences with TDM-guided dosing are reviewed, highlighting their impact on optimizing injection timing and maintaining adequate drug concentrations over prolonged periods. In addition, emerging model-informed precision dosing approaches, such as Bayesian forecasting and machine learning-based tools, are discussed as promising strategies to further refine exposure prediction and re-dosing decisions. Overall, proactive TDM represents a valuable tool for optimizing dalbavancin therapy in chronic infections, although prospective multicenter studies are needed to validate target thresholds and standardized implementation strategies.

## Linked entities

- **Chemicals:** dalbavancin (PubChem CID 16134627)

## Full-text entities

- **Genes:** CTRC (chymotrypsin C) [NCBI Gene 11330] {aka CLCR, ELA4}, ALB (albumin) [NCBI Gene 213] {aka FDAHT, HSA, PRO0883, PRO0903, PRO1341}
- **Diseases:** vertebral osteomyelitis (MESH:D010019), phlegmon (MESH:D002481), chronic (MESH:D002908), overweight (MESH:D050177), vascular graft infections (MESH:D006083), Hepatic impairment (MESH:D008107), osteoarticular infections (MESH:D014394), infective endocarditis (MESH:D004696), underweight (MESH:D013851), MRSA (MESH:D013203), streptococcal infection (MESH:D013290), skin and skin structure infections (MESH:D012871), inflammatory (MESH:D007249), injury to (MESH:D014947), infected (MESH:D007239), Gram-positive infections (MESH:D016908), Chronic Infections (MESH:D000088562), necrotizing fasciitis (MESH:D019115), Bone and Joint Infections (MESH:D001847), bacterial (MESH:D001424), renal dysfunction (MESH:D007674), hypoalbuminemia (MESH:D034141), ABSSSI (MESH:D017192), Obesity (MESH:D009765), critically ill (MESH:D016638), toxicity (MESH:D064420), TDM (MESH:D000081015), discitis (MESH:D015299), diabetes (MESH:D003920), bacteraemia (MESH:C531821)
- **Chemicals:** creatinine (MESH:D003404), vancomycin (MESH:D014640), Cmin (-), methicillin (MESH:D008712), Dalbavancin (MESH:C469289), tetracyclines (MESH:D013754), lipoglycopeptide (MESH:D000077427), beta-lactams (MESH:D047090)
- **Species:** Staphylococcus aureus (species) [taxon 1280], Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13024627/full.md

## References

50 references — full list in the complete paper: https://tomesphere.com/paper/PMC13024627/full.md

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Source: https://tomesphere.com/paper/PMC13024627