Population Pharmacokinetic Analysis of Vancomycin in Patients with Hematological Malignancies

  1. García, María José 1
  2. Herrera, Emma V. 2
  3. Dominguez-Gil, Alfonso 13
  4. Buelga, Dolores Santos 1
  5. del Mar Fernandez de Gatta, María 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

  2. 2 Universidad de Puebla
    info

    Universidad de Puebla

    Heróica Puebla de Zaragoza, México

    ROR https://ror.org/02rsx0d74

  3. 3 Hospital Universitario de Salamanca
    info

    Hospital Universitario de Salamanca

    Salamanca, España

    ROR https://ror.org/0131vfw26

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Revista:
Antimicrobial Agents and Chemotherapy

ISSN: 0066-4804,  1098-6596

Año de publicación: 2005

Volumen: 49

Número: 12

Páginas: 4934-4941

Tipo: Artículo

DOI: 10.1128/AAC.49.12.4934-4941.2005 GOOGLE SCHOLAR

Otras publicaciones en: Antimicrobial Agents and Chemotherapy

Resumen

ABSTRACT This study determines vancomycin (VAN) population pharmacokinetics (PK) in adult patients with hematological malignancies. VAN serum concentration data ( n = 1,004) from therapeutic drug monitoring were collected retrospectively from 215 patients. A one-compartment PK model was selected. VAN pharmacokinetics population parameters were generated using the NONMEM program. A graphic approach and stepwise generalized additive modeling were used to elucidate the preliminary relationships between PK parameters and clinical covariates analyzed. Covariate selection revealed that total body weight (TBW) affected V , whereas renal function, estimated by creatinine clearance, and a diagnosis of acute myeloblastic leukemia (AML) influenced VAN clearance. We propose one general and two AML-specific models. The former was defined by CL (liters/h) = 1.08 × CL CR(Cockcroft and Gault) (liters/h); CV CL = 28.16% and V (liters) = 0.98 × TBW; CV V =37.15%. AML models confirmed this structure but with a higher clearance coefficient (1.17). The a priori performance of the models was evaluated in another 59 patients, and clinical suitability was confirmed. The models were fairly accurate, with more than 33% of the measured concentrations being within ±20% of the predicted value. This therapeutic precision is twofold higher than that of a noncustomized population model (16.1%). The corresponding standardized prediction errors included zero and a standard deviation close to unity. The models could be used to estimate appropriate VAN dosage guidelines, which are not clearly defined for this high-risk population. Their simple structure should allow easy implementation in clinical software and application in dosage individualization using the Bayesian approach.

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