Julie A Passarell | PubMed Health

PubMed2006

Pharmacokinetic-pharmacodynamic relationships describing the efficacy of oritavancin in patients with Staphylococcus aureus bacteremia.

Bloodstream infections due to antimicrobial-resistant Staphylococcus aureus occur with increasing frequency and represent an important cause of morbidity and mortality. To date, the evaluation of pharmacokinetic-pharmacodynamic relationships for efficacy among patients with bacteremia has been limited. The objectives of these analyses were to evaluate relationships between microbiological and clinical responses for patients with S. aureus bacteremia and exposures for oritavancin, a novel bactericidal glycopeptide in development. Bayesian oritavancin exposure predictions, following treatment with 5, 6.5, 8, or 10 mg/kg of body weight/day, were derived using a validated population pharmacokinetic model for 55 patients with S. aureus bacteremia. Using classification and regression tree analysis, a breakpoint of the percentage of the dosing interval duration for which free-drug concentrations were above the MIC (free-drug % time > MIC) of 22% was identified for microbiological response; the probabilities of success greater than or equal to and less than this value were 93% and 76%, respectively. Using logistic regression, a relationship was found between microbiological response and free-drug % time > MIC (odds ratio = 4.42, P = 0.09, and odds ratio = 8.84, P = 0.05, when one patient, a medical outlier, was excluded). A similar relationship was found for clinical response. These results will be valuable in supporting dose selection of oritavancin for patients with S. aureus bacteremia.

Anti-Bacterial Agents↗

PubMed2005

Challenges in the transition to model-based development.

Practitioners of the art and science of pharmacometrics are well aware of the considerable effort required to successfully complete modeling and simulation activities for drug development programs. This is particularly true because of the current, ad hoc implementation wherein modeling and simulation activities are piggybacked onto traditional development programs. This effort, coupled with the failure to explicitly design development programs around modeling and simulation, will continue to be an important obstacle to the successful transition to model-based drug development. Challenges with timely data availability, high data discard rates, delays in completing modeling and simulation activities, and resistance of development teams to the use of modeling and simulation in decision making are all symptoms of an immature process capability for performing modeling and simulation. A process that will fulfill the promise of model-based development will require the development and deployment of three critical elements. The first is the infrastructure--the data definitions and assembly processes that will allow efficient pooling of data across trials and development programs. The second is the process itself--developing guidelines for deciding when and where modeling and simulation should be applied and the criteria for assessing performance and impact. The third element concerns the organization and culture--the establishment of truly integrated, multidisciplinary, and multiorganizational development teams trained in the use of modeling and simulation in decision-making. Creating these capabilities, infrastructure, and incentivizations are critical to realizing the full value of modeling and simulation in drug development.

Animals↗