Population pharmacokinetics of apramycin from first-in-human plasma and urine data to support prediction of efficacious dose

BACKGROUND: Apramycin is under development for human use as EBL-1003, a crystalline free base of apramycin, in face of increasing incidence of multidrug-resistant bacteria. Both toxicity and cross-resistance, commonly seen for other aminoglycosides, appear relatively low owing to its distinct chemical structure. OBJECTIVES: To perform a population pharmacokinetic (PPK) analysis and predict an efficacious dose based on data from a first-in-human Phase I trial. METHODS: The drug was administered intravenously over 30 min in five ascending-dose groups ranging from 0.3 to 30 mg/kg. Plasma and urine samples were collected from 30 healthy volunteers. PPK model development was performed stepwise and the final model was used for PTA analysis. RESULTS: A mammillary four-compartment PPK model, with linear elimination and a renal fractional excretion of 90%, described the data. Apramycin clearance was proportional to the absolute estimated glomerular filtration rate (eGFR). All fixed effect parameters were allometrically scaled to total body weight (TBW). Clearance and steady-state volume of distribution were estimated to 5.5 L/h and 16 L, respectively, for a typical individual with absolute eGFR of 124 mL/min and TBW of 70 kg. PTA analyses demonstrated that the anticipated efficacious dose (30 mg/kg daily, 30 min intravenous infusion) reaches a probability of 96.4% for a free AUC/MIC target of 40, given an MIC of 8 mg/L, in a virtual Phase II patient population with an absolute eGFR extrapolated to 80 mL/min. CONCLUSIONS: The results support further Phase II clinical trials with apramycin at an anticipated efficacious dose of 30 mg/kg once daily

From Therapeutic Drug Monitoring to Model-Informed Precision Dosing for Antibiotic

Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have evolved as important tools to inform rational dosing of antibiotics in individual patients with infections. In particular, critically ill patients display altered, highly variable pharmacokinetics and often suffer from infections caused by less susceptible bacteria. Consequently, TDM has been used to individualize dosing in this patient group for many years. More recently, there has been increasing research on the use of MIPD software to streamline the TDM process, which can increase the flexibility and precision of dose individualization but also requires adequate model validation and re-evaluation of existing workflows. In parallel, new minimally invasive and noninvasive technologies such as microneedle-based sensors are being developed, which-together with MIPD software-have the potential to revolutionize how patients are dosed with antibiotics. Nonetheless, carefully designed clinical trials to evaluate the benefit of TDM and MIPD approaches are still sparse, but are critically needed to justify the implementation of TDM and MIPD in clinical practice. The present review summarizes the clinical pharmacology of antibiotics, conventional TDM and MIPD approaches, and evidence of the value of TDM/MIPD for aminoglycosides, beta-lactams, glycopeptides, and linezolid, for which precision dosing approaches have been recommended

Considerations for the optimal management of antibiotic therapy in elderly patients

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Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies

Abstract In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials

Population pharmacokinetics of colistin and the relation to survival in critically ill patients infected with colistin susceptible and carbapenem-resistant bacteria

Objectives: The aim was to analyse the population pharmacokinetics of colistin and to explore the relationship between colistin exposure and time to death. Methods: Patients included in the AIDA randomized controlled trial were treated with colistin for severe infections caused by carbapenem-resistant Gram-negative bacteria. All subjects received a 9 million units (MU) loading dose, followed by a 4.5 MU twice daily maintenance dose, with dose reduction if creatinine clearance (CrCL) 2 mg/L in 94% (195/208) and 44% (38/87) of patients with CrCL ≤120 mL/min, and >120 mL/min, respectively. Colistin methanesulfonate sodium (CMS) and colistin clearances were strongly dependent on CrCL. High colistin exposure to MIC ratio was associated with increased hazard of death in the multivariate analysis (adjusted hazard ratio (95% CI): 1.07 (1.03–1.12)). Other significant predictors included SOFA score at baseline (HR 1.24 (1.19–1.30) per score increase), age and Acinetobacter or Pseudomonas as index pathogen. Discussion: The population pharmacokinetic model predicted that >90% of the patients had colistin concentrations

Association between paclitaxel clearance and tumor response in patients with esophageal cancer

Inter-individual variability in paclitaxel pharmacokinetics may play a role in the response to chemotherapy. Therefore, we studied the association between paclitaxel clearance and treatment response in patients with esophageal cancer. All patients who received paclitaxel (plus carboplatin) treatment for esophageal cancer between 2007 and 2013 were included. The treatment was given as neoadjuvant chemoradiotherapy (nCRT), induction chemotherapy (iCT), or palliative chemotherapy (pCT). The treatment response was assessed by the tumor regression grade (TRG) or by the RECIST1.1 criteria, respectively. The unbound paclitaxel clearance (CL) was estimated with NONMEM. The log-transformed clearance was related to response with ANOVA and independent sample t-tests. A total of 166 patients were included, of whom 113 received nCRT, 23 iCT and 30 pCT. In patients receiving nCRT, paclitaxel clearance was not associated with tumor regression grade (p-value = 0.25), nor with pathologically complete response (geometric mean 561.6 L/h) and residual disease (geometric mean 566.1 L/h, p-value = 0.90). In patients who underwent iCT or pCT, also no association between paclitaxel clearance and RECIST outcome was identified (iCT: p-value = 0.08 and pCT: p-value = 0.81, respectively). In conclusion, systemic paclitaxel exposure was not associated with response to common paclitaxel-based treatment regimens for esophageal cancer. Future studies should focus on tumor exposure in relation to systemic exposure and treatment outcome