Search

Temesgen Sidamo,1 Prakruti S Rao,2 Eleni Aklillu,3 Workineh Shibeshi,1 Yumi Park,4,5 Yong-soon Cho,4,5 Jae-Gook Shin,4,5 Scott K Heysell,2 Stellah G Mpagama,6 Ephrem Engidawork1 1Department of Pharmacology and Clinical Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia; 2Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA; 3Department of Laboratory of Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden; 4Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea; 5Center for Personalized Precision Medicine of Tuberculosis (cPMTb), Inje University College of Medicine, Busan, Republic of Korea; 6Kibong’oto Infectious Diseases Hospital, Sanya Juu, TanzaniaCorrespondence: Ephrem Engidawork, Department of Pharmacology and Clinical Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, P.O. Box 9086, Addis Ababa, Ethiopia, Email ephrem.engidawork@aau.edu.etObjective: This study aimed to explore the population pharmacokinetic modeling (PopPK) of levofloxacin (LFX) and moxifloxacin (MXF), as well as the percent probability of target attainment (PTA) as defined by the ratio of the area under the plasma concentration-time curve over 24 h and the in vitro minimum inhibitory concentration (AUC0-24/MIC) in Ethiopian multidrug resistant tuberculosis (MDR-TB) patients.Methods: Steady state-plasma concentration of the drugs in MDR-TB patients were determined using optimized liquid chromatography-tandem mass spectrometry. PopPK and simulations were run at various doses, and pharmacokinetic parameters were estimated. The effect of covariates on the PK parameters and PTA for maximum mycobacterial kill and resistance prevention was also investigated.Results: LFX and MXF both fit in a one-compartment model with adjustments. Serum-creatinine (Cr) influenced (p = 0.01) the clearance of LFX, whereas body mass index (BMI) influenced (p = 0.01) the apparent volume of distribution (V) of MXF. The PTA for LFX maximal mycobacterial kill at the critical MIC of 0.5 mg/L with the simulated 750 mg, 1000 mg, and 1500 mg doses were 29%, 62%, and 95%, respectively, whereas the PTA for resistance prevention at 1500 mg was only 4.8%, with none of the lower doses achieving this target. At the critical MIC of 0.25 mg/L, there was no change in the PTA for maximum bacterial kill when the MXF dose was increased (600 mg, 800 mg, and 1000 mg), but the PTA for resistance prevention was improved.Conclusion: The standard doses of LFX and MXF may not provide adequate drug exposure. PopPK of LFX is more predictable for maximum mycobacterial kill, whereas MXF’s resistance prevention target increases with dose. Cr and BMI are likely important covariates for dose optimization in Ethiopian patients.Keywords: population pharmacokinetics, probability of target attainment, moxifloxacin, levofloxacin, MDR-TB patients, Ethiopia

Introduction: Probability of target attainment (PTA) analysis using Monte Carlo simulations has become a mainstay of dose optimization. We highlight the technical and clinical factors that may affect PTA for beta-lactams. Methods: We performed a mini review in adults to explore factors relating to cefepime PTA success and how researchers incorporate PTA into dosing decisions. In addition, we investigated, via simulations with a population pharmacokinetic (PK) model, factors that may affect cefepime PTA success. Results: The mini review included 14 articles. PTA results were generally consistent, given the differences in patient populations. However, dosing recommendations were more varied and appeared to depend on the definition of pharmacodynamic (PD) target, definition of PTA success and specific clinical considerations. Only 3 of 14 articles performed formal toxicological analysis. Simulations demonstrated that the largest determinants of cefepime PTA were the choice of PD target, continuous vs. intermittent infusion and creatinine clearance. Assumptions for protein binding, steady state vs. first dose, and simulating different sampling schemes may impact PTA success under certain conditions. The choice of one or two compartments had a minimal effect on PTA. Conclusions: PTA results may be similar with different assumptions and techniques. However, dose recommendation may differ significantly based on the selection of PD target, definition of PTA success and considerations specific to a patient population. Demographics and the PK parameters used to simulate time-concentration profiles should be derived from patient data applicable to the purpose of the PTA. There should be strong clinical rationale for dose selection. When possible, safety and toxicity should be considered in addition to PTA success.

In the light of increasing antimicrobial resistance among gram-negative bacteria and the lack of new more potent antimicrobial agents, new strategies have been explored. Old antibiotics, such as colistin, temocillin, fosfomycin, mecillinam, nitrofurantoin, minocycline, and chloramphenicol, have attracted the attention since they often exhibit in vitro activity against multi-drug-resistant (MDR) gram-negative bacteria, such as Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. The current review provides a summary of the in vitro activity, pharmacokinetics and PK/PD characteristics of old antibiotics. In silico modelling was then performed using Monte Carlo simulation in order to combine all preclinical data with human pharmacokinetics and determine the probability of target (1-log kill in thigh/lung infection animal models) attainment (PTA) of different dosing regimens. The potential of clinical efficacy of a drug against severe infections by MDR gram-negative bacteria was considered when PTA was >95% at the epidemiological cutoff values of corresponding species. In vitro potent activity against MDR gram-negative pathogens has been shown for colistin, polymyxin B, temocillin (against E. coli and K. pneumoniae), fosfomycin (against E. coli), mecillinam (against E. coli), minocycline (against E. coli, K. pneumoniae, A. baumannii), and chloramphenicol (against E. coli) with ECOFF or MIC90 ≤ 16 mg/L. When preclinical PK/PD targets were combined with human pharmacokinetics, Monte Carlo analysis showed that among the old antibiotics analyzed, there is clinical potential for polymyxin B against E. coli, K. pneumoniae, and A. baumannii; for temocillin against K. pneumoniae and E. coli; for fosfomycin against E. coli and K. pneumoniae; and for mecillinam against E. coli. Clinical studies are needed to verify the potential of those antibiotics to effectively treat infections by multi-drug resistant gram-negative bacteria.

Antibiotic dosing strategies are generally based on systemic drug concentrations. However, drug concentrations at the infection site drive antimicrobial effect, and efficacy predictions and dosing strategies should be based on these concentrations. We set out to review different translational pharmacokinetic-pharmacodynamic (PK/PD) approaches from a target site perspective. The most common approach involves calculating the probability of attaining animal-derived PK/PD index targets, which link PK parameters to antimicrobial susceptibility measures. This approach is time efficient but ignores some aspects of the shape of the PK profile and inter-species differences in drug clearance and distribution, and provides no information on the PD time-course. Time–kill curves, in contrast, depict bacterial response over time. In vitro dynamic time–kill setups allow for the evaluation of bacterial response to clinical PK profiles, but are not representative of the infection site environment. The translational value of in vivo time–kill experiments, conversely, is limited from a PK perspective. Computational PK/PD models, especially when developed using both in vitro and in vivo data and coupled to target site PK models, can bridge translational gaps in both PK and PD. Ultimately, clinical PK and experimental and computational tools should be combined to tailor antibiotic treatment strategies to the site of infection.

We conducted population pharmacokinetic (PPK) analysis and Monte Carlo simulations to determine the appropriate prophylactic dose of fluconazole to prevent invasive candidiasis in patients with hematological malignancies. Patients receiving chemotherapy or hematopoietic stem cell transplantation at Yokohama City University Hospital between November 2018 and March 2020 were included. Additionally, patients receiving oral fluconazole for prophylaxis were recruited. We set the free area under the curve/minimum inhibitory concentration (MIC) = 50 as the target and determined the largest MIC (breakpoint MIC) that could achieve more than 90% probability of target attainment. The blood fluconazole concentration of 54 patients (119 points) was used for PPK analysis. The optimal model was the one-compartment model with first-order administration and first-order elimination incorporating creatinine clearance (CLcr) as a covariate of clearance and body weight as a covariate of distribution volume. We conducted Monte Carlo simulation with fluconazole at 200 mg/day or 400 mg/day dosing schedules and patient body weight and CLcr ranging from 40 to 70 kg and 40–140 mL/min, respectively. The breakpoint MICs on the first dosing day and at steady state were 0.5–1.0 μg/mL and 1.0–2.0 μg/mL for 200 mg/day and 1.0–2.0 μg/mL and 2.0–4.0 μg/mL for 400 mg/day, respectively. The recommended dose was 400–700 mg/day for the loading dose and 200–400 mg/day for the maintenance dose. Our findings suggest that the optimal prophylactic dose of fluconazole in hematological malignancy patients depends on CLcr and body weight, and a sufficient loading and maintenance dose may be needed to completely prevent invasive candidiasis.