Limited-sampling strategies for anti-infective agents: systematic review

Pharmacokinetics of Non-β-Lactam β-Lactamase Inhibitors

The growing emergence of drug-resistant bacterial strains is an issue to treat severe infections, and many efforts have identified new pharmacological agents. The inhibitors of β-lactamases (BLI) have gained a prominent role in the safeguard of beta-lactams. In the last years, new β-lactam-BLI combinations have been registered or are still under clinical evaluation, demonstrating their effectiveness to treat complicated infections. It is also noteworthy that the pharmacokinetics of BLIs partly matches that of β-lactams companions, meaning that some clinical situations, as well as renal impairment and renal replacement therapies, may alter the disposition of both drugs. Common pharmacokinetic characteristics, linear pharmacokinetics across a wide range of doses, and known pharmacokinetic/pharmacodynamic parameters may guide modifications of dosing regimens for both β-lactams and BLIs. However, comorbidities (i.e., burns, diabetes, cancer) and severe changes in individual pathological conditions (i.e., acute renal impairment, sepsis) could make dose adaptation difficult, because the impact of those factors on BLI pharmacokinetics is partly known. Therapeutic drug monitoring protocols may overcome those issues and offer strategies to personalize drug doses in the intensive care setting. Further prospective clinical trials are warranted to improve the use of BLIs and their β-lactam companions in severe and complicated infections.

Determining therapeutic trough ranges for linezolid

Linezolid (LZD) is an oxazolidinone approved for the treatment of gram-positive infections. Therapeutic drug monitoring is increasingly used to optimize LZD dosing. The therapeutic target for LZD is to achieve an area under the concentration-time curve over 24 h divided by the MIC (AUC/MIC) > 100. In this study, we determined the trough ranges associated with this therapeutic AUC. Concentration-time profiles for 999 virtual patients were simulated using a previously published pharmacokinetic model for LZD. AUC was estimated for each virtual patient using the trapezoidal method. We determined the trough ranges that achieve the therapeutic target of AUC/MIC > 100 at different MIC values of 1, 2 and 4 μg/mL. Trough samples correlated well with LZD AUC (R² = 0.87). For trough concentration of 2–5 μg/mL, 99% had an AUC_0–24 > 100 µg⋅h⋅ml⁻¹, 23% had an AUC_0–24 > 200 µg⋅h⋅ml⁻¹ and none had an AUC_0–24 > 400 µg⋅h⋅ml⁻¹. For trough concentrations of 5–8 µg/ml, 87% of the patients had an AUC_0–24 > 200 µg⋅h⋅ml⁻¹ and none had an AUC_0–24 > 400 µg⋅h⋅ml⁻¹ To achieve the therapeutic target of an AUC/MIC > 100, it is suggested that trough ranges be set at 2–5 µg/ml if the MIC < 2 and 5–8 µg/ml if the MIC = 2; however, at an MIC of 4 µg/ml, it is difficult to achieve an AUC/MIC > 100 without increasing the risk of LZD toxicity.

Population Pharmacokinetic Model and Limited Sampling Strategies for Personalized Dosing of Levofloxacin in Tuberculosis Patients

Levofloxacin is an antituberculosis drug with substantial interindividual pharmacokinetic variability; therapeutic drug monitoring (TDM) could therefore be helpful to improve treatment results. TDM would be more feasible with limited sampling strategies (LSSs), a method to estimate the area under the concentration curve for the 24-h dosing interval (AUC0-24) by using a limited number of samples. This study aimed to develop a population pharmacokinetic (popPK) model of levofloxacin in tuberculosis patients, along with LSSs using a Bayesian and multiple linear regression approach. The popPK model and Bayesian LSS were developed using data from 30 patients and externally validated with 20 patients. The LSS based on multiple linear regression was internally validated using jackknife analysis. Only clinically suitable LSSs (maximum time span, 8 h; minimum interval, 1 h; 1 to 3 samples) were tested. Performance criteria were root-mean-square error (RMSE) of <15%, mean prediction error (MPE) of <5%, and r2 value of >0.95. A one-compartment model with lag time best described the data while only slightly underestimating the AUC0-24 (mean, -7.9%; standard error [SE], 1.7%). The Bayesian LSS using 0- and 5-h postdose samples (RMSE, 8.8%; MPE, 0.42%; r2 = 0.957) adequately estimated the AUC0-24, with a mean underestimation of -4.4% (SE, 2.7%). The multiple linear regression LSS using 0- and 4-h postdose samples (RMSE, 7.0%; MPE, 5.5%; r2 = 0.977) was internally validated, with a mean underestimation of -0.46% (SE, 2.0%). In this study, we successfully developed a popPK model and two LSSs that could be implemented in clinical practice to assist TDM of levofloxacin. (This study has been registered at ClinicalTrials.gov under identifier NCT01918397.).

Recommendations for Optimizing Tuberculosis Treatment: Therapeutic Drug Monitoring, Pharmacogenetics, and Nutritional Status Considerations

Although tuberculosis is largely a curable disease, it remains a major cause of morbidity and mortality worldwide. Although the standard 6-month treatment regimen is highly effective for drug-susceptible tuberculosis, the use of multiple drugs over long periods of time can cause frequent adverse drug reactions. In addition, some patients with drug-susceptible tuberculosis do not respond adequately to treatment and develop treatment failure and drug resistance. Response to tuberculosis treatment could be affected by multiple factors associated with the host-pathogen interaction including genetic factors and the nutritional status of the host. These factors should be considered for effective tuberculosis control. Therefore, therapeutic drug monitoring (TDM), which is individualized drug dosing guided by serum drug concentrations during treatment, and pharmacogenetics-based personalized dosing guidelines of anti-tuberculosis drugs could reduce the incidence of adverse drug reactions and increase the likelihood of successful treatment outcomes. Moreover, assessment and management of comorbid conditions including nutritional status could improve anti-tuberculosis treatment response.

Limited sampling strategy and target attainment analysis for levofloxacin in patients with tuberculosis

There is an urgent need to improve and shorten the treatment of tuberculosis (TB) and multidrug resistant tuberculosis (MDR-TB). Levofloxacin, a newer fluoroquinolone, has potent activity against TB both in vitro and in vivo. Levofloxacin dosing can be optimized to improve the treatment of both TB and MDR-TB. Levofloxacin efficacy is linked primarily to the ratio of the area under the concentration-time curve for the free fraction of drug (fAUC) to the MIC. Since obtaining a full-time concentration profile is not feasible in the clinic, we developed a limited sampling strategy (LSS) to estimate the AUC. We also utilized Monte Carlo simulations to evaluate the dosing of levofloxacin. Pharmacokinetic data were obtained from 10 Brazilian TB patients. The pharmacokinetic data were fitted with a one-compartment model. LSSs were developed using two methods: linear regression and Bayesian approaches. Several LSSs predicted levofloxacin AUC with good accuracy and precision. The most accurate were the method using two samples collected at 4 and 6 h (R(2) = 0.91 using linear regression and 0.97 using Bayesian approaches) and that using samples collected at 2 and 6 h (R(2) = 0.90 using linear regression and 0.96 using Bayesian approaches). The 2-and-6-h approach also provides a good estimate of the maximum concentration of the drug in serum (Cmax). Our target attainment analysis showed that higher doses (17 to 20 mg/kg of body weight) of levofloxacin might be needed to improve its activity. Doses in the range of 17 to 20 mg/kg showed good target attainment for MICs from 0.25 to 0.50. At an MIC of 2, poor target attainment was observed across all doses. This LSS for levofloxacin can be used for therapeutic drug monitoring and for future pharmacokinetic/pharmacodynamic studies.

Clinical pharmacokinetics of clopidogrel and its metabolites in patients with cardiovascular diseases

Background and Objective

Approximately 5–40 % of patients treated with clopidogrel do not display an adequate antiplatelet response. Clopidogrel resistance may be caused by insufficient drug absorption or impaired metabolic activation of the drug. The aim of this study was to evaluate the pharmacokinetics of clopidogrel and its metabolites in plasma samples from patients treated with high and low doses of clopidogrel, to obtain a possible explanation for antiplatelet resistance.

Methods

The study included patients receiving either a single 300 mg loading dose of clopidogrel (n = 17) or a 75 mg dose (n = 45) for at least 7 days before sample collection. The concentrations of clopidogrel and its metabolites—the inactive H3 and the pharmacologically active H4 isomers of the thiol metabolite and the inactive carboxylic acid metabolite—in plasma samples (stabilized with 2-bromo-3′-methoxyacetophenone) from three patients after 300 mg and from 41 patients after 75 mg of the drug were determined using a validated high-performance liquid chromatography method with tandem mass spectrometry. The non-stabilized samples from the remaining patients were analysed using a validated capillary electrophoresis method. The calculated concentrations were used to determine the pharmacokinetic parameters of the analytes. The pharmacodynamic response to clopidogrel treatment, expressed as adenosine diphosphate-induced platelet aggregation, was measured using a Multiplate analyser.

Results

The pharmacokinetic parameter values for the H3 and H4 isomers determined in the studied group of patients treated with clopidogrel 75 mg (maximum plasma concentration [C_max] 5.29 ± 5.54 and 7.13 ± 6.32 ng/mL for H3 and H4, respectively; area under the plasma concentration-time curve from time zero to time t [AUC_t] 7.37 ± 6.71 and 11.30 ± 9.58 ng·h/mL for H3 and H4, respectively) were lower than those reported in healthy volunteers, according to the literature data. Platelet aggregation measured with a Multiplate analyser ranged between 37 and 747 AU·min. A significant correlation was found between the C_max of the active H4 isomer and platelet aggregation (p = 0.025).

Conclusion

The C_max of the active H4 isomer and platelet aggregation measured by the Multiplate analyser may serve as markers of the patient response to clopidogrel therapy.