Sherwin K. B. Sy

Population pharmacokinetic modeling of tramadol and its O-desmethyl metabolite in plasma and breast milk

PurposeThe aim of this investigation was to demonstrate that nonlinear mixed-effects population pharmacokinetic (PK) modeling can be used to evaluate data from studies of drug transport/excretion into human milk and hence to estimate infant exposure.MethodsA sparse dataset from a previously published study of the use of oral tramadol for post-cesarean pain management in 75 lactating women was used. Milk and plasma samples were collected during days 2–4 of lactation, and tramadol and O-desmethyltramadol (ODT) concentration measurements in these samples were available. Absolute infant dose was obtained from the concentration measurements and estimated milk volume ingested, and expressed in micrograms per kilogram per day. Relative infant dose was calculated as a percentage of the absolute infant dose divided by the maternal dose (μg/kg/day). Nonlinear mixed-effects modeling was used to fit a population PK model to the data.ResultsThe disposition of tramadol and ODT in plasma and the transition of these substances into milk were characterized by a five-compartment population PK mixture model with first-order absorption. The polymorphic ODT formation clearance in the plasma compartment was able to be characterized in both CYP2D6-poor and -extensive metabolizers. Milk creamatocrit was a significant covariate in ODT transfer between the plasma and milk compartments. The estimated relative infant doses in extensive and poor metabolizers, respectively, were 2.16 ± 0.57 and 2.60 ± 0.57% for tramadol, and 0.93 ± .20 and 0.47 ± 0.10% for ODT.ConclusionsThis study demonstrates that a population PK approach with sparse sampling of analytes in milk and plasma can yield quality information about the transfer process and that it also can be used to estimate the extent of infant exposure to maternal drugs via milk.

In vitro pharmacokinetics/pharmacodynamics of the combination of avibactam and aztreonam against MDR organisms.

OBJECTIVES The combination of aztreonam and avibactam has been proposed for the treatment of infections caused by metallo-β-lactamase-producing Gram-negative organisms, given the stability of aztreonam against metallo-β-lactamases plus the broad coverage of avibactam against AmpC β-lactamases and ESBLs. This study aimed to evaluate the efficacy of the combination against four clinical isolates with defined but diverse β-lactamase profiles. METHODS The MICs of aztreonam were determined without and with avibactam (1, 2, 4, 8 and 16 mg/L). Using the MIC values, the static time-kill kinetic studies were designed to encompass aztreonam concentrations of 0.25, 0.5, 1, 2 and 4 times the MIC at the respective avibactam concentrations from 0 to 8 mg/L. Aztreonam and avibactam concentrations were determined by LC-MS/MS during the course of the time-kill kinetic studies to evaluate whether avibactam protects aztreonam from degradation. RESULTS Three of the four isolates had aztreonam MICs ≥128 mg/L in monotherapy. Dramatically increasing susceptibility associated with a decrease in aztreonam MIC was observed with increasing avibactam concentration. Against all isolates, the combinations resulted in greater killing with a much lower dose requirement for aztreonam. The resulting changes in base-10 logarithm of cfu/mL at both the 10 h and 24 h references (versus 0 h) were synergistic. In contrast, a significantly higher concentration of aztreonam in the monotherapy was required to produce the same kill as that in the combination therapy, due to rapid aztreonam degradation in two isolates. CONCLUSIONS The aztreonam/avibactam combination protects aztreonam from hydrolysis and provides synergy in antimicrobial activity against multiple β-lactamase-expressing strains with a wide MIC range.

Pharmacokinetics and pharmacodynamics in antibiotic dose optimization

Introduction: Identifying the optimized dosing regimen and algorithm is critical in the development of antibiotics. Suboptimal regimens and inappropriate choice of drug give rise to drug-resistant bacteria which have limited the therapeutic utility of many commercially available antimicrobial agents. Strategies to optimize therapy of antimicrobial candidates to speed up the development process are urgently needed. Areas covered: We examined pharmacokinetics and pharmacodynamics of antimicrobial agents with modeling and simulation approaches. The approach that is based on minimum inhibitory concentration to evaluate antimicrobial dosing strategy is widely utilized in drug development. The modeling approach utilizing information from time-kill kinetic studies is a tool that can provide more information on the time-course of bacterial response to a particular dosing regimen. Animal studies of dosing regimens that mimic human pharmacokinetics are another option to evaluate antimicrobial efficacy. Empirical, semi-mechanistic and mechanistic models of bacterial dynamics and development of drug resistance in response to drug therapy are discussed. Expert opinion: Both theories and applications of these approaches provide an overall understanding of how the tools can streamline drug development process and help make crucial decisions. Many opportunities and potentials are presented to incorporate more rigorous integration of PK-PD modeling approaches even at preclinical stage to extrapolate to clinical settings, thus enabling successful trials and optimizing dosing strategies in relevant populations where the drug is mostly used.

Pharmacodynamic evaluation of the potential clinical utility of fosfomycin and meropenem in combination therapy against KPC-2 producing Klebsiella pneumoniae

ABSTRACT KPC-producing Klebsiella pneumoniae causes serious infections associated with high death rates worldwide. Combination therapy consisting of fosfomycin and a carbapenem is better than monotherapy to combat multidrug-resistant microorganisms, but no dosages for the combination have been defined. The MICs of meropenem and fosfomycin were evaluated against 18 clinical isolates of KPC-2-producing K. pneumoniae. The activities of combination antimicrobials were also determined by the checkerboard method. The MIC50 and MIC90 of each agent alone and in combination were challenged against short (1.5-h) or prolonged (3-h) infusion regimens of meropenem (1 g every 8 h [q8h], 1.5 g q6h, 2 g q8h) and fosfomycin (4 g q8h, 6 g q6h, 8 g q8h) by Monte Carlo simulation to evaluate the time above the MIC of the free drug concentration as a percentage of the dosing interval (fT>MIC). The monotherapy MIC50s and MIC90s were 32 and 256 mg/liter for meropenem and 64 and 512 mg/liter for fosfomycin, respectively. Antimicrobial combination increased bacterial susceptibility to 1/4 the MIC50s and to 1/8 to 1/16 the MIC90s of monotherapy. The antimicrobial combination demonstrated a synergistic effect for at least two-thirds of the isolates. In combination therapy, fosfomycin regimens of 6 g q6h and 8 g q8h as a 3-h infusion against the MIC50 and MIC90 had better chances of achieving ≥90% probability of target attainment (PTA) of 70% fT>MIC. Meropenem regimens of 1.5 g q6h and 2 g q8h in prolonged infusion can achieve close to 90% PTA of 40% fT>MIC for MIC50 but not MIC90. The significant reduction in the MIC values and the achievement of appropriate PTA demonstrated that regimens containing fosfomycin with meropenem can be effective against KPC-2-producing K. pneumoniae.

Evaluation of in vitro synergy between vertilmicin and ceftazidime against Pseudomonas aeruginosa using a semi-mechanistic pharmacokinetic/pharmacodynamic model

The aim of this study was to develop a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model to evaluate the in vitro synergy between vertilmicin and ceftazidime against Pseudomonas aeruginosa. The in vitro antimicrobial activity of vertilmicin alone was initially assessed by static and dynamic time-kill experiments against three bacterial strains, including MSSA, MRSA and P. aeruginosa. The combined killing effect with ceftazidime was then evaluated in a static time-kill study against P. aeruginosa. Vertilmicin displayed a concentration-dependent killing effect against the three bacterial strains, and its short half-life may possibly have a dramatic impact on antimicrobial activities. A two-compartment pharmacodynamic model consisting of drug-susceptible and -resistant compartments was developed to characterise the relationship between drug exposure and bacterial response for the time-kill curves from both monotherapy and combination therapy. Loewe additivity was incorporated into the pharmacodynamic model to describe the drug-drug interactive effect in the combination therapy. For monotherapy, the estimated EC50 of the dynamic time-kill study against each strain was close to its MIC but was higher than that of the static time-kill study. The EC50 of combination therapy was estimated at 2.67 mg/L compared with 4.54 mg/L in monotherapy, indicating an enhanced bactericidal capacity. The drug-drug interactive effect was not significantly synergistic but highly varied at each specific combination. Potential synergistic combinations could be screened using PK/PD modelling and simulation. These results demonstrated that PK/PD modelling provides an innovative approach to assist dose selection of combination vertilmicin and ceftazidime for future clinical study design.

Pharmacometrics in Bacterial Infections

Pharmacometrics plays an important role in rational dosing design of antimicrobial therapy by defining the relationship between dose, dosing interval, drug concentration, bacterial response, infection outcome, and toxicity, as well as the variability surrounding these variables. Modeling and simulation is becoming more extensively used to characterize antimicrobial strategies to combat emergence of drug-resistant bacteria, which is imposing a high cost on patient survivability and the overall health-care system. In this chapter, we discuss how pharmacometric approaches are being utilized to tackle these challenges. The approaches in designing treatment strategies using minimum inhibitory concentrations and in vitro time course of bacterial killing are explained to provide a thorough overview of the current state-of-the-art exploration of antimicrobial pharmacokinetic–pharmacodynamic (PKPD) modeling and simulation. In turn, the information from in vitro and animal studies is connected with clinical outcome for the purpose of determining key information that can improve therapeutic success in the clinic. This chapter also discusses the strategies for optimized dosing regimens and combination therapies that are currently explored to tackle the challenges posed by development of drug-resistant infections.

Pharmacokinetics of para-Aminosalicylic Acid in HIV-Uninfected and HIV-Coinfected Tuberculosis Patients Receiving Antiretroviral Therapy, Managed for Multidrug-Resistant and Extensively Drug-Resistant Tuberculosis

ABSTRACT The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis prompted the reintroduction of para-aminosalicylic acid (PAS) to protect companion anti-tuberculosis drugs from additional acquired resistance. In sub-Saharan Africa, MDR/XDR tuberculosis with HIV coinfection is common, and concurrent treatment of HIV infection and MDR/XDR tuberculosis is required. Out of necessity, patients receive multiple drugs, and PAS therapy is frequent; however, neither potential drug interactions nor the effects of HIV infection are known. Potential drug-drug interaction with PAS and the effect of HIV infection was examined in 73 pulmonary tuberculosis patients; 22 (30.1%) were HIV coinfected. Forty-one pulmonary MDR or XDR tuberculosis patients received 4 g PAS twice daily, and in a second crossover study, another 32 patients were randomized, receiving 4 g PAS twice daily or 8 g PAS once daily. A PAS population pharmacokinetic model in two dosing regimens was developed; potential covariates affecting its pharmacokinetics were examined, and Monte Carlo simulations were conducted evaluating the pharmacokinetic-pharmacodynamic index. The probability of target attainment (PTA) to maintain PAS levels above MIC during the dosing interval was estimated by simulation of once-, twice-, and thrice-daily dosing regimens not exceeding 12 g daily. Concurrent efavirenz (EFV) medication resulted in a 52% increase in PAS clearance and a corresponding >30% reduction in mean PAS area under the concentration curve in 19 of 22 HIV-M. tuberculosis-coinfected patients. Current practice recommends maintenance of PAS concentrations at ≥1 μg/ml (the MIC of M. tuberculosis), but the model predicts that at only a minimum dose of 4 g twice daily can this PTA be achieved in at least 90% of the population, whether or not EFV is concomitantly administered. Once-daily dosing of 12 g PAS will not provide PAS concentrations exceeding the MIC over the entire dosing interval if coadministered with EFV, while 4 g twice daily ensures concentrations exceeding MIC over the entire dosing interval, even in HIV-infected patients who received EFV.

Principles of Applied Pharmacokinetic–Pharmacodynamic Modeling

An effective dosing strategy for anti-infectives requires a thorough understanding of the complex interactions between drug, microbe, and the host immune system. Pharmacokinetic and pharmacodynamic (PKPD) modeling has been utilized to describe these relationships to aid the dose selection and dose optimization of antimicrobial agents. The complexity of PKPD models for anti-infective has increased over time with increasing improvement in in vitro methodologies, which have progressed from limited PD (a single minimum inhibition concentration measurement) to full PD analysis (dynamic kill curve). Capturing the time course of microbial dynamics in a kill-curve system provides an opportunity for complex PKPD modeling that has been used to evaluate challenging topics such as antimicrobial resistance.

Gentamicin dosing strategy in patients with end-stage renal disease receiving haemodialysis: evaluation using a semi-mechanistic pharmacokinetic/pharmacodynamic model

OBJECTIVES Gentamicin is widely used in end-stage renal disease (ESRD) patients for the treatment of infections. The goal of this study was to find the most reasonable dosing regimen for gentamicin in ESRD patients receiving haemodialysis. METHODS The in vitro antimicrobial activity of gentamicin was evaluated by static and dynamic time-kill experiments against three bacterial strains of MSSA, MRSA and Pseudomonas aeruginosa. A semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model was established afterwards, allowing the characterization of the antibacterial effect of gentamicin in the human body. The model was utilized to assess dosing regimens of gentamicin in ESRD patients receiving haemodialysis, taking both efficacy and safety into account. RESULTS The PK/PD model was capable of describing the bacterial response to gentamicin exposure in all three strains. Simulation based on the PK/PD model showed that pre-dialysis and post-dialysis dosing would bring comparable benefit to the ESRD patient regardless of whether the PK/PD target (fCmax/MIC >8-fold) was achieved, while the post-dialysis dosing resulted in a significantly lower trough concentration. The result of simulated dose fractionation demonstrated that both fCmax/MIC and fAUC(0-24)/MIC are strong predictors of drug effectiveness, but the PK/PD model would provide a more precise prediction of antibacterial activity as well as valuable information on dose selection in ESRD patients receiving haemodialysis. CONCLUSIONS Our study supports the original FDA label with regard to the dosing regimen of gentamicin in ESRD patients, which offers adequate clinical benefit as well as an acceptable safety profile.

Introduction to Pharmacometrics and Quantitative Pharmacology with an Emphasis on Physiologically Based Pharmacokinetics

Pharmacometrics, enabled by advanced pharmacostatistical modeling and simulation technique, has gained significant importance as a tool for model-based drug development and for pharmacotherapy. This introductory chapter provides an overview of the models and mathematical framework that are commonly used in the field. Both pharmacokinetic and pharmacodynamic models are discussed; the physiologically based pharmacokinetic (PBPK) approach is emphasized through illustrations and examples. We have shown how systemic and tissue clearances are related through mathematical proofs using the PBPK approach. As for the pharmacodynamic section, models for continuous and noncontinuous responses were explained, as well as a disease progression model. We also provided select examples of systems pharmacology models that investigate physiological and biochemical processes. This chapter also discusses the software tools and their specific application. As pharmacometrics and quantitative pharmacology has evolved as a discipline, more and more different types of statistical and mathematical models have been utilized. The advancement of computing software and systems facilitate these complex computations.