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Background: The pharmacodynamics of propofol in children have previously been described with the proprietary bispectral index (BIS) as an effect‐site marker, and it has been suggested that the rate of onset of propofol might be age dependent, that is, a shorter time to peak effect in younger children. However, these analyses were potentially confounded by co‐administered drugs, in particular opioids and benzodiazepines. Thus, the goal of this prospective study was to characterize the influence of age and weight on the onset of hypnotic effects from propofol, reflected by the time to peak of propofol effect‐site concentration in the absence of additional drugs. Methods: A total of 46 healthy children aged 2–12 years presenting for elective surgery were included in our observational cohort study. Solely propofol was administered via a target‐controlled infusion pump programmed with the Paedfusor pharmacokinetic model. The BIS and infusion pump data were recorded. The effect of an induction "bolus" was recorded having stopped the pump once a propofol plasma target concentration of 7 μg.mL−1 was achieved. A direct‐response and an indirect‐response model in the context of nonlinear mixed‐effects modeling was used to characterize and compare BIS data in children aged 2–6 years and older children aged 8–12 years. Results: Time to peak of propofol effect‐site concentration had a difference (p‐value <.01) for age and weight, that is 84 [74, 96] (median [IQR] secs for children aged 2–6 years vs. 99 [91, 113] secs for children aged 8–12 years and 82 [71, 95] secs for weight 11–25 kg vs. 99 [91, 114] secs for weight 30–63 kg). The plasma effect‐site equilibration rate constant for propofol had a heterogeneous distribution with a median of 2.36 (IQR: 2.05–2.93; range: 0.83–7.31) per minute but showed a weight‐dependent effect in patients with weight below 45 kg. Conclusions: In children, the age and weight have an influence on time to peak effect of propofol. In the absence of opioids and benzodiazepines, time to peak effect was approximately 20% longer in children aged 8–12 years as compared to younger children. Such clinically relevant age and weight effects are an important consideration in the individualized titration of propofol dosing. [ABSTRACT FROM AUTHOR]

A model‐based meta‐analysis was performed with reported data from obese subjects and patients with type 2 diabetes (T2DM) to characterize the effects of dipeptidyl peptidase 4 (DPP4) inhibitors, gastric inhibitory polypeptides (GIPs), glucagon‐like peptide‐1 (GLP1), and dual GIP/GLP1 agonists, or a combination of these antidiabetic drugs (ADs) on heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A systematic literature search and review after the Cochrane method identified sources for investigational and approved ADs resulted in a comprehensive database with data from 178 clinical studies in obese subjects and patients with T2DM. Results indicated that there were AD class‐dependent effects on HR and SBP, whereas no clear AD‐related effects on DBP were found. All AD classes, except for DPP4 inhibitors, increased HR. The largest increase of 12 bpm was seen with GLP1 receptor agonists. All AD classes appeared to decrease SBP. DPP4 inhibitors were associated with a marginal decrease of ~ 1 mmHg, whereas GLP1 and GIP/GLP1 dual agonists exhibited the largest decrease of ~ 3 mmHg in SBP. AD‐related effects were similar in obese subjects and patients with T2DM. In conclusion, there are clinically relevant AD‐related effects on both HR and SBP, but not on DBP. DPP4 inhibitors are associated with the smallest (if at all) effects on HR and SBP, whereas GLP1 inhibitors exhibited the largest effects on these two cardiovascular end points. Additional studies are warranted to further investigate how AD‐related SBP decreases combined with HR increases affect long‐term cardiovascular mortality. [ABSTRACT FROM AUTHOR]

Providing the optimal dosing strategy of a drug for an individual patient is an important task in pharmaceutical sciences and daily clinical application. We developed and validated an optimal dosing algorithm (OptiDose) that computes the optimal individualized dosing regimen for pharmacokinetic–pharmacodynamic models in substantially different scenarios with various routes of administration by solving an optimal control problem. The aim is to compute a control that brings the underlying system as closely as possible to a desired reference function by minimizing a cost functional. In pharmacokinetic–pharmacodynamic modeling, the controls are the administered doses and the reference function can be the disease progression. Drug administration at certain time points provides a finite number of discrete controls, the drug doses, determining the drug concentration and its effect on the disease progression. Consequently, rewriting the cost functional gives a finite-dimensional optimal control problem depending only on the doses. Adjoint techniques allow to compute the gradient of the cost functional efficiently. This admits to solve the optimal control problem with robust algorithms such as quasi-Newton methods from finite-dimensional optimization. OptiDose is applied to three relevant but substantially different pharmacokinetic–pharmacodynamic examples. [ABSTRACT FROM AUTHOR]

Sepsis remains a major cause of mortality and morbidity in neonates, and, as a consequence, antibiotics are the most frequently prescribed drugs in this vulnerable patient population. Growth and dynamic maturation processes during the first weeks of life result in large inter- and intrasubject variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of antibiotics. In this review we (1) summarize the available population PK data and models for primarily renally eliminated antibiotics, (2) discuss quantitative approaches to account for effects of growth and maturation processes on drug exposure and response, (3) evaluate current dose recommendations, and (4) identify opportunities to further optimize and personalize dosing strategies of these antibiotics in preterm and term neonates. Although population PK models have been developed for several of these drugs, exposure-response relationships of primarily renally eliminated antibiotics in these fragile infants are not well understood, monitoring strategies remain inconsistent, and consensus on optimal, personalized dosing of these drugs in these patients is absent. Tailored PK/PD studies and models are useful to better understand relationships between drug exposures and microbiological or clinical outcomes. Pharmacometric modeling and simulation approaches facilitate quantitative evaluation and optimization of treatment strategies. National and international collaborations and platforms are essential to standardize and harmonize not only studies and models but also monitoring and dosing strategies. Simple bedside decision tools assist clinical pharmacologists and neonatologists in their efforts to fine-tune and personalize the use of primarily renally eliminated antibiotics in term and preterm neonates. [ABSTRACT FROM AUTHOR]

OBJECTIVE--To examine the effect of dapagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, on themajor components of renal glucose reabsorption (decreasedmaximum renal glucose reabsorptive capacity [TmG], increased splay, and reduced threshold), using the pancreatic/stepped hyperglycemic clamp (SHC) technique. RESEARCH DESIGN AND METHODS--Subjects with type 2 diabetes (n = 12) and matched healthy subjects (n = 12) underwent pancreatic/SHC (plasma glucose range 5.5-30.5 mmol/L) at baseline and after 7 days of dapagliflozin treatment. A pharmacodynamic model was developed to describe the major components of renal glucose reabsorption for both groups and then used to estimate these parameters from individual glucose titration curves. RESULTS- At baseline, type 2 diabetic subjects had elevated TmG, splay, and threshold compared with controls. Dapagliflozin treatment reduced the TmG and splay in both groups. However, the most significant effect of dapagliflozin was a reduction of the renal threshold for glucose excretion in type 2 diabetic and control subjects. CONCLUSIONS--The SGLT2 inhibitor dapagliflozin improves glycemic control in diabetic patients by reducing the TmG and threshold at which glucose is excreted in the urine. [ABSTRACT FROM AUTHOR]

Aim(s) This study assessed the effect of differences in renal function on the pharmacokinetics and pharmacodynamics of dapagliflozin, a renal sodium glucose co-transporter-2 (SGLT2) inhibitor for the treatment of type 2 diabetes mellitus ( T2DM). Methods A single 50 mg dose of dapagliflozin was used to assess pharmacokinetics and pharmacodynamics in five groups: healthy non-diabetic subjects; patients with T2DM and normal kidney function and patients with T2DM and mild, moderate or severe renal impairment based on estimated creatinine clearance. Subsequently, 20 mg once daily multiple doses of dapagliflozin were evaluated in the patients with T2DM. Formation rates of dapagliflozin 3- O-glucuronide ( D3OG), an inactive metabolite, were evaluated using human isolated kidney and liver microsomes. Results Plasma concentrations of dapagliflozin and D3OG were incrementally increased with declining kidney function. Steady-state Cmax for dapagliflozin were 4%, 6% and 9% higher and for D3OG were 20%, 37% and 52% higher in patients with mild, moderate and severe renal impairment, respectively, compared with normal function. AUC(0,τ) was likewise higher. D3OG formation in kidney microsomes was three-fold higher than in liver microsomes and 109-fold higher than in intestine microsomes. Compared with patients with normal renal function, pharmacodynamic effects were attenuated with renal impairment. Steady-state renal glucose clearance was reduced by 42%, 83% and 84% in patients with mild, moderate or severe renal impairment, respectively. Conclusions These results indicate that both kidney and liver significantly contribute to dapagliflozin metabolism, resulting in higher systemic exposure with declining kidney function. Dapagliflozin pharmacodynamics in diabetic subjects with moderate to severe renal impairment are consistent with the observation of reduced efficacy in this patient population. [ABSTRACT FROM AUTHOR]

Background and objective: Telithromycin, a ketolide antibacterial, demonstrates concentration-dependent bactericidal activity against the major pathogens causing community-acquired respiratory tract infections. The objective of this study was to explore the relationships between pharmacokinetic/pharmacodynamic predictor variables, such as area under the plasma concentration-time curve (AUC) over minimum inhibitory concentration (MIC) [AUC/MIC], maximum plasma concentration (Cmax) over MIC (Cmax/MIC) and microbiological outcome from telithromycin therapy for community-acquired pneumonia (CAP). Patients and methods: Data were pooled from five phase III studies of oral telithromycin (800mg once daily for 7–10 days) for the outpatient treatment of adults with CAP. Only subjects with a single pathogen isolated at baseline, a telithromycin MIC determination and at least one plasma pharmacokinetic sample were included. Bacteriologically modified intent-to-treat (bmITT) and bacteriologically evaluable per protocol (PPb) populations were analysed. Individual AUC and Cmax Bayesian estimates were obtained with a population pharmacokinetic model. Logistic regression, nonparametric smoothing, and classification analysis and regression tree (CART) were used to assess the relationship between AUC/MIC and Cmax/MIC and microbiological outcome by pathogen. Results: The bmITT population included 224 patients (Streptococcus pneumoniae in 113, Haemophilus influenzae in 89 and Staphylococcus aureus in 22). Median telithromycin MIC was 0.015 µg/mL for S. pneumoniae, 2.0 µg/mL for H. influenzae and 0.12 µg/mL for S. aureus, with median AUC/MIC of 907.1, 6.9 and 98.4, and median Cmax/MIC of 172.0, 1.3 and 20.4 for the three pathogens, respectively. Both logistic regression and nonparametric smoothing showed the probability of microbiological cure to be consistently greater than 90% over the observed range of predictor variables. No reliable AUC/MIC or Cmax/MIC breakpoints were identified by CART. Conclusion: Telithromycin exhibits near-maximal efficacy against three major pathogens causing CAP at a dose of 800mg once daily. [ABSTRACT FROM AUTHOR]

Objective: To describe the relationship between plasma magnesium (Mg(2+)) concentration and blood pressure response in pregnant women with preeclampsia.Methods: Fifty-one preeclamptic women were studied after receiving two consecutive magnesium sulfate infusions (120 mg/kg for 1 hour and 24 mg/kg for 5 hours). Mg(2+) concentration and systolic/diastolic blood pressure were measured at 0, 0.5, 1, 2, 4, 6, 7, 9, 11, 13 and 15 hours after the beginning of the first infusion. A population pharmacokinetic-pharmacodynamic model was fitted to the data with the computer program NONMEM.Results: Pharmacokinetics were described by a two-compartment model. Population parameter estimates were 5.0 L/h for body clearance (CL), 24L for central volume (V(c), 25L for peripheral volume ((V)(p)) and 5.6 L/h for intercompartment clearance (Q). The interindividual variability in CL, V(c), V(p) and Q was 39, 26, 38, and 59%, respectively. The mean population estimates for systolic (diastolic) blood pressure were 36.8 (27.8) mm Hg for the maximum decrease (E(max)), 0.75 (0.88) mmol/L for the Mg2+ concentration (above baseline) eliciting half-maximum effect (EC(50)) and 0.76 (0.5) h(-1) for the equilibrium rate (k(eo)) of the effect compartment model.Conclusion: Mg(2+ )concentrations within the range (2-4 mmol/L) proposed for treatment of preeclampsia produce greater than half-maximal lowering of systolic and diastolic blood pressure. [ABSTRACT FROM AUTHOR]

An introduction is presented for this issue which focuses on the field of pharmacometrics, the American Society of Pharmacometrics, and innovation.

Systematic implementation of model-based drug development (MBDD) to drug discovery and development has the potential to significantly increase the rate of medical breakthroughs and make available new and better treatments to patients. An analysis of the strengths, weaknesses, opportunities, and threats (ie, SWOT) was conducted through focus group discussions that included 24 members representing 8 pharmaceutical companies to systematically assess the challenges to implementing MBDD into the drug development decision-making process. The application of the SWOT analysis to the successful implementation of MBDD yielded 19 strengths, 27 weaknesses, 34 opportunities, and 22 threats, which support the following conclusions. The shift from empirical drug development to MBDD requires a question-based mentality; early, proactive planning; dynamic access to multisource data; quantitative knowledge integration; multidisciplinary collaboration; effective communication and leadership skills; and innovative, impactful application of pharmacometrics focused on enhancing quantitative decision making. The ultimate goal of MBDD is to streamline discovery and development of innovative medicines to benefit patients. [ABSTRACT FROM PUBLISHER]

An introduction is presented for this issue which focuses on the impact of methodological and technological innovation as well as multidisciplinary integration of research in the field of pharmacometrics.

The article comments on the planning for the American Conference on Pharmacometrics (ACoP) meetings in Tucson, Arizona in 2008 and Mystic, Connecticut in 2009, and calls on the pharmacometric community to volunteer their time and skills to the organization via the ACOP Web site which is at www.to-acop.org.

Model-based drug development (MBDD) is a promising approach to improve decision making in drug development. The pharmaceutical industry has made substantial progress from engaging in empirical decision making to increasingly using pharmacometrics (ie, modeling and simulation [M&S]) as a quantitative decision-making tool. Focusing on culture and an organizational structure perspective, this commentary summarizes experiences and vision from industry M&S leaders on implementing MBDD. A culture for MBDD needs to have wide acceptance of MBDD, enhanced decision making with probability-based evidence and transparent rationale, quantitative impact metrics, and a brand that emphasizes cross-disciplinary collaboration and ownership. An organizational structure for MBDD needs to have a dedicated pharmacometrics function, fine balance between quick wins and impact on long-term R&D goals, and collaborative MBDD teams among clinical pharmacologists, statisticians, pharmacometricians, and clinicians. Pharmaceutical companies with these characteristics are prepared to fully embrace and implement MBDD. [ABSTRACT FROM PUBLISHER]