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Population pharmacokinetic (popPK) approaches have spread widely throughout clinical pharmacology research, and every clinician should have some understanding of them. After a general introduction on the fundamentals and fields of application of these approaches, this review focuses on parametric popPK methods to provide the clinicians with the conceptual tools to interpret appropriately the results of parametric popPK analyses and to understand their clinical utility. The emphasis is put on the clinical questions that popPK methods are best suited to address. The basic principles of the methodology are introduced first, and then the main algorithms and reference software programs used in such analyses are presented. The description of data analysis and clinical applications of the parametric popPK approach (ie, use in simulations and therapeutic drug monitoring) are illustrated with the example of the antiretroviral drug efavirenz. [ABSTRACT FROM AUTHOR]

Population pharmacokinetics consists of analyzing pharmacokinetic (PK) data collected in groups of individuals. Population PK is widely used to guide drug development and to inform dose adjustment via therapeutic drug monitoring and model‐informed precision dosing. There are 2 main types of population PK methods: parametric (P) and nonparametric (NP). The characteristics of P and NP population methods have been previously reviewed. The aim of this article is to answer some frequently asked questions that are often raised by scholars, clinicians, and researchers about P and NP population PK methods. The strengths and limitations of both approaches are explained, and the characteristics of the main software programs are presented. We also review the results of studies that compared the results of both approaches in the analysis of real data. This opinion article may be informative for potential users of population methods in PK and guide them in the selection and use of those tools. It also provides insights on future research in this area. [ABSTRACT FROM AUTHOR]

Objectives: Imipenem is a broad-spectrum antibacterial agent used in critically ill neonates after failure of first-line treatments. Few studies have described imipenem disposition in this population. The objectives of our study were: (i) to characterize imipenem population pharmacokinetics (PK) in a cohort of neonates; and (ii) to conduct model-based simulations to evaluate the performance of six different dosing regimens aiming at optimizing PK target attainment.Methods: A total of 173 plasma samples from 82 neonates were collected over 15 years at the Lausanne University Hospital, Switzerland. The majority of study subjects were preterm neonates with a median gestational age (GA) of 27 weeks (range: 24-41), a postnatal age (PNA) of 21 days (2-153) and a body weight (BW) of 1.16 kg (0.5-4.1). PK data were analysed using non-linear mixed-effect modelling (NONMEM).Results: A one-compartment model best characterized imipenem disposition. Population PK parameters estimates of CL and volume of distribution were 0.21 L/h and 0.73 L, with an interpatient variability (CV%) of 20.1% on CL in a representative neonate (GA 27 weeks, PNA 21 days, BW 1.16 kg, serum creatinine, SCr 46.6 μmol/L). GA and PNA exhibited the greatest impact on PK parameters, followed by SCr. These covariates explained 36% and 15% of interindividual variability in CL, respectively.Simulated regimens using a dose of 20-25 mg/kg every 6-12 h according to postnatal age led to the highest PTA (T>MIC over 100% of time).Conclusions: Dosing adjustment according to BW, GA and PNA optimizes imipenem exposure in neonates. [ABSTRACT FROM AUTHOR]

Although no gold standard exists to assess a patient's anticholinergic burden, a review identified 19 anticholinergic burden scales (ABSs). No study has yet evaluated whether a high anticholinergic burden measured with all 19 ABSs is associated with in‐hospital mortality and length of stay (LOS). We conducted a cohort study at a Swiss tertiary teaching hospital using patients' electronic health record data from 2015–2018. Included were patients aged ≥65 years, hospitalised ≥48 h without stays and >24 h in intensive care. Patients' cumulative anticholinergic burden score was classified using a binary (<3: low, ≥3: high) and categorical approach (0: no, 0.5–3: low, ≥3: high). In‐hospital mortality and LOS were analysed using multivariable logistic and linear regression, respectively. We included 27,092 patients (mean age 78.0 ± 7.5 years, median LOS 6 days). Of them, 913 died. Depending on the evaluated ABS, 1370 to 17,035 patients were exposed to anticholinergics. Patients with a high burden measured by all 19 ABSs were associated with a 1.32‐ to 3.03‐fold increase in in‐hospital mortality compared with those with no/low burden. We obtained similar results for LOS. To conclude, discontinuing drugs with anticholinergic properties (score ≥3) at admission might be a targeted intervention to decrease in‐hospital mortality and LOS. [ABSTRACT FROM AUTHOR]

Background: People living with HIV (PLWH) are aging and experience age-related physiological changes and comorbidities. Atorvastatin is a widely prescribed lipid-lowering agent metabolized by cytochrome P450 (CYP) 3A4, whose hepatocyte uptake is facilitated by organic anion transporting polypeptide (OATP) 1B1/1B3. Inhibition or induction of this enzyme and hepatic transporter can increase or decrease atorvastatin exposure, respectively. Objective: This study aimed to describe the pharmacokinetic profile of atorvastatin and its major metabolite, and to evaluate drug–drug interactions (DDIs) with antiretrovirals (ARVs). Methods: The atorvastatin pharmacokinetic profile was best described by a two-compartment model with first-order absorption and elimination. Metabolite concentrations were described by considering both linear metabolism from atorvastatin and presystemic metabolism. The influence of demographic and clinical covariates on drug and metabolite pharmacokinetics was assessed using NONMEM®. Model-based simulations were performed to evaluate the magnitude of DDIs with ARVs. Results: Full pharmacokinetic profiles (98 atorvastatin + 62 o-OH-atorvastatin concentrations) and sparse concentrations (78 and 53 for atorvastatin and o-OH-atorvastatin, respectively) were collected in 59 PLWH. Interindividual variability was high. The coadministration of boosted ARVs decreased atorvastatin clearance by 58% and slowed down o-OH-atorvastatin formation by 88%. Atorvastatin clearance increased by 78% when coadministered with CYP3A4 inducers. Simulations revealed a 180% increase and 44% decrease in atorvastatin exposure (area under the curve) in the presence of ARVs with inhibiting and inducing properties, respectively. Conclusion: This study showed an important interindividual variability in atorvastatin pharmacokinetics that remains largely unexplained after the inclusion of covariates. Since boosted ARVs double atorvastatin exposure, the initial dosage might be reduced by half, and titrated based on individual clinical targets. [ABSTRACT FROM AUTHOR]

Background In human immunodeficiency virus (HIV), the relative contribution of genetic background, clinical risk factors, and antiretrovirals to chronic kidney disease (CKD) is unknown. Methods We applied a case-control design and performed genome-wide genotyping in white Swiss HIV Cohort participants with normal baseline estimated glomerular filtration rate (eGFR >90 mL/minute/1.73 m2). Univariable and multivariable CKD odds ratios (ORs) were calculated based on the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) score, which summarizes clinical CKD risk factors, and a polygenic risk score that summarizes genetic information from 86 613 single-nucleotide polymorphisms. Results We included 743 cases with confirmed eGFR drop to <60 mL/minute/1.73 m2 (n = 144) or ≥25% eGFR drop to <90 mL/minute/1.73 m2 (n = 599), and 322 controls (eGFR drop <15%). Polygenic risk score and D:A:D score contributed to CKD. In multivariable analysis, CKD ORs were 2.13 (95% confidence interval [CI], 1.55–2.97) in participants in the fourth (most unfavorable) vs first (most favorable) genetic score quartile; 1.94 (95% CI, 1.37–2.65) in the fourth vs first D:A:D score quartile; and 2.98 (95% CI, 2.02–4.66), 1.70 (95% CI, 1.29–2.29), and 1.83 (95% CI, 1.45–2.40), per 5 years of exposure to atazanavir/ritonavir, lopinavir/ritonavir, and tenofovir disoproxil fumarate, respectively. Participants in the first genetic score quartile had no increased CKD risk, even if they were in the fourth D:A:D score quartile. Conclusions Genetic score increased CKD risk similar to clinical D:A:D score and potentially nephrotoxic antiretrovirals. Irrespective of D:A:D score, individuals with the most favorable genetic background may be protected against CKD. [ABSTRACT FROM AUTHOR]

Oral anticoagulants and antiplatelet drugs are commonly prescribed to lower the risk of cardiovascular diseases, such as venous and arterial thrombosis, which represent the leading causes of mortality worldwide. A significant percentage of patients taking antithrombotics will nevertheless experience bleeding or recurrent ischemic events, and this represents a major public health issue. Cardiovascular medicine is now questioning the one-size-fits-all policy, and more personalized approaches are increasingly being considered. However, the available tools are currently limited and they are only moderately able to predict clinical events or have a significant impact on clinical outcomes. Predicting concentrations of antithrombotics in blood could be an effective means of personalization as they have been associated with bleeding and recurrent ischemia. Target concentration interventions could take advantage of physiologically based pharmacokinetic (PBPK) and population-based pharmacokinetic (POPPK) models, which are increasingly used in clinical settings and have attracted the interest of governmental regulatory agencies, to propose dosages adapted to specific population characteristics. These models have the benefit of combining parameters from different sources, such as experimental in vitro data and patients' demographic, genetic, and physiological in vivo data, to characterize the dose-concentration relationships of compounds of interest. As such, they can be used to predict individual drug exposure. In the near future, these models could therefore be a valuable means of predicting personalized antithrombotic blood concentrations and, hopefully, of preventing clinical non-response or bleeding in a given patient. Existing approaches for personalization of antithrombotic prescriptions will be reviewed using practical examples for P2Y12 inhibitors and direct oral anticoagulants. The review will additionally focus on the existing PBPK and POPPK models for these two categories of drugs. Lastly, we address potential scenarios for their implementation in clinics, along with the main limitations and challenges. [ABSTRACT FROM AUTHOR]

Aims: The aims of this study were to characterize escitalopram pharmacokinetic profile, to identify factors influencing drug exposure, notably drug–drug interactions with antiretrovirals, and to simulate expected exposure under standard dosage regimen. Methods: A population pharmacokinetic analysis was performed using NONMEM. A total of 159 plasma concentration measurements were obtained from 39 human immunodeficiency virus (HIV)‐infected and 71 uninfected psychiatric patients. The influence of age, weight, sex, HIV and psychiatric cohorts, racemic citalopram treatment, and comedications on oral clearance was examined. Simulations served to calculate the percentage of patients expected to be under‐ or over‐exposed, considering established therapeutic targets (15–80 ng/mL). Results: A 1‐compartment model with first‐order absorption and elimination described the data adequately. The average escitalopram clearance and volume of distribution were 23.1 L/h (interindividual variability 51%), and 920 L, respectively. Escitalopram disposition did not differ between HIV‐infected and uninfected patients, and was not affected by antiretroviral treatments. Coadministration of at least 1 proton‐pump inhibitor (CYP2C19 inhibitor) modestly influenced escitalopram elimination (clearance decreased by 19%), with limited clinical relevance. Model‐based simulations showed that, under a standard regimen of 10 mg once daily, a significant proportion of patients (56%) might be under‐exposed. Conclusion: The variability in escitalopram disposition is large and poorly explained by demographic, clinical and environmental covariates, thus suggesting a role for dosage individualization based on therapeutic drug monitoring in case of poor clinical response. Escitalopram disposition is modestly impacted by comedications and therefore no a priori dosage adjustments are needed in patients receiving antiretroviral treatments, including boosted regimens. [ABSTRACT FROM AUTHOR]

Purpose: The abstinence rate to tobacco after varenicline treatment is moderate and might be partially affected by variability in varenicline concentrations. This study aimed at characterizing the sources of variability in varenicline pharmacokinetics and to relate varenicline exposure to abstinence. Methods: The population pharmacokinetic analysis (NONMEM®) included 121 varenicline concentrations from 82 individuals and tested the influence of genetic and non-genetic characteristics on apparent clearance (CL/F) and volume of distribution (V/F). Model-based average concentrations over 24 h (Cav) were used to test the impact of varenicline exposure on the input rate (Kin) expressed as a function of the number of cigarettes per day in a turnover model of 373 expired carbon monoxide levels. Results: A one-compartment model with first-order absorption and elimination appropriately described varenicline concentrations. CL/F was 8.5 L/h (coefficient of variation, 26%), V/F was 228 L, and the absorption rate (ka) was fixed to 0.98 h−1. CL/F increased by 46% in 100-kg individuals compared to 60-kg individuals and was found to be 21% higher in UGT2B7 rs7439366 TT individuals. These covariates explained 14% and 9% of the interindividual variability in CL/F, respectively. No influence of varenicline Cav was found on Kin in addition to the number of cigarettes. Conclusions: Body weight mostly and to a smaller extent genetic polymorphisms of UGT2B7 can influence varenicline exposure. Dose adjustment based on body weight and, if available, on UGT2B7 genotype might be useful to improve clinical efficacy and tolerability of varenicline. [ABSTRACT FROM AUTHOR]

Background: Hospital admission and discharge are weakness points in the transition of care.Objective: To lower the risk of errors and improve medication information transfer to primary care physician (PCP), we conducted an experimental study using a structured medication reconciliation form (SMRF) in an Acute Care for Elders unit.Results: 1242 drugs of 173 patients were reconciliated at admission, optimized during the stay, and transmitted via the SMRF to the 143 corresponding PCPs. While the optimization led to 779 adaptations from admission to discharge, of which 39.0% were omissions, exposure to polypharmacy was reduced from 83.2 to 74.6% (P < 0.05). One-month post-discharge, with an answer rate of 62.2% among PCPs, the adherence to recommendations was high (85.0%) and the exposure to polypharmacy was further decreased (67.7%; P < 0.05).Conclusion: These results provide elements to consider SMRF as an example of good practice for which the impact should be analyzed at larger scale. [ABSTRACT FROM AUTHOR]