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Background: Effective delirium prevention could benefit from automatic risk stratification of older inpatients using routinely collected clinical data. Aim: Primary aim was to develop and validate a delirium prediction model (DELIKT) suitable for implementation in hospitals. Secondary aim was to select an anticholinergic burden scale as a predictor. Method: We used one cohort for model development and another for validation with electronically available data collected within the first 24 h of admission. Included were patients aged ≥ 65, hospitalised ≥ 48 h with no stay > 24 h in an intensive care unit. Predictors, such as administrative and laboratory variables or an anticholinergic burden scale, were selected using a combination of feature selection filter method and forward/backward selection. The final model was based on logistic regression and the DELIKT was derived from the β-coefficients. We report the following performance measures: area under the curve, sensitivity, specificity and odds ratio. Results: Both cohorts were similar and included over 10,000 patients each (mean age 77.6 ± 7.6 years) with 11% experiencing delirium. The model included nine variables: age, medical department, dementia, hemi-/paraplegia, catheterisation, potassium, creatinine, polypharmacy and the anticholinergic burden measured with the Clinician-rated Anticholinergic Scale (CrAS). The external validation yielded an AUC of 0.795. With a cut-off at 20 points in the DELIKT, we received a sensitivity of 79.7%, specificity of 62.3% and an odds ratio of 5.9 (95% CI 5.2, 6.7). Conclusion: The DELIKT is a potentially automatic tool with predictors from standard care including the CrAS to identify patients at high risk for delirium. [ABSTRACT FROM AUTHOR]

Introduction: Pharmaceutical interventions can reduce negative outcomes related to potentially inappropriate prescriptions (PIPs). Objective: The objective of this study was to compare the impact of interventions on the reduction of PIPs and on different clinical outcomes using two electronic explicit tools. Methods: A randomized controlled trial was conducted in patients hospitalized between 2018 and 2019 at the Acute Care for Elders unit at Lausanne University Hospital in Switzerland. A medication review was conducted using PIM-Check in the first arm and STOPP/START in the second arm. Proposed interventions were communicated to the physicians. Clinical outcomes evaluated were incidence of falls, delirium, activities of daily living (ADL), length of stay, number of drugs at discharge and hospital readmission. Results: The 123 included patients (60 in the first arm and 63 in the second arm) were 86.3 ± 6.6 years old, had 3.5 ± 1.7 diseases and were treated by 6.2 ± 2.7 drugs at admission. There was a significant decrease in PIPs in each arm, but no significant difference between arms. The deprescription of nervous system drugs was significantly higher with STOPP/START than with PIM-Check (Chi-square p = 0.025). ADL scores between home and discharge were significantly higher in the STOPP/START arm than in the PIM-Check arm (4.42 vs 3.77; p = 0.040). The predictors of ADL score improvement were the deprescription of nervous system drugs (β = 0.423; 95% CI 0.034–0.812; p = 0.033), the use of STOPP/START (β = 0.798, 95% CI 0.305–1.290; p = 0.002) and a shorter length of hospital stay (β = −0.033, 95% CI − 0.056 to − 0.010; p = 0.005). Conclusions: Although PIM-Check was non-inferior to STOPP/START in reducing the number of PIPs, STOPP/START had a significantly higher impact on ADL. The use of STOPP/START or the deprescription of two nervous system drugs would allow the patient to acquire almost one more basic function of living. On the other hand, a loss of one point on the ADL score was observed per month of hospitalization. Clinical Trials Registration Number: NCT04028583. [ABSTRACT FROM AUTHOR]

Not monitoring adherence to oral anticancer therapies (OAT) can lead to poor clinical outcomes, including premature death as reported by Foulon et al. (Acta Clin Belg 66(2):85–96, 2011) and Greer et al. (Oncologist 21(3):354–76, 2016). Barriers to the implementation of supportive cancer care interventions in medication adherence occur with multiple hospital sites, cancer diagnoses, and numerous healthcare professionals. This commentary describes challenges and strategies from two OAT adherence trials in Australia and Switzerland to assist researchers in the design and implementation of future interprofessional trials. [ABSTRACT FROM AUTHOR]

Purpose: Potentially inappropriate prescribing (PIP) is a source of preventable adverse drug events. The objective of this study was a comparative analysis (quantitative and qualitative) between two tools used to detect PIP, PIM-Check and STOPP/START. Methods: First, a qualitative analysis (QAC) was conducted to evaluate the concordance between the criteria, which constitute PIM-Check and the gold standard STOPP/START. Second, a retrospective comparative and observational study was performed on the list of treatment at the admission of 50 older patients hospitalized in an acute geriatric ward of a university hospital in Switzerland in 2016 using both tools. Results: The QAC has shown that 50% (57 criteria) of STOPP/START criteria are fully or partially concordant with those of PIM-Check. The retrospective study was performed on 50 patients aged 87 years, suffering from 5 co-morbidities (min–max 1–11) and treated by of 8 drugs (min–max 2–16), as medians. The prevalence of the detected PIP was 80% by PIM-Check and 90% by STOPP/START. Medication review shows that 4.2 PIP per patient were detected by PIM-Check and 3.5 PIP by STOPP/START among which 1.9 PIP was commonly detected by both tools, as means. PIM-Check detected more PIP related to cardiology, angiology, nephrology, and endocrinology in older patients but missed the PIP related to geriatric syndromes (e.g., fall, dementia, Alzheimer) detected by STOPP/START. Conclusions: By using PIM-Check in geriatric settings, some PIP will not be detected. It is considered as a limitation for this tool in this frail population but brings a certain complementarity in other areas of therapy not covered by STOPP/START. [ABSTRACT FROM AUTHOR]

Purpose: Ceftriaxone total and unbound pharmacokinetics (PK) can be altered in critically ill patients with septic shock and hypoalbuminemia receiving continuous veno-venous hemodiafiltration (CVVHDF). The objective of this study was to determine the dosing strategy of ceftriaxone that maximizes the probability of maintaining the concentration above the MIC of the susceptible bacteria (≤2 mg/L by the EUCAST) for a 100% of the dosing interval (100% ƒuT>MIC). Methods: In a prospective PK study in the intensive care units of two tertiary Spanish hospitals, six timed blood samples were collected per patient; for each sample, ceftriaxone total and unbound concentrations were measured using a liquid chromatography coupled to tandem mass spectrometry method. Population PK analysis and Monte-Carlo simulations were performed using NONMEMv.7.3®. Results: We enrolled 8 critically ill patients that met the inclusion criteria (47 blood samples). Median age (range) was 70 years (47-85), weight 72.5 kg (40-95), albumin concentration 24.2 g/L (22-34), APACHE II score at admission 26 (17-36), and SOFA score on the day of study 12 (9-15). The unbound fraction (ƒu) of ceftriaxone was 44%, and total CL was 1.27 L/h, 25-30% higher than the CL reported in septic critically ill patients not receiving renal replacement therapies, and dependent on albumin concentration and weight. Despite this increment in ƒu and CL, Monte-Carlo simulations showed that a dose of 1 g once-daily ceftriaxone is sufficient to achieve a 100% ƒuT>MIC for MICs ≤2 mg/L for any range of weight and albumin concentration. Conclusion: Once-daily 1 g ceftriaxone provides optimal exposure in critically ill patients with septic shock and hypoalbuminemia receiving CVVHDF. [ABSTRACT FROM AUTHOR]

Purpose: Drug-drug interactions (DDIs) with antiretroviral drugs (ARVs) represent an important issue in elderly people living with HIV (PLWH). Amlodipine is a commonly prescribed antihypertensive drug metabolized by CYP3A4, thus predisposed to a risk of DDIs. Guidance on the management of DDIs is mostly based on theoretical considerations derived from coadministration with other CYP3A4 inhibitors. This study aimed at characterizing the magnitude of DDIs between amlodipine and ARV drugs in order to establish dosing recommendations. Methods: A population pharmacokinetic analysis was developed using non-linear mixed effect modelling (NONMEM) and included 163 amlodipine concentrations from 55 PLWH. Various structural and error models were compared to characterize optimally the concentration-time profile of amlodipine. Demographic and clinical characteristics as well as comedications were tested as potential influential covariates. Model-based simulations were performed to compare amlodipine exposure (i.e. area under the curve, AUC) between coadministered ARV drugs. Results: Amlodipine concentration-time profile was best described using a one-compartment model with first-order absorption and a lag-time. Amlodipine apparent clearance was influenced by both CYP3A4 inhibitors and efavirenz (CYP3A4 inducer). Model-based simulations revealed that amlodipine AUC increased by 96% when coadministered with CYP3A4 inhibitors, while efavirenz decreased drug exposure by 59%. Conclusion: Coadministered ARV drugs significantly impact amlodipine disposition in PLWH. Clinicians should adjust amlodipine dosage accordingly, by halving the dosage in PLWH receiving ARV with inhibitory properties (mainly ritonavir-boosted darunavir), whereas they should double amlodipine doses when coadministering it with efavirenz, under appropriate monitoring of clinical response and tolerance. [ABSTRACT FROM AUTHOR]

Background: Rosuvastatin is a lipid-lowering agent widely prescribed in people living with HIV, which is actively transported into the liver, making it a potential victim of drug–drug interactions with antiretroviral agents. Objectives: The aims of this study were to characterise the pharmacokinetic profile of rosuvastatin and to describe the relationship between rosuvastatin concentrations and non-high-density lipoprotein (HDL)-cholesterol levels in people living with HIV. Methods: A population pharmacokinetic model (NONMEM) was developed to quantify the influence of demographics, clinical characteristics and comedications on rosuvastatin pharmacokinetics. This model was combined with an indirect effect model to describe non-HDL-cholesterol measurements. Results: A two-compartment model with sequential zero- and first-order absorption best fitted the 154 rosuvastatin concentrations provided by 65 people living with HIV. None of the tested covariates significantly influenced rosuvastatin pharmacokinetics. A total of 403 non-HDL cholesterol values were available for pharmacokinetic-pharmacodynamic modelling. Baseline non-HDL cholesterol decreased by 14% and increased by 12% with etravirine and antiretroviral drugs with a known impact on the lipid profile (i.e. protease inhibitors, efavirenz, cobicistat), respectively. The baseline value was surprisingly 43% lower in people living with HIV aged 80 years compared with those aged 40 years. Simulations based on the covariate-free model predicted that, under standard rosuvastatin dosages of 5 mg and 20 mg once daily, 31% and 64% of people living with HIV would achieve non-HDL-cholesterol targets, respectively. Conclusions: The high between-subject variability that characterises both rosuvastatin pharmacokinetic and pharmacodynamic profiles remained unexplained after the inclusion of usual covariates. Considering its limited potential for drug–drug interactions with antiretroviral agents and its potent lipid-lowering effect, rosuvastatin prescription appears safe and effective in people living with HIV with hypercholesterolaemia. Clinical Trial Registration No.: NCT03515772. [ABSTRACT FROM AUTHOR]

Purpose: Older people are at risk of anticholinergic side effects due to changes affecting drug elimination and higher sensitivity to drug's side effects. Anticholinergic burden scales (ABS) were developed to quantify the anticholinergic drug burden (ADB). We aim to identify all published ABS, to compare them systematically and to evaluate their associations with clinical outcomes. Methods: We conducted a literature search in MEDLINE and EMBASE to identify all published ABS and a Web of Science citation (WoS) analysis to track validation studies implying clinical outcomes. Quality of the ABS was assessed using an adapted AGREE II tool. For the validation studies, we used the Newcastle-Ottawa Scale and the Cochrane tool Rob2.0. The validation studies were categorized into six evidence levels based on the propositions of the Oxford Center for Evidence-Based Medicine with respect to their quality. At least two researchers independently performed screening and quality assessments. Results: Out of 1297 records, we identified 19 ABS and 104 validations studies. Despite differences in quality, all ABS were recommended for use. The anticholinergic cognitive burden (ACB) scale and the German anticholinergic burden scale (GABS) achieved the highest percentage in quality. Most ABS are validated, yet validation studies for newer scales are lacking. Only two studies compared eight ABS simultaneously. The four most investigated clinical outcomes delirium, cognition, mortality and falls showed contradicting results. Conclusion: There is need for good quality validation studies comparing multiple scales to define the best scale and to conduct a meta-analysis for the assessment of their clinical impact. [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: Amisulpride is an antipsychotic used in a wide range of doses. One of the major adverse events of amisulpride is hyperprolactinemia, and the drug might also induce body weight gain.Objective: The aims of this work were to characterize the pharmacokinetics of amisulpride in order to suggest optimal dosage regimens to achieve the reference range of trough concentrations at steady-state (Cmin,ss) and to describe the relationship between drug pharmacokinetics and prolactin and body weight data.Methods: The influence of clinical and genetic characteristics on amisulpride pharmacokinetics was quantified using a population approach. The final model was used to simulate Cmin,ss under several dosage regimens, and was combined with a direct Emax model to describe the prolactin data. The effect of model-based average amisulpride concentrations over 24 h (Cav) on weight was estimated using a linear model.Results: A one-compartment model with first-order absorption and elimination best fitted the 513 concentrations provided by 242 patients. Amisulpride clearance significantly decreased with age and increased with lean body weight (LBW). Cmin,ss was higher than the reference range in 65% of the patients aged 60 years receiving 400 mg twice daily, and in 82% of the patients aged > 75 years with a LBW of 30 kg receiving 200 mg twice daily. The pharmacokinetic/pharmacodynamic model included 101 prolactin measurements from 68 patients. The Emax parameter was 53% lower in males compared with females. Model-predicted prolactin levels were above the normal values for Cmin,ss within the reference range. Weight gain did not depend on Cav.Conclusions: Amisulpride treatment might be optimized when considering age and body weight. Hyperprolactinemia and weight gain do not depend on amisulpride concentrations. Modification of the amisulpride dosage regimen is not appropriate to reduce prolactin concentrations and alternative treatment should be considered. [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]

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]

Background and Objectives: A better understanding of the highly variable pharmacokinetics (PK) of tamoxifen and its active metabolite endoxifen in breast cancer patients is crucial to support individualised treatment. This study used a modelling and simulation approach to quantitatively assess the influence of cytochrome P450 (CYP) 2D6 activity and other relevant factors on tamoxifen and endoxifen PK to identify subgroups at risk for subtherapeutic endoxifen concentrations.Methods: Simulations were performed using two previously published PK models jointly describing tamoxifen and endoxifen with CYP2D6 and CYP3A4/5 enzyme activities implemented as covariates. Steady-state predictions were compared between models and with the literature values. Factors potentially causing between-model discrepancies were explored. A previously published threshold (6 ng/mL) was used to identify patients with subtherapeutic endoxifen concentrations and to perform a dose adaptation study.Results: Steady-state predictions of tamoxifen and endoxifen were considerably different between the models. The factors, differences in sampling time, adherence and bioavailability, were not able to fully capture between-model variability. Endoxifen steady-state fluctuations within a dosing interval were minimal (<6%). Poor (97%) and intermediate (54%) CYP2D6 metabolisers failed to achieve therapeutic endoxifen concentrations, suggesting adapted doses of tamoxifen 80 and 40 mg, respectively, achieving therapeutic endoxifen concentrations in 89.7% of patients (standard dosing 45.2%). However, interindividual variability remained.Conclusions: To achieve therapeutic endoxifen concentrations early in treatment, it is advisable to initiate treatment by CYP2D6 genotype/phenotype-guided dosing, followed by therapeutic drug monitoring at steady-state. We strongly advocate to adequately measure, report and prospectively investigate influential factors (i.e. adherence, bioavailability, time to PK steady-state) in clinical trials. [ABSTRACT FROM AUTHOR]

The emergence of oral targeted anticancer agents transformed several cancers into chronic conditions with a need for long-term oral treatment. Although cancer is a life-threatening condition, oncology medication adherence-the extent to which a patient follows the drug regimen that is intended by the prescriber-can be suboptimal in the long term, as in any other chronic disease. Poor adherence can impact negatively on clinical outcomes, notably because most of these drugs are given as a standard non-individualized dosage despite marked inter-individual variabilities that can lead to toxic or inefficacious drug concentrations. This has been especially studied with the prototypal drug imatinib. In the context of therapeutic drug monitoring (TDM), increasingly advocated for oral anticancer treatment optimization, unreported suboptimal adherence affecting drug intake history may lead to significant bias in the concentration interpretation and inappropriate dosage adjustments. In the same way, suboptimal adherence may also bias the results of pharmacokinetic modeling studies, which will affect in turn Bayesian TDM interpretation that relies on such population models. Detailed knowledge of the influence of adherence on plasma concentrations in pharmacokinetic studies or in routine TDM programs is however presently missing in the oncology field. Studies on this topic are therefore eagerly awaited to better pilot the treatment of cancer with the new targeted agents and to find their optimal dosage regimen. Hence, the development and assessment of effective medication adherence programs are warranted for these treatments. [ABSTRACT FROM AUTHOR]

Background and Objectives: Methadone is a μ-opioid agonist widely used for the treatment of pain, and for detoxification or maintenance treatment in opioid addiction. It has been shown to exhibit large pharmacokinetic variability and concentration-QTc relationships. In this study we investigated the relative influence of genetic polymorphism and other variables on the dose concentration-QTc relationship.Patients and Methods: A population model for methadone enantiomers in 251 opioid-dependent patients was developed using non-linear mixed effect modeling (NONMEM®). Various models were tested to characterize the pharmacokinetics of (R)- and (S)-methadone and the pharmacokinetic-pharmacodynamic relationship, while including demographics, physiological conditions, co-medications, and genetic variants as covariates. Model-based simulations were performed to assess the relative increase in QTc with dose upon stratification according to genetic polymorphisms involved in methadone disposition.Results: A two-compartment model with first-order absorption and lag time provided the best model fit for (R)- and (S)-methadone pharmacokinetics. (S)-methadone clearance was influenced by cytochrome P450 (CYP) 2B6 activity, ABCB1 3435C>T, and α-1 acid glycoprotein level, while (R)-methadone clearance was influenced by CYP2B6 activity, POR*28, and CYP3A4*22. A linear model described the methadone concentration-QTc relationship, with a mean QTc increase of 9.9 ms and 19.2 ms per 1000 ng/ml of (R)- and (S)-methadone, respectively. Simulations with different methadone doses up to 240 mg/day showed that <8 % of patients presented with a QTc interval above 450 ms; however, this might reach 12 to 18 % for (R)- and (S)-methadone, respectively, in patients with a genetic status associated with a decreased methadone elimination at doses exceeding 240 mg/day.Conclusion: Risk factor assessment, electrocardiogram monitoring, and therapeutic drug monitoring are beneficial to optimize treatment in methadone patients, especially for those who have low levels despite high methadone doses, or who are at risk of overdosing. [ABSTRACT FROM AUTHOR]

Background: High interindividual variability in plasma concentrations of risperidone and its active metabolite, 9-hydroxyrisperidone, may lead to suboptimal drug concentration.Objective: Using a population pharmacokinetic approach, we aimed to characterize the genetic and non-genetic sources of variability affecting risperidone and 9-hydroxyrisperidone pharmacokinetics, and relate them to common side effects.Methods: Overall, 150 psychiatric patients (178 observations) treated with risperidone were genotyped for common polymorphisms in NR1/2, POR, PPARα, ABCB1, CYP2D6 and CYP3A genes. Plasma risperidone and 9-hydroxyrisperidone were measured, and clinical data and common clinical chemistry parameters were collected. Drug and metabolite concentrations were analyzed using non-linear mixed effect modeling (NONMEM(®)). Correlations between trough concentrations of the active moiety (risperidone plus 9-hydroxyrisperidone) and common side effects were assessed using logistic regression and linear mixed modeling.Results: The cytochrome P450 (CYP) 2D6 phenotype explained 52% of interindividual variability in risperidone pharmacokinetics. The area under the concentration-time curve (AUC) of the active moiety was found to be 28% higher in CYP2D6 poor metabolizers compared with intermediate, extensive and ultrarapid metabolizers. No other genetic markers were found to significantly affect risperidone concentrations. 9-hydroxyrisperidone elimination was decreased by 26% with doubling of age. A correlation between trough predicted concentration of the active moiety and neurologic symptoms was found (p = 0.03), suggesting that a concentration >40 ng/mL should be targeted only in cases of insufficient, or absence of, response.Conclusions: Genetic polymorphisms of CYP2D6 play an important role in risperidone, 9-hydroxyrisperidone and active moiety plasma concentration variability, which were associated with common side effects. These results highlight the importance of a personalized dosage adjustment during risperidone treatment. [ABSTRACT FROM AUTHOR]

Liquid-chromatography (LC) high-resolution (HR) mass spectrometry (MS) analysis can record HR full scans, a technique of detection that shows comparable selectivity and sensitivity to ion transitions (SRM) performed with triple-quadrupole (TQ)-MS but that allows de facto determination of 'all' ions including drug metabolites. This could be of potential utility in in vivo drug metabolism and pharmacovigilance studies in order to have a more comprehensive insight in drug biotransformation profile differences in patients. This simultaneous quantitative and qualitative (Quan/Qual) approach has been tested with 20 patients chronically treated with tamoxifen (TAM). The absolute quantification of TAM and three metabolites in plasma was realized using HR- and TQ-MS and compared. The same LC-HR-MS analysis allowed the identification and relative quantification of 37 additional TAM metabolites. A number of new metabolites were detected in patients' plasma including metabolites identified as didemethyl-trihydroxy-TAM-glucoside and didemethyl-tetrahydroxy-TAM-glucoside conjugates corresponding to TAM with six and seven biotransformation steps, respectively. Multivariate analysis allowed relevant patterns of metabolites and ratios to be associated with TAM administration and CYP2D6 genotype. Two hydroxylated metabolites, α-OH-TAM and 4′-OH-TAM, were newly identified as putative CYP2D6 substrates. The relative quantification was precise (<20 %), and the semiquantitative estimation suggests that metabolite levels are non-negligible. Metabolites could play an important role in drug toxicity, but their impact on drug-related side effects has been partially neglected due to the tremendous effort needed with previous MS technologies. Using present HR-MS, this situation should evolve with the straightforward determination of drug metabolites, enlarging the possibilities in studying inter- and intra-patients drug metabolism variability and related effects. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Background and Objective: Memantine, a frequently prescribed anti-dementia drug, is mainly eliminated unchanged by the kidneys, partly via tubular secretion. Considerable inter-individual variability in plasma concentrations has been reported. We aimed to investigate clinical and genetic factors influencing memantine disposition. Methods: A population pharmacokinetic study was performed including data from 108 patients recruited in a naturalistic setting. Patients were genotyped for common polymorphisms in renal cation transporters ( SLC22A1/2/5, SLC47A1, ABCB1) and nuclear receptors ( NR1I2, NR1I3, RXR, PPAR) involved in transporter expression. Results: The average clearance was 5.2 L/h with a 27 % inter-individual variability (percentage coefficient of variation). Glomerular filtration rate ( p = 0.007) and sex ( p = 0.001) markedly influenced memantine clearance. NR1I2 rs1523130 was identified as the unique significant genetic covariate for memantine clearance ( p = 0.006), with carriers of the NR1I2 rs1523130 CT/TT genotypes presenting a 16 % slower memantine elimination than carriers of the CC genotype. Conclusion: The better understanding of inter-individual variability of memantine disposition might be beneficial in the context of individual dose optimization. [ABSTRACT FROM AUTHOR]

Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation. [ABSTRACT FROM AUTHOR]

Background: The imatinib trough plasma concentration (C(min)) correlates with clinical response in cancer patients. Therapeutic drug monitoring (TDM) of plasma C(min) is therefore suggested. In practice, however, blood sampling for TDM is often not performed at trough. The corresponding measurement is thus only remotely informative about C(min) exposure.Objectives: The objectives of this study were to improve the interpretation of randomly measured concentrations by using a Bayesian approach for the prediction of C(min), incorporating correlation between pharmacokinetic parameters, and to compare the predictive performance of this method with alternative approaches, by comparing predictions with actual measured trough levels, and with predictions obtained by a reference method, respectively.Methods: A Bayesian maximum a posteriori (MAP) estimation method accounting for correlation (MAP-ρ) between pharmacokinetic parameters was developed on the basis of a population pharmacokinetic model, which was validated on external data. Thirty-one paired random and trough levels, observed in gastrointestinal stromal tumour patients, were then used for the evaluation of the Bayesian MAP-ρ method: individual C(min) predictions, derived from single random observations, were compared with actual measured trough levels for assessment of predictive performance (accuracy and precision). The method was also compared with alternative approaches: classical Bayesian MAP estimation assuming uncorrelated pharmacokinetic parameters, linear extrapolation along the typical elimination constant of imatinib, and non-linear mixed-effects modelling (NONMEM) first-order conditional estimation (FOCE) with interaction. Predictions of all methods were finally compared with 'best-possible' predictions obtained by a reference method (NONMEM FOCE, using both random and trough observations for individual C(min) prediction).Results: The developed Bayesian MAP-ρ method accounting for correlation between pharmacokinetic parameters allowed non-biased prediction of imatinib C(min) with a precision of ±30.7%. This predictive performance was similar for the alternative methods that were applied. The range of relative prediction errors was, however, smallest for the Bayesian MAP-ρ method and largest for the linear extrapolation method. When compared with the reference method, predictive performance was comparable for all methods. The time interval between random and trough sampling did not influence the precision of Bayesian MAP-ρ predictions.Conclusion: Clinical interpretation of randomly measured imatinib plasma concentrations can be assisted by Bayesian TDM. Classical Bayesian MAP estimation can be applied even without consideration of the correlation between pharmacokinetic parameters. Individual C(min) predictions are expected to vary less through Bayesian TDM than linear extrapolation. Bayesian TDM could be developed in the future for other targeted anticancer drugs and for the prediction of other pharmacokinetic parameters that have been correlated with clinical outcomes. [ABSTRACT FROM AUTHOR]

Variable antiretroviral therapy (ART) drug response likely reflects the combined influence of environment, underlying disease, concurrent drugs, and genetics. Gender exerts modest or negligible effects on ART disposition, and it is expected to have limited clinical implication, although it should be accounted for in large population studies. Ethnic denominations have, with the notable exception of efavirenz, no clear influence on ART disposition. Exploration of genetic factors might offer a better comprehension to the largely unpredictable and unresolved variability in ART concentrations and related toxicity or treatment outcome. Despite the negative perception of genetic research among the general public, this type of investigation is now widely accepted by concerned parties: patients, relatives, and study volunteers. [ABSTRACT FROM AUTHOR]

Objective: To compare the pharmacokinetic and pharmacodynamic characteristics of angiotensin II receptor antagonists as a therapeutic class.Design: Population pharmacokinetic-pharmacodynamic modelling study.Methods: The data of 14 phase I studies with 10 different drugs were analysed. A common population pharmacokinetic model (two compartments, mixed zero- and first-order absorption, two metabolite compartments) was applied to the 2685 drug and 900 metabolite concentration measurements. A standard nonlinear mixed effect modelling approach was used to estimate the drug-specific parameters and their variabilities. Similarly, a pharmacodynamic model was applied to the 7360 effect measurements, i.e. the decrease of peak blood pressure response to intravenous angiotensin challenge recorded by finger photoplethysmography. The concentration of drug and metabolite in an effect compartment was assumed to translate into receptor blockade [maximum effect (Emax) model with first-order link].Results: A general pharmacokinetic-pharmacodynamic (PK-PD) model for angiotensin antagonism in healthy individuals was successfully built up for the 10 drugs studied. Representatives of this class share different pharmacokinetic and pharmacodynamic profiles. Their effects on blood pressure are dose-dependent, but the time course of the effect varies between the drugs.Conclusions: The characterisation of PK-PD relationships for these drugs gives the opportunity to optimise therapeutic regimens and to suggest dosage adjustments in specific conditions. Such a model can be used to further refine the use of this class of drugs. [ABSTRACT FROM AUTHOR]

Objectives: To determine fluconazole population pharmacokinetics and explore the relationships between fluconazole average concentration and treatment effectiveness or microbiological resistance induction during a study aimed at evaluating the efficacy, tolerability and resistance induction after secondary prevention with fluconazole (150 mg weekly) versus placebo in human immunodeficiency virus-positive (HIV+) patients with oropharyngeal candidiasis. Methods: Population pharmacokinetic parameters of fluconazole determined from 458 serum drug concentration measurements obtained over 37 months in 132 HIV+ patients not receiving highly active antiretroviral therapy. Mean estimates and variabilities were generated using non-linear regression analysis. Logistic and linear regression analyses were used to explore the relationships between the estimated average concentration of fluconazole and candidiasis relapse or fungal resistance towards fluconazole. Results: Fluconazole kinetics were best described by a one-compartment model with first-order oral absorption from the gastrointestinal tract. The pharmacokinetics were influenced only by body weight. No effect was observed for gender, age, height or lymphocyte CD4 counts. The mean apparent population clearance was 0.79 l/h, the volume of distribution 57 l and the absorption constant (ka) 0.93 h–1. Inter-occasion variability in clearance (45%) was large relative to inter-subject variability (21%). Taking into account the average fluconazole concentration or the time above the minimal inhibitory concentrations did not clinically improve the prediction of the occurrence of oropharyngeal relapse or microbiological resistance. Conclusion: The relationship between fluconazole concentrations and preventive effectiveness was poor. Together with the rather large inter-occasion variability in fluconazole clearance, this suggests no role of therapeutic drug monitoring in optimising fluconazole treatment for secondary prevention. [ABSTRACT FROM AUTHOR]