Clinical Pharmacokinetics最新文献

Background and objective: Remdesivir is a nucleotide analog prodrug approved for the treatment of COVID-19. This study evaluated the pharmacokinetics and safety of remdesivir and its metabolites (GS-704277 and GS-441524) in participants with varying degrees of renal impairment. Results of this phase I study, along with those of a phase III study, contributed to an extension of indication for remdesivir in the USA and Europe for use in patients with COVID-19 with all stages of renal impairment, including those on dialysis, with no dose adjustment.

Methods: This phase I, open-label, parallel-group study enrolled participants who had mild (n = 12), moderate (n = 11), or severe (n = 10) renal impairment or kidney failure (n = 6 with dialysis, n = 4 without dialysis). Healthy matched controls were enrolled as reference. Remdesivir was given as single intravenous doses of 100 mg (mild and moderate renal impairment), 40 mg (severe renal impairment, kidney failure predialysis), and 20 mg (kidney failure postdialysis and without dialysis).

Results: Plasma pharmacokinetics of remdesivir were not affected by mild, moderate, or severe renal impairment or kidney failure. Geometric least squares mean ratios ranged from 0.8 to 1.2 for remdesivir area under the plasma concentration-time curve (AUC). GS-704277 AUC was up to 2.8-fold higher and GS-441524 AUC up to 7.9-fold higher in participants with renal impairment. Adverse events and laboratory abnormalities were consistent with the existing safety profile for remdesivir.

Conclusions: Observed pharmacokinetics for remdesivir and its metabolites in participants with renal impairment aligned with expected changes based on known routes of elimination. Remdesivir was generally safe and well tolerated in participants with renal impairment, and no new safety concerns were identified. These results, along with those from the phase III study in patients with COVID-19 with severely reduced kidney function, support the use of remdesivir in patients with any degree of renal impairment with no dose adjustments.

Trial registration: EudraCT no. 2020-003441-10; 9 July 2020.

Background: Predicting metabolic drug-drug interactions (DDIs) via cytochrome P450 enzymes (CYP) is essential in drug development, but controversy has reemerged recently about whether in vitro-in vivo extrapolation (IVIVE) using static models can replace dynamic models for some regulatory filings and label recommendations.

Objective: The aim of this study was to determine if static and dynamic models are equivalent for the quantitative prediction of metabolic DDIs arising from competitive CYP inhibition.

Methods: Drug parameter spaces were varied to simulate 30,000 DDIs between hypothetical substrates and inhibitors of CYP3A4. Predicted area under the plasma concentration-time profile ratios for substrates (AUCr = AUC_{(presence of precipitant)}/AUC_{(absence of precipitant)}) were compared between dynamic simulations (Simcyp^® V21) and corresponding static calculations, giving an inter-model discrepancy ratio (IMDR = AUCr_dynamic/AUCr_static). Dynamic simulations were conducted using a 'population' representative and a 'vulnerable patient' representative with maximal concentration (C_max) or average steady-state concentration (C_avg,ss) as the inhibitor driver concentrations. IMDRs outside the interval 0.8-1.25 were defined as discrepancy between models.

Results: The highest rate of IMDR <0.8 and IMDR >1.25 discrepancies in the 'population' representative was 85.9% and 3.1%, respectively, when using C_avg,ss as the inhibitor driver concentration. Using the 'vulnerable patient' representative showed the highest rate of IMDR >1.25 discrepancies at 37.8%.

Conclusion: Static models are not equivalent to dynamic models for predicting metabolic DDIs via competitive CYP inhibition across diverse drug parameter spaces, particularly for vulnerable patients. Caution is warranted in drug development if static IVIVE approaches are used alone to evaluate metabolic DDI risks.

In recent years, many population pharmacokinetic (popPK) models have been developed for echinocandins to better understand the pharmacokinetics (PK) of these antifungals. This comprehensive review aimed to summarize popPK models of echinocandins (micafungin, caspofungin, anidulafungin, and rezafungin), by focusing on dosage optimization to maximize the probability of attaining the PK/PD target proposed in special populations. A search in PubMed, Embase, Web of Science, and Scopus, supplemented by the bibliography of relevant articles, was conducted from inception to March 2024, including both observational and prospective trials. A total of 1126 articles were identified, 47 of them were included in the review (22 for micafungin, 13 for caspofungin, 9 for anidulafungin, and 3 for rezafungin). A two-compartment model was more frequently used to describe the PK parameters of echinocandin (78.7% of developed models), although more complex structural models with three and four compartments have also been developed. The covariates to estimate the PK parameters such as clearance (CL) and volume of distribution (V_d) differed between models. Weight total (WT) was the most frequently reported to be a significant predictor for both parameters, especially for estimating the CL in pediatrics. The PD parameter most widely reported assessing the drug exposure-efficacy relationship was the area under the concentration-time curve to minimum inhibitory concentration (MIC) ratio (AUC_0-24/MIC) with different targets proposed for each echinocandin. In certain populations such as patients that are critically ill, obese, receiving extracorporeal membrane oxygenation (ECMO) and/or continuous renal replacement therapy (CRRT), or pediatric patients and/or patients with cancer or that are immunocompromised, the fixed dosing strategies recommended in the drug prescribing information may not reach the PK/PD target. For these populations, different strategies have been proposed, such as a dosing regimen based on body weight or increasing the loading and/or maintenance dose. Despite echinocandins' favorable safety profile and predictable PK, certain groups at risk of suboptimal drug exposure can benefit from therapeutic drug monitoring (TDM) to prevent clinical failures. Numerous popPK models of echinocandins have been developed. However, an external validation of the suggested dosing regimens in conjunction with an analysis of population subgroups should be conducted before implementing a popPK model in clinical practice.

Background and objective: Psilocybin is currently being extensively studied as a potential therapeutic agent for multiple psychiatric disorders. Here, a systematic literature review of all published pharmacokinetic data on the pharmacologically active metabolite of psilocybin, psilocin, is presented.

Methods: The review includes clinical studies that reported pharmacokinetic data and/or parameters after psilocybin administration in humans. In addition, raw pharmacokinetic data from these studies was requested and/or extracted to further compare results across studies.

Results: In total, 309 publications were identified, of which 19 publications were ultimately included, which covered 12 unique clinical datasets. Except for one study that investigated intravenous psilocybin, all included studies administered psilocybin orally. Psilocybin acts as a pro-drug and is rapidly absorbed and transformed to psilocin after oral administration. In the majority of studies, unconjugated psilocin was measured while some also measured conjugated and total concentrations. Psilocin's biphasic concentration-time profiles demonstrates fast and extensive disposition with an apparent distribution volume of 505-1267 L and a terminal half-life of 1.23-4.72 h. Only 1.5-3.4% of the dose is excreted as psilocin in urine. Psilocin is mainly transformed to 4-hydroxyindole-3-acetic acid and in less amounts to conjugated psilocin, where 4-hydroxyindole-3-acetic acid formation may occur prior to systemic psilocin absorption. Information on the absolute bioavailability of psilocin was limited, and estimated at 55% in one study. No covariates nor food effects have been reported, based on four studies with known fasting status.

Conclusions: Overall, we found the pharmacokinetic parameters of psilocin to be consistent between studies. This review may guide the further clinical development of psilocybin-based therapies.

Background and objective: The objective of this study was to characterize the effects of risankizumab on the pharmacokinetics of cytochrome P450 (CYP) 1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A substrates in patients with moderately to severely active Crohn's disease (CD) or ulcerative colitis (UC) using a cocktail approach.

Methods: Patients with CD or UC (n = 20) received single doses of probe substrates for CYP1A2 (caffeine 100 mg), CYP2C9 (warfarin 10 mg), CYP2C19 (omeprazole 20 mg), CYP2D6 (metoprolol 50 mg), and CYP3A (midazolam 2 mg) before and after intravenous infusions of risankizumab 1800 mg once every 4 weeks for four doses. Serial blood samples were collected for determination of concentrations of the CYP probe drugs and metabolites with and without risankizumab. Trough samples for risankizumab were collected at sparse timepoints.

Results: The point estimates and 90% confidence intervals for maximum plasma concentration (C_max) and the area under the plasma concentration-time curve from time zero to infinity (AUC_inf) ratios for the CYP probe substrates administered with risankizumab versus without risankizumab were mostly within the 0.8-1.25 equivalence bounds, except for omeprazole and caffeine. While the upper 90% CI for caffeine AUC_inf exceeded 1.25, the point estimate was a modest 1.13 and the C_max ratio was well within 0.8-1.25. For omeprazole, while the lower bound of the 90% CI for AUC_t (0.715) and AUC_inf (0.624) extended slightly below the default equivalence limit, the exposures of its metabolite, 5-hydroxy-omeprazole, formed via CYP2C19, were comparable before and after risankizumab treatment, indicating a limited impact of risankizumab. No new safety issues were identified in this study.

Conclusion: The totality of data indicated a lack of clinically relevant impact of risankizumab on the evaluated CYP enzymes in patients with CD/UC.

Clinicaltrials: GOV: NCT04254783.

Background and objective: Adherence to urate-lowering therapy among people with gout is poor, so it is important to understand which day-to-day medication-taking ('implementation') patterns are most likely to lead to suboptimal serum urate concentrations and worsen clinical outcomes. This study aimed to (1) determine the relative forgiveness (RF) of allopurinol with hypothetical and real-life implementation patterns in people with gout, (2) explore the use of RF as a means of identifying suboptimal implementation patterns, (3) assess the impact of suboptimal implementation patterns on clinical outcomes.

Methods: A simulation study was conducted using a pharmacokinetic-pharmacodynamic model for allopurinol and serum urate to determine the RF of allopurinol implementation patterns.

Results: With 100% ('perfect') implementation, the probability of adequate urate control (> 90% of days with urate < 0.36 mmol/L over 360 days) for a 300 mg dose of allopurinol was 0.549. Simulations based on real-life individual implementation patterns over a year yielded a median RF of 0.51, indicating that half of the patterns studied were at least 50% less likely to achieve adequate urate control than perfect implementation.

Conclusion: Our study provides evidence that missing one or two doses of allopurinol, even repeatedly over a year, does not significantly impact serum urate target achievement or clinical outcomes. However, people who take repeated drug holidays of more than 3 days in a row (followed by less than 15 consecutive days of dosing) are less than 0.3 times as likely (at least 70% less likely) to achieve adequate urate control than those with perfect implementation and may see an increase in the frequency of gout flares.

Population pharmacokinetic (popPK) models are an essential tool when implementing therapeutic drug monitoring (TDM) and to overcome dosing challenges in neonates in clinical practice. Since vancomycin, gentamicin, and amikacin are among the most prescribed antibiotics for the neonatal population, we aimed to characterize the popPK models of these antibiotics and the covariates that may influence the pharmacokinetic parameters in neonates and infants with no previous pathologies. We searched the PubMed, Embase, Web of Science, and Scopus databases and the bibliographies of relevant articles from inception to the beginning of February 2024. The search identified 2064 articles, of which 68 met the inclusion criteria (34 for vancomycin, 21 for gentamicin, 13 for amikacin). A one-compartment popPK model was more frequently used to describe the pharmacokinetics of the three antibiotics (91.2% vancomycin, 76.9% gentamicin, 57.1% amikacin). Pharmacokinetic parameter (mean ± standard deviation) values calculated for a "typical" neonate weighing 3 kg were as follows: clearance (CL) 0.34 ± 0.80 L/h for vancomycin, 0.27 ± 0.49 L/h for gentamicin, and 0.19 ± 0.07 L/h for amikacin; volume of distribution (V_d): 1.75 ± 0.65 L for vancomycin, 1.54 ± 0.53 L for gentamicin, and 1.67 ± 0.27 L for amikacin for one-compartment models. Total body weight, postmenstrual age, and serum creatinine were common predictors (covariates) for describing the variability in CL, whereas only total body weight predominated for V_d. A single universal popPK model for each of the antibiotics reviewed cannot be implemented in the neonatal population because of the significant variability between them. Body weight, renal function, and postmenstrual age are important predictors of CL in the three antibiotics, and total body weight for V_d. TDM represents an essential tool in this population, not only to avoid toxicity but to attain the desired pharmacokinetic/pharmacodynamic index. The characteristics of the neonatal population, coupled with the lack of prospective studies and external validation of most models, indicate a need to continue investigating the pharmacokinetics of these antibiotics in neonates.

Background and objectives: The pharmacokinetics (PK) of piperacillin/tazobactam (PIP/TAZ) is highly variable across different patient populations and there are controversies regarding non-linear elimination as well as the fraction unbound of PIP (f_{UNB_PIP}). This has led to a plethora of subgroup-specific models, increasing the risk of misusing published models when optimising dosing regimens. In this study, we aimed to develop a single model to simultaneously describe the PK of PIP/TAZ in diverse patient populations and evaluate the current dosing recommendations by predicting the PK/pharmacodynamics (PD) target attainment throughout life.

Methods: Population PK models were separately built for PIP and TAZ based on data from 13 studies in various patient populations. In the development of those single-drug models, postnatal age (PNA), postmenstrual age (PMA), total body weight (TBW), height, and serum creatinine (SCR) were tested as covariates. Subsequently, a combined population PK model was established and the correlations between the PK of PIP and TAZ were tested. Monte Carlo simulations were performed based on the final combined model to evaluate the current dosing recommendations.

Results: The final combined model for PIP/TAZ consisted of four compartments (two for each drug), with covariates including TBW, PMA, and SCR. For a 70-kg, 35-year-old patient with SCR of 0.83 mg L^-1, the PIP values for V₁, CL, V₂ and Q₂ were 10.4 L, 10.6 L h^-1, 11.6 L and 15.2 L h^-1, respectively, and the TAZ values were 10.5 L, 9.58 L h^-1, 13.7 L and 16.8 L h^-1, respectively. The CL for both drugs show maturation in early life, reaching 50% at 54.2 weeks PMA. With advancing age, CL of TAZ declines to 50% at 61.6 years PMA, whereas CL of PIP declines more slowly, reaching 50% at 89.1 years PMA. The f_{UNB_PIP} was estimated as 64.5% and non-linear elimination was not supported by our data. The simulation results indicated considerable differences in PK/PD target attainment for different patient populations under current recommended dosing regimens.

Conclusions: We developed a combined population PK model for PIP/TAZ across a broad range of patients covering the extremes of patient characteristics. This model can be used as a robust a priori model for Bayesian forecasting to achieve individualised dosing. The simulations indicate that adjustments based on the allometric theory as well as maturation and decline of CL of PIP may help the current dosing recommendations to provide consistent target attainment across patient populations.