Clinical Pharmacokinetics最新文献

Background and Objective

Daprodustat is a first-in-class hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) approved in the USA for treatment of anemia owing to chronic kidney disease (CKD) in dialysis-dependent adults and in Japan for treatment of CKD in dialysis- and non-dialysis dependent adults. This analysis characterized the population pharmacokinetics (PopPK) of daprodustat in adults with CKD and evaluated the influence of intrinsic and extrinsic factors.

Methods

This PopPK analysis included data from one phase 2B and four phase 3 studies comprising 707 CKD subjects dose titrated to prespecified target hemoglobin levels with daprodustat doses ranging from 1 to 24 mg once daily and 2 to 48 mg given three times a week (TIW). Model development leveraged a previous phase 1/2 PopPK model. Stepwise covariate analysis included 20 extrinsic and intrinsic factors. Model evaluation used standard goodness-of-fit and visual predictive checks.

Results

Daprodustat PopPK was adequately characterized using a three-compartment distribution model with first-order elimination. The absorption phase was described using five transit compartments. Oral clearance and volume of distribution was 24.6 L/h and 26.9 L, respectively. Body weight dependence (with fixed allometric coefficients) of clearance and volume terms was a statistically significant covariate. Concomitant use of clopidogrel (moderate CYP2C8 inhibitor) decreased oral clearance, resulting in higher area under the plasma concentration-time curve (AUC) ratio of 1.59 (90% CI: 1.39–1.82), subjects’ dialysis status (non-dialysis versus dialysis) had an effect on absorption, with C_max ratio of 1.19 (90% CI: 1.09–1.30). None of the other investigated intrinsic or extrinsic covariates, including concomitant administration with phosphate binders, oral iron and acid reducing agents resulted in a significant change in daprodustat systemic exposure.

Conclusion

The PopPK of daprodustat in the CKD population with anemia was adequately characterized. Allometrically-scaled body weight on clearance and volume, dialysis status on absorption and clopidogrel on clearance were statistically significant covariates.

In recent years, virtual drug study, as an emerging research strategy, has become increasingly important in guiding and promoting new drug research and development. Researchers can integrate a variety of technical methods to improve the efficiency of all phases of new drug research and development, including the use of artificial intelligence, modeling and simulation for target identification, compound screening and pharmacokinetic characteristics evaluation, and the application of clinical trial simulation to carry out clinical research. This paper aims to elaborate on the application of virtual drug study in the key stages of new drug research and development and discuss the opportunities and challenges it faces in supporting new drug research and development.

Background and objective: Lupus nephritis (LN), a severe manifestation of systemic lupus erythematosus, has greater severity in children versus adults. Belimumab is approved for systemic lupus erythematosus treatment in patients aged ≥ 5 years, and for active LN in adults in the European Union, China, Japan and Latin America, and patients aged ≥ 5 years in the USA. Low prevalence of paediatric active LN makes conducting a clinical study within a reasonable period unfeasible. We describe a model-based extrapolation of belimumab efficacy and pharmacokinetics from adults to children with LN to support US Food and Drug Administration approval of intravenous belimumab 10 mg/kg (administered every 4 weeks after the loading dose) in children (aged 5-17 years) with active LN.

Methods: This concept assumed that disease progression, response to belimumab, exposure-response, and the target belimumab exposure for efficacy are similar across adult and paediatric systemic lupus erythematosus and LN, evaluated against the published literature for paediatric LN and belimumab systemic lupus erythematosus and LN clinical trial data in adults and children. A two-compartmental population pharmacokinetic model, previously developed for adults with LN, was used to extrapolate belimumab pharmacokinetics to children with LN.

Results: The model captured the dependence of time-varying proteinuria on belimumab clearance, and therefore exposure. Sufficient target exposures for efficacy were achieved in children with active LN. A small proportion of children aged 5-11 years are predicted to have exposures below adult levels but no impact to efficacy is expected.

Conclusions: Our model demonstrated that intravenous belimumab 10 mg/kg every 4 weeks is appropriate for children aged 5-17 years with active LN.

Background and objective: Because of the pathophysiological changes associated with critical illness and the use of extracorporeal life support (ECLS) such as continuous renal replacement therapy (CRRT) and extracorporeal membrane oxygenation (ECMO), the pharmacokinetics of drugs are often altered. The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model for anakinra in children that accounts for the physiological changes associated with critical illness and ECLS technology to guide appropriate pharmacotherapy.

Methods: A PBPK model for anakinra was first developed in healthy individuals prior to extrapolating to critically ill children receiving ECLS. To account for the impact of anakinra clearance by the dialysis circuit, a CRRT compartment was added to the pediatric PBPK model and parameterized using data from a previously published ex-vivo study. Additionally, an ECMO compartment was added to the whole-body structure to create the final anakinra ECLS-PBPK model. The final model structure was validated by comparing predicted concentrations with observed patient data. Due to limited information in guiding anakinra dosing in this population, in-silico dose simulations were conducted to provide baseline recommendations.

Results: By accounting for changes in physiology and the addition of ECLS compartments, the final ECLS-PBPK model predicted the observed plasma concentrations in an adolescent receiving subcutaneous anakinra. Furthermore, dosing simulations suggest that anakinra exposure in adolescents receiving ECLS is similar to that in healthy counterparts.

Conclusion: The anakinra ECLS-PBPK model developed in this study is the first to predict plasma concentrations in a population receiving simultaneous CRRT and ECMO. Dosing simulations provided may be used to inform anakinra use in critically ill children and guide future clinical trial planning.

Background and objective: Axicabtagene ciloleucel (axi-cel, Yescarta) is an autologous, anti-CD19, chimeric antigen receptor (CAR) T-cell therapy approved for patients with relapsed and refractory non-Hodgkin's lymphoma. Substantial inter-individual variability in cellular kinetics has been observed with CAR-T therapies and factors impacting CAR-T cellular kinetics remain poorly understood. This work reports a population cellular kinetic model of axi-cel in relapsed and patients with refractory non-Hodgkin's lymphoma and investigated the impact of covariates on early and late kinetic phases of CAR-T exposure.

Methods: A population cellular kinetic model (NONMEM^® version 7.4) for axi-cel was developed using data from 410 patients (2050 transgene observations) after a single intravenous infusion of 2 × 10⁶ anti-CD19 CAR+ T cells/kg in patients with non-Hodgkin's lymphoma (ZUMA-1, ZUMA-5, and ZUMA-7 clinical studies). A large panel of covariates was assessed to decipher the variability of CAR-T cell kinetics including patient characteristics, product characteristics, and disease types.

Results: Axi-cel cellular kinetics were well described by a piecewise model of cellular growth kinetics characterized by an exponential growth phase followed by a triphasic decline phase including a long-term persistence phase. The final cellular kinetic model retained in vitro doubling time during CAR-T cell manufacturing and total number of T cells infused as covariates impacting the duration of the growth phase, which, however, did not substantially influence maximum concentration, area under the concentration-time curve over the first 28 days, or long-term persistence. A statistically significant relationship was observed between maximum concentration and the probability to receive tocilizumab and/or corticosteroids.

Conclusions: No covariates considered in this study were found to significantly and substantially predict the exposure profile of axi-cel. Tocilizumab and steroid use were related to maximum concentration, but they were used reactively to treat toxicities that are associated with a higher maximum concentration. Further CAR-T kinetic analyses should consider additional factors to explain the observed variability in cellular kinetics or help establish a dose-exposure relationship.

Clinical trial registration: NCT02348216 (ZUMA-1), NCT03105336 (ZUMA-5), and NCT03391466 (ZUMA-7).

Introduction: In the last decade, various Machine Learning techniques have been proposed aiming to individualise the dose of anticancer drugs mostly based on a presumed drug effect or measured effect biomarkers. The aim of this scoping review was to comprehensively summarise the research status on the use of Machine Learning for precision dosing in anticancer drug therapy.

Methods: This scoping review was conducted in accordance with the interim guidance by Cochrane and the Joanna Briggs Institute. We systematically searched the databases Medline (via PubMed), Embase and the Cochrane Library for research articles and reviews including results published after 2016. Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.

Results: A total of 17 relevant studies was identified. In 12 of the included studies, Reinforcement Learning methods were used, including Classical, Deep, Double Deep and Conservative Q-Learning and Fuzzy Reinforcement Learning. Furthermore, classical Machine Learning methods were compared in terms of their performance and an artificial intelligence platform based on parabolic equations was used to guide dosing prospectively and retrospectively, albeit only in a limited number of patients. Due to the significantly different algorithm structures, a meaningful comparison between the various Machine Learning approaches was not possible.

Conclusion: Overall, this review emphasises the clinical relevance of Machine Learning methods for anticancer drug dose optimisation, as many algorithms have shown promising results enabling model-free predictions with the potential to maximise efficacy and minimise toxicity when compared to standard protocols.

Background and objective: Dalbavancin is increasingly used for the long-term treatment of chronic osteoarticular infections. A population pharmacokinetic/pharmacodynamic (PK/PD) analysis for assessing the relationship between dalbavancin exposure and C-reactive protein (C-RP) over time was conducted.

Methods: Non-linear mixed-effect modeling was fitted to dalbavancin and C-RP concentrations. Monte Carlo simulations assessed the weekly percentage of C-RP reduction associated with different dosing regimens, starting from baseline to < 1 mg/dL.

Results: A total of 45 patients were retrospectively included in the analysis. The PK of dalbavancin was described by a two-compartment model, and the PD of C-RP was described by an indirect turnover maximum inhibition model. The total dalbavancin concentration model estimate producing 50% of maximum C-RP production inhibition (IC₅₀) was 0.70 mg/L. Monte Carlo simulations showed that in patients with staphylococcal osteoarticular infections targeting total dalbavancin concentrations at > 14.5 mg/L at any time point may achieve C-RP production inhibition over time in > 95% of patients. Based on this, the findings showed that a cumulative dose of 3000 mg administered in the first 3 weeks may lead to a > 90% C-RP decrease versus baseline in approximately 5-6 weeks. In patients needing treatment prolongation, an additional 1500 mg dose after this period may maintain C-RP concentrations < 1 mg/dL for other 3 weeks.

Conclusions: A decrease in C-RP is related to dalbavancin exposure in osteoarticular infections. Targeting dalbavancin plasma concentrations above the efficacy threshold may be associated with effective treatment.

Introduction: Endometriosis, a common and distressing gynecological condition, affects fertility and causes pain, is often managed with medications such as Elagolix. The present study aimed to construct a physiologically based pharmacokinetic (PBPK) model for elagolix to predict its pharmacokinetics in different populations, including those with special conditions, to enhance treatment strategies for endometriosis.

Methods: The PBPK model was optimized using observational data based on the oral administration of elagolix in a healthy Chinese population under fasting conditions. Model accuracy was further verified by comparing the predicted postprandial elagolix concentration data for healthy Chinese individuals with observed data and by comparing these values with the predicted values in a US population model with renal injury or following multiple-dose administration.

Results: Elagolix pharmacokinetic (PK) profiles in the Chinese and American populations exhibited no differences that were attributable to ethnicity. The model predicted in vivo PK in adolescents aged 14-18 years, revealing no clinically significant differences in the effects of elagolix between adolescents and adults. In addition, no predicted PK differences in individuals with overweight were observed. However, notable variations emerged in those classified as obesity class 2 and above compared to healthy individuals.

Conclusion: Our study presents a novel PBPK model for elagolix in healthy Chinese women, addressing a clinical data gap for its use in adolescents and obese patients. By validating the model with real-world factors, including diet and renal impairment, we provide initial pharmacokinetic predictions for these populations, contributing to a more informed clinical approach.

Background and objective: Asciminib is approved in patients with Philadelphia chromosome-positive chronic myeloid leukemia in chronic phase (Ph+ CML-CP) treated with ≥ 2 prior tyrosine kinase inhibitors. Here, we aimed to demonstrate similarity in efficacy/safety of asciminib 80 mg once daily (q.d.) versus 40 mg twice daily (b.i.d.) in patients with CML-CP without T315I mutation and support the use of the 200-mg b.i.d. dosage in patients harboring T315I, using model-informed drug development.

Methods: Data were collected from 199 patients in the phase I (NCT02081378; 10-200 mg b.i.d. or 10-400 mg q.d.) and 154 patients in the phase III (NCT03106779; 40 mg b.i.d.) studies. Evaluations were based on population pharmacokinetics (PopPK) and exposure-response (efficacy/safety) analyses.

Results: PopPK showed comparable exposure (area under the curve, AUC_0-24h) for 40 mg b.i.d. and 80 mg q.d. (12,638 vs 12,646 ng*h/mL); average maximum and minimum plasma concentrations for 80 mg q.d. were 1.61- and 0.72-fold those of 40 mg b.i.d., respectively. Exposure-response analyses predicted similar major molecular response rates for 40 mg b.i.d. and 80 mg q.d. (Week 24: 27.6% vs 24.8%; Week 48: 32.3% vs 30.6%). Results also established adequacy of 200 mg b.i.d. in patients with T315I mutation (Week 24: 20.7%; Week 48: 23.7%), along with a similar safety profile for all dose regimens.

Conclusions: Similarity between 40 mg b.i.d. and 80 mg q.d. regimens was investigated, demonstrating similar and substantial efficacy with well-tolerated safety in patients without T315I mutation. The 200-mg b.i.d. dose was deemed safe and effective for patients with T315I mutation.