Clinical Pharmacokinetics最新文献

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.

Background: The present literature offers conflicting views on the importance of changes in plasma protein binding in clinical therapeutics. Furthermore, there are no methods to calculate a new dosing regimen when such changes occur.

Methods: Previous models developed by Balaz et al. and Greenblat et al. were used to calculate a plasma protein binding (PPB) score for individual drugs based on the volume of distribution for total concentration and the bound fraction of drug. The models were further used to calculate a new drug dosing interval for cases of altered plasma protein binding. The equations apply best for drugs with fast absorption and fast distribution; they can be used as approximations for drugs with slow distribution by using the volume of distribution at steady state and the rate constant of the elimination phase.

Results: The newly developed equations show that changes in plasma protein binding are relevant only for drugs with a positive PPB score; such drugs must have a volume of distribution for total concentration below 1.3 L/kg and high protein binding. It is further shown that the drug dosing interval should be reduced when the remaining fraction of plasma protein binding is below the PPB score.

Conclusion: A new method to rank drugs according to the impact of changes in plasma protein binding on their pharmacokinetic profile was developed. The new method was applied to show that drugs with high PPB scores need reductions in their dosing interval when the level of protein binding decreases.

Background and objective: The dosage of daptomycin is usually based on body weight. However, it has been shown that this approach yields too high an exposure in obese patients. Pharmacokinetic and pharmacodynamic indexes (PK/PD) have been proposed for daptomycin's antibacterial effect (AUC/CMI >666) and toxicity (C0 > 24.3 mg/L). We previously developed machine learning (ML) algorithms to predict starting doses based on Monte Carlo simulations. We propose a new way to perform probability of target attainment based on an ML algorithm to predict the daptomycin starting dose.

Methods: The Dvorchik model of daptomycin was implemented in the mrgsolve R package and 4950 pharmacokinetic profiles were simulated with doses ranging from 4 to 12 mg/kg. We trained and benchmarked four machine learning algorithms and selected the best to iteratively search for the optimal dose of daptomycin maximizing the event (AUC/CMI > 666 and C0 < 24.3 mg/L). The ML algorithm was evaluated in simulations and an external database of real patients in comparison with population pharmacokinetics.

Results: The performance of the Xgboost algorithms developed to predict the event (ROC AUC) in the training and test set were 0.762 and 0.761, respectively. The most important prediction variables were dose, creatinine clearance, body weight and sex. In the external database of real patients, the starting dose administered based on the ML algorithm significantly improved the target attainment by 7.9% (p-value = 0.02929) in comparison with the dose administered based on body weight.

Conclusion: The developed algorithm improved the target attainment for daptomycin in comparison with weight-based dosing. We built a Shiny app to calculate the optimal starting dose.

Background and objective: Physiologically based pharmacokinetic (PBPK) models are valuable for translating in vitro absorption, distribution, metabolism, and excretion (ADME) data to predict clinical pharmacokinetics, and can enable discovery and early clinical stages of pharmaceutical research. However, in predicting pharmacokinetics of organic anion transporting polypeptide (OATP) 1B substrates based on in vitro transport and metabolism data, PBPK models typically require additional empirical in vitro-to-in vivo scaling factors (ESFs) in order to accurately recapitulate observed clinical profiles. As model simulation is very sensitive to ESFs, a critical evaluation of ESF estimation is prudent. Previously studies have applied classic 'two-stage' and 'naïve pooled data' approaches in identifying a set of compound independent ESFs. However, the 'two-stage' approach has the parameter identification issue in separately fitting data for individual compounds, while the 'naïve pooled data' approach ignores interstudy variability, leading to potentially biased ESF estimates.

Methods: In this study, we have applied a nonlinear mixed-effect approach in estimating ESF of the PBPK model and incorporated additional data from 86 runs of in vitro uptake assay and 49 clinical studies of 12 training compounds in model development to further enhance the translation of in vitro data to predict the pharmacokinetics of OATP1B substrate drugs. To test predication accuracy of the model, a 'leave-one-out' analysis has been performed.

Results: The established model can reasonably describe the clinical observations, with both mean values and interstudy variabilities quantified for ESF and volume of distribution parameters. The mean estimates are largely consistent with values in the previous reports. The interstudy variabilities of these parameters are estimated to be at least 50% (as coefficient of variation). Most compounds can be reasonably predicted in the 'leave-one-out' analysis.

Conclusion: This study improves the confidence in predicting the pharmacokinetics of OATP1B substrates in individual studies of small sample sizes, and quantifies the variability associated with the prediction.