Journal of Pharmacokinetics and Pharmacodynamics最新文献

Brain amyloid beta neuritic plaque accumulation is associated with an increased risk of progression to Alzheimer's disease (AD) [Pfeil, J., et al. in Neurobiol Aging 106: 119-129, 2021]. Several studies estimate rates of change in amyloid plaque over time in clinically heterogeneous cohorts with different factors impacting amyloid plaque accumulation from ADNI and AIBL [Laccarino, L., et al. in Annals Clin and Trans Neurol 6: 1113 1120, 2019, Vos, S.J., et al. in Brain 138: 1327-1338, 2015, Lim, Y.Y., et al. in Alzheimer's Dementia 9: 538-545, 2013], but there are no reports using non-linear mixed effect model for amyloid plaque progression over time similar to that existing of disease-modifying biomarkers for other diseases [Cook, S.F. and R.R. Bies in Current Pharmacol Rep 2: 221-230, 2016, Gueorguieva, I., et al. in Alzheimer's Dementia 19: 2253-2264, 2023]. This study describes the natural progression of amyloid accumulation with population mean and between-participant variability for baseline and intrinsic progression rates quantified across the AD spectrum. 1340 ADNI participants were followed over a 10-year period with ¹⁸F-florbetapir PET scans used for amyloid plaque detection. Non-linear mixed effect with stepwise covariate modelling (scm) was used. Change in natural amyloid plaque levels over 10 year period followed an exponential growth model with an intrinsic rate of approx. 3 centiloid units/year. Age, gender, APOE4 genotype and disease stage were important factors on the baseline in the natural amyloid model. In APOE4 homozygous carriers mean baseline amyloid was increased compared to APOE4 non carriers. These results demonstrate natural progression of amyloid plaque in the continuum of AD.

Alogliptin is a highly selective inhibitor of dipeptidyl peptidase-4 and primarily excreted as unchanged drug in the urine, and differences in clinical outcomes in renal impairment patients increase the risk of serious adverse reactions. In this study, we developed a comprehensive physiologically-based quantitative systematic pharmacology model of the alogliptin-glucose control system to predict plasma exposure and use glucose as a clinical endpoint to prospectively understand its therapeutic outcomes with varying renal function. Our model incorporates a PBPK model for alogliptin, DPP-4 activity described by receptor occupancy theory, and the crosstalk and feedback loops for GLP-1-GIP-glucagon, insulin, and glucose. Based on the optimization of renal function-dependent parameters, the model was extrapolated to different stages renal impairment patients. Ultimately our model adequately describes the pharmacokinetics of alogliptin, the progression of DPP-4 inhibition over time and the dynamics of the glucose control system components. The extrapolation results endorse the dose adjustment regimen of 12.5 mg once daily for moderate patients and 6.25 mg once daily for severe and ESRD patients, while providing additional reflections and insights. In clinical practice, our model could provide additional information on the in vivo fate of DPP4 inhibitors and key regulators of the glucose control system.

Sepiapterin is an exogenously synthesized new chemical entity that is structurally equivalent to endogenous sepiapterin, a biological precursor of tetrahydrobiopterin (BH₄), which is a cofactor for phenylalanine hydroxylase. Sepiapterin is being developed for the treatment of hyperphenylalaninemia in pediatric and adult patients with phenylketonuria (PKU). This study employed concentration-QT interval analysis to assess QT prolongation risk following sepiapterin treatment. Data from three phase 1 studies and one phase 3 study were pooled for this analysis. Pediatric and adult PKU patients ≥ 2 years received multiple doses at 60 mg/kg and adult healthy volunteers received a single or multiple doses at 20 or 60 mg/kg. Time-matched triplicate ECG measurements and plasma samples for pharmacokinetic analysis were collected. Prespecified linear mixed models relating ΔQTcF to concentrations of sepiapterin and the major active circulating metabolite BH₄ were developed for the analysis. The analysis demonstrated that there is no QTcF prolongation risk in patients with PKU following sepiapterin dosing at the highest therapeutic dose, 60 mg/kg/day. The final model showed a marginal but negligible QTcF reduction with increasing sepiapterin and BH₄ concentrations. The effect on ΔQTcF was estimated to -2.72 [-3.72, -1.71] and - 1.25 [-2.75, 0.25] ms at mean baseline adjusted BH₄ C_max of 332 ng/mL (therapeutic exposure) and 675 ng/mL (supratherapeutic exposure) at dose 60 mg/kg, respectively, in PKU patients with food and in healthy volunteers with a high fat diet. Various covariates, such as clinical study ID, age, sex, food effect, race, body weight, and disease status, on QTcF interval were investigated and were found insignificant, except for food effect and age. This study concludes that there is no QTcF prolongation risk in patients with PKU following sepiapterin dosing up to 60 mg/kg/day, and BH₄ and sepiapterin concentrations minimally affect ΔQTcF after adjustment for time, sex, and meal.

P-glycoprotein (P-gp) is a key efflux transporter and may be involved in drug-drug interactions (DDIs) at the blood-brain barrier (BBB), which could lead to changes in central nervous system (CNS) drug exposure. Morphine is a P-gp substrate and therefore a potential victim drug for P-gp mediated DDIs. It is however unclear if P-gp inhibitors can induce clinically relevant changes in morphine CNS exposure. Here, we used a physiologically-based pharmacokinetic (PBPK) model-based approach to evaluate the potential impact of DDIs on BBB transport of morphine by clinically relevant P-gp inhibitor drugs.The LeiCNS-PK3.0 PBPK model was used to simulate morphine distribution at the brain extracellular fluid (brain_ECF) for different clinical intravenous dosing regimens of morphine, alone or in combination with a P-gp inhibitor. We included 34 commonly used P-gp inhibitor drugs, with inhibitory constants and expected clinical P-gp inhibitor concentrations derived from literature. The DDI impact was evaluated by the change in brain_ECF exposure for morphine alone or in combination with different inhibitors. Our analysis demonstrated that P-gp inhibitors had a negligible effect on morphine brain_ECF exposure in the majority of simulated population, caused by low P-gp inhibition. Sensitivity analyses showed neither major effects of increasing the inhibitory concentration nor changing the inhibitory constant on morphine brain_ECF exposure. In conclusion, P-gp mediated DDIs on morphine BBB transport for the evaluated P-gp inhibitors are unlikely to induce meaningful changes in clinically relevant morphine CNS exposure. The developed CNS PBPK modeling approach provides a general approach for evaluating BBB transporter DDIs in humans.

In oncology drug development, overall response rate (ORR) is commonly used as an early endpoint to assess the clinical benefits of new interventions; however, ORR benefit may not always translate into a long-term clinical benefit such as overall survival (OS). Most of the work on developing endpoints based on tumor growth dynamics relies on empirical validation, leading to a lack of generalizability of the endpoints across indications and therapeutic modalities. Additionally, many of these metrics are model-based and do not use data from all the patients. The objective of this work is to use longitudinal tumor size data and new lesion information (that is, the same information used by the ORR) to develop novel endpoints that can improve early clinical decision-making compared to the ORR. We investigate in this work multiple candidate novel endpoints based on tumor size ratio that utilize longitudinal tumor size data from all the patients regardless of their follow-up, rely only on tumor size and new lesion information, and are model-free. An extensive simulation study is conducted, exploring a wide spectrum of tumor size data and overall survival outcomes by modulating a variety of trial characteristics such as slow vs fast tumor growth, high vs low drug efficacy rates, variability in patients' responses, variations in the number of patients, follow-up periods, new lesion rates and survival curve shapes. The proposed novel endpoints based on tumor size ratio consistently outperform the ORR by having a comparable or higher correlation with the OS. Further, the novel endpoints exhibit superior accuracy compared to the ORR in predicting the long-term OS benefit. Retrospective empirical validation on BMS clinical trials confirms our simulation findings. These findings suggest that the tumor size ratio-based endpoints could replace ORR for early clinical decision-making in oncology drug development.

M6495 is a first-in-class NANOBODY^® molecule and an inhibitor of ADAMTS-5, with the potential to be a disease modifying osteoarthritis drug. In order to investigate the PK/PD (pharmacokinetic and pharmacodynamic) properties of M6495, a single dose study was performed in cynomolgus monkeys with doses up to 6 mg/kg, with the goal of understanding the PK/PD properties of M6495. The neo-epitope ARGS (Alanine-Arginine-Glycine-Serine) generated by cleavage of aggrecan by ADAMTS-5 was used as a target-engagement biomarker. A long-lasting dose-dependent decrease in serum ARGS could be observed after a single dose of M6495 in cynomolgus monkeys. The serum biomarker ARGS decreased to levels below the limit of quantification of the assay in animals which received doses of M6495 of 6 mg/kg and higher, indicating a strong inhibition of ADAMTS-5. Data from the single-dose PK/PD study was combined with data from a multiple dose study, and a non-linear mixed effects model was used to explore the relationship between plasma concentrations of M6495 and the reduction of serum ARGS. The model was subsequently used to inform the clinical phase 1 study design and was successful in predicting the human clinical pharmacokinetics and pharmacodynamics of M6495. In addition to having enabled a Phase 1 trial with M6495, this is the first PK/PD model describing the pharmacodynamics of the neo-epitope ARGS after ADAMTS5 inhibition. It is expected that in the future, this model can be used or adapted to explore the PK/PD relationship between M6495 serum concentrations and the ARGS serum biomarker.

Antibody drug conjugates (ADC) are a promising class of oncology therapeutics consisting of an antibody conjugated to a payload via a linker. DYP688 is a novel ADC comprising of a signaling protein inhibitor payload (FR900359) that undergoes unique on-antibody inactivation in plasma, resulting in complex pharmacology. To assess the impact of FR inactivation on DYP688 pharmacology and clinical developability, we performed translational modeling of preclinical PK and tumor growth inhibition (TGI) data, accompanied by mechanistic Krogh cylinder tumor modeling. Using a PK-TGI model, we identified a composite exposure-above-tumorostatic concentration (AUC_TSC) metric as the PK-driver of efficacy. To underpin the mechanisms behind AUC_TSC as the driver of efficacy, we performed quantitative systems pharmacology (QSP) modeling of DYP688 intratumoral pharmacokinetics and pharmacodynamics. Through exploratory simulations, we show that by deviating from canonical ADC design dogma, DYP688 has optimal FR900359 activity despite its transient inactivation. Finally, we performed the successful preclinical to clinical translation of DYP688 PK, including the payload inactivation kinetics, evidenced by good agreement of the predicted PK to the observed interim clinical PK. Overall, this work highlights early quantitative pharmacokinetics as a missing link in the ADC design-developability chasm.

Current treatment recommendations mainly rely on rule-based protocols defined from evidence-based clinical guidelines, which are difficult to adapt for high-risk patients such as those with renal impairment. Consequently, unsuccessful therapies and the occurrence of adverse drug reactions are common. Within the context of personalized medicine, that tries to deliver the right treatment dose to maximize efficacy and minimize toxicity, the concept of model-informed precision dosing proposes the use of mechanistic models, like physiologically based pharmacokinetic (PBPK) modeling, to predict drug regimes outcomes. Nonetheless, PBPK models have limited capability when computing patients' centric optimized drug doses. Consequently, reinforcement learning (RL) has been previously used to personalize drug dosage. In this work we propose the first PBPK and RL-based precision dosing system for an orally taken drug (benazepril) considering a virtual population of patients with renal disease. Population based PBPK modeling is used in combination with RL for obtaining patient tailored dose regimes. We also perform patient stratification and feature selection to better handle dose tailoring problems. Based on patients' characteristics with best predictive capabilities, benazepril dose regimes are obtained for a population with features' diversity. Obtained regimes are evaluated based on PK parameters considered. Results show that the proof-of-concept approach herein is capable of learning good dosing regimes for most patients. The use of a PopPBPK model allowed to account for intervariability of patient characteristics and be more inclusive considering also non-frequent patients. Impact analysis of patients' features reveals that renal impairment is the main driver affecting RL capabilities.

Composite scale data consists of numerous categorical questions/items that are often summed as a total score and are commonly utilized as primary endpoints in clinical trials. These endpoints are conceptually discrete and constrained by nature. Item response theory (IRT) is a powerful approach for detecting drug effects in composite scale data from clinical trials, but estimating all parameters requires a large sample size and all item information, which may not be available. Therefore, total score models are often utilized. The most popular total score models are continuous variable (CV) models, but this strategy establishes assumptions that go against the integer nature, and typically also the bounded nature, of data. Bounded integer (BI) and Coarsened grid (CG) models respect the nature of the data. However, their power to detect drug effects has not been as thoroughly studied in clinical trials. When an IRT model is accessible, IRT-informed models (I-BI and I-CV) are promising methods in which the mean and variability of the total score at any position are extracted from the existing IRT model. In this study, total score data were simulated from the MDS-UPDRS motor subscale. Then, the power, type 1 error, and treatment effect bias of six total score models for detecting drug effects in clinical trials were explored. Further, it was investigated how the power, type 1 of error, and treatment effect bias for the I-BI and I-CV models were affected by mis-specified item information from the IRT model. The I-BI model demonstrated the highest statistical power, maintained an acceptable Type I error rate, and exhibited minimal bias, approaching zero. Following that, the I-CV, BI, and CG with Czado transformation (CG_Czado) models provided the maximum power. However, the CG_Czado model had inflated type 1 error under low sample size scenarios in each arm of clinical trials. The CG model among total score models displayed the lowest power and the most inflated type 1 error. Therefore, the results favor the I-BI model when an IRT model is available; otherwise, the BI model.