Journal of Pharmacokinetics and Pharmacodynamics最新文献

Automatic differentiation (AD), a key method for accurately and efficiently computing derivatives in modern machine learning, is now implemented in Phoenix® NLME™ 8.6 for the first time and applied to the first-order conditional estimation extended least squares (FOCE ELS), Laplacian, and adaptive Gaussian quadrature (AGQ) algorithms. We name the AD implementation as 'automatic-differentiation-assisted parametric optimization' (ADPO), which can be enabled by checking the 'Fast Optimization' option. We present in detail how ADPO is implemented in the frequently used FOCE ELS algorithm, and analyze its performance from the benchmarks based on four PK/PD models. We show both ADPO and traditional FOCE ELS which uses gradients obtained from finite difference (FD) are reasonably accurate and robust, while the main advantage of ADPO being that it considerably reduces computation time no matter what ODE solvers are used: in general ADPO reduces the total run time by around 20% to 50% compared to traditional FOCE ELS. In a case for the realistic voriconazole model using 'auto-detect' ODE solver, 95% reduction in the total run time is observed.

Model-informed Drug Development (MIDD) is an essential framework for advancing drug development and supporting regulatory decision-making. The current review presents a strategic blueprint to closely align MIDD tools with key questions of interests (QOI), content of use (COU), and model impact across stages of development -from early discovery to post-market lifecycle management. To demonstrate how the strategy works, we have also provided examples of how the MIDD tools can be applied to enhance the target identification, assist with lead compound optimization, improve preclinical prediction accuracy, facilitate First-in-Human (FIH) studies, optimize clinical trial design including dosage optimization, describe clinical population pharmacokinetics/exposure-response (PPK/ER) characteristics, and support label updates during post-approval stages. Additionally, the roles of some commonly used modeling methodologies, such as quantitative structure-activity relationship (QSAR), physiologically based pharmacokinetic (PBPK), semi-mechanistic pharmacokinetics/pharmacodynamics (PK/PD), PPK/ER, and quantitative systems pharmacology (QSP), are highlighted. What is more, we also explored the evolving role of MIDD in the context of emerging technologies, such as artificial intelligence (AI) and machine learning (ML) approaches. Further, MIDD utilities in development and regulatory evaluation of 505(b) (2) and generic drug products, as well as practical considerations of MIDD in regulatory interactions and asset acquisitions, are briefly discussed. At the end of the review, we briefly addressed the potential challenges faced by MIDD, such as lack of appropriate resources and slow organizational acceptance and alignment, as well as our perspectives on future opportunities of how MIDD could be further expanded.

Concentration-dependent binding to red blood cells is a characteristic of several drugs, complicating the understanding of how pathophysiological factors influence drug behavior. This study utilized user-friendly, physiologically-based pharmacokinetic (PBPK) models to compare concentration-dependent and independent blood-to-plasma drug concentration ratios (B/P), using tacrolimus as a case study. Two models were developed and validated for tacrolimus using clinical data from healthy volunteers; Model 1 accounted for saturable blood binding, and Model 2 used a constant B/P level. The differences between the two models based on the two binding assumptions were also studied across clinically relevant hematocrit (HCT) and dose levels. For intravenous (IV) infusions, varying HCT from 15 to 45% resulted in a predicted difference in the area under the concentration-time curve (AUC) of 6-9% for total drug concentration in blood and 37-39% for unbound drug concentration in plasma. Increasing IV doses increased the predicted differences in blood AUC. For oral dosing to steady state, predicted differences in trough concentrations ranged between 50% and 130%, peak concentrations (78-284%), and AUC (up to 125%) according to HCT, dose, and biological medium, e.g., trough differences ranged from 50% (blood, 5 mg) to 130% (plasma, 10 mg). A hypothetical scenario of tacrolimus dose levels increasing above clinically relevant doses revealed a reducing difference in outcomes between the two binding assumptions. Although PBPK models ignoring concentration-dependent binding may adequately fit observed data, they can necessitate compensatory adjustments in disposition parameters, limiting their ability to predict clinical scenarios beyond the model's original development settings.

Pharmacokinetics and toxicological studies how the body reacts to a specific administered substance, such as a drug, toxin, or food. Each substance experiences these four steps: absorption, distribution, metabolism, and excretion, which are the main parameters in pharmacokinetics studies. Many toxic endpoints exist. There are three main ways to measure toxicology and pharmacokinetic parameters: in vivo, in vitro, and in-silico. Knowing toxicological and pharmacokinetic parameters before developing a new drug candidate could save time and resources, as clinical studies are highly cost-demanding. This review aims to gather studies using in-silico methodologies to predict pharmacokinetic properties.

Background: The ε4 allele of the apolipoprotein E gene (APOE4) is a major risk factor for developing sporadic Alzheimer's disease (AD). APOE4 homozygosity has been recently proposed as the defining signature of a genetic form of AD. The goal was to assess the role, if any, of APOE4 in AD progression using a mixed-effects disease progression model-informed approach.

Methods: Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed for 2092 participants categorized as cognitively normal (CN), subjective memory concerns (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), or AD. Each included subject had a median of 5.00 (IQR: 3-8) follow-ups; there were n = 13,699 follow-ups. Demographics, APOE4 genotype, cerebrospinal fluid biomarkers, MRI measures, and neuropsychological tests from baseline to 6-years of follow-up visits were analyzed. Linear mixed-effects models were used to evaluate the impact of the APOE4 genotype on disease progression.

Results: APOE4 heterozygous and homozygous frequencies were higher in AD vs. CN (p < 0.001). APOE4-positive groups were associated with lower levels of amyloid β1-42, higher levels of Tau and phosphorylated tau-181 proteins, lower hippocampus and entorhinal volumes, and worse AD Assessment Scale Cognitive-11 (ADAS-COG11), ADAS-COG13, and Mini-Mental State Examination neuropsychological test scores. The progression of the biomarkers over time was not associated with APOE4 positivity. The progression of all MRI measures and neuropsychological test scores was associated with APOE4 positivity.

Conclusions: APOE4 genotypes adversely influence the levels of biomarkers and the progression of neuroimaging and cognitive outcomes in AD.

Concentration-QTc (C-QTc) analysis was accepted to serve as an alternative to the by-time point analysis with intersection-union test (IUT) as the primary basis for decisions to classify the arrhythmogenic risk of a drug by ICH E14 Q&As (R3) in December 2015. Since then, this analysis method has been widely applied by industry as it significantly reduces the sample size to achieve the same power as with IUT. There are many model-based power calculation approaches available for C-QTc through simulation in the literature, however, there is still no standard method with a clear formula to determine the sample size for C-QTc analysis to exclude a small effect on the QTc interval. The current model-based simulation approaches are too complicated to prevent them from being widely used, which is not commensurate with the popular status. We have developed a systematic method based on t-tests to determine the sample size for different study designs using the C-QTc analysis method and applied it to many studies. The results of the sample sizes utilizing this method are consistent with simulation studies and validated by real analyses.

Proficiency in English is essential in scientific disciplines; however, it is unevenly distributed globally, creating barriers for those with limited training. Research in neuroscience supports the benefits of teaching in a student's native language. Consequently, pharmacometrics, a complex and growing field, stands to gain significantly from overcoming language barriers to better train future scientists. One effective strategy is to offer pharmacometrics education in various languages, particularly in regions with low English proficiency, such as French-speaking African countries. Recently, two French-led pharmacometrics training programs were conducted in Africa, demonstrating the positive impact of such initiatives. These programs are adaptable to other countries and languages, and ultimately, they could contribute to global health improvements by making pharmacometrics education more accessible worldwide.

Antibody-drug conjugates (ADCs) are advanced cancer therapeutics that link monoclonal antibodies to cytotoxic drugs, enhancing targeted delivery to tumors. Since the FDA's first ADC approval in 2000, 14 ADCs have received approval to date (March 2025), underscoring their therapeutic value across cancer types. A prolonged QT interval is a known risk factor for the development of torsades de pointes (TdP), a potentially fatal ventricular arrhythmia. Therefore, assessing and mitigating the potential for QT prolongation is a fundamental aspect of drug development, especially for oncology therapeutics where patients may already be at an increased risk of cardiovascular complications or receiving other QT-prolonging drugs. Traditional QT risk assessment, as outlined in the ICH E14 guidance, is challenging in oncology due the safety profile of anticancer drugs, which precludes study in healthy participants, and the ethical complications of placebo-controlled studies in patients with cancer; therefore, dedicated QT studies and/or concentration-corrected QT (QTc) assessments have been used as alternative approaches. This review investigates QT risk assessment for FDA-approved ADCs, examining nonclinical and clinical approaches and summarizing the strategies used in informing each ADC's labeling. Findings suggest that ADCs generally exhibit low proarrhythmic risk, attributed to the low systemic concentration of their payloads, and minimal QT effects have been observed in clinical settings. This analysis advocates a streamlined, fit-for-purpose QT risk assessment strategy in ADC development, reducing reliance on dedicated QT studies and promoting integrated assessments in early-phase trials. This approach can optimize ADC safety evaluation, supporting ongoing innovation and therapeutic application in oncology.