Journal of Pharmacokinetics and Pharmacodynamics最新文献

Monoclonal antibodies (mAbs) are becoming an important therapeutic option in veterinary medicine, and understanding the pharmacokinetic (PK) of mAbs in higher-order animal species is also important for human drug development. To better understand the PK of mAbs in these animals, here we have expanded a platform physiological-based pharmacokinetic (PBPK) model to characterize the disposition of mAbs in three different preclinical species: cats, sheep, and dogs. We obtained PK data for mAbs and physiological parameters for the three different species from the literature. We were able to describe the PK of mAbs following intravenous (IV) or subcutaneous administration in cats, IV administration in sheep, and IV administration dogs reasonably well by fixing the physiological parameters and just estimating the parameters related to the binding of mAbs to the neonatal Fc receptor. The platform PBPK model presented here provides a quantitative tool to predict the plasma PK of mAbs in dogs, cats, and sheep. The model can also predict mAb PK in different tissues where the site of action might be located. As such, the mAb PBPK model presented here can facilitate the discovery, development, and preclinical-to-clinical translation of mAbs for veterinary and human medicine. The model can also be modified in the future to account for more detailed compartments for certain organs, different pathophysiology in the animals, and target-mediated drug disposition.

Recently, immunotherapies for antitumoral response have adopted conditionally activated molecules with the objective of reducing systemic toxicity. Amongst these are conditionally activated antibodies, such as PROBODY® activatable therapeutics (Pb-Tx), engineered to be proteolytically activated by proteases found locally in the tumor microenvironment (TME). These PROBODY® therapeutics molecules have shown potential as PD-L1 checkpoint inhibitors in several cancer types, including both effectiveness and locality of action of the molecule as shown by several clinical trials and imaging studies. Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for triple-negative breast cancer (TNBC), to computationally predict the effectiveness and targeting specificity of a PROBODY® therapeutics drug compared to the non-modified antibody. We begin with the analysis of anti-PD-L1 immunotherapy in non-small cell lung cancer (NSCLC). As a first contribution, we have improved previous virtual patient selection methods using the omics data provided by the iAtlas database portal compared to methods previously published in literature. Furthermore, our results suggest that masking an antibody maintains its efficacy while improving the localization of active therapeutic in the TME. Additionally, we generalize the model by evaluating the dependence of the response to the tumor mutational burden, independently of cancer type, as well as to other key biomarkers, such as CD8/Treg Tcell and M1/M2 macrophage ratio. While our results are obtained from simulations on NSCLC, our findings are generalizable to other cancer types and suggest that an effective and highly selective conditionally activated PROBODY® therapeutics molecule is a feasible option.

A basic FcRn-regulated clearance mechanism is investigated using the method of matched asymptotic expansions. The broader aim of the work is to obtain further insight on the mechanism, thereby providing theoretical support for future pharmacologically-based pharmacokinetic modelling efforts. The corresponding governing equations are first non-dimensionalised and the order of magnitudes of the model parameters are assessed based on their values reported in the literature. Under the assumption of high FcRn-binding affinity, analytical approximations are derived that are valid over the characteristic phases of the problem. Additionally, relatively simple equations relating clearance and AUC to physiological model parameters are derived, which are valid over the longest characteristic time scale of the problem. For lower to moderate doses clearance is effectively linear, whereas for higher doses it is nonlinear. It is shown that for all doses sufficiently high the leading-order approximation for the IgG concentration in plasma, over the longest characteristic time scale, is independent of the initial dose. This is because IgG that is in 'excess' of FcRn is eliminated over a time scale much shorter than that of the terminal phase. In conclusion, analytical approximations of the basic FcRn mechanism have been derived using matched asymptotic expansions, leading to a simple equation relating clearance to FcRn binding affinity, the ratio of degradation and FcRn concentration, and the volumes of the system.

In this paper, we use Time Scale Calculus (TSC) to formulate and solve pharmacokinetic models exploring multiple dose dynamics. TSC is a mathematical framework that allows the modeling of dynamical systems comprising continuous and discrete processes. This characteristic makes TSC particularly suited for multi-dose pharmacokinetic problems, which inherently feature a blend of continuous processes (such as absorption, metabolization, and elimination) and discrete events (drug intake). We use this toolkit to derive analytical expressions for blood concentration trajectories under various multi-dose regimens across several flagship pharmacokinetic models. We demonstrate that this mathematical framework furnishes an alternative and simplified way to model and retrieve analytical solutions for multi-dose dynamics. For instance, it enables the study of blood concentration responses to arbitrary dose regimens and facilitates the characterization of the long-term behavior of the solutions, such as their steady state.

Congenital adrenal hyperplasia (CAH) is characterized by impaired adrenal cortisol production. Hydrocortisone (synthetic cortisol) is the drug-of-choice for cortisol replacement therapy, aiming to mimic physiological cortisol circadian rhythm. The hypothalamic-pituitary-adrenal (HPA) axis controls cortisol production through the pituitary adrenocorticotropic hormone (ACTH) and feedback mechanisms. The aim of this study was to quantify key mechanisms involved in the HPA axis activity regulation and their interaction with hydrocortisone therapy. Data from 30 healthy volunteers was leveraged: Endogenous ACTH and cortisol concentrations without any intervention as well as cortisol concentrations measured after dexamethasone suppression and single dose administration of (i) 0.5-10 mg hydrocortisone as granules, (ii) 20 mg hydrocortisone as granules and intravenous bolus. A stepwise model development workflow was used: A newly developed model for endogenous ACTH and cortisol was merged with a refined hydrocortisone pharmacokinetic model. The joint model was used to simulate ACTH and cortisol trajectories in CAH patients with varying degrees of enzyme deficiency, with or without hydrocortisone administration, and healthy individuals. Time-dependent ACTH-driven endogenous cortisol production and cortisol-mediated feedback inhibition of ACTH secretion processes were quantified and implemented in the model. Comparison of simulated ACTH and cortisol trajectories between CAH patients and healthy individuals showed the importance of administering hydrocortisone before morning ACTH secretion peak time to suppress ACTH overproduction observed in untreated CAH patients. The developed framework allowed to gain insights on the physiological mechanisms of the HPA axis regulation, its perturbations in CAH and interaction with hydrocortisone administration, paving the way towards cortisol replacement therapy optimization.

Forward addition/backward elimination (FABE) has been the standard for population pharmacokinetic model selection (PPK) since NONMEM® was introduced. We investigated five machine learning (ML) algorithms (Genetic algorithm [GA], Gaussian process [GP], random forest [RF], gradient boosted random tree [GBRT], and particle swarm optimization [PSO]) as alternatives to FABE. These algorithms were applied to PPK model selection with a focus on comparing the efficiency and robustness of each of them. All machine learning algorithms included the combination of ML algorithms with a local downhill search. The local downhill search consisted of systematically changing one or two "features" at a time (a one-bit or a two-bit local search), alternating with the ML methods. An exhaustive search (all possible combinations of model features, N = 1,572,864 models) was the gold standard for robustness, and the number of models examined leading prior to identification of the final model was the metric for efficiency.All algorithms identified the optimal model when combined with the two-bit local downhill search. GA, RF, GBRT, and GP identified the optimal model with only a one-bit local search. PSO required the two-bit local downhill search. In our analysis, GP was the most efficient algorithm as measured by the number of models examined prior to finding the optimal (495 models), and PSO exhibited the least efficiency, requiring 1710 unique models before finding the best solution. Additionally, GP was also the algorithm that needed the longest elapsed time of 2975.6 min, in comparison with GA, which only required 321.8 min.

Physiologically based pharmacokinetic (PBPK) models can be used to leverage physiological and in vitro data to predict monoclonal antibody (mAb) concentrations in serum and tissues. However, it is currently not known how consistent predictions of mAb disposition are across PBPK modelling platforms. In this work PBPK simulations of IgG, adalimumab and infliximab were compared between three platforms (Simcyp, PK-Sim, and GastroPlus). Accuracy of predicted serum and tissue concentrations was assessed using observed data collected from the literature. Physiological and mAb related input parameters were also compared and sensitivity analyses were carried out to evaluate model behavior when input values were altered. Differences in serum kinetics of IgG between platforms were minimal for a dose of 1 mg/kg, but became more noticeable at higher dosages (> 100 mg/kg) and when reference (healthy) physiological input values were altered. Predicted serum concentrations of both adalimumab and infliximab were comparable across platforms, but were noticeably higher than observed values. Tissue concentrations differed remarkably between the platforms, both for total- and interstitial fluid (ISF) concentrations. The accuracy of total tissue concentrations was within a three-fold of observed values for all tissues, except for brain tissue concentrations, which were overpredicted. Predictions of tissue ISF concentrations were less accurate and were best captured by GastroPlus. Overall, these simulations show that the different PBPK platforms generally predict similar mAb serum concentrations, but variable tissue concentrations. Caution is therefore warranted when PBPK models are used to simulate effect site tissue concentrations of mAbs without data to verify the predictions.

Pharmacokinetics and pharmacodynamics of many biologics are influenced by their complex binding to biological receptors. Biologics consist of diverse groups of molecules with different binding kinetics to its receptors including IgG with simple one-to-one drug receptor bindings, bispecific antibody (BsAb) that binds to two different receptors, and antibodies that can bind to six or more identical receptors. As the binding process is typically much faster than elimination (or internalization) and distribution processes, quasi-equilibrium (QE) binding models are commonly used to describe drug-receptor binding kinetics of biologics. However, no general QE modeling framework is available to describe complex binding kinetics for diverse classes of biologics. In this paper, we describe novel approaches of using differential algebraic equations (DAE) to solve three QE multivalent drug-receptor binding (QEMB) models. The first example describes the binding kinetics of three-body equilibria of BsAb that binds to 2 different receptors for trimer formation. The second example models an engineered IgG variant (Multabody) that can bind to 24 identical target receptors. The third example describes an IgG with modified neonatal Fc receptor (FcRn) binding affinity that competes for the same FcRn receptor as endogenous IgG. The model parameter estimates were obtained by fitting the model to all data simultaneously. The models allowed us to study potential roles of cooperative binding on bell-shaped drug exposure-response relationships of BsAb, and concentration-depended distribution of different drug-receptor complexes for Multabody. This DAE-based QEMB model platform can serve as an important tool to better understand complex binding kinetics of diverse classes of biologics.

Incorporating realistic sets of patient-associated covariates, i.e., virtual populations, in pharmacometric simulation workflows is essential to obtain realistic model predictions. Current covariate simulation strategies often omit or simplify dependency structures between covariates. Copula models are multivariate distribution functions suitable to capture dependency structures between covariates with improved performance compared to standard approaches. We aimed to develop and evaluate a copula model for generation of adult virtual populations for 12 patient-associated covariates commonly used in pharmacometric simulations, using the publicly available NHANES database, including sex, race-ethnicity, body weight, albumin, and several biochemical variables related to organ function. A multivariate (vine) copula was constructed from bivariate relationships in a stepwise fashion. Covariate distributions were well captured for the overall and subgroup populations. Based on the developed copula model, a web application was developed. The developed copula model and associated web application can be used to generate realistic adult virtual populations, ultimately to support model-based clinical trial design or dose optimization strategies.

Hereditary angioedema (HAE) due to C1-inhibitor deficiency is a rare, debilitating, genetic disorder characterized by recurrent, unpredictable, attacks of edema. The clinical symptoms of HAE arise from excess bradykinin generation due to dysregulation of the plasma kallikrein-kinin system (KKS). A quantitative systems pharmacology (QSP) model that mechanistically describes the KKS and its role in HAE pathophysiology was developed based on HAE attacks being triggered by autoactivation of factor XII (FXII) to activated FXII (FXIIa), resulting in kallikrein production from prekallikrein. A base pharmacodynamic model was constructed and parameterized from literature data and ex vivo assays measuring inhibition of kallikrein activity in plasma of HAE patients or healthy volunteers who received lanadelumab. HAE attacks were simulated using a virtual patient population, with attacks recorded when systemic bradykinin levels exceeded 20 pM. The model was validated by comparing the simulations to observations from lanadelumab and plasma-derived C1-inhibitor clinical trials. The model was then applied to analyze the impact of nonadherence to a daily oral preventive therapy; simulations showed a correlation between the number of missed doses per month and reduced drug effectiveness. The impact of reducing lanadelumab dosing frequency from 300 mg every 2 weeks (Q2W) to every 4 weeks (Q4W) was also examined and showed that while attack rates with Q4W dosing were substantially reduced, the extent of reduction was greater with Q2W dosing. Overall, the QSP model showed good agreement with clinical data and could be used for hypothesis testing and outcome predictions.