Song IH, Chen G, Hayes S
… +4 more, Farrell C, Jomphe C, Gosselin NH, Sun K
J Pharmacokinet Pharmacodyn
· 2024 Dec · PMID 39333337
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Maribavir is approved for management of post-transplant cytomegalovirus (CMV) infections refractory and/or resistant to CMV therapies at a dose of 400 mg twice daily (BID). Population pharmacokinetic (PopPK) and exposure...Maribavir is approved for management of post-transplant cytomegalovirus (CMV) infections refractory and/or resistant to CMV therapies at a dose of 400 mg twice daily (BID). Population pharmacokinetic (PopPK) and exposure-response analyses were conducted to support the appropriateness of 400 mg BID dosing. A PopPK model was developed using non-linear mixed-effects modeling with pooled maribavir plasma concentration-time data from phase 1 and 2 studies (from 100 mg up to 1200 mg as single or repeated doses) and the phase 3 SOLSTICE study (400 mg BID). Exposure-response analyses were performed for efficacy, safety, and viral resistance based on data collected in the SOLSTICE study. Maribavir PK after oral administration was adequately described by a two-compartment model with first-order elimination, first-order absorption, and an absorption lag-time. There was no evidence that maribavir PK was affected by age, sex, race, diarrhea, vomiting, disease characteristics, or concomitant use of histamine H blockers, or proton pump inhibitors. In the SOLSTICE study, higher maribavir exposure was not associated with increased probability of achieving CMV DNA viremia clearance, nor with reduced probability of treatment-emergent maribavir-resistant CMV mutations. A statistically significant association with maribavir exposure was identified for taste disturbance, fatigue, and treatment-emergent serious adverse events, while transplant type, enrollment region, CMV DNA level at baseline, and/or CMV resistance at baseline were identified as additional risk factors for these safety outcomes. In conclusion, the findings of these PopPK and exposure-response analyses provide further support for the recommended maribavir dose of 400 mg BID.
The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical an...The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increase available data (e.g., in low-density regions-i.e., for patients with under-represented characteristics). Generative methods employ a family of solutions for generating synthetic data. The objective of this research is to benchmark numerous state-of-the-art deep-learning generative methods across different scenarios and clinical datasets comprising patient covariates and several pharmacokinetic/pharmacodynamic endpoints. We did this by implementing various probabilistic models aimed at generating synthetic data, such as the Multi-layer Perceptron Conditioning Generative Adversarial Neural Network (MLP cGAN), Time-series Generative Adversarial Networks (TimeGAN), and a more traditional approach like Probabilistic Autoregressive (PAR). We evaluated their performance by calculating discriminative and predictive scores. Furthermore, we conducted comparisons between the distributions of real and synthetic data using Kolmogorov-Smirnov and Chi-square statistical tests, focusing respectively on covariate and output variables of the models. Lastly, we employed pharmacometrics-related metric to enhance interpretation of our results specific to our investigated scenarios. Results indicate that multi-layer perceptron-based conditional generative adversarial networks (MLP cGAN) exhibit the best overall performance for most of the considered metrics. This work highlights the opportunities to employ synthetic data generation in the field of clinical pharmacology for augmentation and sharing of proprietary data across institutions.
During the space travel mission, astronauts' physiological and psychological behavior will alter, and they will start consuming terrestrial drug products. However, factors such as microgravity, radiation exposure, temper...During the space travel mission, astronauts' physiological and psychological behavior will alter, and they will start consuming terrestrial drug products. However, factors such as microgravity, radiation exposure, temperature, humidity, strong vibrations, space debris, and other issues encountered, the drug product undergo instability This instability combined with physiological changes will affect the shelf life and diminish the pharmacokinetic and pharmacodynamic profile of the drug product. Consequently, the physicochemical changes will produce a toxic degradation product and a lesser potency dosage form which may result in reduced or no therapeutic action, so the astronaut consumes an additional dose to remain healthy. On long-duration missions like Mars, the drug product cannot be replaced, and the astronaut may relay on the available medications. Sometimes, radiation-induced impurities in the drug product will cause severe problems for the astronaut. So, this review article highlights the current state of various space-related factors affecting the drug product and provides a comprehensive summary of the physiological changes which primarly focus on absorption, distribution, metabolism, and excretion (ADME). Along with that, we insist some of the strategies like novel formulations, space medicine manufacturing from plants, and 3D printed medicine for astronauts in longer-duration missions. Such developments are anticipated to significantly contribute to new developments with applications in both human space exploration and on terrestrial healthcare.
Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especia...Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especially in oncology. While methods accounting for dropouts have appeared in literature, they vary in assumptions, flexibility, and performance, and the differences between them are not widely understood. This manuscript aims to elucidate which methods can be used to handle VPC with dropout and when, along with a more informative VPC approach using confidence intervals. Additionally, we propose constructing the confidence interval based on the observed data instead of the simulated data. The theoretical framework for incorporating dropout in VPCs is developed and applied to propose two approaches: full and conditional. The full approach is implemented using a parametric time-to-event model, while the conditional approach is implemented using both parametric and Cox proportional-hazard (CPH) models. The practical performances of these approaches are illustrated with an application to the tumor growth dynamics (TGD) modeling of data from two cancer clinical trials of nivolumab and docetaxel, where patients were followed until disease progression. The dataset consisted of 3504 tumor size measurements from 855 subjects, which were described by a TGD model. The dropout of subjects was described by a Weibull or CPH model. Simulated datasets were also used to further illustrate the properties of the VPC methods. The results showed that the more familiar full approach might not provide meaningful improvement for TGD model evaluation over the naive approach of not adjusting for dropout, and could be outperformed by the conditional approach using either the Weibull model or the Cox proportional hazard model. Overall, including confidence intervals in VPC should improve interpretation, the conditional approach was shown to be more generally applicable when dropout occurs, and the nonparametric approach could provide additional robustness.
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 i...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.
J Pharmacokinet Pharmacodyn
· 2024 Dec · PMID 39060788
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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 con...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.
Bindellini D, Michelet R, Aulin LBS
… +7 more, Melin J, Neumann U, Blankenstein O, Huisinga W, Whitaker MJ, Ross R, Kloft C
J Pharmacokinet Pharmacodyn
· 2024 Dec · PMID 38977635
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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 cor...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.
Clinical trial endpoints are often bounded outcome scores (BOS), which are variables having restricted values within finite intervals. Common analysis approaches may treat the data as continuous, categorical, or a mixtur...Clinical trial endpoints are often bounded outcome scores (BOS), which are variables having restricted values within finite intervals. Common analysis approaches may treat the data as continuous, categorical, or a mixture of both. The appearance of BOS data being simultaneously continuous and categorical easily leads to confusions in pharmacometrics regarding the appropriate domain for model evaluation and the circumstances under which data likelihoods can be compared. This commentary aims to clarify these fundamental issues and facilitate appropriate pharmacometric analyses.
Janssen A, Bennis FC, Cnossen MH
… +2 more, Mathôt RAA, OPTI-CLOT study group SYMPHONY consortium
J Pharmacokinet Pharmacodyn
· 2024 Dec · PMID 38965175
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This work focusses on extending the deep compartment model (DCM) framework to the estimation of mixed-effects. By introducing random effects, model predictions can be personalized based on drug measurements, enabling the...This work focusses on extending the deep compartment model (DCM) framework to the estimation of mixed-effects. By introducing random effects, model predictions can be personalized based on drug measurements, enabling the testing of different treatment schedules on an individual basis. The performance of classical first-order (FO and FOCE) and machine learning based variational inference (VI) algorithms were compared in a simulation study. In VI, posterior distributions of the random variables are approximated using variational distributions whose parameters can be directly optimized. We found that variational approximations estimated using the path derivative gradient estimator version of VI were highly accurate. Models fit on the simulated data set using the FO and VI objective functions gave similar results, with accurate predictions of both the population parameters and covariate effects. Contrastingly, models fit using FOCE depicted erratic behaviour during optimization, and resulting parameter estimates were inaccurate. Finally, we compared the performance of the methods on two real-world data sets of haemophilia A patients who received standard half-life factor VIII concentrates during prophylactic and perioperative settings. Again, models fit using FO and VI depicted similar results, although some models fit using FO presented divergent results. Again, models fit using FOCE were unstable. In conclusion, we show that mixed-effects estimation using the DCM is feasible. VI performs conditional estimation, which might lead to more accurate results in more complex models compared to the FO method.
Li X, Sale M, Nieforth K
… +7 more, Craig J, Wang F, Solit D, Feng K, Hu M, Bies R, Zhao L
J Pharmacokinet Pharmacodyn
· 2024 Dec · PMID 38941056
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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 algorit...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.
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 suppo...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.
Kulesza A, Couty C, Lemarre P
… +2 more, Thalhauser CJ, Cao Y
J Pharmacokinet Pharmacodyn
· 2024 Dec · PMID 38904912
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Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulat...Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.
Ippolito A, Wang H, Zhang Y
… +2 more, Vakil V, Popel AS
J Pharmacokinet Pharmacodyn
· 2024 Dec · PMID 38858306
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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® a...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.
Guo Y, Guo T, Knibbe CAJ
… +2 more, Zwep LB, van Hasselt JGC
J Pharmacokinet Pharmacodyn
· 2024 Dec · PMID 38844624
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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...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.
Authors' Response to Letter to Editor from Hinpetch Daungsupawong and Viroj Wiwanitkit.Authors' Response to Letter to Editor from Hinpetch Daungsupawong and Viroj Wiwanitkit.
This is a correspondence on "Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM". Additional concern on using ChatGPT and Gemini is provided.This is a correspondence on "Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM". Additional concern on using ChatGPT and Gemini is provided.
Sexton D, Nguyen HQ, Juethner S
… +4 more, Luo H, Zhang Z, Jasper P, Zhu AZX
J Pharmacokinet Pharmacodyn
· 2024 Dec · PMID 38734778
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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...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.