Journal of Pharmacokinetics and Pharmacodynamics最新文献

The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T₂) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T₂. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T₂ of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T₂ model variables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T₂. Sigmoid I_max and E_max models best captured the profiles of 6MWD and MRI-T₂ over age. Steroid use, baseline 6MWD, and baseline MRI-T₂ were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T₂ is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T₂ successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T₂, supporting the use of MRI-T₂. The developed models will guide drug developers in using the MRI-T₂ to most efficient use in DMD clinical trials.

Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8–1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework.

The objective of this study was to compare the effectiveness of subcutaneous (SC) and sublingual (SL) formulations of apomorphine for the treatment of motor fluctuations in Parkinson’s disease using a pharmacokinetics (PK)/pharmacodynamics (PD) modeling approach. The PK of SC and SL apomorphine are best described by a one-compartment model with first-order absorption and a two-compartment model with delayed absorption, respectively. The PK/PD model relating apomorphine plasma concentrations to the Unified Parkinson’s Disease Rating Scale (UPDRS) motor scores was described by a sigmoidal E_max model assuming effective concentration = drug concentration in an effect compartment. Apomorphine concentrations and UPDRS motor scores were simulated from the PK/PD models using 500 hypothetical subjects. UPDRS motor score change from baseline was evaluated using time to clinically relevant response, response duration, area under the curve, maximal response, and time to maximal response. Higher doses of each apomorphine formulation were associated with shorter time to response, longer response duration, and greater maximal response. Although the mean maximal responses to SC and SL apomorphine were comparable, the time to response was four times shorter (7 vs. 31 min) and time to maximal response was two times shorter (27 vs. 61 min) for 4 mg SC vs. 50 mg SL. Thus, faster onset of action was observed for the SC formulation compared to SL. These data may be useful for physicians when selecting “on demand” therapy for patients with Parkinson’s disease experiencing motor fluctuations.

Efficiently finding covariate model structures that minimize the need for random effects to describe pharmacological data is challenging. The standard approach focuses on identification of relevant covariates, and present methodology lacks tools for automatic identification of covariate model structures. Although neural networks could potentially be used to approximate covariate-parameter relationships, such approximations are not human-readable and come at the risk of poor generalizability due to high model complexity.In the present study, a novel methodology for the simultaneous selection of covariate model structure and optimization of its parameters is proposed. It is based on symbolic regression, posed as an optimization problem with a smooth loss function. This enables training of the model through back-propagation using efficient gradient computations.Feasibility and effectiveness are demonstrated by application to a clinical pharmacokinetic data set for propofol, containing infusion and blood sample time series from 1031 individuals. The resulting model is compared to a published state-of-the-art model for the same data set. Our methodology finds a covariate model structure and corresponding parameter values with a slightly better fit, while relying on notably fewer covariates than the state-of-the-art model. Unlike contemporary practice, finding the covariate model structure is achieved without an iterative procedure involving manual interactions.

Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. However, SCM is based on a local search strategy, in which the model-building process iteratively tests the addition or elimination of a single covariate at a time given all the others. This introduces a heuristic to limit the searching space and then the computational complexity, but, at the same time, can lead to a suboptimal solution. The application of genetic algorithms (GAs) for covariate selection has been proposed as a possible solution to overcome these limitations. However, their actual use during model building is limited by the extremely high computational costs and convergence issues, both related to the number of models being tested. In this paper, we proposed a new GA for covariate selection to address these challenges. The GA was first developed on a simulated case study where the heuristics introduced to overcome the limitations affecting currently available GA approaches resulted able to limit the selection of redundant covariates, increase replicability of results and reduce convergence times. Then, we tested the proposed GA on a real-world problem related to remifentanil. It obtained good results both in terms of selected covariates and fitness optimization, outperforming the SCM.

Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.

To systematically assess the ChatGPT large language model on diverse tasks relevant to pharmacokinetic data analysis. ChatGPT was evaluated with prototypical tasks related to report writing, code generation, non-compartmental analysis, and pharmacokinetic word problems. The writing task consisted of writing an introduction for this paper from a draft title. The coding tasks consisted of generating R code for semi-logarithmic graphing of concentration-time profiles and calculating area under the curve and area under the moment curve from time zero to infinity. Pharmacokinetics word problems on single intravenous, extravascular bolus, and multiple dosing were taken from a pharmacokinetics textbook. Chain-of-thought and problem separation were assessed as prompt engineering strategies when errors occurred. ChatGPT showed satisfactory performance on the report writing, code generation tasks and provided accurate information on the principles and methods underlying pharmacokinetic data analysis. However, ChatGPT had high error rates in numerical calculations involving exponential functions. The outputs generated by ChatGPT were not reproducible: the precise content of the output was variable albeit not necessarily erroneous for different instances of the same prompt. Incorporation of prompt engineering strategies reduced but did not eliminate errors in numerical calculations. ChatGPT has the potential to become a powerful productivity tool for writing, knowledge encapsulation, and coding tasks in pharmacokinetic data analysis. The poor accuracy of ChatGPT in numerical calculations require resolution before it can be reliably used for PK and pharmacometrics data analysis.

In-vitro to in-vivo correlations (IVIVC), relating in-vitro parameters like IC50 to in-vivo drug exposure in plasma and tumour growth, are widely used in oncology for experimental design and dose decisions. However, they lack a deeper understanding of the underlying mechanisms. Our paper therefore focuses on linking empirical IVIVC relations for small-molecule kinase inhibitors with a semi-mechanistic tumour-growth model. We develop an approach incorporating parameters like the compound's peak-trough ratio (PTR), Hill coefficient of in-vitro dose-response curves, and xenograft-specific properties. This leads to formulas for determining efficacious doses for tumor stasis under linear pharmacokinetics equivalent to traditional empirical IVIVC relations, but enabling more systematic analysis. Our findings reveal that in-vivo xenograft-specific parameters, specifically the growth rate (g) and decay rate (d), along with the average exposure, are generally more significant determinants of tumor stasis and effective dose than the compound's peak-trough ratio. However, as the Hill coefficient increases, the dependency of tumor stasis on the PTR becomes more pronounced, indicating that the compound is more influenced by its maximum or trough values rather than the average exposure. Furthermore, we discuss the translation of our method to predict population dose ranges in clinical studies and propose a resistance mechanism that solely relies on specific in-vivo xenograft parameters instead of IC50 exposure coverage. In summary, our study aims to provide a more mechanistic understanding of IVIVC relations, emphasizing the importance of xenograft-specific parameters and PTR on tumor stasis.

Efmarodocokin alfa (IL-22Fc) is a fusion protein of human IL-22 linked to the crystallizable fragment (Fc) of human IgG4. It has been tested in multiple indications including inflammatory bowel disease (IBD). The purposes of the present analyses were to describe the population pharmacokinetics (PK) of efmarodocokin alfa and perform pharmacodynamic (PD) analysis on the longitudinal changes of the PD biomarker REG3A after efmarodocokin alfa treatment as well as identify covariates that affect efmarodocokin alfa PK and REG3A PD. The data used for this analysis included 182 subjects treated with efmarodocokin alfa in two clinical studies. The population PK and PD analyses were conducted sequentially. Efmarodocokin alfa concentration-time data were analyzed using a nonlinear mixed-effects modeling approach, and an indirect response model was adopted to describe the REG3A PD data with efmarodocokin alfa serum concentration linked to the increase in REG3A. The analysis software used were NONMEM and R. A 3-compartment model with linear elimination best described the PK of efmarodocokin alfa. The estimated population-typical value for clearance (CL) was 1.12 L/day, and volume of central compartment was 6.15 L. Efmarodocokin alfa CL increased with higher baseline body weight, C-reactive protein, and CL was 27.6% higher in IBD patients compared to healthy subjects. The indirect response PD model adequately described the longitudinal changes of REG3A after efmarodocokin alfa treatment. A popPK and PD model for efmarodocokin alfa and REG3A was developed and covariates affecting the PK and PD were identified.