Evaluation of the estimation and classification performance of NONMEM when applying mixture model for drug clearance

Maximum likelihood estimation of parameters involving mixture model is known to have significant and specific patterns of errors. Population pharmacokinetic (PopPK) modeling using NONMEM is no exception. A few relevant studies on estimation and classification performance were done, but a comprehensive study was not yet available. The current study aims to evaluate performance and likelihood ratio test (LRT)‐based true covariate detection rate when fitting a bimodal mixture of drug clearance (CL) in NONMEM. A large number of PopPK datasets with various settings were simulated and then estimated. The estimates were compared to the simulated values and summarized. The separation between the CL distributions of the two subpopulations is systematically overestimated. The major factor associated with the performance is the change in the minimum objective function value after removing the mixture component (dOFV). Other significant factors include estimated disparity index (DI), estimated mixing proportion, and number of subjects in the dataset. Small dOFV and large estimated DI are associated with the worst performance. Omitting a true mixture resulted in reduced true covariate detection rates. It is recommended that on top of routinely generated standard errors and model diagnostics, dOFV, and other factors when necessary, should be taken into account for the evaluation of performance when fitting mixture model using NONMEM. In addition, when fitting mixture model for CL is intended, the mixture component should be introduced prior to LRT‐based covariate model development for CL.

Population Pharmacokinetic Study and Individual Dose Adjustments of High-Dose Methotrexate in Chinese Pediatric Patients With Acute Lymphoblastic Leukemia or Osteosarcoma

High-dose methotrexate (>0.5 g/m² ) is among the first-line chemotherapeutic agents used in treating acute lymphoblastic leukemia (ALL) and osteosarcoma in children. Despite rapid hydration, leucovorin rescue, and routine therapeutic drug monitoring, severe toxicity is not uncommon. This study aimed at developing population pharmacokinetic (popPK) models of high-dose methotrexate for ALL and osteosarcoma and demonstrating the possibility and convenience of popPK model-based individual dose optimization using R and shiny, which is more accessible, efficient, and clinician-friendly than NONMEM. The final data set consists of 36 ALL (354 observations) and 16 osteosarcoma (585 observations) patients. Covariate model building and parameter estimations were done using NONMEM and Perl-speaks-NONMEM. Diagnostic Plots and bootstrapping validated the models' performance and stability. The dose optimizer developed based on the validated models can obtain identical individual parameter estimates as NONMEM. Compared to calling a NONMEM execution and reading its output, estimating individual parameters within R reduces the execution time from 8.7-12.8 seconds to 0.4-1.0 second. For each subject, the dose optimizer can recommend (1) an individualized optimal dose and (2) an individualized range of doses. For osteosarcoma, recommended optimal doses by the optimizer resemble the final doses at which the subjects were eventually stabilized. The dose optimizers developed demonstrated the potential to inform dose adjustments using a model-based, convenient, and efficient tool for high-dose methotrexate. Although the dose optimizer is not meant to replace clinical judgment, it provides the clinician with the individual pharmacokinetics perspective by recommending the (range of) optimal dose.

In silico drug absorption tract: An agent-based biomimetic model for human oral drug absorption

Background

An agent-based modeling approach has been suggested as an alternative to traditional, equation-based modeling methods for describing oral drug absorption. It enables researchers to gain a better understanding of the pharmacokinetic (PK) mechanisms of a drug. This project demonstrates that a biomimetic agent-based model can adequately describe the absorption and disposition kinetics both of midazolam and clonazepam.

Methods

An agent-based biomimetic model, in silico drug absorption tract (ISDAT), was built to mimic oral drug absorption in humans. The model consisted of distinct spaces, membranes, and metabolic enzymes, and it was altogether representative of human physiology relating to oral drug absorption. Simulated experiments were run with the model, and the results were compared to the referent data from clinical equivalence trials. Acceptable similarity was verified by pre-specified criteria, which included 1) qualitative visual matching between the clinical and simulated concentration-time profiles, 2) quantitative similarity indices, namely, weighted root mean squared error (RMSE), and weighted mean absolute percentage error (MAPE) and 3) descriptive similarity which requires less than 25% difference between key PK parameters calculated by the clinical and the simulated concentration-time profiles. The model and its parameters were iteratively refined until all similarity criteria were met. Furthermore, simulated PK experiments were conducted to predict bioavailability (F). For better visualization, a graphical user interface for the model was developed and a video is available in Supporting Information.

Results

Simulation results satisfied all three levels of similarity criteria for both drugs. The weighted RMSE was 0.51 and 0.92, and the weighted MAPE was 5.99% and 8.43% for midazolam and clonazepam, respectively. Calculated PK parameter values, including area under the curve (AUC), peak plasma drug concentration (C_max), time to reach C_max (T_max), terminal elimination rate constant (Kel), terminal elimination half life (T_1/2), apparent oral clearance (CL/F), and apparent volume of distribution (V/F), were reasonable compared to the referent values. The predicted absolute oral bioavailability (F) was 44% for midazolam (literature reported value, 31–72%) and 93% (literature reported value, ≥ 90%) for clonazepam.

Conclusion

The ISDAT met all the pre-specified similarity criteria for both midazolam and clonazepam, and demonstrated its ability to describe absorption kinetics of both drugs. Therefore, the validated ISDAT can be a promising platform for further research into the use of similar in silico models for drug absorption kinetics.

An Agent-Based Approach to Dynamically Represent the Pharmacokinetic Properties of Baicalein

Baicalein, a typical flavonoid presented in Scutellariae radix, exhibits a unique metabolic profile during first-pass metabolism: parallel glucuronidation and sulfation pathways, with possible substrate inhibition in both pathways. In this project, we aimed to construct an agent-based model to dynamically represent baicalein pharmacokinetics and to verify the substrate inhibition hypothesis. The model consisted of three 3D spaces and two membranes: apical space (S1), intracellular space (S2), basolateral space (S3), apical membrane (M1), and basolateral membrane (M2). In silico enzymes (UDP-glucuronosyltransferases (UGTs) and sulfotransferases (SULTs)) and binder components were placed in S2. The model was then executed to simulate one-pass metabolism experiments of baicalein. With the implementation of a two-site enzyme design, the simulated results captured the preset qualitative and quantitative features of the wet-lab observations. The feasible parameter set showed that substrate inhibition happened in both conjugation pathways of baicalein. The simulation results suggested that the sulfation pathway was dominant at low concentrations and that SULT was more inclined to substrate inhibition than UGT. Cross-model validation was satisfactory. Our findings were consistent with a previously reported catenary model. We conclude that the mechanisms represented by our model are plausible. Our novel modeling approach could dynamically represent the metabolic pathways of baicalein in a Caco-2 system.

Dose Optimization of Efavirenz Based on Individual CYP2B6 Polymorphisms in Chinese Patients Positive for HIV

The purpose of this study was to investigate the impact of CYP2B6‐G516T polymorphisms on the pharmacokinetics (PKs) of efavirenz among the Chinese population and to propose doses for different genotypic populations that optimize therapeutic outcomes. Nonlinear mixed‐effect modeling was applied to describe PKs of efavirenz in Chinese patients with human immunodeficiency virus (HIV). Probabilities of successful treatment at different doses were obtained by simulations using the developed model to identify the optimal doses. The model was based on data from 163 individuals. Efavirenz clearance was found to be significantly influenced by CYP2B6‐G516T polymorphisms and body weight. The typical values of oral clearance were 10.2 L/h, 7.33 L/h, and 2.38 L/h and simulation results suggested that the optimal daily oral doses are 550 mg, 350 mg, and 100 mg for the GG, GT, and TT populations, respectively. The effect of CYP2B6‐G516T polymorphisms on efavirenz clearance was successfully quantified. Pharmacogenetics‐based dose individualization of efavirenz may optimize patient outcomes by promoting efficacy while minimizing central nervous system (CNS) side effects.

Mechanistic insight from in silico pharmacokinetic experiments: roles of P-glycoprotein, Cyp3A4 enzymes, and microenvironments

Saquinavir exhibits paradoxical transport across modified Caco-2 cell monolayers (doi: 10.1124/jpet.103.056390) expressing P-glycoprotein and Cyp3A4. The data implicate complicated intracellular transport mechanisms. Drawing on recent discrete event modeling and simulation advances, we built an in silico analog of the confluent, asymmetric cell monolayer used in the cited work. We call it in silico experimental Caco-2 (cell monolayer) culture (ISECC). Concrete, working, hypothesized spatial mechanisms were implemented. Validation was achieved when in silico experimental results met similarity measure (SM) expectations that targeted 16 wet-lab experimental conditions. Initial mechanistic hypotheses turned out to be necessary parts of a more complicated explanation. We progressed through four stages of an iterative refinement and validation protocol that enabled and facilitated discovery of plausible, new mechanistic details. The process exercised abductive reasoning, a primary means of scientific knowledge creation and creative cognition. The ISECC that survived the most stringent SM challenge produced transport data that was statistically indistinguishable from referent wet-lab observations. It required a 7:1 ratio of apical transporters to metabolizing enzymes, a 97% reduction of efflux activity by an inhibitor, a biased distribution of metabolizing enzymes, heterogeneous intracellular spaces, and restrictions on intracellular drug movement. Experimenting on synthetic analogs such as ISECC provides a former unavailable means of discovering new mechanistic details and testing their plausibility. The approach thus provides a powerful new expansion of the scientific method: an independent, scientific means to challenge, explore, better understand, and improve any inductive mechanism and, importantly, the assumptions on which it rests.

At the biological modeling and simulation frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine.

Mechanistic simulations explain paradoxical saquinavir metabolism during in vitro vectorial transport study

We were confronted by an unexpected observation: in a Transwell study of confluent, Cyp3A4 and P-gp-expressing Caco-2 cells, higher intracellular saquinavir levels, yet less first metabolite (M7) formation were observed following apical dosing, compared to basal dosing. To test two seemingly plausible hypothesized explanations, we constructed an in silico working analogue using the synthetic method. Neither mechanism alone was sufficient, but when combined and tuned within the analogue, the results generated were a semi-quantitative match to the experimental data. After 60 cycles, more of the simulated dose was present within analogue cells as parent drug after apical dosing. Furthermore, less M7 was present after apical dose. The paradox disappeared by having simulated drugs equilibrate among separate intracellular zones. Building, studying, and exploring mechanistic explanations for complex wet-lab phenomena using the new methods improved insight into the referent system, while providing a straightforward, scientific means of testing the plausibility of mechanistic hypotheses.

Applying models of targeted drug delivery to gene delivery

Gene delivery requires targeted delivery systems. Exploratory simulations using models of targeted drug delivery helps one assess the worthiness of such systems, and helps quantify the expected therapeutic benefits of the systems. The drug targeting index (DTI), a ratio of availabilities, is a measure of pharmacokinetic benefit of the delivery device, based on a combination of a physiologically-based pharmacokinetic model and a single pharmacodynamic E<inf>max</inf>model. Pharmacodynamic outcomes are quantified by the degree of separation between the dose-response and dose-toxicity curves (SRT). Simulations are undertaken to investigate the potential linkage of DTI and SRT, a pharmacodynamic outcome. A significant positive linear relationship is found between the DTI and SRT. The relationship can be translated into a minimum pharmacokinetic requirement that can be used to guide making decisions regarding whether or not further pursue the development of a candidate gene-delivery device as a therapeutic agent.