GITNUXSOFTWARE REVIEW
Data Science AnalyticsTop 10 Best Pk Modeling Software of 2026
Compare pk modeling software tools for pharmacokinetic modeling, highlighting features, workflows, and tradeoffs to shortlist the right fit for teams.
How we ranked these tools
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Framework: Features 40% · Ease 30% · Value 30%
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GastroPlus is the best overall choice for pharmaceutical R&D teams needing precise PBPK simulation of oral GI absorption because its mechanistic ACAT model handles gastrointestinal transit and absorption with clinical-style physiologic data, whereas PK-Sim fits academic users well since its open-source population PBPK with organ-level detail and life-stage variability keeps experimentation low-friction.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
GastroPlus
Proprietary ACAT™ model for mechanistic simulation of gastrointestinal absorption and transit
Best for: Pharmaceutical R&D teams in drug discovery and development requiring precise PBPK simulations for oral drug candidates.
PK-Sim
Editor pickSophisticated physiologically-based models accounting for organ-level details, population variability, and life-stage changes like pediatrics and geriatrics
Best for: Academic researchers and pharmacokinetic modelers focused on PBPK simulations in diverse virtual populations.
Phoenix NLME
Editor pickIts proprietary NLME solver with first-order conditional estimation (FOCE) and Bayesian methods, delivering unmatched speed and robustness for the most challenging hierarchical models.
Best for: Experienced pharmacometricians and pharmaceutical R&D teams needing top-tier population PK/PD modeling for clinical trials and regulatory filings.
Comparison Table
GastroPlus
enterpriseComprehensive PBPK/PD modeling platform for ADME predictions and simulations.
Proprietary ACAT™ model for mechanistic simulation of gastrointestinal absorption and transit
GastroPlus from Simulations Plus provides PBPK workflows that connect mechanistic absorption models with system physiology to predict plasma and tissue exposure. The ACAT framework models dissolution, permeability, solubility, and gastrointestinal transit, and it can translate formulation and preclinical inputs into concentration-time profiles. Model outputs support oral bioavailability analysis and exposure matching against clinical observations for compounds with variable absorption behavior.
A tradeoff is that accurate predictions depend on assigning realistic compound and formulation parameters for dissolution, permeability, and physiological inputs before running large scenario sets. A common usage situation is iterating oral formulation changes or dose strategies to narrow which candidates justify costly clinical studies. Another frequent use is exploring food effects and gastrointestinal variability when interpreting discrepancies between in vitro performance and observed human PK.
- +Sophisticated PBPK and ACAT models for accurate GI absorption predictions
- +Extensive library of physiological data and validation against clinical trials
- +Powerful visualization and reporting tools for PK simulations
- –Steep learning curve for non-experts due to complex modeling options
- –High licensing costs limit accessibility for small teams or academics
- –Primarily focused on absorption/PK, less emphasis on advanced systems pharmacology
Oral formulation scientists
Test dissolution and absorption scenarios
Prioritizes formulation candidates
Clinical pharmacology teams
Match predicted and observed PK
Improves dose selection
Show 1 more scenario
PBPK modelers in drug discovery
De-risk oral bioavailability predictions
Reduces late-stage failures
Runs scenario analyses to assess risk from solubility limits and permeability constraints on oral BA.
Best for: Pharmaceutical R&D teams in drug discovery and development requiring precise PBPK simulations for oral drug candidates.
PK-Sim
specializedOpen-source tool for multi-scale physiologically-based PK modeling.
Sophisticated physiologically-based models accounting for organ-level details, population variability, and life-stage changes like pediatrics and geriatrics
PK-Sim provides physiologically-based pharmacokinetic modeling that maps drug-specific parameters onto detailed anatomical and physiological systems for virtual human simulations. The tool supports absorption, distribution, metabolism, and excretion workflows, including covariates for age, sex, ethnicity, and disease states to study population and condition-specific PK behavior. A modular building-block approach helps assemble complex models for repeated trial simulations across multiple virtual subjects.
A key tradeoff is that model setup requires substantial physiological and drug-parameter configuration compared with simpler compartment models. PK-Sim fits best when there is a need to justify exposure drivers such as tissue distribution and metabolic clearance mechanisms, especially for mechanistic ADME studies and PK/PD model handoffs with MoBi.
- +Free and open-source with no licensing costs
- +Advanced PBPK capabilities including population simulations and ontogeny models
- +Modular building-block interface for flexible model construction
- –Steep learning curve for users new to PBPK modeling
- –Requires significant computational resources for large simulations
- –Community-driven support lacks the immediacy of commercial vendors
PBPK modelers
Mechanistic ADME modeling across populations
Exposure profiles by subgroup
Clinical pharmacology teams
Dose selection using virtual cohorts
Rationalized dosing ranges
Show 2 more scenarios
Translational scientists
Ontogeny and disease progression PK
Mechanistic clearance explanations
Model ontogeny and impaired organ states to quantify clearance changes and resulting concentration-time shifts.
Systems pharmacology groups
PK/PD integration with MoBi
End-to-end PK/PD scenarios
Pass PBPK results into MoBi workflows to support PK-to-PD mechanistic translation and scenario testing.
Best for: Academic researchers and pharmacokinetic modelers focused on PBPK simulations in diverse virtual populations.
Phoenix NLME
enterpriseIndustry-leading platform for nonlinear mixed-effects modeling in population PK/PD analysis.
Its proprietary NLME solver with first-order conditional estimation (FOCE) and Bayesian methods, delivering unmatched speed and robustness for the most challenging hierarchical models.
Phoenix NLME, developed by Certara, is a premier nonlinear mixed-effects (NLME) modeling software designed for advanced pharmacokinetics (PK) and pharmacodynamics (PD) analysis in drug development. It excels in population modeling, handling complex hierarchical models with sparse data from clinical trials to estimate fixed and random effects, variability, and covariates. The tool supports Bayesian and frequentist approaches, model diagnostics, and simulation for optimal dosing strategies, making it a cornerstone for regulatory submissions.
- +Exceptionally powerful NLME engine for handling massive datasets and complex models with high precision
- +Advanced diagnostics, visualization, and simulation capabilities tailored for PK/PD workflows
- +Validated for regulatory use with seamless integration into Phoenix suite (e.g., WinNonlin)
- –Steep learning curve requiring pharmacometrics expertise
- –High computational demands, especially for large simulations
- –Premium pricing limits accessibility for small teams or academics
Best for: Experienced pharmacometricians and pharmaceutical R&D teams needing top-tier population PK/PD modeling for clinical trials and regulatory filings.
SimBiology
specializedMATLAB toolbox for quantitative systems pharmacology and PK/PD modeling.
Graphical model builder for intuitive construction of biological reaction networks with PK compartments
SimBiology is a MATLAB toolbox from MathWorks specialized in mechanistic modeling of biological systems, with robust support for pharmacokinetics (PK) and pharmacodynamics (PD) simulations. It enables users to construct complex compartmental models, perform parameter estimation, sensitivity analysis, and optimal design using both graphical and programmatic interfaces. Ideal for systems pharmacology, it handles deterministic and stochastic simulations while integrating seamlessly with MATLAB's ecosystem for advanced data analysis.
- +Highly flexible for building custom mechanistic PK/PD models
- +Advanced simulation capabilities including ODEs, SDEs, and parameter estimation
- +Deep integration with MATLAB toolboxes for optimization and visualization
- –Steep learning curve requiring MATLAB proficiency
- –Expensive licensing model tied to MATLAB
- –Less intuitive GUI compared to dedicated standalone PK software
Best for: Experienced modelers in pharma R&D comfortable with programming who need customizable, complex PK systems modeling.
NONMEM
enterpriseGold standard software for advanced population pharmacokinetic and pharmacodynamic modeling.
FOCE with Interaction method for superior bias reduction in variance-covariance estimation
NONMEM, developed by ICON plc, is a cornerstone software for nonlinear mixed-effects modeling (NLME) in population pharmacokinetics (PK) and pharmacodynamics (PD). It excels at analyzing sparse and complex datasets from clinical trials using advanced estimation methods like FOCE, Laplace, and SAEM. Widely adopted in the pharmaceutical industry, it supports model-based drug development and regulatory submissions with high precision and flexibility.
- +Gold standard accuracy for population PK/PD modeling
- +Robust handling of large, unbalanced datasets
- +Extensive library of estimation algorithms including FOCE-INTERACTION
- –Steep learning curve due to control stream syntax
- –Limited graphical user interface; primarily command-line driven
- –High licensing costs with no free version
Best for: Experienced pharmacometricians in pharma R&D teams handling complex regulatory PK modeling.
mrgsolve
specializedR package for efficient simulation from nonlinear mixed-effects PK models.
Compiled C++ solver delivering unmatched speed for simulating thousands of virtual subjects in seconds
mrgsolve is an open-source R package specialized for fast simulation of pharmacokinetic/pharmacodynamic (PK/PD) models in pharmacometrics. Users define models using a concise, C++-inspired syntax that compiles to highly efficient code, enabling rapid simulations of large populations. It excels in handling complex ODE-based models, dosing events, covariates, and integration with R's data analysis ecosystem for tasks like trial simulation and posterior predictive checks.
- +Blazing-fast simulation speeds for large-scale population PK/PD analyses
- +Flexible model syntax supporting advanced features like time-varying covariates and nested compartments
- +Deep integration with R/tidyverse for seamless data handling and visualization
- –Steep learning curve for the model specification syntax without prior pharmacometrics experience
- –Focused on simulation rather than parameter estimation or full model fitting
- –No graphical user interface; requires R programming proficiency
Best for: Experienced R users and pharmacometricians focused on efficient PK/PD model simulation and diagnostics.
Monolix
specializedUser-friendly suite for stochastic approximation EM-based population PK/PD modeling.
Proprietary Stochastic Approximation Expectation-Maximization (SAEM) algorithm for superior convergence speed and accuracy in NLME estimation
Monolix, developed by Lixoft (lixoft.com), is a leading software for nonlinear mixed-effects modeling (NLME) in population pharmacokinetics (PK) and pharmacodynamics (PD). It excels in parameter estimation, model diagnostics, simulation via Mlxplore, and visualization tools tailored for drug development workflows. Integrated with R through mlxR, it supports complex models including stochastic differential equations and is widely used in pharma R&D.
- +Highly efficient SAEM algorithm for fast and robust parameter estimation even with sparse data
- +User-friendly GUI with drag-and-drop model building and rich diagnostics
- +Seamless integration with R and extensive library of PK/PD models
- –Steeper learning curve for users new to NLME concepts
- –Limited built-in support for non-compartmental analysis (NCA) compared to competitors
- –High commercial licensing costs may deter small teams or academics without discounts
Best for: Experienced pharmacometricians in pharmaceutical companies handling complex population PK/PD modeling during drug development.
nlmixr2
specializedOpen-source R package for flexible nonlinear mixed-effects PK/PD modeling.
Intuitive event-based model syntax that mimics mathematical PK equations for rapid prototyping of complex dosing regimens and covariates
nlmixr2 is an open-source R package for nonlinear mixed-effects (NLME) modeling, primarily used in pharmacometrics for population PK/PD analysis. It leverages the rxode2 engine for fast ODE solving and simulation, supporting estimation methods like FOCEi, SAEM, and Bayesian via brms or Stan. The package enables building complex compartmental models, handling event-based dosing, and integrating seamlessly with the R ecosystem for data processing and visualization.
- +Extremely powerful for advanced NLME PK/PD modeling with multiple estimation algorithms
- +Lightning-fast ODE solving and simulation via rxode2 integration
- +Free, open-source, and highly extensible within R/tidyverse workflows
- –Steep learning curve requiring solid R programming skills
- –Model specification syntax can be tricky for beginners
- –Limited GUI; relies heavily on command-line scripting
Best for: Experienced R users and pharmacometricians seeking customizable, high-performance population PK modeling without commercial software costs.
Berkeley Madonna
otherHigh-performance numerical solver for ordinary differential equations in PK modeling.
Proprietary high-speed stiff ODE integrators that simulate complex PK models in seconds
Berkeley Madonna is a specialized numerical modeling software for solving ordinary differential equations (ODEs), widely used in pharmacokinetics (PK) for simulating compartmental models and predicting drug concentration-time profiles. It supports rapid prototyping of PK models with tools for parameter fitting, sensitivity analysis, and bifurcation diagrams. While powerful for deterministic simulations, it is less suited for advanced population PK or nonlinear mixed-effects modeling compared to dedicated tools.
- +Extremely fast ODE solvers for quick simulations of stiff PK systems
- +Intuitive graphical interface for model building and visualization
- +Built-in optimization and sensitivity analysis tools
- –No native support for population PK or NLME modeling
- –Limited PK-specific libraries and data import/export options
- –Windows-only with a somewhat dated user interface
Best for: Individual researchers, students, and educators needing fast prototyping and simulation of basic to intermediate deterministic PK models.
Simcyp Simulator
enterprisePopulation-based PBPK platform for predicting drug metabolism and DDI risks.
Advanced population simulator with ontogeny, ethnicity, and disease-specific virtual populations for highly realistic variability modeling
Simcyp Simulator, developed by Certara, is a population-based physiologically-based pharmacokinetic (PBPK) modeling platform used for predicting drug absorption, distribution, metabolism, and excretion (ADME) in virtual populations. It supports drug development by integrating extensive libraries of compounds, enzymes, transporters, and demographic data to simulate clinical outcomes and assess drug-drug interactions. The software is widely used in pharmaceutical R&D for regulatory submissions and optimizing clinical trial designs.
- +Comprehensive PBPK modeling with built-in libraries of over 1,000 compounds and physiological parameters
- +Strong support for population variability and drug-drug interaction predictions
- +High regulatory acceptance by FDA, EMA, and other agencies
- –Steep learning curve requiring expertise in pharmacokinetics
- –High computational demands and resource-intensive simulations
- –Enterprise-level pricing not suitable for small teams or academics
Best for: Large pharmaceutical companies and research teams needing advanced PBPK simulations for population-based drug predictions and regulatory filings.
Conclusion
After evaluating 10 pk modeling software, GastroPlus stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Pk Modeling Software
This buyer's guide helps teams choose PK modeling software for population PK/PD and PBPK workflows using tools like Phoenix NLME, NONMEM, Monolix, nlmixr2, GastroPlus, Simcyp Simulator, PK-Sim, SimBiology, mrgsolve, and Berkeley Madonna. It connects each tool to concrete capabilities such as FOCE or SAEM estimation, ACAT™ absorption modeling, event-based dosing syntax, and fast ODE or compiled simulation engines. The guide also highlights common setup traps that show up across NLME, PBPK, and general ODE solvers.
What Is Pk Modeling Software?
PK modeling software builds mathematical models that generate drug concentration and exposure over time using compartmental or physiologically-based structures. Population PK/PD tools estimate fixed effects, random effects, variability, and covariate influence from sparse clinical trial data using estimation methods like FOCE or SAEM. PBPK tools like GastroPlus and Simcyp Simulator predict ADME processes in virtual populations by simulating absorption, distribution, metabolism, and excretion. These tools are used by pharmacometricians, pharmacology modelers, and pharmaceutical R&D teams to support trial design, simulation, diagnostics, and regulatory submissions.
Key Features to Look For
The right PK modeling tool needs the right estimation or simulation engine, the right dosing and model specification workflow, and the right diagnostics for PK/PD decision-making.
NLME estimation with FOCE and Bayesian methods
Phoenix NLME provides a proprietary NLME solver with FOCE and Bayesian methods for hierarchical population models that require speed and robustness. NONMEM complements this with FOCE-INTERACTION for bias reduction in variance-covariance estimation when modeling complex datasets for regulatory work.
SAEM convergence speed for sparse data
Monolix uses a proprietary Stochastic Approximation Expectation-Maximization algorithm for fast and robust parameter estimation, especially when clinical data are sparse. This makes Monolix well-suited for population PK/PD work that emphasizes reliable convergence with user-facing workflows.
Event-based dosing syntax for rapid model prototyping
nlmixr2 provides intuitive event-based model syntax that mimics mathematical PK equations, which speeds up prototyping of dosing regimens and covariates. mrgsolve also supports dosing events and covariates in simulation-focused workflows, making it practical for large-scale scenario runs.
PBPK absorption modeling with ACAT™
GastroPlus uses the proprietary ACAT™ model for mechanistic gastrointestinal absorption and transit, which helps generate oral bioavailability-driven PK predictions. For teams focused on oral candidates and GI mechanistic assumptions, GastroPlus delivers a focused PBPK approach.
Population PBPK simulation with virtual populations and DDI libraries
Simcyp Simulator includes built-in libraries with over 1,000 compounds and physiological parameters to simulate metabolism and drug-drug interaction risk. It also supports ontogeny, ethnicity, and disease-specific virtual populations for realistic variability modeling in regulatory-style analyses.
High-speed execution for ODE-based and large-population runs
mrgsolve compiles models to a highly efficient solver and delivers fast simulation of thousands of virtual subjects for posterior predictive checks and trial simulations. Berkeley Madonna focuses on fast stiff ODE integrators for quick deterministic PK model prototyping, while SimBiology provides ODE and SDE simulation inside the MATLAB ecosystem for systems-level modeling.
How to Choose the Right Pk Modeling Software
A practical selection starts by matching the modeling goal to the engine type, then checking whether the workflow supports dosing events, diagnostics, and simulation scale.
Match the software to the modeling goal: NLME vs PBPK vs deterministic ODE
Choose Phoenix NLME, NONMEM, or Monolix when the goal is population PK/PD parameter estimation from sparse clinical data. Choose GastroPlus or Simcyp Simulator when the goal is PBPK predictions of ADME and absorption, including oral candidates and drug-drug interactions. Choose PK-Sim or SimBiology when the goal is mechanistic physiologically-based modeling with flexible integration into broader modeling ecosystems. Choose mrgsolve or Berkeley Madonna when the goal is fast ODE-based simulation workflows or deterministic compartment prototyping.
Pick the right estimation engine for the data and the modeling complexity
For regulatory-grade hierarchical modeling with a mix of fixed effects, random effects, covariates, and diagnostics, Phoenix NLME pairs FOCE and Bayesian methods with strong visualization and simulation. For bias reduction in variance-covariance estimates, NONMEM’s FOCE-INTERACTION helps when modeling complex PK/PD structures. For fast and robust convergence under sparsity, Monolix’s SAEM algorithm supports stable parameter estimation in population workflows.
Validate the model-building workflow around dosing events and covariates
When dosing schedules are complex, nlmixr2’s event-based syntax helps align code structure with PK mathematics for rapid iteration. When simulation scale is the priority, mrgsolve supports dosing events, covariates, and time-varying effects in a code-first approach designed for speed. When using a GUI-first modeling approach, Berkeley Madonna provides a graphical model builder for deterministic PK simulations and fast sensitivity analysis.
Check diagnostics and simulation capabilities for the decisions being made
For optimal dosing strategy simulation and advanced diagnostics in population PK/PD, Phoenix NLME is built for complex hierarchical models and regulatory submissions. For mechanistic GI absorption simulations and mechanistic de-risking, GastroPlus emphasizes ACAT™ GI absorption and transit with strong visualization and reporting. For population variability and DDI risk decisions, Simcyp Simulator emphasizes virtual population simulation and drug interaction libraries tied to metabolism and enzyme or transporter assumptions.
Plan for the learning curve and the toolchain integration style
For command-line driven pharmacometrics workflows, NONMEM and nlmixr2 rely heavily on syntax, so training and internal standards matter. For R-first workflows, nlmixr2 and mrgsolve integrate into R ecosystems and support deep data processing and visualization pipelines. For GUI-oriented prototyping, Monolix and Berkeley Madonna support more interactive model building than command-only engines, while PK-Sim and SimBiology require comfort with PBPK or MATLAB-based systems modeling.
Who Needs Pk Modeling Software?
PK modeling software serves specialized needs across NLME estimation, PBPK prediction, and high-speed simulation for drug development decisions.
Experienced pharmacometricians and pharma R&D teams doing regulatory population PK/PD
Phoenix NLME fits this group because it targets complex hierarchical NLME models with a proprietary solver using FOCE and Bayesian methods, plus advanced diagnostics, visualization, and simulation for regulatory submissions. NONMEM also fits this group for gold-standard population PK/PD modeling with FOCE, Laplace, SAEM options, and FOCE-INTERACTION bias reduction.
Pharmacometricians optimizing NLME estimation under sparse clinical data
Monolix fits teams that need fast and robust convergence through its SAEM algorithm paired with a user-friendly GUI and rich diagnostics. This also suits organizations that want R integration through mlxR while keeping model building accessible.
R users and pharmacometricians prioritizing extensibility and fast simulation
nlmixr2 fits teams that want an open-source, R-integrated NLME modeling workflow with rxode2-based fast ODE solving and simulation. mrgsolve fits teams that prioritize rapid simulation speeds for large populations using compiled execution and a syntax designed for event-based dosing and covariates.
Drug discovery and development teams focused on mechanistic PBPK for oral candidates and ADME de-risking
GastroPlus fits teams that need mechanistic oral absorption predictions using ACAT™ gastrointestinal absorption and transit models. Simcyp Simulator fits large teams that need advanced population PBPK with virtual populations and built-in compound and enzyme or transporter libraries for drug-drug interaction risk.
Common Mistakes to Avoid
Several recurring selection and workflow mistakes come from choosing an engine that does not match the modeling objective or underestimating the specification and computational demands.
Buying an ODE solver for population PK/PD fitting
Berkeley Madonna is designed for deterministic ODE simulation with parameter fitting and sensitivity analysis, but it has no native support for population PK or NLME modeling. Choose Phoenix NLME, NONMEM, or Monolix when the goal is nonlinear mixed-effects estimation with sparse clinical trial data.
Underestimating how much NLME syntax and model specification matter
NONMEM and nlmixr2 require steep learning tied to control stream or R syntax, so teams often spend time on correct model specification before achieving stable estimates. Monolix provides a drag-and-drop GUI model building approach that can reduce friction for population PK/PD modeling.
Choosing PBPK tooling without confirming the needed mechanistic scope
GastroPlus focuses on absorption and GI mechanistic modeling via ACAT™, so it is not positioned as a systems pharmacology replacement for all mechanistic PD needs. Simcyp Simulator emphasizes population variability and drug-drug interaction predictions using virtual populations and built-in libraries, so it is the better match when DDI risk is central.
Relying on simulation speed without checking diagnostic requirements
mrgsolve excels at fast simulation and is focused on simulation rather than full model fitting, so teams must pair it with an estimation workflow when parameter estimation is required. Phoenix NLME provides simulation and model diagnostics for complex hierarchical modeling when diagnostics drive decision-making.
How We Selected and Ranked These Tools
we evaluated every tool on three sub-dimensions: features with a weight of 0.40, ease of use with a weight of 0.30, and value with a weight of 0.30. Each tool’s overall rating is computed as overall = 0.40 × features + 0.30 × ease of use + 0.30 × value. Phoenix NLME separated from lower-ranked tools by combining top-tier features for complex hierarchical NLME modeling with strong execution support from its proprietary NLME solver using FOCE and Bayesian methods. That combination made Phoenix NLME the best match for regulatory-grade population PK/PD work where simulation scale and diagnostic confidence drive outcomes.
Frequently Asked Questions About Pk Modeling Software
Which tools support population PK and PK/PD estimation from sparse clinical sampling?
How do PK modeling tools handle event-based dosing and concentration-time records?
When does physiologically based pharmacokinetic modeling become the right modeling approach?
What breaks if a workflow needs heavy automation and programmatic integration rather than interactive modeling?
How do NLME tools differ in solver strategy and convergence behavior for hierarchical models?
Which tools support Bayesian parameter estimation without rewriting the entire model workflow?
How do modeling workflows handle diagnostics like goodness-of-fit and predictive checks?
Which tools support extensibility when the model must incorporate custom covariates, dosing logic, or new components?
How should security and administration requirements be evaluated for PK modeling work involving regulated datasets?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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