NONMEM 7.6 is the latest release of ICON’s industry-standard nonlinear mixed-effects modeling software for population pharmacokinetic and pharmacodynamic (PK/PD) analysis . It is developed and licensed by ICON Early Clinical (versions up to VI were property of the Regents of the University of California) .
Key New Features in Version 7.6
Advanced Estimation & Bayesian Methods
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NUTS (Hamiltonian No-U-Turn Sampling): A new Markov-Chain Monte Carlo Bayesian method alongside existing Gibbs sampling, with the ability to store random samples of individual parameters or any user-defined variable .
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SAEM Enhancements: Stochastic Approximation Expectation-Maximization now allows storage of individual parameter samples or user-defined variables for deeper analysis .
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FAST Algorithm: A Monte Carlo search algorithm introduced to improve FOCE estimation efficiency .
Delay Differential Equation (DDE) Solvers
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ADVAN16: Uses RADAR5 by Guglielmi and Hairer (implicit 4th-order Runge-Kutta) for stiff delay equations .
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ADVAN17: For stiff delay differential-algebraic equations .
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Distributed delay convolution algorithms are also included for modeling complex delay systems .
Performance & Efficiency
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Parallel Computing: A single problem can be distributed across multiple cores or computers for estimation, covariance assessment, simulation, nonparametric analysis, and posthoc evaluation, significantly reducing completion time .
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Dynamic Memory Allocation: Improved to handle very large problems without recompiling the NONMEM program for unusually large models .
Optimal Design & Robust Analysis
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Clinical Trial Design Evaluation: Includes robust design analysis and optimal design evaluation tools .
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WFIMS Module: New user-defined module for adjusting objective function weights in robust design .
Bug Fixes & Stability
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Fixed memory access conflicts in ADVAN5/7/8 during $TABLE steps .
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Resolved parallelization conflicts with FPI (File Passing Interface) and importance sampling .
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Fixed COVSIRparallelizationissuesandTABLE ETAS() output errors .
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Addressed empty subject data handling in parallel computing .

