Computing optimal drug dosing with OptiDose: implementation in NONMEM

Abstract

Finding a drug dosing recommendation with a PKPD model for an individual or a target population can be a laborious and complex task. Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal doses for any pharmacometrics / PKPD model for a given dosing scenario. In this work, we reformulate the underlying optimal control problem and elaborate how to solve it with standard commands in the software NONMEM. To demonstrate the potential of the OptiDose implementation in NONMEM, four relevant but substantially different optimal dosing tasks are solved. In addition, the impact of different dosing

Computing optimal drug dosing regarding efficacy and safety - the enhanced OptiDose method in NONMEM

“…The unconstrained OCP Eq. ( 9) is of the same type as the OCP in [2] and can be solved in NONMEM as described in detail there and briefly summarized in the following, with few additional comments arising due to the penalty function.…”

“…Mathematically, this is a state-constrained optimal control problem (OCP). Such a state-constrained OCP is transformed into a classical unconstrained OCP that can be solved in NONMEM as described in [2]. The idea of the transformation is to introduce a penalty function, which indirectly includes the state constraint in the optimization by measuring the violation of the state constraint, e.g.,…”

“…Previously [2], the therapeutic goal was characterized by a reference function describing the desired time course of the biomarker transitioning from elevated towards the healthy level. Different choices of the reference function resulted in different optimal doses [2]. The enhanced OptiDose method makes the previously mandatory reference function and discussed choices [2] obsolete.…”

“…OptiDose is an optimal drug dosing algorithm [1,2] recently implemented in NONMEM [3] utilizing standard commands. This allows users to advance from modelingand-simulation towards modeling-and-optimization by solving their own optimal dosing tasks, i.e., computing optimal drug doses with any pharmacometrics (PMX) model for a given dosing scenario.…”

Building an adaptive dose simulation framework to aid dose and schedule selection

“…This package allows simulations from hierarchical, closed‐form solutions and ordinary differential equation (ODE)–based models that are widely employed in the pharmacometrics community to support drug development. Although alternative implementations of adaptive simulation frameworks are viable (e.g., NONMEM, 17 nlmixr2/rxode2, 18,19 OptiDose 20 ), we chose mrgsolve for the following benefits: (1) the C++ − based coding provides fast output compared with alternatives, with well‐established and tested integration into R; (2) the free and open source nature facilitates dissemination of this framework and provides opportunities for collaboration and wide applications; (3) the package can be integrated with commonly used R‐based packages for data management (dplyr), plotting (ggplot, lattice), and user interface (Shiny); and (4) implementation and coding of the model and its parameters are compatible with output from NONMEM.…”