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Forward addition/backward elimination (FABE) has been the standard for population pharmacokinetic model selection (PPK) since NONMEM® was introduced. We investigated five machine learning (ML) algorithms (Genetic algorithm [GA], Gaussian process [GP], random forest [RF], gradient boosted random tree [GBRT], and particle swarm optimization [PSO]) as alternatives to FABE. These algorithms were applied to PPK model selection with a focus on comparing the efficiency and robustness of each of them. All machine learning algorithms included the combination of ML algorithms with a local downhill search. The local downhill search consisted of systematically changing one or two "features" at a time (a one-bit or a two-bit local search), alternating with the ML methods. An exhaustive search (all possible combinations of model features, N = 1,572,864 models) was the gold standard for robustness, and the number of models examined leading prior to identification of the final model was the metric for efficiency.All algorithms identified the optimal model when combined with the two-bit local downhill search. GA, RF, GBRT, and GP identified the optimal model with only a one-bit local search. PSO required the two-bit local downhill search. In our analysis, GP was the most efficient algorithm as measured by the number of models examined prior to finding the optimal (495 models), and PSO exhibited the least efficiency, requiring 1710 unique models before finding the best solution. Additionally, GP was also the algorithm that needed the longest elapsed time of 2975.6 min, in comparison with GA, which only required 321.8 min.
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Cichoric acid (CA), a natural phenolic compound found in many plants, has been reported to have antioxidant, anti-inflammatory, hypoglycemic, and other effects. The aim of this study was to determine the potential role and underlying mechanisms of CA in isoproterenol (ISO)-induced myocardial fibrosis (MF). The MF model was induced by subcutaneous ISO injection in mice. Blood and heart tissue were collected for examination. Hematoxylin and eosin staining and Masson's trichrome staining were used to evaluate the histopathological changes and collagen deposition. The production of reactive oxygen species markers was observed by fluorescence microscopy, the degree of cardiomyocyte microstructure injury was observed by transmission electron microscope, and oxidative stress factors were detected by kit method, and the effect of CA on inflammatory factors was detected by ELISA. The expression levels of collagen proteins and signaling pathways were further investigated by western blotting. The results showed that CA inhibited the expression of ISO-induced proinflammatory factors (TNF-α, IL-1ß, and IL-18) and proteins (HK1, NLRP3, caspase-1, cleaved-caspase-1, and ASC), and regulated the expression of apoptotic factors (caspase-3, cleaved-caspase-3, Bax, and Bcl-2). The results indicated that CA may regulate the HK1/NLRP3 inflammasome pathway by inhibiting HK1 expression and play a protective role in MF.
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pyDarwin is an open-source Python package for nonlinear mixed-effect model selection. pyDarwin combines machine-learning algorithms and NONMEM to perform a global search for the optimal model in a user-defined model search space. Compared with traditional stepwise search, pyDarwin provides an efficient platform for conducting an objective, robust, less labor-intensive model selection process without compromising model interpretability. In this tutorial, we will begin by introducing the essential components and concepts within the package. Subsequently, we will provide an overview of the pyDarwin modeling workflow and the necessary files needed for model selection. To illustrate the entire process, we will conclude with an example utilizing quetiapine clinical data.