In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.

Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic (PK/PD) model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. However, SCM is based on a local search and on the assumption of independent covariate effects. Moreover, it is manly designed to test few covariate-parameter relationships, often chosen ad hoc or based on prior knowledge. The application of genetic algorithms (GA) for covariate selection has been proposed as a possible solution to overcome these limitations. However, their actual use during model building is limited by the extremely high computational costs and convergence issues, both related to the number of models being tested. In this paper, we proposed a new genetic algorithm for covariate selection to address these challenges. The GA was first developed on a simulated case study; then, its performances were evaluated on a real-world problem related to Remifentanil. The implemented GA showed good results both in terms of correctness of the selected model and fitness optimization, and it was able to outperform the SCM in both scenarios. Furthermore, the heuristics introduced to overcome the limitations affecting currently available GA approaches allowed to reduce convergence times, obtain replicable results, and limit the number of selected covariates.

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