Population pharmacokinetic model selection assisted by machine learning

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches

Fast screening of covariates in population models empowered by machine learning

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. Methods are not equivalent with respect to computational speed; SCM is 30 and 100-times slower than NN and SVR, respectively. The results are validated in an additional scenario involving 100 covariates. Taken together, the results indicate that ML methods can greatly increase the efficiency of population covariate model building in the case of large datasets or complex models that require long run-times. This can provide fast initial covariate screening, which can be followed by more conventional PMX approaches to assess the clinical relevance of selected covariates and build the final model

Les enjeux sociaux de la mise en oeuvre de la politique agri-environnementale en France et en Europe

International audiencePartie intégrante de la réforme de la PAC, l'ensemble des mesures agri-environnementales tend à introduire un contrôle sur les pratiques agricoles dans plusieurs pays de l'Union européenne. S'appuyant essentiellement sur le cas français, mais utilisant des comparaisons avec d'autres pays, l'article analyse le développement ambigu des préoccupations sociales tenant à la dégradation de l'environnement par les activités agricoles. Plusieurs causes de ce phénomène sont examinées : la crise du modèle agricole productiviste, les nouvelles formes de pression urbaine sur les usages des territoires ruraux, et la montée plus globale des inquiétudes environnementales. L'auteur met l'accent sur le fait que les agriculteurs se voient attribuer le rôle ambivalent d'être dans le même temps les destructeurs et les gestionnaires des qualités de l'environnement rural. En conclusion, l'auteur s'interroge sur la durabilité du soutien public à une politique basée sur des compromis locaux et tendant à accroître la différenciation territoriale