title: Benefits of a new Metropolis-Hasting based algorithm, in non-linear regression for estimation of ex vivo antimalarial sensitivity in patients infected with two strains.
abstract: Malaria is one of the world׳s most widespread parasitic diseases. The parasitic protozoans of the genus Plasmodium have developed resistance to several antimalarial drugs. Some patients are therefore infected by two or more strains with different levels of antimalarial drug sensitivity. We previously developed a model to estimate the drug concentration (IC50) that inhibits 50% of the growth of the parasite isolated from a patient infected with one strain. We propose here a new Two-Slopes model for patients infected by two strains. This model involves four parameters: the proportion of each strain and their IC50, and the sigmoidicity parameter. To estimate the parameters of this model, we have developed a new algorithm called PGBO (Population Genetics-Based Optimizer). It is based on the Metropolis-Hasting algorithm and is implemented in the statistical software R. We performed a simulation study and defined three evaluation criteria to evaluate its properties and compare it with three other algorithms (Gauss-Newton, Levenberg-Marquardt, and a simulated annealing). We also evaluated it using in vitro data and three ex vivo datasets from the French Malaria Reference Center. Our evaluation criteria in the simulation show that PGBO gives good estimates of the parameters even if the concentration design is poor. Moreover, our algorithm is less sensitive than Gauss-Newton algorithms to initial values. Although parameter estimation is good, interpretation of the results can be difficult if the proportion of the second strain is close to 0 or 1. For these reasons, this approach cannot yet be implemented routinely.
authors.affiliation: MAP5, UMR 8145, CNRS, 45 rue des Saints Përes, F-75006 Paris, France. Electronic address: rebecca.bauer@inserm.fr.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Bauer
authors.affiliation: IAME UMR 1137, INSERM, Paris, France; IAME UMR 1137, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
authors.forename: France
authors.initials: F
authors.lastname: Mentré
authors.affiliation: Paris Descartes University, France.
authors.forename: Halima
authors.initials: H
authors.lastname: Kaddouri
authors.affiliation: PRES Sorbonne Paris Cité, Université Paris Descartes, UMR 216, faculté de Pharmacie, 75270 Paris, France; IRD, UMR 216, Mëre et enfants face aux infections tropicales, faculté de Pharmacie, 75270 Paris, France.
authors.forename: Jacques
authors.initials: J
authors.lastname: Le Bras
authors.affiliation: IAME UMR 1137, INSERM, Paris, France; IAME UMR 1137, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
authors.forename: Hervé
authors.initials: H
authors.lastname: Le Nagard
mesh_terms: D000465:Algorithms; D000962:Antimalarials / Q000494:pharmacology* / Q000627:therapeutic use; D060085:Coinfection; D003198:Computer Simulation; D006801:Humans; D008288:Malaria / Q000188:drug therapy* / Q000469:parasitology*; D008954:Models, Biological; D017711:Nonlinear Dynamics; D010961:Plasmodium / Q000187:drug effects*
chemical_list: D000962:Antimalarials
keywords: Antimalarials; Gauss–Newton algorithms; Metropolis–Hasting algorithms; Modeling; Nonlinear regression; Plasmodium; Sensitivity; Simulated annealing algorithms