Biopharmaceuticals, especially monoclonal antibodies, have been increasingly used to treat several chronic inflammatory diseases. Due to the complexity of their pharmacokinetics and concentration–effect relationship, therapeutic drug monitoring (TDM) has been used to optimize their dosing regimen. Up to date, several decisional algorithms have been developed to provide tools for monoclonal antibodies' therapeutic drug monitoring. However, these algorithms are unable to determine the individual optimal dosing scheme. The aim of this article is to deal with population pharmacokinetic (PK) and pharmacokinetic–pharmacodynamic (PK-PD) modeling. Allowing the quantification of the variability of the dose-concentration–response relationship, population pharmacokinetic–pharmacodynamic modeling may be a valuable tool to determine the optimal dosing scheme. Based on population modeling, Bayesian estimators may be developed to optimize dosing schemes for each patient using limited sampling strategies. These estimators may allow accurate dosing adjustment for each patient individually.