Infliximab is an anti-tumour necrosis factor alpha monoclonal antibody used to treat inflammatory diseases. Many patients fail during induction and others respond initially but relapse during maintenance therapy. Although anti-drug antibodies (ADA) are associated with some clinical failures, there is evidence that some failures may be due to subtherapeutic exposure. Adapting doses based on clinical outcomes and trough concentrations can improve response and reduce the proportion that develop ADA, but identification of appropriate doses in the presence of time-varying patient factors is complicated. Several adaptive dosing strategies (label recommendations versus therapeutic drug monitoring with an established stepwise algorithm or proportional dose adjustments or Bayesian population pharmacokinetic model-based dosing) were simulated on a virtual population (constructed with time-varying covariates and random effects on individual pharmacokinetic parameters) using R to assess their relative performance. Strategies were evaluated on their ability to maintain trough infliximab concentrations above an established target, 3 mg/L, during maintenance phase. Model-based dosing was superior in maintaining target trough concentrations, showing individuals in maintenance achieving concentrations above the target faster and a lower proportion of individuals who developed ADA. Model-based dosing results were consistent across a range of baseline covariate groups. This in silico assessment of adaptive dosing strategies demonstrated that, when challenged with dynamic covariate and random effect changes occurring in individual pharmacokinetic parameters, model-based approaches were superior to other strategies. Model-based dosing has not been tested clinically; however, the potential benefits of model-based dosing for infliximab suggest that it should be investigated to reduce subtherapeutic exposure.