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While most conventional drugs are given daily, the effect of monoclonal antibodies often lasts for months, and hence, these biologics require less frequent dosing. A good understanding of the time\u2010changing effect of the biologic for different doses is needed to determine both an adequate dose and an appropriate time\u2010interval between doses. Clinical trials provide data to estimate the dose\u2010time\u2010response relationship with semi\u2010mechanistic nonlinear regression models. We investigate how to best choose the doses and corresponding sample size allocations in such clinical trials, so that the nonlinear dose\u2010time\u2010response model can be precisely estimated. We consider both local and conservative Bayesian D\u2010optimality criteria for the design of clinical trials with biologics. For determining the optimal designs, computer\u2010intensive numerical methods are needed, and we focus here on the particle swarm optimization algorithm. This metaheuristic optimizer has been successfully used in various areas but has only recently been applied in the optimal design context. The equivalence theorem is used to verify the optimality of the designs. The methodology is illustrated based on results from a clinical study in patients with gout, treated by a monoclonal antibody. 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