| Every year millions of dollars are spent in clinical research on various aspects of drug development by many pharmaceutical companies in the world. Once an efficacious drug is found and developed, regulatory officials would require the sponsor to introduce the safest dose of the newly found drug. In addition, pharmaceutical companies try to develop not only the most efficacious and safe dose, also the most profitable one. To achieve this goal, often many years of clinical research are needed to optimize safety, efficacy and profitability at the same time. Therefore, the problem of dose optimization is often of great interest to scientists at the pharmaceutical companies. Although our upcoming optimization findings do not necessarily apply to every problem of dose optimization in clinical research, it could certainly serve as a guide in dose escalating studies were often multiple doses of the same drug are investigated, or combined therapies when a combinations of several drugs are administered to patients. These findings, when applicable, not only provides different approaches to optimization within already established safe therapeutic range of a drug, it also addresses the required sample size at each optimal dose level given the assumptions of our methodology.; There are many optimization criteria that are discussed in the literature from which we only discuss and expand on C- and D-optimality criteria. Although first-degree, 2- and 3-parameter models, as well as non-linear 2-parameter models are discussed before, our new cubic logistic model optimization methodology broadens the horizons of possibilities and model selection in the field of optimization. Sequential optimal designs and their possible benefits are also discussed. |
