Quantitative dose optimization in early phase clinical trials for investigational new drugs has been expanding across drug modalities and disease indications in response to the limitations of non-quantitative or algorithmic methods of dose progression. In the context of the FDA's Project Optimus Initiative, which emphasizes dose optimization rather than reliance on the maximum tolerated dose, these challenges motivate the development of alternative quantitative frameworks tailored to gene therapies. In this manuscript we describe the state of the art for quantitative dose optimization with an eye to applications in cell and gene therapy modalities. The application of quantitative dose progression methods in this setting poses unique challenges, but the regulatory, scientific, medical, and statistical environment has advanced to the point where new approaches can be employed to characterize the safety and efficacy profile of these powerful biopharmaceutical products. We discuss the current use of quantitative dose optimization, the limitations and pitfalls of these options for gene therapy indications, and provide a tutorial example of how an established Bayesian logistic regression framework can be adapted to distinguish clinically distinct toxicity classes. The example is intended to illustrate safety-constrained decision logic in a sparse early-phase setting, rather than to establish comparative superiority over existing dose-finding designs. The proposed framework should be viewed as one component of broader dose optimization that may also incorporate pharmacodynamic, exposure-response, efficacy, durability, and practical administration considerations.
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