Model-based Characterisation of Safety & Toxicity; NOAEL and Risk Assessment in Early Clinical Development
09 / 2006 - 09 / 2010
Safety drives the choice of starting dose and dosing increments in early clinical studies. However, the ultimate objective of exploratory studies is the identification of the concentration-exposure range that provides the best risk-benefit ratio. Underlying this objective is the assumption that an optimal dose and therapeutic window can be assessed during clinical development. Few decisions in drug development have as large an impact as the failure to determine an adequate dose range to be used in clinical trials. An inadequate dose will result in failure to demonstrate efficacy; an excessive dose can lead to irrelevant pharmacological effects and unacceptable adverse events, which may prevent further development of a compound. This is particularly important for chronic indications. The NOAEL (dose, Cmax and AUC) is commonly used a limit for dose escalation in humans. However, very often inaccurate estimates are obtained that can be largely biased or do not represent the underlying exposure-toxicity relationship. Furthermore, the current approach doesn t account for the time dependencies (time to event), as toxicology data from different studies are not evaluated in an integrated manner, particularly for overt signs. A subtle line separates caution from unnecessary exposure of human subjects to a dose that provides little relevant clinical information, at either edge of the concentration-effect relationship curve. The question to be posed is not whether one could escalate exposure up to or above a pre-clinical NOAEL if required, but how to establish a NOAEL that accurately reflects the timing and rate of exposure-related toxicity and subsequently link it to specific dosing regimens. The objective of this research proposal is to develop a strategy that allows pre-clinical data to be combined and appended with information from ongoing clinical studies, including a quantitative estimation of its predictive value.