Model-based drug development applied to oncology

Abstract

Model-based drug development (MBDD) is an approach that is used to organize the vast and complex data streams that feed the drug development pipelines of small molecule and biotechnology sponsors. Such data streams are ultimately reviewed by the global regulatory community as evidence of a drug’s potential to treat and/or harm patients. Some of this information is captured in the scientific literature and prescribing compendiums forming the basis of how new and existing agents will ultimately be administered and further evaluated in the broader patient community. As this data stream evolves, the details of data qualification, the assumptions and/or critical decisions based on these data are lost under conventional drug development paradigms. MBDD relies on the construction of quantitative relationships to connect data from discrete experiments conducted along the drug development pathway. These relationships are then used to ask questions relevant at critical development stages, hopefully, with the understanding that the various scenarios explored represent a path to optimal decision making. Oncology, as a therapeutic area, presents a unique set of challenges and perhaps a different development paradigm as opposed to other disease targets. The poor attrition of development compounds in the recent past attests to these difficulties and provides an incentive for a different approach. In addition, given the reliance on multimodal therapy, oncological disease targets are often treated with both new and older agents spanning several drug classes. As MBDD becomes more integrated into the pharmaceutical research community, a more rational explanation for decisions regarding the development of new oncology agents as well as the proposed treatment regimens that incorporate both new and existing agents can be expected. Hopefully, the end result is a more focussed clinical development programme, which ultimately provides a means to optimize individual patient care.

Keywords::

• ADME
• clinical pharmacology
• in silico
• model-based drug development
• pharmacodynamics
• pharmacokinetics

Notes