Drug discovery is a process with high attrition rates and extended timelines. Despite the size of the workforce or the budget, few companies can afford to continue most research with strict limits on the number of failures. Therefore, go/no-go decisions must be taken as early as possible, and computational tools capable of supporting the different design steps are pivotal to drug development.
Systems biology can provide mechanistic models that incorporate multi-level descriptions of biological phenomena and drug interactions. In addition, systems-level tools of explorations, like sensitivity analysis, can guide the identification of new potential drug targets by highlighting the key components of metabolism that can affect overall behavior while also assessing any adverse effects. For this reason, systems biology, toxicology, and pharmacology are already providing support in the drug development phases through quantitative systems pharmacology (QSP).
The aim of this Research Topic is to demonstrate how systems-level thinking and QSP can support drug design and development by focusing on both the open challenges within industry and the current state-of-the-art innovations. The final goal is to showcase how this can provide a more knowledge-based process drug development pipeline with information on the mechanism of actions, biological targets, and off-target effects, thus supporting the selection of candidate lead.
The Research Topic will include original research that shed light on new possible methodologies or applications of systems-level thinking and QSP in supporting drug development. In addition, the Research Topic will consider review and perspective articles that recapitulate existing literature and/or discuss emerging or established trends devoted to this topic.
Drug discovery is a process with high attrition rates and extended timelines. Despite the size of the workforce or the budget, few companies can afford to continue most research with strict limits on the number of failures. Therefore, go/no-go decisions must be taken as early as possible, and computational tools capable of supporting the different design steps are pivotal to drug development.
Systems biology can provide mechanistic models that incorporate multi-level descriptions of biological phenomena and drug interactions. In addition, systems-level tools of explorations, like sensitivity analysis, can guide the identification of new potential drug targets by highlighting the key components of metabolism that can affect overall behavior while also assessing any adverse effects. For this reason, systems biology, toxicology, and pharmacology are already providing support in the drug development phases through quantitative systems pharmacology (QSP).
The aim of this Research Topic is to demonstrate how systems-level thinking and QSP can support drug design and development by focusing on both the open challenges within industry and the current state-of-the-art innovations. The final goal is to showcase how this can provide a more knowledge-based process drug development pipeline with information on the mechanism of actions, biological targets, and off-target effects, thus supporting the selection of candidate lead.
The Research Topic will include original research that shed light on new possible methodologies or applications of systems-level thinking and QSP in supporting drug development. In addition, the Research Topic will consider review and perspective articles that recapitulate existing literature and/or discuss emerging or established trends devoted to this topic.