Systems biology-enabled computational modeling can be useful in almost every step of drug discovery and development, including drug target identification and clinical outcome analysis. Various modeling strategies can be used at different stages.
For example, during the basic scientific research stage, systems biology approaches and data mining techniques are useful for analyzing laboratory data. Such approaches would allow the development of computational models for the studies of cellular mechanisms including metabolic processes, signaling pathways, transcription factors, and protein interactions (Melas et al., 2013). Such models can be applied for drug target identification, analysis of the drug’s mode of action (MOA), and drug efficacy evaluations.
During the drug development stage, computational analysis and systems biology methods can assist pharmacokinetic/pharmacodynamic (PK/PD) modeling for safety analysis and dosage testing. Such modeling would be useful for the studies of the associations among drug input, plasma concentration, as well as PD responses (Melas et al., 2013).
The development of predictive preclinical PK/PD models during the early-stage of clinical trials would help overcome obstacles in the drug discovery processes (Zhou and Gallo, 2011). For example, physiologically based (PB) PK modeling methods such as the whole-body PBPK model have been found especially useful.
Based on the systems biology approaches, a systems pharmacology platform can be established to facilitate the development of predictive PK/PD models. Such models would benefit the processes in both drug discovery and drug development.
Melas IN, Kretsos K, Alexopoulos LG. Leveraging systems biology approaches in clinical pharmacology. Biopharm Drug Dispos. 2013 Aug 23. doi: 10.1002/bdd.1859.
Zhou Q, Gallo JM. The pharmacokinetic/pharmacodynamic pipeline: translating anticancer drug pharmacology to the clinic. AAPS J. 2011 Mar;13(1):111-20. doi: 10.1208/s12248-011-9253-1.