Support precision medicine: In the past, many diseases were treated as uniform conditions using a "one size fits all" approach. However, we have started to realize that many diseases comprise a variety of distinct conditions that affect different patient subpopulations. By utilizing Quantitative Systems Pharmacology (QSP), sponsors can strategically plan which patient subpopulation to target before conducting critical Phase 2 trials. This approach can significantly impact the success or failure of these trials.
Increase the likelihood of demonstrating drug efficacy: QSP is built on insights gained from PBPK modeling. Once we determine the concentration of the drug at the site of action, we need to understand how it will influence cellular signaling to produce a pharmacological effect. What specific actions will it have on that particular organ? Addressing these questions will help illuminate the mechanisms behind drug efficacy.
Provide insight into mechanisms of toxicity: QSP assesses organ exposure to predict potential side effects. This method connects pharmacokinetics (drug exposure) with pharmacodynamics (pharmacological effects).
Perform “what if” scenarios: QSP (Quantitative Systems Pharmacology) can be utilized from the early stages of drug discovery to help identify biological pathways and disease determinants. This approach allows researchers to explore questions such as: "Which drug has a better pharmacological profile: drug A or drug B?"
For example, if drug A has a stronger impact on biological pathway Y while drug B has a greater effect on biological pathway Z, researchers can evaluate which drug is likely to be more effective overall. QSP modeling enables the investigation of a wide range of hypothetical scenarios to assess the anticipated efficacy of a drug without the need for extensive clinical trials, thus facilitating lead optimization early in the discovery process.
Support discovery of new drugs: The pharmaceutical industry is turning to Quantitative Systems Pharmacology (QSP) to leverage the immense volume of data generated from the omics sciences, which include genomics, proteomics, and metabolomics. In recent years, we have gained access to vast amounts of data that were previously unavailable. By utilizing QSP models and other biosimulation tools, we can integrate this new data into pharmaceutical research and development (R&D) to support the discovery of new medicines.