Systems Pharmacology

Quantitative Systems Pharmacology (QSP): Integrating Quantitative Drug Data with Knowledge of Its Mechanism of Action

One of the biggest challenges – and, hence, the biggest opportunity for QSP – is drug attrition in Phase 2 clinical trials. Investigational medicines are usually tested for the first time in patients in phase 2 clinical trials. This is the point when many drug programs fail. In fact, approximately 80 percent of new drugs that move into Phase 2 fail. The major reason for this failure is that the drug doesn’t show efficacy or is not safe. This high failure rate wastes lots of money and time. Using QSP to augment current biosimulation technology (modeling and simulation) could help tackle this issue.

What is QSP?

QSP is a relatively new discipline with enormous potential to improve pharma R&D productivity. Most major pharma organizations are investing in it. QSP may also be able to take advantage of the enormous amounts of information we now have access to, including genomics and proteomic data.

QSP combines computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process. This emerging discipline integrates quantitative drug data with knowledge of its mechanism of action. QSP models predict how drugs modify cellular networks in space and time and how they impact and are impacted by human pathophysiology. Additionally, QSP facilitates evaluating complex, heterogeneous diseases such as cancer, immunological, metabolic and CNS diseases that probably will require combination therapies to fully control them.

Benefits of QSP

  • Support precision medicine: In the past, we treated many diseases as monolithic. We used a “one size fits all” approach for everyone. We’ve now started to recognize that many diseases are actually a plethora of different diseases affecting distinct subpopulations of patients. By leveraging QSP, sponsors can rationally plan which patient subpopulation to target before running that make-or-break Phase 2 trial. That could make the difference between failure and success in Phase 2.
  • Increase the likelihood of demonstrating drug efficacy: QSP builds on insights gained from PBPK. Once we know how much drug is at the site of action, how will it modulate cellular signaling to exert a pharmacological effect? What pharmacological action will it have at that particular organ? Answering these questions will provide insight into the mechanisms of drug efficacy.
  • Provide insight into mechanisms of toxicity: QSP determines the exposure at various organs to predict potential side effects. This approach translates pharmacokinetics (drug exposure) to pharmacodynamics (pharmacological effects).
  • Perform “what if” scenarios: QSP can be used from the early stages of discovery onwards, helping identify biological pathways and determinants of disease. You can ask questions such as: “Does drug A or drug B have a better pharmacological profile?” If you determine that drug A has a stronger impact on biological path Y than biological path Z, is that going to be more effective than drug B which has a stronger effect on biological path Z than Y? QSP modeling allows investigation of a wide range of what-if scenarios to determine what the likely efficacy of the drug is going to be, without having to do clinical investigation and facilitating lead optimization very early on in the discovery process.
  • Support discovery of new drugs: Pharma looks to QSP to utilize the tremendous amount of data now being generated from the “omics sciences” (genomics, proteomics, and metabolomics). Today, we have access to vast quantities of data that we have only been able to generate within the last few years. Using QSP models and other biosimulation tools will help integrate this new data into pharmaceutical R&D to support the discovery of new medicines.
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