Quantitative Systems Pharmacology

Quantitative systems pharmacology (QSP) is an exciting and powerful convergence of biological pathways, pharmacology, and mathematical models for drug development. QSP modeling helps drug developers understand the interaction of drugs in biological systems. Individually, the concepts and components of QSP are not new. However, merging these disciplines has the potential to significantly impact modern medicine by facilitating the discovery and utilization of newly identified molecular pathways and drug targets, especially in the pursuit of new therapies and individualized medicine.

What is Quantitative Systems Pharmacology? 

QSP is a computational method that mechanistically describes the interaction of a drug in the body. QSP models can be informed by in vitro data, animal models, and clinical endpoints in patients. QSP can be used to identify novel therapeutic targets, verify new therapeutic approaches to current targets, design virtual patient populations, and predict clinical exposure-response and efficacy outcomes for the design of clinical trials.

By utilizing “big data” (such as genomics, proteomics, and metabolomics), QSP can help guide appropriate study design or suggest additional experiments to make more informed drug development decisions. Similarly, QSP can significantly reduce missteps that might prolong the drug development process or even result in an unnecessary failure.

QSP has benefited from the insights gained in developing physiologically based pharmacokinetic (PBPK) models (predicting PK outcomes by differences in physiological variables) and has truly taken the power of systems biology and pharmacodynamics (PD) to a new level.

Quantitative Systems Pharmacology vs. Physiologically Based Pharmacokinetics

PBPK modeling is traditionally used to predict changes in PK outcomes in patient populations with changing physiological conditions. PD components can be added to PBPK models to integrate the effects of drug on the cellular processes that change the inherent physiological response.

QSP involves the development of mathematical models (e.g. ordinary differential equations) to describe biological systems relevant to a specific therapeutic target and to understand the mechanism(s) of action. These models are then used to predict clinically relevant PD responses (i.e. predicting heart-rate changes, muscle growth rate, etc.). QSP approaches can be particularly useful during early development when attempting to predict PD effects in humans based on early nonclinical in vitro and in vivo data.

Quantitative Systems Pharmacology Services

QSP is becoming an increasingly powerful component of drug development that can be employed at any stage (pre-clinical to Phase 3). The power of combining experimental, biological, and physiochemical data for model-informed decision making is just beginning to be harnessed. Allucent’s QSP services include:

  • Evaluation of therapeutic targets in drug discovery
  • Pre-clinical to clinical translation (PK, efficacy, and safety)
  • Prediction of PD response and efficacious dose
  • PK/PD design and dose recommendation for Phase 2 studies
  • Virtual patient populations

Benefits of Quantitative Systems Pharmacology

QSP can potentially save valuable time and resources during drug development. Applying QSP modeling early can guide the design of therapeutics from the very beginning of the drug discovery process. QSP can help facilitate the decision-making process in order to get the right drug, to the right patient, and at the right time.

Many drug candidates fail in Phase 1 due to poor PK properties or fail in Phase 2 due to less than expected efficacy. QSP offers the potential to predict and evaluate critical aspects related to the efficacy of a drug candidate and provide a “road map” to designing Phase 2 clinical studies to potentially improve outcomes.

Ideally, QSP is applied throughout the drug development process, from pre-clinical through clinical development, to harness its true power and capacity. In programs where the first clinical study will be in a patient population such as in a rare disease or in cellular and gene therapies, the need to predict a therapeutic dose for the first in human dose is crucial. QSP offers the ability to help make these critical decisions with more confidence.

QSP can incorporate evolving nonclinical and clinical data to better inform development decisions such as identification of novel indications, selection of doses, and evaluation of a drug’s potential to have a clinically meaningful impact for patients.