Quantitative systems pharmacology (QSP) is an exciting and powerful convergence of biological pathways, pharmacology, and mathematical models for drug development. Individually, the concepts and components of QSP (understanding the interaction of drugs in biological systems) 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 therapeutics and individualized medicine.
What is QSP?
QSP is a computational method that mechanistically describes the interaction of the drug in the body. QSP models can be informed by in vitro data (e.g., receptor interaction), animal models (biomarker response in animal models of disease), and clinical endpoints in patients.
Utilizing big data (i.e., genomics, proteomics, 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 can be leveraged 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 early clinical trials. QSP has benefited from the insights gained in developing physiological based pharmacokinetic (PBPK) models (ex. predicting PK outcomes by differences in physiological variables) and has truly taken the power of systems biology and pharmacodynamics (PD) to a new level.
QSP 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 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.). The QSP approach can be particularly useful during early development when attempting to predict pharmacodynamic effects in humans based on early nonclinical in vitro and in vivo data.
|Question||PK model||PD model||PBPK model||QSP model|
|Effect of drug on body||X||X||X|
|Effect of body on drug||X||X||X|
|Interaction of drug and disease||X||X|
|Mechanistic explanation for interaction of drug and disease||X|
Benefits of QSP
QSP can save valuable resources during drug development and, importantly, reduce the time to getting therapies to the patients in need. QSP can facilitate the decision-making process in order to get the right drug, to the right patient, for the right disease, at the right time, and in the right dose. Early application of QSP can guide the design of therapeutics from the very beginning of the drug discovery process.
Many drug candidates fail in Phase 1 due to poor pharmacokinetic (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 preclinical through clinical development, to harness its true power and capacity. For example, in programs where the first clinical study will be in a patient population such as in a rare disease or in cell 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 and ethical 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.
Nuventra’s QSP Services
- Evaluation of therapeutic targets in drug discovery
- Preclinical 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
Nuventra’s QSP Experience
QSP can be employed at all stages of drug development (preclinical to Phase 3). Nuventra’s consultants have many years of experience designing quantitative and translational pharmacology strategy.