Quantitative Systems Pharmacology meets Systems Biology

This article collection brings together cutting-edge research at the intersection of Quantitative Systems Pharmacology (QSP) and Systems Biology, showcasing how integrative, model-informed approaches are transforming our understanding and treatment of complex biomedical challenges. The first article explores the potential of QSP and precision medicine in developing disease-modifying therapies for Parkinson’s disease, emphasizing the crucial role of genetic discoveries, biomarkers, and the regulatory push for model-informed drug development. The second piece leverages mathematical modeling to optimize machine perfusion strategies for organ preservation, providing a quantitative framework for minimizing ischemia/reperfusion injury through targeted temperature and antioxidant control. Finally, the third article presents a semi-mechanistic model elucidating the entrainment of peripheral circadian clocks by temperature cues, underscoring the power of systems approaches in decoding physiological synchronization mechanisms. Collectively, these studies illustrate the power of systems-level, quantitative modeling both to interpret complex biological data and to enable rational design of innovative therapeutic and experimental interventions.

---

Quantitative Systems Pharmacology (QSP) has emerged as the convolution of four distinct areas:
a) systems biology, which focuses on modeling the dynamics of molecular and cellular mechanisms and networks;
b) systems pharmacology, which aims at incorporating links between therapeutic interventions and drug mechanisms;
c) systems physiology, which describes disease mechanisms driving the dynamics of the onset and resolution of a disease and its symptoms in the context of a patient’s physiology, genetics, age, sex, behaviors, and lifestyle; and
d) data science, which enables the integration of relevant, yet diverse, biomarkers of efficacy and safety, disease pathology and phenotype, treatment, and clinical endpoints.

A QSP model always refers to an in silico model, either mathematical (equation-based, mostly ordinary of partial differential equations) or computational (agent-based, rule-based, Boolean, or Bayesian, to name a few), describing spatiotemporal dynamic evolution of biomarkers, or outcomes of interest. In QSP, the aim is, broadly, to use (semi-)mechanistic rules that express fundamental phenomena approximating first principles, such as mass action, transport, binding, etc., as a guide

Thus, QSP has benefited from several advanced in the field of systems biology (SB), including modeling methodologies, algorithmic innovations, and mathematical advances. However, the exact boundaries between an SB and a QSP model become blurry. Although both belong to the broader modeling continuum, the distinction between the two is often a matter of contention.

In this special issue, we wish to examine the role SB plays in developing QSP mode and, using this as the starting point, solicit contributions examining whether and how QSP extends and expands SB and identify opportunities for further synergies. We solicit contributions that discuss applications, theoretical advances, and issues related to the assessment and standardization of QSP/SB models. Finally, we welcome contributions discussing the need for developing “best practices” in developing these models.

The special issue will include original scientific papers, perspectives, and reviews that fit the theme. Contributions not fitting the theme will be considered general submissions to the journal.

This topic aims to expand on discussion from the 9th International Conference on the Foundations of Systems Biology in Engineering (FOSBE 2022).

Keywords: Quantitative Systems Pharmacology, systems pharmacology, systems physiology, data science, in silico models, best practices

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.