Quantitative systems pharmacology (QSP) is a field of biomedical research that aims to model the mechanisms behind disease progression and quantify the pharmacokinetics and pharmacodynamics of pharmaceuticals using mathematical computer models.
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In silico modeling of pharmacokinetics and pharmacodynamics (PK-PD) is a popular concept in drug design and discovery, and mechanism-based PK-PD modeling is used to characterize specific processes to correlate a causal path between drug exposure and drug response. Systems pharmacology aims to model the effect of drugs on complex biological systems computationally, instead of focusing on specific interactions between molecules as in PK-PD modeling.
This may mean that the technique is more appropriately applied to discovering emergent properties and general trends, and could be considered holistic rather than reductionist. For example, modeling a complex protein signaling network allows the interaction of a drug with the system to be characterized. Systems pharmacology is distinct from bioinformatics in the focus on dynamic systems, rather than the analysis of static blocks of data.
QSP combines PK-PD modeling with systems pharmacology, modeling molecules on the PK-PD mechanistic level and analyzing the systemic effects and emergent trends. Aspects of drug administration: dose, dosing regimen, concentration at a target site, and affinity of drug with receptor can each be independently controlled and tested against various aspects of target biology at the molecular, cellular, and full-body level.
How is quantitative systems pharmacology used?
Quantitative systems pharmacology is increasingly being employed in drug discovery efforts to assist with target selection, describe mechanisms of action, and perform scenario analysis testing. It has been used to optimize dosing regimens and plot regimens for those being administered with combinations of drugs.
In order to perform QSP a researcher must have access to calibration data relevant to the system being simulated, with more rigorous data collected from literature allowing for more accurate simulations to be programmed for. Once a system has been simulated, the sensitivity of the dynamic system to particular changes and inputs can be tested. For example, exposure to specifically sized and timed doses of drugs.
As QSP is predictive of systemic effects it can be used to infer the mechanism of action of a drug under scrutiny. Hypothesized mechanisms can be programmed into the QSP simulation, and the resulting changes compared to observed changes in in vivo or in vitro testing. Early discovery of probable mechanisms by in silico testing can potentially save a great deal of lab time by massively reducing the number of key molecules that must be tracked in in vivo or in vitro testing.
In addition to use in early-stage drug discovery, QSP models can be used to compare a drug quantitatively against a common same-purpose drug or market competitor for commercial purposes. Overall efficacy, safety, frequency of specific target in the population, cost per dose of drug, and economies of drug combinations can be evaluated using QSP, allowing pharmaceutical companies to make better-informed decisions regarding the likely market share of their product.
Case study: Cardiovascular benefits of SGLT2 inhibitors
Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a class of medications prescribed to lessen cardiovascular events and control blood glucose levels. Many patients prescribed these medications show significant improvements in cardio-renal systems, with the underlying mechanisms not yet fully elucidated.
Quantitative systems pharmacology modeling was used to simulate the flow of blood flow through the kidney, the rate of filtration and reabsorption of substances, blood pressure, the resulting systemic balance and distribution of fluids and electrolytes, and the neurohormonal processes that regulate these systems. The study (Helmlinger et al., 2019) utilized data collected from a drug-drug interaction study of urinary biomarker responses to SGLT2 inhibitors in healthy subjects.
Three hypothetical mechanisms were tested using the system established: direct SGLT2 inhibition, the inclusion of glucose-induced osmotic diuresis, and coupled inhibition of both SGLT2 and sodium-hydrogen antiporter 3 (another transport protein that is mechanistically associated). SGLT2 inhibition alone as a mechanism underestimated the glucose, water, Na+, and creatinine response, while including the two latter variables produced data that was in line with that published in the literature.
Additionally, QSP modeling identified an unknown mechanism for Na+ removal, as the concentration is not observed to rise as the volume of water in the system lowers, both in clinical studies and in silico. Other studies have suggested the involvement of a peripheral nonosmotic sodium storage compartment, which explained the observed phenomenon one added to the simulation.
These results support that each of these mechanisms is taking place, explaining the observed renal and cardiac benefits of SGLT2 inhibitors. In chronic heart failure renal retention of sodium and water results in interstitial fluid expansion, to which the kidney responds with an effective reducing in blood volume, inducing further renal retention.
One additional novel finding of the QSP model was that the drug affects the body by lowering interstitial fluid to a greater degree than blood volume, alleviating this feedback loop. This trend was only observable by QSP thanks to the pathophysiological model employed, and several trials have since been launched to deploy SGLT2 inhibitors for renal and cardiovascular indications.
- Helmlinger, G. et al. (2019) Quantitative Systems Pharmacology: An Exemplar Model‐Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development. CPT: Pharmacokinetics & Systems Pharmacology, 8(6). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617832/
- Masuda, T. et al. (2020) Osmotic diuresis by SGLT2 inhibition stimulates vasopressin-induced water reabsorption to maintain body fluid volume. Physiological Reports, 8(2). https://pubmed.ncbi.nlm.nih.gov/31994353/
- Fioretto, P., Zambon, A., Rossato, M., Busetto, L. & Vettor, R. (2016) SGLT2 Inhibitors and the Diabetic Kidney. Diabetes Care, 39. https://care.diabetesjournals.org/content/39/supplement_2/s165.abstract
- Boran, A. D. W. & Iyengar, R. (2011) Systems Pharmacology. Mount Sinai Journal of Medicine, 77(4). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3113679/
- Danhof, M., Lange, E. C. M., Pasqua, O. E. D., Ploeger, B. A. & Voskuyl, R. A. (2008) Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research. Trends in pharmacological sciences, 29(4). https://pubmed.ncbi.nlm.nih.gov/18353445/#:~:text=Mechanism%2Dbased%20PK%2DPD%20models,drug%20exposure%20and%20drug%20response.&text=Ultimately%2C%20mechanism%2Dbased%20PK%2D,disease%20processes%20and%20disease%20progression.
- Miller, W. L. (2016) Fluid Volume Overload and Congestion in Heart Failure. Circulation: Heart Failure, 9(8). https://www.ahajournals.org/doi/full/10.1161/CIRCHEARTFAILURE.115.00292