A Quantitative Systems Physiology Model of Renal Function and Blood Pressure Regulation: Application in Salt-Sensitive Hypertension

Quantitative Systems Toxicology Modeling Informed Safe Dose Selection of Emvododstat in Acute Myeloid Leukemia Patients

Clinical investigation of emvododstat for the treatment of solid tumors was halted after two patients who were heavily treated with other anticancer therapies experienced drug-induced liver failure. However, preclinical investigations supported that emvododstat at lower doses might be effective in treating acute myeloid leukemia (AML) and against severe acute respiratory syndrome-coronavirus 2 as a dihydroorotate dehydrogenase inhibitor. Therefore, a quantitative systems toxicology model, DILIsym, was used to predict liver safety of the proposed dosing of emvododstat in AML clinical trials. In vitro mechanistic toxicity data of emvododstat and its desmethyl metabolite were integrated with in vivo exposure within DILIsym to predict hepatotoxicity responses in a simulated human population. DILIsym simulations predicted alanine aminotransferase elevations observed in prior emvododstat clinical trials in patients with solid tumors, but not in the prospective AML clinical trial with the proposed dosing regimens. Exposure predictions based on physiologically-based pharmacokinetic modeling suggested that reduced doses of emvododstat would produce clinical exposures that would be efficacious to treat AML. In the AML clinical trial, only eight patients experienced aminotransferase elevations, all of which were mild (grade 1), all resolving within a short period of time, and no patient showed symptoms of hepatotoxicity, confirming the prospective prediction of liver safety. Overall, retrospective DILIsym simulations adequately predicted the liver safety liabilities of emvododstat in solid tumor trials and prospective simulations predicted the liver safety of reduced doses in an AML clinical trial. The modeling was critical to enabling regulatory approval to proceed with the AML clinical trial wherein the predicted liver safety was confirmed.

Quantitative Systems Pharmacology for Rare Disease Drug Development

Though hundreds of drugs have been approved by the US Food and Drug Administration (FDA) for treating various rare diseases, most rare diseases still lack FDA-approved therapeutics. To identify the opportunities for developing therapies for these diseases, the challenges of demonstrating the efficacy and safety of a drug for treating a rare disease are highlighted herein. Quantitative systems pharmacology (QSP) has increasingly been used to inform drug development; our analysis of QSP submissions received by FDA showed that there were 121 submissions as of 2022, for informing rare disease drug development across development phases and therapeutic areas. Examples of published models for inborn errors of metabolism, non-malignant hematological disorders, and hematological malignancies were briefly reviewed to shed light on use of QSP in drug discovery and development for rare diseases. Advances in biomedical research and computational technologies can potentially enable QSP simulation of the natural history of a rare disease in the context of its clinical presentation and genetic heterogeneity. With this function, QSP may be used to conduct in-silico trials to overcome some of the challenges in rare disease drug development. QSP may play an increasingly important role in facilitating development of safe and effective drugs for treating rare diseases with unmet medical needs.

Sodium-Glucose Cotransporter 2 Inhibitor Canagliflozin Antagonizes Salt-Sensitive Hypertension Through Modifying Transient Receptor Potential Channels 3 Mediated Vascular Calcium Handling

Background Salt-sensitive hypertension is highly prevalent and associated with cardiorenal damage. Large clinical trials have demonstrated that SGLT2 (sodium-glucose cotransporter 2) inhibitors exert hypotensive effect and cardiorenal protective benefits in patients with hypertension with and without diabetes. However, the underlying mechanism remains elusive. Methods and Results Dahl salt-sensitive rats and salt-insensitive controls were fed with 8% high-salt diet and some of them were treated with canagliflozin. The blood pressure, urinary sodium excretion, and vascular function were detected. Transient receptor potential channel 3 (TRPC3) knockout mice were used to explain the mechanism. Canagliflozin treatment significantly reduced high-salt-induced hypertension and this effect was not totally dependent on urinary sodium excretion in salt-sensitive hypertensive rats. Assay of vascular function and proteomics showed that canagliflozin significantly inhibited vascular cytoplasmic calcium increase and vasoconstriction in response to high-salt diet. High salt intake increased vascular expression of TRPC3 in salt-sensitive rats, which could be alleviated by canagliflozin treatment. Overexpression of TRPC3 mimicked salt-induced vascular cytosolic calcium increase in vitro and knockout of TRPC3 erased the antihypertensive effect of canagliflozin. Mechanistically, high-salt-induced activation of NCX1 (sodium-calcium exchanger 1) reverse mode increased cytoplasmic calcium level and vasoconstriction, which required TRPC3, and this process could be blocked by canagliflozin. Conclusions We define a previously unrecognized role of TRPC3/NCX1 mediated vascular calcium dysfunction in the development of high-salt-induced hypertension, which can be improved by canagliflozin treatment. This pathway is potentially a novel therapeutic target to antagonize salt-sensitive hypertension.

Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease

Here we present a modular agent-based mathematical model of the human cardiovascular and renal systems. It integrates the previous models primarily developed by A. C. Guyton, F. Karaaslan, K. M. Hallow, and Y. V. Solodyannikov. We performed the model calibration to find an equilibrium state within the normal vital sign ranges for a healthy adult. We verified the model's abilities to reproduce equilibrium states with abnormal physiological values related to different combinations of cardiovascular diseases (such as systemic hypertension, chronic heart failure, pulmonary hypertension, etc.). For the model creation and validation, we involved over 200 scientific studies covering known models of the human cardiovascular and renal functions, biosimulation platforms, and clinical measurements of physiological quantities in normal and pathological conditions. We compiled detailed documentation describing all equations, parameters and variables of the model with justification of all formulas and values. The model is implemented in BioUML and available in the web-version of the software.

Sir George Johnson FRCP (1818-96), high blood pressure and the continuing altercation about its origins

NEW FINDINGS

What is the topic of this review? The review takes a historical approach to examining where in the body it might be possible to identify the most common cause, or causes, of long-term hypertension. It gathers evidence from histology, human and animal physiology, and computational modelling. The burden of decades of controversy is noted. What advances does it highlight? The review highlights the distinctive pathology of the afferent renal circulation and what its consequences are for the widespread view that essential hypertension is caused by elevated peripheral vascular resistance.

ABSTRACT

The widely promulgated notion that long-term elevation in mean arterial blood pressure (MAP) can be caused by raised peripheral vascular resistance remains a subject of vigorous debate. According to the 1967 mathematical model of Guyton and Coleman, such a causal relationship is impossible, kidney function being the determining factor. We explore this altercation starting with Sir George Johnson's 19th-century renal vascular histological observations in patients with Bright's disease. We note the striking physiological measurements in hypertensives by Gómez and Bolomey in the 1950s, moving on to the mathematical modelling of the circulation from the 1960s up to the ∼100-parameter computer models of the present day. Confusion has been generated by the fact that peripheral resistance is raised in hypertension in close proportion to MAP whilst cardiac output often stays normal, an apparent autoregulation, the mechanism of which is poorly understood. All models allowing for the circulation to be an open system show that isolated changes in peripheral resistance cannot lead to long-term hypertension, but models fail so frequently to account for results from experiments such as salt loading that their credibility with regard to this key finding is compromised. Laboratory animal models of adrenergic renal actions resonate with a contemporary emphasis on the sympathetic nerve supply to the kidney as contributing to the characteristically markedly elevated renal afferent resistance that appears to be the most common cause of hypertension. Remarkably, there remains no account of the way in which the fixed structural changes in vessels observed by Johnson relate to this sympathetic overactivity, which can itself be modified by drugs in the medium term. In this account, we seek to locate the crime scene and identify a smoking gun.

Cardiorenal Systems Modeling: Left Ventricular Hypertrophy and Differential Effects of Antihypertensive Therapies on Hypertrophy Regression

Cardiac and renal function are inextricably connected through both hemodynamic and neurohormonal mechanisms, and the interaction between these organ systems plays an important role in adaptive and pathophysiologic remodeling of the heart, as well as in the response to renally acting therapies. Insufficient understanding of the integrative function or dysfunction of these physiological systems has led to many examples of unexpected or incompletely understood clinical trial results. Mathematical models of heart and kidney physiology have long been used to better understand the function of these organs, but an integrated model of renal function and cardiac function and cardiac remodeling has not yet been published. Here we describe an integrated cardiorenal model that couples existing cardiac and renal models, and expands them to simulate cardiac remodeling in response to pressure and volume overload, as well as hypertrophy regression in response to angiotensin receptor blockers and beta-blockers. The model is able to reproduce different patterns of hypertrophy in response to pressure and volume overload. We show that increases in myocyte diameter are adaptive in pressure overload not only because it normalizes wall shear stress, as others have shown before, but also because it limits excess volume accumulation and further elevation of cardiac stresses by maintaining cardiac output and renal sodium and water balance. The model also reproduces the clinically observed larger LV mass reduction with angiotensin receptor blockers than with beta blockers. We further provide a mechanistic explanation for this difference by showing that heart rate lowering with beta blockers limits the reduction in peak systolic wall stress (a key signal for myocyte hypertrophy) relative to ARBs.

Impact of sex and pathophysiology on optimal drug choice in hypertensive rats: quantitative insights for precision medicine

Less than half of all hypertensive patients receiving treatment are successful in normalizing their blood pressure. Despite the complexity and heterogeneity of hypertension, the current antihypertensive guidelines are not tailored to the individual patient. As a step toward individualized treatment, we develop a quantitative systems pharmacology model of blood pressure regulation in the spontaneously hypertensive rat (SHR) and generate sex-specific virtual populations of SHRs to account for the heterogeneity between the sexes and within the pathophysiology of hypertension. We then used the mechanistic model integrated with machine learning tools to study how variability in these mechanisms leads to differential responses in rodents to the four primary classes of antihypertensive drugs. We found that both the sex and the pathophysiological profile of the individual play a major role in the response to hypertensive treatments. These results provide insight into potential areas to apply precision medicine in human primary hypertension.

Relationship between saline infusion and blood pressure variability in non-critically patients with hypertension: A retrospective study

Saline is a commonly used intravenous solvent, however, its excessive infusion may increase drug-induced sodium intake. To investigate the effects of saline infusion on blood pressure variability (BPV) in patients with hypertension, a retrospective study was performed in 1010 patients with hypertension. The patients who received saline infusion before surgery for continuous 3 to 5 days were divided into 2 groups according to the saline infusion volume during the hospitalization, which are >500 mL per day group and <500 mL per day group. The overall incidence of abnormal BPV was 11.58%. As for the incidence of abnormal BPV in the <500 mL per day group with 698 patients was 9.17%, while that in the >500 mL per day group with 312 patients was as high as 16.99%. Additionally, >500 mL of daily saline infusion for continuous 3 to 5 days (P for trend = .004, odds ratio [OR] = 1.911, 95% confidence interval [CI] for OR 1.226-2.977), medical history of diabetes mellitus (P < .001, OR = 4.856, 95% CI for OR 3.118-7.563) and cardiovascular diseases (P < .001, OR = 2.498, 95% CI for OR 1.549-4.029) may be risk factors of abnormal BPV; while anti-hypertensive therapy with diuretics (P < .001, OR = 0.055, 95% CI for OR 0.024-0.125) may be the protective factor. Our study suggests that >500 mL of daily saline infusion for continuous 3 to 5 days may have disadvantages in the blood pressure control for hypertensive patients, especially for the patients with diabetes mellitus and cardiovascular diseases.

Renal Effects of Dapagliflozin in People with and without Diabetes with Moderate or Severe Renal Dysfunction: Prospective Modeling of an Ongoing Clinical Trial

Sodium glucose cotransporter 2 inhibitors (SGLT2i) reduce cardiovascular events and onset and progression of renal disease by mechanisms that remain incompletely understood but may include clearance of interstitial congestion and reduced glomerular hydrostatic pressure. The ongoing DAPASALT mechanistic clinical study will evaluate natriuretic, diuretic, plasma/extracellular volume, and blood pressure responses to dapagliflozin in people with type 2 diabetes with normal or impaired renal function (D-PRF and D-IRF, respectively) and in normoglycemic individuals with renal impairment (N-IRF). In this study, a mathematical model of renal physiology, pathophysiology, and pharmacology was used to prospectively predict changes in sodium excretion, blood and interstitial fluid volume (IFV), blood pressure, glomerular filtration rate, and albuminuria in DAPASALT. After validating the model with previous diabetic nephropathy trials, virtual patients were matched to DAPASALT inclusion/exclusion criteria, and the DAPASALT protocol was simulated. Predicted changes in glycosuria, blood pressure, glomerular filtration rate, and albuminuria were consistent with other recent studies in similar populations. Predicted albuminuria reductions were 46% in D-PRF, 34.8% in D-IRF, and 14.2% in N-IRF. The model predicts a similarly large IFV reduction between D-PRF and D-IRF and less, but still substantial, IFV reduction in N-IRF, even though glycosuria is attenuated in groups with impaired renal function. When DAPASALT results become available, comparison with these simulations will provide a basis for evaluating how well we understand the cardiorenal mechanism(s) of SGLT2i. Meanwhile, these simulations link dapagliflozin's renal mechanisms to changes in IFV and renal biomarkers, suggesting that these benefits may extend to those with impaired renal function and individuals without diabetes. SIGNIFICANCE STATEMENT: Mechanisms of SGLT2 inhibitors' cardiorenal benefits remain incompletely understood. We used a mathematical model of renal physiology/pharmacology to prospectively predict responses to dapagliflozin in the ongoing DAPASALT study. Key predictions include similarly large interstitial fluid volume (IFV) reductions between subjects with normal and impaired renal function and less, but still substantial, IFV reduction in those without diabetes, even though glycosuria is attenuated in these groups. Comparing prospective simulations and study results will assess how well we understand the cardiorenal mechanism(s) of SGLT2 inhibitors.

On the role of the epithelium in a model of sodium exchange in renal tubules

In this study we present a mathematical model describing the transport of sodium in a fluid circulating in a counter-current tubular architecture, which constitutes a simplified model of Henle's loop in a kidney nephron. The model explicitly takes into account the epithelial layer at the interface between the tubular lumen and the surrounding interstitium. In a specific range of parameters, we show that explicitly accounting for transport across the apical and basolateral membranes of epithelial cells, instead of assuming a single barrier, affects the axial concentration gradient, an essential determinant of the urinary concentrating capacity. We present the solution related to the stationary system, and we perform numerical simulations to understand the physiological behaviour of the system. We prove that when time grows large, our dynamic model converges towards the stationary system at an exponential rate. In order to prove rigorously this global asymptotic stability result, we study eigen-problems of an auxiliary linear operator and its dual.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices

Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.

Multiscale Mathematical Model of Drug-Induced Proximal Tubule Injury: Linking Urinary Biomarkers to Epithelial Cell Injury and Renal Dysfunction

Drug-induced nephrotoxicity is a major cause of acute kidney injury, and thus detecting the potential for nephrotoxicity early in the drug development process is critical. Various urinary biomarkers exhibit different patterns following drug-induced injury, which may provide greater information than traditional biomarkers like serum creatinine. In this study, we developed a multiscale quantitative systems pharmacology model relating drug exposure to proximal tubule (PT) epithelial cell injury and subsequently to expression of multiple urinary biomarkers and organ-level functional changes. We utilized urinary kidney injury molecule-1 (Kim-1), alpha glutathione S-transferase, albumin (αGST), glucose, and urine volume time profiles as well as serum creatinine and histopathology data obtained from rats treated with the nephrotoxicant cisplatin to develop the model. Although the model was developed using single-dose response to cisplatin, the model predicted the serum creatinine response to multidose cisplatin regimens. Further, using only the urinary Kim-1 response to gentamicin (a nephrotoxicant with a distinctly different injury time course than cisplatin), the model detected and predicted mild to moderate PT injury, as confirmed with histopathology, even when serum creatinine was unchanged. Thus, the model is generalizable, and can be used to deconvolute the underlying degree and time course of drug-induced PT injury and renal dysfunction from a small number of urinary biomarkers, and may provide a tool to determine optimal dosing regimens that minimize renal injury.

Benchmarking renin suppression and blood pressure reduction of direct renin inhibitor imarikiren through quantitative systems pharmacology modeling

Multiple classes of antihypertensive drugs inhibit components of the renin-angiotensin-aldosterone system (RAAS). The primary physiological effector of the RAAS is angiotensin II (AngII) bound to the AT1 receptor (AT1-bound AngII). There is a strong non-linear feedback from AT1-bound AngII on renin secretion. Since AT1-bound AngII is not readily measured experimentally, plasma renin concentration (PRC) and/or activity (PRA) are typically measured to indicate RAAS suppression. We investigated the RAAS suppression of imarikiren hydrochloride (TAK-272; SCO-272), a direct renin inhibitor currently under clinical development. We employed a previously developed quantitative system pharmacology (QSP) model to benchmark renin suppression and blood pressure regulation with imarikiren compared to other RAAS therapies. A pharmacokinetic (PK) model of imarikiren was linked with the existing QSP model, which consists of a mechanistic representation of the RAAS pathway coupled with a model of blood pressure regulation and volume homeostasis. The PK and pharmacodynamic effects of imarikiren were calibrated by fitting drug concentration, PRA, and PRC data, and trough AT1-bound AngII suppression was simulated. We also prospectively simulated expected mean arterial pressure reduction in a cohort of hypertensive virtual patients. These predictions were benchmarked against predictions for several other (previously calibrated) RAAS monotherapies and dual-RAAS therapies. Our analysis indicates that low doses (5-10 mg) of imarikiren are comparable to current RAAS therapies, and at higher doses (25-200 mg), RAAS suppression may be equivalent to existing dual-RAAS combinations (at registered doses). This study illustrates application of QSP modeling to predict phase II endpoints from phase I data.

Evaluation of renal and cardiovascular protection mechanisms of SGLT2 inhibitors: model-based analysis of clinical data

The mechanisms of cardiovascular and renal protection observed in clinical trials of sodium-glucose cotransporter 2 (SGLT2) inhibitors (SGLT2i) are incompletely understood and likely multifactorial, including natriuretic, diuretic, and antihypertensive effects, glomerular pressure reduction, and lowering of plasma and interstitial fluid volume. To quantitatively evaluate the contribution of proposed SGLT2i mechanisms of action on changes in renal hemodynamics and volume status, we coupled a mathematical model of renal function and volume homeostasis with clinical data in healthy subjects administered 10 mg of dapagliflozin once daily. The minimum set of mechanisms necessary to reproduce observed clinical responses (urinary sodium and water excretion, serum creatinine and sodium) was determined, and important unobserved physiological variables (glomerular pressure, blood and interstitial fluid volume) were then simulated. We further simulated the response to SGLT2i in diabetic virtual patients with and without renal impairment. Multiple mechanisms were required to explain the observed response: 1) direct inhibition of sodium and glucose reabsorption through SGLT2, 2) SGLT2-driven inhibition of Na⁺/H⁺ exchanger 3 sodium reabsorption, and 3) osmotic diuresis coupled with peripheral sodium storage. The model also showed that the consequences of these mechanisms include lowering of glomerular pressure, reduction of blood and interstitial fluid volume, and mild blood pressure reduction, in agreement with clinical observations. The simulations suggest that these effects are more significant in diabetic patients than healthy subjects and that while glucose excretion may diminish with renal impairment, improvements in glomerular pressure and blood volume are not diminished at lower glomerular filtration rate, suggesting that cardiorenal benefits of SGLT2i may be sustained in renally impaired patients.

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.