Improving the Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Rieger, Theodore R.; Bystricky, Lukas; Chen, Yuzhou; Colopy, Glen Wright; Cui, Yifan; González, Angélica; Liu, Yifei; White, Rebekah; Everett, Rebecca A.; Banks, H. T.; Musante, Cynthia J.

doi:10.1101/196089

Cited by 8 publications

(9 citation statements)

References 9 publications

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“…To evaluate the ability of our model to support the construction of a virtual population, 51 we examined the response to resampling a subset of parameters that describe the relative strength of virus infectivity and the immune system (see Supplementary Material s ).…”

Section: Resultsmentioning

confidence: 99%

A Prototype QSP Model of the Immune Response to SARS‐CoV‐2 for Community Development

Dai

Rao

Sher

et al. 2020

CPT Pharmacom & Syst Pharma

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic requires the rapid development of efficacious treatments for patients with life-threatening coronavirus disease (COVID-19). Quantitative Systems Pharmacology (QSP) models are mathematical representations of pathophysiology for simulating and predicting the effects of existing or putative therapies. The application of model-based approaches, including QSP, have accelerated the development of some novel therapeutics. Nevertheless, the development of disease-scale mechanistic models can be a slow process, often taking years to be validated and considered mature. Furthermore, emerging data may make any QSP model quickly obsolete. We present a prototype QSP model to facilitate further development by the scientific community. The model accounts for the interactions between viral dynamics, the major host immune response mediators, and tissue damage and regeneration. The immune response is determined by viral activation of innate and adaptive immune processes that regulate viral clearance and cell damage. The prototype model captures two physiologically relevant outcomes following infection: a 'healthy' immune response that appropriately defends against the virus, and an uncontrolled alveolar inflammatory response that is characteristic of acute respiratory distress syndrome. We aim to significantly shorten the typical QSP model development and validation timeline by encouraging community use, testing, and refinement of this prototype model. It is our expectation that the model will be further advanced in an open science approach; i.e. by multiple contributions towards a validated quantitative platform in an open forum, with the ultimate goal of informing and accelerating the development of safe and effective treatment options for patients.

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Section: Resultsmentioning

confidence: 99%

A Prototype QSP Model of the Immune Response to SARS‐CoV‐2 for Community Development

Dai

Rao

Sher

et al. 2020

CPT Pharmacom & Syst Pharma

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“…Building on this previous study, we produced a virtual population of patients with HF-rEF by varying model parameters over physiologically plausible ranges and reflecting the baseline characteristics of the DAPA-HF clinical study population. [34][35][36][37][38] The predicted cardiac hemodynamic responses to SGLT2i vary from patient to patient due to their diverse physiological states (Figure 8) relative to the findings from 1 HF-rEF virtual patient. 17 Simulating the DAPA-HF per study protocol, the initial diuresis and natriuresis with SGLT2 inhibition is predicted to reduce blood volume and IFV within the first 14 days (Figure 6), lowering preload and afterload on the heart.…”

Section: Discussionmentioning

confidence: 99%

“…However, the resultant cardiac improvements for a single virtual patient cannot reflect the varieties of patients in a clinical trial. Building on this previous study, we produced a virtual population of patients with HF‐rEF by varying model parameters over physiologically plausible ranges and reflecting the baseline characteristics of the DAPA‐HF clinical study population 34–38 . The predicted cardiac hemodynamic responses to SGLT2i vary from patient to patient due to their diverse physiological states (Figure 8) relative to the findings from 1 HF‐rEF virtual patient 17 .…”

Section: Discussionmentioning

confidence: 99%

Predicted Cardiac Hemodynamic Consequences of the Renal Actions of SGLT2i in the DAPA‐HF Study Population: A Mathematical Modeling Analysis

Tang

Greasley

et al. 2020

The Journal of Clinical Pharma

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The Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure (DAPA-HF) study demonstrated that dapagliflozin, a sodium-glucose cotransporter-2 inhibitor (SGLT2i), reduced heart failure hospitalization and cardiovascular death in patients with heart failure with reduced ejection fraction (HF-rEF), with and without type 2 diabetes mellitus. Multiple potential mechanisms have been proposed to explain this benefit, which may be multifactorial. This study aimed to quantify the contribution of the known natriuretic/diuretic effects of SGLT2is to changes in cardiac hemodynamics, remodeling, and fluid homeostasis in the setting of HF-rEF. An integrated cardiorenal mathematical model was used to simulate inhibition of SGLT2 and its consequences on cardiac hemodynamics in a virtual population of HF-rEF patients generated by varying model parameters over physiologically plausible ranges and matching to baseline characteristics of individual DAPA-HF trial patients. Cardiovascular responses to placebo and SGLT2i over time were then simulated. The baseline characteristics of the HF-rEF virtual population and DAPA-HF were in good agreement. SGLT2i-induced diuresis and natriuresis that reduced blood volume and interstitial fluid volume, relative to placebo within 14 days. This resulted in decreased left ventricular end-diastolic volume and pressure, indicating reduced cardiac preload. Thereafter, blood volume and interstitial fluid volume again began to accumulate, but pressures and volumes remained shifted lower relative to placebo. After 1 year, left ventricle mass was lower and ejection fraction was higher than placebo. These simulations considered only hemodynamic consequences of the natriuretic/diuretic effects of SGLT2i, as other mechanisms may contribute additional benefits besides those predictions.

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“…Evaluating the treatment effects of therapeutic regimens and exploring the MoA by integrating preclinical/clinical data of drugs and disease phenotypes (TCMSP, Virtual Tumour, CancerHSP, etc.) [52][53][54][55][56][57][58][59][60][61][62][63][64] Virtual patient A simplified model to translate complex biological processes into a series of intuitive equations [65][66][67][68] and the high-throughput virtual screening is accomplished by analyzing the binding affinity between compound and target. For example, Omer discovered two novel antiviral molecules (Calanolide A and Chaetochromin B) and their target HRAS by molecular docking and molecular dynamics simulation [73].…”

Section: Molecular Level Evaluation Of Molecular Characteristicsmentioning

confidence: 99%

“…However, the preclinical and clinical data for the establishment of models is lacking in some aspects. Many scientists simplified the models by alternative parameterization to reduce the need for data, and this method is also called "virtual patient" [65][66][67][68]97]. Moreover, a mechanistically-based weighting method to match clinical trial statistics at population level was introduced in a comprehensive analysis (virtual population) [60].…”

Section: Virtual Patientmentioning

confidence: 99%

Computational methods and applications for quantitative systems pharmacology

Xie

2019

Quant. Biol.

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Background: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. Results: Various computational methods such as ADME/T evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient for QSP have been developed. We reviewed the major progresses and broad applications in medical guidance, drug discovery and exploration of pharmacodynamic material basis and mechanism of traditional Chinese medicine. Conclusion: QSP has significant achievements in recent years and is a promising approach for quantitative evaluation of drug efficacy and systematic exploration of mechanisms of action of drugs.Author summary: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. This review is an attempt to introduce the computational methods for QSP, including ADME/T (absorption, distribution, metabolism, excretion and toxicity) evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient as well as their applications in medical guidance, drug discovery and explorations of pharmacodynamics material basis and mechanism of traditional Chinese medicine.

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Improving the Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Cited by 8 publications

References 9 publications

A Prototype QSP Model of the Immune Response to SARS‐CoV‐2 for Community Development

A Prototype QSP Model of the Immune Response to SARS‐CoV‐2 for Community Development

Predicted Cardiac Hemodynamic Consequences of the Renal Actions of SGLT2i in the DAPA‐HF Study Population: A Mathematical Modeling Analysis

Computational methods and applications for quantitative systems pharmacology

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