While anti-PD-1 and anti-PD-L1 anti-PD-(L)1 monotherapies are effective treatments for many types of cancer, high variability in patient responses is observed in clinical trials. Understanding the ...sources of response variability can help prospectively identify potential responsive patient populations. Preclinical data may offer insights to this point and, in combination with modeling, may be predictive of sources of variability and their impact on efficacy. Herein, a quantitative systems pharmacology (QSP) model of anti-PD-(L)1 was developed to account for the known pharmacokinetic properties of anti-PD-(L)1 antibodies, their impact on CD8
T cell activation and influx into the tumor microenvironment, and subsequent anti-tumor effects in CT26 tumor syngeneic mouse model. The QSP model was sufficient to describe the variability inherent in the anti-tumor responses post anti-PD-(L)1 treatments. Local sensitivity analysis identified tumor cell proliferation rate, PD-1 expression on CD8
T cells, PD-L1 expression on tumor cells, and the binding affinity of PD-1:PD-L1 as strong influencers of tumor growth. It also suggested that treatment-mediated tumor growth inhibition is sensitive to T cell properties including the CD8
T cell proliferation half-life, CD8
T cell half-life, cytotoxic T-lymphocyte (CTL)-mediated tumor cell killing rate, and maximum rate of CD8
T cell influx into the tumor microenvironment. Each of these parameters alone could not predict anti-PD-(L)1 treatment response but they could shift an individual mouse's treatment response when perturbed. The presented preclinical QSP modeling framework provides a path to incorporate potential sources of response variability in human translation modeling of anti-PD-(L)1.
Fructose is a major component of Western diets and is implicated in the pathogenesis of obesity and type 2 diabetes. In response to an oral challenge, the majority of fructose is cleared during ..."first-pass" liver metabolism, primarily via phosphorylation by ketohexokinase (KHK). A rare benign genetic deficiency in KHK, called essential fructosuria (EF), leads to altered fructose metabolism. The only reported symptom of EF is the appearance of fructose in the urine following either oral or intravenous fructose administration. Here we develop and use a mathematical model to investigate the adaptations to altered fructose metabolism in people with EF. First, the model is calibrated to fit available data in normal healthy subjects. Then, to mathematically represent EF subjects, we systematically implement metabolic adaptations such that model simulations match available data for this phenotype. We hypothesize that these modifications represent the major metabolic adaptations present in these subjects. This modeling approach suggests that several other aspects of fructose metabolism, beyond hepatic KHK deficiency, are altered and contribute to the etiology of this benign condition. Specifically, we predict that fructose absorption into the portal vein is altered, peripheral metabolism is slowed, renal reabsorption of fructose is mostly ablated, and alternate pathways for hepatic metabolism of fructose are upregulated. Moreover, these findings have implications for drug discovery and development, suggesting that the therapeutic targeting of fructose metabolism could lead to unexpected metabolic adaptations, potentially due to a physiological response to high-fructose conditions.
Non-alcoholic fatty liver disease is a metabolic and inflammatory disease that afflicts many people worldwide and presently has few treatment options. To enhance the preclinical to clinical ...translation and the design of early clinical trials for novel therapeutics, we developed a Quantitative Systems Pharmacology model of human hepatocyte lipid metabolism. The intended application of the model is for simulating anti-steatotic therapies for reversing fatty liver. We parameterized the model using literature data from humans with both normal and elevated liver fat. We assessed that the model construct was sufficient to generate a virtual population of NAFLD patients that matched relevant statistics of a published clinical cohort, and then validated the model response to treatment by simulating pioglitazone and diet intervention in the virtual population. Finally, a sensitivity analysis was performed to determine the best points of intervention for reducing hepatic steatosis. Analysis of the model suggests the most potent method for reducing hepatic steatosis is by limiting non-esterified fatty acid flux from the adipose to the liver.
A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. ...Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment, we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting mAb and antiviral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different time points post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post symptom onset prior to treatment.
Quantitative systems pharmacology (QSP) models aim to describe mechanistically the pathophysiology of disease and predict the effects of therapies on that disease. For most drug development ...applications, it is important to predict not only the mean response to an intervention but also the distribution of responses, due to inter-patient variability. Given the necessary complexity of QSP models, and the sparsity of relevant human data, the parameters of QSP models are often not well determined. One approach to overcome these limitations is to develop alternative virtual patients (VPs) and virtual populations (Vpops), which allow for the exploration of parametric uncertainty and reproduce inter-patient variability in response to perturbation. Here we evaluated approaches to improve the efficiency of generating Vpops. We aimed to generate Vpops without sacrificing diversity of the VPs’ pathophysiologies and phenotypes. To do this, we built upon a previously published approach (Allen et al., 2016) by (a) incorporating alternative optimization algorithms (genetic algorithm and Metropolis-Hastings) or alternatively (b) augmenting the optimized objective function. Each method improved the baseline algorithm by requiring significantly fewer plausible patients (precursors to VPs) to create a reasonable Vpop.
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 2019 ...(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 toward 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.
In ventricular myocytes, stimulation of β-adrenergic receptors activates critical cardiac signaling pathways, leading to shorter action potentials and increased contraction strength during the ...“fight-or-flight” response. These changes primarily result, at the cellular level, from the coordinated phosphorylation of multiple targets by protein kinase A. Although mathematical models of the intracellular signaling downstream of β-adrenergic receptor activation have previously been described, only a limited number of studies have explored quantitative interactions between intracellular signaling and electrophysiology in human ventricular myocytes. Accordingly, our objective was to develop an integrative mathematical model of β-adrenergic receptor signaling, electrophysiology, and intracellular calcium (Ca2+) handling in the healthy human ventricular myocyte. We combined published mathematical models of intracellular signaling and electrophysiology, then calibrated the model results against voltage clamp data and physiological changes occurring after stimulation of β-adrenergic receptors with isoproterenol. We subsequently: (1) explored how molecular variability in different categories of model parameters translated into phenotypic variability; (2) identified the most important parameters determining physiological cellular outputs in the model before and after β-adrenergic receptor stimulation; and (3) investigated which molecular level alterations can produce a phenotype indicative of heart failure with preserved ejection fraction (HFpEF). Major results included: (1) variability in parameters that controlled intracellular signaling caused qualitatively different behavior than variability in parameters controlling ion transport pathways; (2) the most important model parameters determining action potential duration and intracellular Ca2+ transient amplitude were generally consistent before and after β-adrenergic receptor stimulation, except for a shift in the importance of K+ currents in determining action potential duration; and (3) decreased Ca2+ uptake into the sarcoplasmic reticulum, increased Ca2+ extrusion through Na+/Ca2+ exchanger and decreased Ca2+ leak from the sarcoplasmic reticulum may contribute to HFpEF. Overall, this study provided novel insight into the phenotypic consequences of molecular variability, and our integrated model may be useful in the design and interpretation of future experimental studies of interactions between β-adrenergic signaling and cellular physiology in human ventricular myocytes.
•β-adrenergic regulation in human myocytes was explored with mathematical modeling•Between-cell functional variability depends on the source of molecular variability•β-adrenergic activation affects the importance of individual ion transport pathways•SR calcium leak may be reduced in heart failure with preserved ejection fraction
PF-05231023, a long-acting FGF21 analogue, is a promising potential pharmacotherapy for the treatment of obesity and associated comorbidities. Previous studies have shown the potential of FGF21 and ...FGF21-like compounds to decrease body weight in mice, non-human primates, and humans; the precise mechanisms of action remain unclear. In particular, there have been conflicting reports on the degree to which FGF21-induced weight loss in non-human primates is attributable to a decrease in food intake versus an increase in energy expenditure. Here, we present a semi-mechanistic mathematical model of energy balance and body composition developed from similar work in mice. This model links PF-05231023 administration and washout to changes in food intake, which in turn drives changes in body weight. The model is calibrated to and compared with recently published data from cynomolgus macaques treated with PF-05231023, demonstrating its accuracy in describing pharmacotherapy-induced weight loss in these animals. The results are consistent with the hypothesis that PF-05231023 decreases body weight in cynomolgus macaques solely by a reduction in food intake, with no direct effect on energy expenditure.
Metabolic flux analysis (MFA) involves model‐based estimation of metabolic reaction rates (i.e., fluxes) and, in some cases, metabolite content (i.e., pool sizes) from experimental measurements. ...Applying MFA to biological data helps determine the fate of substrates and the activity of specific pathways within metabolic networks. However, reliably estimating fluxes by using simplified “core” models to predict the dynamics of larger metabolic networks remains a challenge. One point of uncertainty relates to the advantages and potential pitfalls of including pool size measurements as experimental inputs for isotopically nonstationary MFA (INST‐MFA). Here, we directly assessed the role of pool sizes using various core models and simulated datasets. To investigate the effects of pool size measurements on INST‐MFA, we assessed the accuracy and precision of flux estimates obtained using different subsets of data (e.g., with or without pool size measurements) and simple network models that either matched or differed from the true network. The inclusion of pool size measurements provided incremental improvements to the precision of the flux estimates. However, adding pool size measurements increased the sensitivity of the flux solution to unmodeled reactions outside the core network. These results were confirmed using a large Escherichia coli model that is representative of realistic metabolic networks examined in MFA studies. Our findings indicate that accurate flux estimates can be obtained in the absence of pool size measurements, even when using core models that lack full network coverage. Addition of pool size measurements to INST‐MFA datasets may reveal the activity of non‐core reactions that influence the labeling dynamics and therefore necessitate network expansion in order to reconcile all available data to the model. Our findings also emphasize the key role that goodness‐of‐fit testing plays in assessing the quality of model fits obtained with INST‐MFA.
Graphical and Lay Summary
Intracellular pool size measurements have been used to constrain flux estimates in isotopically non‐stationary metabolic flux analysis (INST‐MFA). In this study, we demonstrate that pool size measurements are not necessary inputs for INST‐MFA. Fitting INST‐MFA models to isotope labeling datasets with pool size measurements may require model expansion to obtain acceptable fits, whereas fitting datasets without pool size measurements can still provide accurate and precise flux estimates based on simplified core models. This work emphasizes the pros and cons of including pool size measurements and the critical role of goodness‐of‐fit testing in metabolic flux analysis.
► Large-scale disease models for drug discovery and clinical advancement. ► Clinical phenotype-driven models of disease pathophysiology. ► Biological network simulations. ► Modeling and simulation ...predictions for the safety and efficacy of torcetrapib. ► Computational approaches for biomarker identification.
A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research.
Advancement in the fundamental understanding of physiologic processes has enabled the development of large computational models of disease that can assist in the evaluation and prioritization of drug targets.
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