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KRAS is a small GTPase family protein that relays extracellular growth signals to cell nucleus. KRAS G12C mutations lead to constitutive proliferation signaling and are prevalent across human cancers. ASP2453 is a novel, highly potent, and selective inhibitor of KRAS G12C. Although preclinical data suggested impressive efficacy, it remains unclear whether ASP2453 will show more favorable clinical response compared to more advanced competitors, such as AMG 510. Here, we developed a quantitative systems pharmacology (QSP) model linking KRAS signaling to tumor growth in patients with non‐small cell lung cancer. The model was parameterized using in vitro ERK1/2 phosphorylation and in vivo xenograft data for ASP2453. Publicly disclosed clinical data for AMG 510 were used to generate a virtual population, and tumor size changes in response to ASP2453 and AMG 510 were simulated. The QSP model predicted ASP2453 exhibits greater clinical response than AMG 510, supporting potential differentiation and critical thinking for clinical trials.

Alzheimer's disease (AD) is an irreversible, progressive brain disorder that impairs memory and cognitive function. Dysregulation of the amyloid-β (Aβ) pathway and amyloid plaque accumulation in the brain are hallmarks of AD. Aducanumab is a human, immunoglobulin gamma 1 monoclonal antibody targeting aggregated forms of Aβ. In phase Ib and phase III studies, aducanumab reduced Aβ plaques in a dose dependent manner, as measured by standard uptake value ratio of amyloid positron emission tomography imaging. The goal of this work was to develop a quantitative systems pharmacology model describing the production, aggregation, clearance, and transport of Aβ as well as the mechanism of action for the drug to understand the relationship between aducanumab dosing regimens and changes of different Aβ species, particularly plaques in the brain. The model was used to better understand the pharmacodynamic effects observed in the clinical trials of aducanumab and assist in the clinical development of future Aβ therapies.

Introduction Despite strong evidence linking amyloid beta (Aβ) to Alzheimer's disease, most clinical trials have shown no clinical efficacy for reasons that remain unclear. To understand why, we developed a quantitative systems pharmacology (QSP) model for seven therapeutics: aducanumab, crenezumab, solanezumab, bapineuzumab, elenbecestat, verubecestat, and semagacestat. Methods Ordinary differential equations were used to model the production, transport, and aggregation of Aβ; pharmacology of the drugs; and their impact on plaque. Results The calibrated model predicts that endogenous plaque turnover is slow, with an estimated half-life of 2.75 years. This is likely why beta-secretase inhibitors have a smaller effect on plaque reduction. Of the mechanisms tested, the model predicts binding to plaque and inducing antibody-dependent cellular phagocytosis is the best approach for plaque reduction. Discussion A QSP model can provide novel insights to clinical results. Our model explains the results of clinical trials and provides guidance for future therapeutic development.

The coronavirus disease 2019 (COVID‐19) pandemic has caused respiratory failure and associated mortality in numbers that have overwhelmed global health systems. Thrombotic coagulopathy is present in nearly three quarters of patients with COVID‐19 admitted to the intensive care unit, and both the clinical picture and pathologic findings are consistent with microvascular occlusive phenomena being a major contributor to their unique form of respiratory failure. Numerous studies are ongoing focusing on anticytokine therapies, antibiotics, and antiviral agents, but none to date have focused on treating the underlying thrombotic coagulopathy in an effort to improve respiratory failure in COVID‐19. There are animal data and a previous human trial demonstrating a survival advantage with fibrinolytic therapy to treat acute respiratory distress syndrome. Here, we review the extant and emerging literature on the relationship between thrombotic coagulopathy and pulmonary failure in the context of COVID‐19 and present the scientific rationale for consideration of targeting the coagulation and fibrinolytic systems to improve pulmonary function in these patients.

Crigler-Najjar syndrome type 1 (CN1) is an autosomal recessive disease caused by a marked decrease in uridine-diphosphate-glucuronosyltransferase (UGT1A1) enzyme activity. Delivery of hUGT1A1-modRNA (a modified messenger RNA encoding for UGT1A1) as a lipid nanoparticle is anticipated to restore hepatic expression of UGT1A1, allowing normal glucuronidation and clearance of bilirubin in patients. To support translation from preclinical to clinical studies, and first-in-human studies, a quantitative systems pharmacology (QSP) model was developed. The QSP model was calibrated to plasma and liver mRNA, and total serum bilirubin in Gunn rats, an animal model of CN1. This QSP model adequately captured the observed plasma and liver biomarker behavior across a range of doses and dose regimens in Gunn rats. First-in-human dose projections made using the translated model indicated that 0.5 mg/kg Q4W dose should provide a clinically meaningful and sustained reduction of >5 mg/dL in total bilirubin levels.

Crigler‐Najjar syndrome type 1 (CN1) is an autosomal recessive disease caused by a marked decrease in uridine‐diphosphate‐glucuronosyltransferase (UGT1A1) enzyme activity. Delivery of hUGT1A1‐modRNA (a modified messenger RNA encoding for UGT1A1) as a lipid nanoparticle is anticipated to restore hepatic expression of UGT1A1, allowing normal glucuronidation and clearance of bilirubin in patients. To support translation from preclinical to clinical studies, and first‐in‐human studies, a quantitative systems pharmacology (QSP) model was developed. The QSP model was calibrated to plasma and liver mRNA, and total serum bilirubin in Gunn rats, an animal model of CN1. This QSP model adequately captured the observed plasma and liver biomarker behavior across a range of doses and dose regimens in Gunn rats. First‐in‐human dose projections made using the translated model indicated that 0.5 mg/kg Q4W dose should provide a clinically meaningful and sustained reduction of >5 mg/dL in total bilirubin levels.

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety.

A quantitative systems pharmacology (QSP) model of oncolytic immunotherapies based on the myxoma virus (MYXV) platform was constructed to project systemic cytokine exposure after intravenous (IV) administration, and to determine safe doses. Oncolytic viruses selectively replicate in and lyse tumor cells and provide stimulation to the immune system. The genome of MYXV is relatively large and is amenable to engineering for expression of transgenic proteins. The QSP model mechanistically describes virus administration, infection of competent cells, promoter-dependent gene transcription, cytokine payload production and secretion, and secondary cytokine responses. An in vitro model was calibrated to cytokine release data from virus infected PBMCs. A mouse model was developed to recapitulate virus PK data and cytokine data collected in the serum of tumor-bearing mice who received various IV doses of oncolytic virus constructs. The cytokine data include time-resolved measurements of transgenic cytokine expression (the viral payload), the immediate endogenous cytokine response to the virus vehicle itself, as well as secondary cytokine responses. Learnings from the in vitro model, the in vivo mouse model as well as from human specific literature information were used to create the human model. The resulting human model was used to project systemic expression of cytokine payloads and subsequent secondary cytokine responses for various IV doses of the oncolytic virus construct. Projected cytokine levels were compared to previously established cytokine exposure at maximum tolerated doses of IV administered cytokines to assess potential safety implications. The impact of uncertainties in model predictions stemming from variability in the available data and from the assumptions made during model building was evaluated. The upper range of predicted systemic cytokine exposure in humans is still expected to fall within known safety margins at the planned doses of oncolytic immunotherapies. In the absence of human data, QSP modeling represents a holistic approach of integrating all available knowledge and preclinical data for the purpose of human prediction. It yields valuable insight on the system-wide effects of IV oncolytic viral administration and can be expanded in the future to include assessment of efficacy in the form of tumor growth inhibition. Citation Format: A. Katharina Wilkins, Lina S. Franco, Diana H. Marcantonio, Karyn L. Sutton, Joshua F. Apgar, John M. Burke, Grant McFadden, Fei Hua, Leslie L. Sharp. Prediction of systemic cytokine exposure in human after IV administration of oncolytic myxoma virus, using quantitative systems pharmacology modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1919.

Purpose: Many biologics exhibit target-mediated drug disposition (TMDD). That is, given the high affinity and low doses typical for antibody-based therapeutics, drug clearance is often mediated through target binding and endocytosis, in addition to 1st-order elimination processes. Accounting for the process of TMDD in pharmacokinetic (PK) models is thus necessary to describe and predict drug exposure. TMDD is typically characterized by dose-dependent clearance, wherein drug elimination is faster at lower doses. In standard TMDD-models, target concentration and turnover are assumed to be constant. While this is generally a good approximation, the assumption does not always hold. Immune-stimulating drugs are the largest class of agents currently being developed in oncology, including checkpoint inhibitors (PD-1/PD-L1, CTLA4), cytokines (IL-2, IL-15), and immune agonists (GITR, OX40, ICOS). For immune-stimulating agents, drug treatment may induce lymphocyte proliferation, expanding the pool of target along with the target-expressing cells. TMDD in such cases is thus induced by drug treatment, and thereby target burden is dynamic; both dose- and time- dependent. PK dose response profiles in such cases could exhibit very different characteristics from standard (static) TMDD. Methods: The goal of this work is to invest the role of target dynamics on drug pharmacokinetics (PK) and receptor occupancy (RO). To do so, we have developed a mathematical modelling construct termed ‘Drug-induced TMDD’, wherein drug-target binding induces increase of the target synthesis rate, subsequently target concentration, leading to enhanced TMDD. We incorporate this construct into a 2-compartment model typical of monoclonal antibodies. Simulations were then used to characterize how dose, drug-target affinity, and the amplitude and the delay in target up-regulation affect the PK and RO profiles, and compared static vs. drug-induced TMDD behavior. Results: For Drug-induced TMDD, target expression increases with increased target engagement. This creates a pseudo-negative-feedback circuit, wherein the rate of TMDD-mediated clearance is dependent upon drug concentration. As a result, drug clearance does not appear dose-proportional at low doses, as is typical of static-TMDD. The AUC of drug concentration for drug-induced TMDD can be higher than static TMDD at low doses, but lower at higher doses. Similar results are predicted for the AUC of RO as well with the nominal parameter set. Increasing target affinity (decreasing KD) however results in faster clearance, typical of both cases of TMDD. Conclusions: Dynamic-TMDD could lead to different dose-dependent PK and RO behaviors from a static TMDD. Based on these characteristic profiles presented, one can determine whether a static vs. drug-induced TMDD model may be required for PK modelling, particularly important consideration for immune-stimulating biologics. Citation Format: Fei Hua, David Flowers, Daniel C. Kirouac, John M. Burke, Joshua F. Apgar. Drug-induced TMDD: a novel class of PK models relevant to Immune-stimulating therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 678.

Despite the success of checkpoint blockade immunotherapy, most patients do not respond adequately to treatment and tumors effectively evade T lymphocyte and Natural Killer (NK) cell immune surveillance. Within the tumor microenvironment, the activities of immune cells are regulated by a multitude of signals including the local cytokine milieu. Intratumoral administration of our cytokine mRNA cocktail therapy, consisting of transcripts encoding for IL-12sc, IFNα2b, IL-15sushi and GM-CSF, has induced regression in tumor-bearing mice. Leveraging in vitro, in vivo and in silico data, we have developed a QSP platform that captures the observed preclinical tumor responses and allows for systemic predictions under a variety of conditions. A 5-compartment model was employed to account for the pharmacokinetics, cellular interactions and signaling within the tumor, lymph node, peritoneum, blood and periphery. We model the intratumoral (i.t.) injection of the mRNA mixture and its subsequent uptake, translation and secretion of activated cytokines that induce an immune response. Specifically, when released into the tumor, these cytokines promote tumor killing and trigger the activation and proliferation of CD4+/CD8+ T lymphocytes and NK cells. The model’s simulation results are well aligned with our preclinical data; indicating increased tumor regression with escalating doses of the mRNA monotherapy. The wealth of data and the intertwined information derived from the interaction of receptors and ligands defining the T cell phenotype necessitate a systems approach in order to identify suitable targets and mechanisms of drug action. Our murine immuno-oncology QSP model could be translated across species and be used as part of a computational platform to test various assets with similar MoA. Citation Format: Kamau Pierre, Lin Lin, Lore Gruenbaum, Carissa Young, Joshua Apgar, Christian Hotz, Timothy Wagenaar, Spyros K. Stamatelos. Development of a multiscale QSP model to characterize the tumor suppressive effects of a cytokine mRNA immunotherapy in a preclinical melanoma mouse model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 685.

Purpose: Pembrolizumab (Pembro), an anti-PD-1 antibody, has been approved for several cancer indications at 200 mg or 2 mg/kg IV every 3 weeks (Q3W). Understanding the percent target receptor occupancy (RO) for PD-1 at this approved dose can help with dose selection for other anti-PD-1 drugs in development. However, RO can be challenging to determine through direct experimental measurements. A mechanistic PKRO model was developed to fit to Pembro phase 1 PK data and was then used to predict RO at 2 mg/kg IV dosing Q3W. Methods: A mechanistic PKRO model describes linear clearance of Pembro, tissue distribution of Pembro, binding to PD-1, binding to soluble PD-1 (sPD-1) and clearance of drug-PD-1 complex, clearance of drug-sPD-1 complex as well as drug distribution to tumor and binding to PD-1 in the tumor compartment. Binding between Pembro and PD-1 was fixed to 29 pM (Pembro BLA, Pharmacology Review) and half-life of internalization of drug-PD-1 complex was set to 2 hours. Results: Since a wide range of sPD-1 concentration has been reported, different sPD-1 concentration was used to fit to the PK data. The model predicted PD-1 expression level in circulation to be approximately 10.8 pM with no soluble PD-1. In the presence of soluble PD-1, PD-1 expression is predicted to be higher to capture the non-linear PK. The model was then used to predict RO at 2 mg/kg IV dosing Q3W and > 98% RO was predicted in the tumor compartment with a range of sPD-1 concentrations from 0 to 3000 pg/mL tested. The impact of Kd and drug-PD-1 turnover rate on predicted RO was explored. When either of the parameters was varied by 10-fold higher or lower from its nominal value, the model still predicted greater than 90% RO at 2 mg/kg dose. Conclusion: A mechanistic PKRO model was built to capture Pembro phase I PK data and then predicted near complete PD-1 target receptor occupancy at the approved dose. This approach with expanded model scope has the potential to be used to inform dose selection for other anti-PD-1, or I/O therapeutics, as a single therapy or in combination. Citation Format: Fei Hua, Lin Lin, Lore Gruenbaum, Bjorn L. Millard, John M. Burke, Joshua F. Apgar. Inferring target occupancy from fitting nonlinear-PK data with mechanistic PKRO model for Pembrolizumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4126.

The pharmacokinetics of antibody drug conjugate (ADC) therapeutics typically show a discrepancy between the PK of total antibody (conjugated and unconjugated antibody) and that of conjugated antibody, carrying one or more payload molecules. This discrepancy is often attributed strictly to deconjugation (Kamath, 2014), however recent evidence suggests that the underlying mechanisms may be more complex. This work employs a computational quantitative systems pharmacology (QSP) approach that generates a hypotheses to better understand the impact of drug antibody ratio (DAR) and the resulting changes in molecular properties on overall PK and relative payload disposition as observed in preclinical and clinical studies. Our work establishes the benefit of using computational models to design novel ADCs and to optimize the discovery and development of existing ADCs to better enable clinical trial design. References: Kamath, A. V., and Iyer, S. (2014). Preclinical Pharmacokinetic Considerations for the Development of Antibody Drug Conjugates. Pharmaceutical Research, 32(11), 3470-3479. Citation Format: A. Katharina Wilkins, Andrew Matteson, Lore Gruenbaum, Jennifer Park, John M. Burke, Joshua F. Apgar. Computational exploration of mechanistic determinants of ADC pharmacokinetics using QSP modeling strategies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4266.

Inhibition of the IGF1R, INSRA, and INSRB receptor tyrosine kinases represents an attractive approach of pharmacologic intervention in cancer, owing to the roles of the IGF1R and INSRA in promoting cell proliferation and survival. However, the central role of the INSRB isoform in glucose homeostasis suggests that prolonged inhibition of this kinase could result in metabolic toxicity. We describe here the profile of the novel compound BI 885578, a potent and selective ATP-competitive IGF1R/INSR tyrosine kinase inhibitor distinguished by rapid intestinal absorption and a short in vivo half-life as a result of rapid metabolic clearance. BI 885578, administered daily per os, displayed an acceptable tolerability profile in mice at doses that significantly reduced the growth of xenografted human GEO and CL-14 colon carcinoma tumors. We found that treatment with BI 885578 is accompanied by increases in circulating glucose and insulin levels, which in turn leads to compensatory hyperphosphorylation of muscle INSRs and subsequent normalization of blood glucose within a few hours. In contrast, the normalization of IGF1R and INSR phosphorylation in GEO tumors occurs at a much slower rate. In accordance with this, BI 885578 led to a prolonged inhibition of cell proliferation and induction of apoptosis in GEO tumors. We propose that the remarkable therapeutic window observed for BI 885578 is achieved by virtue of the distinctive pharmacokinetic properties of the compound, capitalizing on the physiologic mechanisms of glucose homeostasis and differential levels of IGF1R and INSR expression in tumors and normal tissues. Mol Cancer Ther; 14(12); 2762–72. ©2015 AACR.

ICOS (inducible co-stimulator molecule) is a co-stimulatory molecule and a member of the CD28 superfamily expressed primarily on T lymphocytes. Analysis of cancer patient samples as well as rodent preclinical data have implicated a role for the ICOS pathway in cancer immunotherapy. We have generated a panel of anti-ICOS monoclonal antibodies with in vitro agonistic properties. The anti-ICOS antibodies are efficacious as monotherapies and in combination with anti-PD1 in multiple syngeneic tumor models. Mechanistic studies demonstrate that tumor regression is associated with enhanced ratios of cytotoxic CD8:T regulatory (Treg) cells as well as preferential reduction in ICOS-high Tregs in the tumor microenvironment. JTX-2011, a species cross-reactive high affinity humanized agonist monoclonal antibody, has been selected for development. Evaluation of JTX-2011 in nonhuman primate models will be presented, including data informing safety and PK parameters. Our preclinical data provides rational for clinical development of JTX-2011 as a cancer immunotherapeutic to be tested as both a monotherapy as well as in combination with immunotherapies in solid tumor indications. Citation Format: Jennifer S. Michaelson, Christopher J. Harvey, Kutlu G. Elpek, Ellen Duong, Matthew Wallace, Chengyi J. Shu, Sriram Sathyanarayanan, Robert Mabry, Lindsey Shallberg, Tong Zi, Amit Deshpande, Stephen L. Sazinsky, Joshua Apgar, Deborah Law. Preclinical evaluation of JTX-2011, an anti-ICOS agonist antibody. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 573.

The goal of this collaboration was to enable early quantitative thought experiments and risk assessments by developing and interrogating a quantitative systems pharmacology (QSP) model of the co-modulation inhibitory receptors PD-1 and TIM-3 in immuno-oncology. The QSP model was to: (1) provide predictions of best-in-class profiles for a PD-1 and TIM-3 fixed dose combination (FDC) and dual antagonist platforms, and (2) provide biological insights. The QSP model was based on first principles as a system of elementary mass-action, mechanistic PKPD, ordinary differential equations. The model parameters and reactions were based on biophysics, and are interpretable. The model reactions include protein synthesis and elimination, ligand-receptor and drug-target formation and turnover, and drug administration and first order clearance. There were four versions of the model: PD-1 monospecific, TIM-3 monospecific, PD-1 x TIM-3 bispecific and fixed dose combination (FDC) targeting PD-1 and TIM-3. The monospecific models were then benchmarked against published data such that model parameter values were set to known values and unknown parameters were estimated. Once benchmarked, the FDC and bispecific models were analyzed by systematically investigating how tuning the model parameters (e.g., affinity, avidity, dose, half-life, target expression, etc.) impacted target inhibition, and to simulate patient variability. The model predictions were in good agreement with published clinical data from Nivolumab and Pembrolizumab, providing a hypothesis for the apparent inconsistencies in similar clinical doses for therapeutics with affinities differing by several orders of magnitude, and data from RMT3-23 in the TIM-3 driven mouse model. QSP model analysis predicted: (1) there would be diminishing returns on very tight binding biologics due to Target Mediated Drug Disposition (TMDD) that offsets potency, and (2) there is no advantage between FDC, 2-2 bispecific, and 2-1 bispecific formats, which are predicted to be roughly equivalent. Citation Format: Joshua Apgar, Jamie Wong, Ryan Phennicie, Robert Mabry, Tatiana Novobrantseva, Michael Briskin, John M. Burke. Quantitative systems pharmacology modeling and analysis provides biological insights into anti-PD-1 dosing and predicts optimal PD-1 x TIM-3 therapeutic properties for bispecifics and fixed dose combinations in immuno-oncology. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5001.

The goal of this collaboration was to provide early quantitative decision making guidance for the project team by developing and interrogating a quantitative systems pharmacology (QSP) model of the co-modulation inhibitory receptors PD-1 and TIM-3 in immuno-oncology. The QSP model was to: (1) provide predictions of the best-in-class profile for a PD-1 and TIM-3 dual antagonist biologic(s), (2) accelerate project timelines, (3) provide biological insights, and (4) reduce experimental costs. The QSP model was based on first principles as a system of elementary mass-action, mechanistic PKPD, ordinary differential equations. The model parameters and reactions were based on biophysics, and are interpretable. The model reactions include protein synthesis and elimination, ligand-receptor and drug-target formation and turnover, and drug administration and first order clearance. There were four versions of the model: PD-1 monospecific, TIM-3 monospecific, PD-1 x TIM-3 bispecific and fixed dose combination (FDC) targeting PD-1 and TIM-3. The monospecific models were then benchmarked against published data such that model parameter values were set to known values and unknown parameters were estimated. Once benchmarked, the FDC and bispecific models were analyzed by systematically investigating how tuning the model parameters (e.g., affinity, avidity, dose, half-life, target expression, etc.) impacted target inhibition, and to simulate patient variability. The model was in good agreement with published clinical data from nivolumab and pembrolizumab, and data from RMT3-23 in the TIM-3 driven mouse model. QSP model analysis predicted: (1) there would be diminishing returns on very tight binding biologics due to Target Mediated Drug Disposition (TMDD) that offsets potency, and (2) there is no advantage between FDC, 2-2 bispecific, and 2-1 bispecific formats, which are predicted to be roughly equivalent. As a result of these analyses, there was a significant reduction in the number of experiments, and acceleration of project timelines by (1) eliminating rounds of affinity maturation, as drug leads were in predicted optimal drug parameter ranges, and (2) eliminating the need to construct and evaluate bi-specific constructs and proceed with FDCs. Citation Format: Joshua F. Apgar, Jamie Wong, Ryan Phennicie, Mike Briskin, John M. Burke. Quantitative systems pharmacology and immunotherapy: accelerating lead generation and optimization of a PD-1 x TIM-3 biotherapeutic in immuno-oncology. [abstract]. In: Proceedings of the Fourth AACR International Conference on Frontiers in Basic Cancer Research; 2015 Oct 23-26; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2016;76(3 Suppl):Abstract nr B34.

available in PMC 2012 November 10., We welcome the commentary from Chachra, Transtrum, and Sethna1 regarding our paper “Sloppy models, parameter uncertainty, and the role of experimental design,”2 as their intriguing work shaped our thinking in this area.3 Sethna and colleagues introduced the notion of sloppy models, in which the uncertainty in the values of some combinations of parameters is many orders of magnitude greater than others.4 In our work we explored the extent to which large parameter uncertainties are an intrinsic characteristic of systems biology network models, or whether uncertainties are instead closely related to the collection of experiments used for model estimation. We were gratified to find the latter result –– that parameters are in principle knowable, which is important for the field of systems biology. The work also showed that small parameter uncertainties can be achieved and that the process can be greatly accelerated by using computational experimental design approaches5–9 deployed to select sets of experiments that effectively exercise the system in complementary directions.

Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by trajectory measurements. We examined this question in the context of a pathway model of epidermal growth factor (EGF) and neuronal growth factor (NGF) signaling. Computationally, we examined a palette of experimental perturbations that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, experimental design methodology identified a set of five complementary experiments that could. These results suggest optimism for the prospects for calibrating even large models, that the success of parameter estimation is intimately linked to the experimental perturbations used, and that experimental design methodology is important for parameter fitting of biological models and likely for the accuracy that can be expected from them., National Institutes of Health (U.S.) (U54 CA112967), MIT-Portugal Program, Singapore-MIT Alliance for Research and Technology

The goal of this collaboration was to provide early quantitative decision making guidance for the project team by developing and interrogating a quantitative systems pharmacology (QSP) model of the co-modulation inhibitory receptors PD-1 and TIM-3 in immuno-oncology. The QSP model was to: (1) provide predictions of the best-in-class profile for a PD-1 and TIM-3 dual antagonist, (2) accelerate project timelines, (3) provide biological insights, and (4) reduce experimental costs. The QSP model was based on first principles as a system of elementary mass-action, mechanistic PKPD, ordinary differential equations. The model parameters and reactions were based on biophysics, and are interpretable. The model reactions include protein synthesis and elimination, ligand-receptor and drug-target formation and turnover, and drug administration and first order clearance. There were four versions of the model: PD-1 monospecific, TIM-3 monospecific, PD-1 x TIM-3 bispecific and fixed dose combination (FDC) targeting PD-1 and TIM-3. The monospecific models were then benchmarked against published data such that model parameter values were set to known values and unknown parameters were estimated. Once benchmarked, the FDC and bispecific models were analyzed by systematically investigating how tuning the model parameters (e.g., affinity, avidity, dose, half-life, target expression, etc.) impacted target inhibition, and to simulate patient variability. The model was in good agreement with published clinical data from nivolumab and pembrolizumab, and data from RMT3-23 in the TIM-3 driven mouse model. QSP model analysis predicted: (1) there would be diminishing returns on very tight binding biologics due to Target Mediated Drug Disposition (TMDD) that offsets potency, and (2) there is no advantage between FDC, 2-2 bispecific, and 2-1 bispecific formats, which are predicted to be roughly equivalent. As a result of these analyses, there was a significant reduction in the number of experiments, and acceleration of project timelines by (1) eliminating rounds of affinity maturation, as drug leads were in predicted optimal drug parameter ranges, and (2) eliminating the need to construct and evaluate bi-specific constructs and proceed with FDCs. Citation Format: Joshua F. Apgar, Jamie Wong, Ryan Ryan Phennicie, Mike Briskin, John M. Burke. Quantitative systems pharmacology approaches accelerate lead generation and optimization of a PD-1 x TIM-3 therapeutic in immuno-oncology. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A151.

Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus–response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody–ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models., Author Summary A major focus of systems biology is the development of mechanism-based models of cell signaling pathways. These models hold the promise of encapsulating our understanding of complex biological processes while also predicting new behavior. However, as these models become more complex, it can be difficult to distinguish between model alternatives. One means of improved model discrimination involves making measurements of additional components in the biological system to provide more detailed data. Here we present an alternative, which is to apply a time-varying input while monitoring the same network components. This new method was able to discriminate among models with subtle mechanistic differences. A particular advantage is that for many cases, time-varying input stimulation is fairly easy to apply experimentally, whereas measuring additional network components can involve the creation of new reagents or measurement assays. Thus, we believe that the application of time-varying input stimulation will become a powerful tool in the field of systems biology as the community places increased emphasis on the development of quantitative, mechanistic, and predictive models of biological network behavior.

One of the central functions of actin cytoskeleton is to provide the mechanical support required for the establishment and maintenance of cell morphology. The mechanical properties of actin filament assemblies are a consequence of both the available polymer concentration and the actin regulatory proteins that direct the formation of higher order structures. By monitoring the displacement of well-dispersed microspheres via fluorescence microscopy, we probe the degree of spatial heterogeneity of F-actin gels and networks in vitro. We compare the distribution of the time-dependent mean-square displacement (MSD) of polystyrene microspheres imbedded in low- and high-concentration F-actin solutions, in the presence and absence of the F-actin-bundling protein fascin. The MSD distribution of a 2.6-μM F-actin solution is symmetric and its standard deviation is similar to that of a homogeneous solution of glycerol of similar zero-shear viscosity. However, increasing actin concentration renders the MSD distribution wide and asymmetric, an effect enhanced by fascin. Quantitative changes in the shape of the MSD distribution correlate qualitatively with the presence of large heterogeneities in F-actin solutions produced by increased filament concentration and the presence of actin bundles, as detected by confocal microscopy. Multiple-particle tracking offers a new, quantitative method to characterize the organization of biopolymers in solution.