Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Rieger, Theodore R.; Musante, Cynthia J.

doi:10.1101/028548

Cited by 31 publications

(62 citation statements)

References 19 publications

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“…Methods to statistically calibrate alternative QSP model parameterizations, also known as virtual patients or virtual subjects, to both preclinical and clinical data to develop virtual populations have been demonstrated and applied in several therapeutic areas. [106][107][108][109][110][111] In 1 study, a set of virtual patients in a model of rheumatoid arthritis was calibrated to multiple phase 3 trials for adalimumab, rituximab, and tocilizumab to develop a virtual population calibrated to methotrexate-inadequate responding and anti-TNF-inadequate responding patient trial data. 110 Subsequently, new clinical regimens of reduced dosing were implemented with the existing therapies in the calibrated virtual population.…”

Section: Pharmacometric Strategies: Application Of Systems Modelingmentioning

confidence: 99%

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

Sheng

Srivastava

Sanghavi

et al. 2017

The Journal of Clinical Pharma

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Immuno-oncology works through activation of the patient's immune system against cancer, with several advantages over other treatment approaches, including cytotoxic agents and molecular-targeted therapies. The most notable feature of immuno-oncology treatments is the nature of the patient responses achieved, which can be more durable and sustained than with other modalities. Increased understanding of immune system complexity has provided a number of opportunities to advance several strategies for the development of immuno-oncology therapies. This review outlines the clinical pharmacology characteristics and development challenges for the 6 approved immunomodulatory monoclonal antibodies that target 2 immune checkpoint pathways: ipilimumab (an anti-cytotoxic T-lymphocyte antigen-4 antibody) and, more recently, nivolumab and pembrolizumab (both anti-programmed death-1 antibodies) and atezolizumab, avelumab, and durvalumab (all anti-programmed death ligand-1 antibodies). These agents have revealed much about the clinical pharmacology features of immune checkpoint inhibitors as a class, as well as the pharmacometric approaches used to support their clinical development and regulatory approval. The development experiences with these pioneering immuno-oncology agents are likely to serve as useful guides in the discovery, progression, and approval of future drugs or combination of drugs in this class. This review includes summaries of the pharmacokinetics and exposure-response of the immune checkpoint inhibitors approved to date, as well as an overview of some quantitative systems pharmacology approaches. The ability of immuno-oncology to meet its full potential will depend on overcoming development challenges, including the need for clear strategies to determine optimal dose and scheduling for monotherapy as well as combination approaches. Keywords immunopharmacology, oncology, clinical pharmacology, clinical trials, pharmacology, immunotherapy, checkpoint inhibitorsIt could be said that if there were a book chronicling the history and progression of cancer treatment, we are now moving on from the chapter describing the time of sole reliance on chemotherapy, the chapter outlining the advent of targeted therapy is partially completed, and a new chapter on immuno-oncology is just beginning. The past few years have seen the regulatory approval of several immuno-oncology agents (Figure 1), including 6 immune checkpoint inhibitors: ipilimumab (Yervoy; Bristol-Myers Squibb), pembrolizumab (Keytruda; Merck), nivolumab (Opdivo; Bristol-Myers Squibb), atezolizumab (Tecentriq; Genentech), avelumab (Bavencio; EMD Serono), and durvalumab (Imfinzi; AstraZeneca). 1,2Immunotherapy in oncology is a switch from the cell-killing modality using relatively nonspecific cytotoxic methods (chemotherapy) or therapies that target cancer-specific pathways to methods that employ the patients' immune system to attack cancer. The immuno-oncology approach calls on the understanding of complex signaling processes of effector and regulatory...

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Section: Pharmacometric Strategies: Application Of Systems Modelingmentioning

confidence: 99%

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

Sheng

Srivastava

Sanghavi

et al. 2017

The Journal of Clinical Pharma

View full text Add to dashboard Cite

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“…Therefore, it is important to evaluate the impacts of known variability and uncertainty, where QSP models often use ensembles of alternative parameterizations that appear to be plausibly in agreement with observed data (8,25,26). As one concrete example, it is not uncommon to find quantitatively different in vitro measures of the same process reported by two different labs.…”

Section: Variabilitymentioning

confidence: 99%

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Cheng

Thalhauser

Smithline

et al. 2017

AAPS J

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Abstract. Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

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“…These virtual populations are alternative parameterizations to the model in Eqs. 1-6 that, when pooled together, in theory capture the underlying heterogeneity in the full patient population (16). The 20 treatment orderings considered in ref.…”

Section: Resultsmentioning

confidence: 99%

“…Others have given consideration to further preserve the covariance structure among all variables; see, for instance, refs. 16,23,25,26,29, and 30. The second constraint imposed is that only virtual populations whose parameter values are all within their respective 95% credible intervals were considered; this can be thought of as an "inclusion-exclusion" criterion that refines the virtual population pool to be statistically similar to the experimental population (29), including mirroring its heterogeneity.…”

Section: Methodsmentioning

confidence: 99%

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Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

Barish

Ochs

Sontag

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.robust therapies | cancer treatment | mathematical modeling | virotherapy | immunotherapy H eterogeneity is a defining feature of cancer (1, 2). Interpatient heterogeneity manifests clinically in variable disease progression and treatment response between patients with the same diagnosis, whereas intrapatient heterogeneity describes variations that exist between tumor cells in a single patient. Intrapatient heterogeneity can be broken down further into intratumor heterogeneity, intrametastatic heterogeneity, intermetastatic heterogeneity, and temporal heterogeneity (2, 3). Intratumor heterogeneity is evident through the presence of multiple genetic subclones within a primary tumor (2), which have even been shown to exist in spatially distinct regions of the primary tumor (4). Intrametastatic heterogeneity is similar to intratumor heterogeneity, but describes heterogeneity within a single metastatic lesion instead of within the primary tumor (2). Intermetastatic heterogeneity, on the other hand, describes variations in subclones between different metastases in the same patient (2). Finally, temporal heterogeneity is defined as changes that take place in the tumor over time, whether they are a result of genomic instability, natural selection, non-Darwinian evolution, or selective pressures imposed by treatment (4-6). Note that, in each case, heterogeneity need not be genetic but may also be epigenetic, phenotypic, or microenvironmental (2, 7, 8).For decades, cancer patients have been treated using standard of care, meaning they receive the best known treatment that has been deemed as efficacious and safe in epidemiological studies (9). However, in the face of such int...

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Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Cited by 31 publications

References 19 publications

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

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