FDA Advisory Meeting Clinical Pharmacology Review Utilizes a Quantitative Systems Pharmacology (QSP) Model: A Watershed Moment?

Mc, Peterson; Riggs, Matthew M.

doi:10.1002/psp4.20

Cited by 71 publications

(80 citation statements)

References 7 publications

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“…This Bsystems^approach can better inform target selection and the decision process for advancing compounds through preclinical and clinical research (3); as such, it is becoming increasingly important in pharmaceutical research and development as a potential means of reducing attrition and improving productivity (4)(5)(6)(7). QSP models often are developed to impact drug discovery and development, and often enable the investigation of relationships between Electronic supplementary material The online version of this article (doi:10.1208/s12248-017-0100-x) contains supplementary material, which is available to authorized users.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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show abstract

“…Recently, an advanced mechanism-based QSP model was used for simulating the impact of human recombinant parathyroid hormone [43] with regard to alternative dosing regimens. This suggests that regulatory agencies are becoming increasingly interested in advanced multiscale modeling and simulation beyond the more traditional PK/PD modeling.…”

Section: Other Examples Of Modeling Influencing Regulatory Decisionsmentioning

confidence: 99%

Big data to smart data in Alzheimer's disease: Real‐world examples of advanced modeling and simulation

Haas¹,

Stephenson

Romero

et al. 2016

Alzheimer's & Dementia

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Many disease-modifying clinical development programs in Alzheimer's disease (AD) have failed to date, and development of new and advanced preclinical models that generate actionable knowledge is desperately needed. This review reports on computer-based modeling and simulation approach as a powerful tool in AD research. Statistical data-analysis techniques can identify associations between certain data and phenotypes, such as diagnosis or disease progression. Other approaches integrate domain expertise in a formalized mathematical way to understand how specific components of pathology integrate into complex brain networks. Private-public partnerships focused on data sharing, causal inference and pathway-based analysis, crowdsourcing, and mechanism-based quantitative systems modeling represent successful real-world modeling examples with substantial impact on CNS diseases. Similar to other disease indications, successful real-world examples of advanced simulation can generate actionable support of drug discovery and development in AD, illustrating the value that can be generated for different stakeholders.

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“…1, 2), in our experience some early QSP initiatives were overambitious and lacked predefined specification of question(s) to be answered by the model, which would have determined the required model complexity and whether that was achievable. As a result, these modeling efforts, while scientifically sound, did not turn out to provide valuable return on investment (unfortunately, such “failures” do not tend to get published; see Ref.…”

Section: Optimal Granularity and Design Of Quantitative Systems Pharmmentioning

confidence: 99%

Methodologies for Quantitative Systems Pharmacology (QSP) Models: Design and Estimation

Ribba

Grimm

Agoram

et al. 2017

CPT Pharmacometrics Syst. Pharmacol.

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With the increased interest in the application of quantitative systems pharmacology (QSP) models within medicine research and development, there is an increasing need to formalize model development and verification aspects. In February 2016, a workshop was held at Roche Pharma Research and Early Development to focus discussions on two critical methodological aspects of QSP model development: optimal structural granularity and parameter estimation. We here report in a perspective article a summary of presentations and discussions.

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FDA Advisory Meeting Clinical Pharmacology Review Utilizes a Quantitative Systems Pharmacology (QSP) Model: A Watershed Moment?

Cited by 71 publications

References 7 publications

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

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

Big data to smart data in Alzheimer's disease: Real‐world examples of advanced modeling and simulation

Methodologies for Quantitative Systems Pharmacology (QSP) Models: Design and Estimation

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