Article

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

2017-09-29

Abstract excerpt

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...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ddb9b7ce-1e6b-5da4-9695-372414a3201d
DOI: 10.1101/196089

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Improving the Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology ModelsDOI 10.1101/196089Read

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology ModelsSemantic proximity Read

Approaches to generating virtual patient cohorts with applications in oncologySemantic proximity Read

Generation of virtual populations for quantitative systems pharmacology through advanced sampling methodsSemantic proximity Read

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative Proteomics.Semantic proximity Read

Application of different approaches to generate virtual patient populations for the Quantitative Systems Pharmacology model of erythropoiesisSemantic proximity Read

Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial OutcomesSemantic proximity Read

A surrogate workflow for virtual population development: designing an amenable proxy for a hypothesis-test inspired nonlinear goodness-of-fitSemantic proximity Read

Identification and characterization of virtual sub-populations through phenotype-guided filtering. The challenging case of nonidentifiable models in the context of therapeutic evaluation.Semantic proximity Read

A Quantitative Systems Pharmacology Model of the Pathophysiology and Treatment of COVID-19 Predicts Optimal Timing of Pharmacological InterventionsSemantic proximity Read

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibitionSemantic proximity Read

Generation of realistic virtual adult populations using a model-based copula approachSemantic proximity Read

Assessing the Role of Model Complexity in Virtual Clinical Trial OutcomesSemantic proximity Read