Author

A. L. Jenner

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 4 Cell Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 5 Research Focus:drug response / drug discovery· 4 Organism:human· 4 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Assay:flow cytometry· 1 Research Focus:host-pathogen & immune response· 1 Approach:mechanistic / dynamical model· 1

Frequent Co-Authors

M. Craig· 4 shared A. M. Smith· 1 shared A. P. Browning· 1 shared A. P. Johnston· 1 shared A. P. Smith· 1 shared A. Surendran· 1 shared C. Burlot· 1 shared C. Drovandi· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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4 shared papersCancer Biology

M. Craig

Mutual co-authors in cluster

Author profile →

6 papers

Recent·Most cited

Impact of resistance on therapeutic design: a Moran model of cancer growth

M. S. Lacy, A. L. Jenner

This study uses a Moran model to predict cancer recurrence probabilities under different treatment strategies, finding sustained-low dose therapies ineffective against resistant clones.

bioRxiv · Aug 28, 2023Cancer Biology

Deconstructing the contributions of heterogeneity to combination treatment of hormone sensitive breast cancer

S. Linn, J. Moore-Ott, R. Shuttleworth, W. Zhang, M. Craig, A. L. Jenner

The paper develops a mathematical model to study how tumor heterogeneity affects the response to combination therapy in hormone receptor-positive breast cancer, finding that pharmacokinetics and pharmacodynamics of fulvestrant drive treatment outcomes.

bioRxiv · May 19, 2023 · PreprintCancer Biology

Approaches to generating virtual patient cohorts with applications in oncology

A. Surendran, J. L. Sauteur-Robitaille, D. Kleimeier, J. L. Gevertz, K. P. Wilkie, A. L. Jenner, M. Craig

This paper discusses methods for generating virtual patient cohorts in oncology to predict treatment responses and capture inter-individual variability.

bioRxiv · May 24, 2022 · PreprintCancer Biology

Establishing combination PAC-1 and TRAIL regimens for treating ovarian cancer based on patient-specific pharmacokinetic profiles using in silico clinical trials

O. Cardinal, C. Burlot, Y. Fu, P. Crosley, M. Hitt, M. Craig, A. L. Jenner

This paper uses in silico clinical trials to optimize combination PAC-1 and TRAIL therapy for ovarian cancer based on patient-specific pharmacokinetic profiles, identifying optimal treatment schedules and patient subgroups.

bioRxiv · Mar 29, 2022Cancer Biology

Identifying cell-to-cell variability in internalisation using flow cytometry

A. P. Browning, N. Ansari, C. Drovandi, A. P. Johnston, M. J. Simpson, A. L. Jenner

The paper develops a mathematical model to quantify cell-to-cell variability in internalisation using flow cytometry data from human B-cell lymphoblastoid cells.

bioRxiv · Nov 24, 2021 · PreprintCell Biology

COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes

A. L. Jenner, R. A. Aogo, S. Alfonso, V. Crowe, A. P. Smith, P. A. Morel, C. L. Davis, A. M. Smith, M. Craig

The paper develops a computational model of COVID-19 immunopathology to identify immune mechanisms driving severe disease outcomes.

bioRxiv · Jan 5, 2021Immunology