Submitting an abstract for the PAGE meeting
Abstracts must be submitted online to the PAGE web site (www.page-meeting.org) by clicking on 'Register / submit abstract' under the heading for the upcoming meeting. You must register as a participant before you can submit an abstract and you can only register after you have created an account. When you click "Submit" you will immediately receive an email with your abstract attached. This e-mail will also be sent to the committee responsible for peer review in the selected category. Therefore, only click "Submit" when you are done editing. Abstracts will remain invisible until release of the final program. Each participant is only allowed to submit one abstract, but does not need to be the first author. Do not submit separate oral and poster abstracts: if your request for an oral presentation is not granted, you will be contacted to switch your abstract from an oral to a poster category. The abstracts are text-only: no figures are possible.
All abstracts are reviewed and abstracts that do not comply with the guidelines given here, may run the risk of not being accepted. If the abstract is not satisfactory after review, the abstract may be rejected and will not be published at the PAGE website. A minimum requirement concerning the contents of the abstract is that concrete results are included. Accordingly, abstracts without results will be rejected, i.e. statements such as “…will be shown…”, “…will be available at the time of the conference…” are not acceptable. Furthermore, abstracts for an anonymous drug (drug X) will not be accepted unless the work clearly describes a new methodology/new disease model for a specific therapeutic area (or similar), i.e. is of value to the pharmacometric community without the identity of the drug. Accordingly, a 2-compartment population PK model for drug X will not be acceptable. Please note the minimum and maximum abstract length, to allow a better assessment of the intended presentation.
A structured abstract is required (Objectives / Methods / Results / Conclusions / References) with number of characters (including spaces) not exceeding 4,500 but not less than 3,000 for the abstract itself (i.e. excluding Title/ Authors/ Affiliation and References). An example is provided below.
There are separate fields for entering your abstract title, the authors, the associated institution or affiliation and the type of abstract you wish to present (oral or poster category):
Title: The title of your abstract (DO NOT USE ALL CAPITALS)
Author: Author1 (1), Author2 (1), Author3 (2) (DO NOT USE ALL CAPITALS)
Institution: (1) Affiliation1, (2) Affiliation2
Type: Please choose the appropriate oral or poster category from the following categories, which will facilitate the review of abstracts and to help structure the poster sessions.
- Oral: Clinical Applications (see clarification below)
- Oral: Drug/Disease Modelling - Oncology (see clarification below)
- Oral: Drug/Disease Modelling - Other Topics (see clarification below)
- Oral: Lewis Sheiner Student Session
- Oral: Methodology - New Modelling Approaches
- Oral: Methodology - New Tools
- Oral: Real-world data (RWD) in pharmacometrics
- Oral: Other Topics
- Poster: Clinical Applications (see clarification below)
- Poster: Drug/Disease Modelling - Absorption & PBPK
- Poster: Drug/Disease Modelling - CNS
- Poster: Drug/Disease Modelling - Endocrine
- Poster: Drug/Disease Modelling - Infection
- Poster: Drug/Disease Modelling - Oncology
- Poster: Drug/Disease Modelling - Other Topics
- Poster: Drug/Disease Modelling - Paediatrics
- Poster: Drug/Disease Modelling - Safety
- Poster: Methodology - AI/Machine Learning
- Poster: Methodology - Covariate/Variability Models
- Poster: Methodology - Estimation Methods
- Poster: Methodology - Model Evaluation
- Poster: Methodology - New Modelling Approaches
- Poster: Methodology - Other topics
- Poster: Methodology - Study Design
- Software Demonstration
Special abstract topic this year: Regulation and use of real-world data (RWD) in pharmacometrics
Data originating from sources other than traditional clinical trials are becoming increasingly important for clinical drug research, drug development and regulatory decision making. Real-world evidence (RWE) derived from real-world data (RWD), such as data from electronic health records, patient registries and observational data, can complement clinical trial evidence to support drug development and regulatory decisions, e.g. label expansion, as there is a growing payer interest in RWE. Historically, the 'population approach' was designed to enable analysis of observational TDM data and pharmacometric tools are very suitable to analyze RWD to generate RWE, e.g. disease progression modelling of natural history data. There are, however, potential limitations (ethical, legal and methodological) and other practical pitfalls and extrapolation issues that may complicate the use of RWD in pharmacometric analyses. We are inviting abstracts for oral presentations on case studies as well as methodological studies describing how and when RWD can be used in pharmacometrics and MIDD, with a focus on what obstacles were experienced and how these were overcome. Oral abstract submissions on this topic are very welcome!
Clinical Applications: these abstracts are expected to deal with applications of pharmacometrics whose aim is to improve individual patient treatment. They may include studies that have identified covariates that can be applied to calculate the dose, or even better, if the application was able to take patient response (concentration, biomarker) and further improve the individual dose. They can include simulation studies that explore how to improve patient treatment by dose individualization. The abstract must indicate an algorithm demonstrating how an individual patient dose is calculated.
Drug-Disease modelling categories: these abstracts are expected to be description of and applications of PK, PKPD, disease progression models for all type of data (continuous, categorical, TTE, count etc, preclinical/clinical/in silico). These may have suggestions for clinical practice application as a side-benefit (e.g. covariates) but if that is not the main focus then they do not belong in the Clinical Application category and should be submitted to one of the Drug-Disease Modelling categories.
There are two editor windows, one for the core abstract and one for the references to allow counting the number characters. The core abstract text itself must have the following layout:
Introduction/Objectives: Text regarding objectives.
Methods: Text regarding methods.
Results: Text regarding results. Concrete results need to be included in the abstract. Statements such as “…will be shown…”, “…will be available at the time of the conference…” are not acceptable. Abstracts for an anonymous drug (drug X) will not be accepted unless the work clearly describes a new methodology/new disease model for a specific therapeutic area (or similar), i.e. is of value to the pharmacometric community without the identity of the drug.
Conclusions: Text regarding conclusions.
The references, in the separate references editor, must have the following layout:
References:
[1] Text for reference 1.
[2] Text for reference 2, etc etc
Separate the different sections in the core abstract window with a simple (hard return), but separate the different references so you do not get extra white lines between the references.
The "PDF poster/presentation" option below the editor window will allow you to add the PDF of your final poster or presentation as a service to your audience, and is not intended for a PDF of your abstract, and is not required at the time of abstract submission.
How to produce such an abstract
In contrast to previous years, abstracts can be prepared in your favourite text editor and simply pasted in the online abstract text window. This should remove almost all formatting except the allowed minimum (such as bold text).
Example abstract:
Title: nlmixr: an open-source package for pharmacometric modelling in R
Author: Rik Schoemaker (1,6), Matt Fidler (2,6), Justin Wilkins (1,6), Teun Post (3,6), Richard Hooijmakers (3,6), Mirjam Trame (2,6) , Yuan Xiong (4,6), Christian Laveille (5), Wenping Wang (2,6)
Institution: (1) Occams, The Netherlands, (2) Novartis Pharmaceuticals, USA, (3) LAP&P Consultants, The Netherlands, (4) Certara Strategic Consulting, USA, (5) Calvagone, France, (6) The nlmixr team
Editor window:
Introduction: nlmixr (www.nlmixr.org) is an open-source R package, freely available on CRAN[1] and GitHub[2]. It builds on RxODE[3], an R package for simulation of nonlinear mixed effect models using ordinary differential equations (ODEs), providing an efficient and versatile way to specify pharmacometric models and dosing scenarios, with rapid execution due to compilation in C. By combining the simulation core with population-type estimation routines, a versatile pharmacometric eco-system entirely contained within R becomes feasible. Currently, estimation routines comprise the nlme[4] package in R, a custom-built SAEM[5] implementation, and a proof-of-concept FOCE-I implementation, as well as adaptive Gaussian quadrature for 'odd-type' data. Both closed-form and ODE model definitions are included in nlmixr.
nlmixr is under active development, and exciting new additions are:
- a unified user interface (UUI) that provides a common language for model definition for the different estimation routines,
- an interface[6] to the new xpose package[7] for graphical model diagnostics, and
- a ShinyMixR interface[8] for nlmixr project management that can be used to define and run nlmixr models in a friendly interface, and structure and examine nlmixr output.
In addition, parallel implementation of simulation and parameter estimation is at the horizon.
Objectives:
- Examine the estimation properties of the nlme and SAEM routines implemented in nlmixr for both rich data models and for a sparse sampling example
- Compare the results from the nlme and SAEM routines implemented in nlmixr with NONMEM FOCE-I
Methods: Richly sampled profiles were simulated for 4 different dose levels (10, 30, 60 and 120 mg) of 30 subjects each as single dose (over 72h), multiple dose (4 daily doses), single and multiple dose combined, and steady state dosing, for a range of test models: 1- and 2-compartment disposition, with and without 1st order absorption, with either linear or Michaelis-Menten (MM) clearance (MM without steady state dosing). This provided a total of 42 test cases. All inter-individual variabilities (IIVs) were set at 30%, residual error at 20% and overlapping PK parameters were the same for all models. A similar set of models was previously used to compare NONMEM and Monolix[9]. Additionally, a sparse data estimation situation was investigated where 500 datasets of 600 subjects each (150 per dose) were generated consisting of 4 random time point samples in 24 hours per subject, using a first-order absorption, 1-compartment disposition, linear elimination model. NONMEM®[10] with FOCE-I was used as a comparator to test the various nlmixr estimation routines for both closed-form and ODE implementations.
Results: Theta parameter estimates were comparable across estimation methods. The SAEM routine in nlmixr was particularly stable compared to the nlme routine, and consistently provided accurate results. For nlme, IIV estimates were regularly estimated close to 0% in nlmixr, whereas both NONMEM, and SAEM in nlmixr, provided estimates close to the original simulation values, for rich and sparse sampling alike.
Conclusion: These findings suggest that nlmixr provides a viable open-source parameter estimation package for nonlinear mixed effects pharmacometric models within the R environment. With a stable release on CRAN, and encouraging developments regarding new estimation routines (like FOCE-I) on their way, the nlmixr project is moving from prototype to mature application, ready for input from and adoption by the pharmacometric community.
References window:
References:
[1] https://cran.r-project.org/web/packages/nlmixr/index.html
[2] https://github.com/nlmixrdevelopment/nlmixr
[3] Wang W et al. CPT:PSP (2016) 5, 3–10.
[4] Pinheiro J et al. (2016). nlme: Linear and Nonlinear Mixed Effects Models.
[5] Kuhn E and Lavielle M. M. Comput Stat Data An, 49:1020–1038, 2005.
[6] https://github.com/nlmixrdevelopment/xpose.nlmixr
[7] https://CRAN.R-project.org/package=xpose
[8] https://github.com/nlmixrdevelopment/ShinyMixR
[9] Laveille C et al PAGE 17 (2008) Abstr 1356 [www.page-meeting.org/?abstract=1356]
[10] Beal SL et al. 1989-2011. NONMEM Users Guides. Icon Development Solutions, Ellicott City, Maryland, USA.
Retractions
Retraction is possible for one of the following reasons:
- ethically unacceptable issues or gross errors have been identified, or
- fabrication or fraudulent data have been used, which make the abstract scientifically invalid.
Minor errors should be processed as amendments (i.e. errata) rather than retraction. An updated version can be uploaded.
Without such justification we cannot process such a request, as abstracts can be cited as references in scientific publications.
Requests for retraction should be submitted in writing to the PAGE Board of Directors, including signed agreement from all authors and co-authors.
If the request is approved, the title and reference link on the website will be maintained, but a note will be added: "Retracted by the authors on DD.MM.YYYY due to [reason]."
