Literature DB >> 27818254

Defining drug response for stratified medicine.

Mike Lonergan1, Stephen J Senn2, Christine McNamee3, Ann K Daly4, Robert Sutton5, Andrew Hattersley6, Ewan Pearson1, Munir Pirmohamed7.   

Abstract

The premise for stratified medicine is that drug efficacy, drug safety, or both, vary between groups of patients, and biomarkers can be used to facilitate more targeted prescribing, with the aim of improving the benefit:risk ratio of treatment. However, many factors can contribute to the variability in response to drug treatment. Inadequate characterisation of the nature and degree of variability can lead to the identification of biomarkers that have limited utility in clinical settings. Here, we discuss the complexities associated with the investigation of variability in drug efficacy and drug safety, and how consideration of these issues a priori, together with standardisation of phenotypes, can increase both the efficiency of stratification procedures and identification of biomarkers with the potential for clinical impact.
Copyright © 2016 Elsevier Ltd. All rights reserved.

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Year:  2016        PMID: 27818254     DOI: 10.1016/j.drudis.2016.10.016

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  7 in total

1.  Precision Medicine.

Authors:  Michael R Kosorok; Eric B Laber
Journal:  Annu Rev Stat Appl       Date:  2019-03       Impact factor: 5.810

2.  Response to Comment on Dawed et al. Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas. Diabetes Care 2021;44:2673-2682.

Authors:  Adem Y Dawed; Sook Wah Yee; Kaixin Zhou; Nienke van Leeuwen; Yanfei Zhang; Moneeza K Siddiqui; Amy Etheridge; Federico Innocenti; Fei Xu; Josephine H Li; Joline W Beulens; Amber A van der Heijden; Roderick C Slieker; Yu-Chuan Chang; Josep M Mercader; Varinderpal Kaur; John S Witte; Ming Ta Michael Lee; Yoichiro Kamatani; Yukihide Momozawa; Michiaki Kubo; Colin N A Palmer; Jose C Florez; Monique M Hedderson; Leen M 't Hart; Kathleen M Giacomini; Ewan R Pearson
Journal:  Diabetes Care       Date:  2022-04-01       Impact factor: 17.152

3.  Demonstrating Heterogeneity of Treatment Effects Among Patients: An Overlooked but Important Step Toward Precision Medicine.

Authors:  Jennifer S Gewandter; Michael P McDermott; Hua He; Shan Gao; Xueya Cai; John T Farrar; Nathaniel P Katz; John D Markman; Stephen Senn; Dennis C Turk; Robert H Dworkin
Journal:  Clin Pharmacol Ther       Date:  2019-03-12       Impact factor: 6.875

4.  Costs and Treatment Pathways for Type 2 Diabetes in the UK: A Mastermind Cohort Study.

Authors:  Peter Eibich; Amelia Green; Andrew T Hattersley; Christopher Jennison; Mike Lonergan; Ewan R Pearson; Alastair M Gray
Journal:  Diabetes Ther       Date:  2017-09-06       Impact factor: 2.945

Review 5.  Nursing Personnel in the Era of Personalized Healthcare in Clinical Practice.

Authors:  Marios Spanakis; Athina E Patelarou; Evridiki Patelarou
Journal:  J Pers Med       Date:  2020-06-29

6.  [Establishment of a rapid identification of adverse drug reaction program in R language implementation based on monitoring data].

Authors:  Dongsheng Hong; Jian Ni; Wenya Shan; Lu Li; Xi Hu; Hongyu Yang; Qingwei Zhao; Xingguo Zhang
Journal:  Zhejiang Da Xue Xue Bao Yi Xue Ban       Date:  2020-05-25

Review 7.  Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials.

Authors:  Ralf-Dieter Hilgers; Malgorzata Bogdan; Carl-Fredrik Burman; Holger Dette; Mats Karlsson; Franz König; Christoph Male; France Mentré; Geert Molenberghs; Stephen Senn
Journal:  Orphanet J Rare Dis       Date:  2018-05-11       Impact factor: 4.123

  7 in total

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