Diabetes Management and N of 1 Trials In Silico
P. Augstein*, L. Vogt, E. Salzsieder
Identifiers and Pagination:Year: 2009
First Page: 35
Last Page: 37
Publisher Id: TODIAJ-2-35
Article History:Received Date: 2/04/2009
Revision Received Date: 10/04/2009
Acceptance Date: 29/04/2009
Electronic publication date: 26/6/2009
Collection year: 2009
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
N of 1 trials are a new strategy to determine the best personal therapeutic choice for a diabetic patient. Here, we discuss the idea of performing N of 1 trials in silico using the knowledge-based decision support system KADIS®. This Diabetes Management System acts as interactive computer program that first generates a personal in silico copy of the glucose/insulin metabolism of the patient and then allows in silico N of 1 trials in order to identify the optimal therapeutic choice for the patient. KADIS® simulates and predicts therapeutic options as medication timing, dosage, and different formulas of oral anti-diabetic drugs or insulin, as well as life style changes like exercise and reduced carbohydrate intake by the patient to support drug therapy. Self control data are the data base for KADIS®. Over the past year decision support was generated for 384 diabetic patients treated by 132 general practitioners and 30 diabetes specialists. Application of KADIS® -based recommendations reduced HbA1c during the follow-up by 0.2% (7.1% to 6.9%) after 3 months and by 0.4% (7.1% to 6.7%) after 6 months in routine diabetes care. The reduction in HbA1c was strongly related to significantly improved 24-hour glucose profiles. Taken together, performing N of 1 trials in silico has the potential to determine the optimal patient-oriented therapy and to predict the clinical outcomes in the management of single patients.