1. Literature databases: integrating information on diseases and their treatments Vandemeulebroecke M, Demin I, Luttringer O, McDevitt H, Ramakrishna R, Sander O Advanced Quantitative Sciences Novartis Pharma AG, Basel, Switzerland Background Drug development relies on thorough knowledge of the disease in focus, in particular with respect to its natural course and the efficacy and safety of already existing treatment options 1,2. Such knowledge allows to put a therapy under development into context and to inform decision making throughout development. A great wealth of information is available for this purpose in published literature. However, it is rarely compiled systematically into one integrated, well- documented and easy-to-search source database. This study was supported by Novartis Pharma AG, Basel, Switzerland. Copyright © 2013 Novartis Pharma AG, Basel, Switzerland. All rights reserved. Poster presented at the Population Approach Group Europe (PAGE), June 11-14, 2013, Glasgow, UK. The development of infrastructure for building and maintaining indication-specific literature databases has been described previously 3. Building upon this, literature sources for each database are systematically identified, curated according to a standard process, condensed into a standard format database. The methods are illustrated by the following motivating example 4. Summary-level longitudinal data on the clinical efficacy of biologics for Rheumatoid Arthritis (RA) are available in the literature, including patient characteristics and concomitant medications. We carried out a systematic review of the published literature on clinical trials of biological treatments in RA. The data were extracted using robust processes into a literature database, which contained all the relevant information necessary to build a drug– disease model. The aim of this work was twofold: first, to quantify the time course of the ACR20 score across approved drugs and patient populations, and second, to apply this knowledge in the decision- making process for an internal compound. The integrated analysis included data from 37 phase II– III studies describing 13,474 patients (Figure 1). With this, the efficacy of the internal compound could be put into perspective, and decision-making processes could be effectively supported. The framework can be applied to any other compound targeting RA, thereby supporting internal and external decision making at all clinical development stages Methods To build comprehensive literature-based summary level databases on selected indications and their major treatments, including longitudinal outcome data and covariates to facilitate dynamic modeling. To implement a relational database infrastructure as a generic IT solution that allows state-of-the art creation and maintenance of such literature databases. Objectives Significant lead time and effort is required to assemble the relevant data for comprehensive literature databases, as well as for the implementation of a generic IT solution to host such databases. However, great value can be derived from this investment. Results Discussion References: Conclusion Current range of applications Table 1 provides an overview of the literature databases that have been built so far, along with their major applications. For two databases currently under construction, the intended application is given. Each database assembles comprehensive longitudinal (group-level) data on a wide range of endpoints for all major drugs of the respective indication also including demographic and background characteristics based on all relevant publications in the field. A great wealth of information is available in published literature on the natural course of diseases and the effect of available treatments options. However, it is rarely compiled into comprehensive quantitative databases. If available, such databases allow to assess the effect of a new compound quickly and accurately in the context of the competitive landscape. For this reason, Novartis has been building several literature databases on selected medical indications. This requires significant lead time and effort, but great value can be derived from this investment The range of successful applications spans from dose selection to supporting Go/Nogo milestones. Building on this experience, we have recently built a more generic IT infrastructure to capture these and future databases. This solution has an intuitive web- based interface, while still retaining the full flexibility of a relational database in the background. DatabaseNumber of publica- tions included Major applications COPD 39Comparison of in vivo performance of different bronchodilators / devices Type 2 Diabetes 45Benchmarking against competition and standard of care Rheumatoid Arthritis 128Support of Go/Nogo milestone Optimizing clinical trial design Hepatitis C Virus 21Benchmarking against competition Predicting sustained viral response from early viral response Multiple Sclerosis 40Benchmarking against competition Chronic Kidney Disease 21Quantifying the competitive landscape Psoriasis 23Competitive profiling including benefit/risk assessment Supporting dose selection Osteoporosis 20Assessment of compound‘s efficacy Dyslipidemia* -Explore lipid lowering potential of own compounds on top of standard of care Heart Failure* -Calibration of a simulation platform for the cardiovascular/renal system Relational database solution Managing the literature data presents some unique challenges: Data need to be kept up-to-date, and updates should be traceable Data need to be traceable back to the source references Data need to be queried and searched in a meaningful way Our literature databases were implemented as comprehensive Excel spreadsheets in the past. However, Excel spreadsheets do not provide an ideal way of addressing these needs. A relational database system has therefore been developed in collaboration with an external vendor (GVKBio) that addresses these requirements. Existing Excel spreadsheets can be uploaded, and new data can be entered directly. Once the data have been verified, they can be queried and searched through a simple user interface (Figure 2). The system can be developed further based on growing practical experience. Table 1. Novartis AQS literature databases with major applications * currently being built Figure 2: User interface of the relational database application Figure 1: Model-based predictions of median ACR20 responder rates together with their 90% Bayesian confidence intervals for approved biologics and placebo. All treatments including placebo are given in combination with methotrexate (MTX) to patients with previous exposure to MTX. The blue stripe represents the 90% Bayesian confidence interval for certolizumab. 1 Mandema et al.: „Model-based development of gemcabene, a new lipid-altering agent“, AAPS J 2005 2 Ito et al.: „Disease progression meta-analysis model in Alzheimer‘s disease“, Alzheimer‘s & Dementia 2010 3 McDevitt et al.: „Infrastructure development for building, maintaining and modeling indication-specific summary-level literature databases to support model-based drug development”, PAGE 2009 4 Demin et al.: „Longitudinal model-based meta-analysis in rheumatoid arthritis: an application toward model-based drug development”, Clin Pharmacol Ther 2012