Publicly available clinical trial data represents an underutilized source of information. If properly extracted and analyzed, they provide valuable information to support drug development decisions. When you think of the volumes of public information and databases that are available to determine, for example, commercial viability of a therapeutic in development, your first inclination is—“great!” But then you begin to realize how overwhelming it can be to sort through all the data. Where do I start? How do I know if the information I retrieve is up-to-date? Am I missing important data that may impact how to effectively position and differentiate a new drug in a highly competitive market? Do I have sufficient information to demonstrate commercial viability?
The What: what are the Clinical Trial Outcomes Databases?
Based on years of experience exploring and analyzing publicly available data to perform model-based meta-analysis (MBMA) for our clients, we created an extensive collection of analysis-ready Clinical Trial Outcomes Databases for a wide range of therapeutic areas. These up-to-date databases capture high-quality public source data on drug efficacy and safety, drug, trial, and disease characteristics, trial design, and other relevant information to make key development and commercial decisions.
We have accumulated 40 databases that provide comprehensive up-to-date information on major therapeutic areas such as CNS & Pain, Oncology, Immunology, CV, Metabolic, Infectious Diseases, and more.
For easy central access, our Collaborate portal can be used to explore the databases through the integrated Clinical Outcomes Database Explorer (CODEx) interface. CODEx enables users to quickly visualize, explore, analyze, and communicate database content using a variety of highly interactive tools. Check out this video for an overview on the databases and CODEx, or browse the database collection for a closer look at specific therapeutic areas.
The How: how can the Clinical Trial Outcomes Databases be used to streamline drug development?
The Clinical Trial Outcomes Databases provide clinical trial data that can help you answer questions to ensure trial, program, portfolio, and market success. Here are some of the important ways you can use these databases to help drug development.
As a knowledge repository
The Clinical Trial Outcomes Databases can help answer the following questions:
- What new clinical evidence are available for our main competitors and specific products?
- How have trial design and patient characteristics evolved in the past decades? For example, are patients under better disease control in recent years and what implication does it have on patient selection?
- What emerging adverse events are becoming a concern for newer classes of drugs?
- What regulatory strategies are our competitors following?
As a source of information to perform pairwise, network, or model-based meta-analysis
Several different types of meta-analysis are used in drug development. Pairwise meta-analysis (PMA) compares treatment in pairs. PMA is a good place to start any evidence synthesis in which all studies are assessed in context—the key to more coherent and efficient research.
Network meta-analysis (NMA) provides an efficient use of information by combining trials with different primary treatments and comparators within a single framework.
Model-based meta-analysis incorporates parametric pharmacology models (eg, with dose and duration) for the effect of treatment, time, and patient population characteristics on the outcomes of trials. MBMA informs on comparative safety and efficacy, characterizes endpoint-to-endpoint relationships, and quantifies the impact of treatment, time, and patient characteristics on the therapeutic outcome in a trial.
Clinical Trial Outcomes Databases can be used in these meta-analysis applications, for example, to evaluate comparative effectiveness or in endpoint scaling to determine short term effect to long term clinical outcomes, scaling from one indication to another.
Solve specific problems
- Facilitate Early Go/No Go Decisions—Can we differentiate our drug as best-in-class? How can we best position a drug between existing and developing competitors?
- Evaluate Competitive Landscape—How will our drug perform in a crowded and competitive therapeutic area? What are the key competitor compounds in development? How many competitor trials are available for certain outcomes? What are characteristics of these trials in terms of key covariates such as populations, baseline values, and demographics?
Optimize dose and dose regimen for a compound
Dose-response models can help sponsors understand how differences in patient populations or trial design aspects may result in differential responses to a drug. Is there any evidence of dose-response? What are typical time courses and how do they vary across drugs? What are the characteristics of the dose-response curves for existing drugs that are in the same class as a new compound? What are typical dose ranges? How does onset of effect differ between drug classes? How do baseline characteristics or background treatments impact drug response? Clinical trial outcomes data are a rich source of information to examine and evaluate dose response of a drug in development.
Optimize trial design
What is the impact of trial design features (eg, time, endpoints) on treatment effects? How are specific subsets of the population represented? What is the impact of region? How do biomarker and clinical endpoint results compare? Can we predict trial results?
The wealth of publicly available clinical trial data provides valuable information on drug efficacy and safety, drug, disease and trial characteristics, trial design, and the competitive landscape—critical for achieving commercial success. Further, the Clinical Trial Outcomes Databases—using Collaborate for central access and the CODEx interface for interactive exploration and analysis—provides a solution for the daunting task of gathering clinical data.
To learn more, watch this webinar on how MBMA can be used to increase the likelihood of success in drug development.