Rapid identification of potential therapeutic targets for medical product development has been facilitated by widely accessible biological information, sophisticated bioinformatics tools to map pathways and build systems biology models, and high throughput screening methods. There has also been significant progress in understanding how genomic variations alter an individual's response to activation or inhibition of these therapeutic targets, enabling potential improvements in the clinical use of existing therapeutics and opening up the possibility of co-developing therapies and tests that can be used to tailor treatment to individual patients (personalized medicine). Indeed, genomic information has already been added to drug labels to identified patients who will most benefit from a drug, patients who will be at most risk of an adverse drug reaction, and select the optimal dose for a given patient. Despite this progress, the process of translating new scientific findings into safe and effective use of medical products and optimizing the use of existing products for all populations remains a major challenge. Clinical development programs are lengthy and expensive with uncertain outcomes. There is an imperative to speed efficacious medical products to patients by increasing efforts to reduce the uncertainties in this process.
Clinical development programs for medical products (drugs, biologics, and devices intended to treat disease) are dependent on the availability of tools, such as endpoints predictive of efficacy and toxicity, as well as effective clinical trial design and analysis methods. Central to effective clinical evaluation is the availability of clinically meaningful measures. These often utilize analytical measurements which employ physiologic, imaging, or genomic endpoints in addition to traditional lab tests. Efforts to assure the accuracy and consistency of analytical measurements while reducing inter-platform and inter-site variability are critical.
Stimulating innovation in the clinical development and evaluation of products requires multiple stakeholders. Addressing challenges in the early phases of drug and biologics discovery, for example, falls within the purview of companies and the biomedical research community. FDA's regulatory scientists, because of their broad and cumulative experience evaluating FDA-regulated product submissions, have a unique ability to facilitate development of knowledge and clinical evaluation tools needed for successful translation of discoveries into viable products 1, 2. Because the scale of data and effort needed to develop, validate or qualify clinical evaluation tools is enormous, intramural efforts are and should be supplemented by collaborative projects involving an array of external partners including academia, industry, and global regulatory agencies.
FDA will collaborate with others to help develop the new tools and approaches needed to catalyze the development of personalized medicine and to modernize and advance the science and conduct of clinical trials. Areas of need include:
- Develop and refine clinical trial designs, endpoints and analysis methods:
- Continue to refine clinical trial design and statistical methods of analysis to address issues such as missing data, multiple endpoints, patient enrichment, and adaptive designs;
- Identify and evaluate improved clinical endpoints and related biomarkers for trials in areas where optimal endpoints are lacking (e.g., efficacy and safety endpoints for osteoarthritis in humans and animals, for gene therapy, for ophthalmic indications, for tumor vaccines, and for stem cell-derived therapies);
- Develop novel trial designs and endpoints for special needs (e.g., small trials for orphan indications, designs and endpoints for pediatric trials including neonatal trials);
- Continue to refine the use of modeling and simulation in clinical trial design to enhance the effectiveness of clinical studies;
- Work with a broad coalition of partners to identify key opportunities for improving the conduct and efficiency of clinical trials; and
- Continue development and refinement of tools and approaches for assessing benefit/risk.
- Leverage existing and future clinical trial data:
- Develop quantitative models and measures of disease progression; and
- Utilize large, pooled clinical trial datasets to identify potential trial endpoints, explore differences in specific populations and subpopulations (e.g., stage of disease, chronic disease states, sex, race and ethnicity, pediatrics and other age groups) and different subsets of diseases, improve understanding of relationships between clinical parameters and outcomes, and evaluate clinical utility of potential biomarkers (also see section 5).
- Identify and qualify biomarkers and study endpoints:
- Facilitate identification and qualification of new and improved biomarkers for safety and efficacy, pharmacodynamic response - dose selection, disease severity, progression and prognosis, and pharmacogenomics (to predict safety and efficacy or guide dosing);
- Develop and evaluate novel approaches for biomarker identification, including 'omics, systems biology, and high throughput methods; and
- Monitor new developments in personalized medicine as they pertain to regulated medical products.
- Increase the accuracy and consistency, and reduce inter-platform variability of analytical methods to measure biomarkers:
- Reduce inter-platform variability of analytical methods to measure biomarkers through identification of standards and standardized characterization;
- Develop evidentiary requirements to demonstrate accuracy and reliability of devices that measure biomarkers using novel or innovative technologies (e.g., whole genome sequencing, new proteomics approaches, and image analysis);
- Promote and develop scientific tools to better characterize and standardize measurements which have relied on subjective reading (e.g., computer-assisted diagnostics in imaging, digital pathology devices); and
- Continue participation in collaborative efforts, such as the MicroArray Quality Control Consortium, to evaluate the quality of validation strategies for emerging technologies.
- Develop a virtual physiologic patient:
- Encourage the development of computer models that incorporate radiological imaging data of healthy and diseased anatomy from a range of relevant diseases;
- Ensure the integration of these models with genomic and other physiological data to promote development of complete physiological models and simulations that can be used in the development and testing of medical devices and other medical products; and
- Create a library of models so that models validated by FDA are easily accessible to researchers.
Recent biomedical breakthroughs are pushing medicine toward tailored therapeutics, or personalized medicine. Part of this movement means an increase in companion diagnostics—the tests that are used to determine whether a particular therapy may work for a patient. To address this issue, FDA issued the draft guidance In Vitro Companion Diagnostic Devices on July 12, 2011, to communicate to industry how FDA defines these devices and what the Agency’s regulatory requirements are for them. FDA hopes that by clarifying these topics for industry, we can streamline the process to approve companion diagnostics that accurately steer patients toward targeted therapies so the right patients receive the right drug at the right dose.
Public Health Impact:
The public health impact includes the following:
- Facilitate translation of the huge investment in basic sciences by optimizing product development and improving patient and consumer outcomes;
- Further basic knowledge needed to develop human biomarkers of harm from tobacco constituents;
- Maximize individual therapeutic efficacy and minimize harm by identifying predictors of individual outcomes; and
- Facilitate drug and device development for special populations (such as for children, women, and patients with rare or neglected diseases) for which safe and effective therapies are very much needed.
2. Advancing Regulatory Science for Public Health accessible at: http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/RegulatoryScience/UCM228444.pdf