OR WAIT 15 SECS
Transfer of knowledge between pre-clinical and clinical research is necessary to deliver effective medicines to patients.
Translational medicine (TM) is the emerging discipline involving the translation of laboratory findings into the design and implementation of early-stage clinical trials. TM focuses on translating pre-clinical data from in vivo, in vitro, and in silico research into the clinic to help design trials, determine methods, and choose the biomarkers. In addition, TM uses the data from clinical studies to feed back into pre-clinical experiments to improve future drug discovery. Biomarkers are an essential piece of the translational effort and critical to understanding an individual patient's disease and response to experimental treatments. TM uses a very patient-driven approach to drug development and is a result of the practical application of the improvements made in biomarker discovery in the era of personalized medicine (PM).
Many pharmaceutical companies are introducing TM departments charged with the task of facilitating the transition of basic research into practical treatments and clinical trials. These organizational changes are based on the need for an improved, dynamic exchange of information between late pre-clinical efforts and early stage clinical trials.
Traditionally, oncology has been at the forefront of the biomarker development and PM. However, as technologies advance, fields such as neuroscience and immunological, inflammatory, and metabolic diseases are expanding their use of biomarkers for PM. Personalized medicine seeks to identify individuals who will receive the most clinical benefit and least harm from a specific treatment by targeting genetic or other targets associated with their disease. Enabled by technological advances and expansion of the use of biomarkers, researchers can stratify patients into disease subtypes and evaluate targeted therapies aimed at treating them. With the cost of developing a successful drug typically exceeding $1 billion, the need has never been greater to effectively translate pre-clinical research into the clinic and learn from early stage clinical trials.
Over the past 10 years, advances in early phase studies have focused on collecting and applying more clinical pharmacology data sooner to inform dose determination, endpoint identification, and patient selection in Phase II trials. Phase 0 trials are emerging, particularly in oncology, to help translate preclinical research into humans before Phase I. Phase 0 evaluations leverage the incorporation of biomarkers to gain more information from first-in-human experience.
In Phase 0, microdoses of experimental drugs are administered to volunteers. The dose is expected to be well below toxicity and efficacy points, but can be used to make an initial assessment of pharmacokinetics (PK) and pharmacodynamics (PD) effects. Positron emission tomography (PET) and accelerated mass spectrometry (AMS) imaging are used to assess drug distribution and other clinical pharmacology measures. Phase 0 biomarker evaluations can provide direction regarding drug targets, mode of action, and pharmacology to make data collection in Phase I more selective.
Traditionally, Phase I studies assess the safety, tolerability, PK, and PD of an investigational drug to determine dose, dose schedule, and route of administration. Phase I investigations use biomarkers to identify and recruit targeted patient populations, as well as evaluate patients' response to drug treatment. Phase I evaluations are designed to inform Phase II endpoint selection, streamline trials to demonstrate proof of concept, and facilitate more efficient approval pathways.
Patient stratification biomarkers. Biomarkers that can identify patients expected to respond to a targeted therapy will increase the chance for delivering the right medicine to the right patient. Current targets include BRAF gene mutations in metastatic melanoma, HER-2 mutations in breast cancer, and ALK gene translocation in non-small-cell lung cancer. If preclinical research has not delivered patient stratification biomarkers prior to entry into humans, Phase I and Phase II data may be useful to discern them after treatment. Correlations between patient response and biomarker measurements can be used to retrospectively discover patient stratification biomarkers that have the potential to be used in later trials. This retrospective analysis is facilitated by a translational medicine team dedicated to extracting information from biomarker measurements.
Pharmacodynamic biomarkers. Monitoring patient response to therapy in Phase I is critical to determining the correct dose for Phase II. Biomarkers for toxicity and target engagement can help researchers decide upper limits of dosing. In healthy volunteer studies, biomarkers can also help predict response in patients where none is expected in non-diseased individuals. In targeted drug development, Phase I is the starting point for tissue collection, genetic profiling, antibody testing, and tumor typing that support clinical research and patient selection.
Phase II trials are deemed "proof-of-concept" trials as the goal is to determine if the drug has the hoped for biological effect. Phase II determines drug efficacy and evaluates Phase I safety assessments in the expanded number of subjects. Biomarkers continue to play a critical role in Phase II trials. Safety biomarkers remain standard practice for monitoring adverse events. Patient stratification and pharmacodynamic biomarkers also continue to be assessed. A large panel of exploratory biomarkers that is assessed in Phase I trials can be reduced in Phase II to those that showed the most promise. Biomarkers that have been previously correlated with long-term patient outcomes can be used as surrogate or secondary endpoints.
Critical to translational medicine is the conversion of insights from pre-clinical research into early clinical trials. In addition, TM fosters ongoing evaluation of clinical biomarker data to both inform the next stage of trials and to provide feedback into additional pre-clinical studies. Pre-clinical data is used to predict a mechanism of action for the drug and choose biomarkers appropriate for patient stratification and response to treatment. Because pre-clinical models are known to be imperfect at predicting human response, the translational medicine teams must use biomarkers to measure as much information as they can as early as possible in the clinic. Further pre-clinical research can explore hypotheses generated from the clinical biomarker data to predict more effective biomarkers for the next stage of clinical testing. This translation of knowledge between pre-clinical and clinical research is necessary to design and run effective clinical trials and ultimately deliver effective medicines to patients.
Holly Hilton, PhD, is Director, Biomarkers and Translational Sciences, Rand Jenkins, is Director, Laboratory Operations, and Steve Lobel, PhD, is Vice President, Global Laboratory Operations all at PPD, 929 North Front Street, Wilmington, NC.
Related Content:Trial Design