Corporate Comment


New paths forward in translational medicine


T


ranslational research lies at the heart of drug development and is the process whereby basic science discoveries are harnessed to develop new drugs, devices and therapeutic approaches for use in human patients1. A classic success story in translational research was the development of insulin therapy for diabetes, which began in 1869 with the discovery of the pancreatic islets of Langerhans, contin- ued with Nobel Prize-winning experiments on the digestive physiology of dogs in the 1920s, and culminated in the large-scale commercial production of genetically-engi- neered insulin in 19822,3. In a more recent example, the 1995 discovery that spinal muscular atrophy (SMA) is caused by muta- tions in the Survival Motor Neuron 1 (SMN1) gene led directly to the develop- ment of the first therapy for SMA, an anti- sense drug that was approved by the FDA in December 20164,5.


Despite significant advances over the last 30 years in biomedical technology and basic science insights into disease mecha- nisms, there has been an increasingly high failure rate of new candidate drugs devel- oped during the same period6. Currently, it takes longer than a decade and US$ 2.6 bil- lion on average to develop a new drug from target discovery to market entry, and only one out of 10 drug candidates entering clinical trials receives market approval7. Ninety percent of drug candidates of that fail in clinical trials have been found to do so because of low efficacy and safety issues8. Failure rates are highest for cancer, mental health disorders, cardiovascular


Drug Discovery World Fall 2017


disease and neurological disease – four of the leading causes of morbidity and mortal- ity worldwide9.


This situation has prompted extensive reappraisals of current approaches to translational research10-17. Major issues that have been identified as contributing to failure of new drug candidates in clinical trials include: l Sub-optimal trial design, poor choices of patient populations (for example, studying patients whose disease has already pro- gressed too far for successful treatment), lack of validated disease and target engage- ment biomarkers, failure of drugs to engage their intended targets at the doses used, and insufficiently sensitive outcome measures. l Lack of rigor in preclinical animal stud- ies, including inadequate sample sizes, poor study design, inappropriate statistical meth- ods and the failure to seek replication of positive results or report negative results. l Poor predictive value of many currently used preclinical in vitro and in vivo model systems.


Drug discovery relies heavily on geneti- cally-engineered animal models of disease, especially mice. The cancer field also makes heavy use of mouse xenograft models: immunodeficient mice into which patient- derived tumours or tumour cell lines have been transplanted. However, it is has become increasingly clear that many mouse models fail to accurately recapitulate the human disease and/or to predict the effica- cy and clinical side-effects of candidate drugs. Fortunately, there is a seismic shift


happening in the field of translational research, as emerging technologies offer new possibilities for creating more accu- rate, informative, less expensive and high- er-throughput biological models for drug discovery. Genome-editing techniques such as TALENS and CRISPR have revolu- tionised the precision, scale and speed with which we can generate new disease models, in large animals as well as rodents18-20. Growing databases of human ‘omics’ data are enabling the reverse translation of clin- ical findings to inform preclinical studies, generate new animal models and test the validity of existing models. Furthermore, developments in the field of bioengineering are spawning new in vitro systems for studying disease biology and the effects of novel drugs on human cells. In theory, the ideal animal model of dis- ease would have the following features (reviewed in references21, 22):


(1) Replicate the human disease phenotype (at all levels from the molecular to the behavioural).


(2) Share underlying biological mecha- nisms with the human disease. (3) Have predictive validity with respect to drug efficacy and safety in humans.


In practice, these criteria are rarely, if


ever, met. For example, the Alzforum web- site now lists 127 genetically-engineered mouse models of Alzheimer’s disease (AD), and not one of them has yet been shown to completely fulfill any of these criteria23-25. Key pathological features of AD in humans include not only amyloid plaques but also


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