The drug development landscape is awash with candidates that have shown enormous promise and efficacy in preclinical models but failed when administered to clinical trial subjects. Although such failures occur for different reasons, one of the most pervasive causes is the inability of preclinical models to recapitulate human physiology accurately. Despite advances with both in vitro and in vivo models, improving those toward a more accurate avatar of the human physiological process remains a challenge. Central to that effort will be incorporating results from human clinical trials that show investigators why some patients respond to a given therapy but others do not. Such knowledge could guide researchers to design their models more accurately. By reflecting human physiology, models can predict clinical responses to particular drug treatments more precisely.
Translational Medicine — Reversed
Over the past several years, a surge of translational medicine research across multiple therapeutic areas has brought novel drugs into clinical trials and many ultimately to FDA approval. In oncology alone, the advent of immunotherapy is altering the treatment landscape. Failure of oncology compounds in clinical trials has both informed and fueled a transformation in how we approach preclinical development for this therapeutic area and what models we use to test cancer drugs. Conventional murine studies using xenograft models implant tumors into animals that lack intact immune systems, so such studies can’t accurately reflect a physiological environment. Thus, preclinical studies have produced spectacular curative indications that were ineffective in human clinical settings.
Recent preclinical developments in immunooncology have shunted toward syngeneic (allograft) mouse models. In these studies, the tumors share the same genetic background as their host mice, which also possess intact immune systems. These models have proven to be excellent tools for testing immunooncology candidates. Moreover, expansion along these lines includes the development of “humanized models.” A human immune system is engrafted into a rodent model, thus better recapitulating drug responses in humans. Coupled with patient-derived tumor xenograft (PDX) models, which have become increasingly popular for preclinical drug evaluation, biomarker identification and personalized medicine represent further advances that retain the principal histologic and genetic characteristics of a donor tumor.
Advances in stem cell biology, tissue engineering, and other innovations have inspired the creation of in vitro assays that mimic the sophisticated architecture surrounding host and cancerous tissue. For example, two-dimenional (2-D) angiogenesis assays (using endothelial cells, fibroblasts, and other extra cellular matrix tissues) have enabled development of novel antiangiogenic therapeutics. Although not completely physiological, such assays nonetheless indicate whether a specific candidate might be efficacious in a clinical setting. Ultimately, human diseases involve multiple interactions among several different cell types. Those interactions can be investigated by bioprinting three-dimensional (3-D) organ-like structures to create multidimensional structures of living cells or by using “organ-on-a-chip” models that mimic disease states in a physiological setting.
Another example of reverse translation is deep-learning and large data mining to help repurpose drugs across all therapeutic areas. By capturing the vast reservoir of information available in scientific literature — both published academic research and data from clinical trials — some companies are developing software that can harness pertinent information from many sources. Using sophisticated algorithms, that information is collated to predict, with increasing accuracy, which drug might work best to treat a specific condition.
For example, a hypothetical drug that undergoes preclinical development for pancreatic cancer but fails in clinical trials might be better suited for treating brain tumor glioblastoma multiforme. Biotechnology and pharmaceutical companies can use the vast amount of preexisting data on such drugs, lessening the need to spend millions of dollars on drug discovery.
Furthering the Discussion
In addition to the above-mentioned advances, we have seen a progressive approach to using genomics, proteomics, and other technologies to improve understanding of a drug response or elucidate which tumor will respond to a given drug. The ultimate goal of personalized medicine is to move toward tailored treatments with specific and fine-tuned therapeutics that ultimately lead to disease regression or cure.
At the Inaugural Charles River World Congress on Animal Models in Drug Discovery and Development in Boston, MA on 26–27 September 2017, speakers from across academia and industry discussed the impact of reverse translational medicine in drug development with presentations on genomic data, animal models, and ex/in vivo methods. For more information, see http://breakthroughs.criver.com.
Former oncology director for in vivo biosciences in integrated drug discovery at Charles River Laboratories, Joseph Murphy, PhD, is director of immunology applications at Caprion Biosciences Inc., 201 President-Kennedy Avenue, Montreal, QC, Canada H2X 3Y7; 1-781 664-4897; firstname.lastname@example.org; www.caprion.com.