Editor’s Note: Sharon Presnell discussed the use of bioprinting and 3D tissue to improve predictability in preclinical drug discovery this week at the BIO 2014 convention in San Diego. —BVB
The human race consists of a large population of genetically diverse organisms. This inherent diversity is compounded daily by the individualistic way we go about living – the air we breathe, the food we ingest, and the drugs, chemicals, micro-organisms, plants and animals that we contact. Studies that describe epigenetic mechanisms in the regulation of prevalent diseases such as diabetes, addiction, and cancer reveal the true complexity of homeostasis and pathogenesis.
Denis Noble, a renowned Oxford professor of cardiovascular physiology, elegantly described the genome as being “like an immense organ with 30,000 pipes,” pointing out that the music itself is not created by the organ alone, but is a product of the pipes that are available and the actions of an organist. The presence of a particular gene (pipe on the organ) establishes the potential for a particular event to occur (note being played), but it is the combination of genetic and epigenetic regulation (the organist) that determines the outcome (the music). Evidence continues to mount in support of these concepts, as genomic studies find that genes associated with a specific disease are absent in some people who have the disease, and present in some people who do not. So how do we accurately model physiology and disease in ways that emulate the inherent complexity and heterogeneity of the human being?
Beginning with the discovery that mammalian cells could be isolated and kept alive outside of the body (around the turn of the 20th century), scientists have endeavored to grow cells in a culture dish (in vitro) to model and predict outcomes within a living organism (in vivo). The reductionists in all of us have enthusiastically taken complex tissues, separated them into their cellular components, and undertaken experiments aimed at understanding how a particular cell type responds to a stimulus—painstakingly picking apart genomic, proteomic, and phenotypic responses so we can better understand the details. We use the data from these experiments to draw conclusions about the mechanisms of homeostasis and disease, to identify new drug targets, and to test the therapeutic or toxic effects of drugs. Results from experiments conducted with cell lines or purified primary cells are extrapolated to predict what would happen at the tissue level or in a living organism. When we have been informed by the in vitro experiments, we often leap to animal studies to validate hypotheses at the organism level, hoping the outcomes will predict human in vivo responses.
The inherent challenges with classical in vitro cell-based assays and in vivo animal models are twofold: In vitro cell cultures provide a great opportunity to study pathways and assess responses, but lack the broader context and structure of intact tissue as well as the systemic effects of a whole organism; In vivo animal studies enable the assessment of organism-level responses, but lack the context of a human system. In order to bridge the gap between current preclinical models and clinical trials, significant efforts are being expended to develop predictive models that can provide a greater degree of human tissue context.
Seminal work by Berkeley’s Mina Bissell and her collaborators provided clear evidence that three-dimensional cellular aggregates comprised of multiple breast tissue-relevant cell types yielded superior predictive results to traditional two-dimensional monocellular cultures. These results, and others, sparked a revolution aimed at developing
