All models are wrong — but some models are less wrong than others. At the interface of tissue engineering and microfluidic technology, tumor-on-chip systems offer tantalizing possibilities of unraveling human tumor biology and drug responses, while minimizing the time, cost, and ethical concerns of animal research.
In biomedical research, questions of animal ethics usually pertain to the use of whole animals to model disease and test pharmaceutical efficacy. In truth, the problem extends far beyond this. The global antibody industry, which today relies heavily on animals, is worth $80 billion. As well as contributing unnecessarily to animal suffering, this is an industry polluted by poor quality antibodies, leading to scientific inconsistency, confusion, money-wasting and meaningless results. Regardless of whether animal ethics are high on your agenda, the need for an antibody revolution today is undeniable. This is an issue that Animal-Friendly Affinity Reagents – a high quality and cost-effective alternative to animal antibodies – might help to address.
Immunotherapy has enormous potential to provide cancer patients with a treatment which is more personalised, more precise, and more effective than current therapies, but evidently its promises will only come to fruition with the assistance of improved predictive algorithms and bioinformatics tools. Recent explosions in publicly available cancer genomic data, coupled with advancements in machine learning methods is ensuring that the marriage of computation and biology will help to address challenges facing immunotherapy in the coming decade. Here we will take a look at how scientists are starting to implement artificial intelligence methods to predict neoepitopes for cancer vaccine development.