SUMMARY More efficient processes for drug development make possible new effective therapies for rare cancers that have unmet clinical needs. In the pharmaceutical industry, narrowing from 10,000 or more possible chemical entities hits to a single FDA approved drug could take 11-16 years and cost $1-2B for that single drug. The earliest stages of this process are Research & Development (R&D) and then Preclinical Research with animals. The US expenditures for R&D and preclinical processes are $26B annually, equating to $474M per FDA-approved drug. The first R&D step is fast and automated, but preclinical animal studies are comparatively slower and more expensive. But what if R&D could “preview” animal studies and only refer the most promising compounds for Preclinical research with mammalian models? We hypothesize that pharma can forego much of the expense of failed mouse studies by pushing animal studies earlier into the discovery phase using our shell-free quail xenograft assay – an assay that can be executed in a standard laboratory instead of a vivarium, without the regulatory need for IACUC oversight and as a simple hardware + bioware Kit. To product-engineer & streamline our proof of concept, easy-to use Kit, we will: (Aim 1A) Refine and test a CAM Assay Hardware Product Line by optimizing throughput in restricting use of quail eggs of a specific weight range we will determine; and comparing durability of an egg-cracking device manufactured with environmentally- friendly materials versus aluminum. Completing this aim, we will achieve proof of concept for an optimized hardware component of our kit. We will also: Aim 1B. Develop and test a prototype CAM Assay Bioware Kit product line by developing a Bioware kit with shipping-safe, temperature appropriate packaging; refining the assay protocol with careful attention to user time and total time required. A special effort will be made to simplify the placement of the drug-delivery beads; defining reproducibility of the kit within an experiment and for the same experiment across different weeks using a drug range dose-response experiment for a cancer cell line xenograft; benchmarking the throughput for an experienced user vs a beginner user. Completing these sub- aims, will have documented performance for a proof of concept bioware kit to be brought to beta-testing for drug lead validation in early pharmaceutical R&D labs investigating cancer therapeutics.