The ultimate goal of precision medicine is a unique drug for each patient. However, this N-of-1 model directly conflicts with the current economic and regulatory system, which incentivizes developing drugs for large populations to recoup massive R&D and approval costs.

The fear of toxicity pushes many companies to pursue the same few well-validated targets, leading to an average of nine assets per target. This hyper-competition not only crowds the market but, more importantly, leaves vast patient populations without effective options because their diseases lack these "popular" targets.

While targeting intracellular peptide-MHC complexes opens the entire proteome as potential cancer targets, the approach is limited by HLA restriction. This means a drug might only be applicable to 30-40% of patients, a major commercial and clinical drawback that complicates development despite the potential for exquisite specificity.

Instead of creating therapies for hundreds of specific driver mutations, which vary widely between patients, Earli's platform targets downstream commonalities—the "hallmarks of cancer" like rapid cell proliferation. These pathways are where diverse mutations converge, creating a more universal and reliable target across different cancers.

The 'safety first' mandate in drug development is flexible. For cancers like leukemia with high cure rates, highly aggressive therapies with severe side effects are deemed acceptable. The risk-benefit calculation shifts dramatically when a cure, not just management, is the goal.

Despite scientific breakthroughs and better technology, the cost per approved drug has steadily increased over the last 60 years. This phenomenon, the reverse of Moore's Law, is called Eroom's Law and highlights a fundamental productivity problem in the biopharma industry, with costs approaching $1B+ per successful drug.

With over 5,000 oncology drugs in development and a 9-out-of-10 failure rate, the current model of running large, sequential clinical trials is not viable. New diagnostic platforms are essential to select drugs and patient populations more intelligently and much earlier in the process.

Despite billions invested over 20 years in targeted and genome-based therapies, the real-world benefit to cancer patients has been minimal, helping only a small fraction of the population. This highlights a profound gap and the urgent need for new paradigms like functional precision oncology.

The fastest, cheapest path to drug approval involves showing a small survival benefit in terminally ill patients. This economic reality disincentivizes the longer, more complex trials required for early-stage treatments that could offer a cure.