Simple cell viability screens fail to identify powerful drug combinations where each component is ineffective on its own. AI can predict these synergies, but only if trained on mechanistic data that reveals how cells rewire their internal pathways in response to a drug.

A key conceptual shift is viewing ctDNA not as a statistical risk marker, but as direct detection of molecular residual disease (MRD). This framing, similar to how a CT scan identifies metastases, explains its high positive predictive value and justifies its use in making critical treatment decisions.

The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.

An individual tumor can have hundreds of unique mutations, making it impossible to predict treatment response from a single genetic marker. This molecular chaos necessitates functional tests that measure a drug's actual effect on the patient's cells to determine the best therapy.

By continuously measuring a drug's effect on the body (pharmacodynamics), the wearable device provides a real-time view of a patient's phenotype. This granular data can revolutionize clinical trial design, safety monitoring, and drug dosing, moving beyond static genomic data to understand real-world drug response.

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.

The progress of AI in predicting cancer treatment is stalled not by algorithms, but by the data used to train them. Relying solely on static genetic data is insufficient. The critical missing piece is functional, contextual data showing how patient cells actually respond to drugs.

Alt-Pep's SOBA blood test is a crucial companion diagnostic for its SOBIN-AD therapeutic. It allows for patient stratification by confirming the presence of the drug's target—toxic oligomers. This creates a rare, direct link between biomarker, target, and mechanism, significantly increasing the probability of clinical success.

The low-hanging fruit of finding a single predictive biomarker is gone. The next frontier for bioinformatics is developing complex, 'multimodal models' that integrate several data points to predict outcomes. The key challenge is creating sophisticated models that still yield practical, broadly applicable clinical insights.