The Biological Algorithm for Cancer Therapy

Modern cancer therapy is largely grounded in a linear paradigm in which drug–target interaction is assumed to be the dominant determinant of therapeutic outcome. Yet across virtually all tumor types and treatment modalities, clinical responses remain profoundly unpredictable: patients with nearly identical disease characteristics often exhibit radically different outcomes to the same therapy, and biomarker-positive tumors can be either exquisitely sensitive or entirely refractory. These inconsistencies expose fundamental limitations of a single-dimension, drug-centered framework and indicate the presence of deeper system-level determinants.Here, I introduce the concept of the high-dimensional biological algorithm—the nonlinear, multidimensional computational logic by which the tumor–host system integrates all internal and external variables to generate therapeutic outcomes. This algorithm is not mutable code but is embodied in the invariant laws and principles of physics, chemistry, and biology (such as biochemistry, biophysics, molecular biology, cell biology, immunology, physiology) in the tumor-host system. While these governing principles remain constant, the algorithm faithfully processes its inputs; thus, system output is determined entirely by the nature and configuration of those inputs.Within this framework, a drug is not a direct cause of outcome but one input among many entering a system that simultaneously processes biochemical, metabolic, signaling, immunological, structural, and microenvironmental variables. The quantities and qualities of these variables differ between patients, between tumors, within individual tumors, and over time. Consequently, when the same drug is introduced into tumor–host systems with different computational baselines, it enters fundamentally distinct input landscapes. Although processed by the same underlying algorithm, these differing baseline conditions necessarily drive computation along divergent trajectories, yielding heterogeneous therapeutic outcomes.Thus, variability in therapeutic response is not a failure of the drug nor a change in the governing biological algorithm, but a predictable consequence of applying identical inputs to heterogeneous computational baselines. Achieving consistent and durable therapeutic efficacy therefore requires reconditioning the baseline inputs presented to the algorithm, rather than attempting to modify the algorithm itself.A second-dimension input, distinct from molecular targeting, is therefore required to constrain or recondition the computational baseline through which the algorithm processes therapeutic inputs, thereby favoring convergent and therapeutically productive outputs. Concentrated bicarbonate provides a proof-of-concept example: by sharply alkalizing extracellular and intracellular pH, it reconditions global baseline parameters—including enzymatic kinetics, metabolic flux distribution, mitochondrial energetics, immune visibility, and signaling thresholds. In clinical studies, including bicarbonate-integrated TACE and bicarbonate-augmented anti–PD-1 therapy, this conditioning transformed previously variable responses into uniform, high-magnitude outcomes, despite unchanged drugs, doses, and delivery routes.Together, this framework suggests that future progress in oncology will require moving beyond an exclusive focus on drug–target interactions, toward strategies that combine molecular targeting therapies with deliberate manipulation of the computational baseline that governs how therapeutic inputs are interpreted and executed. Integrating pharmacology with system-level input conditioning offers a principled path toward more predictable, controllable, and robust cancer therapies.