Algorithmic pharmacology: from pharmacotherapy to therapy by algorithms
DOI:
https://doi.org/10.15407/visn2026.04.015Keywords:
algorithmic pharmacology; algorithmic therapy; artificial intelligence in medicine; digital therapeutics; algorithmodynamics; algorithmokinetics; algorithmic dose; AI regulatory framework; precision medicine; informational medicine.Abstract
This paper proposes the concept of algorithmic pharmacology as a novel interdisciplinary field of medical science that studies algorithms and artificial intelligence systems as potential informational therapeutic agents. The evolution of therapeutic paradigms — from surgery and pharmacotherapy to algorithm-based therapy — is analyzed, and the transition of medicine toward an informational level of therapeutic intervention is theoretically substantiated. The prerequisites for the emergence of algorithmic pharmacology are examined, including the development of medical artificial intelligence, digital therapeutics, precision medicine, and big medical data. A structural model of the discipline is proposed, encompassing theoretical, experimental, and clinical levels. The possibility of transferring classical pharmacological categories (dose, pharmacodynamics, pharmacokinetics, toxicity) to algorithmic systems is justified based on the principle of structural isomorphism. Special attention is given to the classification of medical algorithms, the concept of a “pharmacopoeia of algorithms,” regulatory frameworks, algorithmic safety, ethical considerations, and future prospects for clinical integration. A tiered model of algorithmic autonomy is introduced, outlining regulatory requirements for therapeutic AI systems. Algorithmic pharmacology is presented as a conceptual foundation for the transition from material pharmacotherapy to informational therapy by algorithms, opening new horizons for the future development of medicine.
Cite this article:
Golovenko M.Ya. Algorithmic pharmacology: from pharmacotherapy to therapy by algorithms. Visn. Nac. Akad. Nauk Ukr. 2026. (4): 15—27. https://doi.org/10.15407/visn2026.04.015
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