A method, system and device for generating drug interaction prediction and dosage reference information

By constructing a PBPK model and integrating enzyme inhibition or induction parameters, the interaction between Bruton's tyrosine kinase inhibitors and antiretroviral drugs is simulated, generating dose reference information. This solves the problem of the lack of automated prediction tools in existing technologies and enables efficient and accurate drug interaction assessment and dose adjustment.

CN122417469APending Publication Date: 2026-07-17CHONGQING UNIV CANCER HOSPITAL
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING UNIV CANCER HOSPITAL
Filing Date
2026-04-29
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Current technologies lack quantitative and automated predictive tools for combinations of Bruton's tyrosine kinase inhibitors and antiretroviral drugs, making it difficult to assess drug interactions in HIV patients during combination therapy, increasing the risk of treatment toxicity or reducing efficacy.

Method used

A physiological pharmacokinetic (PBPK) model is constructed, integrating enzyme inhibition or induction parameters of interacting drugs. Drug interactions are predicted through computer simulation, and dose reference information is generated. The process includes parameter acquisition, basic model validation, interaction model construction and validation, interaction simulation prediction, and dose reference information generation.

Benefits of technology

It provides high-fidelity quantitative prediction tools that automatically simulate changes in blood drug concentrations under different dosages, generate actionable dose adjustment reference values, support clinical decision-making, and avoid high-risk, high-cost clinical trials.

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Abstract

The application discloses a drug interaction prediction and dose reference information generation method, system and device, and the method comprises the following steps: acquiring parameters of a Bruton's tyrosine kinase inhibitor as a target drug and an antiretroviral drug as an interaction drug; constructing and verifying a basic physiological pharmacokinetic model of each drug; integrating inhibition or induction parameters of the interaction drug on corresponding enzymes, constructing and verifying an interaction prediction model; running the verified model to simulate and calculate predicted exposure data of the target drug when the drugs are used in combination; and generating dose reference information meeting preset bioequivalence conditions by iteratively adjusting dose parameters of the target drug and re-simulating. The application realizes automatic, quantitative prediction and personalized dose scheme generation of specific drug combination interactions, and can provide key decision support for clinical combination drug use.
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