Collaborative anti-cancer pharmaceutical combination prediction method and pharmaceutical composition

Abstract

The invention relates to a collaborative anti-cancer pharmaceutical combination prediction method and a pharmaceutical composition. The collaborative anti-cancer pharmaceutical combination prediction method comprises the following steps: 1) data collection: according to different disease treatment effects of a pharmaceutical combination, classifying and obtaining a known collaborative anti-cancer pharmaceutical combination and a corresponding target; 2) model establishment: for the known collaborative anti-cancer pharmaceutical combination and an unknown pharmaceutical combination, calculating a characteristic of the collaborative anti-cancer pharmaceutical combination, and establishing a collaborative anti-cancer pharmaceutical combined prediction model; and and 3) result filtration: expressing spectrum information with the pharmacy, exploring and inducing the characteristic of the known collaborative anti-cancer pharmaceutical combination, and conducting screening with prediction results of the step 2). An anti-breast cancer pharmaceutical combination and an anti-lung cancer pharmaceutical combination can be acquired on the base of the collaborative anti-cancer pharmaceutical combination prediction method. Compared to the prior art, according to the invention, the collaborative anti-cancer pharmaceutical combination prediction method comprehensively uses various characteristics of the pharmaceutical combination, is designed dexterously, predicts accurately, has an important practical application and is suitable for large-scale popularization.

Description

technical field [0001] The invention relates to a method for predicting anticancer drug combinations, in particular to a method for predicting synergistic anticancer drug combinations based on drug target network features and expression profile information features and a pharmaceutical composition. Background technique [0002] With the in-depth study of disease mechanisms, people are increasingly aware that most diseases are the result of the joint influence of multiple pathogenic factors, leading to an imbalance in the regulatory network. In many cases, inhibiting a target does not cause phenotypic changes, and may even activate other factors in the disease system to protect the stability of the system, resulting in drug loss of efficacy or toxic side effects. Clinically, two or more drugs are often used in combination to achieve multiple therapeutic purposes, produce synergistic effects, or reduce adverse reactions. Compared with traditional single-component, single-targ...

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Problems solved by technology

However, the efficiency of current prediction models is generally low. The latest results released by the Dialogue for Reverse Engineering Assessments and Methods (DREAM), an international organization, show that the best existing prediction methods are only slightly better than random guessing (NatBiotechnol32, 1213-1222 (2014))

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