Drug-Drug Relationship Analysis

Through network pharmacology analysis, two or more drugs are compared in terms of symptoms, side effects, gene expression, etc., so as to determine the similarities and differences between different drugs better and reduce the high investment in actual testing. Moreover, based on the analysis results of network pharmacology, it can provide new ideas for " Drug Repositioning".

Strategies Development

Similarity analysis of ADMET properties

The properties of ADMET refer to the absorption, distribution, metabolism, excretion and toxicity of molecules in the organism. The description of ADMET can help to eliminate compounds with bad properties of ADMET in time, in order to avoid costly structural modification later. Besides, the effect of structural optimization can be evaluated to determine whether ADMET properties are indeed improved. It helps to avoid synthesizing unwanted products. ADMET analysis can determine the similarities and differences in the bioavailability and in vivo effects of drugs, clearly explaining the possibility of new uses for old drugs. We can use the DS software to complete the above operations to achieve this goal.

The properties of ADMET that can be calculated using Discovery Studio include:

  • aqueous solubility at 25℃
  • Blood brain barrier penetration (BBB)
  • Cytochrome inhibition
  • Hepatotoxicity
  • Human intestinal absorption (HIA)
  • Plasma protein binding

Similarity analysis of drug toxicological properties

Analysis of the toxicological properties of different drugs is also a necessary stage to complete the reutilization of the drugs. It can clarify the feasibility of repurposing existing drugs. Similar to ADMET property similarity analysis, DS is used to predict the toxicological properties of compounds (TOPKAT). TOPKAT is based on the 2D structure of the molecule to quickly and accurately calculate the toxicity of compounds. The process uses a series of powerful and cross-validated quantitative structural toxicity relationship (QSTR) models to evaluate various toxicity prediction results.

Based on this process, we can complete the following tasks:

  • Quickly evaluate a series of toxicological properties of organic compounds
  • Check the substructure and the toxicity relationship of structural modification and related mechanism of action
  • Molecular ranking for experimental testing or further development
  • Design new compounds

Therefore, the TOPKAT process for toxicity prediction plays a very important role in drug R&D. It can save the time of drug commercialization, reduce animal experiments, and assess human health risks.

Gene-Expression Signatures Analysis

After completing the analysis of ADMET and toxicological properties of drugs or compounds, it is necessary to analyze the gene expression characteristics.

From a biological point of view, genes with similar expression patterns may have common characteristics, such as being regulated by a certain gene at the same time, or have similar functions or common cellular origin. Although there are many unexpected situations, a large number of functionally related genes do have very similar expression profiles under a set of related conditions, especially those genes regulated by common transcription factors, or their products constitute the same protein complex, or participate in the same regulatory path. Therefore, in specific applications, clusters of genes with similar expression profiles can be used to infer the functions of other genes. In this way, we can use the analysis software STEM (Short Time-series Expression Miner) to complete this step.

STEM is written in JAVA language, and is mainly used to cluster, compare and visualize gene expression data of short time series (less than or equal to 8 time points). STEM allows researchers to identify significant temporal expression profiles, accurately and intuitively screen out genes related to these expression profiles, and compare the expression trends of these genes under different conditions. By evaluating the degree of correlation between gene expression trends and sample shape changes, isolate gene groups that have nothing to do with sample changes, Extract and highlight the trend of mainstream gene expression.

Molecular Dynamics Analysis

Molecular dynamics (MD) is one of the most commonly used methods in molecular simulation. This method is based on the molecular standpoint and can dynamically describe the motion process of molecules. Molecular dynamics has a wide range of applications in the field of life sciences, such as the study of the mechanism of protein folding, enzyme-catalyzed reactions, etc. The MD analysis can be done using Molecular Dynamics in DS.

Molecular dynamics simulation mainly includes the following steps:

  • Simulate the heating process of the system
  • Simulate system balance process
  • Simulation system sampling process
  • The target nature of the analysis system

In summary, we can use AI methods and technologies to test the similarity of drugs for the same disease in various ways. Our in silico drug repositioning platform represents an alternative and complementary approach for drug discovery.

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