CN102622532A - Method for building complex drug material group in vivo and vitro associated metabolic network - Google Patents

Abstract

Aiming at the problems of low concentration, large interference, extensive metabolism, lack of standard products, complex data and difficult analysis of complex components in biological matrices, the present invention provides a rapid detection of complex component metabolites and extensive step-by-step classification of metabolic pathways Methods for the prediction and comprehensive construction of "prototype component-metabolite" association networks. The steps of the method are as follows: 1. preparation of preparation and biological sample; 2. multi-stage mass spectrometry detection; 3. selection of main components and elimination of interference; 4. confirmation of prototype components in vivo; Confirm metabolic pathways and identify structures of metabolites. Each step can use computer programming to realize automatic operation, which is easy to operate, accurate and efficient. The present invention does not rely on standard reference substances, does not rely on the structure of prototype components, solves the technical problems of "non-target" full component analysis of single or complex drugs in the body and interpretation of the substance basis of drug efficacy, and is applicable to the environment, medical engineering, food, etc. research in many fields.

Description

A method for constructing metabolic network by association of in vivo and in vitro substance groups of complex drugs

technical field

The invention belongs to the field of medicine, and relates to the rapid detection of complex component metabolites of drugs, the step-by-step classification and extensive prediction of metabolic pathways, and the comprehensive construction of a "prototype component-metabolite" association network.

Background technique

Traditional Chinese medicine is a treasure of traditional Chinese medicine, but the current research on Chinese medicine at home and abroad cannot systematically and clearly clarify the relationship between the complex components of traditional Chinese medicine and their metabolites, and lacks corresponding theoretical and technical support. Therefore, the process in the body of traditional Chinese medicine has long been regarded as The "black box" is the main bottleneck in the modernization research of traditional Chinese medicine. Lack of knowledge about biodisposal is one of the major barriers to providing evidence for efficacy and risk assessment of Chinese herbal medicines. Most natural compounds are structurally more fragile than chemical drugs and thus more susceptible to extensive biological metabolism. More importantly, a large part of the medicinal effects of discovered natural compounds do not come from the parent compound, but from their active metabolites. In addition, many traditional Chinese medicine ingredients are characterized by poor oral bioavailability, and the metabolites produced in the gastrointestinal tract are more likely to be absorbed into the circulation system than Chinese herbal medicine ingredients, and then extensively metabolized. Therefore, the detection and identification of metabolites of Chinese medicinal ingredients is not only a key step in the discovery of pharmacologically active ingredients of Chinese herbal medicines, but may also provide additional resources for the discovery of new drugs from natural compounds.

However, the detection and identification of metabolites is by no means easy, even for a single chemical drug, due to difficulties such as numerous endogenous interferences and low metabolite abundance in biological matrices. Even though detection of components at low concentrations can be achieved with high-resolution (such as TOF and FT Obitrap) mass spectrometers, some additional software tools such as mass deficit filters, background subtraction, and ion screening are still necessary for data processing , in order to analyze the relevant information of metabolites from the huge and complex data. However, natural compounds often have more specific metabolic pathways, and the component detection and metabolite identification of natural compounds face a wider range of interference factors and uncertainties, which is undoubtedly a more difficult task. Most of the existing studies on metabolites of Chinese herbal medicines are limited to single compounds that have been isolated, and a few studies on the complex metabolic components of Chinese herbal medicines also rely heavily on several confirmed components of known metabolic pathways, that is, relying on standard Product targeting research. In view of the multi-component, multi-target mechanism of action of Chinese herbal medicine, low concentration in vivo, difficulty in detection in biological matrices, huge amount of data, unclear effective target components, and lack of standards, it is urgent to develop efficient, fast, and automated analysis methods , for standard-independent global detection and identification of non-targeted components in complex biological matrices.

Contents of the invention

Technical problem: The present invention proposes for the first time a method for complex medicines to solve the problems of low concentration, large interference, lack of standard products, difficult detection, widely unknown metabolic pathways, complex data, and lack of effective data analysis means for the complex components of Chinese herbal medicines in biological matrices. An efficient, rapid and automated analysis method for the detection of metabolites of components in complex matrices, the prediction of metabolic pathways, and the construction of "prototype component-metabolite" association networks.

Technical solution: The purpose of the present invention is to provide a general method for the detection of metabolites of complex pharmaceutical ingredients in biological matrices, the prediction of metabolic pathways, and the construction of a "prototype ingredient-metabolite" association network. The core idea of this method is that the prototype components and metabolites have similar fragmentation methods under the same mass spectrometry conditions. Based on the multiple correspondences of the components, a "prototype component-metabolite" association network was constructed. For each pair of associated prototype components and metabolites, use the precise molecular weight difference and fragment ion comparison between the two to quickly predict their metabolic pathways. The method can be completed through the following steps (see Figure 1), and each step utilizes computer programming to realize automatic operation.

1. Apply LC-MS to analyze pharmaceutical preparation extract samples and biological samples after administration, and output the chromatographic and mass spectral data of samples by setting certain standards:

According to the characteristics of pharmaceutical preparations and administered biological samples, select chromatography (including column length, inner diameter, filler, column temperature, flow rate, mobile phase composition, elution gradient, etc.), mass spectrometry conditions (set parameters of the instrument), and application LC-MS scans the sample to obtain its total ion current chromatogram. According to the actual situation, the ions with higher intensity and suitable retention time are extracted, detected and determined from the total ion flow as the main target components. Acquire multi-level mass spectrometry information such as molecular ions and fragment ions of each main component, and each level of mass spectrometry can achieve high resolution, high mass accuracy and high sensitivity at the same time.

2. Deduct endogenous interference and construct a data matrix of drug-derived ingredients:

The spectrum of the administered biological sample is compared with the spectrum of the blank biological sample, and the endogenous components in the spectrum of the administered sample are deducted.

3. Strip off the prototype components and obtain the metabolite spectrum:

The drug-derived component data matrix is compared with the data of the drug preparation sample, and the prototype components measured in vivo are stripped to obtain the in vivo metabolite profile.

4. Correlation analysis between prototype components and metabolites to identify metabolites and metabolic pathways:

1) Association analysis between prototype components and metabolites

Correlate substances with similar mass spectrum behavior (with the same fragment or fragment neutral loss) in the prototype component spectrum and metabolite spectrum, and use the MR (Match Ratio) parameter to measure the degree of structural correlation between the two. The higher the MR value Larger means that the relationship between the pair of compounds is closer. The formula for calculating the MR value is as follows: MR=VIN*2/(PFN+MFN), where VIN represents the number of identical or consistent fragments between a pair of prototype components and metabolites, PFN and MFN represent the total number of mass spectrometric fragments of prototypical components and metabolites, respectively. Then, through the multiple correspondences between all the prototype components and metabolites, the whole formula "prototype components-metabolites" association network was constructed.

2) Metabolic pathway prediction and metabolite identification

For each pair of associated prototype components and metabolites, the accurate mass difference of their molecular ions is matched with the generalized metabolic reaction library, and the metabolic relationship between the pair of compounds is predicted step by step, including degradation reactions, phase I One-step, two-step, and multi-step metabolic reactions of metabolism, phase II metabolism, and mixed reaction types, and verify the reliability of the candidate pathway by comparing fragments. Metabolites with proven reliable metabolic pathways can be excluded from the profile to simplify the next step of prediction of the remaining unknown metabolites and avoid generating too many candidate pathways. If the structure of the prototype component is known, the structure of the metabolite can be identified by comparing the fragments of the prototype component and the metabolite.

Beneficial effect:

1. Aiming at the multi-component and multi-target mechanism of Chinese herbal medicine, the present invention establishes an association network of "prototype components-metabolites", which can accurately and quickly predict and identify the metabolic pathways and metabolites of all or specified drug components, comprehensively It explains the disposal process and mutual influence of drug components in the body, and solves the problem that the in vivo research of traditional Chinese medicine compound is limited to several monomers, which is not enough to characterize the overall characteristics of the compound preparation.

2. Aiming at the problems of low in vivo concentration of natural products, unclear effective target components, widely unknown metabolic pathways, and lack of standard products for metabolites, the present invention utilizes the metabolic laws and mass spectrum fragmentation characteristics of compounds to carry out non-targeting without relying on standard products Metabolite search has realized the transformation from traditional known metabolite analysis to unknown metabolite analysis, and solved the technical problems of "non-target" full component analysis of traditional Chinese medicine in vivo and interpretation of pharmacodynamic material basis.

3. The present invention can clearly predict the metabolic process between prototype components and metabolites, including one-step, two-step, and multi-step metabolic reactions of degradation reactions, phase I metabolism, phase II metabolism and mixed reaction types. Through the step-by-step and classified operation steps, the candidate pathway set with the least number and the highest correct rate can be predicted, and the metabolites of the verified and reliable metabolic pathways can be removed from the metabolite profile to simplify the next search. In the present invention, the structure of metabolites can be deduced from the known structures of prototype components.

4. The present invention aims at the problems of low concentration, large interference, difficult detection, complex data, and lack of effective data analysis means of complex pharmaceutical ingredients in biological matrices. It adopts the means of multi-stage mass spectrometry accurate mass determination and computer programming to realize the whole analysis process. Automated and easy to operate, it has achieved a geometric progression in efficiency and accuracy compared with traditional manual analysis methods, and is suitable for complex system research in various fields such as environmental science, food, medicine, biochemical engineering, and metabolomics.

Detailed ways

Example 1: Association network analysis of "prototype components-metabolites" of Mailuoning injection in rat urine

This embodiment is carried out on the premise of this technical solution, and the detailed implementation and operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

1. Animal experiment protocol

Take 10 SD rats and place them in metabolic cages, fasting but free to drink water overnight, inject Mailuoning injection into the tail vein at a dose of 10mL kg -24h urine, stored at -20°C.

2. Sample pretreatment

After mixing the urine at each time point with equal volumes of 0.5% ammonia water, the samples were loaded onto the activated MAX column, washed with 50% ammonia water and methanol, and then eluted with 90% methanol (containing 5% formic acid). After the eluate was evaporated to dryness, it was dissolved with 90% methanol (containing 5% formic acid), and then analyzed after high-speed centrifugation at 20,000 rpm.

3. Chromatography and mass spectrometry parameters

Instrument: LC/MS-IT-TOF (Shimadzu, Japan).

Chromatographic parameters: column type, Synergi C ₁₈ Hydro-RP 80A, 250mm×4.6mm id, 4μm (Phenomenex, USA); column temperature, 35°C; injection volume, 5μl; flow rate, 0.8ml/min; mobile phase composition, Water (containing 0.025% formic acid, A) and methanol (B); elution gradient, 0-15min: 8-12% (B), 15-40min: 12-60% (B), 40-50min: 60% ( B), 50-55min: 60-8% (B), 55-65min: 8% (B). After post-column splitting, the 0.2ml/min mobile phase enters the mass spectrometer detector.

Mass Spectrometry Parameters: Ionization mode, ESI(-); Nebulizing gasflow rate, 1.5L min-1; Drying gas pressure, 0.1Mpa; Applied voltage, -3.5KV; CDL voltage (CDL voltage), Constant mode; CDL temperature (CDL temperature), 200°C; analysis mode (Analysis mode), MS measurement; measurement range (Measurement range), Auto MS/MS mode (MS: m /z 100-1500; MSn: m/z 50-1500); ion accumulation time (Ion accumulation time), 30ms; bombardment energy (Energy), 30%, 80% and 150%.

Under the above conditions, the total ion current chromatogram of Mailuoning injection in rat urine 0-4h after administration was obtained by full scanning, as shown in Figure 2.

3. Determine the main components from the total ion current chromatogram and subtract endogenous interference:

In Figure 2, 527 ion peaks with an intensity greater than 1,000,000 and a retention time between 3-52 minutes were identified as the main components in rat urine 0-4 hours after administration of Mailuoning injection. Then compare the component spectrum of the rat blank urine, deduct the components (mass error not greater than 10mDa, retention time drift not greater than 0.2min) in the sample and the blank control, to obtain 180 drug-derived components, the retention time , measured accurate molecular weight ([MH] ^- ) and mass spectrum fragments are shown in Table 1.

Table 1: Main drug-derived components in rat urine 0-4h after administration of Mailuoning Injection

4. Strip off the prototype components in the body and obtain the metabolite spectrum:

Comparing the spectrum of drug-derived components with the data of the original injection sample, a total of 19 prototype components (with an accurate mass error of no more than 15mDa and a retention time shift of no more than 0.5min from the components of the injection) measured in vivo were obtained. After prototyping the components, in vivo metabolite profiles were obtained.

5. Correlation analysis between prototype components and metabolites to identify metabolites and metabolic pathways:

One-step, two-step, three-step, and multi-step reactions for "prototype component-metabolite" association and metabolic pathway prediction. For each step of analysis, the "prototype component-metabolite" is first correlated based on the behavior of similar mass spectra (with the same fragment or fragment neutral loss). In order to improve the accuracy of the correlation, the lower limit of the MR parameter for each step of search is set to 0.04-0.4 range. Then match the accurate mass difference of each pair of compound molecular ions with the conventional metabolic reaction library to predict different types of metabolic relationships between "prototype components-metabolites", including degradation reactions, phase II metabolism, and phase I metabolism , mixed type, and verified by comparing the fragment features. It is more possible to use the structure of the prototype component and the correlation between the two fragments to identify the structure of the metabolite

For the metabolic components of Mailuoning injection in rat urine 0-4h after administration, a total of 12 degradation products were identified, 32 were one-step I-phase metabolites, one-step II-phase metabolites were 21, and two-step I-phase metabolites were identified. 26 products, 15 two-step II metabolites, 32 two-step mixed type metabolites, 38 three-step metabolites and 3 multi-step metabolites, there are crossovers between various types of metabolites, and the prediction of all metabolites The metabolic pathways are listed in Table 2. The association network of all prototype components-metabolites is shown in Figure 3. The metabolic relationship and structural identification of some compounds are as follows:

Table 2 Identification of main metabolic pathways of Mailuoning injection in rat urine 0-4h after administration

The above descriptions are only preferred embodiments of the present invention, and the scope of protection of the present invention is not limited to the above embodiments, but all equivalent modifications or changes made by those of ordinary skill in the art according to the disclosure of the present invention should be included within the scope of protection described in the claims.

Claims (5)

1. the method that a complicated metabolin detects, metabolic pathway prediction and inside and outside material group related network make up, comprising: preparation prepares with biological sample; Multi-stage ms detects; Choose principal ingredient, get rid of and disturb; Prototype composition in the conclusive evidence body; Whole or selected prototype composition and metabolic product are carried out association analysis, the inside and outside material group related network of construct; Prediction and prove conclusively the metabolic pathway between related prototype composition and the metabolic product, and utilize the structure of prototype composition to identify the structure of metabolic product.

2. the method that a kind of complicated metabolin according to claim 1 detects, metabolic pathway is predicted and inside and outside material group related network makes up, it is characterized in that can be to having standard reference material and no standard reference material, having the prototype composition and the metabolin of documents and materials reports (information such as compound mass spectrum, chromatogram, physicochemical property, structure) and no documents and materials report analyze.

3. according to claim 1,2 described a kind of complicated metabolins detect, metabolic pathway prediction and inside and outside material group related network make up method; It is characterized in that and can analyze the metabolin of known and unknown, target and non-target, can be to all carrying out metabolic analysis with the prototype composition of part.

4. the method for prototype composition according to claim 1 and metabolic product association analysis; It is characterized in that utilizing the similarity degree of compound mass spectrum behavior to weigh its related tightness degree; And the multiple corresponding relation through prototype composition and metabolic product, material group related network inside and outside the construct.

5. the method for prediction according to claim 1 and conclusive evidence metabolic pathway; It is characterized in that substep, predict that the metabolism between prototype composition and the metabolic product concerns taxonomically; The step, two steps, the multistep metabolic response that comprise degradation reaction, the metabolism of I phase, the metabolism of II phase and hybrid reaction type, and verify the reliability of candidate's approach through the comparison fragment.

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