CN120600339B - Method and system for detecting hepatotoxicity of antifungal drug - Google Patents
Method and system for detecting hepatotoxicity of antifungal drugInfo
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Abstract
本发明涉及医学诊断技术领域，尤其是一种抗真菌药物肝毒性检测方法及系统，方法包括以下步骤：获取因侵袭性真菌感染而接受抗真菌药物治疗、且并发细菌感染并出现肝损伤的个体的血源性样本；检测血源性样本中，与所述抗真菌药物作用下真菌裂解相关的第一特征组分的浓度，得到第一浓度值；检测所述血源性样本中，与所述并发细菌感染相关的第二特征组分的浓度，得到第二浓度值；基于所述第一浓度值与所述第二浓度值，计算表征所述第一特征组分与所述第二特征组分相对强度的归因指数；通过检测与真菌裂解和细菌感染相关的特征组分浓度，计算归因指数，从而区分肝损伤的优势诱因，具有能够区分肝损伤的优势诱因，为临床决策提供依据的优点。
The present invention relates to the field of medical diagnosis technology, and in particular to a method and system for detecting hepatotoxicity of antifungal drugs. The method comprises the following steps: obtaining a blood sample from an individual who is receiving antifungal drug treatment for an invasive fungal infection and has a concurrent bacterial infection and liver damage; detecting the concentration of a first characteristic component related to fungal lysis under the action of the antifungal drug in the blood sample to obtain a first concentration value; detecting the concentration of a second characteristic component related to the concurrent bacterial infection in the blood sample to obtain a second concentration value; calculating an attribution index characterizing the relative strengths of the first characteristic component and the second characteristic component based on the first concentration value and the second concentration value; and calculating the attribution index by detecting the concentrations of characteristic components related to fungal lysis and bacterial infection, thereby distinguishing the dominant causes of liver damage. The method has the advantage of being able to distinguish the dominant causes of liver damage and provide a basis for clinical decision-making.
Description
Technical Field
The invention relates to the technical field of medical diagnosis, in particular to a method and a system for detecting hepatotoxicity of an antifungal drug.
Background
In clinical practice, invasive fungal infections are one of the leading causes of death in critically ill patients, and effective antifungal agents are critical in controlling such infections. Like echinocandins, the mechanism of action is to destroy the cell wall structure of fungi, by inhibiting beta-1, 3-glucan synthase, the critical structure of the fungal cell wall is destroyed, leading to rapid lysis and death of the fungal cells. However, this highly effective sterilization process may be accompanied by a particular drug-related hepatotoxicity. In particular, a large number of lysed fungal cells release structural fragments of their cell walls into the blood, which fragments, as exogenous danger signals, are recognized and cleared by immune cells (mainly kupfu cells) in the liver. When the amount of fragments released in a short period of time is too large, excessive activation of these immune cells is caused, and a large amount of inflammatory mediators are released, so that collateral damage is caused to adjacent liver cells, and the acute rise of liver function indexes such as serum transaminase is shown.
In complex clinical care settings, particularly in intensive care units, patients undergoing antifungal therapy are often susceptible to systemic bacterial infections due to hypoimmunity, intracorporeal indwelling materials, and the like. Bacterial cell wall components, such as peptidoglycans and lipoteichoic acids, are also strong immune activators. They also activate the same immune cell population as when cleared of fungal debris after entering the liver and trigger a similar inflammatory injury pathway, ultimately leading to liver dysfunction that is indistinguishable in clinical manifestations and biochemical indicators. In this case, the clinician is faced with a serious diagnosis dilemma, whether the newly developed liver injury is a secondary effect generated under the high-efficiency action of the antifungal drug or sepsis related liver injury caused by concurrent bacterial infection, and the pathophysiological pathways of two possible reasons tend to be consistent at the downstream, so that the conventional liver function and inflammation markers cannot be clearly attributed. Wrong attribution will lead to catastrophic treatment decisions, for example, if the antifungal is deactivated by misjudgment as drug toxicity, it may lead to uncontrolled fungal infection, whereas if drug hepatotoxicity is ignored for continued administration, the risk of liver failure is exacerbated.
Therefore, when the biochemical index of acute liver injury of such patients is increased, and systematic bacterial infection is diagnosed at the same time, in view of the fact that the cell components of fungus cells and bacterial pathogens which are cracked under the action of antifungal drugs can trigger seemingly indistinguishable immune liver injury by activating the same type of immune cells in the liver, how to establish a detection method which can specifically distinguish and quantitatively evaluate the currently observed liver cell injury based on a single-time collected blood sample, wherein the dominant upstream trigger source of the liver cell injury is derived from a fungus cracking product or from a concurrent bacterial infection, thereby providing clear attribution basis for adjusting the treatment scheme of the antifungal drugs and being a clinically important technical problem.
In view of the above, there is a need in the art for improvements.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a method and a system for detecting the hepatotoxicity of an antifungal drug.
In a first aspect, the present invention provides a method for detecting hepatotoxicity of an antifungal agent, for determining that a dominant cause of liver injury occurs when a subject receiving treatment with the antifungal agent is infected with a fungus and the liver injury occurs, the method comprising the steps of:
obtaining a blood-derived sample of an individual receiving anti-fungal drug treatment due to an invasive fungal infection, and having a concurrent bacterial infection and developing liver damage;
Detecting the concentration of a first characteristic component related to fungus cracking under the action of the antifungal drug in the blood-borne sample to obtain a first concentration value;
Detecting the concentration of a second characteristic component related to the concurrent bacterial infection in the blood-borne sample to obtain a second concentration value;
calculating an attribution index characterizing the relative intensities of the first and second characteristic components based on the first and second concentration values;
And judging that the dominant cause of the liver injury is attributed to fungal lysis or concurrent bacterial infection according to the comparison result of the attribution index and a preset reference interval.
The method has the core innovation points that the technical problem that when fungal infection is combined with bacterial infection and liver injury occurs, the dominant cause of the liver injury is difficult to distinguish is solved by quantifying the concentration of the first characteristic component related to fungal lysis under the action of an antifungal drug and the concentration of the second characteristic component related to concurrent bacterial infection and calculating the attribution index based on the relative intensity of the first characteristic component and the second characteristic component, so that the effect of providing clear basis for clinical treatment decision is achieved.
In a second aspect, there is provided an antifungal drug hepatotoxicity detection system for determining, when a bacterial infection is concurrent and liver damage occurs to a subject suffering from a fungal infection treated with an antifungal drug, the dominance cause of the liver damage, the system comprising:
A sample acquisition module for acquiring a blood-derived sample of an individual receiving anti-fungal drug treatment due to invasive fungal infection and having a concurrent bacterial infection and developing liver injury;
The first concentration detection module is used for detecting the concentration of a first characteristic component which is related to fungus cracking under the action of the antifungal drug in the blood source sample to obtain a first concentration value;
The second concentration detection module is used for detecting the concentration of a second characteristic component related to the concurrent bacterial infection in the blood source sample to obtain a second concentration value;
An attribution index calculation module for calculating an attribution index characterizing the relative intensities of the first feature component and the second feature component based on the first concentration value and the second concentration value;
and the dominant cause judging module is used for judging that the dominant cause of the liver injury is attributed to fungal lysis or concurrent bacterial infection according to the comparison result of the attribution index and a preset reference interval.
Compared with the prior art, the invention has the following beneficial effects:
by detecting the concentrations of characteristic components related to fungus cracking and bacterial infection and calculating attribution indexes, the dominant causes of liver injury are distinguished, and the method has the advantages of being capable of distinguishing the dominant causes of liver injury and providing basis for clinical decision.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of a system structure according to the present invention.
In the figure, a sample acquisition module 201, a first concentration detection module 202, a second concentration detection module 203, an attribution index calculation module 204 and an advantage incentive judgment module 205 are shown.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
When an individual who receives antifungal drug treatment due to invasive fungal infection and is complicated with bacterial infection and has liver injury is attributed to liver injury by the traditional clinical diagnosis method, the technical problem that whether the liver injury is caused by fungal cleavage products under the action of the antifungal drug or the complicated bacterial infection is difficult to distinguish exists. Both cases can cause phenotypically similar liver damage by activating the same immune cells in the liver, so that conventional detection means cannot provide clear attribution basis.
To illustrate this problem more clearly, for example, it is assumed that a subject receiving caspofungin treatment for invasive candida blood has a steep rise in serum transaminase levels during the course of treatment, suggesting acute hepatocyte damage. Depending on the mechanism of action of the drug, this may be related to the large number of cell wall fragments such as beta-glucan released by caspofungin causing lysis of candida, which activate immune cells of the liver to cause inflammatory lesions. However, the individual is at this point complicated by methicillin-resistant staphylococcus aureus bacteremia, where the cell wall components of staphylococcus aureus, such as peptidoglycans, are also strong immunoactivators, capable of causing liver damage through similar pathways. In this particular technical scenario, the clinician cannot judge by conventional means whether the currently observed liver injury is primarily due to fungal lysate or bacterial infection.
In such a scenario, it would be difficult for a clinician to make an accurate treatment decision without solving the above-mentioned technical problem of liver damage attribution ambiguity. Misjudging liver damage caused by bacterial infection as drug toxicity may lead to improper disabling of critical antifungal drugs, thereby causing uncontrolled fungal infection and endangering individual life. Conversely, if the anti-fungal drug-related liver injury is not recognized and the drug administration is continued, the liver injury may be aggravated, and even liver failure may result. This uncertainty severely affects the effectiveness and safety of clinical treatment.
Therefore, the method for detecting the hepatotoxicity of the antifungal drug, shown in the figure 1, is used for judging the dominant cause attribution of the liver injury when the individual receiving the antifungal drug treatment is infected by the fungus and is infected by bacteria and has the liver injury, and comprises the following steps:
S101, obtaining a blood-derived sample of an individual who receives antifungal drug treatment due to invasive fungal infection and is complicated with bacterial infection and liver injury;
s102, detecting the concentration of a first characteristic component related to fungus cracking under the action of an antifungal drug in a blood source sample to obtain a first concentration value;
s103, detecting the concentration of a second characteristic component related to concurrent bacterial infection in the blood source sample to obtain a second concentration value;
S104, calculating attribution indexes representing the relative intensities of the first characteristic component and the second characteristic component based on the first concentration value and the second concentration value;
s105, judging that the dominant cause of the liver injury is attributed to fungal lysis or concurrent bacterial infection according to the comparison result of the attribution index and a preset reference interval.
Wherein a blood-derived sample refers to blood or blood derivatives, such as whole blood, plasma or serum, taken from an individual, with the aim of obtaining a detection matrix comprising biomarkers reflecting pathological conditions in the body;
Wherein the first characteristic component refers to a specific component released into blood after the fungal cells are lysed under the action of the antifungal drug, which can be realized by adopting structural components of fungal cell walls or cell membranes, such as beta- (1, 3) -D-glucan, which is mainly used for quantifying the cell damage degree caused by the antifungal drug to kill fungi;
Wherein the second characteristic component refers to a specific component present in the blood-borne sample in relation to the concurrent bacterial infection, which can be achieved with a bacterial cell wall component, a bacterial secreted toxin or a bacterial metabolite, such as peptidoglycan, lipoteichoic acid or bacterial DNA, which is primarily used to quantify the extent of the concurrent bacterial infection or its effect on the body;
The attribution index is a numerical value which is calculated based on the first concentration value and the second concentration value and represents the relative intensity of the first characteristic component and the second characteristic component, can be calculated by adopting a mode of a ratio, a difference value or a weighted combination of the two, and is mainly used for comprehensively evaluating the relative contribution of fungal pyrolysis and bacterial infection to liver injury;
the preset reference interval refers to a numerical range which is determined according to a large amount of clinical data or experimental study and is used for comparing with an attribution index, and aims to provide an objective judgment standard for judging the dominant cause of liver injury.
The present application provides methods for treating an individual with an antifungal agent due to an invasive fungal infection by obtaining a blood-borne sample from the individual, with a concomitant bacterial infection and liver damage. Such individuals are selected because of their complex clinical manifestations and indistinguishable causes of liver injury. Subsequently, the sample is examined to obtain a concentration value of the first characteristic component reflecting the degree of fungal lysis and a concentration value of the second characteristic component reflecting the degree of bacterial infection, respectively. The two potential markers related to the liver injury causes are quantized at the same time, so that a foundation is laid for subsequent differential diagnosis. Based on this, an attribution index is calculated based on the two concentration values, and the attribution index integrates the information of the two markers in a mathematical manner, so that the relative strength of the attribution index on the influence of the two markers on liver injury is represented. And finally, comparing the calculated attribution index with a preset reference interval. This comparison mechanism enables an objective determination of whether the currently observed liver damage is more likely to be due to the effects of fungal lysate or the effects of concurrent bacterial infection, depending on the range of the index. The whole process forms a complete diagnosis chain from sample acquisition, marker quantification, relative strength evaluation to final attribution judgment, and effectively solves the clinical diagnosis problem.
As one embodiment of the present invention, the step of calculating an attribution index characterizing the relative intensities of the first characteristic component and the second characteristic component based on the first concentration value and the second concentration value includes:
When the second characteristic component comprises a first bacterial characteristic component and a second bacterial characteristic component having different biological efficacy, the step of detecting the concentration of the second characteristic component is embodied as detecting the concentration of the first bacterial characteristic component to obtain a first bacterial concentration value, and detecting the concentration of the second bacterial characteristic component to obtain a second bacterial concentration value;
Calculating a corrected bacterial load value based on the first bacterial concentration value, the second bacterial concentration value, and a predetermined correction factor that characterizes the relative biological effectiveness between the first bacterial characteristic component and the second bacterial characteristic component;
an attribution index is calculated based on the first concentration value and the corrected bacterial load value.
Wherein the first bacterial characteristic component and the second bacterial characteristic component refer to specific bacteria related to concurrent bacterial infection and having different biological effectiveness or components generated by the specific bacteria and capable of inducing liver immune response, the method can be implemented by detecting DNA/RNA (deoxyribonucleic acid)/cell wall components (peptidoglycan, lipopolysaccharide), secreted toxins and the like of the specific bacteria, the purpose of distinguishing differences of the bacterial infection on liver is to quantify the relative biological effectiveness between the first bacterial characteristic component and the second bacterial characteristic component, the preset correction coefficient refers to a numerical value for quantifying the relative biological effectiveness between the first bacterial characteristic component and the second bacterial characteristic component, the coefficient can be predetermined based on in vitro experiments, animal model data or clinical observation data, the purpose of the coefficient is to weight the concentration of different bacteria so as to reflect the actual liver injury potential, the correction bacterial load value refers to a numerical value obtained by comprehensively considering the concentration of the different bacterial characteristic components and the relative biological effectiveness thereof and used for characterizing the whole liver injury potential of bacterial infection, and the value can be calculated by adopting a weighted sum or other mathematical model so as to provide an accurate bacterial infection influence evaluation compared with a single concentration value.
According to the scheme, the problem that different bacteria affect liver injury when multiple bacterial infections are concurrent is solved by refining detection of the second characteristic components related to the concurrent bacterial infections. In particular, the protocol no longer universally detects the total concentration of the second characteristic component, but rather distinguishes and detects the respective concentrations of the first and second bacterial characteristic components having different biological efficacy, resulting in a first and second bacterial concentration value. This differential detection enables finer capture of the composition of bacterial infections. Further, the protocol incorporates a preset correction factor reflecting the relative biological effectiveness of liver injury between the first and second bacterial trait components. And calculating a corrected bacterial load value based on the detected first bacterial concentration value, the detected second bacterial concentration value and a preset correction coefficient. This corrected bacterial load value integrates the concentration of different bacteria and their potential for actual effects on the liver, and thus represents the overall load of bacterial infection on the liver more accurately than the total concentration of bacteria alone. Finally, the attribution index is calculated with the first concentration value using this more accurate corrected bacterial load value instead of the original second concentration value. In this way, the attribution index can more accurately reflect the relative contribution of the fungal pyrolysis product and bacterial infection to liver injury, thereby improving the accuracy of judging the dominant cause of liver injury. Compared with a scheme using only a single second concentration value, the method overcomes evaluation deviation caused by different bacterial biological efficacy differences by finely quantifying and correcting bacterial infection, so that the liver injury inducement can be judged more reliably in a complex clinical scene.
As an embodiment of the present invention, before the step of calculating a corrected bacterial load value, the method further comprises:
Contacting the normalized reactive cells with a first bacterial trait component and a second bacterial trait component, respectively, in a reactive environment comprising a humoral factor taken from the individual;
Detecting the amount of a reaction product produced by the standardized reaction cell after contacting the standardized reaction cell with the first bacterial feature component and the second bacterial feature component to obtain a first reaction product amount and a second reaction product amount;
calculating an instant correction coefficient based on the first reaction product magnitude and the second reaction product magnitude;
and based on the first bacteria concentration value, the second bacteria concentration value and the preset correction coefficient, the step of calculating a corrected bacteria load value specifically includes:
and replacing a preset correction coefficient by using the instant correction coefficient, and calculating to obtain a corrected bacterial load value based on the first bacterial concentration value, the second bacterial concentration value and the instant correction coefficient.
Wherein the reaction environment comprising humoral factors taken from the individual means a liquid medium that mimics the in vivo environment of the individual, comprising serum, plasma or other humoral components from the individual, with the aim of providing a reaction system that reflects the specific biological state of the individual; standardized response cells refer to cells that have been specifically treated or selected to have stable and reproducible response characteristics, such as specific cell lines or isolated and purified primary cells, with the objective of acting as carriers for stimulating and producing detectable reaction products from components characteristic of the receptive bacteria, ensuring comparability of the reaction results; the first bacterial trait component and the second bacterial trait component refer to molecular components derived from different types of bacteria capable of eliciting a host immune response, such as lipopolysaccharide of gram negative bacteria or peptidoglycan of gram positive bacteria, which are aimed at mimicking the effect of different bacterial infections on the host immune system as a stimulus, the reaction product amounts refer to the total amount of molecules or substances released or produced by the standardized reaction cells upon stimulation by the bacterial trait components, such as cytokines, chemokines or enzymes, which are able to be quantitatively detected, which are aimed at quantifying the biological response strength of the standardized reaction cells to the different bacterial trait components, the first reaction product amount and the second reaction product amount refer to the reaction product amounts detected upon contact of the standardized reaction cells with the first bacterial trait component and the second bacterial trait component, respectively, which are aimed at quantifying the difference in biological effects of the different bacterial trait components elicited under specific individual humoral environments, and the immediate correction coefficient refers to the difference calculated based on the first reaction product amount and the second reaction product amount, the aim of the method is to provide a parameter for correcting individual differences by using an instantaneous correction factor instead of a preset correction factor, which is to use the instantaneous correction factor obtained based on the actual response of an individual rather than a universal preset value when calculating the corrected bacterial load value, and to increase the degree of reflection of the corrected bacterial load value to the actual condition of the individual.
The protocol of the present application simulates the actual biological effects of different bacterial trait components in an in vivo environment of an individual by contacting standardized response cells with a first bacterial trait component and a second bacterial trait component, respectively, in a response environment comprising humoral factors taken from the individual. Thus, by detecting the amount of reaction product produced by the standardized reaction cells after contact, a first reaction product amount and a second reaction product amount are obtained that directly reflect the degree of activation of the standardized reaction cells by different bacterial characteristic components in an individual-specific body fluid environment. Based on these magnitudes, an immediate correction coefficient is calculated that accurately quantifies the relative biological efficacy differences of the different bacterial trait components in the individual's body fluid environment. And then, replacing the original preset correction coefficient by the instant correction coefficient, and combining the first bacteria concentration value and the second bacteria concentration value to calculate and obtain a corrected bacteria load value. Due to the fact that the immediate correction coefficient reflecting the actual situation of the individual is used, the corrected bacterial load value can more accurately evaluate the comprehensive contribution of different bacterial characteristic components to liver injury in the individual. The method for correcting based on the body fluid environment of the individual overcomes the limitation that the preset correction coefficient cannot reflect individual differences, so that the calculated corrected bacterial load value is closer to the actual bacterial infection load of the individual and the biological effect thereof, the accuracy of the subsequent attribution index calculation is improved, and a more reliable basis is provided for judging the dominant cause of liver injury finally.
As one embodiment of the invention, the step of detecting the amount of reaction product produced by the standardized reaction cell upon contact with the first bacterial trait component and the second bacterial trait component to obtain a first reaction product amount and a second reaction product amount comprises:
detecting the amount of a reaction product in a reaction environment under the condition that the first bacterial characteristic component and the second bacterial characteristic component are not added, so as to obtain the amount of a background product;
Detecting the total amount of reaction products generated in the reaction environment after the standardized reaction cells are contacted with the first bacterial characteristic component and the second bacterial characteristic component respectively to obtain a first total amount of reaction products and a second total amount of reaction products;
the total amount of first reaction product is subtracted from the amount of background product to determine a first reaction product amount and the total amount of second reaction product is subtracted from the amount of background product to determine a second reaction product amount.
The protocol of the present application is achieved by first measuring the amount of background product in the reaction environment that does not contain the bacterial trait component and then measuring the total amount of reaction product in the reaction environment that contains the bacterial trait component when detecting the amount of reaction product that occurs after contacting the standardized reaction cells with the bacterial trait component. This is done because there may be reaction products in the reaction environment that are not caused by the bacterial trait that would be superimposed on the reaction products caused by the bacterial trait, resulting in a directly measured total amount value that is higher than the amount actually caused by the bacterial trait. It is the background product amount that is measured and subtracted from the total product amount that results in a more accurate first reaction product amount due to the first bacterial characteristic component and a more accurate second reaction product amount due to the second bacterial characteristic component that eliminates the effects of background interference. The accurate reaction product quantity value can more truly reflect the biological effectiveness difference of different bacterial characteristic components in a specific body fluid environment, provides basic data for the accurate immediate correction coefficient for subsequent calculation, further improves the accuracy of correcting bacterial load values, and finally improves the reliability of liver injury cause judgment.
In some embodiments of the present application described above, it is proposed that the attribution index characterizing the relative intensities of the first characteristic component and the second characteristic component is calculated based on the first concentration value and the second concentration value, which may be obtained by simply calculating the ratio or difference between the first concentration value and the second concentration value, so that the relative contents of the two components may be primarily reflected, however, in the implementation, there may be a complex non-proportional relationship between the concentrations of the first characteristic component and the second characteristic component and the biological effects they actually produce, and if the concentration values are directly used for calculation, deviation of attribution index may be caused, thereby affecting the accuracy of the liver damage causing judgment.
In this regard, the present application further proposes that the step of calculating an attribution index characterizing the relative intensities of the first characteristic component and the second characteristic component based on the first concentration value and the second concentration value comprises:
converting the first concentration value into a first effect contribution value based on a preset first corresponding relation reflecting the non-proportional relation between the concentration of the first characteristic component and the biological effect thereof;
Converting the second concentration value into a second effect contribution value based on a preset second corresponding relation reflecting the non-proportional relation between the concentration of the second characteristic component and the biological effect thereof;
and calculating an attribution index based on the first effect contribution value and the second effect contribution value.
The preset first corresponding relation refers to a pre-established data set or mathematical model capable of reflecting nonlinear correlation between biological effect intensities generated by the first characteristic component under different concentrations, and the data set or mathematical model can be realized by adopting a concentration-effect curve graph, a lookup table or a mathematical function. The preset second corresponding relation refers to a pre-established data set or mathematical model capable of reflecting nonlinear correlation between intensities of biological effects generated by the second characteristic component at different concentrations, and the data set or mathematical model can be realized by adopting a concentration-effect curve chart, a lookup table or a mathematical function. The first effect contribution value refers to a value which is obtained by conversion of the first concentration value according to a preset first corresponding relation and represents the actual biological effect intensity of the first characteristic component. The second effect contribution value refers to a value which is obtained by conversion of the second concentration value according to a preset second corresponding relation and represents the actual biological effect intensity of the second characteristic component. The attribution index is an index calculated based on the first effect contribution value and the second effect contribution value and used for quantitatively evaluating the relative influence degree of the first characteristic component and the second characteristic component on liver injury.
According to the scheme, the first concentration value and the second concentration value are converted into the first effect contribution value and the second effect contribution value respectively based on the preset first corresponding relation and the preset second corresponding relation, and then the attribution index is calculated based on the effect contribution values. The core of the process is to introduce a correspondence reflecting a non-proportional relationship between concentration and biological effects, thereby converting raw concentration data into effect contribution data more representative of actual biological effects. Due to the conversion, the subsequent attribution index calculation based on the effect contribution value can more accurately reflect the relative driving effect of two characteristic components on liver injury in the biological level, and the deviation possibly caused by directly using the concentration value is overcome. The calculation mode based on biological effect rather than simple concentration can enable attribution indexes to reflect the dominant cause of liver injury more truly, so that the reliability of judgment is improved.
As one embodiment of the present invention, the step of calculating the attribution index based on the first effect contribution value and the second effect contribution value includes:
acquiring an index for representing direct hepatotoxicity potential of the antifungal drug;
converting the index into a third effect contribution value based on a preset corresponding relation reflecting the relation between the index and the direct hepatotoxicity;
an attribution index is calculated based on the first effect contribution value, the second effect contribution value, and the third effect contribution value.
Wherein, the index for characterizing the direct hepatotoxicity potential of the antifungal drug refers to a quantitative value reflecting the direct hepatic injury capability of the antifungal drug, which can be determined by adopting half inhibition concentration (IC 50), half lethal concentration (LC 50) measured by an in vitro cytotoxicity experiment, dose-effect data of hepatic injury observed in an animal model, or clinical pharmacokinetic parameters are determined by combining known toxicity intensity, and the aim is to quantify the potential contribution of chemical toxicity of the drug to hepatic injury. The preset correspondence reflecting the relationship between the index and the direct liver toxicity refers to a rule or model for mapping the toxicity potential index value to the third effect contribution value, which can be implemented by adopting a preset lookup table or applying a preset mathematical function model, and aims to uniformly convert the toxicity potential indexes in different forms into the comparable effect contribution value. Wherein the third effect contribution value refers to the quantitative contribution of the direct liver toxicity of the antifungal drug to the total liver injury, with the aim of incorporating the toxic effect of the drug itself into the attribution calculation. Wherein the first effect contribution value refers to the quantitative contribution of the fungal lysate to liver damage, which aims at quantifying the biological effect of the fungal lysate. Wherein the second effect contribution value refers to a quantified contribution of a bacterial infection-related component to liver injury, which aims at quantifying the biological effect of the bacterial infection. Wherein, the attribution index is the relative contribution of three factors of representing fungal cracking products, bacterial infection and direct hepatotoxicity of antifungal drugs to liver injury, and aims to provide a comprehensive index for judging the dominant cause of liver injury.
Based on the technical characteristics, the scheme of the application quantifies the direct chemical injury capability of the drug to the liver by acquiring the index for representing the direct hepatotoxicity potential of the antifungal drug. The index is converted into a third effect contribution value based on a preset corresponding relation, so that the toxic effect of the drug is converted into a quantifiable contribution degree. On the basis, the attribution index is comprehensively calculated based on the three effect contribution values by combining the first effect contribution value and the second effect contribution value which are already determined. The attribution index can reflect the potential causative constitution of the liver injury more comprehensively just by taking the chemical toxicity contribution of the medicine and the biological effect contribution of the fungus cracking products and bacterial infection into consideration, so that the accuracy of judging the dominant causative constitution of the liver injury is improved.
As an embodiment of the present invention, the step of obtaining an index characterizing the direct hepatotoxic potential of an antifungal drug comprises:
obtaining respective concentrations of the primary metabolites of the antifungal drug that are toxic to the liver produced in vivo by the antifungal drug;
And determining a comprehensive drug toxicity load value according to the concentration of the original antifungal drug, the concentration of the main metabolite and the toxicity intensity of each of the original antifungal drug and the main metabolite to the liver, and taking the comprehensive drug toxicity load value as an index for representing the direct hepatotoxicity potential of the antifungal drug.
Wherein, the antifungal drug precursor refers to the antifungal drug molecule itself that has not been chemically or biologically converted in vivo. The major metabolites with hepatotoxicity are chemical substances which are produced by the antifungal drugs after enzymatic or other reactions in the living body and have significant adverse effects on liver cells or functions, and are usually intermediates or end products of the drugs during in vivo clearance or activation. The respective concentrations refer to the content of the antifungal drug in the specific biological sample, as such, or as the content of the substance per unit volume of the main metabolite thereof, which can be obtained by chromatographic-mass spectrometry, enzyme-linked immunosorbent assay or other suitable quantitative detection methods. Toxicity strength refers to the degree of damage or potential risk level to the liver caused by a unit mass or unit molar amount of an antifungal drug precursor or its major metabolite, which can be determined based on in vitro cytotoxicity experimental data, animal model studies, clinical pharmacokinetic/pharmacodynamic correlation analysis, or known pharmacotoxicology data. The integrated drug toxicity load value refers to the combination of the actual exposure level of the antifungal drug precursor and its major metabolites with hepatotoxicity in the body with its inherent hepatotoxic potential, and the quantitative assessment of the overall drug toxicity load on the liver can be calculated by using weighted summation, multiplication or other mathematical model, wherein the weight or functional relationship reflects the contribution of concentration and toxicity intensity to the total toxicity load. The index for representing the direct hepatotoxicity potential of the antifungal drug refers to a numerical value or quantitative representation for reflecting the possibility or degree of direct damage to the liver caused by the antifungal drug and the metabolite thereof, and in the scheme, the index is specifically a comprehensive drug toxicity load value.
The scheme of the application comprehensively considers the actual in-vivo existence form and level of the drug by acquiring the original form of the antifungal drug and the respective concentration of main metabolites with hepatotoxicity. Meanwhile, the inherent potential of different components on liver injury is quantified by combining the toxicity intensity of each of the original shape of the medicine and the main metabolite to the liver. By combining the concentration and toxicity intensity, a combined drug toxicity load value is determined. This integrated value reflects not only the amount of the drug but also the quality of the drug, thereby more accurately characterizing the overall direct toxic burden of the antifungal drug to the liver. The comprehensive drug toxicity load value is used as an index for representing the direct hepatotoxicity potential of the antifungal drug, and can be used for subsequent analysis of liver injury attribution. For example, this index may be converted into a third effect contribution value, which, along with effect contribution values of other factors, is used to calculate the attribution index. By using a more accurate index to represent the direct toxicity of the drug, the accuracy of the final calculated attribution index can be improved, thereby more reliably determining the leading cause of liver injury. This approach avoids bias from drug concentrations or simple evaluations alone, making the attribution of liver damage finer and more reliable.
As one embodiment of the present invention, the step of converting the first concentration value into the first effect contribution value based on a preset first correspondence reflecting a non-proportional relationship between the concentration of the first characteristic component and the biological effect thereof includes:
Converting the first concentration value into a first effect contribution value by referring to a preset concentration-effect correspondence table;
or converting the first concentration value into a first effect contribution value by applying a preset mathematical function model.
The preset concentration-effect corresponding table is a lookup table established through experimental measurement or clinical observation data and records the relation between different concentration values of the first characteristic components and corresponding first effect contribution values, and aims to directly reflect the nonlinear corresponding relation between the concentration and the effect.
In the scheme of the application, in the process of converting the first concentration value into the first effect contribution value, a simple linear relation is not adopted, but the conversion is carried out based on a preset first corresponding relation reflecting the non-proportional relation between the concentration of the first characteristic component and the biological effect thereof. Such non-proportional relationships may be embodied in a pre-set concentration-effect correspondence table, which by consulting may be directly mapped to a pre-determined value reflecting its true biological effect contribution. Or the non-proportional relation can be described by a preset mathematical function model, and the model can output a corresponding first effect contribution value through nonlinear calculation according to an input first concentration value. Both the two modes can capture the nonlinear relation between the concentration and the effect more accurately, and deviation caused by linear assumption is avoided. By this more accurate conversion, the resulting first effect contribution value can more truly characterize the potential contribution strength of the fungal lysate to liver damage. After converting both the first concentration value and the second concentration value into effect contribution values, an attribution index is calculated based on the effect contribution values. The accurate conversion method provided herein makes the input of the first effect contribution value more accurate, thereby improving the reliability of the attribution index obtained by final calculation. A more reliable attribution index can more accurately reflect the relative contribution of fungus cracking products and bacterial infection products to liver injury, and provides a firmer foundation for the subsequent judgment of the dominant cause of liver injury. Therefore, the scheme provided herein is combined with the overall framework, and the reliability and clinical application value of the whole liver injury attribution method are enhanced by improving the accuracy of key intermediate steps.
As one embodiment of the present invention, the step of calculating the attribution index based on the first effect contribution value, the second effect contribution value, and the third effect contribution value includes:
a weighted sum or difference operation is performed on the first, second, and third effect contribution values to determine an attribution index.
The weighted summation refers to a mathematical operation of multiplying a plurality of values by a weight coefficient and then adding the multiplied values, and can be implemented in a linear combination mode. The difference operation refers to a mathematical operation that calculates the difference between two or more values, which may be implemented using simple subtraction or more complex combination operations. Attribution index refers to a comprehensive numerical index used to quantitatively evaluate the relative contribution or impact strength of different potential causes to a particular result, which may be represented by the result of a weighted sum or difference operation.
The scheme of the application determines the attribution index by carrying out weighted summation or difference operation on the first effect contribution value, the second effect contribution value and the third effect contribution value. The first effect contribution value, the second effect contribution value and the third effect contribution value quantify the potential effect of fungal lysis, bacterial infection and drug direct toxicity on liver injury, respectively. By adopting a weighted summation mode, the overall contribution of different factors to liver injury can be comprehensively estimated by adjusting weights according to the actual influence degree of the different factors, so that an attribution index reflecting the comprehensive effect of multiple factors is obtained. The relative influence intensities among different factors can be directly compared by adopting a difference operation mode, for example, the difference between the fungus cracking effect contribution value and the bacterial infection effect contribution value is calculated, and which factor is the main driving force of the current liver injury can be intuitively judged. The two operation modes or the combination of the two operation modes are used, so that the calculation method of the attribution index has flexibility, and can be optimized and adjusted according to clinical data and experience, thereby more effectively utilizing the three effect contribution values and obtaining the attribution index capable of accurately reflecting the attribution of the dominant cause of the liver injury. The calculation method is combined with the previous method for acquiring and quantifying the effect contribution values, so that a complete technical flow capable of quantitatively evaluating the relative intensities of different causes is formed, and a feasible way is provided for solving the problem of liver injury attribution.
In some preferred embodiments, the attribution index may be calculated by a weighted summation, for example, attribution index=w1×first effect contribution+w2×second effect contribution+w3×third effect contribution, where w1, w2, w3 are preset weighting coefficients, and the values thereof may be determined according to a number of clinical data analysis results or expert consensus to reflect the relative importance of different effect contribution values in actual liver injury attribution. As another specific embodiment, the attribution index may be calculated by means of a difference operation, for example, attribution index=first effect contribution value-second effect contribution value, and the dominant cause is determined by comparing the contribution difference of the fungal lysis effect and the bacterial infection effect. A combination of weighted summation and difference operations may also be employed, e.g., attributing an index = (w1×first effect contribution + w3×third effect contribution) -w2×second effect contribution, taking into account the effects of fungal lysis and drug toxicity, and comparing with the effects of bacterial infection.
An antifungal drug hepatotoxicity test system as shown in FIG. 2 for determining the dominance cause of liver injury when a subject receiving antifungal drug treatment is infected with a fungus and has liver injury, comprising:
A sample acquisition module 201 for acquiring a blood-derived sample of an individual receiving an antifungal drug treatment due to an invasive fungal infection, and having a concurrent bacterial infection and developing liver damage;
the first concentration detection module 202 is configured to detect a concentration of a first characteristic component related to fungal lysis under an antifungal drug in a blood-borne sample, to obtain a first concentration value;
the second concentration detection module 203 is configured to detect a concentration of a second characteristic component related to a concurrent bacterial infection in the blood-borne sample, to obtain a second concentration value;
An attribution index calculation module 204 for calculating an attribution index characterizing the relative intensities of the first feature component and the second feature component based on the first concentration value and the second concentration value;
the dominant cause determination module 205 is configured to determine that the dominant cause of the liver injury is due to fungal lysis or concurrent bacterial infection according to a comparison result of the attribution index and a preset reference interval.
According to the scheme, the method for detecting the hepatotoxicity of the antifungal drug is integrated into one system, so that automation and standardization of a detection flow are realized. Specifically, the sample acquisition module 201 first provides the basis for analysis of the overall system, ensuring the sample source for subsequent testing. Subsequently, the first concentration detection module 202 and the second concentration detection module 203 respectively perform quantitative analysis on the key biomarkers in the sample independently or in parallel to obtain a first concentration value reflecting the fungal lysis degree and a second concentration value reflecting the bacterial infection degree. It is by the ability to quantify these two potential causative related biomarkers simultaneously or separately that subsequent attribution analysis is made possible. The attribution index calculation module 204 receives the two concentration values and calculates an attribution index that quantifies the relative contribution strengths of the two causative factors according to a preset algorithm. Finally, the dominance incentive determination module 205 compares the calculated attribution index to a preset clinical reference interval to automatically determine whether the currently observed liver injury is more likely to be attributed to fungal lysis or concurrent bacterial infection. The modularized design and the information circulation mechanism enable the whole detection process to be efficient and objective, avoid subjectivity and error possibly introduced by manual operation, and accordingly provide attribution information of liver injury for clinicians rapidly and accurately, and effectively solve the technical problem that liver injury causes are difficult to distinguish under complex clinical conditions.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims.
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| CN102257389A (en) * | 2008-08-28 | 2011-11-23 | 沙路特里亚制药有限责任公司 | Screening method for identifying patients at risk of adverse hepatologic events |
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| US7452678B2 (en) * | 2003-06-24 | 2008-11-18 | Bristol-Myers Squibb Company | Identification of biomarkers for liver toxicity |
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| CN102257389A (en) * | 2008-08-28 | 2011-11-23 | 沙路特里亚制药有限责任公司 | Screening method for identifying patients at risk of adverse hepatologic events |
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