A method and system for detecting hepatotoxicity of antifungal drugs

Abstract

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

[0001] The present invention relates to the technical field of medical diagnosis, and in particular to a method and system for detecting hepatotoxicity of antifungal drugs. Background Art

[0002] In clinical practice, invasive fungal infections are a major cause of death in critically ill patients, and effective antifungal drugs are key to controlling such infections. Drugs like the echinocandins work by disrupting the fungal cell wall structure. By inhibiting β-1,3-glucan synthase, they destroy key structures in the fungal cell wall, leading to rapid lysis and death of the fungal cells. However, this highly effective bactericidal process may be accompanied by a unique drug-related hepatotoxicity. Specifically, large numbers of lysed fungal cells release structural fragments of their cell wall into the bloodstream. These fragments, acting as exogenous danger signals, are recognized and cleared by immune cells in the liver (primarily Kupffer cells). When excessive amounts of fragments are released in a short period of time, these immune cells become overactivated, releasing large amounts of inflammatory mediators, which in turn cause collateral damage to adjacent hepatocytes, manifesting as acute elevations in liver function markers such as serum transaminases.

[0003] In complex clinical care settings, particularly in intensive care units (ICUs), patients receiving antifungal therapy are often susceptible to systemic bacterial infections due to compromised immune function and indwelling parasites. Bacterial cell wall components, such as peptidoglycan and lipoteichoic acid, are also potent immune stimulators. Once in the liver, they activate the same immune cell populations that clear fungal debris and trigger similar inflammatory pathways, ultimately leading to liver dysfunction that is indistinguishable clinically and biochemically. In this setting, clinicians face a daunting diagnostic dilemma: is the newly observed liver injury a secondary effect of the highly effective antifungal drug or sepsis-related liver injury caused by a concurrent bacterial infection? Because the pathophysiological pathways underlying these two possible causes converge downstream, conventional liver function tests and inflammatory markers cannot definitively attribute the cause. Misattribution can lead to disastrous treatment decisions. For example, discontinuing antifungal therapy due to misdiagnosis of drug toxicity can lead to uncontrolled fungal infection; conversely, ignoring drug hepatotoxicity and continuing the drug can increase the risk of liver failure.

[0004] Therefore, when such patients develop elevated biochemical indicators of acute liver injury and are diagnosed with systemic bacterial infection at the same time, given that the fungal cell components lysed under the action of antifungal drugs and the cellular components of bacterial pathogens can both activate the same type of immune cells in the liver to cause seemingly indistinguishable immune liver injury, how to establish a detection method that can specifically distinguish and quantitatively evaluate the currently observed liver cell damage based on a single blood sample, and determine whether its dominant upstream trigger source is derived from fungal lysis products or from concurrent bacterial infection, thereby providing a clear attribution basis for adjusting the antifungal drug treatment plan, is an important technical issue facing clinical practice.

[0005] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for detecting hepatotoxicity of antifungal drugs.

[0007] In a first aspect, the present invention provides a method for detecting hepatotoxicity of antifungal drugs, which is used to determine the dominant cause of liver damage in individuals with fungal infections who are receiving antifungal drug treatment and subsequently have a concurrent bacterial infection. The method comprises the following steps:

[0008] Obtain blood samples from individuals receiving antifungal therapy for invasive fungal infection who also have concurrent bacterial infection and liver damage;

[0009] detecting, in the blood-derived sample, a concentration of a first characteristic component associated with fungal lysis under the action of the antifungal drug to obtain a first concentration value;

[0010] detecting the concentration of a second characteristic component associated with the concurrent bacterial infection in the blood-derived sample to obtain a second concentration value;

[0011] 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;

[0012] Based on the comparison result of the attribution index with the preset reference interval, it is determined that the dominant cause of the liver injury is attributable to fungal lysis or concurrent bacterial infection.

[0013] The core innovation of this application is that by quantifying the concentration of the first characteristic component related to fungal lysis under the action of antifungal drugs and the concentration of the second characteristic component related to concurrent bacterial infection, and calculating the attribution index based on the relative strength of the two, it solves the technical problem of difficulty in distinguishing the dominant cause of liver damage when fungal infection is combined with bacterial infection and liver damage occurs, and achieves the effect of providing a clear basis for clinical treatment decisions.

[0014] In a second aspect, a system for detecting hepatotoxicity caused by antifungal drugs is provided for determining the dominant cause of liver damage in individuals with fungal infections who are receiving antifungal drug treatment and subsequently have a concurrent bacterial infection. The system comprises:

[0015] a sample acquisition module for obtaining blood samples from individuals who are receiving antifungal treatment for invasive fungal infection and have concurrent bacterial infection and liver damage;

[0016] a first concentration detection module, configured to detect the concentration of a first characteristic component in the blood-derived sample, which is related to fungal lysis under the action of the antifungal drug, and obtain a first concentration value;

[0017] a second concentration detection module, configured to detect the concentration of a second characteristic component associated with the concurrent bacterial infection in the blood-derived sample to obtain a second concentration value;

[0018] an attribution index calculation module, configured to calculate an attribution index characterizing relative intensities of the first characteristic component and the second characteristic component based on the first concentration value and the second concentration value;

[0019] The dominant cause determination module is used to determine whether the dominant cause of the liver injury is attributable to fungal lysis or concurrent bacterial infection based on the comparison result of the attribution index and a preset reference interval.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] By detecting the concentrations of characteristic components related to fungal lysis and bacterial infection and calculating the attribution index, the dominant causes of liver injury can be distinguished. This has the advantage of being able to distinguish the dominant causes of liver injury and provide a basis for clinical decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Flow chart of the method of the present invention.

[0023] Figure 2 Schematic diagram of the system structure of the present invention.

[0024] In the figure: 201, sample acquisition module; 202, first concentration detection module; 203, second concentration detection module; 204, attribution index calculation module; 205, advantage inducement determination module. DETAILED DESCRIPTION

[0025] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0027] Conventional clinical diagnostic methods face technical challenges in attributing liver damage in individuals who are receiving antifungal treatment for invasive fungal infections and who also develop a concurrent bacterial infection and liver injury. These challenges present difficulties in distinguishing whether the liver damage is due to fungal lysis products induced by the antifungal drugs or the concurrent bacterial infection. Both conditions can lead to phenotypically similar liver damage by activating the same immune cells in the liver, making conventional testing unable to provide definitive attribution.

[0028] To illustrate this issue more clearly, for example, suppose an individual receiving caspofungin for invasive candidemia experiences a sharp increase in serum transaminase levels during treatment, indicating acute liver cell damage. Based on the drug's mechanism of action, this may be related to caspofungin causing a large amount of Candida to lyse and release cell wall fragments such as β-glucan, which activate liver immune cells and cause inflammatory damage. However, this individual also develops methicillin-resistant Staphylococcus aureus bacteremia at this time. Staphylococcus aureus cell wall components such as peptidoglycan are also strong immune activators and can cause liver damage through similar pathways. In this specific technical scenario, clinicians cannot use conventional means to determine whether the currently observed liver damage is mainly caused by fungal lysis products or bacterial infection.

[0029] In such a scenario, if the technical problem of unclear attribution of liver injury mentioned above is not resolved, clinicians will find it difficult to make accurate treatment decisions. Misdiagnosing liver injury caused by bacterial infection as drug toxicity may lead to the inappropriate discontinuation of key antifungal drugs, thereby causing the fungal infection to get out of control and endanger the individual's life. Conversely, if antifungal drug-related liver injury is not identified and the drug is continued, it may aggravate liver damage and even lead to liver failure. This uncertainty seriously affects the effectiveness and safety of clinical treatment.

[0030] To this end, this application Figure 1The present invention provides a method for detecting hepatotoxicity of antifungal drugs, which is used to determine the dominant cause of liver damage in individuals with fungal infections who are receiving antifungal drugs and have concurrent bacterial infections and liver damage. The method comprises the following steps:

[0031] S101. Obtain blood samples from individuals who are receiving antifungal therapy for invasive fungal infection and have concurrent bacterial infection and liver damage;

[0032] S102. Detecting the concentration of a first characteristic component related to fungal lysis under the action of an antifungal drug in the blood-derived sample to obtain a first concentration value;

[0033] S103, detecting the concentration of a second characteristic component associated with concurrent bacterial infection in the blood-derived sample to obtain a second concentration value;

[0034] S104. Calculating an attribution index representing relative intensities of the first characteristic component and the second characteristic component based on the first concentration value and the second concentration value;

[0035] S105. Based on the comparison result of the attribution index and the preset reference interval, determine whether the dominant cause of the liver injury is attributable to fungal lysis or concurrent bacterial infection.

[0036] Among them, blood-derived samples refer to blood or blood derivatives such as whole blood, plasma or serum taken from an individual, the purpose of which is to obtain a test matrix containing biomarkers reflecting the pathological state in the body;

[0037] The first characteristic component refers to a specific component released into the blood after fungal cells are lysed under the action of antifungal drugs. It can be realized by structural components of the fungal cell wall or cell membrane, such as β-(1,3)-D-glucan, which is mainly used to quantify the degree of cell damage caused by antifungal drugs in killing fungi;

[0038] The second characteristic component refers to a specific component related to concurrent bacterial infection and present in the blood sample. It can be realized by bacterial cell wall components, bacterial toxins or bacterial metabolites, such as peptidoglycan, lipoteichoic acid or bacterial DNA. It is mainly used to quantify the extent of concurrent bacterial infection or its impact on the body.

[0039] The attribution index refers to a value calculated based on the first and second concentration values, representing the relative intensities of the first and second characteristic components. It can be calculated using the ratio, difference, or weighted combination of the two. It is primarily used to comprehensively assess the relative contributions of fungal lysis and bacterial infection to liver injury.

[0040] Among them, the preset reference interval refers to a numerical range determined based on a large amount of clinical data or experimental research and used for comparison with the attribution index. Its purpose is to provide an objective judgment standard for determining the dominant cause of liver damage.

[0041] The approach of this application involves obtaining a blood sample from an individual who is receiving antifungal treatment for an invasive fungal infection and who also has a concurrent bacterial infection and liver damage. This type of individual is selected because their clinical presentation is complex and the cause of the liver damage is difficult to distinguish. The sample is then tested to obtain concentrations of a first characteristic component, reflecting the degree of fungal lysis, and a second characteristic component, reflecting the degree of bacterial infection. The simultaneous quantification of these two potential markers associated with liver damage lays the foundation for subsequent differential diagnosis. Based on these two concentrations, an attribution index is calculated. This index mathematically integrates the information from the two markers to characterize their relative impact on liver damage. Finally, the calculated attribution index is compared with a pre-set reference interval. This comparison mechanism enables objective determination, based on the index's range, of whether the observed liver damage is more likely to be attributable to the effects of fungal lysis products or the concurrent bacterial infection. The entire process forms a complete diagnostic chain, from sample acquisition, marker quantification, relative intensity assessment, to final attribution determination, effectively addressing clinical diagnostic challenges.

[0042] As an 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:

[0043] When the second characteristic component comprises a first bacterial characteristic component and a second bacterial characteristic component having different biological effects, the step of detecting the concentration of the second characteristic component is specifically implemented 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;

[0044] Calculating a corrected bacterial load value based on the first bacterial concentration value, the second bacterial concentration value, and a preset correction coefficient representing the relative biological efficacy between the first bacterial characteristic component and the second bacterial characteristic component;

[0045] An attribution index is calculated based on the first concentration value and the corrected bacterial load value.

[0046] Among them, the first bacterial characteristic component and the second bacterial characteristic component refer to specific bacteria with different biological efficacy related to concurrent bacterial infection or the components produced by them that can cause liver immune response. This can be achieved by detecting the DNA / RNA, cell wall components (such as peptidoglycan, lipopolysaccharide), secreted toxins, etc. of specific bacteria. Its purpose is to distinguish the differences in the effects of different types of bacterial infections on the liver; the preset correction coefficient refers to the numerical value used to quantify the relative biological efficacy between the first bacterial characteristic component and the second bacterial characteristic component. This coefficient can be predetermined based on in vitro experiments, animal model data or clinical observation data. Its purpose is to weight the concentrations of different bacteria so that it can better reflect the actual liver damage potential; the corrected bacterial load value refers to the numerical value that characterizes the overall liver damage potential of bacterial infection after comprehensively considering the concentrations of different bacterial characteristic components and their relative biological efficacy. This value can be calculated by weighted summation or other mathematical models. Its purpose is to provide a more accurate assessment of the impact of bacterial infection than a single concentration value.

[0047] The solution of this application addresses the issue of varying liver damage caused by different bacteria in cases of multiple bacterial infections by refining the detection of a second characteristic component associated with concurrent bacterial infection. Specifically, the solution no longer simply measures the total concentration of the second characteristic component. Instead, it differentiates and measures the concentrations of the first and second bacterial characteristic components, each with different biological potencies, to obtain a first bacterial concentration value and a second bacterial concentration value. This differentiated detection allows for a more refined capture of the composition of the bacterial infection. Furthermore, the solution incorporates a preset correction factor that reflects the relative liver-damaging biological potency of the first and second bacterial characteristic components. Based on the detected first and second bacterial concentration values ​​and the preset correction factor, a corrected bacterial load value is calculated. This corrected bacterial load value integrates the concentrations of different bacteria and their actual potential to impact the liver, thus more accurately representing the overall burden of bacterial infection on the liver than a single total bacterial concentration. Ultimately, this more accurate corrected bacterial load value replaces the original second concentration value and is used together with the first concentration value to calculate the attribution index. In this way, the attribution index can more accurately reflect the relative contributions of fungal lysis products and bacterial infection to liver injury, thereby improving the accuracy of determining the dominant cause of liver injury. Compared with methods that only use a single second concentration value, this method quantifies and corrects for bacterial infection more precisely, overcoming assessment bias caused by differences in the biological potency of different bacteria, making the judgment of the cause of liver injury more reliable in complex clinical scenarios.

[0048] As an embodiment of the present invention, before the step of calculating a corrected bacterial load value, the method further includes:

[0049] contacting the standardized reaction cells with the first bacterial characteristic component and the second bacterial characteristic component in a reaction environment containing humoral factors taken from an individual;

[0050] detecting the amount of a reaction product produced by the standardized reaction cells after contact with the first bacterial characteristic component and the second bacterial characteristic component to obtain a first reaction product amount value and a second reaction product amount value;

[0051] Calculating an instantaneous correction coefficient based on the first reaction product amount value and the second reaction product amount value;

[0052] Furthermore, the step of calculating a corrected bacterial load value based on the first bacterial concentration value, the second bacterial concentration value, and the preset correction coefficient specifically includes:

[0053] The instant correction coefficient is used to replace the preset correction coefficient, and the corrected bacterial load value is calculated based on the first bacterial concentration value, the second bacterial concentration value and the instant correction coefficient.

[0054] Among them, the reaction environment containing humoral factors taken from an individual refers to a liquid medium that simulates the in vivo environment of an individual, which contains serum, plasma or other body fluid components from the individual, and its purpose is to provide a reaction system that can reflect the individual's specific biological state; standardized reaction cells refer to cells that have been specifically treated or selected and have stable and repeatable reaction characteristics, such as specific cell lines or isolated and purified primary cells, and their purpose is to serve as a carrier that senses the stimulation of bacterial characteristic components and produces detectable reaction products to ensure the comparability of the reaction results; the first bacterial characteristic component and the second bacterial characteristic component refer to molecular components derived from different types of bacteria that can trigger the host immune response, such as lipopolysaccharide of Gram-negative bacteria or peptidoglycan of Gram-positive bacteria, and their purpose is to serve as stimuli to simulate the effects of different bacterial infections on the host immune system; the amount of reaction products refers to the total amount of molecules or substances that can be quantitatively detected and released or produced by standardized reaction cells after being stimulated by bacterial characteristic components, for example Such as cytokines, chemokines or enzymes, its purpose is to quantify the biological response intensity of standardized reaction cells to different bacterial characteristic components; the first reaction product quantity value and the second reaction product quantity value refer to the reaction product quantities detected after the standardized reaction cells contact the first bacterial characteristic component and the second bacterial characteristic component, respectively, and its purpose is to quantify the differences in biological effects caused by different bacterial characteristic components in the body fluid environment of a specific individual; the immediate correction coefficient refers to the numerical value calculated based on the first reaction product quantity value and the second reaction product quantity value, which reflects the relative biological efficacy between the first bacterial characteristic component and the second bacterial characteristic component in the body fluid environment of a specific individual, and its purpose is to provide a parameter for correcting individual differences; the use of an immediate correction coefficient instead of a preset correction coefficient means that when calculating the corrected bacterial load value, the immediate correction coefficient obtained based on the actual response of the individual is used, rather than a universal, pre-set value, and its purpose is to improve the degree to which the corrected bacterial load value reflects the actual situation of the individual.

[0055] The solution of the present application simulates the actual biological effects of different bacterial characteristic components in an individual's internal environment by contacting standardized reaction cells with a first bacterial characteristic component and a second bacterial characteristic component in a reaction environment containing humoral factors taken from an individual. Thus, by detecting the amount of reaction products produced by the standardized reaction cells after contact, the first reaction product quantity value and the second reaction product quantity value are obtained. These quantities directly reflect the degree of activation of the standardized reaction cells by different bacterial characteristic components in the individual's specific body fluid environment. Based on these quantities, an immediate correction coefficient is calculated, which can accurately quantify the relative biological efficacy differences of different bacterial characteristic components in the individual's body fluid environment. Subsequently, this immediate correction coefficient is used to replace the original preset correction coefficient, and combined with the first bacterial concentration value and the second bacterial concentration value, a corrected bacterial load value is calculated. It is precisely because of the use of the immediate correction coefficient that reflects the actual individual situation that the corrected bacterial load value can more accurately assess the comprehensive contribution of different bacterial characteristic components to liver damage in the individual. This correction method based on the individual body fluid environment overcomes the limitation that the preset correction coefficient cannot reflect individual differences, making the calculated corrected bacterial load value closer to the individual's true bacterial infection load and its biological effects, thereby improving the accuracy of subsequent attribution index calculations, and ultimately providing a more reliable basis for determining the dominant cause of liver damage.

[0056] As an embodiment of the present invention, the steps of detecting the amount of a reaction product produced by the standardized reaction cells after contact with the first bacterial characteristic component and the second bacterial characteristic component to obtain the first reaction product amount value and the second reaction product amount value include:

[0057] Under the condition that the first bacterial characteristic component and the second bacterial characteristic component are not added, detecting the amount of the reaction product in the reaction environment to obtain the background product amount;

[0058] Respectively detecting the total amount of reaction products produced in the reaction environment after the standardized reaction cells are in contact with the first bacterial characteristic component and the second bacterial characteristic component to obtain a first reaction product total amount value and a second reaction product total amount value;

[0059] The amount of background product is subtracted from the total amount of the first reaction product to determine the first reaction product amount, and the amount of background product is subtracted from the total amount of the second reaction product to determine the second reaction product amount.

[0060] The solution of the present application is to first measure the amount of background products in a reaction environment that does not contain bacterial characteristic components when detecting the amount of reaction products produced after the standardized reaction cells come into contact with bacterial characteristic components, and then measure the total amount of reaction products in a reaction environment that contains bacterial characteristic components. The reason for adopting this approach is that there may be reaction products in the reaction environment that are not caused by bacterial characteristic components. These background products will be superimposed on the reaction products caused by bacterial characteristic components, resulting in the total amount directly measured being higher than the amount actually caused by bacterial characteristic components. It is precisely because the amount of background products is measured and deducted from the total amount of products that the amount of the first reaction product caused by the first bacterial characteristic component and the amount of the second reaction product caused by the second bacterial characteristic component are more accurate, eliminating the influence of background interference. This accurate reaction product amount can more truly reflect the difference in biological efficacy of different bacterial characteristic components in a specific body fluid environment, provide basic data for the subsequent calculation of accurate instant correction coefficients, thereby improving the accuracy of the corrected bacterial load value, and ultimately improving the reliability of the determination of the cause of liver injury.

[0061] In some of the above-mentioned embodiments of the present application, it is proposed to calculate an attribution index characterizing the relative intensity of the first characteristic component and the second characteristic component based on the first concentration value and the second concentration value. The attribution index calculated based on the first concentration value and the second concentration value can be obtained by simply calculating the ratio or difference between the first concentration value and the second concentration value, which can preliminarily reflect the relative content of the two components. However, in its implementation process, the concentrations of the first characteristic component and the second characteristic component and their actual biological effects may not have a simple linear relationship, but a complex non-proportional relationship. If the concentration values ​​are directly used for calculation, it may lead to deviations in the attribution index, thereby affecting the accuracy of the determination of the cause of liver injury.

[0062] In this regard, the present application further proposes that the steps 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 include:

[0063] converting the first concentration value into a first effect contribution value based on a preset first corresponding relationship reflecting a non-proportional relationship between the concentration of the first characteristic component and its biological effect;

[0064] converting the second concentration value into a second effect contribution value based on a preset second corresponding relationship reflecting a non-proportional relationship between the concentration of the second characteristic component and its biological effect;

[0065] Based on the first effect contribution value and the second effect contribution value, the attribution index is calculated.

[0066] Among them, the preset first correspondence refers to a pre-established data set or mathematical model that can reflect the nonlinear correlation between the biological effect intensities produced by the first characteristic component at different concentrations, which can be implemented using a concentration-effect curve, a lookup table or a mathematical function. The preset second correspondence refers to a pre-established data set or mathematical model that can reflect the nonlinear correlation between the biological effect intensities produced by the second characteristic component at different concentrations, which can be implemented using a concentration-effect curve, a lookup table or a mathematical function. The first effect contribution value refers to a numerical value that characterizes the actual biological effect intensity of the first characteristic component, converted from the first concentration value according to the preset first correspondence. The second effect contribution value refers to a numerical value that characterizes the actual biological effect intensity of the second characteristic component, converted from the second concentration value according to the preset second correspondence. The attribution index refers to an indicator calculated based on the first effect contribution value and the second effect contribution value, which is used to quantitatively evaluate the relative impact of the first characteristic component and the second characteristic component on liver damage.

[0067] The solution of the present application converts the first concentration value and the second concentration value into a first effect contribution value and a second effect contribution value based on a preset first correspondence and a preset second correspondence, respectively, and then calculates the attribution index based on these effect contribution values. The core of this process is to introduce a correspondence that reflects the non-proportional relationship between concentration and biological effect, thereby converting the original concentration data into effect contribution data that is more representative of the actual biological impact. It is precisely because of this conversion that the subsequent attribution index calculation based on the effect contribution value can more accurately reflect the relative driving effect of the two characteristic components on liver damage at the biological level, overcoming the deviation that may be caused by the direct use of concentration values. This calculation method based on biological effects rather than simple concentrations enables the attribution index to more truly reflect the dominant inducement of liver damage, thereby improving the reliability of the judgment.

[0068] As an 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:

[0069] Obtaining indicators to characterize the direct hepatotoxic potential of antifungal drugs;

[0070] The indicator is converted into a third effect contribution value based on a preset correspondence reflecting the relationship between the indicator and direct liver toxicity;

[0071] The attribution index is calculated based on the first effect contribution value, the second effect contribution value, and the third effect contribution value.

[0072] The index characterizing the direct hepatotoxic potential of an antifungal drug refers to a quantitative value reflecting the drug's ability to directly damage hepatocytes. This index can be determined using the half-maximal inhibitory concentration (IC50) or half-lethal concentration (LC50) measured in in vitro cytotoxicity experiments, dose-response data on liver injury observed in animal models, or clinical pharmacokinetic parameters combined with known toxicity. Its purpose is to quantify the potential contribution of the drug's inherent chemical toxicity to liver injury. The predefined mapping relationship between this index and direct hepatotoxicity refers to a rule or model that maps the toxicity index value to a third-effect contribution value. This can be achieved using a predefined lookup table or applying a predefined mathematical function model. The purpose is to uniformly convert different toxicity indexes into comparable effect contribution values. The third-effect contribution value represents the quantified contribution of the antifungal drug's direct hepatotoxicity to total liver injury, aiming to incorporate the drug's inherent toxic effects into attribution calculations. The first-effect contribution value represents the quantified contribution of fungal cleavage products to liver injury, aiming to quantify the biological effects of fungal cleavage products. The second effect contribution value refers to the quantitative contribution of bacterial infection-related components to liver injury, and its purpose is to quantify the biological effects of bacterial infection. The attribution index represents the relative contribution of three factors, namely fungal lysis products, bacterial infection, and direct hepatotoxicity of antifungal drugs, to liver injury, and its purpose is to provide a comprehensive indicator for determining the dominant cause of liver injury.

[0073] Based on the above technical features, the scheme of the present application obtains an indicator that characterizes the direct hepatotoxicity potential of antifungal drugs, which quantifies the direct chemical damage ability of the drug to the liver. The indicator is converted into a third effect contribution value based on a preset correspondence, thereby converting the toxic effect of the drug itself into a quantifiable contribution. On this basis, combined with the already determined first effect contribution value and second effect contribution value, the attribution index is calculated based on these three effect contribution values. It is precisely because the chemical toxicity contribution of the drug itself is taken into consideration together with the biological effect contribution of fungal lysis products and bacterial infection that the attribution index can more comprehensively reflect the potential inducement composition of liver damage, thereby improving the accuracy of the determination of the dominant inducement of liver damage.

[0074] As one embodiment of the present invention, the step of obtaining an indicator characterizing the direct hepatotoxic potential of an antifungal drug comprises:

[0075] Obtain the concentrations of the parent antifungal drug and the main hepatotoxic metabolites produced by the antifungal drug in the body;

[0076] Based on the concentration of the antifungal drug prototype, the concentration of the main metabolite, and the toxicity intensity of the antifungal drug prototype and the main metabolite to the liver, a comprehensive drug toxicity load value is determined as an indicator to characterize the direct liver toxicity potential of antifungal drugs.

[0077] The "protoform" antifungal drug refers to the antifungal drug molecule itself before chemical or biotransformation in the body. Hepatotoxic major metabolites are chemical substances produced by enzymatic or other reactions in the body that have significant adverse effects on liver cells or function. These metabolites are typically intermediates or end products during the drug's clearance or activation process in the body. Respective concentrations refer to the amount of substance per unit volume of the protoform antifungal drug or its major metabolite in a specific biological sample. These concentrations can be obtained using chromatography-mass spectrometry, enzyme-linked immunosorbent assay (ELISA), or other applicable quantitative detection methods. Toxicity refers to the degree of damage or potential risk to the liver caused by the protoform antifungal drug or its major metabolite per unit mass or molar amount. These concentrations can be determined based on in vitro cytotoxicity data, animal model study results, clinical pharmacokinetic / pharmacodynamic correlation analysis, or known drug toxicology data. The comprehensive drug toxicity burden (CTB) value is a quantitative assessment of the toxic burden of the drug on the liver, which is a combination of the actual in vivo exposure levels of the antifungal drug parent and its major hepatotoxic metabolites and their inherent hepatotoxic potential. This value can be calculated using weighted summation, multiplication, or other mathematical models, where the weights or functional relationships reflect the contribution of concentration and toxicity intensity to the total toxicity burden. An indicator characterizing the direct hepatotoxic potential of an antifungal drug is a numerical value or quantitative representation reflecting the likelihood or degree of direct liver damage caused by the antifungal drug itself and its metabolites. In this protocol, this indicator is specifically the comprehensive drug toxicity burden value.

[0078] The proposed approach comprehensively considers the actual drug form and levels in the body by measuring the concentrations of both the parent antifungal drug and its major hepatotoxic metabolites. Furthermore, by combining the hepatotoxicity of both parent antifungal drug and its major metabolites, the inherent potential of different components to cause liver damage is quantified. By combining concentration and toxicity, a comprehensive drug toxicity burden (DTL) value is determined. This combined value reflects not only the quantity but also the quality of the drug, more accurately characterizing the overall direct toxic burden of the antifungal drug on the liver. This comprehensive DTL value, as an indicator of the direct hepatotoxic potential of the antifungal drug, can be used in subsequent liver injury attribution analysis. For example, this index can be converted into a third-effect contribution value and used, along with the effect contributions of other factors, to calculate an attribution index. By using a more accurate indicator to represent direct drug toxicity, the accuracy of the final calculated attribution index can be improved, leading to more reliable identification of the primary cause of liver injury. This approach avoids the bias associated with relying solely on parent drug concentration or simplistic assessments, resulting in more refined and reliable attribution of liver injury.

[0079] As an embodiment of the present invention, the step of converting the first concentration value into a first effect contribution value based on a preset first corresponding relationship reflecting the non-proportional relationship between the concentration of the first characteristic component and its biological effect includes:

[0080] By referring to a preset concentration-effect correspondence table, the first concentration value is converted into a first effect contribution value;

[0081] Alternatively, the first concentration value is converted into the first effect contribution value by applying a preset mathematical function model.

[0082] Among them, the preset concentration-effect correspondence table refers to a lookup table established through experimental measurement or clinical observation data, which records the relationship between different concentration values ​​of the first characteristic component and the corresponding first effect contribution values, and its purpose is to directly reflect the nonlinear correspondence between concentration and effect; the preset mathematical function model refers to a mathematical model that describes the relationship between the concentration of the first characteristic component and the first effect contribution value through a mathematical formula, and its purpose is to achieve nonlinear conversion from concentration to effect contribution value through calculation.

[0083] The solution of the present application does not use a simple linear relationship in the process of converting the first concentration value to the first effect contribution value. Instead, the conversion is based on a preset first correspondence relationship that reflects the non-proportional relationship between the concentration of the first characteristic component and its biological effect. This non-proportional relationship can be reflected in a preset concentration-effect correspondence table. By consulting the table, the measured first concentration value can be directly mapped to a predetermined value that reflects its true biological effect contribution. Alternatively, this non-proportional relationship can be described by a preset mathematical function model, which can output the corresponding first effect contribution value through nonlinear calculation based on the input first concentration value. Both methods can more accurately capture the nonlinear relationship between concentration and effect, avoiding the deviation caused by the linear assumption. Through this more accurate conversion, the obtained first effect contribution value can more realistically characterize the potential contribution intensity of fungal lysis products to liver damage. After both the first concentration value and the second concentration value are converted to effect contribution values, the attribution index is calculated based on these effect contribution values. The precise conversion method provided here makes the input of the first effect contribution value more accurate, thereby improving the reliability of the attribution index finally calculated. A more reliable attribution index can more accurately reflect the relative contributions of fungal lysis products and bacterial infection products to liver injury, providing a more solid foundation for subsequent determination of the dominant cause of liver injury. Therefore, the proposed scheme, combined with the overall framework, enhances the reliability and clinical application value of the entire liver injury attribution method by improving the accuracy of key intermediate steps.

[0084] As an 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:

[0085] A weighted sum or difference operation is performed on the first effect contribution value, the second effect contribution value, and the third effect contribution value to determine the attribution index.

[0086] Among them, weighted summation refers to a mathematical operation that multiplies multiple values ​​by a weight coefficient and then adds them together. This can be achieved through linear combination. Difference operation refers to a mathematical operation that calculates the difference between two or more values. This can be achieved through simple subtraction or more complex combination operations. Attribution index is a comprehensive numerical indicator used to quantitatively assess the relative contribution or impact of different potential factors on a specific outcome. It can be expressed as the result of weighted summation or difference operation.

[0087] The scheme of the present application determines the attribution index by performing a weighted sum 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 respectively quantify the potential impact of fungal lysis, bacterial infection and direct drug toxicity on liver damage. By adopting the weighted summation method, the overall contribution of different factors to liver damage can be comprehensively evaluated by adjusting the weights according to the actual impact of different factors, thereby obtaining an attribution index that reflects the comprehensive effect of multiple factors. By adopting the difference operation method, the relative impact intensity between different factors can be directly compared. For example, by calculating the difference between the fungal lysis effect contribution value and the bacterial infection effect contribution value, it can be intuitively judged which factor is the main driving force of the current liver damage. These two operation methods or their combined use make the calculation method of the attribution index flexible, and can be optimized and adjusted according to clinical data and experience, so as to more effectively utilize the three effect contribution values ​​and obtain an attribution index that can accurately reflect the attribution of the dominant inducement of liver damage. This calculation method, combined with previous methods for obtaining and quantifying the contribution values ​​of these effects, forms a complete technical process that can quantitatively evaluate the relative strength of different inducements, providing a feasible way to solve the problem of liver injury attribution.

[0088] In some preferred embodiments, the attribution index can be calculated by weighted summation, for example, attribution index = w1 * first effect contribution value + w2 * second effect contribution value + w3 * third effect contribution value, where w1, w2, w3 are preset weight coefficients, and their values ​​can be determined based on a large amount of clinical data analysis results or expert consensus to reflect the relative importance of different effect contribution values ​​in the actual attribution of liver injury. As another specific embodiment, the attribution index can be calculated by difference operation, for example, attribution index = first effect contribution value - second effect contribution value, and the dominant inducement is determined by comparing the contribution difference between the fungal lysis effect and the bacterial infection effect. A combination of weighted summation and difference operation can also be used, for example, attribution index = (w1 * first effect contribution value + w3 * third effect contribution value) - w2 * second effect contribution value, comprehensively considering the effects of fungal lysis and drug toxicity, and comparing them with the effects of bacterial infection.

[0089] like Figure 2 The present invention provides an antifungal drug hepatotoxicity detection system for determining the dominant cause of liver damage in individuals with fungal infections who are receiving antifungal drug treatment and then have concurrent bacterial infections and liver damage. The system comprises:

[0090] The sample acquisition module 201 is used to obtain a blood sample from an individual who is receiving antifungal treatment for an invasive fungal infection and has a concurrent bacterial infection and liver damage;

[0091] A first concentration detection module 202 is configured to detect the concentration of a first characteristic component in the blood-derived sample that is related to fungal lysis under the action of an antifungal drug, and obtain a first concentration value;

[0092] A second concentration detection module 203 is configured to detect the concentration of a second characteristic component associated with concurrent bacterial infection in the blood-derived sample to obtain a second concentration value;

[0093] An attribution index calculation module 204 is configured to calculate an attribution index representing relative intensities of the first characteristic component and the second characteristic component based on the first concentration value and the second concentration value;

[0094] The dominant cause determination module 205 is used to determine whether the dominant cause of liver injury is fungal lysis or concurrent bacterial infection based on the comparison result of the attribution index and the preset reference interval.

[0095] The solution of the present application realizes the automation and standardization of the detection process by integrating the antifungal drug hepatotoxicity detection method into a system. Specifically, the sample acquisition module 201 first provides the analysis basis for the entire system, ensuring the source of samples for subsequent detection. Subsequently, the first concentration detection module 202 and the second concentration detection module 203 independently or in parallel quantitatively analyze the key biomarkers in the sample to obtain a first concentration value reflecting the degree of fungal lysis and a second concentration value reflecting the degree of bacterial infection. It is precisely because of the ability to quantify the biomarkers related to these two potential inducers simultaneously or separately that subsequent attribution analysis becomes possible. The attribution index calculation module 204 receives these two concentration values ​​and calculates the attribution index according to a preset algorithm. The index quantifies the relative contribution strength of the two inducements. Finally, the dominant inducement determination module 205 compares the calculated attribution index with the preset clinical reference interval to automatically determine whether the currently observed liver damage is more likely to be attributed to fungal lysis or concurrent bacterial infection. This modular design and information flow mechanism makes the entire detection process efficient and objective, avoiding the subjectivity and errors that may be introduced by manual operations, thereby quickly and accurately providing clinicians with attribution information on liver damage, effectively solving the technical problem of difficulty in distinguishing the causes of liver damage in complex clinical situations.

[0096] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. Various changes and improvements are possible without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the invention as claimed.

Claims

1. A method for detecting hepatotoxicity of antifungal drugs, for determining the dominant cause of liver damage in individuals with fungal infections receiving antifungal drug treatment who also have bacterial infections and liver damage, characterized in that: The method comprises the following steps: Obtain blood samples from individuals receiving antifungal therapy for invasive fungal infection who also have concurrent bacterial infection and liver damage; detecting, in the blood-derived sample, a concentration of a first characteristic component associated with fungal lysis under the action of the antifungal drug to obtain a first concentration value; detecting the concentration of a second characteristic component associated with the concurrent bacterial infection in the blood-derived sample to obtain a second concentration value; 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; Based on the comparison result of the attribution index with the preset reference interval, it is determined that the dominant cause of the liver injury is attributable to fungal lysis or concurrent bacterial infection.

2. The method for detecting hepatotoxicity of antifungal drugs according to claim 1, wherein: 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 effects, the step of detecting the concentration of the second characteristic component is specifically implemented 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 preset correction coefficient characterizing the relative biological efficacy between the first bacterial characteristic component and the second bacterial characteristic component; The attribution index is calculated based on the first concentration value and the corrected bacterial load value.

3. The method for detecting hepatotoxicity of antifungal drugs according to claim 2, wherein: Before the step of calculating a corrected bacterial load value, the method further comprises: contacting the standardized reaction cells with the first bacterial characteristic component and the second bacterial characteristic component respectively in a reaction environment containing humoral factors taken from an individual; detecting the amount of a reaction product produced by the standardized reaction cells after contact with the first bacterial characteristic component and the second bacterial characteristic component to obtain a first reaction product amount value and a second reaction product amount value; calculating an instantaneous correction coefficient based on the first reaction product amount and the second reaction product amount; Furthermore, the step of calculating a corrected bacterial load value based on the first bacterial concentration value, the second bacterial concentration value, and the preset correction coefficient specifically includes: The instant correction coefficient is used to replace the preset correction coefficient, and the corrected bacterial load value is calculated based on the first bacterial concentration value, the second bacterial concentration value and the instant correction coefficient.

4. The method for detecting hepatotoxicity of antifungal drugs according to claim 3, wherein: The step of detecting the amount of the reaction product produced by the standardized reaction cells after contacting the first bacterial characteristic component and the second bacterial characteristic component to obtain the first reaction product amount value and the second reaction product amount value comprises: Under the condition that the first bacterial characteristic component and the second bacterial characteristic component are not added, detecting the amount of the reaction product in the reaction environment to obtain the background product amount; Respectively detecting the total amount of reaction products produced in the reaction environment after the standardized reaction cells are in contact with the first bacterial characteristic component and the second bacterial characteristic component to obtain a first reaction product total amount value and a second reaction product total amount value; The first reaction product amount is determined by subtracting the background product amount from the first reaction product amount, and the second reaction product amount is determined by subtracting the background product amount from the second reaction product amount.

5. The method for detecting hepatotoxicity of antifungal drugs according to claim 1, wherein: 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: converting the first concentration value into a first effect contribution value based on a preset first corresponding relationship reflecting a non-proportional relationship between the concentration of the first characteristic component and its biological effect; converting the second concentration value into a second effect contribution value based on a preset second corresponding relationship reflecting a non-proportional relationship between the concentration of the second characteristic component and its biological effect; The attribution index is calculated based on the first effect contribution value and the second effect contribution value.

6. The method for detecting hepatotoxicity of antifungal drugs according to claim 5, characterized in that: The step of calculating the attribution index based on the first effect contribution value and the second effect contribution value includes: Obtaining indicators characterizing the direct hepatotoxic potential of the antifungal drug; converting the indicator into a third effect contribution value based on a preset corresponding relationship reflecting the relationship between the indicator and the direct liver toxicity; The attribution index is calculated based on the first effect contribution value, the second effect contribution value, and the third effect contribution value.

7. The method for detecting hepatotoxicity of antifungal drugs according to claim 6, characterized in that: The step of obtaining an indicator characterizing the direct hepatotoxic potential of the antifungal drug comprises: Obtaining the concentrations of the parent antifungal drug and the main liver-toxic metabolites produced in the body by the antifungal drug; Based on the concentration of the antifungal drug prototype, the concentration of the main metabolite, and the toxicity intensity of the antifungal drug prototype and the main metabolite to the liver, a comprehensive drug toxicity load value is determined as an indicator to characterize the direct liver toxicity potential of the antifungal drug.

8. The method for detecting hepatotoxicity of antifungal drugs according to claim 5, characterized in that: The step of converting the first concentration value into a first effect contribution value based on a preset first corresponding relationship reflecting the non-proportional relationship between the concentration of the first characteristic component and its biological effect comprises: Converting the first concentration value into the first effect contribution value by referring to a preset concentration-effect correspondence table; Alternatively, the first concentration value is converted into the first effect contribution value by applying a preset mathematical function model.

9. The method for detecting hepatotoxicity of antifungal drugs according to claim 6, wherein: 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 effect contribution value, the second effect contribution value, and the third effect contribution value to determine the attribution index.

10. An antifungal drug hepatotoxicity detection system for determining the dominant cause of liver damage in individuals with fungal infections receiving antifungal drug treatment who also have bacterial infections and liver damage, characterized in that: The system includes: a sample acquisition module for obtaining blood samples from individuals who are receiving antifungal treatment for invasive fungal infection and have concurrent bacterial infection and liver damage; a first concentration detection module, configured to detect the concentration of a first characteristic component in the blood-derived sample, which is related to fungal lysis under the action of the antifungal drug, and obtain a first concentration value; a second concentration detection module, configured to detect the concentration of a second characteristic component associated with the concurrent bacterial infection in the blood-derived sample to obtain a second concentration value; an attribution index calculation module, configured to calculate an attribution index characterizing relative intensities of the first characteristic component and the second characteristic component based on the first concentration value and the second concentration value; The dominant cause determination module is used to determine whether the dominant cause of the liver injury is attributable to fungal lysis or concurrent bacterial infection based on the comparison result of the attribution index and a preset reference interval.

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