A method and device for predicting changes in drug content in a living body

Abstract

The present invention discloses a method and device for predicting changes in the content of a drug in a living body. The present invention uses microsomes to construct a kinetic reaction system of a test substance in vitro, allowing the test substance to fully react in the microsomes, and determining the reaction conditions for conducting in vitro kinetic experiments using microsomes. Through the constructed microsomal reaction system, the metabolic rate of the test substance in the liver and / or small intestine of the living body is calculated, and its metabolic kinetic parameters are written into calculus equations using a computer, and then solved to finally obtain the kinetic process of the test substance, thereby making up for the shortcomings of conducting toxicity experiments using in vitro biological tissues. The present invention can analyze the toxicity characteristics of drugs in the liver of mice and predict the dose that produces liver toxicity without the need for animal experiments.

Description

Technical Field

[0001] The present invention belongs to the field of food safety detection, and in particular relates to a method and a device for predicting changes in drug content in a living body. Background Art

[0002] In the field of food safety, toxicity evaluation of contaminants present in food is required to ensure the health and safety of consumers. Generally, toxicity evaluation experiments are conducted using mice. However, animal experiments are time-consuming and costly, and it is difficult to quickly obtain the toxicity characteristics and toxic doses of contaminants. On the other hand, if faced with a new type of contaminant, in the absence of its toxicity information and data, it is impossible to design an experimental dose to conduct animal experiments. In order to make up for the shortcomings of animal toxicity experiments, in vitro tissue and cell culture have become the main method for judging the toxic effects of pollutants. However, in vitro experiments lack the kinetic processes of simulating the absorption, distribution, metabolism and excretion of substances in the body, so there is an uncertainty problem in extrapolating the toxic dose of in vitro experiments to the toxic dose in vivo.

[0003] Currently, acute hepatotoxicity assessment is primarily conducted using mice. Experiments typically determine the median lethal dose (LD50) of a test compound. The basic approach involves administering the test compound to multiple groups of mice at a range of doses. The number of animals that die in each group over a specified period of time is observed and recorded. Statistical methods are then used to calculate the dose that kills 50% of the group. The number of mice used is generally 10 per group, with a minimum of 6 mice. Six dose groups are typically used. If the dose does not achieve the desired number of deaths, the dose is adjusted and repeated until the desired number is met. The observation period for acute toxicity in animals should last until the majority of animals that exhibit typical symptoms of toxicity but survive have fully recovered, typically 24 to 48 hours. The number of deaths during the observation period is recorded. It is best to maintain the animals for another week after observation. If symptoms of toxicity or death occur 24 hours after administration of the test compound, this indicates delayed toxicity, and the observation period should be extended to two or four weeks. From the above, it can be seen that conducting acute animal toxicity experiments using mice is not only time-consuming and costly, but most of the time it takes multiple experiments to achieve the desired result.

[0004] At present, in vitro tissue and cell culture are mainly used to replace animal toxicity tests internationally. However, in vitro tissue culture cannot simulate the kinetic processes of absorption, distribution, metabolism and excretion of the test substance in the body, which often leads to uncertainty in the extrapolation of in vitro experimental toxic doses to actual in vivo toxic doses (Groothuis, FA, Heringa, MB, Nicol, B., Hermens, JL, Blaauboer, BJ, Kramer, NI (2015). Dosemetric considerations in in vitro assays to improve quantitative in vitro–invivo dose extrapolations. Toxicology, 332, 30-40.; Algharably, EAH, Kreutz, R., Gundert-Remy, U. (2019). Importance of in vitro conditionsformodeling the in vivo dose in humans by in vitro–in vivo extrapolation(IVIVE). Archives of Toxicology, 93(3), 615-621.). Summary of the Invention

[0005] In order to fully solve the problem that in vitro experiments cannot simulate the kinetic process of the test substance in vivo, the present invention has developed a device (system) that can replace the acute hepatotoxicity experiment in mice. Without the need for animal experiments, it can analyze the toxicity characteristics of drugs in the mouse liver and predict the dose that will produce hepatotoxicity.

[0006] Thus, the first aspect of the present invention provides a method for predicting changes in drug content in an organism, comprising:

[0007] Obtain the in vitro kinetic parameters of the test substance, the physicochemical properties of the test substance, and the physiological parameters of the organism. Calculate the maximum enzyme reaction rate of the test substance in the liver and / or small intestine based on the in vitro kinetic parameters of the test substance. Then, predict the changes in the content of the test substance in the organism using a physiologically based pharmacokinetics (PBK) model that simulates the kinetic process of the drug in vivo.

[0008] Wherein, the compartment structures of the physiological pharmacokinetic model simulating the kinetic process of the drug in vivo include: gastrointestinal tract, small intestine, liver, slowly perfused tissue, quickly perfused tissue, fat and / or blood;

[0009] The in vitro kinetic parameters of the test substance include the maximum enzyme reaction rate of the test substance in in vitro microsomes and the Michaelis constant of the test substance in in vitro microsomes.

[0010] In the present invention, the microsomes include liver microsomes and small intestinal microsomes.

[0011] In the present invention, the content change of the test substance in the organism includes the content change of the test substance in the small intestine, liver, fat, fast perfusion tissue, slow perfusion tissue and / or blood.

[0012] The maximum enzyme reaction rate of the test substance in the in vitro microsomes and the Michaelis constant of the test substance in the in vitro microsomes are calculated based on the in vitro microsome experiments of the test substance.

[0013] In the present invention, based on the metabolism of the test substance at different time points in an in vitro microsomal experiment, a time-concentration curve is obtained. The maximum enzyme reaction velocity Vmax and the Michaelis-Menten constant Km of the test substance in the microsomes are calculated using the Michaelis-Menten equation: V = Vmax × [S] / (Km + [S]), where S is the concentration of the test substance.

[0014] In the present invention, the maximum enzyme reaction rate of the test substance in the liver is VMaxLM1 (µmol / h) = VMaxLM1c / 1000×60×MPL×L×BW, where MPL is 35 mg microsomes per gram of liver (mg / g liver), L is the liver weight index = VLC×1000 (g / kg bw), and VmaxLM1c is the maximum enzyme reaction rate of the test substance in liver microsomes in vitro (nmol min -1 (mg protein) -1 ), BW is the body weight (kg). In a specific embodiment of the present invention, the test substance is senecioline, and the organism is a mouse. The VmaxLM1c value obtained through liver microsome experiments is 7.948. The Michaelis-Menten constant KmLM1 of senecioline in liver microsomes in vitro is 166.3 (µmol / L). This Michaelis-Menten constant is used to calculate changes in the metabolic concentration of senecioline in the liver.

[0015] In the present invention, the maximum enzyme reaction rate of the test substance in the small intestine is VMaxSiM2 (µmol / h) = VMaxSiM2c / 1000×60×MPSi×Si×BW, wherein MPSi is 20.6 mg of microsomes per gram of small intestine (mg / g smallintestine), Si is the small intestine weight index = VSic×1000 (g / kg bw), and VmaxSiM2c is the maximum enzyme reaction rate of the test substance in the small intestinal microsomes (nmol min -1 (mg protein) -1 ), BW is the organism's body weight (kg). In a specific embodiment of the present invention, the test substance is seneciopine, and the organism is a mouse. The VmaxSiM2c value obtained through liver microsome experiments is 0.06577. The Michaelis constant of seneciopine in small intestinal microsomes, KmSiM2, is 434.2 (µmol / L). This Michaelis constant is used in the calculation of changes in the metabolic concentration of seneciopine in the small intestine.

[0016] In the present invention, the blood includes venous blood and arterial blood.

[0017] In the present invention, the organism includes mammals and humans. The mammals include but are not limited to mice and rats. As a preferred embodiment, the organism is a mouse.

[0018] According to a specific embodiment of the present invention, the physicochemical property parameters of the test substance include the absorption rate constant Ka (h -1 ), molecular weight of the test substance MWL (g / mol), partition coefficient of the test substance between liver and blood PLL, partition coefficient of the test substance between fat and blood PFL, partition coefficient of the test substance between rapidly perfused tissues and blood PRL, partition coefficient of the test substance between slowly perfused tissues and blood PSL, partition coefficient of the test substance between small intestine and blood PIL, content of the test substance in the gastrointestinal tract AGI.

[0019] In the present invention, the content of the test substance in the gastrointestinal tract AGI is equal to the content of the test substance exposed orally.

[0020] In the present invention, the absorption rate constant Ka of the test substance in the intestine is calculated based on the permeability and molecular properties of the test substance in the small intestine.

[0021] In the present invention, the equation for calculating Ka is: log Papp = -5.469 + 0.236 log P; log Papp seneciphylline / Ka seneciphylline = log Papp adonifoline / 0.6.

[0022] In the present invention, the calculation method of Ka is derived from: TJ Hou, W. Zhang, K. Xia, XB Qiao, XJ Xu. ADME evaluation in drug discovery. 5. Correlation of Caco-2 permeation with simple molecular properties J. Chem. Inf. Comput. Sci., 44(2004), pp. 1585-1600.

[0023] In the present invention, the partition coefficient is obtained based on the octanol-water partition coefficient Kow of the test substance, and the calculation method is obtained according to the reference: J. DeJongh, HJ Verhaar, JL Hermens. A quantitativeproperty-property relationship (QPPR) approach to estimate in vitro tissue-blood partition coefficients of organic chemicals in rats and humans. Arch.Toxicol., 72 (1997), pp. 17-25.

[0024] According to a specific embodiment of the present invention, the physiological parameters of the organism include the body weight BW (kg), adipose tissue volume fraction VFc, liver volume fraction VLc, small intestine volume fraction VSic, arterial blood volume fraction Vac, venous blood volume fraction VVc, blood volume fraction VBc, fast perfusion tissue volume fraction VRc, slow perfusion tissue volume fraction VSc, adipose tissue volume VF (L or Kg), liver volume VL (L or Kg), small intestine volume VSi (L or Kg), fast perfusion tissue volume VR (L or Kg), slow perfusion tissue volume VS (L or Kg), arterial blood volume VA (L or Kg), venous blood volume VV (L or Kg), blood volume VB (L or Kg), blood flow to fat fraction QFc, blood flow to liver fraction QLc, blood flow to small intestine fraction QSic, blood flow to fast perfusion tissue fraction QRc, blood flow to slow perfusion tissue fraction QSc, cardiac output QC (L / h), adipose tissue blood flow QF (L / h), liver blood flow QL (L / h), small intestine blood flow QSi (L / h), rapidly perfused tissue blood flow QR (L / h), and slowly perfused tissue blood flow QS (L / h).

[0025] In the present invention, the physiological parameters of the organism are obtained by searching the literature. In a specific embodiment of the present invention, the organism is a mouse. The physiological parameters of mice are referenced in the literature: Brown, RP, Delp, MD, Lindstedt, SL, Rhomberg, LR, Beliles, RP, 1997. Physiological Parameter Values ​​for Physiologically Based Pharmacokinetic Models. Toxicol Ind Health. 13, 407-484.

[0026] In the present invention, the physiological parameters of the mice are specifically as follows: mouse body weight BW=0.025 (Kg); adipose tissue fraction VFc=0.07; liver fraction VLc=0.034; small intestine fraction VSic=0.014; arterial blood fraction VAc=0.0185; venous blood fraction VVc=0.0555; rapidly perfused tissue fraction VRc=0.09-VLc-VSic; slowly perfused tissue fraction VSc=0.82-VFc; adipose tissue volume VF=VFc×BW (L or kg); liver volume VL=VLc×BW (L or Kg); small intestine volume VSi=VSic×BW (L or Kg); rapidly perfused tissue volume VR=VRc×BW (L or Kg); slowly perfused tissue volume VS=VSc×BW (L or Kg); arterial blood volume VA=VAc×BW (L or (kg); venous blood volume VV = VVc × BW (L or kg); cardiac output QC = 15 × BW ^ 0.74 (L / h); blood flow fraction to fat QFc = 0.09; blood flow fraction to the liver QLc = 0.25-QSic; blood flow fraction to the small intestine QSic = 0.183; blood flow fraction to rapidly perfused tissues QRc = 0.76-QLc-QSic; blood flow fraction to slowly perfused tissues QSc = 0.24-QFc; adipose tissue blood flow QF = QFc × QC (L / h); liver blood flow QL = QLc × QC (L / h); small intestine blood flow QSi = QSic × QC (L / h); rapidly perfused tissue blood flow QR = QRc × QC (L / h); slowly perfused tissue blood flow QS = QSc × QC (L / h).

[0027] According to a specific embodiment of the present invention, the physiological pharmacokinetic model that simulates the kinetic process of the drug in the body is established using a universal calculus equation solver, and the physiological pharmacokinetic model includes a mass conservation differential equation that describes the dynamic changes in the content of the test substance in each chamber structure every hour.

[0028] In the present invention, the physiologically based pharmacokinetic (PBK) model that simulates the kinetic process of a drug in vivo is a mathematical model that includes multiple calculus equations and is used to describe the change in the content of the drug in a living body over time.

[0029] In the present invention, a universal calculus equation solver is used to solve the mass conservation differential equation describing the dynamic changes in the test substance content in each compartment structure every hour, thereby obtaining the dynamic changes in the test substance content in each compartment structure every hour.

[0030] In the present invention, the universal calculus equation solver includes but is not limited to Berkeley Madonna, Matlab, Gastro, and Simcyp.

[0031] According to a specific embodiment of the present invention, the universal calculus equation solver is BerkeleyMadonna.

[0032] According to a specific embodiment of the present invention, the model for simulating the kinetic process of a drug in vivo comprises a plurality of submodules, each of which is used to describe the kinetic process of the test drug in different tissues or organs.

[0033] According to a specific embodiment of the present invention, the physiological pharmacokinetic model for simulating the kinetic process of a drug in vivo comprises the following submodules:

[0034] The gastrointestinal simulation module is used to calculate the concentration change of the test substance before and after it enters the gastrointestinal tract for reaction. It performs the following calculation formula: the dynamic change of the test substance metabolite content in the small intestine per hour AMSiM2' (µmol / h) = VmaxSiM2×CVSi / (KmSiM2+CVSi), where CVSi is the concentration of the test substance flowing into the venous blood after metabolism in the small intestine (µmol / L) = CSi / PIL, where the partition coefficient of the test substance between the small intestine and blood PIL = 1.04, and CSi (µmol / L) is the concentration of the test substance in the small intestine = ASi / VSi, ASi is the content of the test substance in the small intestine (µmol); VSi is the volume of the small intestine (L).

[0035] The initial content of the test substance in the small intestine is 0: Init ASi = 0;

[0036] The initial concentration of metabolites generated by the test substance in the small intestine is 0: Init AMSiM2 =0.

[0037] The liver simulation module is used to calculate the change in the concentration of the test substance before and after the test substance flows out of the gastrointestinal tract and enters the liver. It performs the calculation according to the following formula: the change in the content of the test substance metabolite in the liver per hour AMLM1' (µmol / h) = VmaxLM1×CVL / (KmLM1+CVL), where CVL is the concentration of the test substance flowing into the venous blood after metabolism in the liver (µmol / L) = CL / PLL, where the partition coefficient of the test substance between the liver and blood PLL = 1.04, and CL is the concentration of the test substance in the liver (µmol / L) = AL / VL, AL is the content of the test substance in the liver (µmol), and VL is the liver volume (L).

[0038] The initial content of the test substance in the liver is 0: Init AL = 0;

[0039] The initial level of metabolites produced by the test substance in the liver is 0: Init AMLM1 = 0.

[0040] The fat simulation module is used to calculate the concentration change of the test substance before and after it enters the fat. It performs the following calculation formula: the concentration of the test substance flowing into the venous blood after metabolism in the fat is CVF (µmol / L) = CF / PFL, where the distribution coefficient of the test substance between fat and blood PFL = 1.70, where CF is the concentration of the test substance in the fat (µmol / L) = AF / VF, AF is the content of the test substance in the fat (µmol), and VF is the fat volume (L).

[0041] The initial total amount of the test substance in the fat is 0: Init AF = 0.

[0042] The rapid perfusion tissue simulation module is used to calculate the concentration change of the test substance before and after entering the rapidly perfused tissue. It performs the following calculation formula: the concentration of the test substance flowing into the venous blood after metabolism in the rapidly perfused tissue is CVR (µmol / L) = CR / PRL, where the partition coefficient of the test substance between the rapidly perfused tissue and the blood is PRL = 1.04, where CR is the concentration of the test substance in the rapidly perfused tissue (µmol / L) = AR / VR, AR is the content of the test substance in the rapidly perfused tissue (µmol), and VR is the volume of the rapidly perfused tissue (L).

[0043] The initial content of the test substance in the rapidly perfused tissue is 0: Init AR = 0.

[0044] The slow perfusion tissue simulation module is used to calculate the concentration change of the test substance before and after entering the rapidly perfused tissue. It performs the following calculation formula: the concentration of the test substance flowing into the venous blood after metabolism in the slowly perfused tissue CVS (µmol / L) = CS / PSL, where the partition coefficient of the test substance between the slowly perfused tissue and the blood PSL = 1.02, where CS is the concentration of the test substance in the slowly perfused tissue (µmol / L) = AS / VS, AS is the content of the test substance in the slowly perfused tissue (µmol), and VS is the volume of the slowly perfused tissue (L).

[0045] The initial content of the test substance in the slowly perfused tissue is 0: Init AS = 0.

[0046] The blood simulation module is used to calculate the concentration change of the test substance in the blood, and performs the following calculation formula:

[0047] The concentration of the test substance in arterial blood is equal to the concentration in venous blood CA = CV;

[0048] The initial content of the test substance in venous blood is 0: Init AB = 0.

[0049] The change in the concentration of the test substance in the blood is CB = AB / VB, where AB is the test substance content in the blood and VB is the blood volume.

[0050] Draw a curve with time as the horizontal axis and CB as the vertical axis.

[0051] The initial maximum area under the curve of the test substance in blood is 0: init AUC = 0.

[0052] The dynamic change of the maximum area under the curve of the test substance in the blood is AUC' = CB×Time.

[0053] According to a specific embodiment of the present invention, the mass conservation differential equation describing the dynamic change of the test substance content in the small intestine per hour is shown in formula (1):

[0054] ASi' = QSi×(CB-CVSi) + Ka×AGI-AMSiM2'(1),

[0055] Among them, ASi' is the dynamic change of the content of the test substance in the small intestine per hour (µmol / h), QSi is the small intestinal blood flow (L / h), CB is the concentration of the test substance in the blood (µmol / L), CVSi is the concentration of the test substance flowing into the venous blood after metabolism in the small intestine (µmol / L), Ka is the absorption rate constant of the test substance in the intestine (h -1 ), AGI is the content of the test substance in the gastrointestinal tract (µmol), and AMSiM2' is the dynamic change of the content of the test substance metabolite in the small intestine per hour.

[0056] The mass conservation differential equation describing the dynamic change AL' of the test substance content in the liver per hour is shown in formula (2):

[0057] AL' = QL×CB +QSi×CVSi-(QL+QSi)×CVL-AMLM1'(2),

[0058] Among them, AL' is the dynamic change of the test substance content in the liver per hour (µmol / h), QL is the liver blood flow (L / h), QSi is the small intestine blood flow (L / h), CVSi is the concentration of the test substance flowing into the venous blood after metabolism in the small intestine (µmol / L), CVL is the concentration of the test substance flowing into the venous blood after metabolism in the liver (µmol / L), and AMLM1' is the dynamic change of the content of the test substance metabolite in the liver per hour.

[0059] According to a specific embodiment of the present invention, the dynamic changes in the content of the test substance metabolite in the small intestine per hour are expressed according to the following formula (3):

[0060] AMSiM2'=VmaxSiM2×CVSi / (KmSiM2+CVSi)(3), where VmaxSiM2 is the maximum enzyme reaction rate of the test substance in the small intestine (µmol / h), and KmSiM2 is the Michaelis constant of the test substance in in vitro small intestinal microsomes (µmol / L).

[0061] According to a specific embodiment of the present invention, the dynamic changes in the content of the metabolites of the test substance in the liver per hour are expressed according to the following formula (4):

[0062] AMLM1'=VmaxLM1×CVL / (KmLM1+CVL)(4), where VmaxLM1 is the maximum enzyme reaction rate of the test substance in the liver (µmol / h) and KmLM1 is the Michaelis constant of the test substance in in vitro liver microsomes (µmol / L).

[0063] According to a specific embodiment of the present invention, the dynamic change of the content of the test substance in fat per hour is expressed according to the following formula (5):

[0064] AF'=QF×(CB-CVF)(5),

[0065] Where AF' is the dynamic change of the test substance content in fat per hour (µmol / h), QF is the adipose tissue blood flow (L / h), and CVF is the concentration of the test substance flowing into venous blood after metabolism in fat (µmol / L).

[0066] The dynamic changes in the content of the test substance in the rapidly perfused tissue every hour are expressed according to the following formula (6):

[0067] AR'=QR×(CB-CVR)(6),

[0068] Wherein, AR' is the dynamic change of the test substance content in the rapidly perfused tissue per hour (µmol / h), QR is the blood flow of the rapidly perfused tissue (L / h), and CVR is the concentration of the test substance flowing into the venous blood after metabolism in the rapidly perfused tissue (µmol / L).

[0069] The dynamic changes of the test substance content in the slowly perfused tissue per hour are expressed according to the following formula (7):

[0070] AS'=QS×(CB-CVS)(7),

[0071] Wherein, AS' is the dynamic change of the test substance content in the slowly perfused tissue per hour (µmol / h), QS is the blood flow of the slowly perfused tissue (L / h), and CVS is the concentration of the test substance flowing into the venous blood after metabolism in the slowly perfused tissue (µmol / L).

[0072] The dynamic changes of the test substance content in the blood per hour are expressed according to the following formula (8):

[0073] AB'=(QF×CVF+(QL+QSi)×CVL+QR×CVR+QS×CVS-QC×CB)(8), where AB' is the dynamic change of the test substance content in the blood per hour (µmol / h), and QC is the cardiac output (L / h).

[0074] According to a specific embodiment of the present invention, the method further comprises drawing a time-concentration change curve of the test substance in the organism.

[0075] According to a specific embodiment of the present invention, the test substance is a Senecio drug. According to a specific embodiment of the present invention, the test substance is Senecio phenanthemine.

[0076] A second aspect of the present invention provides a device for predicting changes in drug content in an organism, which uses the method described in the first aspect of the present invention to predict changes in drug content in an organism, and the device comprises:

[0077] a data input module, for inputting kinetic parameters of a test substance, physicochemical property parameters of the test substance, and physiological parameters of an organism, wherein the in vitro kinetic parameters of the test substance include the maximum enzyme reaction velocity and Michaelis-Menten constant of the test substance in in vitro microsomes, the microsomes including liver microsomes and / or small intestinal microsomes, and the maximum enzyme reaction velocity and Michaelis-Menten constant of the test substance in in vitro microsomes are calculated based on in vitro microsome experiments of the test substance;

[0078] A data preprocessing module is used to calculate the maximum enzyme reaction rate of the test substance in the liver and small intestine based on the in vitro kinetic parameters of the test substance;

[0079] The model prediction module is used to predict the concentration change of the test substance in the organism by using the physiological pharmacokinetic model that simulates the kinetic process of the drug in the body as described in the first aspect of the present invention.

[0080] According to a specific embodiment of the present invention, the data preprocessing module performs the calculation of the following formula:

[0081] The maximum rate of the enzyme reaction of the test substance in the liver is VMaxLM1 (µmol / h) = VMaxLM1c / 1000×60×MPL×L×BW, or the maximum rate of the enzyme reaction of the test substance in the small intestine is VMaxSiM2 (µmol / h) = VMaxSiM2c / 1000×60×MPSi×Si×BW.

[0082] In some embodiments, the device further comprises an output module for outputting a threshold value of liver damage caused by the test substance.

[0083] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores an execution code, and when the executable code is executed by the processor, the processor executes the method described in the first aspect of the present invention.

[0084] A fourth aspect of the present invention provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method described in the first aspect of the present invention.

[0085] Beneficial effects:

[0086] The advantages of the present invention are that it can not only solve the shortcomings of animal experiments that are time-consuming and expensive, but also effectively solve the problem that in vitro cell and tissue culture cannot simulate the kinetic processes of absorption, distribution, metabolism and excretion of the test substance, and can effectively solve the uncertainty problem of extrapolating in vitro toxic doses to in vivo toxic doses. If only animal experiments are performed, especially for drugs with relatively insufficient toxicity data or information, it is difficult to successfully obtain the toxicity characteristics and toxicity-producing doses in mice in a single experiment. In vitro cell or tissue culture cannot obtain the kinetic processes of the test substance, resulting in the unknown concentration at which it actually exerts its toxic effect in vivo. To address the shortcomings of in vivo animal experiments and in vitro toxicity experiments, the present invention uses mouse liver / intestinal microsomes to build a kinetic reaction system that can simulate the metabolic transformation of the test substance in the liver / intestinal tract. The physiological parameters of the mouse, the physicochemical parameters of the test substance, and the kinetic parameters are computer-modeled. The model can simulate and analyze the absorption, distribution, metabolism and excretion processes of the test substance in the mouse liver and body, the concentration in various organs, and the concentration changes in the blood, etc., which are in vivo toxicity characteristics. On this basis, the model constructed by the present invention can be used to correct and analyze the toxicity data of the test substance obtained from in vitro tissue or cell culture, solving the uncertainty problem of extrapolating in vitro toxicity doses to in vivo toxicity doses. Therefore, by combining biological means with computer modeling, the present invention effectively solves the problems of long-term and high-cost acute hepatotoxicity in animals, and effectively solves the problem that in vitro toxicity experiments cannot simulate the kinetic processes of the test substance's absorption, distribution, metabolism, and excretion in the body.

[0087] The key to this invention lies in the calculus equation developed to predict liver toxicity in mice. To successfully develop this mathematical formula, the present invention established an in vitro liver kinetic reaction system using mouse liver microsomes. Successful development of this reaction system requires optimizing the time, concentration, and liver microsome concentration required for the test substance reaction to reach saturation, thereby constructing a model that accurately predicts toxic effects in the test subjects. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 The model of the present invention predicts the toxic dose-effect relationship curve of seneciophylline in mice;

[0089] Figure 2 This is a schematic diagram of the principle process of the present invention;

[0090] Figure 3 It is an organ compartment model;

[0091] Figure 4 This is a comparison chart of the prediction results of the model of the present invention and the results of mouse experiments. DETAILED DESCRIPTION

[0092] Before further describing the specific embodiments of the present invention, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments described below; it should also be understood that the terms used in the examples of the present invention are for describing specific embodiments rather than for limiting the scope of protection of the present invention.

[0093] When the embodiments provide numerical ranges, it should be understood that, unless otherwise specified in the present invention, both endpoints of each numerical range and any numerical value between the two endpoints may be selected. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those generally understood by those skilled in the art. In addition to the specific methods, equipment, and materials used in the embodiments, according to the understanding of the prior art by those skilled in the art and the description of the present invention, any methods, equipment, and materials of the prior art similar or equivalent to the methods, equipment, and materials described in the embodiments of the present invention may also be used to implement the present invention.

[0094] Unless otherwise stated, all experimental methods, detection methods, and preparation methods not described in detail herein are based on conventional techniques in the art.

[0095] The present invention uses mouse liver microsomes to construct a kinetic reaction system of the test substance in vitro, allowing the test substance to fully react in the liver, and after optimizing different reaction times, liver microsome concentrations and test substance concentrations, the optimal reaction system for conducting in vitro kinetic experiments using liver microsomes is determined. Through the constructed liver microsome reaction system, the metabolic rate of the test substance in the liver is calculated, and the metabolic kinetic parameters of the test substance are written into a mathematical formula code using a computer, and then solved to finally obtain the kinetic process of the test substance. The principle and process of the present invention are as follows Figure 2 shown.

[0096] Example 1

[0097] 1. Use mouse microsomes to establish an in vitro kinetic reaction system

[0098] C57 mouse liver and small intestinal microsomes were purchased from CHI Scientific Inc. (Maynard, MA, USA). In vitro incubation reaction systems for liver and small intestine were established, respectively, containing 0.1 M potassium phosphate buffer (pH 7.4), NADPH regeneration system (5 mM MgCl2, 1 mM NADP + C57 mouse liver or intestinal microsomes containing 10 mM glucose-6-phosphate, 2 U / mL glucose-6-phosphate dehydrogenase, 2 mM GSH, and 0.5 mg protein / mL were mixed with the test compound (seneciophylline) at concentrations ranging from 2 to 500 µM. The final reaction volume was 100 µL. The reaction was incubated in a 37°C water bath for 30 minutes for liver microsomes and 2 hours for small intestinal microsomes. The reaction was then stopped by adding 100 µL of ice-cold methanol. Finally, the reaction was centrifuged at 15,000 g for 10 minutes, and the supernatant was collected and analyzed by LC-MS / MS. The reaction was described by the Michaelis-Menten equation: V = Vmax × [S] / (Km + [S]). The maximum enzyme reaction velocity and Michaelis-Menten constant in the microsomes were calculated based on the time-dependent changes in test compound concentration obtained from in vitro microsomal experiments.

[0099] 2. Body

[0100] In order to accurately simulate the kinetic response of the test substance in mice, the present invention divides the model into two major parts: one is the body part, and the other is the characteristic part of the test substance. The body part mainly describes the physiological characteristics of the experimental animal to be simulated by the model, including: body weight, blood perfusion rate, tissue and organ volume and percentage of body weight, etc. Specifically:

[0101] Mouse body weight BW = 0.0250 (Kg);

[0102] adipose tissue fraction VFc = 0.07;

[0103] liver fraction VLc = 0.034;

[0104] Small bowel fraction VSic = 0.014;

[0105] Arterial blood fraction VAc = 0.0185;

[0106] Venous blood fraction VVc = 0.0555;

[0107] Rapidly perfused tissue fraction VRc = 0.09-VLc-VSic;

[0108] Slowly perfused tissue fraction VSc = 0.82-VFc;

[0109] Adipose tissue volume VF = VFc × BW (L or kg);

[0110] Liver volume VL = VLc × BW;

[0111] Small intestinal volume VSi = VSic × BW;

[0112] Rapidly perfused tissue volume VR = VRc × BW;

[0113] Slowly perfused tissue volume VS = VSc × BW;

[0114] Arterial blood volume VA = VAc × BW;

[0115] Venous blood volume VV = VVc × BW;

[0116] Cardiac output QC = 15×BW^0.74 (L / h) (Brown, RP, Delp, MD, Lindstedt,SL, Rhomberg, LR, Beliles, RP(1997). Physiological Parameter Values ​​forPhysiologically Based Pharmacokinetic Models. Toxicol Ind Health. 13, 407-484.);

[0117] The fraction of blood flow to fat, QFc = 0.09;

[0118] The fraction of blood flowing to the liver is QLc = 0.25 -QSic;

[0119] The fraction of blood flow to the small intestine, QSic = 0.183;

[0120] The fraction of blood flowing to rapidly perfused tissues QRc = 0.76-QLc-QSic;

[0121] The fraction of blood flowing to slowly perfused tissues is QSc = 0.24-QFc;

[0122] Adipose tissue blood flow QF = QFc × QC (L / h);

[0123] Liver blood flow QL = QLc × QC;

[0124] Small intestinal blood flow QSi = QSic × QC;

[0125] Rapid perfusion tissue blood flow QR = QRc × QC;

[0126] Slowly perfused tissue blood flow QS = QSc×QC.

[0127] 3. Metabolic characteristics of the test substance

[0128] According to the principle of mass conservation, the mass balance differential equations of the liver and small intestine are established to simulate the metabolic process of the test substance in the liver and calculate the inflow and outflow of the test substance in the liver and small intestine. Specifically, the metabolic process in the liver can be described as:

[0129] The intestinal absorption rate constant of the test substance is Ka = 0.55 / h (Hou, TJ, Zhang, W., Xia, K., Qiao, XB, Xu, XJ (2004). ADME evaluation in drug discovery. 5. Correlation of Caco-2 permeation with simple molecular properties. J Chem InfComput Sci. 44, 1585-1600); log Papp = -5.469 + 0.236 logP

[0130] Each gram of mouse liver contains 35 mg of hepatic microsomal protein (MPL) (Malekinejad, H., Maas-Bakker, RF, Fink-Gremmels, J. (2005). Bioactivation of zearalenone by porcine hepatic biotransformation. Veterinary Research, 36(5-6), 799-810.).

[0131] Liver weight index L=VL C ×1000(g / kg bw);

[0132] The maximum enzyme reaction rate of the test substance in liver microsomes is VmaxLM1c=7.948 (nmol min -1 (mg protein) -1 );

[0133] The maximum enzyme reaction rate of the test substance in the liver is VMaxLM1 (µmol / h) = VMaxLM1c / 1000 × 60 × MPL × L × BW;

[0134] The Michaelis constant for the metabolism of the test substance in the liver was KmLM1 = 166.3 (µmol / L);

[0135] In the present invention, the Michaelis constant of the test substance's metabolism in the liver is characterized by its Michaelis constant in in vitro liver microsomes.

[0136] Small intestine:

[0137] Each gram of mouse small intestine contains 20.6 mg of microsomal protein (MPSi = 20.6 mg / g small intestine). (Cubitt, HE, Houston, JB, Galetin, A. (2011). Prediction of human drug clearance by multiple metabolic pathways: integration of hepatic and intestinal microsomal and cytosolic data. Drug metabolism and disposition, 39(5), 864-873.)

[0138] Small intestine weight index Si =VSic×1000 (g / kg bw);

[0139] The maximum enzyme reaction rate of the test substance in the small intestinal microsomes is VmaxSiM2c = 0.06577 (nmol min -1 (mg protein) -1 ) ;

[0140] The maximum enzyme reaction rate of the test substance in the small intestine is VMaxSiM2 (µmol / h) = VMaxSiM2c / 1000×60×MPSi×Si×BW;

[0141] The Michaelis constant for the metabolism of the test substance in the small intestine was KmSiM2 = 434.2 (µmol / L);

[0142] In the present invention, the Michaelis constant of the test substance's metabolism in the small intestine is characterized by its Michaelis constant in in vitro small intestinal microsomes.

[0143] After the metabolic process of the test substance in the liver and small intestine is described, a calculus equation is written based on the principle of conservation of mass to describe the concentration changes of the test substance after it enters other organs and tissues through the bloodstream and undergoes metabolic reactions and then flows into the blood. Specifically:

[0144] Fat:

[0145] Dynamic changes in the content of the test substance in fat per hour AF' = QF × (CB-CVF) (unit: µmol / h);

[0146] The initial content of the test substance in fat is 0 Init AF = 0;

[0147] The concentration of the test substance in fat CF = AF / VF;

[0148] The concentration of the test substance flowing into venous blood after metabolism in fat is CVF = CF / PFL.

[0149] Rapid perfusion of tissue:

[0150] The dynamic change of the test substance content in the rapidly perfused tissue per hour AR' = QR × (CB-CVR), unit: µmol / h;

[0151] The initial content of the test substance in the rapidly perfused tissue is 0 Init AR = 0;

[0152] The concentration of the test substance in the rapidly perfused tissue is CR = AR / VR;

[0153] The concentration of the test substance flowing into the venous blood after metabolism in the rapidly perfused tissue is CVR = CR / PRL.

[0154] Slowly perfused tissue:

[0155] The dynamic changes of the test substance content in the slow perfused tissue per hour AS' = QS × (CB-CVS), unit: µmol / h;

[0156] The initial content of the test substance in the slowly perfused tissue is 0 Init AS = 0;

[0157] The concentration of the test substance in the slowly perfused tissue is CS = AS / VS;

[0158] The concentration of the test substance in the venous blood after metabolism in the slowly perfused tissue is CVS = CS / PSL.

[0159] After the above metabolic process characteristics are described, the model operation process is set up and the various organs and tissues are connected in series through mathematical formulas, specifically:

[0160] Model run settings:

[0161] The molecular weight of the test substance is MWL = 333.38 (g / mol);

[0162] Assume that the oral exposure content of the test substance GDOSE = 10 (mg / kg bw);

[0163] The units of oral exposure to the test substance are converted into the concentration units of in vitro exposure: ODOSE = GDOSE × 1E -3 / MWL×1E6 (µmol / kg bw);

[0164] In vitro exposure concentration DOSE = ODOSE × BW (µmol);

[0165] Model running start time Starttime = 0(h -1 );

[0166] Model running end time Stoptime = 24(h -1 );

[0167] Description of the metabolic process of the test substance in various organs and tissues through the bloodstream after oral administration:

[0168] Oral exposure:

[0169] Dynamic changes in the content of the test substance in the gastrointestinal tract per hour AGI' (µmol / h) = -ka×AGI;

[0170] The initial concentration of the test substance in the gastrointestinal tract is equal to the in vitro exposure concentration

[0171] Init AGI = dose;

[0172] Dynamic changes of the test substance content in the liver per hour AL' = QL×CB +QSi×CVSi - (QL+QSi)×CVL- AMLM1';

[0173] The concentration of the test substance flowing into the venous blood after liver metabolism is CVL = CL / PLL;

[0174] Dynamic changes in the content of test substance metabolites in the liver every hour: AMLM1' = VmaxLM1×CVL / (KmLM1 +CVL);

[0175] Dynamic changes in the content of the test substance in the small intestine per hour: ASi' = QSi×(CB -CVSi) + ka×AGI -AMSiM2';

[0176] The concentration of the test substance flowing into the venous blood after intestinal metabolism is CVSi = CSi / PIL;

[0177] Dynamic changes in the content of test substance metabolites in the intestine every hour: AMSiM2'= VmaxSiM2×CVSi / (KmSiM2 + CVSi);

[0178] The concentration of the test substance in arterial blood is CA = CV;

[0179] Dynamic changes in the test substance content in blood per hour AB' = (QF × CVF + (QL + QSi) × CVL + QR × CVR + QS × CVS - QC × CB);

[0180] The dynamic change of the area under the curve of the test substance in the blood every hour is AUC' = CB×Time.

[0181] After the metabolic calculation formulas for each organ and tissue are established, they are integrated into the Berkeley Madonna algorithm platform to implement the above simulation process. The algorithm platform includes the following modules:

[0182] Data input module: used to input the kinetic parameters, physicochemical properties and physiological parameters of the test substance and mice;

[0183] Data initial processing module: used to realize the extrapolation of in vitro liver microsome and small intestinal microsome reaction concentrations to the concentrations in the liver and small intestine of mice. It performs the calculation of the following formula:

[0184] Each gram of liver contains 35 mg of microsomes, MPL=35 (mg / g liver);

[0185] Liver weight index L=VL C ×1000(g / kg bw);

[0186] The maximum enzyme reaction rate of the test substance in liver microsomes is VmaxLM1c=7.948 (nmol min -1 (mg protein) -1 );

[0187] The maximum enzyme reaction rate of the test substance in the liver is VMaxLM1 (µmol / h) = VMaxLM1c / 1000 × 60 × MPL × L × BW;

[0188] The Michaelis constant for the metabolism of the test substance in the liver was KmLM1 = 166.3 (µmol / L);

[0189] Each gram of small intestine contains 20.6 mg of microsomes, MPSi=20.6 (mg / g small intestine);

[0190] Small intestine weight index Si = VSiC × 1000 (g / kg bw);

[0191] The maximum enzyme reaction rate of the test substance in the small intestinal microsomes is VmaxSiM2c = 0.06577 (nmol min -1 (mg protein) -1 );

[0192] The maximum enzyme reaction rate of the test substance in the small intestine is VMaxSiM2 (µmol / h) = VMaxSiM2c / 1000×60×MPSi×Si×BW;

[0193] The Michaelis-Menten constant for the metabolism of the test substance in the small intestine was KmSiM2 = 434.2 (µmol / L).

[0194] The data simulation module includes the following submodules:

[0195] Gastrointestinal simulation is used to calculate the change in the concentration of the test substance before and after the test substance enters the gastrointestinal tract for reaction. It performs the following calculation formula:

[0196] The initial content of the test substance in the small intestine is 0 Init ASi = 0,

[0197] The initial content of metabolites generated by the test substance in the small intestine is 0 Init AMSiM2 = 0,

[0198] The concentration of the test substance in the small intestine CSi = ASi / VSi,

[0199] The concentration of the test substance in the venous blood after metabolism in the small intestine is CVSi = CSi / PIL,

[0200] The dynamic changes in the content of metabolites in the small intestine every hour AMSiM2'= VmaxSiM2×CVSi / (KmSiM2 +CVSi).

[0201] The liver simulation module is used to calculate the change in the concentration of the test substance before and after the test substance flows out of the gastrointestinal tract and enters the liver. It performs the following calculation formula:

[0202] The initial content of the test substance in the liver is 0 Init AL = 0,

[0203] The initial level of test substance metabolites generated in the liver is 0 Init AMLM1 = 0,

[0204] The concentration of the test substance in the liver CL = AL / VL,

[0205] The concentration of the test substance in the venous blood after metabolism in the liver is CVL = CL / PLL.

[0206] The dynamic changes in the content of metabolites in the liver every hour are AMLM1' = VmaxLM1×CVL / (KmLM1 + CVL).

[0207] The fat simulation module is used to calculate the concentration change of the test substance before and after it enters the fat. It performs the following calculation formula:

[0208] The initial content of the test substance in fat is 0 Init AF = 0,

[0209] The concentration of the test substance in fat CF = AF / VF,

[0210] The concentration of the test substance in fat that flows into venous blood after metabolism is CVF = CF / PFL.

[0211] The rapid perfusion tissue simulation module is used to calculate the concentration change of the test substance before and after it enters the rapid perfusion tissue. It performs the following calculation formula:

[0212] The initial content of the test substance in the rapidly perfused tissue is 0 Init AR = 0,

[0213] The concentration of the test substance in the rapidly perfused tissue is CR = AR / VR,

[0214] The concentration of the test substance flowing into the venous blood after metabolism in the rapidly perfused tissue is CVR = CR / PRL.

[0215] The slow perfusion tissue simulation module is used to calculate the concentration change of the test substance before and after it enters the fast perfusion tissue. It performs the following calculation formula:

[0216] The initial content of the test substance in the slowly perfused tissue is 0 Init AS = 0,

[0217] The concentration of the test substance in the slowly perfused tissue is CS = AS / VS,

[0218] The concentration of the test substance in the venous blood after metabolism in the slowly perfused tissue is CVS = CS / PSL.

[0219] The blood flow simulation module is used to calculate the concentration change of the test substance in the blood, and it performs the following calculation formula:

[0220] The concentration of the test substance in arterial blood is equal to the concentration in venous blood CA = CV,

[0221] The initial content of the test substance in the blood is 0 Init AB = 0,

[0222] The concentration of the test substance in the blood CB = AB / VB,

[0223] The initial maximum area under the curve of the test substance in the blood is 0 init AUC = 0,

[0224] The dynamic change of the maximum area under the curve of the test substance in the blood is AUC' = CB×Time.

[0225] The result output module is used to output the predicted concentration of the test substance in various organs, tissues and blood after entering the body and the toxicity threshold that causes acute liver damage in mice. It is calculated by the following formulas:

[0226] Dynamic changes in the content of the test substance in the small intestine ASi' = QSi×(CB -CVSi) + ka×AGI -AMSiM2',

[0227] Dynamic changes in the content of the test substance in the liver AL' = QL×CB +QSi×CVSi - (QL+QSi)×CVL -AMLM1',

[0228] Dynamic changes in the content of the test substance in fat: AF' = QF × (CB-CVF),

[0229] Dynamic changes in the content of the test substance in rapidly perfused tissues: AR' = QR×(CB-CVR),

[0230] The dynamic changes of the content of the test substance in the slow perfusion tissue are AS' = QS×(CB-CVS),

[0231] The dynamic changes of the test substance content in the blood are AB' = (QF×CVF + (QL+QSi)×CVL + QR×CVR+ QS×CVS - QC×CB).

[0232] Figure 3 This is the compartment model diagram of the physiologically based pharmacokinetic (PBK) model.

[0233] After running the established model using Berkeley Madonna, the time-concentration change curve of the drug in the liver in vivo is obtained. The values ​​under this curve are put into the BMD (Benchmark Dose) software for calculation and fitting, and the threshold value of drug-induced hepatotoxicity can be obtained.

[0234] Upload the data to the BMD calculation software and set the parameters. The BMD software will automatically perform a fitting to obtain the dose-effect curve of seneciophylline in mice.

[0235] Example 2 Verification Implementation

[0236] Model verification includes two aspects: one is the verification of the model parameters themselves, and the other is the verification of the accuracy of the model prediction results.

[0237] The validation of the model parameters is mainly carried out through sensitivity analysis. During the model operation, the value of each parameter is increased by 5% and then compared with the initial value. If the absolute value is greater than 0.1, and the error prompt of the model is less than 1, it means that the parameter setting is highly sensitive. The formula for sensitivity test is:

[0238] SC = ((C′-C) / (P′-P))×(P / C).

[0239] The model of the present invention was validated using a pyrrolizidine alkaloid, seneciodine. Following the aforementioned steps and parameter entry, all relevant parameters for seneciodine were entered and executed into a prepared mathematical formula, and the model was then run. The SC value of each parameter was analyzed according to the sensitivity test formula, resulting in a sensitivity range of 0.3-1.0 for the model of the present invention, and an error value of 0.1 for the run, indicating that the model parameters were correctly set and the model operated normally.

[0240] The sensitivity value of the model of the present invention ranges from 0.3 to 1.0, and the error value is 0.1. The dose-effect relationship curve of the toxicity of seneciophylline in mice predicted by this model is as follows: Figure 1 shown.

[0241] To validate the model's predictions, the results were compared with the results of a toxicity experiment on mice fed different concentrations of senecioline. The predicted results were compared with the results of an acute hepatotoxicity experiment on C57 BL / 6J male mice exposed to a single oral dose of senecioline (Table 1).

[0242] Table 1 Acute hepatotoxicity test of seneciodine in mice

[0243]

[0244] Source:

[0245] Wang, W., Yang, X., Chen, Y., Ye, X., Jiang, K.,&Wang, Z. (2020). Seneciphylline, a main pyrrolizidine alkaloid in Gynura japonica, induceshepatotoxicity in mice and primary hepatocytes via activating mitochondria‐mediated apoptosis. Journal of Applied Toxicology, 40(11), 1534-1544.

[0246] Michael J. Clayton. (2023). Relative hepatotoxicity, carcinogenicity, and toxic genomics of select dehydropyrrolizidine alkaloids in mice. PP:124.

[0247] To verify the accuracy of the model of the present invention in predicting acute hepatotoxicity, the model of the present invention was used to predict the threshold value of acute liver damage in mice caused by the test substance. The predicted result showed: 11.6-33.7 mg / kg bw / day. In contrast to the acute hepatotoxicity experiment in mice, the threshold value of liver cell necrosis observed in Experiment 1 was: 7-70 mg / kg bw / day, and the dose when inflammation of the mouse liver was observed in Experiment 2 was: 2-4 mg / kg bw / day. The results of the acute hepatotoxicity experiment in mice were compared with the results of the hepatotoxicity threshold predicted by the model of the present invention ( Figure 4). The toxicity threshold ranges predicted by the model of the present invention all fall within the dose range obtained from the mouse animal experiment, and the difference is within 10 times. According to the World Health Organization's "Principles for Assessing the Risks of Chemical Exposure to Human Health", when the model extrapolation results deviate from the mouse in vivo experimental results by 3 to 10 times, the accuracy of the model prediction results is deemed acceptable. (International Programme on Chemical Safety (IPCS). Principles for the assessment of risks to human health from exposure to chemicals. Geneva: World Health Organization, 1999). The dose of the test substance that is predicted to cause acute hepatotoxicity by the present invention completely falls within the range of the dose that causes hepatotoxicity in animal experiments. The prediction results are accurate and reliable, and can achieve the purpose of replacing animal acute hepatotoxicity. This shows that the alternative model established by the present invention can accurately predict the acute hepatotoxicity threshold of mice and can completely replace the acute hepatotoxicity experiment of mice. Comparison between the prediction results of the model of the present invention and the experimental results of mice. Figure 4 shown.

[0248] The above description of the embodiments is intended to facilitate understanding and use of the invention by those skilled in the art. It will be apparent that those skilled in the art can readily make various modifications to these embodiments and apply the general principles described herein to other embodiments without requiring inventive effort. Therefore, the present invention is not limited to the above-described embodiments. Improvements and modifications made by those skilled in the art based on the disclosure of the present invention, without departing from the scope of the present invention, should be within the scope of protection of the present invention.

Claims

1. A method for predicting changes in drug content in an organism, characterized in that: The method includes: Obtaining in vitro kinetic parameters of the test substance, physicochemical property parameters of the test substance, and physiological parameters of the organism, calculating the maximum enzyme reaction rate of the test substance in the liver and / or small intestine based on the in vitro kinetic parameters of the test substance, and then predicting the content change of the test substance in the organism using a physiological pharmacokinetic model that simulates the kinetic process of the drug in vivo; the test substance is a Senecio drug; Wherein, the compartment structures of the physiological pharmacokinetic model simulating the kinetic process of the drug in vivo include the gastrointestinal tract, small intestine, liver, slowly perfused tissue, rapidly perfused tissue, fat and / or blood; The in vitro kinetic parameters of the test substance include the maximum enzyme reaction rate of the test substance in in vitro microsomes and the Michaelis constant of the test substance in in vitro microsomes; The changes in the content of the test substance in the organism include changes in the content of the test substance in the small intestine, liver, fat, fast perfusion tissue, slow perfusion tissue and / or blood; The microsomes include liver microsomes and / or small intestinal microsomes; The maximum enzyme reaction rate of the test substance in in vitro microsomes and the Michaelis constant of the test substance in in vitro microsomes are calculated based on in vitro microsome experiments of the test substance; The physiological pharmacokinetic model includes a mass conservation differential equation that describes the dynamic changes in the content of the test substance in each compartment structure every hour; The mass conservation differential equation describing the dynamic changes in the test substance content in the small intestine every hour is shown in formula (1): ASi' = QSi×(CB-CVSi) + Ka×AGI-AMSiM2' (1), Among them, ASi' is the dynamic change of the test substance content in the small intestine per hour, in µmol / h, QSi is the small intestinal blood flow, in L / h, CB is the concentration of the test substance in the blood, in µmol / L, CVSi is the concentration of the test substance flowing into the venous blood after metabolism in the small intestine, in µmol / L, Ka is the absorption rate constant of the test substance in the intestine, in h -1 , AGI is the content of the test substance in the gastrointestinal tract, the unit is μmol, AMSiM2' is the dynamic change of the content of the test substance metabolite in the small intestine per hour, the unit is μmol / h; The mass conservation differential equation describing the dynamic changes in the test substance content in the liver every hour is shown in formula (2): AL' = QL×CB +QSi×CVSi-(QL+QSi)×CVL-AMLM1' (2), Among them, AL' is the dynamic change of the test substance content in the liver per hour, in µmol / h, QL is the liver blood flow, in L / h, QSi is the small intestine blood flow, in L / h, CVSi is the concentration of the test substance flowing into the venous blood after metabolism in the small intestine, in µmol / L, CVL is the concentration of the test substance flowing into the venous blood after metabolism in the liver, in µmol / L, and AMLM1' is the dynamic change of the test substance metabolite content in the liver per hour, in µmol / h.

2. The method according to claim 1, characterized in that The dynamic changes in the content of the test substance metabolites in the small intestine per hour are expressed according to the following formula (3): AMSiM2'=VmaxSiM2×CVSi / (KmSiM2+CVSi) (3); Wherein, VmaxSiM2 is the maximum enzyme reaction rate of the test substance in the small intestine, in µmol / h; KmSiM2 is the Michaelis constant of the test substance in in vitro small intestinal microsomes, in µmol / L; The dynamic changes in the content of the test substance metabolites in the liver per hour are expressed according to the following formula (4): AMLM1'=VmaxLM1×CVL / (KmLM1+CVL) (4); Wherein, VmaxLM1 is the maximum enzyme reaction velocity of the test substance in the liver, in µmol / h, and KmLM1 is the Michaelis constant of the test substance in in vitro liver microsomes, in µmol / L; The dynamic changes of the test substance content in fat per hour are expressed according to the following formula (5): AF'=QF×(CB-CVF) (5), Wherein, AF' is the dynamic change of the test substance content in fat per hour, unit is μmol / h, QF is the adipose tissue blood flow, unit is L / h, CVF is the concentration of the test substance flowing into venous blood after metabolism in fat, unit is μmol / L; The dynamic changes in the content of the test substance in the rapidly perfused tissue every hour are expressed according to the following formula (6): AR'=QR×(CB-CVR) (6), Wherein, AR' is the dynamic change of the test substance content in the rapidly perfused tissue per hour, in µmol / h, QR is the blood flow of the rapidly perfused tissue, in L / h, and CVR is the concentration of the test substance flowing into the venous blood after metabolism in the rapidly perfused tissue, in µmol / L; The dynamic changes of the test substance content in the slowly perfused tissue per hour are expressed according to the following formula (7): AS'=QS×(CB-CVS) (7), Wherein, AS' is the dynamic change of the test substance content in the slowly perfused tissue per hour, in µmol / h, QS is the blood flow of the slowly perfused tissue, in L / h, and CVS is the concentration of the test substance flowing into the venous blood after metabolism in the slowly perfused tissue, in µmol / L; The dynamic changes of the test substance content in the blood per hour are expressed according to the following formula (8): AB'=(QF×CVF+(QL+QSi)×CVL+QR×CVR+QS×CVS-QC×CB) (8); Wherein, AB' is the dynamic change of the test substance content in the blood per hour, in µmol / h, and QC is the cardiac output, in L / h.

3. The method according to claim 1, characterized in that The method further comprises drawing a time-concentration change curve of the test substance in the organism.

4. A device for predicting changes in drug content in a living body, which uses the method according to any one of claims 1 to 3 to predict changes in drug content in a living body, characterized in that: The device comprises: a data input module, for inputting in vitro kinetic parameters of a test substance, physicochemical property parameters of the test substance, and physiological parameters of an organism, wherein the in vitro kinetic parameters of the test substance include the maximum enzyme reaction velocity of the test substance in in vitro microsomes and the Michaelis constant of the test substance in in vitro microsomes, the microsomes including liver microsomes and / or small intestinal microsomes, and the maximum enzyme reaction velocity and Michaelis constant of the test substance in microsomes are calculated based on in vitro microsome experiments of the test substance; A data preprocessing module, used to calculate the maximum enzyme reaction rate of the test substance in the liver and / or small intestine based on the in vitro kinetic parameters of the test substance; The model prediction module is used to predict the content changes of the test substance in the organism by simulating the physiological pharmacokinetic model of the drug's kinetic process in the body.

5. The device according to claim 4, characterized in that The device also includes an output module for outputting the threshold value of liver damage caused by the test substance.

6. An electronic device comprising a processor and a memory, characterized in that: The memory stores an execution code, and when the executable code is executed by the processor, the processor executes the method according to any one of claims 1 to 3.

7. A computer-readable storage medium having executable code stored thereon, characterized in that: When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method according to any one of claims 1 to 3.

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