Methods for determining the efficacy of treatment with a drug combination in a subject diagnosed with a disease and methods for classifying the utility of a drug combination in the treatment of said subject

Abstract

The present invention relates to methods and systems for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, and to methods and systems for classifying the utility of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease. Also disclosed herein is a method for improving the accuracy of estimating the synergistic effect of a drug combination.

Description

Field of the Invention

[0001] The present invention relates to a method for determining the efficacy of treatment with a drug combination in a subject diagnosed with a disease. The present invention also relates to a method for classifying the utility of a drug combination in the treatment of a subject diagnosed with a disease. The present invention also relates to a method for improving the accuracy of estimating the synergistic effect of a drug combination. Background of the Invention

[0003] Drug combinations are generally used to treat diseases, particularly cancers of hematopoietic and lymphoid tissues, such as acute myeloid leukemia. However, not all such drug combinations show the same degree of synergy in all patients and therefore show the same efficacy to the disease. Therefore, a problem of the present invention is to provide a method for determining the efficacy of a given drug combination for treating a specific disease in a given subject (i.e., the sensitivity or drug resistance of the disease to the drug combination in the subject), thereby providing an indication of the degree of synergy provided by the drug combination for the disease in the subject.

[0004] Additionally, since various drug combinations are generally available for treating any given disease, one problem of the present invention is to provide a method for classifying different drug combinations according to their efficacy or effectiveness in treating the disease in a given subject, thereby providing a clinician or subject with treatment guidance as to the drug combination with the greatest or least effectiveness in treating the disease in the subject.

[0005] Furthermore, since clinical trials of a given drug combination for a given disease are required to obtain its regulatory approval, a problem of the present invention is also to provide a companion diagnostic (CDx) biomarker for said drug combination, which allows the selection of subjects (patients) in which said disease is susceptible to treatment for inclusion in said trial.

[0006] Another problem of the present invention is to ensure that each of the above methods exhibits high accuracy and reduced variability, so that each method provides reliable analysis and improved correlation with clinical output, while also minimizing the amount of tissue sampled from each subject. SUMMARY OF THE INVENTION

[0008] The present invention relates to a method for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, wherein the method comprises the following steps:

[0009] (a) separating a tissue sample obtained from the subject into subsamples;

[0010] (b) perform the following steps:

[0011] (i) incubating the subsample in the presence of said drug A at a concentration X for a time T ranging from 2 to 168 hours; and

[0012] (ii) repeating step (b)(i) an additional (N-1) times, each time using a different subsample, using a different value of X from the value of X used in the previous repetition of step (b)(i);

[0013] wherein N is an integer selected from 5 to 10, inclusive;

[0014] as well as

[0015] (iii) incubating the subsample in the presence of the drug B at a concentration Y for the time T; and

[0016] (iv) repeating step (b)(iii) an additional (M-1) times, each time using a different subsample, using a different value of Y from the value of Y used in the previous repetition of step (b)(iii);

[0017] wherein M is an integer selected from 5 to 10, inclusive;

[0018] as well as

[0019] (v) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug B, wherein

[0020] - the concentration of drug A is a concentration X corresponding to X obtained in said population from each subject diagnosed with said disease 50,A The percentile value P of the distribution of values Hα,A The concentration at which the percentile value P Hα,A Calculated by formula (A):

[0021] P Hα,A =cos(α°)x H

[0022] (A)

[0023] in:

[0024] H corresponds to the reference percentile of the group selected from:

[0025] and 90, where:

[0026] r is an integer selected from 2 to (R-1), including 2 and R-1;

[0027] α is in degrees and is calculated as follows:

[0028]

[0029] in:

[0030] w is an integer selected from 1 to W, including 1 and W;

[0031] - the concentration of drug B is a concentration Y corresponding to Y obtained in the population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values Hα,B The concentration at which the percentile value P Hα,B Calculated by formula (B):

[0032] P Hα,B =cos(90°–α°)x H

[0033] (B)

[0034] (vi) repeating step (b)(v) an additional (R-1) times, each time using a different subsample, using a different value of H than the value of H used in the previous repetition of step (b)(v), and using the same value of w used in step (b)(v);

[0035] (vii) repeating steps (b)(v) and (b)(vi) an additional (W-1) times, each time using a different subsample, using a value of w that is different from the value of w used in the previous repetition of steps (b)(v) and (b)(vi);

[0036] in;

[0037] R is an integer selected from 3 to 10, inclusive;

[0038] W is an integer selected from 3 to 10, inclusive;

[0039] And among them:

[0040] ·X 50,A is the concentration of drug A that exerts half of its maximal activity in the subject estimated according to step (e)(i) below;

[0041] ·Y 50,B is the concentration of drug B that exerts half of its maximal activity in the subject estimated according to step (e)(i) below;

[0042] as well as

[0043] (viii) incubating the subsample for the time T;

[0044] (c) adding at least one marker to each subsample incubated in step (b) to identify at least one cell type therein (CT i );

[0045] (d) counting the number of viable cells (LCTi) of each cell type identified in step (c) that remain after incubating each subsample according to step (b);

[0046] (e) for each cell type identified in step (c), determine:

[0047] (i) Pharmacodynamic parameter values, including X 50,A Value, LCTi 0,A Value, E max,A Value, γ A Value, Y 50,B Value, LCTi 0,B Value, E max,B Value and / or gamma B Values, where:

[0048] ·The X 50，A Value, LCTi 0,A Value, E max,A Value and γ A The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (I) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(i) and (b)(ii) obtained for each concentration X of drug A:

[0049]

[0050] -The Y 50,B Value, LCTi 0,B Value, E max,B Value and γ B The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (II) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(iii) and (b)(iv) obtained for each concentration Y of drug B:

[0051]

[0052] wherein the population comprises the subject and other subjects diagnosed with the disease;

[0053] in:

[0054] X = concentration of drug A;

[0055] ·X 50,A It is the concentration of drug A that exerts half of its maximal activity;

[0056] ·LCTi 0,Ais the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii);

[0057] ·E max,A It is LCTi caused by drug A 0,A The maximum score is reduced;

[0058] γ A is the steepness of the LCTi-concentration curve of drug A;

[0059] Y = concentration of drug B;

[0060] ·Y 50,B is the concentration of drug B that exerts half of its maximal activity;

[0061] ·LCTi 0,B is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii);

[0062] ·E max，B , is LCTi caused by drug B 0，B The maximum score is reduced;

[0063] γ B is the steepness of the LCTi-concentration curve of drug B;

[0064] (ii) Activity marker values, including AUC xy，A Value, AUC xy，B Value, α AB Value and / or VUS AB Values, where:

[0065] - The AUC xy，A The value is calculated using formula (III):

[0066] AUC xy，A =AUC x，A -A y：10-90，A

[0067] (III)

[0068] in:

[0069] The AUC x，A The value is the integral between two drug concentrations X' and X" of the function derived from formula (I) for % survival after incubation subsamples according to steps (b)(i) and (b)(ii) obtained for each concentration x of drug A and calculated using formula (IV), where LCTi 0，A Considered as 100% survival rate,

[0070]

[0071] wherein drug concentrations X' and X" correspond to X obtained in each subject diagnosed with the disease 50，A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50，A calculated according to steps (a) to (e)(i);

[0072] as well as

[0073] The A y：10-90，A Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x，A Surface, where LCTi 0，A Considered as 100% survival rate;

[0074] as well as

[0075] - The AUC xy，B The value is calculated using formula (V):

[0076] AUC xy，B =AUC x，B -A y：10-90，B

[0077] (V)

[0078] in:

[0079] The AUC x，B The value is the integral between the two drug concentrations Y' and Y" of the function derived from formula (II) for % survival after incubation subsamples according to steps (b)(iii) and (b)(iv) obtained for each concentration Y of drug B and calculated using formula (VI), where LCTi 0,B Considered as 100% survival rate,

[0080]

[0081] wherein drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to steps (a) to (e)(i);

[0082] as well as

[0083] The A y:10-90,B Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x,B Surface, where LCTi0,A Considered as 100% survival rate;

[0084] as well as

[0085] -The VUS AB The value is calculated using formula (VII), where VUS AB The value is the double integral between two drug concentrations X' and X" of drug A and two drug concentrations Y' and Y" of drug B of the model function of the natural logarithm of the LCTi counted after incubation of the subsamples according to steps (b)(v) to (b)(vii), where LCTi 0,A =LCTi 0，B , and is considered as 100% survival rate,

[0086]

[0087] in:

[0088] Drug concentrations X' and X" correspond to the X obtained in each subject diagnosed with the disease in the population 50,A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50，A calculated according to steps (a) to (e)(i);

[0089] Drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease in the population 50，B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50，B calculated according to steps (a) to (e)(i);

[0090] ·E max，A = Maximum score reduction of LPC caused by drug A;

[0091] ·E max，B = Maximum score reduction of LPC caused by drug B;

[0092] ·X 50，A = Play E max,A Half of the EC of drug A 50 concentration;

[0093] ·Y 50，B = Play E max，B Half of the EC of drug B 50 concentration;

[0094] X = concentration of drug A;

[0095] Y = concentration of drug B;

[0096] ·

[0097] in:

[0098] -γA = steepness of the LCTi-concentration curve of drug A;

[0099] -γB = steepness of the LCTi-concentration curve of drug B; and

[0100] α AB = the synergy parameter estimated by a two-drug surface interaction model determined by fitting formula (VII') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to the steps mentioned in (b)(i) and (b)(ii) obtained for each concentration X of Drug A, the steps mentioned in (b)(iii) and (b)(iv) obtained for each concentration Y of Drug B, and the steps mentioned in b(v), b(vi) and b(vii) obtained for each pair of concentrations of the combination of Drug A and Drug B:

[0101]

[0102] (iii) Normalized marker values, including NAUC A ,NAUC B and / or NVUS AB ,in:

[0103] -NAUC A is the AUC calculated using formula (VIII) xy，A The normalized value of

[0104] NAUC A = 100 x AUC xy，A / AUC max，A (VIII)

[0105] -NAUC B is the AUC calculated using formula (IX) xy，B The normalized value of

[0106] NAUC B = 100 x AUC xy，B / AUC max，B (IX)

[0107] -NVUS AB is the VUS calculated using formula (X) AB The normalized value of

[0108] NVUS AB =100 x VUS AB / VUS max，AB(X)

[0109] in:

[0110] AUC max，A = AUC obtained in each population of subjects diagnosed with the disease xy，A The maximum value of the AUC for each subject in the population xy，A calculated according to steps (a) to (e)(ii);

[0111] AUC max，B = the AUC obtained in the population of each subject diagnosed with the disease xy，B The maximum value of the AUC for each subject in the population xy，B calculated according to steps (a) to (e)(ii); and

[0112] ·VUS max，AB = VUS obtained in the population per subject diagnosed with the disease AB The maximum value of the VUS for each subject in the population AB calculated according to steps (a) to (e)(ii);

[0113] (f)Select:

[0114] (i) the pharmacodynamic parameter value or values ​​determined according to step (e)(i) for each subject in said population of subjects; and / or

[0115] (ii) the activity marker value or values ​​determined according to step (e)(ii) for each subject in said population of subjects; and / or

[0116] (iii) a normalized marker value or multiple normalized marker values ​​determined according to step (e)(iii) for each subject in the population of subjects; and / or

[0117] (iv) a value of a clinical variable or values ​​of a plurality of clinical variables for each subject in said population of subjects,

[0118] it depends on clinical resistance or clinical sensitivity to the drug combination, whereby a value is dependent when the probability that the value is independent of clinical resistance or clinical sensitivity is less than or equal to 0.05;

[0119] (g) using at least one of the values ​​selected in step (f), creating a response function using a generalized linear model or a generalized additive model for the drug combination for the population of subjects, wherein the area under the curve of a receiver operating characteristic curve derived from the model function is equal to or greater than 0.8 and the lower limit of a 95% confidence interval for the area under the curve is greater than 0.5;

[0120] (h) calculating the threshold of the response function created in step (g) from the points on the receiver operating characteristic curve:

[0121] - the point at which sensitivity and specificity are maximized and have equal values; or

[0122] - specificity takes precedence over sensitivity at that point; or

[0123] - The point is closest to the (1,0) coordinate plane.

[0124] (j) calculating the S / R value of the drug combination for the disease in the subject using the response function created in step (g) and the following:

[0125] (i) the pharmacodynamic parameter value or multiple pharmacodynamic parameter values ​​of the subject determined according to step (e)(i); and / or

[0126] (ii) the activity marker value or values ​​of the subject determined according to step (e)(ii); and / or

[0127] (iii) a normalized marker value or multiple normalized marker values ​​determined according to step (e)(iii) for the subject; and / or

[0128] (iv) a value of a clinical variable or values ​​of multiple clinical variables of the subject,

[0129] which is a variable in the response function; and

[0130] (k) determining the efficacy of using the drug combination to treat the disease in the subject by comparing the S / R value calculated in step (j) with the threshold calculated in step (h), wherein

[0131] - when the S / R value is equal to or greater than the threshold, the disease is sensitive to treatment with the drug combination in the subject; and

[0132] - When the S / R value is less than the threshold, the disease is resistant to treatment with the drug combination in the subject.

[0133] The present invention also relates to a system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, wherein the system comprises:

[0134] (a) a device for performing the following steps: separating a tissue sample obtained from the subject into subsamples;

[0135] (b) an apparatus for carrying out the following steps:

[0136] (i) incubating the subsample in the presence of said drug A at a concentration X for a time T ranging from 2 to 168 hours; and

[0137] (ii) repeating the steps mentioned in (b)(i) an additional (N-1) times, each time using a different subsample, using a different value of X from the value of X used in the previous repetition of the steps mentioned in (b)(i);

[0138] wherein N is an integer selected from 5 to 10, inclusive;

[0139] as well as

[0140] (iii) incubating the subsample in the presence of the drug B at a concentration Y for the time T; and

[0141] (iv) repeating the steps mentioned in (b)(iii) an additional (M-1) times, each time using a different subsample, using a different value of Y from the value of Y used in the previous repetition of the steps mentioned in (b)(iii);

[0142] wherein M is an integer selected from 5 to 10, inclusive;

[0143] as well as

[0144] (v) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug B, wherein

[0145] - the concentration of drug A is a concentration X corresponding to X obtained in said population from each subject diagnosed with said disease 50,A The percentile value P of the distribution of values Hα,A The concentration at which the percentile value P Hα,A Calculated by formula (A):

[0146] P Hα,A =cos(α°)x H

[0147] (A)

[0148] in:

[0149] H corresponds to the reference percentile of the group selected from:

[0150] and 90, where:

[0151] r is an integer selected from 2 to (R-1), including 2 and R-1;

[0152] α is in degrees and is calculated as follows:

[0153]

[0154] in:

[0155] w is an integer selected from 1 to W, including 1 and W;

[0156] - the concentration of drug B is a concentration Y corresponding to Y obtained in the population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values Hα,B The concentration at which the percentile value P Hα,B Calculated by formula (B):

[0157] P Hα,B =cos(90°–α°)x H

[0158] (B)

[0159] (vi) repeating the step (b)(v) an additional (R-1) times, each time using a different subsample, using a different value of H than the value of H used in the previous repeat of the step (b)(v), and using the same value of w used in the step (b)(v); and

[0160] (vii) repeating the steps referenced in (b)(v) and (b)(vi) an additional (W-1) times, each time using a different subsample, using a value of w that is different from the value of w used in the previous repetition of the steps referenced in (b)(v) and (b)(vi);

[0161] in;

[0162] R is an integer selected from 3 to 10, inclusive;

[0163] W is an integer selected from 3 to 10, inclusive;

[0164] And among them:

[0165] ·X 50,A is the concentration of Drug A that exerts half of its maximal activity in the subject estimated according to the steps mentioned in (e)(i) below;

[0166] ·Y 50,Bis the concentration of drug B that exerts half of its maximal activity in the subject estimated according to the steps mentioned in (e)(i) below;

[0167] as well as

[0168] (viii) incubating the subsample for the time T;

[0169] (c) means for performing the following steps: adding at least one marker to each subsample incubated in step (b) to identify at least one cell type (CT) therein i );

[0170] (d) means for counting the number of viable cells (LCTi) of each cell type identified in step (c) remaining after incubation of each subsample according to the step mentioned in (b);

[0171] (e) means for performing the following steps: for each cell type identified in step (c), determining:

[0172] (i) Pharmacodynamic parameter values, including X 50,A Value, LCTi 0,A Value, E max,A Value, γ A Value, Y 50,B Value, LCTi 0,B Value, E max,B Value and / or gamma B Values, where:

[0173] ·The X 50,A Value, LCTi 0,A Value, E max,A Value and γ A The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (I) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to the steps mentioned in (b)(i) and (b)(ii) obtained for each concentration X of drug A:

[0174]

[0175] -The Y 50,B Value, LCTi 0,B Value, E max,B Value and γ BThe value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (II) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to the steps mentioned in (b)(iii) and (b)(iv) obtained for each concentration Y of drug B:

[0176]

[0177] wherein the population comprises the subject and other subjects diagnosed with the disease;

[0178] in:

[0179] X = concentration of drug A;

[0180] ·X 50，A It is the concentration of drug A that exerts half of its maximal activity;

[0181] ·LCTi 0，A is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii);

[0182] ·E max，A It is LCTi caused by drug A 0，A The maximum score is reduced;

[0183] γ A is the steepness of the LCTi-concentration curve of drug A;

[0184] Y = concentration of drug B;

[0185] ·Y 50，B is the concentration of drug B that exerts half of its maximal activity;

[0186] ·LCTi 0，B is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii);

[0187] ·E max，B It is LCTi caused by drug B 0，B The maximum score is reduced;

[0188] γ B is the steepness of the LCTi-concentration curve of drug B

[0189] (ii) Activity marker values, including AUC xy，A Value, AUC xy，B Value, α ABValue and / or VUS AB Values, where:

[0190] - The AUC xy，A The value is calculated using formula (III):

[0191] AUC xy，A =AUC x，A -A y：10-90，A

[0192] (III)

[0193] in:

[0194] The AUC x，A The value is the integration between the two drug concentrations X' and X" of the function derived from formula (I) for % survival after incubation of the subsamples according to the steps mentioned in (b)(i) and (b)(ii) obtained for each concentration of drug A and calculated using formula (IV), where LCTi 0，A Considered as 100% survival rate,

[0195]

[0196] wherein drug concentrations X' and X" correspond to X obtained in each subject diagnosed with the disease 50，A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50，A calculated according to steps (a) to (e)(i);

[0197] as well as

[0198] The A y：10-90，A Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x，A Surface, where LCTi 0，A Considered as 100% survival rate;

[0199] as well as

[0200] - The AUC xy，B The value is calculated using formula (V):

[0201] AUC xy，B =AUC x，B -A y：10-90，B

[0202] (V)

[0203] in:

[0204] The AUC x，BThe value is the integration between the two drug concentrations Y' and Y" of the function derived from formula (II) for % survival after incubation of the subsamples according to the steps mentioned in (b)(iii) and (b)(iv) obtained for each concentration of drug B and calculated using formula (VI), where LCTi 0，B Considered as 100% survival rate,

[0205]

[0206] wherein drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease 50，B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50，B calculated according to steps (a) to (e)(i);

[0207] as well as

[0208] The A y：10-90，B Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x，B Surface, where LCTi 0，A Considered as 100% survival rate;

[0209] as well as

[0210] -The VUS AB The value is calculated using formula (VII), where VUS AB The value is the double integral between two drug concentrations X' and X" of drug A and two drug concentrations Y' and Y" of drug B of the model function of the natural logarithm of the LCTi counted after incubation of the subsamples according to steps (b)(v) to (b)(vii), where LCTi 0,A =LCTi 0,B , and was considered as 100% survival rate and calculated using formula (VII),

[0211]

[0212] in:

[0213] Drug concentrations X' and X" correspond to the X obtained in each subject diagnosed with the disease in the population 50,A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to steps (a) to (e)(i);

[0214] Drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease in the population 50,BThe 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to steps (a) to (e)(i);

[0215] ·E max,A = Maximum score reduction of LPC caused by drug A;

[0216] ·E max,B = Maximum score reduction of LPC caused by drug B;

[0217] ·X 50,A = Play E max,A Half of the EC of drug A 50 concentration;

[0218] ·Y 50，B = Play E max，B Half of the EC of drug B 50 concentration;

[0219] X = concentration of drug A;

[0220] Y = concentration of drug B;

[0221] ·

[0222] in:

[0223] -γA = steepness of the LCTi-concentration curve of drug A;

[0224] -γB = steepness of the LCTi-concentration curve of drug B; and

[0225] α AB = the synergy parameter estimated by a two-drug surface interaction model determined by fitting formula (VII') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to steps (b)(i) and (b)(ii) obtained for each concentration X of Drug A, steps (b)(iii) and (b)(iv) obtained for each concentration Y of Drug B, and steps b(v), b(vi) and b(vii) obtained for each pair of concentrations of the combination of Drug A and Drug B:

[0226]

[0227] (iii) Normalized marker values, including NAUC A ,NAUC B and / or NVUS AB ,in:

[0228] -NAUCA is the AUC calculated using formula (VIII) xy,A The normalized value of

[0229] NAUC A = 100 x AUC xy,A / AUC max,A (VIII)

[0230] -NAUC B is the AUC calculated using formula (IX) xy,B The normalized value of

[0231] NAUC B = 100 x AUC xy,B / AUC max,B (IX)

[0232] -NVUS AB is the VUS calculated using formula (X) AB The normalized value of

[0233] NVUS AB =100 x VUS AB / VUS max,AB (X)

[0234] in:

[0235] AUC max,A = AUC obtained in each population of subjects diagnosed with the disease xy,A The maximum value of the AUC for each subject in the population xy,A calculated according to the steps mentioned in (a) to (e)(ii);

[0236] AUC max,B = the AUC obtained in the population of each subject diagnosed with the disease xy,B The maximum value of the AUC for each subject in the population xy,B calculated according to the steps referred to in (a) to (e)(ii); and

[0237] ·VUS max,AB = VUS obtained in the population per subject diagnosed with the disease AB The maximum value of the VUS for each subject in the population AB calculated according to the steps mentioned in (a) to (e)(ii);

[0238] (b) means for performing the following steps: selecting:

[0239] (i) a pharmacodynamic parameter value or values ​​determined according to step (e)(i) for each subject in said population of subjects; and / or

[0240] (ii) the activity marker value or values ​​determined according to step (e)(ii) for each subject in said population of subjects; and / or

[0241] (iii) a normalized marker value or multiple normalized marker values ​​determined according to the steps mentioned in (e)(iii) for each subject in the population of subjects; and / or

[0242] (iv) a value for a clinical variable or values ​​for a plurality of clinical variables for each subject in said population of subjects that is dependent on clinical resistance or clinical sensitivity to said drug combination, whereby said value is dependent when the probability that a value is independent of clinical resistance or clinical sensitivity is less than or equal to 0.05;

[0243] (g) means for performing the following steps: using at least one of the values ​​selected in step (f), creating a response function using a generalized linear model or a generalized additive model for the drug combination for the population of subjects, wherein the area under the curve of a receiver operating characteristic curve derived from the model function is equal to or greater than 0.8, and the lower limit of a 95% confidence interval of the area under the curve is greater than 0.5;

[0244] (h) means for calculating the threshold of the response function created in step (g) from points on the receiver operating characteristic curve:

[0245] - the point at which sensitivity and specificity are maximized and have equal values; or

[0246] - specificity takes precedence over sensitivity at that point; or

[0247] -The point is closest to the (1,0) coordinate plane.

[0248] (j) means for calculating the S / R value of the drug combination for the disease in the subject using the response function created in step (g) and:

[0249] (i) a pharmacodynamic parameter value or multiple pharmacodynamic parameter values ​​of the subject determined according to the steps mentioned in (e)(i); and / or

[0250] (ii) the activity marker value or values ​​of the subject determined according to the step mentioned in (e)(ii); and / or

[0251] (iii) a normalized marker value or multiple normalized marker values ​​of the subject determined according to the steps mentioned in (e)(iii); and / or

[0252] (iv) a value of a clinical variable or values ​​of multiple clinical variables of the subject,

[0253] which is a variable in the response function; and

[0254] (k) means for determining the efficacy of using the drug combination to treat the disease in the subject by comparing the S / R value calculated in step (j) with the threshold calculated in step (h), wherein

[0255] - when the S / R value is equal to or greater than the threshold, the disease is sensitive to treatment with the drug combination in the subject; and

[0256] - When the S / R value is less than the threshold, the disease is resistant to treatment with the drug combination in the subject.

[0257] The present invention also relates to a method for classifying the effectiveness of drug combinations each comprising drug A and drug B in the treatment of a subject diagnosed with a disease, wherein the method comprises the following steps:

[0258] (a) separating a tissue sample obtained from the subject into subsamples;

[0259] (b) perform the following steps:

[0260] (i) incubating the subsample in the presence of said drug A at a concentration X for a time T ranging from 2 to 168 hours; and

[0261] (ii) repeating step (b)(i) an additional (N-1) times, each time using a different subsample, using a different value of X from the value of X used in the previous repetition of step (b)(i);

[0262] wherein N is an integer selected from 5 to 10, inclusive;

[0263] as well as

[0264] (iii) incubating the subsample in the presence of the drug B at a concentration Y for the time T; and (iv) repeating step (b)(iii) an additional (M-1) times, each time using a different subsample, using a different value of Y from the value of Y used in the previous repetition of step (b)(iii);

[0265] wherein M is an integer selected from 5 to 10, inclusive;

[0266] as well as

[0267] (v) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug B, wherein

[0268] - the concentration of drug A is a concentration X corresponding to X obtained in said population from each subject diagnosed with said disease 50,A The percentile value P of the distribution of values Hα,A The concentration at which the percentile value P Hα,A Calculated by formula (A):

[0269] P Hα,A =cos(α°)x H

[0270] (A)

[0271] in:

[0272] H corresponds to the reference percentile of the group selected from:

[0273] and 90, where:

[0274] r is an integer selected from 2 to (R-1), including 2 and R-1;

[0275] α is in degrees and is calculated as follows:

[0276]

[0277] in:

[0278] w is an integer selected from 1 to W, including 1 and W;

[0279] - the concentration of drug B is a concentration Y corresponding to Y obtained in the population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values Hα,B The concentration at which the percentile value P Hα,B Calculated by formula (B):

[0280] P Hα,B =cos(90°–α°)x H

[0281] (B)

[0282] (vi) repeating step (b)(v) an additional (R-1) times, each time using a different subsample, using a different value of H than the value of H used in the previous repetition of step (b)(v), and using the same value of w used in step (b)(v);

[0283] (vii) repeating steps (b)(v) and (b)(vi) an additional (W-1) times, each time using a different subsample, using a value of w that is different from the value of w used in the previous repetition of steps (b)(v) and (b)(vi);

[0284] in;

[0285] R is an integer selected from 3 to 10, inclusive;

[0286] W is an integer selected from 3 to 10, inclusive;

[0287] And among them:

[0288] X 50，A is the concentration of drug A that exerts half of its maximal activity in the subject estimated according to step (e)(i) below;

[0289] Y 50，B is the concentration of drug B that exerts half of its maximal activity in the subject estimated according to step (e)(i) below;

[0290] as well as

[0291] (viii) incubating the subsample for the time T;

[0292] (c) adding at least one marker to each subsample incubated in step (b) to identify at least one cell type therein (CT i );

[0293] (d) counting the number of viable cells (LCTi) of each cell type identified in step (c) that remain after incubating each subsample according to step (b);

[0294] (e) for each cell type identified in step (c), determine:

[0295] (i) pharmacodynamic parameter values, the pharmacodynamic parameter values ​​comprising at least one pharmacodynamic parameter value of drug A and / or at least one pharmacodynamic parameter value of drug B, wherein:

[0296] - Each pharmacodynamic parameter value of drug A is estimated from a single drug dose-response pharmacodynamic mixed effects nonlinear population model by fitting the formula to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(i) and (ii):

[0297] - Each pharmacodynamic parameter value of drug B is estimated from a single drug dose-response pharmacodynamic mixed effects nonlinear population model by fitting the formula to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(iii) and (iv):

[0298] wherein the population comprises the subject and other subjects diagnosed with the disease;

[0299] (ii) active marker values, the active marker values ​​comprising at least one active marker value of drug A, at least one active marker value of drug B, and / or at least one active marker value of drugs A and B, wherein:

[0300] - each activity marker value of drug A is calculated from the pharmacodynamic parameter value or multiple pharmacodynamic parameter values ​​of drug A estimated in step (e)(i),

[0301] - each activity marker value of drug B is calculated from the pharmacodynamic parameter value or pharmacodynamic parameter values ​​of drug B estimated in step (e)(i),

[0302] - each activity marker value of drug A and drug B is calculated by a specific model developed by fitting a formula to the pharmacodynamic parameter value or values ​​of drug A and the pharmacodynamic parameter value or values ​​of drug B estimated in step (e)(i) and to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to (b)(v) to (vii);

[0303] as well as

[0304] (iii) normalized marker values, the normalized marker values ​​comprising at least one normalized marker value of drug A, at least one normalized marker value of drug B, and / or at least one normalized marker value of drugs A and B, wherein:

[0305] - each normalized marker value of drug A is calculated by the ratio of each active marker value of drug A calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​from said population;

[0306] - each normalized marker value of drug B is calculated by the ratio of each active marker value of drug B calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​of drug B from said population;

[0307] - each normalized marker value of drug A and drug B is calculated by the ratio of each active marker value of drug A and drug B calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​of drug A and drug B from said population;

[0308] (f)Select:

[0309] (i) the pharmacodynamic parameter value or values ​​determined according to step (e)(i) for each subject in said population of subjects; and / or

[0310] (ii) the activity marker value or values ​​determined according to step (e)(ii) for each subject in said population of subjects; and / or

[0311] (iii) a normalized marker value or multiple normalized marker values ​​determined according to step (e)(iii) for each subject in said population of subjects, and / or

[0312] (iv) a value for a clinical variable or values ​​for a plurality of clinical variables for each subject in said population of subjects that is dependent on clinical resistance or clinical sensitivity to said drug combination, whereby said value is dependent when the probability that a value is independent of clinical resistance or clinical sensitivity is less than or equal to 0.05;

[0313] (g') calculating a score S for treating the subject with the drug A and the drug B, wherein the score corresponds to or is calculated using at least one of the values ​​selected in step (f);

[0314] (h') performing steps (b) to (g') for each drug combination to be classified;

[0315] as well as

[0316] (j') Using the scores determined in steps (g') and (h'), classify each drug combination such that:

[0317] (i) Drug combinations with a score greater than 80 are classified into classification category I with a classification value of 2;

[0318] (ii) drug combinations with a score less than or equal to 80 and greater than 60 were classified into classification category II with a classification value of 1;

[0319] (iii) drug combinations with a score less than or equal to 60 and greater than 40 were assigned to classification category III with a classification value of 0;

[0320] (iv) drug combinations with a score less than or equal to 40 and greater than 20 are classified as classification category IV with a classification value of -1; or

[0321] (v) drug combinations with a score less than or equal to 20 are classified into classification category V with a classification value of -2,

[0322] thereby:

[0323] - each drug combination assigned to a classification category having a positive classification value or a zero classification value has the highest efficacy in the treatment of said disease in said subject; and

[0324] - Each drug combination belonging to the classification category with a negative classification value has the lowest utility in the treatment of said disease in said subject.

[0325] Similarly, the present invention also relates to a system for classifying the utility of drug combinations each comprising drug A and drug B in the treatment of a subject diagnosed with a disease, wherein the system comprises:

[0326] (a) a device for performing the following steps: separating a tissue sample obtained from the subject into subsamples;

[0327] (b) an apparatus for carrying out the following steps:

[0328] (i) incubating the subsample in the presence of said drug A at a concentration X for a time T ranging from 2 to 168 hours; and

[0329] (ii) repeating the steps mentioned in (b)(i) an additional (N-1) times, each time using a different subsample, using a different value of X from the value of X used in the previous repetition of the steps mentioned in (b)(i);

[0330] wherein N is an integer selected from 5 to 10, inclusive;

[0331] as well as

[0332] (iii) incubating the subsample in the presence of the drug B at a concentration Y for the time T; and

[0333] (iv) repeating the steps mentioned in (b)(iii) an additional (M-1) times, each time using a different subsample, using a different value of Y from the value of Y used in the previous repetition of the steps mentioned in (b)(iii);

[0334] wherein M is an integer selected from 5 to 10, inclusive;

[0335] as well as

[0336] (v) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug B, wherein

[0337] - the concentration of drug A is a concentration X corresponding to X obtained in said population from each subject diagnosed with said disease 50，A The percentile value P of the distribution of values Hα，A The concentration at which the percentile value P Hα,A Calculated by formula (A):

[0338] P Hα，A =cos(α°)x H

[0339] (A)

[0340] in:

[0341] H corresponds to the reference percentile of the group selected from:

[0342] and 90, where:

[0343] r is an integer selected from 2 to (R-1), including 2 and R-1;

[0344] α is in degrees and is calculated as follows:

[0345]

[0346] in:

[0347] w is an integer selected from 1 to W, including 1 and W;

[0348] - the concentration of drug B is a concentration Y corresponding to Y obtained in the population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values Hα,B The concentration at which the percentile value P Hα，B Calculated by formula (B):

[0349] P Hα，B =cos(90°–α°)x H

[0350] (B)

[0351] (vi) repeating the step (b)(v) an additional (R-1) times, each time using a different subsample, using a different value of H than the value of H used in the previous repetition of the step (b)(v), and using the same value of w used in the step (b)(v);

[0352] (vii) repeating the steps referenced in (b)(v) and (b)(vi) an additional (W-1) times, each time using a different subsample, using a value of w that is different from the value of w used in the previous repetition of the steps referenced in (b)(v) and (b)(vi);

[0353] in;

[0354] R is an integer selected from 3 to 10, inclusive;

[0355] W is an integer selected from 3 to 10, inclusive;

[0356] And among them:

[0357] ·X 50,A is the concentration of Drug A that exerts half of its maximal activity in the subject estimated according to the steps mentioned in (e)(i) below;

[0358] ·Y 50,B is the concentration of drug B that exerts half of its maximal activity in the subject estimated according to the steps mentioned in (e)(i) below;

[0359] as well as

[0360] (viii) incubating the subsample for the time T;

[0361] (c) means for performing the following steps: adding at least one marker to each subsample incubated in step (b) to identify at least one cell type (CT) therein i );

[0362] (d) means for counting the number of viable cells (LCTi) of each cell type identified in step (c) that remain after incubation of each subsample according to step (b);

[0363] (e) means for performing the following steps: for each cell type identified in step (c), determining:

[0364] (i) pharmacodynamic parameter values, the pharmacodynamic parameter values ​​comprising at least one pharmacodynamic parameter value of drug A and / or at least one pharmacodynamic parameter value of drug B, wherein:

[0365] - Each pharmacodynamic parameter value of drug A is estimated from a single drug dose-response pharmacodynamic mixed effects nonlinear population model by fitting the formula to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(i) and (ii):

[0366] - Each pharmacodynamic parameter value of drug B is estimated from a single drug dose-response pharmacodynamic mixed effects nonlinear population model by fitting the formula to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(iii) and (iv):

[0367] wherein the population comprises the subject and other subjects diagnosed with the disease;

[0368] (ii) active marker values, the active marker values ​​comprising at least one active marker value of drug A, at least one active marker value of drug B, and / or at least one active marker value of drugs A and B, wherein:

[0369] - each activity marker value of drug A is calculated from the pharmacodynamic parameter value or pharmacodynamic parameter values ​​of drug A estimated in step (e)(i),

[0370] - each activity marker value of drug B is calculated from the pharmacodynamic parameter value or pharmacodynamic parameter values ​​of drug B estimated in step (e)(i),

[0371] - each activity marker value of drug A and drug B is calculated by a specific model developed by fitting a formula to the pharmacodynamic parameter value or values ​​of drug A and the pharmacodynamic parameter value or values ​​of drug B estimated in step (e)(i) and to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to (b)(v) to (vii);

[0372] as well as

[0373] (iii) normalized marker values, the normalized marker values ​​comprising at least one normalized marker value of drug A, at least one normalized marker value of drug B, and / or at least one normalized marker value of drugs A and B, wherein:

[0374] - each normalized marker value of drug A is calculated by the ratio of each active marker value of drug A calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​from said population;

[0375] - each normalized marker value of drug B is calculated by the ratio of each active marker value of drug B calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​of drug B from said population;

[0376] - each normalized marker value of drug A and drug B is calculated by the ratio of each active marker value of drug A and drug B calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​of drug A and drug B from said population;

[0377] (b) means for performing the following steps: selecting:

[0378] (i) a pharmacodynamic parameter value or values ​​determined according to step (e)(i) for each subject in said population of subjects; and / or

[0379] (ii) the activity marker value or values ​​determined according to step (e)(ii) for each subject in said population of subjects; and / or

[0380] (iii) a normalized marker value or multiple normalized marker values ​​determined according to the steps mentioned in (e)(iii) for each subject in the population of subjects; and / or

[0381] (iv) a value for a clinical variable or values ​​for a plurality of clinical variables for each subject in said population of subjects that is dependent on clinical resistance or clinical sensitivity to said drug combination, whereby said value is dependent when the probability that a value is independent of clinical resistance or clinical sensitivity is less than or equal to 0.05;

[0382] (g') means for performing the following steps: calculating a score S for treating the subject with the drug A and the drug B, wherein the score corresponds to or is calculated using at least one of the values ​​selected in step (f);

[0383] (h') means for carrying out the steps mentioned in (b) to (g') for each drug combination to be classified;

[0384] as well as

[0385] (j') means for performing the following steps: using the scores determined in steps (g') and (h') to classify each drug combination, such that:

[0386] (i) Drug combinations with a score greater than 80 are classified into classification category I with a classification value of 2;

[0387] (ii) drug combinations with a score less than or equal to 80 and greater than 60 were classified into classification category II with a classification value of 1;

[0388] (iii) drug combinations with a score less than or equal to 60 and greater than 40 were assigned to classification category III with a classification value of 0;

[0389] (iv) drug combinations with a score less than or equal to 40 and greater than 20 are classified as classification category IV with a classification value of -1; or

[0390] (v) drug combinations with a score less than or equal to 20 are classified into classification category V with a classification value of -2,

[0391] thereby:

[0392] - each drug combination assigned to a classification category having a positive classification value or a zero classification value has the highest efficacy in the treatment of said disease in said subject; and

[0393] - Each drug combination belonging to the classification category with a negative classification value has the lowest utility in the treatment of said disease in said subject.

[0394] Therefore, the present invention also relates to the use of a method or system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease in determining the efficacy of treatment with the drug combination in the subject diagnosed with the disease.

[0395] Similarly, the present invention also relates to the use of a method or system for classifying the effectiveness of a drug combination each comprising drug A and drug B in the treatment of a subject diagnosed with a disease in determining a drug combination classified as having the highest effectiveness in the treatment of the disease in the subject diagnosed with the disease.

[0396] The method or system of the present invention for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease may also include means for prescribing a care plan for the subject, wherein when the disease is determined to be sensitive to treatment with the drug combination in the subject, the care plan prescribes the drug combination. Therefore, the present invention also relates to the use of the method or system in prescribing the care plan for the subject.

[0397] Similarly, the method and system of the present invention for classifying the effectiveness of a drug combination each comprising drug A and drug B in the treatment of a subject diagnosed with a disease may also include prescribing a care plan for the subject, wherein the care plan prescribes a drug combination selected from a drug combination classified as having the highest effectiveness in the treatment of the disease in the subject. Therefore, the present invention also relates to the use of the method or system in prescribing the care plan for the subject.

[0398] Furthermore, the present invention relates to a method of treating a subject diagnosed with a disease, the method comprising administering a drug combination to the subject, wherein according to a method or system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, the disease is determined to be sensitive to treatment with the drug combination in the subject.

[0399] Similarly, the present invention relates to a method for treating a subject diagnosed with a disease, the method comprising administering a drug combination selected from a group consisting of drug combinations that are classified as having the highest efficacy in the treatment of the disease in the subject according to a method or system for classifying the efficacy of a drug combination comprising drug A and drug B in the treatment of the subject diagnosed with the disease.

[0400] The present invention also relates to the use of a method or system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease in determining whether a given subject from a population of subjects each diagnosed with a disease is suitable for inclusion in a clinical trial involving treatment with a drug combination comprising drug A and drug B, wherein:

[0401] - when the disease is determined to be susceptible to treatment with the drug combination in the subject, selecting the subject for inclusion in the clinical trial; and

[0402] - when the disease is determined to be resistant to treatment with the drug combination in the subject, the subject is not selected for inclusion in the clinical trial.

[0403] Similarly, the present invention relates to the use of a method and system for classifying the effectiveness of drug combinations each comprising drug A and drug B in the treatment of subjects diagnosed with a disease in determining whether a given subject from a population of subjects each diagnosed with a disease is suitable for inclusion in a clinical trial involving treatment with a drug combination comprising drug A and drug B, wherein:

[0404] - selecting the subject for inclusion in the clinical trial when the drug combination is classified as having the greatest efficacy in the treatment of the disease in the subject; and

[0405] - when the drug combination is classified as having the greatest efficacy in the treatment of the disease in the subject, not selecting the subject for inclusion in the clinical trial.

[0406] Finally, the present invention also relates to the aforementioned method and system for classifying the effectiveness of drug combinations each comprising drug A and drug B in the treatment of a subject diagnosed with a disease, wherein:

[0407] - The disease is acute myeloid leukemia;

[0408] - Drug A is selected from the group consisting of idarubicin, cytarabine, fludarabine, etoposide, thioguanine, clofarabine, cladribine, daunorubicin, mitoxantrone and amsacrine, followed by Drug B being selected from the remaining group of drugs after Drug A has been selected from, and optionally the drug combination comprises Drug C selected from the remaining group of drugs listed under Drug A after Drugs A and B have been selected from

[0409] - a drug combination having a score greater than 80, more preferably greater than 85, even more preferably greater than 90 belongs to classification category I with a classification value of 2 and has the highest efficacy in the treatment of said disease in said subject,

[0410] A drug combination belonging to the classification category as determined by the method and system for classifying the effectiveness of drug combinations is thereby selected in a care plan for prescription in a treatment method. BRIEF DESCRIPTION OF THE DRAWINGS

[0411] Figure 1 .Sequential workflow of the experimental (image A) and analytical (images B and C) methods applied in the study. Whole bone marrow samples [A] were incubated with drugs and drug mixtures, preserving the native microenvironment. Automated flow cytometry [B] and subsequent dot plot analysis [C] allowed for the counting of live pathological cells (LPCs) after incubation in control wells and wells with increasing drug concentrations. Data were uploaded to a LIMS system. The relationship of response relative to drug concentration was analyzed by nonlinear mixed-effects population modeling [E]. In the EC 50 The predicted efficacy curves were integrated between the 80% confidence intervals of the individual estimates to calculate the area under the curve (AUC) used as a single marker of activity. Similarly, double integration of the bivariate interaction surface function allowed calculation of the volume under the surface (VUS) affected by the sign of interaction (synergy or antagonism) [F]. The correlation of the active markers with the clinical output was analyzed by generalized additive models (GAM) [G] and ROC curves [H].

[0412] Figure 2 .General description of the system of the invention. Test conditions and parameters must be predefined and registered in a database. Based on this information, input data from the cytometer files and manual user input are processed by an R runtime script, which retrieves the data from the database, passes it to NONMEM and returns the results, which are post-processed to obtain the final treatment score. The results as well as every intermediate data and process parameter are stored in the database, ensuring full traceability. The results are output in an appropriate html report.

[0413] Figure 3 .Flowchart showing the sequential steps in the process (straight lines) as well as the lateral data migration (dashed lines). The database collects all processed data and test results as well as the required setup and operating parameters. The result processing template (RPT) compiles the required parameter settings for each test definition. The marked (AG) subprocesses are described in detail later.

[0414] Figure 4.Build a population model from scratch using the minimum number of samples (30) or when the cumulative number of samples for the model is greater than 20% of the samples used to build the previous model. An effective model should have an objective function value (OFV) that is at least 4 points lower than the previous model, but should have biologically sensitive model parameters and meet several conditions, such as inter-patient variability (IPV), standard error (SE), and quality control check values. If this is not the case, the model should be rebuilt again after 10 new samples. When the new model conditions are met, all new model data are stored in the database for use next time.

[0415] Figure 5 The result processing template of a .PM test compiles all the required parameters for running the entire process. Most of them are derived from the experimental design in use. Therefore, the protocol ID is the first parameter fixed in the RPT. Associated with it are the drugs and combinations tested and the plate format. The RPT also includes the definition of QC test criteria, modeling execution parameters, post-processing and scoring calculations, clinical treatment evaluations and report design.

[0416] Figure 6 The model was executed in a sequential cycle for all drugs and combinations tested and defined in the protocol and included in the corresponding RPT. While for single drugs a population mixed effects model was applied using the R runtime and NONMEM software (Figure A), the effects of drug combinations were investigated using a single interaction surface model (Figure B) supplied with parameters from the single drug population model. Confidence intervals (CI) were calculated in the case of single drugs by bootstrapping from 1000 simulations using the NONMEM software and the single drug modeling output. For interaction parameters, CIs were calculated by nonparametric bootstrapping (1000).

[0417] Figure 7 The single drug model function is integrated over a specific range of concentration values ​​to generate an area under the curve (AUC) that is used as a marker for optimized activity. Integration limits are set based on statistical analysis of the distribution of stored results. The same limits are used for the volume under the surface (VUS) calculation, which follows the double integration of the two-variable function that defines the surface interaction model. Upper and lower limits (UL and LL) are calculated as extreme values ​​of the confidence intervals for the model parameter estimates.

[0418] Figure 8 Single drug and combination results are input to calculate a score for each treatment included in the test definition of a method for classifying the effectiveness of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease. Thus, the score is used to classify the treatment.

[0419] Fig. 9The system is designed to automatically check for required upgrades. These upgrades will be set after executing a new model or after applying changes in experimental conditions or report format. T )(above the defined threshold (N u )) to rebuild the model, new drugs or combinations included in the test or dynamically included in the existing model may require a new model.

[0420] Fig.10 The application includes a report designer to build report templates. One or more templates can be included in an RPT and applied to sample results. Authentication is a critical step before authorized users can publish reports on the network server. Access to this server will be controlled and granted to authenticated end users.

[0421] Fig.11 . Visual predictive inspection of the population pharmacokinetic model for (Panel A) cytarabine [ln(live tumor cells) vs. cytarabine concentration] and (Panel B) idarubicin [ln(live tumor cells) vs. idarubicin concentration]. The open circles are observed data points, the solid and dashed red lines are the median and 5th-95th percentiles of the observed ln(cells) distribution, respectively, and the translucent red and blue bands represent simulation-based 95% confidence intervals for the median and 5th-95th percentiles of the estimated population distribution of ln(cells), respectively.

[0422] Fig.12 .Regression hyperplanes for predicted probability of resistance (i.e., probability of nonresponder) relative to the AUCs of cytarabine and idarubicin for identifying ex vivo variables associated with hematologic response. The AUC of the concentration-response curve is a summary of the pharmacodynamic parameters used to predict clinical response, so the higher the AUC, the lower the cytotoxic effect (efficacy or potency) of the drug. The regression hyperplanes were obtained using a two-dimensional smoothing function in a binary logistic GAM. When IDA was inactive at the maximum IDA AUC, the AUC of CYT led the prediction in the case of a classical dose-response curve (back curve, right edge of the plane). Conversely, when CYT was inactive, IDA led the prediction in the case of a classical dose-response curve (back curve, left edge of the plane). When one drug was very active (low AUC), the other drug showed a more limited effect, still consistent with a higher AUC corresponding to a higher probability of resistance. This behavior is consistent and expected; a higher AUC (i.e., lower activity) of either drug implies a higher probability of resistance. AUC: area under the curve, GAM: generalized additive model.

[0423] Fig.13Panel A: Empirical and smoothed (binormal) ROC curves of resistance probability obtained in a binary logistic GAM, where open circles are pairs of sensitivity and 1-specificity values ​​at estimated discrete individual values ​​of resistance probability (used as markers to classify patients as responders or resisters), and solid large circles represent pairs of sensitivity and 1-specificity values ​​at selected cut-off points obtained using each of the following three criteria specified in the text: ‘MaxSpSe’ selects the point that maximizes both sensitivity and specificity; ‘Geometric’ selects the point closest to the (1,0) coordinates (upper left corner of the [sensitivity, 1-specificity] plane); and ‘mMCT’ selects the point that minimizes the misclassification cost term that assigns a higher cost to false positives than to false negatives (prioritizing specificity over sensitivity). Panel B: Confusion matrix for MaxSpSe cut-off, where AUC: area under the curve, CI: confidence interval, FPF: false positive fraction, NPV: negative predictive value, PPV: positive predictive value, Se: sensitivity, Sp: specificity, TPF: true positive fraction.

[0424] Fig.14 .Kaplan-Meier curves for overall survival. The three panels (A, B, and C) show pairs of survival functions for patients classified as responders (solid black line) and resistant (solid red line) according to the cut-off points for the estimated probability of resistance obtained for each of the three criteria specified in the text (Panel A: 'MaxSpSe', Panel B: 'Geometric', and Panel C: 'mMCT'). The dashed lines represent the survival functions for clinical responders (black line) and resistant patients (grey line). The hazard ratio for death was obtained from a Cox regression model using patients predicted to be responders as the reference category (patients predicted to be resistant relative to patients predicted to be responders). CI: confidence interval, HR: hazard ratio

[0425] Fig.15 Comparison between clinical relevance of cytogenetics (panels A and B) and PM testing (panels C and D) in a cohort of 111 patients sharing both results. ROC curves (panels A and C) and confusion matrices for MaxSpSe cutoff (panels B and D). For explained bias, cytogenetics was 29.4% and PM testing was 40.9%.

[0426] Fig.16 .A 3x3 drug combination matrix of combinations of drug A and B in which the number of informative data points is maximized, thus maximizing the accuracy of synergy parameter estimates from interaction surface modeling. These data points are calculated based on the data from each EC 50 The intersection between the arcs of the 10th, 50th and 90th percentiles of the drug population values, and the 3 lines dividing the quarter circle into individual 3 sectors of equal area.

[0427] Fig.17 .AUCxy values ​​are normalized to a reference to a base area bounded by a rectangle described by the same limits as above, defined by the entire population results and the 10%-90% response interval.

[0428] Fig.18 . Classification of a plurality of treatments for a subject diagnosed with acute myeloid leukemia based on a normalized sensitivity score for each treatment according to a method for classifying the effectiveness of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease. The treatment to which the acute myeloid leukemia is determined to be sensitive (panel A) falls into the more effective and intermediate-high categories, while the treatment to which the acute myeloid leukemia is determined to be resistant (panel B) falls into the intermediate-low and less effective categories.

[0429] Fig.19 . According to an alternative embodiment of a method for classifying the utility of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease, a ranking of multiple treatments based on a normalized sensitivity score for each treatment. The sensitivity levels are displayed in order from best at the top to worst at the bottom, color coded as green-sensitive (shown as light gray), orange-uncertain, red-resistant (shown as dark gray). Four patients with different degrees of sensitivity are shown, namely sensitive and standard patients (Panel A) and resistant and very resistant patients (Panel B).

[0430] Fig. 20 . Validation of a single CYT+IDA treatment PM test score relative to clinical response in 112 first-line AML samples, where Res. = number of patients resistant to therapy, Sens. = number of patients sensitive to therapy, and %Sens. = percentage of patients sensitive to therapy (Panel A). Comparison of personalized medicine predictive tests relative to correlation tests (Panel B).

[0431] Fig.21 . indicates the EC provided by the sample population stored in the database 50 Box plot of the distribution of values ​​along with 95% confidence intervals associated with the central median estimate. The vertical gray line represents the results shown for patient sample VIVIA-PMMM010431. Drugs used in the combination therapy are highlighted within the box; arrows indicate the EC values ​​of those drugs 50 value.

[0432] Fig. 22 . indicates the EC provided by the sample population stored in the database 50Box plot of the distribution of values ​​along with 95% confidence intervals associated with the central median estimate. The vertical gray line represents the results shown for patient sample VIVIA-PMMM010431. Drugs used in the combination therapy are highlighted within the box; arrows indicate the EC values ​​of those drugs 50 The group boxed within the dashed line identifies highly sensitive results (below the 10th percentile) from the same family of drugs (mTor inhibitors) that may be considered alternative treatments for this patient.

[0433] Fig.23 . indicates the EC provided by the sample population stored in the database 50 Box plot of the distribution of values ​​along with 95% confidence intervals associated with the central median estimate. The vertical grey line represents the results shown for patient sample VIVIA-PMMM130061. The drugs used in the different therapies are highlighted within the continuous line boxes; arrows indicate the EC values ​​of those drugs. 50 Alternative drugs with moderate or high activity compared to the population results are highlighted in the dashed boxes.

[0434] Fig.24 . indicates the EC provided by the sample population stored in the database 50 Box plot of the distribution of values ​​along with 95% confidence intervals associated with the central median estimate. The vertical gray line represents the results shown for patient sample VIVIA-PMALL07002. The drugs used in the combination therapy are highlighted within the continuous line box; the arrows indicate the EC values ​​of those drugs. 50 value.

[0435] Fig.25 . indicates the EC provided by the sample population stored in the database 50 Box plot of the distribution of values ​​with 95% confidence intervals associated with the central median estimate. Vertical gray lines represent results shown for patient sample VIVIA-PMALL04001. Highlighted within the continuous line box are the drugs used in the combination therapy (except prednisolone which could not be tested); arrows indicate the EC values ​​for those drugs 50 value.

[0436] Fig.26 . indicates the EC provided by the sample population stored in the database 50 Box plot of the distribution of values ​​with 95% confidence intervals associated with the central median estimate. The vertical gray line represents the results shown for patient sample VIVIA-PMALL09001. Highlighted within the continuous line box are the drugs used in the combination therapy (except imatinib, which could not be tested); arrows indicate the EC values ​​of those drugs 50 value. DETAILED DESCRIPTION OF THE INVENTION

[0438] The present invention discloses a method for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease. Similarly, the present invention discloses a system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease.

[0439] The present invention also discloses a method for classifying the effectiveness of drug combinations each comprising drug A and drug B in treating a subject diagnosed with a disease. Similarly, the present invention discloses a system for classifying the effectiveness of drug combinations each comprising drug A and drug B in treating a subject diagnosed with a disease.

[0440] The methods and systems for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease each include assessing whether the drug combination will be effective for treating the disease in the subject by determining whether the disease is sensitive to treatment with the drug combination in the subject or whether the disease is resistant to treatment with the drug combination in the subject. Thus, the methods and systems for determining treatment efficacy provide an assessment of whether a given drug combination will be effective for treating the disease in the subject.

[0441] Conversely, the methods and systems for classifying the effectiveness of drug combinations in treating a subject diagnosed with a disease each include scoring each of a plurality of drug combinations based on their effectiveness in treating the disease in the subject, and classifying each drug combination based on the scores. Thus, the methods and systems for classifying the effectiveness of drug combinations in treating a subject diagnosed with a disease provide treatment guidance regarding the most appropriate drug combination for treating the disease in the subject.

[0442] In the present invention, the drug combination comprises at least drug A and drug B. Preferably, the drug combination comprises at most three drugs. Therefore, in one embodiment of the present invention, the drug combination further comprises drug C. The drug A, the drug B and the drug C are different.

[0443] Preferably, the drug A is selected from the group consisting of idarubicin (IDA), cytarabine (CYT or ARA-C), fludarabine (FLU), etoposide (ETO), thioguanine (TIO), clofarabine (CLO), cladribine (CLA), daunorubicin (DNR or DAU), mitoxantrone (MIT), amsacrine (AMS), decitabine, doxorubicin, vincristine, cyclophosphamide, arsenic trioxide, trioxide), 5-azacytidine, bortezomib, bendamustine, prednisolone, dexamethasone, thalidomide, L-asparaginase, imatinib, melphalan, maphosphamide, cisplatin, carmustine, tanespimycin , rapamycin, everolimus, temsirolimus, panobinostat, vorinostat, tipifarnib, perifosine, pomalidomide, lenalidomide, methylprednisolone, hydrocortisone, methotrexate and dasatinib. The drug B is then selected from the remaining drug groups from which drug A has been selected. The drug C is then selected from the remaining drug groups from which drugs A and B have been selected. In the methods and systems for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, drug A is preferably selected from the group of idarubicin and cytarabine, and drug B is the remaining drug after drug A has been selected therefrom (i.e., drug A is idarubicin and drug B is cytarabine, or vice versa).In a preferred embodiment, drug A is idarubicin and drug B is cytarabine, or vice versa, and the drug combination comprises drug C selected from the group of fludarabine, etoposide, thioguanine and clofarabine.

[0444] In the present invention, the subject diagnosed with a disease is a subject diagnosed with a cancer of hematopoietic and lymphoid tissue, preferably leukemia, myeloma or lymphoma (meaning that the efficacy mentioned in the method for determining therapeutic efficacy is the anti-leukemia, anti-myeloma or anti-lymphoma efficacy, respectively), more preferably a blood cancer and even more preferably a leukemia or myeloma, still more preferably acute myeloid leukemia (AML), multiple myeloma (MM) or acute lymphoid leukemia (ALL), most preferably acute myeloid leukemia.

[0445] In a preferred embodiment, when the disease is acute myeloid leukemia, drug A is selected from the group of idarubicin, cytarabine, fludarabine, etoposide, thioguanine, clofarabine, cladribine, daunorubicin, mitoxantrone and amsacrine, and drug B is subsequently selected from the remaining drug group after drug A has been selected from it. More preferably, when the disease is acute myeloid leukemia, drug A is idarubicin, fludarabine, daunorubicin or mitoxantrone and drug B is cytarabine, or vice versa, or drug A is idarubicin, daunorubicin, mitoxantrone or amsacrine and drug B is fludarabine, or vice versa, or drug A is idarubicin, daunorubicin, mitoxantrone or amsacrine and drug B is etoposide, or vice versa. Optionally, the drug combination comprises a drug C selected from the remaining group of drugs listed under drug A in the previous preferred embodiment after drugs A and B have been selected therefrom.

[0446] In another preferred embodiment, when the disease is multiple myeloma, drug A is selected from the group of bortezomib, bendamustine, prednisolone, dexamethasone and thalidomide, and drug B is subsequently selected from the remaining drug group after drug A has been selected therefrom, optionally followed by drug C from the remaining drug group after drugs A and B have been selected therefrom. More preferably, when the disease is multiple myeloma, drug A is bendamustine or prednisolone and drug B is bortezomib, or vice versa, or drug A is dexamethasone and drug B is bortezomib, or vice versa, optionally wherein drug C is bendamustine or and thalidomide.

[0447] In yet another preferred embodiment, when the disease is acute lymphocytic leukemia, drug A is selected from the group of prednisolone, idarubicin, cytarabine, fludarabine, daunorubicin, vincristine, cyclophosphamide, L-asparaginase and imatinib, and drug B is subsequently selected from the remaining drug group after drug A has been selected, optionally followed by drug C from the remaining drug group after drugs A and B have been selected. More preferably, when the disease is acute lymphocytic leukemia, drug A is daunorubicin or vincristine, drug B is prednisolone, or vice versa, and drug C is the remaining drug after drugs A and B have been selected therefrom or it is cyclophosphamide, L-asparaginase or imatinib (even more preferably when used to treat acute lymphocytic leukemia, drug A is daunorubicin or vincristine, drug B is prednisolone, or vice versa, drug C is the remaining drug after drugs A and B have been selected therefrom, and the drugs A, B and C are administered in combination with cyclophosphamide and L-asparaginase or with imatinib). Alternatively, drug A is idarubicin, cytarabine or fludarabine and drug B is prednisolone, or vice versa, and drug C is selected from the group of drugs remaining after drugs A and B have been selected (even more preferably when used for the treatment of acute lymphoblastic leukemia, drug A is idarubicin, cytarabine or fludarabine and drug B is prednisolone, or vice versa, drug C is selected from the group of drugs remaining after drugs A and B have been selected, and the drugs A, B and C are administered in combination with the remaining drugs after drugs A, B and C have been selected.

[0448] The subject may be an adult subject diagnosed with the disease (i.e., having reached sexual maturity), preferably an adult subject who has not previously undergone first-line or second-line treatment, an adult subject who has previously undergone first-line treatment, or an adult subject who has previously undergone second-line treatment.

[0449] In this specification, any given step mentioned in any given method described herein discloses a step performed by a device included in the corresponding system described herein with the same label (e.g., step (a) in two methods of the present invention is a step performed by device (a) in two systems of the present invention). Therefore, where the present specification refers to a step with a particular label in one or both of the methods described herein, it is also understood that the step can be performed by one or both of the devices mentioned with the same label in the systems described herein.

[0450] The method of the present invention both comprises the step mentioned under (a), which separates the tissue sample obtained from the subject into subsamples. The tissue sample is a blood, lymph or bone marrow sample. Preferably, the tissue sample is a bone marrow sample. In fact, in one such embodiment of the present invention, the tissue sample comprises bone marrow cells collected before the patient has undergone chemotherapy and / or radiotherapy. In an even more preferred embodiment, the viability of the bone marrow cells is greater than or equal to 60% when incubated for 48 hours in the absence of drug A and / or drug B and / or drug C; and / or when obtained from the subject, the bone marrow cells do not exist in the form of clots.

[0451] The separation of the sample into sub-samples comprises dividing the sub-samples into several portions. In one embodiment of each system of the present invention, the means for performing the step of separating the tissue sample comprises a microfluidic stem cell separation device.

[0452] Both of the methods of the present invention further comprise a step (b) of performing the following steps:

[0453] (i) incubating the subsample in the presence of said drug A at a concentration X for a time T ranging from 2 to 168 hours; and

[0454] (ii) repeating step (b)(i) an additional (N-1) times, each time using a different subsample, using a different value of X from the value of X used in the previous repetition of step (b)(i);

[0455] wherein N is an integer selected from 5 to 10, inclusive;

[0456] as well as

[0457] (iii) incubating the subsample in the presence of the drug B at a concentration Y for the time T; and

[0458] (iv) repeating step (b)(iii) an additional (M-1) times, each time using a different subsample, using a different value of Y from the value of Y used in the previous repetition of step (b)(iii);

[0459] wherein M is an integer selected from 5 to 10, inclusive;

[0460] as well as

[0461] (v) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug B, wherein

[0462] - the concentration of drug A is a concentration X corresponding to X obtained in said population from each subject diagnosed with said disease 50,A The percentile value P of the distribution of valuesHα,A The concentration at which the percentile value P Hα,A Calculated by formula (A):

[0463] P Hα,A =cos(α°)x H

[0464] (A)

[0465] in:

[0466] H corresponds to the reference percentile of the group selected from:

[0467] and 90, where:

[0468] r is an integer selected from 2 to (R-1), including 2 and R-1;

[0469] α is in degrees and is calculated as follows:

[0470]

[0471] in:

[0472] w is an integer selected from 1 to W, including 1 and W;

[0473] - the concentration of drug B is a concentration Y corresponding to Y obtained in the population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values Hα,B The concentration at which the percentile value P Hα,B Calculated by formula (B):

[0474] P Hα,B =cos(90°–α°)x H

[0475] (B)

[0476] (vi) repeating step (b)(v) an additional (R-1) times, each time using a different subsample, using a value of H different from the value of H used in the previous repetition of step (b)(v), and using the same value of α used in step (b)(v);

[0477] (vii) repeating steps (b)(v) and (b)(vi) an additional (W-1) times, each time using a different subsample, using a different value of α from the value of α used in the previous repetition of steps (b)(v) and (b)(vi);

[0478] in;

[0479] R is an integer selected from 3 to 10, inclusive;

[0480] W is an integer selected from 3 to 10, inclusive;

[0481] And among them:

[0482] ·X 50,A is the concentration of drug A that exerts half of its maximal activity in the subject estimated according to step (e)(i) below;

[0483] ·Y 50,B is the concentration of drug B that exerts half of its maximal activity in the subject estimated according to step (e)(i) below;

[0484] as well as

[0485] (viii) incubating the subsample for said time T.

[0486] In one embodiment of each method of the present invention, step (b) further comprises:

[0487] (ix) incubating the subsample in the presence of the drug C at concentration Z for the time T; and

[0488] (x) repeating step (b)(ix) an additional (L-1) times, each time using a different subsample, using a different value of Z from the value of Z used in the previous repetition of step (b)(ix);

[0489] wherein L is an integer selected from 5 to 10, inclusive;

[0490] as well as

[0491] (xi) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug C, wherein

[0492] - the concentration of drug A is a concentration X corresponding to X obtained in said population from each subject diagnosed with said disease 50,A The percentile value P of the distribution of values H’α’,A The concentration at which the percentile value P H’α’,A Calculated by formula (C):

[0493] P H’α’,A =cos(α'°)x H'

[0494] (C)

[0495] in:

[0496] H' corresponds to the reference percentile of the group selected from:

[0497] and 90, where:

[0498] r' is an integer selected from 2 to (R'-1), including 2 and R'-1;

[0499] α' is in degrees and is calculated by:

[0500]

[0501] in:

[0502] w' is an integer selected from 1 to W', including 1 and W';

[0503] - the concentration of drug C is a concentration Z corresponding to Z obtained in said population from each subject diagnosed with said disease 50,C The percentile value P of the distribution of values H’α’,C The concentration at which the percentile value P H’α’，C Calculated by formula (D):

[0504] P H’α’，C =cos(90°–α'°)x H'

[0505] (D)

[0506] (xii) repeating step (b)(xi) an additional (R'-1) times, each time using a different subsample, using a value of H' different from the value of H' used in the previous repetition of step (b)(xi), and using the same value of α' used in step (b)(xi); and

[0507] (xiii) repeating steps (b)(xi) and (b)(xii) an additional (W'-1) times, each time using a different subsample, using a different value of α' from the value of α' used in the previous repetition of steps (b)(xi) and (b)(xii);

[0508] in;

[0509] R' is an integer selected from 3 to 10, inclusive;

[0510] W' is an integer selected from 3 to 10, inclusive;

[0511] as well as;

[0512] (xiv) incubating the subsample for said time T in the presence of a drug combination comprising said drug B and said drug C, wherein

[0513] - the concentration of drug B is a concentration Y corresponding to Y obtained in the population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values H”α”,B The concentration at which the percentile value PH”α”，B Calculated by formula (E):

[0514] P H”α”,B =cos(α”°)x H”

[0515] (E)

[0516] in:

[0517] H" corresponds to the reference percentile of the group selected from:

[0518] and 90, where:

[0519] R" is an integer selected from 2 to (R"-1), including 2 and R"-1;

[0520] α" is in degrees and is calculated by the following formula:

[0521]

[0522] in:

[0523] w" is an integer selected from 1 to W", including 1 and W";

[0524] - the concentration of drug C is a concentration Z corresponding to Z obtained in said population from each subject diagnosed with said disease 50,C The percentile value P of the distribution of values H”α”,C The concentration at which the percentile value P H”α”,C Calculated by formula (F):

[0525] P H”α”,C =cos(90°–α”°)x H”

[0526] (F)

[0527] (xv) repeating step (b)(xiv) an additional (R"-1) times, each time using a different subsample, using a value of H" different from the value of H" used in the previous repetition of step (b)(xiv), and using the same value of α" used in step (b)(xiv);

[0528] (xvi) repeating steps (b)(xiv) and (b)(xv) an additional (W″-1) times, each time using a different subsample and using a different value of α″ from the value of α″ used in the previous repetition of steps (b)(xiv) and (b)(xv);

[0529] in;

[0530] R" is an integer selected from 3 to 10, inclusive;

[0531] W" is an integer selected from 3 to 10, including 3 and 10;

[0532] And among them:

[0533] ·X 50,A is the concentration of Drug A that exerts half of its maximal activity in the subject, as estimated according to step (e)(i);

[0534] ·Y 50,B is the concentration of drug B that exerts half of its maximal activity in the subject, estimated according to step (e)(i);

[0535] ·Z 50,C is the concentration of drug C that exerts half of its maximal activity in the subject estimated according to step (e)(i), as follows.

[0536] Each X obtained in the population of subjects diagnosed with the disease 50,A , Y 50,B and Z 50,C The values ​​are preferably obtained from a population of at least 5, even more preferably at least 10, still more preferably at least 20, most preferably at least 30 of said subjects. Each of said values ​​is obtained for each subject in said population of subjects.

[0537] Use the above minimum number of samples or when the model accumulates the number of samples (N T ) is greater than the specified threshold number (N u ), the population model is constructed from scratch. Preferably, N u Nu is the number corresponding to 10% to 20% of the samples used to build the previous model, and more preferably Nu is the number corresponding to 20% of the samples used to build the previous model. The effective model should have an objective function value (OFV) that is at least 4 points lower than the previous model, and should also have biologically sensitive model parameters and meet several conditions, such as inter-patient variability (IPV), standard error (SE) and quality control check values. If not, the model should be rebuilt again after 10 new samples. When the new model conditions are met, all new model data are stored in the database and used until replaced by a subsequent model.

[0538] The step for determining the respective concentrations X, Y and Z of the drugs A, B and C used in steps (b)(i) and (b)(ii), (b)(iii) and (b)(iv) and (b)(ix) and (b)(x) of the present invention preferably first comprises selecting for each drug the endpoint concentrations u1 and u2 of the U% trimmed range of the concentration profile, said drug here causing a reduction of greater than V% in the number of viable cells LCTi of each cell type identified according to step (c) obtained in said population of subjects, wherein U is from 1% to 50% and V is at least 1%, more preferably wherein U is from 2% to 30% and V is at least 5%, even more preferably wherein U is from 5% to 75% and V is at least 10%, still more preferably wherein the U% trimmed range is a decile or interquartile range and V i' is at least 10%. The concentrations are determined by:

[0539] (i) Select a concentration from the group consisting of:

[0540] and u2, where:

[0541] - n' is an integer selected from 2 to (N'-1), inclusive; and

[0542] (ii) repeating step (i) an additional (N′-1) times, each time using a concentration value different from the concentration value used in the previous repetition of step (i);

[0543] wherein N' is N, M or P, depending on whether the drug is drug A, drug B or drug C, respectively;

[0544] The steps for determining the respective concentrations X, Y and Z of drugs A, B and C used in steps (b)(v) to (b)(vii), (b)(xi) to (b)(xiii) and (b)(xiv) to (b)(xvi) of the present invention include steps of a method for improving the accuracy of the estimate of the synergistic effect of drug combinations. The method is a statistical method that provides a concentration distribution of any two drugs in a combination experiment (where the number of information data points is maximized) and maximizes the accuracy of the synergistic parameter estimate from interaction surface modeling. In theory, drug interaction experiments require up to 50 data points of paired concentrations of each drug to appropriately cover enough area to fit well to the surface interaction model and allow accurate estimation of synergy. Experimentally, this is a serious limitation because it requires a sample size that will never be available. Based on historically stored data of dose response curves from multiple patient samples (herein, the EC values ​​of each drug) 50 distribution), the methods described herein minimize the number of points at which each drug is most representative of the drug concentration at which it is active.

[0545] Dose response curves from multiple patient samples were analyzed to determine the concentration ratio that most accurately simulated the interaction surface. Figure 7 These (R=3)x(W=3)=9 points correspond to each EC 50 The 10th, 50th and 90th percentiles of the drug population value are plotted, thereby using each drug percentile as the radius and using the origin as the center to draw three quarter circles. In addition, three straight lines are drawn with the origin as the starting point and using a given angle to divide different circles into sectors of the same size angle. Finally, the intersection of the line and the circle is calculated and converted to concentration units. The X value from each point belongs to drug A and the Y value belongs to drug B.

[0546] In a preferred embodiment, N=M and R=W, wherein N(=M) is selected from an integer of 5 to 10 (including 5 and 10), and R(=W) is selected from an integer of 3 to 10 (including 3 and 10). In a more preferred embodiment of the present invention, N=M and R=W, wherein N(=M) is selected from the group of integers 8, 9 and 10, and R(=W) is selected from the group of integers 3, 4 and 5, still more preferably N(=M)=8 and R(=W)=3. When R=W is 3, α is selected from the group of 22.5°, 45° and 67.5°. When the drug combination further comprises drug C, preferably N=M=L and R=W=R'=W'=R"=W", wherein N(=M=L) is selected from an integer of 5 to 10 (including 5 and 10), and R(=W=R'=W'=R"=W") is selected from an integer of 3 to 10 (including 3 and 10). In an even more preferred embodiment of the present invention, N=M=L and R=W=R'=W'=R"=W", wherein N(=M=L) is selected from the group of integers 8, 9 and 10, and R(=W=R'=W'=R"=W") is selected from the group of integers 3, 4 and 5, still more preferably N(=M=L)=8 and R(=W=R'=W'=R"=W")=3. When R=W=R'=W'=R"=W" is 3, each of α, α' and α" is selected from the group of 22.5°, 45° and 67.5°.

[0547] Each of the incubation steps comprises incubating a subsample which has not undergone a previous incubation with the drug combination according to the method of the invention.

[0548] Preferably, the incubation time T is a time of 24 to 72 hours, more preferably a time of 36 to 60 hours, and in an even more preferred embodiment is 48 hours.In one embodiment of each system of the invention, the means for performing the step of incubating comprises a cell culture incubator.

[0549] Both of the methods of the present invention comprise the step (c): adding at least one marker to each subsample incubated in step (b) to identify at least one cell type (CT) therein. i ). Each i-th cell type identified is different depending on the added marker. In one embodiment of the method of the present invention, step (c) comprises:

[0550] (i) adding at least one conjugated antibody to each subsample incubated in step (b) to identify at least one pathological cell type therein; and

[0551] (ii) adding at least one cell death or apoptosis marker (such as anexin, iodurepropidium, 7-AAD, Draq5, Hoechst or DAPI) to each subsample incubated in step (b) to identify apoptotic cells therein.

[0552] Preferably, the conjugated antibody identifies cancer cells, more preferably cancer cells of hematopoietic and lymphoid tissues, even more preferably leukemia or lymphoma cells, still more preferably leukemia cells, and most preferably acute myeloid leukemia cells. By adding the at least one marker (preferably at least one conjugated antibody and at least one cell death or apoptosis marker) to each subsample incubated in step (b), live pathological cells of the disease are identified in preparation for step (d). In one embodiment of each system of the present invention, the device for performing the addition of at least one marker comprises a pipette or an injector such as a syringe or a dispenser.

[0553] Therefore, both of the methods of the present invention further include a step (d): counting the number of live cells (LCTi) of each cell type identified in step (c) that remain after incubating each subsample according to step (b). In the aforementioned embodiment in which at least one conjugated antibody and at least one cell death or apoptosis marker are added as markers in step (c), step (d) includes counting the number of live cells (LCTi) that remain after incubating each subsample by counting the number of cells of the at least one pathological cell type identified according to step (c) (i) that are not identified as apoptotic according to step (c) (ii). In another embodiment of each system of the present invention, the device for performing the step of counting the number of live pathological cells includes a cytometer, preferably a flow cytometer, an image cytometer or a cell photometer, more preferably a flow cytometer.

[0554] Both of the methods of the present invention further comprise the step (e): for each cell type identified in step (c), determining:

[0555] (i) pharmacodynamic parameter values;

[0556] (ii) activity marker value;

[0557] as well as

[0558] (iii) normalizing the marker values. Thus, step (e) comprises for each cell type (CT) identified in step (c) i ) determines at least one of each of said values, wherein:

[0559] - pharmacodynamic parameter values, the pharmacodynamic parameter values ​​comprising at least one pharmacodynamic parameter value of drug A and / or at least one pharmacodynamic parameter value of drug B, wherein:

[0560] - Each pharmacodynamic parameter value of drug A is estimated from a single drug dose-response pharmacodynamic mixed effects nonlinear population model by fitting the formula to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(i) and (ii):

[0561] - Each pharmacodynamic parameter value of drug B is estimated from a single drug dose-response pharmacodynamic mixed effects nonlinear population model by fitting the formula to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(iii) and (iv):

[0562] wherein the population comprises the subject and other subjects diagnosed with the disease;

[0563] - an activity marker value, the activity marker value comprising at least one activity marker value of drug A, at least one activity marker value of drug B and / or at least one activity marker value of drugs A and B, wherein:

[0564] - each activity marker value of drug A is calculated from the pharmacodynamic parameter value or pharmacodynamic parameter values ​​of drug A estimated in step (e)(i),

[0565] - each activity marker value of drug B is calculated from the pharmacodynamic parameter value or pharmacodynamic parameter values ​​of drug B estimated in step (e)(i),

[0566] - each activity marker value of drug A and drug B is calculated by a specific model developed by fitting a formula to the pharmacodynamic parameter value or values ​​of drug A and the pharmacodynamic parameter value or values ​​of drug B estimated in step (e)(i) and to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to (b)(v) to (vii); and

[0567] - normalized marker values, the normalized marker values ​​comprising at least one normalized marker value of drug A, at least one normalized marker value of drug B, and / or at least one normalized marker value of drugs A and B, wherein:

[0568] - each normalized marker value of drug A is calculated by the ratio of each active marker value of drug A calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​from said population;

[0569] - each normalized marker value of drug B is calculated by the ratio of each active marker value of drug B calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​of drug B from said population;

[0570] - Each normalized marker value for drugs A and B is calculated by the ratio of each active marker value for drugs A and B calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​for drugs A and B from said population.

[0571] In one embodiment of the method of the present invention, when the drug combination comprises drug A, drug B and drug C,

[0572] - The pharmacodynamic parameter values ​​determined in step (e)(i) optionally further include at least one pharmacodynamic parameter value of drug C, wherein:

[0573] - Each pharmacodynamic parameter value of drug C is estimated from a single drug dose-response pharmacodynamic mixed effects nonlinear population model by fitting the formula to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(ix) and (x):

[0574] - the active marker values ​​determined in step (e)(ii) optionally further include at least one active marker value for drug C, at least one active marker value for drugs A and C, and / or at least one active marker value for drugs B and C, wherein:

[0575] - each activity marker value of drug C is calculated from the pharmacodynamic parameter value or pharmacodynamic parameter values ​​of drug C estimated in step (e)(i),

[0576] - each activity marker value of drug A and C is calculated by a specific model developed by fitting a formula to the pharmacodynamic parameter value or values ​​of drug A and the pharmacodynamic parameter value or values ​​of drug C estimated in step (e)(i) and to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(xi) to (xiii);

[0577] - each activity marker value of drug B and drug C is calculated by a specific model developed by fitting a formula to the pharmacodynamic parameter value or values ​​of drug B and the pharmacodynamic parameter value or values ​​of drug C estimated in step (e)(i) and to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to (b)(xiv) to (xvi);

[0578] - the normalized marker values ​​determined in step (e)(iii) optionally further include normalized marker values ​​comprising at least one normalized marker value for drug C, at least one normalized marker value for drugs A and C, and / or at least one normalized marker value for drugs B and C, wherein:

[0579] - each normalized marker value of drug C is calculated by the ratio of each active marker value of drug C calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​from said population;

[0580] - each normalized marker value for drugs A and C is calculated by the ratio of each active marker value for drugs A and C calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​for drugs A and C from said population;

[0581] - Each normalized marker value for drugs B and C is calculated by the ratio of each active marker value for drugs B and C calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​for drugs B and C from said population.

[0582] In one embodiment of each of the methods of the present invention, the pharmacodynamic parameter value determined in step (e)(i) comprises X 50,A Value, LCTi 0,A Value, E max,A Value, γ A Value, Y 50,B Value, LCTi 0,B Value, E max,B Value and / or gamma B Values, where:

[0583] -X 50,A Value, LCTi 0,A Value, E max,A Value and γ A The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (I) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(i) and (b)(ii) obtained for each concentration X of drug A:

[0584]

[0585] -The Y 50,B Value, LCTi 0,B Value, E max,B Value and γ B The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (II) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(iii) and (b)(iv) obtained for each concentration Y of drug B:

[0586]

[0587] wherein the population comprises the subject and other subjects diagnosed with the disease;

[0588] in:

[0589] X = concentration of drug A;

[0590] ·X 50,A It is the concentration of drug A that exerts half of its maximal activity;

[0591] ·LCTi 0,A is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii);

[0592] ·E max,A It is LCTi caused by drug A 0,A The maximum score is reduced;

[0593] γ A is the steepness of the LCTi-concentration curve of drug A;

[0594] Y = concentration of drug B;

[0595] ·Y 50,B is the concentration of drug B that exerts half of its maximal activity;

[0596] ·LCTi 0,B is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii);

[0597] ·E max,B It is LCTi caused by drug B 0,B The maximum score is reduced;

[0598] γB is the steepness of the LCTi-concentration curve of drug B.

[0599] In a more preferred embodiment of each of the methods of the present invention, when the drug combination further comprises drug C, the pharmacodynamic parameter value determined in step (e)(i) optionally further comprises Z 50,C Value, LCTi 0,C Value, E max，C Value and / or gamma C Values, where:

[0600] -Z 50，C Value, LCTi 0，C Value, E max，C Value and γ C The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (XI) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(ix) and (b)(x) obtained for each concentration Z of drug C:

[0601]

[0602] in:

[0603] Z = concentration of drug C;

[0604] ·Z 50,C It is the concentration of drug C that exerts half of its maximal activity;

[0605] ·LCTi 0,C is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii);

[0606] ·E max，C It is LCTi caused by drug C 0，C The maximum score is reduced;

[0607] γ C is the steepness of the LCTi-concentration curve of drug C.

[0608] In one embodiment of each of the methods of the invention, the activity marker value determined in step (e)(ii) comprises AUC xy，A Value, AUC xy，B Value, α AB Value and / or VUS AB Values, where:

[0609] - The AUC xy，A The value is calculated using formula (III):

[0610] AUC xy，A =AUC x，A -A y：10-90，A

[0611] (III)

[0612] in:

[0613] The AUC x，A The value is the integral between two drug concentrations X' and X" after incubation of the subsamples according to steps (b)(i) and (b)(ii) obtained for each concentration x of drug A by the function of formula (I) derived from the survival rate %, where LCTi 0，A was considered as 100% survival and was calculated using formula (IV):

[0614]

[0615] wherein drug concentrations X' and X" correspond to X obtained in each subject diagnosed with the disease 50，A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50，A calculated according to steps (a) to (e)(i);

[0616] as well as

[0617] The A y：10-90，A Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x，A Surface, where LCTi 0，A Considered as 100% survival rate;

[0618] as well as

[0619] - The AUC xy，B The value is calculated using formula (V):

[0620] AUC xy，B =AUC x，B -A y：10-90，B

[0621] (V)

[0622] in:

[0623] The AUC x，B The value is the integral between two drug concentrations Y' and Y" after incubation of the subsamples according to steps (b)(iii) to (b)(iv) obtained for each concentration Y of drug B by the function of formula (II) derived from the survival rate %, where LCTi 0，B was considered as 100% survival and was calculated using formula (VI):

[0624]

[0625] wherein drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50，B calculated according to steps (a) to (e)(i);

[0626] as well as

[0627] The A y:10-90,B Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x,B Surface, where LCTi 0,A Considered as 100% survival rate;

[0628] as well as

[0629] -The VUS AB The value is calculated using formula (VII), where VUS AB The value is the double integral between two drug concentrations X' and X" of drug A and two drug concentrations Y' and Y" of drug B of the model function of the natural logarithm of LCTi counted after incubation of the subsamples in steps (b)(v) to (b)(vii), where LCTi 0,A =LCTi 0,B , and was considered as 100% survival rate and calculated using formula (VII),

[0630]

[0631] in:

[0632] Drug concentrations X' and X" correspond to the X obtained in each subject diagnosed with the disease in the population 50,A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to steps (a) to (e)(i);

[0633] Drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease in the population 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50，B calculated according to steps (a) to (e)(i);

[0634] ·E max,A = Maximum score reduction of LPC caused by drug A;

[0635] ·E max,B = Maximum score reduction of LPC caused by drug B;

[0636] ·X 50，A = Play E max，A Half of the EC of drug A 50 concentration;

[0637] ·Y 50,B = Play E max,B Half of the EC of drug B 50 concentration;

[0638] X = concentration of drug A;

[0639] Y = concentration of drug B;

[0640] ●

[0641] in:

[0642] -γA = steepness of the LCTi-concentration curve of drug A;

[0643] -γB = steepness of the LCTi-concentration curve of drug B; and

[0644] α AB = the synergy parameter estimated by a two-drug surface interaction model determined by fitting formula (VII') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to steps (b)(i) and (b)(ii) obtained for each concentration X of Drug A (where Y=Z=0), steps (b)(iii) and (b)(iv) obtained for each concentration Y of Drug B (where X=Z=0), and steps b(v), b(vi) and b(vii) obtained for each pair of concentrations of the combination of Drug A and Drug B:

[0645]

[0646] In a more preferred embodiment of each of the methods of the present invention, when the drug combination further comprises drug C, the activity marker value determined in step (e)(ii) optionally also includes AUC xy，C Value, α AC Value, VUS AC Value, α BC Value and / or VUS BC Values, where:

[0647] - The AUC xy，C The value is calculated using formula (XII):

[0648] AUC xy，C =AUC x，C -A y：10-90，C

[0649] (XII)

[0650] in:

[0651] The AUC x，C The value is the integral between two drug concentrations Z' and Z" after incubation of subsamples according to steps (b)(ix) and (b)(x) obtained for each concentration Z of drug C by the function of formula (XI) derived from the survival rate %, where LCTi 0,C was considered as 100% survival and was calculated using formula (XIII):

[0652]

[0653] wherein drug concentrations Z' and Z" correspond to Z obtained in each subject diagnosed with the disease 50,C The 20th and 80th percentile concentrations of the values ​​where the Z of each subject in the population 50,C calculated according to steps (a) to (e)(i);

[0654] as well as

[0655] The A y:10-90,C Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x,C Surface, where LCTi 0,C Considered as 100% survival rate;

[0656] as well as

[0657] -The VUS AC The value is calculated using formula (XIV), where the VUS AC The value is the double integral between two drug concentrations X' and X" of drug A and two drug concentrations Z' and Z" of drug C of the model function of the natural logarithm of LCTi counted after incubation of the subsamples according to steps (b)(xi) to (b)(xiii), where LCTi 0,A =LCTi 0,C , and is considered as 100% survival rate,

[0658]

[0659] in:

[0660] Drug concentrations X' and X" correspond to the X obtained in each subject diagnosed with the disease in the population 50,AThe 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to steps (a) to (e)(i);

[0661] Drug concentrations Z' and Z" correspond to Z obtained in each subject diagnosed with the disease in the population 50,C The 20th and 80th percentile concentrations of the values ​​where the Z of each subject in the population 50,C calculated according to steps (a) to (e)(i);

[0662] ·E max,A = Maximum score reduction of LPC caused by drug A;

[0663] ·E max,C = Maximum score reduction of LPC caused by drug C;

[0664] ·X 50,A = Play E max,A Half of the EC of drug A 50 concentration;

[0665] ·Z 50,C = Play E max,C Half of the EC of drug C 50 concentration;

[0666] X = concentration of drug A;

[0667] Z = concentration of drug C;

[0668] ·

[0669] in:

[0670] -γA = steepness of the LCTi-concentration curve of drug A;

[0671] -γC = steepness of the LCTi-concentration curve of drug C;

[0672] as well as

[0673] α AC= the synergy parameter estimated by a two-drug surface interaction model determined by fitting formula (XIV') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to steps (b)(i) and (b)(ii) obtained for each concentration x of drug A (where Y=Z=0), steps (b)(ix) and (b)(x) obtained for each concentration z of drug C (where X=Y=0), and steps b(xi), b(xii) and b(xiii) obtained for each pair of concentrations of the combination of drug A and drug C:

[0674]

[0675] -The VUS BC The value is calculated using formula (XV), where the VUS BC The value is the double integral between two drug concentrations Y' and Y" of drug B and two drug concentrations Z' and Z" of drug C of the model function of the natural logarithm of LCTi counted after incubation of the subsamples according to steps (b)(xiv) to (b)(xvi), where LCTi 0,B =LCTi 0,C , and is considered as 100% survival rate,

[0676]

[0677]

[0678] in:

[0679] Drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease in the population 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to steps (a) to (e)(i);

[0680] Drug concentrations Z' and Z" correspond to Z obtained in each subject diagnosed with the disease in the population 50,C The 20th and 80th percentile concentrations of the values ​​where the Z of each subject in the population 50,C calculated according to steps (a) to (e)(i);

[0681] ·E max,B = Maximum score reduction of LPC caused by drug B;

[0682] ·E max,C = Maximum score reduction of LPC caused by drug C;

[0683] ·Y 50,B = Play E max,B Half of the EC of drug B 50 concentration;

[0684] ·Z 50,C = Play E max,C Half of the EC of drug C 50 concentration;

[0685] Y = concentration of drug B;

[0686] Z = concentration of drug C;

[0687] ·

[0688] in:

[0689] -γB = steepness of the LCTi-concentration curve of drug B;

[0690] -γC = steepness of the LCTi-concentration curve of drug C;

[0691] as well as

[0692] α BC = the synergy parameter estimated by a two-drug surface interaction model determined by fitting equation (XV') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to steps (b)(iii) and (b)(iv) mentioned for each concentration Y of drug B (where X=Z=0), steps (b)(ix) and (b)(x) mentioned for each concentration Z of drug C (where X=Y=0), and steps b(xiv), b(xv) and b(xvi) mentioned for each pair of concentrations of the combination of drug B and drug C:

[0693]

[0694] In one embodiment of each of the methods of the invention, the normalized marker value determined in step (e)(ii) comprises NAUC A ,NAUC B and / or NVUS AB ,in:

[0695] -NAUC A is the AUC calculated using formula (VIII) xy,A The normalized value of

[0696] NAUC A = 100 x AUC xy,A / AUC max,A (VIII)

[0697] -NAUC B is the AUC calculated using formula (IX) xy,B The normalized value of

[0698] NAUC B = 100 x AUC xy,B / AUC max,B (IX)

[0699] -NVUS AB is the VUS calculated using formula (X) AB The normalized value of

[0700] NVUS AB =100 x VUS AB / VUS max,AB (X)

[0701] in:

[0702] AUC max,A = AUC obtained in each population of subjects diagnosed with the disease xy,A The maximum value of the AUC for each subject in the population xy,A calculated according to steps (a) to (e)(ii);

[0703] AUC max,B = the AUC obtained in the population of each subject diagnosed with the disease xy,B The maximum value of the AUC for each subject in the population xy,B calculated according to steps (a) to (e)(ii); and

[0704] ·VUS max,AB = VUS obtained in the population per subject diagnosed with the disease AB The maximum value of the VUS for each subject in the population AB Calculate according to steps (a) to (e)(ii).

[0705] In a more preferred embodiment of each of the methods of the present invention, when the drug combination further comprises drug C, the normalized marker value determined in step (e)(iii) optionally also includes NAUC C NVUS AC and / or NVUS BC ,in:

[0706] -NAUC C is the AUC calculated using formula (XVI) xy,C The normalized value of

[0707] NAUC C = 100 x AUC xy,C / AUC max,C (XVI)

[0708] -NVUS AC is the VUS calculated using formula (XVII) AC The normalized value of

[0709] NVUS AC =100 x VUS AC / VUS max,AC (XVII)

[0710] -NVUS BC is the VUS calculated using formula (XVIII) BC The normalized value of

[0711] NVUS BC =100 x VUS BC / VUS max,BC (XVIII)

[0712] in:

[0713] AUC max,C = the AUC obtained in the population of each subject diagnosed with the disease xy,C The maximum value of the AUC for each subject in the population xy,C calculated according to steps (a) to (e)(ii);

[0714] ·VUS max,AC = VUS obtained in the population per subject diagnosed with the disease AC The maximum value of the VUS for each subject in the population AC calculated according to steps (a) to (e)(ii); and

[0715] ·VUS max,BC = VUS obtained in the population per subject diagnosed with the disease BC The maximum value of the VUS for each subject in the population BC Calculate according to steps (a) to (e)(ii).

[0716] In each of the system embodiments of the present invention, the means for performing the steps of determining a pharmacodynamic parameter value, an active marker value, and a normalized marker value comprises at least one computer program product.

[0717] In the present invention, a computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to execute various aspects of the present invention.

[0718] A computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer readable storage medium may include, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer readable storage media includes the following: a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device (such as a punch card or a raised structure in a groove on which instructions are recorded), and any suitable combination of the foregoing.

[0719] Computer readable program instructions as described herein can be downloaded from a computer readable storage medium to a corresponding computing / processing device or downloaded to an external computer or external storage device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network). The network can include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and sends computer readable program instructions to be stored in a computer readable storage medium in the corresponding computing / processing device.

[0720] The computer-readable program instructions for performing the operation of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" programming language or similar programming languages). The computer-readable program instructions can be executed completely on the user's computer, partially on the user's computer, executed as a software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network (including a local area network (LAN) or a wide area network (WAN)), or can be connected to an external computer (for example, using an Internet service provider through the Internet). In some embodiments, an electronic circuit (including, for example, a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA)) can execute a computer-readable program instruction to personalize the electronic circuit by utilizing the state information of the computer-readable program instructions, thereby performing various aspects of the present invention.

[0721] According to the flowchart illustrations and / or block diagrams and / or steps of the present invention of the methods, devices (systems) and kits of the embodiments, various aspects of the present invention are described herein. It should be understood that each square or diamond-shaped frame of the flowchart illustration and / or block diagram and the combination of frames in the flowchart illustration and / or block diagram can be implemented by biotechnology or computer-readable program instructions or a combination thereof.

[0722] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create a device for implementing the functions / actions specified in the blocks or multiple blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that can guide a computer, a programmable data processing device, and / or other device to operate in a specific manner, so that the computer-readable storage medium in which the instructions are stored includes a manufactured article containing instructions, and the instructions implement aspects of the functions / actions specified in the blocks or multiple blocks in the flowchart and / or block diagram.

[0723] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing device, or other device to cause a series of operational steps to be performed on the computer, other programmable device, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable device, or other device implement the functions / actions specified in a box or multiple boxes in the flowchart and / or block diagram.

[0724] The method of the present invention also includes step (f): selecting:

[0725] (i) the pharmacodynamic parameter value or values ​​determined according to step (e)(i) for each subject in said population of subjects; and / or

[0726] (ii) the activity marker value or values ​​determined according to step (e)(ii) for each subject in said population of subjects; and / or

[0727] (iii) a normalized marker value or multiple normalized marker values ​​determined according to step (e)(iii) for each subject in said population of subjects, and / or

[0728] (iv) a value of a clinical variable or values ​​of a plurality of clinical variables for each subject in said population of subjects,

[0729] It depends on clinical resistance or clinical sensitivity to the drug combination, whereby a value is dependent when the probability that the value is independent of clinical resistance or clinical sensitivity is less than or equal to 0.05. A value depends on clinical resistance or clinical sensitivity when the probability that the value is independent of clinical resistance or clinical sensitivity is less than or equal to 0.05 as determined using a statistical independence test.

[0730] Clinical variables are variables different from any one of the pharmacodynamic parameter value, the active marker value or the normalized marker value. The clinical variables can be selected from the following groups: subject age, sex, race, blood type, white blood cell count presented, the presence of mutations in NPM1 or FLT3 genes, cytogenetic risk group, whether the subject has experienced or not experienced the first-line treatment of the disease, whether the subject has experienced or not experienced the second-line treatment of the disease, the count of mononuclear cells obtained from peripheral blood (WBC in PB), Eastern Cooperative Oncology Group (Eastern Cooperative Oncology Group, ECOG) performance status, blood cancer type (France-US-UK (FAB) classification, new onset relative to secondary AML), karyotype, hematological response, the number of 3+7 induction cycles, response date, the date of the last follow-up, and whether the subject has experienced or not experienced post-remission therapy. Preferably, the clinical variables are selected from the following groups: subject age, sex, race, blood type, white blood cell (WBC) count presented, the presence of mutations in NPM1 or FLT3 genes, cytogenetic risk group, whether the subject has experienced or not experienced the first-line treatment of the disease, whether the subject has experienced or not experienced the second-line treatment of the disease. More preferably, the clinical variables are selected from the following groups: age at diagnosis, sex, cytogenetic risk group, immature cell %, WBC count, FAB subtype, ECOG performance status, the presence of mutations in NPM1 gene, and the presence of mutations in FLT3 gene. The clinical variable value describes each subject in the population of subjects suffering from the disease, and can be a continuous number or an integer, depending on the variable described in this way. For example, when the subject has experienced the first-line treatment of the disease, the clinical variable value can be 0, and when the subject has not experienced the first-line treatment of the disease, the clinical variable value can be 1, or vice versa.

[0731] In each system embodiment of the invention, the means for performing step (f) of selecting comprises at least one computer program product, wherein said computer program product may be different from the computer program product used in step (e).

[0732] Therefore, in a particularly preferred embodiment of the method of the present invention, steps (a) to (f) respectively comprise the following:

[0733] (a) separating a tissue sample obtained from the subject into 20 to 30 sub-samples;

[0734] (b) perform the following steps:

[0735] (i) incubating the subsample in the presence of said drug A at a concentration X for a time T ranging from 36 to 72 hours; and

[0736] (ii) repeating step (b)(i) an additional (N-1) times, each time using a different subsample, using a different value of X from the value of X used in the previous repetition of step (b)(i);

[0737] wherein N is an integer selected from 5 to 10, inclusive;

[0738] as well as

[0739] (iii) incubating the subsample in the presence of the drug B at a concentration Y for the time T; and

[0740] (iv) repeating step (b)(iii) an additional (M-1) times, each time using a different subsample, using a different value of Y from the value of Y used in the previous repetition of step (b)(iii);

[0741] wherein M is an integer selected from 5 to 10, inclusive;

[0742] as well as

[0743] (v) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug B, wherein

[0744] - the concentration of drug A is a concentration X corresponding to X obtained in said population from each subject diagnosed with said disease 50,A The percentile value P of the distribution of values Hα,A The concentration at which the percentile value P Hα,A Calculated by formula (A):

[0745] P Hα,A =cos(α°)x H

[0746] (A)

[0747] in:

[0748] H corresponds to the reference percentile of the group selected from:

[0749] and 90, where:

[0750] - r is an integer selected from 2 to (R-1), including 2 and R-1;

[0751] α is in degrees and is calculated as follows:

[0752]

[0753] in:

[0754] -w is an integer selected from 1 to W, including 1 and W;

[0755] - the concentration of drug B is a concentration Y corresponding to Y obtained in the population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values Hα,B The concentration at which the percentile value P Hα,B Calculated by formula (B):

[0756] P Hα,B =cos(90°–α°)x H

[0757] (B)

[0758] (vi) repeating step (b)(v) an additional (R-1) times, each time using a different subsample, using a different value of H than the value of H used in the previous repetition of step (b)(v), and using the same value of w used in step (b)(v);

[0759] (vii) repeating steps (b)(v) and (b)(vi) an additional (W-1) times, each time using a different subsample, using a different value of w from the value of w used in the previous repetition of steps (b)(v) and (b)(vi);

[0760] in;

[0761] R is 3;

[0762] W is 3,

[0763] And among them:

[0764] ·X 50,A is the concentration of drug A that exerts half of its maximal activity in the subject estimated according to step (e)(i) below;

[0765] ·Y 50,B is the concentration of drug B that exerts half of its maximal activity in the subject estimated according to step (e)(i) below;

[0766] as well as

[0767] (viii) incubating the subsample for the time T;

[0768] (c)(i) adding at least one conjugated antibody to each subsample incubated in step (b) to identify at least one pathological cell type therein; and

[0769] (ii) adding at least one cell death or apoptosis marker to each subsample incubated in step (b) to identify apoptotic cells therein;

[0770] (d) counting the number of viable cells (LCTi) of each cell type identified in step (c) that remain after incubation of each subsample according to step (b) by counting the number of cells of the at least one pathological cell type identified according to step (c)(i) that are not identified as apoptotic according to step (c)(ii);

[0771] (e) for each cell type identified in step (c), determine:

[0772] (i) Pharmacodynamic parameter values, including X 50,A Value, LCTi 0,A Value, E max,A Value, γ A Value, Y 50,B Value, LCTi 0,B Value, E max,B Value and γ B Values, where:

[0773] -X 50,A Value, LCTi 0,A Value, E max,A Value and γ A The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (I) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(i) and (b)(ii) obtained for each concentration X of drug A:

[0774]

[0775] -The Y 50,B Value, LCTi 0,B Value, E max,B Value and γ B The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (II) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(iii) and (b)(iv) obtained for each concentration Y of drug B:

[0776]

[0777] wherein the population comprises the subject and other subjects diagnosed with the disease;

[0778] in:

[0779] X = concentration of drug A;

[0780] ·X 50，AIt is the concentration of drug A that exerts half of its maximal activity;

[0781] ·LCTi 0，A is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii);

[0782] ·E max，A It is LCTi caused by drug A 0，A The maximum score is reduced;

[0783] γ A is the steepness of the LCTi-concentration curve of drug A;

[0784] Y = concentration of drug B;

[0785] ·Y 50，B is the concentration of drug B that exerts half of its maximal activity;

[0786] ·LCTi 0，B is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii);

[0787] ·E max，B It is LCTi caused by drug B 0，B The maximum score is reduced;

[0788] γ B is the steepness of the LCTi-concentration curve of drug B;

[0789] (ii) Activity marker values, including AUC xy，A Value, AUC xy，B Value, α AB Value and VUS AB Values, where:

[0790] - The AUC xy，A The value is calculated using formula (III):

[0791] AUC xy，A =AUC x，A -A y：10-90，A

[0792] (III)

[0793] in:

[0794] The AUC x，AThe value is the integral between two drug concentrations X' and X" after incubation of the subsamples according to steps (b)(i) and (b)(ii) obtained for each concentration x of drug A by the function of formula (I) derived from the survival rate %, where LCTi 0，A was considered as 100% survival and was calculated using formula (IV):

[0795]

[0796] wherein drug concentrations X' and X" correspond to X obtained in each subject diagnosed with the disease 50，A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50，A calculated according to steps (a) to (e)(i);

[0797] as well as

[0798] The A y：10-90，A Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x，A Surface, where LCTi 0，A Considered as 100% survival rate;

[0799] as well as

[0800] - The AUC xy，B The value is calculated using formula (V):

[0801] AUC xy，B =AUC x，B -A y：10-90，B

[0802] (V)

[0803] in:

[0804] The AUC x，B The value is the integral between two drug concentrations Y' and Y" after incubation of the subsamples according to steps (b)(iii) and (b)(iv) obtained for each concentration Y of drug B by the function of formula (II) derived from the survival rate %, where LCTi 0，B was considered as 100% survival and was calculated using formula (VI):

[0805]

[0806] wherein drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease 50，B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50，B calculated according to steps (a) to (e)(i);

[0807] as well as

[0808] The A y：10-90，B Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x，B Surface, where LCTi 0，A Considered as 100% survival rate;

[0809] as well as

[0810] -The VUS AB The value is calculated using formula (VII), where VUS AB The value is the double integral between two drug concentrations X' and X" of drug A and two drug concentrations Y' and Y" of drug B of the model function of the natural logarithm of LCTi counted after incubation of the subsamples in steps (b)(v) to (b)(vii), where LCTi 0，A =LCTi 0，B , and was considered as 100% survival rate and calculated using formula (VII),

[0811]

[0812] in:

[0813] Drug concentrations X' and X" correspond to the X obtained in each subject diagnosed with the disease in the population 50，A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to steps (a) to (e)(i);

[0814] Drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease in the population 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to steps (a) to (e)(i);

[0815] ·E max,A = Maximum score reduction of LPC caused by drug A;

[0816] ·E max,B = Maximum score reduction of LPC caused by drug B;

[0817] ·X 50,A = Play E max,A Half of the EC of drug A 50 concentration;

[0818] ·Y 50,B = Play E max,BHalf of the EC of drug B 50 concentration;

[0819] X = concentration of drug A;

[0820] Y = concentration of drug B;

[0821] ·

[0822] in:

[0823] -γA = steepness of the LCTi-concentration curve of drug A;

[0824] -γB = steepness of the LCTi-concentration curve of drug B; and

[0825] α AB = the synergy parameter estimated by a two-drug surface interaction model determined by fitting formula (VII') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to the steps mentioned in (b)(i) and (b)(ii) obtained for each concentration X of Drug A, the steps mentioned in (b)(iii) and (b)(iv) obtained for each concentration Y of Drug B, and the steps mentioned in b(v), b(vi) and b(vii) obtained for each pair of concentrations of the combination of Drug A and Drug B:

[0826]

[0827] (iii) Normalized marker values, including NAUC A ,NAUC B and / NVUS AB ,in:

[0828] -NAUC A is the AUC calculated using formula (VIII) xy,A The normalized value of

[0829] NAUC A = 100 x AUC xy,A / AUC max,A (VIII)

[0830] -NAUC B is the AUC calculated using formula (IX) xy,B The normalized value of

[0831] NAUC B = 100 x AUC xy,B / AUC max,B (IX)

[0832] -NVUSAB is the VUS calculated using formula (X) AB The normalized value of

[0833] NVUS AB =100 x VUS AB / VUS max,AB (X)

[0834] in:

[0835] AUC max,A = AUC obtained in each population of subjects diagnosed with the disease xy,A The maximum value of the AUC for each subject in the population xy,A calculated according to steps (a) to (e)(ii);

[0836] AUC max,B = the AUC obtained in the population of each subject diagnosed with the disease xy,B The maximum value of the AUC for each subject in the population xy,B calculated according to steps (a) to (e)(ii); and

[0837] ·VUS max,AB = VUS obtained in the population per subject diagnosed with the disease AB The maximum value of the VUS for each subject in the population AB calculated according to steps (a) to (e)(ii);

[0838] (f)Select:

[0839] (i) the pharmacodynamic parameter value or values ​​determined according to step (e)(i) for each subject in said population of subjects; and / or

[0840] (ii) the activity marker value or values ​​determined according to step (e)(ii) for each subject in said population of subjects; and / or

[0841] (iii) a normalized marker value or multiple normalized marker values ​​determined according to step (e)(iii) for each subject in said population of subjects, and / or

[0842] (iv) a value of a clinical variable or values ​​of a plurality of clinical variables for each subject in said population of subjects,

[0843] which depends on clinical resistance or clinical sensitivity to the drug combination, whereby a value is dependent when the probability that the value is independent of clinical resistance or clinical sensitivity is less than or equal to 0.05,

[0844] wherein each of said clinical variables is selected from the group consisting of: age at diagnosis, sex, cytogenetic risk group, % immature cells at diagnosis, WBC count, FAB subtype, ECOG performance status, presence of mutations in the NPM1 gene, and presence of mutations in the FLT3 gene;

[0845] in:

[0846] The drug combination contains 2 or 3 drugs, among which

[0847] - Drug A is cytarabine and Drug B is idarubicin; and

[0848] - the drug combination optionally comprises an additional drug C selected from the group consisting of fludarabine, etoposide, thioguanine and clofarabine;

[0849] The tissue is bone marrow; and

[0850] The disease is acute sporadic leukemia.

[0851] The method for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease further comprises the further subsequent steps (g), (h), (j) and (k). Conversely, the method for classifying the effectiveness of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease further comprises the further final steps (g'), (h') and (j').

[0852] In a method for determining the efficacy of treatment with a drug combination comprising drug A and drug B in subjects diagnosed with a disease, step (g) comprises creating a response function using a generalized linear model (GLM) or a generalized additive model (GAM) for the drug combination for the population of subjects using at least one of the values ​​selected in step (f), wherein the area under the curve of a receiver operating characteristic (ROC) curve derived from the model function is equal to or greater than 0.8, and the lower limit of the 95% confidence interval of the area under the curve is greater than 0.5; in an embodiment of a system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in subjects diagnosed with a disease, the device for performing step (g) of creating a response function comprises at least one computer program product.

[0853] In the method for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, step (h) comprises calculating a threshold limit of the response function created in step (g) from a point on the receiver operating characteristic curve:

[0854] - the point at which sensitivity and specificity are maximized and have equal values ​​(maxSpSe); or

[0855] - specificity takes precedence over sensitivity at that point (mMCT); or

[0856] - The point is closest to the (1,0) coordinate plane (geometry).

[0857] Preferably, sensitivity and specificity are maximized at said point and are equal in value. However, a reduced threshold (achieved when specificity takes precedence over sensitivity) will result in fewer false negatives (and more false positives), corresponding to a rightward shift on the ROC curve. In an embodiment of a system for determining the efficacy of a drug combination comprising drug A and drug B for treatment in a subject diagnosed with a disease, the means for performing step (h) of calculating the threshold of the response function comprises at least one computer program product.

[0858] In the method for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, step (j) comprises calculating the S / R value (i.e., a sensitivity / resistance value indicating clinical sensitivity / resistance) of the drug combination for the disease in the subject using the response function created in step (g) and:

[0859] (i) the pharmacodynamic parameter value or multiple pharmacodynamic parameter values ​​of the subject determined according to step (e)(i); and / or

[0860] (ii) the activity marker value or values ​​of the subject determined according to step (e)(ii); and / or

[0861] (iii) a normalized marker value or multiple normalized marker values ​​determined according to step (e)(iii) for the subject; and / or

[0862] (iv) a value of a clinical variable or values ​​of multiple clinical variables of the subject,

[0863] It is a variable in the response function. In other words, by introducing the values ​​mentioned under (j)(i) to (j)(iv) into the response function (where relevant), the S / R value of the drug combination for the disease in the subject is calculated. In an embodiment of the system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, the means for performing step (j) of calculating the S / R value comprises at least one computer program product.

[0864] In the method for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, step (k) comprises determining the efficacy of treatment of the disease with the drug combination in the subject by comparing the S / R value calculated in step (j) with the threshold calculated in step (h), wherein

[0865] - when the S / R value is equal to or greater than the threshold, the disease is sensitive to treatment with the drug combination in the subject; and

[0866] -When the S / R value is less than the threshold, the disease is resistant to treatment with the drug combination in the subject. In addition, when the S / R value is equal to or greater than the threshold and the difference between the S / R value and the threshold is large (and positive), the disease is more sensitive to treatment with the drug combination in the subject than when the S / R value is equal to or greater than the threshold and the difference between the S / R value and the threshold is small (and positive) or zero. Similarly, when the S / R value is less than the threshold and the difference between the S / R value and the threshold is large (and negative), the disease is more resistant to treatment with the drug combination in the subject than when the S / R value is less than the threshold and the difference between the S / R value and the threshold is small (and negative) or zero. In an embodiment of a system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, the means for performing step (k) of determining the efficacy of treating the disease with the drug combination in the subject comprises at least one computer program product.

[0867] Thus, in one embodiment of a system for determining the efficacy of treatment with a drug combination comprising Drug A and Drug B in a subject diagnosed with a disease:

[0868] - the apparatus for carrying out the step of isolating the tissue sample comprises a microfluidic stem cell isolation device;

[0869] - the apparatus for carrying out the incubation step mentioned in (b) comprises a cell culture incubator;

[0870] - the means for carrying out the step of adding at least one marker comprises a pipette or an injector;

[0871] - the means for carrying out the step of counting the number of viable cells comprises a cytometer;

[0872] - means for carrying out the steps of determining a pharmacodynamic parameter value, an active marker value and a normalized marker value comprising at least one computer program product;

[0873] - the means for performing the step of selecting comprises at least one computer program product;

[0874] - the means for carrying out the step of creating a response function comprises at least one computer program product;

[0875] - means for carrying out the step of calculating the threshold of said response function comprises at least one computer program product;

[0876] - the means for carrying out the step of calculating the S / R value comprises at least one computer program product; and

[0877] - Means for determining the efficacy of treating said disease in said subject using said drug combination comprises at least one computer program product.

[0878] In a method for classifying the effectiveness of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease, step (g') comprises calculating a score S for treating the subject using the drug A and the drug B, wherein the score corresponds to or is calculated using at least one of the values ​​selected in step (f). In a preferred embodiment of the method for classifying the effectiveness of a drug combination, the score S is:

[0879] (i) in step (g') using formula (XIX) to calculate:

[0880]

[0881] in:

[0882] ·(NAUC) d = the normalized NAUC value of each drug included in the treatment;

[0883] ·(NVUS) cd = the normalized NVUS for each drug combination included in the treatment;

[0884] D = the number of drugs included in the treatment;

[0885] C = the number of drug combinations included in the treatment; and

[0886] f = compensation factor for multiple drug treatments, where:

[0887]

[0888] or

[0889] (ii) is selected from (NVUS) cd The value of NVUS (ie, S = NVUS AB, or when the drug combination comprises drug A, drug B and drug C, S is selected from NVUS AB NVUS AC and NVUS BC ).

[0890] In an embodiment of the system for determining a classification of the utility of a drug combination comprising Drug A and Drug B in treating a subject diagnosed with a disease, the means for performing step (g') of calculating a score comprises at least one computer program product.

[0891] In the method for classifying the effectiveness of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease, step (h') includes performing steps (b) to (f) and (g') for each drug combination to be classified. In an embodiment of the system for classifying the effectiveness of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease, the means for performing the steps mentioned in (h') are the same as those according to the steps mentioned in (a) to (f) and (g').

[0892] In the method for classifying the utility of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease, step (j') comprises classifying each drug combination using the scores determined in steps (g') and (h') such that:

[0893] (i) Drug combinations with a score greater than 80 are classified into classification category I with a classification value of 2;

[0894] (ii) drug combinations with a score less than or equal to 80 and greater than 60 were classified into classification category II with a classification value of 1;

[0895] (iii) drug combinations with a score less than or equal to 60 and greater than 40 were assigned to classification category III with a classification value of 0;

[0896] (iv) drug combinations with a score less than or equal to 40 and greater than 20 are classified as classification category IV with a classification value of -1; or

[0897] (v) drug combinations with a score less than or equal to 20 are classified into classification category V with a classification value of -2,

[0898] thereby:

[0899] - each drug combination assigned to a classification class having a positive classification value or a zero classification value has the highest or greatest utility in the treatment of a subject diagnosed with the disease; and

[0900] -Each drug combination belonging to the classification category with a negative classification value has the lowest or minimum utility in the treatment of a subject diagnosed with a disease.Each drug combination belonging to the classification category with a positive classification value or a zero classification value is a drug combination to which the disease is expected to be sensitive in the subject.Similarly, each drug combination belonging to the classification category with a negative classification value is a drug combination to which the disease is expected to be resistant in the subject.Preferably, each drug combination belonging to the classification category with a positive classification value has the maximum utility in the treatment of a subject diagnosed with a disease, and each drug combination belonging to the classification category with a negative classification value has the minimum utility in the treatment of a subject diagnosed with a disease.More preferably, each drug combination belonging to the classification category with the highest positive value (i.e., classification category I) has the maximum utility in the treatment of a subject diagnosed with a disease. In a particularly preferred embodiment, each drug combination having a score greater than 80, more preferably greater than 85, and even more preferably greater than 90 is a drug combination belonging to a classification category (i.e., classification category I) with the highest positive classification value, which is the most effective in the treatment of a subject diagnosed with a disease, and a drug combination belonging to this classification category as determined by the method and system for classifying the effectiveness of a drug combination is selected in the nursing plan for the prescription in the treatment method. Each drug combination falling into this category is a drug combination to which the disease is most sensitive in the subject. Similarly, each drug combination belonging to a classification category (i.e., classification category V) with the lowest negative value has the minimum utility in the treatment of a subject diagnosed with a disease. Each drug combination falling into this category is a drug combination to which the disease is most resistant in the subject predicted. Thus, classification category I corresponds to a classification category that is “more effective” in the treatment of the subject diagnosed with the disease, classification category II corresponds to a classification category that has “intermediate to high” efficacy in the treatment of the subject diagnosed with the disease, classification category III corresponds to a classification category that has “intermediate” efficacy in the treatment of the subject diagnosed with the disease, classification category IV corresponds to a classification category that has “low to intermediate” efficacy in the treatment of the subject diagnosed with the disease, and classification category V corresponds to a classification category that has “less effective” efficacy in the treatment of the subject diagnosed with the disease ( Fig.18 ).

[0901] Alternatively, the classification is such that:

[0902] (i) Drug combinations with a score greater than (100-JJ) are classified into classification category I' with a classification value of 1;

[0903] (ii) drug combinations with a score less than or equal to (100-JJ) and greater than JJ are classified into classification category III' with a classification value of 0; or

[0904] (iii) drug combinations with a score less than or equal to JJ are classified into classification category V' with a classification value of -1,

[0905] wherein JJ is a value selected from 5 to 30, preferably wherein JJ is a value selected from 10 to 20,

[0906] Wherein each drug combination assigned to a classification category with a positive classification value has the greatest utility in the treatment of a subject diagnosed with a disease, and each drug combination assigned to a classification category with a negative classification value has the least utility in the treatment of a subject diagnosed with a disease. In this alternative form of classification, classification category I' corresponds to classification category I disclosed above, classification category III' corresponds to classification categories II, III and IV disclosed above, and classification category V' corresponds to classification category V disclosed above.

[0907] In an alternative to step (j'), the method for classifying the effectiveness of a drug combination comprising drug A and drug B in the treatment of a subject diagnosed with a disease may include step (j'), wherein step (j') includes ranking each drug combination according to the score determined in steps (g') and (h'), such that the drug combination with the highest score S is the drug combination with the greatest effectiveness in the treatment of a subject diagnosed with a disease ( Fig.19 ).

[0908] In an embodiment of a system for ranking the effectiveness of drug combinations comprising Drug A and Drug B in treating a subject diagnosed with a disease, means for ranking each drug combination in a scoring order comprises at least one computer program product.

[0909] Thus, in one embodiment of a system for classifying the utility of a drug combination comprising Drug A and Drug B in the treatment of a subject diagnosed with a disease:

[0910] - the apparatus for carrying out the step of isolating the tissue sample comprises a microfluidic stem cell isolation device;

[0911] - the apparatus for carrying out the incubation step mentioned in (b) comprises a cell culture incubator;

[0912] - the means for carrying out the step of adding at least one marker comprises a pipette or an injector;

[0913] - the means for carrying out the step of counting the number of viable cells comprises a cytometer;

[0914] - means for carrying out the steps of determining a pharmacodynamic parameter value, an active marker value and a normalized marker value comprising at least one computer program product;

[0915] - the means for performing the step of selecting comprises at least one computer program product;

[0916] - the means for performing the step of calculating a score comprises at least one computer program product; and

[0917] - the means for sorting each drug combination in order of score comprises at least one computer program product;

[0918] The apparatus used to carry out the step mentioned in (h') are the same as those used in the steps mentioned in (a) to (g').

[0919] Note that the steps for determining the activity markers (AUC and VUS) disclosed in step (e)(ii) of the present invention include the steps of limiting the calculation of the activity marker to the concentration range and activity range that produces the most informative result (i.e., AUCxy). When the calculation of the activity marker is limited to the concentration range and activity range that produces the most informative result, an improved correlation is achieved compared to the correlation obtained when the calculation of the activity marker uses the entire concentration and activity range.

[0920] For a range of drug concentrations, such limits are defined by the statistical values ​​of the distribution of results from the entire population and correspond to the EC 50 For the activity range (% survival), the normalized values ​​were restricted to the 10% to 90% interval. These limits are Fig.17 , where the rectangle bounded by the limits represents the rectangle over which the correlation is generated.

[0921] Therefore, the present invention also discloses a normalization method using the AUCxy value as a reference for a reference area, which is defined by a rectangle described by the same restrictions as above, which are defined by the entire population results and the 10%-90% response interval. The output value NAUCxy (i.e., N_AUCxy) ranges from 0 to 100% and is equivalent to a percentile value range, but when calculated in a population that does not follow a symmetric normal distribution, it overcomes the limitations of the range percentile. For example, in a very asymmetric distribution, the smallest change in activity may mean a huge change in the range percentile. The distribution of clinical responses to cytotoxic therapy (especially in first-line patients) is often very asymmetric, showing a higher trend towards sensitive responses.

[0922] By considering the behavior of the entire population as a benchmark, this normalization provides a standard value for the response of a patient sample to a treatment. In addition, this value enables the activity observed for multiple treatments to be classified. The classification of activities allows for an estimate of the efficacy of each treatment as well as a rough comparison of the treatments provided. The criteria for classifying the activities can be different from multiple equally spaced intervals or intervals defined by any other criteria, including, for example, references to known response rates.

[0923] Therefore, this normalization enables the generation of a classification of multiple treatments, which can be recommended to each individual patient as a treatment guide. In order to achieve the above classification, several factors are considered, such as activity, sensitivity, clinical efficacy and toxicity. The activity of drugs and combination treatments can be evaluated by calculating AUC and synergy. The result is that there is a range of activity from higher to lower in the patient samples treated each time. Samples showing higher activity represent patients determined to be more sensitive to the treatment. On the contrary, samples showing lower activity represent patients determined to be more resistant to the treatment. In other words, activity reflects the sensitivity or resistance of patients to a given treatment. It is helpful to define a threshold to classify patients as sensitive, uncertain or resistant. For this threshold, the clinical efficacy of treatment is relevant; for example, in acute myeloid leukemia (AML), the standard first-line treatment of cytarabine + idarubicin (CYT + IDA) achieves complete remission (good response) in 70% of patients. Therefore, as shown in Example 1 (CYT-IDA correlation), the prediction algorithm can identify about 70% of first-line patients as sensitive and about 30% of patients as resistant. Alternatively, simpler scores based on AUC and synergy without clinical relevance to validate the thresholds can arbitrarily define safe extreme thresholds, such as estimating the 20% of samples showing the highest activity scores to predict patients who are highly sensitive to the treatment. Preferably, the 20% of samples showing the highest activity scores are those falling into classification category I with scores greater than 80, more preferably 15% of samples showing the highest activity scores are those falling into classification category I with scores greater than 85, and even more preferably 10% of samples showing the highest activity scores are those falling into classification category I with scores greater than 90, and are estimated to predict patients who are highly sensitive to those treatments whose scores fall into said classification category I. Similarly, the 20% of samples showing the lowest activity scores are estimated to predict patients who are highly resistant to said same treatment.

[0924] The above prediction of sensitive patients relative to resistant patients (which identifies the patients most sensitive to a given treatment) is only meaningful in the same treatment. It is difficult to compare such activity score classification with another different treatment, and often leads to confusion. For example, there are several cytotoxic drugs with similar average activity in AML, but many treatments combine 2 drugs and other treatments combine 3 drugs (including these 2 drugs plus the third drug). If we assume that the dosage of all 3 drugs is similar, then 3-drug treatment just includes 2-drug treatment plus an additional third drug. When the third drug has good or significant activity, 3-drug treatment has a greater cytotoxic ability than 2-drug treatment. However, the sensitivity classification of the two treatments does not reflect this fact. On the contrary, sensitivity estimation is unfavorable to 3-drug treatment, because if the sensitivity of the third drug is lower than the other 2 drugs, the overall 3-drug sensitivity will be lower than 2-drug treatment, but 3-drug treatment is stronger. This example shows that the measurement of the sensitivity of the patient to a given treatment (which can be calculated by the activity of the treatment described in the representative population of the patient) cannot be used to compare different treatments of the same patient.

[0925] Different toxicities among different treatments, and even different toxicities among different approved doses of the same treatment, or among younger and healthier versus older and frail patients, make comparisons of different treatments even more challenging.

[0926] This is particularly difficult to compare treatments because the absolute values ​​of their activities (e.g., AUC) are not normalized to compare drug to drug, treatment to treatment. The new method proposed here to normalize AUC based on a population's historical activity record enables direct comparisons between different treatments in terms of their sensitivity to individual patients. In individual patient samples, the activity score (e.g., AUC) can be used to quantitatively estimate the relative sensitivity of the patient sample to each treatment (normalized in a population of similar patients). Fig.19 The ratings obtained by categorizing different types of patients using this particular approach are shown.

[0927] In one embodiment, each of the systems of the present invention further comprises a device for prescribing a care plan for a subject, wherein when the disease is determined to be sensitive to treatment with the drug combination in the subject (the drug combination is determined to be effective for the subject in the treatment of the subject), the care plan prescribes the drug combination. Accordingly, each of the methods or systems of the present invention for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease may also comprise a device for prescribing a care plan for a subject, wherein when the disease is determined to be sensitive to treatment with the drug combination in the subject, the care plan prescribes the drug combination (i.e., the care plan is prescribed for the subject, wherein the drug combination is determined to be effective for the subject in the treatment of the disease in the subject). Therefore, the present invention also relates to the use of the method or system in prescribing the care plan for the subject.

[0928] Similarly, the method and system of the present invention for classifying the effectiveness of a drug combination each comprising drug A and drug B in the treatment of a subject diagnosed with a disease may also include prescribing a care plan for the subject, wherein the care plan prescribes a drug combination selected from a drug combination classified as having the highest effectiveness in the treatment of the disease in the subject. Therefore, the present invention also relates to the use of the method or system in prescribing the care plan for the subject.

[0929] Furthermore, the present invention relates to a method of treating a subject diagnosed with a disease, the method comprising administering a drug combination to the subject, the disease being determined to be sensitive to treatment with the drug combination in the subject when according to a method or system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease.

[0930] Similarly, the present invention relates to a method for treating a subject diagnosed with a disease, the method comprising administering a drug combination selected from a group consisting of drug combinations that are classified as having the highest efficacy in the treatment of the disease in the subject according to a method or system for classifying the efficacy of a drug combination comprising drug A and drug B in the treatment of the subject diagnosed with the disease.

[0931] The present invention also relates to the use of a method or system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease in determining whether a given subject from a population of subjects each diagnosed with a disease is suitable for inclusion in a clinical trial involving treatment with a drug combination comprising drug A and drug B, wherein:

[0932] - when the disease is determined to be susceptible to treatment with the drug combination in the subject, selecting the subject for inclusion in the clinical trial; and

[0933] - when the disease is determined to be resistant to treatment with the drug combination in the subject, the subject is not selected for inclusion in the clinical trial.

[0934] Similarly, the present invention relates to the use of a method and system for classifying the effectiveness of drug combinations each comprising drug A and drug B in the treatment of subjects diagnosed with a disease in determining whether a given subject from a population of subjects each diagnosed with a disease is suitable for inclusion in a clinical trial involving treatment with a drug combination comprising drug A and drug B, wherein:

[0935] - selecting the subject for inclusion in the clinical trial when the drug combination is classified as having the greatest efficacy in the treatment of the disease in the subject; and

[0936] -When the drug combination is classified as having the highest efficacy in the treatment of the disease in the subject, the subject is not selected for inclusion in the clinical trial. Such use of the methods and systems of the present invention determines whether a given subject from a population of subjects each diagnosed with a disease is suitable for inclusion in a clinical trial involving treatment with a drug combination comprising drug A and drug B. These are found to be particularly useful when a drug or combination thereof has not yet been approved by a regulatory agency but a clinical trial is required to obtain approval. Therefore, the drug selected for inclusion in the clinical trial according to the method is a companion diagnostic (CDx) and is a class of biomarkers for the drug treatment. The companion diagnostic is used to select patients who can be treated with the treatment, which in clinical trials means a selection criterion; only patients identified by the CDX as being sensitive to the treatment can be included in the trial. Example

[0937] The following examples illustrate the invention and should not be considered limiting, but rather illustrating the invention.

[0938] Materials and methods

[0939] (i) Sample

[0940] 123 patients 18 years and older with newly diagnosed acute myeloid leukemia (AML) (de novo or secondary to myelodysplastic syndrome, or treatment related) who were monitored were used for correlation analysis. All 123 patients received first-line therapy with idarubicin + cytarabine (IDA+CYT) 3+7 (a dosing regimen consisting of 7 days of standard-dose cytarabine followed by 3 days of idarubicin, as described below). The series used was expanded to 473 patients, including all adult patients treated with other first-line regimens, to establish a population-based pharmacodynamic (PD) model.

[0941] The diagnosis and classification of AML are based on the WHO (World Health Classification) criteria (Vardiman, et al 2002).

[0942] (ii) Chemotherapy regimens, drugs and assessments

[0943] Drugs were sourced from Sigma Aldrich and Selleck Chemicals.

[0944] Induction therapy in Examples 1 and 2 consisted of up to two cycles of the following combination: intravenous (IV) idarubicin (IDA) (12 mg / m 2 / day), and IV continuous infusion of cytarabine (CYT) (200 mg / m 2 / day). After the first cycle, a second 3+7 induction cycle was administered in patients showing partial remission (PR). Patients were considered responders if they achieved complete remission (CR) or incomplete recovery (CRi) within the first two identical 3+7 induction cycles. Patients who died during induction before response assessment were considered unevaluable. The remaining patients were classified as resistant.

[0945] (iii) PharmaFlow PM measurement

[0946] A representative workflow for a PharmaFlow PM test is as follows: Figure 1 As shown, the experimental and analytical methods applied in this study are collected.

[0947] a) Whole bone marrow sample in native environment

[0948] Using the PharmaFlow platform (formerly known as ) for ex vivo drug sensitivity analysis. A minimum BM sample volume of 1 to 2 mL was collected by aspiration at the time of AML diagnosis before starting induction chemotherapy and processed by automated methods 24 hours after extraction. The samples were incubated with CYT, IDA and / or CYT+IDA for 48 hours. A more detailed description of this procedure has been published elsewhere. (Bennett, et al 2014)

[0949] b) Modeling of the in vitro activities of CYT, IDA and their combination

[0950] Drug response was assessed by counting the number of viable pathological cells (LPC) remaining after incubation with increasing drug concentrations (Upton & Mould 2014). Annexin V-FITC was used to exclude dead cells (apoptosis). Pharmacological responses were analyzed using a PD population-based model (Upton & Mould 2014) that fit the dependent variable (natural logarithm of LPC) to a nonlinear mixed-effects model to derive typical population values ​​(fixed effects) as well as between-patient magnitude and residual variability (random effects).

[0951] Model development was performed using the first-order conditional estimation method using the interactive option with the software NONMEM (v7.2) (Beal, et al 1989-2001) according to the following equation:

[0952]

[0953] The parameter LPC0(LCTi0) refers to the number of LPCs (LCTi) after incubation in the absence of drug, Emax represents the maximum fractional decrease in LPCs that can be caused by the drug, and EC 50 Is to play E max , half of the drug concentration, and γ is a parameter that controls the steepness of the LPC versus drug concentration (C) curve. For interaction analysis, the surface interaction model (Greco, et al 1995) was used to estimate the degree of systemic interaction between the two drugs (called the α parameter).

[0954] The inter-patient variability (IPV) associated with all parameters was described by an exponential model of variable components. Additional error structures were used for residual variability. Bone marrow (BM) samples from 473 patients incubated with CYT, 456 patients incubated with IDA, and 443 patients incubated with CYT+IDA were used to construct a population PD model. The Bayesian estimation method was then used to retrieve individual patient parameters based on available exposure-response measurements together with PD population parameters.

[0955] The population PD model was evaluated by visual prediction inspection based on the simulation program (Bergstrand, et al2011). Five hundred experimental scenarios identical to the original experimental scenarios were simulated using the selected model and corresponding parameters. In each simulation group and each concentration level, the 2.5th, 50th and 97.5th percentiles of the LPC distribution were calculated, and then the 95% confidence intervals of the above percentiles were calculated and presented in a graph together with the 2.5th, 50th and 97.5th percentiles obtained from the original data.

[0956] c) Modeling the probability of clinical relevance of clinical outcomes

[0957] Individual response curves normalized to LPC0 (LCTi0) were plotted at the position corresponding to the estimated individual EC 50 The concentration points of the 20th and 80th percentiles of the distribution of values ​​were integrated to obtain the value of the area under the curve (AUC) used as a description of the ex vivo drug effect (i.e., the higher the AUC, the lower the cytotoxic effect (efficacy or potency) of the drug.

[0958] Individual AUC values ​​are associated with actual patient responses after induction therapy (non-responders [PR or resistant disease] versus responders [complete remission (CR) or complete remission but incomplete recovery (CRi)]). The probability of non-responders was modeled using a binary logistic generalized additive model (GAM) based on a binomial distribution, which included a double or two univariate smooth functions of the AUC of CYT and IDA. In addition, univariate smooth functions of LPC0, α, and the difference in the percentage of LPC in the control wells before and after incubation (to detect any possible effects of spontaneous cell death) were also included, but were discarded later because they were not related to clinical response. In addition, the predictive power of relevant patient characteristics on the efficacy data (age and sex, white blood cell count presented, physical status, mutations in the NPM1 or FLT3 genes, and cytogenetic risk groups) was studied by introducing auxiliary models as parameter model terms. The P-spline basis was used as a smoother for the univariate smooth function; the tensor product of the univariate P-spline was used to construct the bivariate smooth function. All smooth bases have dimension three. The coefficients of the smoothing function are estimated using penalized iteratively reweighted least squares. The minimum of the scaled Akaike information criterion is used to find the optimal value of the smoothing parameter.

[0959] Additional pharmacodynamic parameters other than AUC were evaluated one by one by calculating the GAM model for their clinical predictive value (a total of 69 are listed in Table 1). The best parameters were AUC of CYT and IDA and were used to achieve the best correlation function as described above.

[0960] Table 1. Analyzed efficacy parameters

[0961]

[0962]

[0963]

[0964]

[0965] (iv) Data collection and study endpoints

[0966] Demographic data (sex, age) and the following parameters were prospectively collected since diagnosis: count of mononuclear cells obtained from peripheral blood (WBC in PB), Eastern Cooperative Oncology Group (ECOG) performance status, AML type (French-American-British (FAB) classification, de novo vs. secondary AML), karyotype (Grimwade et al., 2010), FLT3 and NPM1 mutation status, hematological response, number of 3+7 induction cycles, date of response, date of last follow-up, and post-remission therapy. All data collection forms and clinical records were monitored.

[0967] The primary endpoint was to assess the predictive power of ex vivo outcomes. First, the CR / CRi rates observed in patients treated with up to two 3+7 induction cycles were recorded and monitored. This correlated with ex vivo drug sensitivity analysis performed in patients in the same cohort. As a secondary endpoint, the probability of overall survival (OS) was also calculated based on observed and predicted responses after induction.

[0968] Since predicting the outcome of first-line induction therapy may be most beneficial in elderly patients, a sensitivity analysis of clinical relevance was performed by re-running the GAM in a cohort older than 60 years (n=31).

[0969] (v) Statistical analysis

[0970] The response probability model is carried out for statistical calculation (Wood 2006) using the mgcv package (v1.8–23) run in the R environment (v3.4.3). For the probability of being a non-responder from each GAM, the empirical receiver operating characteristic (ROC) curve is calculated. The AUC of the ROC curve is calculated using the trapezoidal rule. In addition, three cut-off points are established for each ROC curve to limit positive and derive classification probability (sensitivity and specificity). One uses geometric criteria, by selecting the point closest to (1,0) coordinates (the upper left corner of [sensitivity, 1-specificity] plane), and another is set by maximizing sensitivity and specificity and another is set by minimizing the misclassification cost item (Greiner 1996), wherein a higher cost than a false negative is assigned to a false positive (specificity is given priority relative to sensitivity).

[0971] OS was described using the Kaplan-Meier method and compared between patients predicted to be nonresponders and responders according to the three different cut-off points described above using simple Cox regression.

[0972] Example 1. Prediction of clinical response after induction therapy with idarubicin and cytarabine

[0973] Here is a method to determine the correlation between the CR / CRi rate observed after 3+7 induction with idarubicin (IDA) and cytarabine (CYT) and the leukemia chemosensitivity measured by an ex vivo test of drug activity. This study included bone marrow samples from adult patients with newly diagnosed AML. Whole bone marrow samples were incubated for 48 hours in well plates containing IDA, CYT, or a combination thereof. Pharmacological response parameters were estimated using a population pharmacodynamic model. Patients who achieved CR / CRi in up to two 3+7 induction cycles were classified as responders, and the rest were resistant. A total of 123 patients met the inclusion criteria and could be evaluated for correlation analysis. The strongest clinical predictor was the area under the curve of the concentration response curve for CYT and IDA. The overall accuracy achieved using the MaxSpSe criteria to define positivity was 81%, predicting responders (93%) better than non-responders (60%). Ex vivo testing provides better but similar information than cytogenetics, but can be provided before treatment, representing a valuable and timely addition.

[0974] result

[0975] (vi) Patient characteristics

[0976] Overall, the laboratory received 954 BM samples from patients suspected of AML. Of these, 316 (33%) could not be evaluated due to the following laboratory technical issues:

[0977] 1) Low sample cellularity (187 patients),

[0978] 2) Low cell viability (less than 60%) in control wells after incubation (67 copies),

[0979] 3) Insufficient sample volume (<500 μL) (38 samples), and

[0980] 4) Other reasons such as clot samples (24). The other 26 patients (3%) did not meet the diagnostic criteria. Of the 612 analyzed samples, 139 were used only for analysis adjustment and did not contain the necessary data for the final model. Overall, 473 patient samples (50%) were used to build the PD model, and a complete data set was monitored in 237 of them (50%). Among the monitored patients, 114 could not be evaluated for correlation analysis due to: 1) induced death (20 patients), 2) non-first-line treatment (11), and 3) other induction regimens (83). Finally, 123 monitored patients (52%) met the inclusion criteria defined in the study and could be evaluated for correlation analysis. The main patient and disease characteristics of these 123 patients are shown in Table 2. In summary, the median age was 50 years (range, 19 to 71 years), 109 patients (89%) were diagnosed with de novo AML, and 21 patients (17%) were classified as having high-risk cytogenetics. Only the cytogenetic risk group and, less frequently, the presence of mutations in the NPM1 gene were significantly associated with clinical response to induction and the results of PM testing. Postremission therapy included allogeneic stem cell transplantation (SCT) in 33 patients (27%) and chemotherapy with or without autologous SCT in 66 patients (54%).

[0981] Table 2. Patient characteristics

[0982] (A) Clinical response and geometric criteria.

[0983]

[0984]

[0985] Patients with missing data were not included in the denominator of relative frequencies.

[0986] a Mann-Whitney test; b Pearson Chi-square test

[0987] (B) Criteria for maximizing specificity and sensitivity and prioritizing specificity over sensitivity.

[0988]

[0989]

[0990] Patients with missing data were not included in the denominator of relative frequencies.

[0991] a Mann-Whitney test; b Pearson Chi-square test

[0992] (vii) Ex vivo PharmaFlow test characterization of the CYT-IDA combination

[0993] Visual prediction check plots were generated for the single-drug PD model ( Fig.11 ). Most of the observations were contained within the simulation-based 95% confidence interval of the 2.5th-97.5th population percentile, demonstrating the good predictability of the selected model. The efficacy population parameters as well as the variability and error values ​​are shown in Table 3. The maximum fractional effect (E max ) were all set to 1 and constrained to the range of 0 to 3. The typical value of the α parameter of the interaction model was 1.1 (Table 3), indicating a slight synergistic interaction between IDA and CYT in the in vitro combination experiment.

[0994] Table 3. Estimation of in vitro population efficacy parameters.

[0995]

[0996] Parameters typical and random (variability and residual error percentage) are shown together with the corresponding relative standard errors (calculated as the ratio between the standard error provided by NONMEM and the estimated value). Estimates of interpatient variability (IPV) are expressed as coefficient of variation (%).

[0997] (viii) Clinical responses in AML patients treated with CYT-IDA

[0998] In 92 of 123 patients (75%) included in the correlation study, CR / CRi was achieved after one (88, 96%) or two (4, 4%) identical induction cycles.

[0999] (ix) Correlation between ex vivo activity and clinical response to CYT-IDA

[1000] Fig.12 Prediction surfaces fitted by the GAM are depicted, representing the probability of being a nonresponder for a range of observed individual AUC values. The proposed model used bivariate smoothing functions for CYT and IDA; models using univariate smoothing gave a poorer fit. Higher CYT and IDA AUC values ​​were associated with a greater probability of being a nonresponder, although the relationship was nonmonotonic. Based on the selected cutoff points, sensitivity / specificity values ​​ranged from 81% / 82% to 61% / 95% ( Fig.13 , Figure A). Although the geometric cutoff balances these two aspects, the cutoff point finally chosen in the ROC curve is MaxSpSe to construct the confusion matrix and derive the classification probability, achieving high values ​​of specificity and sensitivity, as well as good PPV and NPV ( Fig.13 , Panel B). The positive / negative predictive values ​​(PPV / NPV) ranged from 60% / 93% to 79% / 88% [i.e., the accuracy obtained by defining positivity using the above MaxSpSe criteria was 81%, predicting responders (93%, NPV) better than non-responders (60%, PPV)]. Fig.13 In Figure 2, panel B shows the confusion matrix obtained using the MaxSpSe cutoff point.

[1001] Sensitivity analyses showed that the predictive power of the PM test remained unchanged in a cohort aged ≥60 years, although in this case most of the discriminatory information was provided by the CYT data; IDAAUC values ​​were generally higher in older patients.

[1002] (x) Overall survival (OS) based on ex vivo activity and observed clinical response

[1003] Regardless of the cut-off point used to classify them, patients predicted as non-responders had significantly shorter OS than patients predicted as responders (in other words, the estimated OS was significantly better for patients predicted as responders). The median OS in patients predicted as non-responders ranged from 344 to 589 days ( Fig.14 ). This was not achieved in patients predicted to be responders. The hazard ratio (HR) for death (patients predicted to be non-responders vs. responders) ranged from 2.46 (1.38-4.36, panel A) to 3.44 (1.88-6.28, panel C). The values ​​for the groups defined by actual clinical response were similar (median OS in non-responders: 279 days; HR [resistant vs. CR / CRi]: 3.17).

[1004] in conclusion

[1005] This novel approach of ex vivo testing using the PharmaFlow PM platform provided a drug sensitivity parameter that was incorporated into a flexible generalized additive logistic regression model with excellent predictive accuracy for hematologic response after first-line induction with IDA-CYT 3+7. Following validation in an external cohort, our diagnostic tool was able to be used to select AML patients for the 3+7 regimen relative to alternative regimens.

[1006] Example 2.

[1007] The precision medicine (PM) test ranks treatments by a score that estimates the activity of each treatment in a population of patient samples. Higher scores represent greater ex vivo sensitivity to the treatment, while lower scores represent less ex vivo sensitivity to the treatment, i.e., greater ex vivo resistance. PM test results in a specific format are displayed in Fig.19In (panels A and B), the rating of treatments by activity score is shown, from 100% = best to 0% = worst (scores can be presented graphically using color / hue gradients or classifications). The following 3 categories are identified:

[1008] 1. Very responsive to treatment, expected to be sensitive in patients, with a score of 80-100% (top)

[1009] 2. Moderately sensitive to treatment, unable to infer patient sensitivity, 20-80% (intermediate)

[1010] 3. Very resistant to treatment, with a score of 0-20% (bottom)

[1011] Fig.19 The PM test format in compares 32 different treatments for each patient type (sensitive, standard, resistant and very resistant patient types), ranking them in lists by score, with the most sensitive treatments at the top of each list. In the case of sensitive patients, there is only one treatment to which the disease is very sensitive in vitro (see the highest treatment), and is therefore expected to be sensitive in the patient. For the sensitive patients, there are five ex vivo treatments at the bottom of the list to which the disease is predicted to be resistant.

[1012] To validate the predictive power of these PM test score results, a set of 123 samples uniformly treated with CYT+IDA used in the correlation study of Example 1 was used. Only 112 samples were used for validation. Fig.19 In the format shown, panel A refers to sensitive patients, and we can only use patients predicted to be sensitive or resistant. Treatments with intermediate scores are considered insufficient to predict any response. In the case of intermediate scores, other factors such as pharmacokinetics may be more important than the score. On the other hand, extreme scores are more likely to outperform other factors such as pharmacokinetics.

[1013] A set of data from 112 patients treated with CYT+IDA represents the first-line treatment of AML, with a response rate of 72%. This means that 72% of these 112 patients achieved complete remission (CR) clinically. Most patients are sensitive, and a few are resistant. Therefore, statistics are improved by calculating the percentage of cases in which the PM test score predicts that the patient is sensitive to ex vivo treatment. This means that only treatments to which the expected patient is very sensitive are selected, with a score of 80-100%. The percentage of accurate predictions of sensitive (responsive) patients is called negative predictive value (NPV).

[1014] Table 4 and Fig. 20The results of this analysis are shown. The table in the top figure shows different thresholds selected for scoring, from 55 to 100. Samples with scores between this variable threshold and 100% are analyzed and predicted to be sensitive because they have high scores. The results of monotherapy CYT and IDA and combination therapy are shown. Emphasis is placed on the combination therapy CYT+IDA administered to patients. Columns 8 to 10 of Table 4 show the results of the combination therapy CYT+IDA of different thresholds. It means that the lowest threshold 55% (row 3) between 55 and 100% identifies 93 of 112 samples that will be predicted to be sensitive. However, the results in columns 8 and 9 of Table 4 show that among these 93 patients, 16 are resistant clinically and 77 are sensitive. Therefore, the prediction accuracy or NPV is 77 / 93=82.8%. As the threshold increases (column 1 of Table 4, decreasing), the score range included narrows because the maximum value is always 100%. Thus, as the threshold increases, the total number of samples included decreases, reaching 0 samples at 100% (100-100%) and 18 at 95%.

[1015] samples. A decrease in the number of sensitive or resistant samples can be observed (Table 4 columns 8 and 9, decreasing). However, for the 95-100% selection, the NPV or prediction accuracy (%SEN, Table 4 column 10) increases to 94.4%. Therefore, the higher the score, the better the prediction (in addition, the intermediate sensitivity values ​​provide no more accurate predictions than the extreme values).

[1016] A threshold of 80% was chosen for the PM test because it provided a 91% NPV predictive accuracy: on an equivalent set of samples (123 instead of 112), it had a similar predictive accuracy to the correlation analysis shown in Example 1. The line for the 80% threshold is highlighted in italics in Table 4. A total of 65 patients (6+59=65) were included in this 80-100% threshold. This means that 65 of the 112 patients had scores in the top 20%, i.e., the distribution of patient samples in the scores was asymmetric: a higher percentage of samples had higher scores compared to lower scores. The reason for this asymmetry is attributed to the high response rate to first-line AML treatment, where 72% of patients were sensitive and achieved a complete response. Therefore, their asymmetric distribution concentrated in samples with higher scores is consistent with the overall clinical response observed. Fig. 20 , Panel A shows the distribution of sensitive and resistant samples classified according to the score using this 80% threshold and a final NPV or prediction accuracy of 91%.

[1017] Table 4: Number of resistant patients and number and percentage of sensitive patients with scores greater than or equal to the indicated score

[1018]

[1019] In this set of CYT+IDA, comparison of the predictions of the PM test score relative to the correlation of the clinical response data showed that, when a threshold of 80% was selected, the PM test predicted that 58% (65) of the 112 samples were sensitive, with a precision (NPV) of 91%. In contrast, the correlation predicted that 66% of the samples were sensitive, with a precision (NPV) of 93% (see Fig. 20 Figure B). The prediction accuracy or NPV of both methods was similar. The clinical response rate was also similar, 72% for the PM test and 75% for the correlation. The key difference was that the correlation predicted 66% of the samples to be sensitive, compared to 75% that were clinically sensitive, while the PM test predicted 58% of the samples to be sensitive, compared to 72% that were clinically sensitive. Therefore, the PM test is a much simpler method than the correlation analysis, but still predicts sensitivity with similar accuracy.

[1020] Example 3. Prediction and outcome of clinical response after induction therapy with a drug combination comprising drug A and drug B in six patients diagnosed with acute myeloid leukemia

[1021] Bone marrow samples were obtained from six adult patients (00281, 00183, 00218, 00096, 00086 and 00082) diagnosed with AML. According to the same experimental design as described in Example 1, the bone marrow samples were incubated in a well plate containing the drug pairs included in the combination disclosed in Tables 5 and 6 or each individual drug in each combination for 72 hours. The same single drug and drug combination efficacy parameters as shown in Table 3 were determined. The pharmacological response parameters were estimated using a population pharmacodynamic model. The strongest clinical predictors were the area under the curve (AUC) of the single drug concentration response curve and the volume under the surface (VUS) in the drug concentration interaction response surface. According to the expression of formula (XIX) defined herein, once normalized, the score (S) of each combination was calculated using AUC and VUS values.

[1022] Independently, patients 00281 and 00096 were treated with a drug combination comprising cytarabine (ARA-C) and idarubicin (IDA), patients 00183, 00086 and 00082 were treated with a drug combination comprising cytarabine (ARA-C) and daunorubicin (DNR), and patient 00218 was treated with a drug combination comprising cytarabine (ARA-C) and mitoxantrone (MIT). The drug combination is selected for each patient before predicting any given aforementioned drug combination, and the clinical response to any given aforementioned drug combination and the clinical outcome of treatment with the drug combination in each patient are determined to be resistant or sensitive to the drug combination, whereby patients who obtain CR / CRi for up to two induction cycles are classified as sensitive to (i.e., responding to) the drug combination, and the rest are classified as resistant.

[1023] Results: Scoring based on ex vivo activity and observed clinical response (S)

[1024] The scores for the specific drug combination used to treat each patient were significantly lower in those patients predicted to be non-responders (00096, 00086, and 0082, see Table 6) than in those patients predicted to be responders (00281, 00183, and 00218, see Table 5), regardless of the cutoff point used to classify them (in other words, the estimated scores were significantly higher in patients predicted to be sensitive to treatment). Although the information from the PM test method of the present invention was not used to prescribe treatment, it can be seen that the PM test method will correctly predict the results of the tested drug combinations. In addition, in the case of patient 00183, the treatment given to the patient was consistent with the drug combination that also gave the highest score using the method of the present invention (cytarabine and daunorubicin).

[1025] Table 5. Drug combinations to which acute myeloid leukemia patients showed sensitivity in in vivo treatment, their scores, and the scores of other drug combinations

[1026]

[1027]

[1028] Table 6. Drug combinations to which acute myeloid leukemia patients were resistant during in vivo treatment, their scores, and the scores of other drug combinations

[1029]

[1030]

[1031] Example 4. Prediction and outcome of clinical response after induction therapy with a drug combination comprising drug A and drug B and optionally drug C (or additional drug) in three subjects diagnosed with multiple myeloma (MM) and three subjects diagnosed with acute lymphoblastic leukemia (ALL)

[1032] Bone marrow samples were obtained from three adult patients (VIVIA-PMMM010431, VIVIA-PMMM060111 and VIVIA-PMMM130061) diagnosed with MM and three adult patients (VIVIA-PMALL07002, VIVIA-PMALL04001 and VIVIA-PMALL09001) diagnosed with ALL. According to the same experimental design as described in Example 1 and 3, the bone marrow samples were added in a 4% flask containing Figures 21 to 26 The single drug indicated was incubated in the wells for 48 hours. After measuring the pathological population by flow cytometry, the pharmacological response parameters were estimated using a population efficacy model. The most representative parameter of drug efficacy, EC, is the statistical distribution of the same parameter observed in a group of samples and stored in the database. 50 For comparative analysis of the results provided by the bone marrow samples. The size of such a group varies from drug to drug, always exceeding a hundred individuals in the case of MM and between 15 and 21 individuals in the case of ALL.

[1033] Fig.21 Comparative analysis of patient VIVIA-PMMM010431 treated with a drug combination comprising bortezomib, bendamustine, and prednisolone and achieving a complete response after induction therapy is shown. Consistently, the ex vivo results showed potent activity of the three drugs, with the EC of bortezomib 50 below the first quartile, and the EC of bendamustine and prednisolone 50 Below the 10th percentile.

[1034] Patient VIVIA-PMMM060111 was treated with a combination of bortezomib and dexamethasone and had a partial clinical response after induction. Test results showed ( Fig. 22 ), bortezomib was moderately active, and dexamethasone was inactive. Consistently, all drugs from the same family provided similar and very sensitive results according to their mechanism of action as mTor inhibitors, indicating that any drug from this family could be considered as an effective alternative treatment for this patient.

[1035] After three different chemotherapy cycles, patient VIVIA-PMMM130061 was resistant to all of them. The first cycle included a combination of bortezomib and dexamethasone. The second cycle included the same drugs plus thalidomide, and in the third cycle bortezomib and dexamethasone were combined with bendamustine instead. The in vitro results of the test showed that the four drugs had very weak activity. Similarly, the test showed that up to six different drugs had moderate or high activity on tumor cell populations, which may provide a higher clinical response ( Fig.23 ).

[1036] ALL patient VIVIA-PMALL07002 achieved a complete response after multiple therapies combining five different drugs: daunorubicin, vincristine, prednisolone, L-asparaginase, and cyclophosphamide. Fig.24 As shown, the ex vivo test results showed that five drugs had moderate or high activity.

[1037] Second-line patients VIVIA-PMALL04001 achieved a complete response after treatment with a combination of four drugs: fludarabine, idarubicin, cytarabine, and prednisolone. Idarubicin showed very potent activity in ex vivo tests, while cytarabine and fludarabine showed low to moderate activity. First-line treatment included drugs such as daunorubicin, mitoxantrone, or vincristine that showed low activity or resistance in ex vivo tests ( Fig.25 ).

[1038] ALL patient VIVIA-PMALL09001 achieved a complete response after multitherapy combining four different drugs: daunorubicin, vincristine, prednisolone, and imatinib. Ex vivo testing showed that all of these drugs were highly active, except for imatinib, which could not be tested ( Fig.26 ).

[1039] References

[1040] 1.Beal,SL,Sheiner,LB,Boeckmann,AJ&Bauer,RJ(1989-2001)NONMEMUsers Guides.Icon Development Solutions,Ellicot City,Maryland.

[1041] 2.Bennett,TA,Montesinos,P.,Moscardo,F.,Martinez-Cuadron,D.,Martinez,J.,Sierra,J.,Garcia,R.,de Oteyza,JP,Fernandez,P.,Serrano,J.,Fernandez,A.,Herrera,P.,Gonzalez,A.,Bethancourt,C.,Rodriguez-Macias,G.,Alons o,A.,Vera,JA,Navas,B.,Lavilla,E.,Lopez,JA,Jimenez,S.,Simiele,A.,Vidriales,B.,Gonzalez,BJ,Burgaleta,C.,Hernandez Rivas,JA,Mascunano,RC,Bautista,G.,Perez Simon,JA,Fuente Ade,L.,Rayon,C.,Troconiz,IF,Janda,A.,Bosanquet,AG,Hernandez-Campo,P.,Primo,D.,Lopez,R.,Liebana,B.,Rojas,JL,Gorrochategui,J.,Sanz,MA&Ballesteros,J.(2014)Pharmacological profiles of acute myeloid leukemia treatments in patient samples by automated flowcytometry: a bridge to individualized medicine.Clin Lymphoma Myeloma Leuk,14,305-318.

[1042] 3.Bergstrand,M.,Hooker,AC,Wallin,JE&Karlsson,MO(2011)Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models.AAPS J,13,143-151.

[1043] 4.Greco,W.R.,Bravo,G.&Parsons,J.C.(1995)The search for synergy:acritical review from a response surface perspective.Pharmacol Rev,47,331-385.

[1044] 5.Greiner,M.(1996)Two-graph receiver operating characteristic(TG-ROC):update version supports optimisation of cut-off values that minimiseoverall misclassification costs.J Immunol Methods,191,93-94.

[1045] 6.Grimwade,D.,Hills,R.K.,Moorman,A.V.,Walker,H.,Chatters,S.,Goldstone,A.H.,Wheatley,K.,Harrison,C.J.&Burnett,A.K.(2010)Refinement ofcytogenetic classification in acute myeloid leukemia:determination ofprognostic significance of rare recurring chromosomal abnormalities among5876 younger adult patients treated in the United Kingdom Medical ResearchCouncil trials.Blood,116,354-365.

[1046] 8.Upton,R.N.&Mould,D.R.(2014)Basic concepts in population modeling,simulation,and model-based drug development:part 3-introduction topharmacodynamic modeling methods.CPT Pharmacometrics Syst Pharmacol,3,e88.

[1047] 9.Vardiman,J.W.,Harris,N.L.&Brunning,R.D.(2002)The World HealthOrganization(WHO)classification of the myeloid neoplasms.Blood,100,2292-2302.

[1048] 10.Wood,S.N.(2006)Generalized Additive Models.An Introduction withR.Chapman&Hall / CRC,Boca Raton,Florida.

Claims

1. A system for determining the efficacy of treatment with a drug combination comprising drug A and drug B in a subject diagnosed with a disease, wherein the system comprises: (a) a device for performing the following steps: separating a tissue sample obtained from the subject into subsamples; (b) an apparatus for carrying out the following steps: (i) incubating the subsample in the presence of said drug A at a concentration X for a time T ranging from 2 to 168 hours; and (ii) repeating the steps mentioned in (b)(i) an additional (N-1) times, each time using a different subsample, using a different value of X from the value of X used in the previous repetition of the steps mentioned in (b)(i); wherein N is an integer selected from 5 to 10, inclusive; as well as (iii) incubating the subsample in the presence of the drug B at a concentration Y for the time T; as well as (iv) repeating the steps mentioned in (b)(iii) an additional (M-1) times, each time using a different subsample, using a different value of Y from the value of Y used in the previous repetition of the steps mentioned in (b)(iii); wherein M is an integer selected from 5 to 10, inclusive; as well as (v) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug B, wherein - the concentration of drug A is a concentration X, which corresponds to X obtained in a population from each subject diagnosed with the disease 50,A The percentile value P of the distribution of values Hα,A The concentration at which the percentile value P Hα,A Calculated by formula (A): P Hα,A =cos(α°)x H in: H corresponds to a reference percentile selected from the group consisting of:

10. and 90, where: - r is an integer selected from 2 to (R-1), including 2 and R-1; α is in degrees and is calculated as follows: in: -w is an integer selected from 1 to W, including 1 and W; - the concentration of drug B is a concentration Y corresponding to Y obtained in a population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values Hα,B The concentration at which the percentile value P Hα,B Calculated by formula (B): P Hα,B =cos(90°–α°)x H (vi) repeating the step (b)(v) an additional (R-1) times, each time using a different subsample, using a different value of H than the value of H used in the previous repetition of the step (b)(v), and using the same value of w used in the step (b)(v); (vii) repeating the steps referenced in (b)(v) and (b)(vi) an additional (W-1) times, each time using a different subsample, using a value of w that is different from the value of w used in the previous repetition of the steps referenced in (b)(v) and (b)(vi); in: R is an integer selected from 3 to 10, inclusive; W is an integer selected from 3 to 10, inclusive; And among them: ·X 50,A is the concentration of Drug A that exerts half of its maximal activity in the subject estimated according to the steps mentioned in (e)(i) below; ·Y 50,B is the concentration of drug B that exerts half of its maximal activity in the subject estimated according to the steps mentioned in (e)(i) below; as well as (viii) incubating the subsample for the time T; (c) means for performing the following steps: adding at least one marker to each subsample incubated in step (b) to identify at least one cell type (CT) therein i ); (d) means for counting the number of viable cells (LCTi) of each cell type identified in step (c) remaining after incubation of each subsample according to the step mentioned in (b); (e) means for performing the following steps: for each cell type identified in step (c), determining: (i) Pharmacodynamic parameter values, including X 50,A Value, LCTi 0,A Value, E max,A Value, γ A Value, Y 50,B Value, LCTi 0,B Value, E max,B Value and / or gamma B Values, where: -X 50,A Value, LCTi 0,A Value, E max,A Value and γ A The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (I) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to the steps mentioned in (b)(i) and (b)(ii) obtained for each concentration X of drug A: -The Y 50,B Value, LCTi 0,B Value, E max,B Value and γ B The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (II) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to the steps mentioned in (b)(iii) and (b)(iv) obtained for each concentration Y of drug B: wherein the population comprises the subject and other subjects diagnosed with the disease; in: X = concentration of drug A; ·X 50,A It is the concentration of drug A that exerts half of its maximal activity; ·LCTi 0,A is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii); ·E max,A It is LCTi caused by drug A 0,A The maximum score is reduced; γ A is the steepness of the LCTi-concentration curve of drug A; Y = concentration of drug B; ·Y 50,B is the concentration of drug B that exerts half of its maximal activity; ·LCTi 0,B is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii); ·E max,B It is LCTi caused by drug B 0,B The maximum score is reduced; γ B is the steepness of the LCTi-concentration curve of drug B (ii) Activity marker values, including AUC xy,A Value, AUC xy,B Value, α AB Value and / or VUS AB Values, where: - The AUC xy,A The value is calculated using formula (III): AUC xy,A =AUC x，A -A y:10-90，A (III) in: The AUC x,A The value is the integration between the two drug concentrations X' and X" of the function derived from formula (I) for % survival after incubation of the subsamples according to the steps mentioned in (b)(i) and (b)(ii) obtained for each concentration of drug A and calculated using formula (IV), where LCTi 0,A Considered as 100% survival rate, wherein drug concentrations X' and X" correspond to X obtained in each subject diagnosed with the disease 50,A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to steps (a) to (e)(i); as well as The A y:10-90,A Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x,A Surface, where LCTi 0,A Considered as 100% survival rate; as well as - The AUC xy,B The value is calculated using formula (V): AUC xy,B =AUC x,B -A y:10-90,B (V) in: The AUC x,B The value is the integration between the two drug concentrations Y' and Y" of the function derived from formula (II) for % survival after incubation of the subsamples according to the steps mentioned in (b)(iii) and (b)(iv) obtained for each concentration of drug B and calculated using formula (VI), where LCTi 0,B Considered as 100% survival rate, wherein drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to steps (a) to (e)(i); as well as The A y:10-90,B Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x,B Surface, where LCTi 0,A Considered as 100% survival rate; as well as -The VUS AB The value is calculated using formula (VII), where VUS AB The value is the double integral between two drug concentrations X' and X" of drug A and two drug concentrations Y' and Y" of drug B of the model function of the natural logarithm of the LCTi counted after incubation of the subsamples according to steps (b)(v) to (b)(vii), where LCTi 0,A =LCTi 0,B , and was considered as 100% survival rate and calculated using formula (VII), SUV AB = in: Drug concentrations X' and X" correspond to the X obtained in each subject diagnosed with the disease in the population 50,A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to steps (a) to (e)(i); Drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease in the population 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to steps (a) to (e)(i); ·E max,A = Maximum score reduction of LPC caused by drug A; ·E max,B = Maximum score reduction of LPC caused by drug B; ·X 50,A = Play E max,A Half of the EC of drug A 50 concentration; ·Y 50,B = Play E max,B Half of the EC of drug B 50 concentration; X = concentration of drug A; Y = concentration of drug B; · in: -γA = steepness of the LCTi-concentration curve of drug A; -γB = steepness of the LCTi-concentration curve of drug B; and α AB = the synergy parameter estimated by a two-drug surface interaction model determined by fitting formula (VII') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to steps (b)(i) and (b)(ii) obtained for each concentration X of Drug A, steps (b)(iii) and (b)(iv) obtained for each concentration Y of Drug B, and steps b(v), b(vi) and b(vii) obtained for each pair of concentrations of the combination of Drug A and Drug B: (iii) Normalized marker values, including NAUC A ,NAUC B and / or NVUS AB ,in: -NAUC A is the AUC calculated using formula (VIII) xy,A The normalized value of is: NAUC A =100x AUC xy,A / AUC max,A -NAUC B is the AUC calculated using formula (IX) xy,B The normalized value of is: NAUC B =100x AUC xy,B / AUC max,B -NVUS AB is the VUS calculated using formula (X) AB The normalized value of is: NVUS AB =100x VUS AB / VUS max,AB in: AUC max，A = AUC obtained in each population of subjects diagnosed with the disease xy,A The maximum value of the AUC for each subject in the population xy,A calculated according to the steps mentioned in (a) to (e)(ii); AUC max,B = the AUC obtained in the population of each subject diagnosed with the disease xy,B The maximum value of the AUC for each subject in the population xy,B calculated according to the steps referred to in (a) to (e)(ii); and ·VUS max,AB = VUS obtained in the population per subject diagnosed with the disease AB The maximum value of the VUS for each subject in the population AB calculated according to the steps mentioned in (a) to (e)(ii); (f) means for performing the following steps: selecting: (i) a pharmacodynamic parameter value or values ​​determined according to step (e)(i) for each subject in said population of subjects; and / or (ii) the activity marker value or values ​​determined according to step (e)(ii) for each subject in said population of subjects; and / or (iii) a normalized marker value or multiple normalized marker values ​​determined according to the step mentioned in (e)(iii) for each subject in the population of subjects; and, optionally, (iv) a value of a clinical variable or values ​​of a plurality of clinical variables for each subject in said population of subjects, which depends on clinical resistance or clinical sensitivity to the drug combination, whereby a value is dependent when the probability that the value is independent of clinical resistance or clinical sensitivity is less than or equal to 0.05, (g) means for performing the following steps: using at least one of the values ​​selected in step (f), creating a response function using a generalized linear model or a generalized additive model for the drug combination for the population of subjects, wherein the area under the curve of a receiver operating characteristic curve derived from the model function is equal to or greater than 0.8, and the lower limit of a 95% confidence interval of the area under the curve is greater than 0.5; (h) means for calculating the threshold of the response function created in step (g) from points on the receiver operating characteristic curve: - the point at which sensitivity and specificity are maximized and have equal values; or - specificity takes precedence over sensitivity at that point; or - the point is closest to the (1,0) coordinate plane; (i) means for performing the following steps: calculating the S / R value of the drug combination for the disease in the subject using the response function created in step (g) and the following: (i) a pharmacodynamic parameter value or multiple pharmacodynamic parameter values ​​of the subject determined according to the steps mentioned in (e)(i); and / or (ii) the activity marker value or values ​​of the subject determined according to the step mentioned in (e)(ii); and / or (iii) a normalized marker value or multiple normalized marker values ​​of the subject determined according to the steps mentioned in (e)(iii); and / or (iv) a clinical variable value or values ​​of multiple clinical variables of the subject, which is a variable in the response function; and (j) means for determining the efficacy of using the drug combination to treat the disease in the subject by comparing the S / R value calculated in step (i) with the threshold calculated in step (h), wherein - when the S / R value is equal to or greater than the threshold, the disease is sensitive to treatment with the drug combination in the subject; and - When the S / R value is less than the threshold, the disease is resistant to treatment with the drug combination in the subject.

2. The system of claim 1, wherein the combination is a combination of drug A and drug B and drug C, wherein: - said device for carrying out the step mentioned in (b) is additionally used for: (ix) incubating the subsample in the presence of the drug C at concentration Z for the time T; and (x) repeating step (b)(ix) an additional (L-1) times, each time using a different subsample, using a different value of Z from the value of Z used in the previous repetition of step (b)(ix); wherein L is an integer selected from 5 to 10, inclusive; as well as (xi) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug C, wherein: - the concentration of drug A is a concentration X corresponding to X obtained in said population from each subject diagnosed with said disease 50,A The percentile value P of the distribution of values H’α’,A The concentration at which the percentile value P H’α’,A Calculated by formula (C): P H’α’,A =cos(α'°)x H' in: H' corresponds to a reference percentile selected from the group consisting of:

10. and 90, where: -r' is an integer selected from 2 to (R'-1), including 2 and R'-1; α' is in degrees and is calculated by: in: -w' is an integer selected from 1 to W', including 1 and W'; - the concentration of drug C is a concentration Z corresponding to Z obtained in said population from each subject diagnosed with said disease 50,C percentile value of the distribution of values P H’α’,C The concentration at which the percentile value P H’α’,C Calculated by formula (D): P H’α’,C =cos(90°–α'°)x H' (xii) repeating step (b)(xi) an additional (R'-1) times, each time using a different subsample, using a value of H' different from the value of H' used in the previous repetition of step (b)(xi), and using the same value of w' used in step (b)(xi); and (xiii) repeating steps (b)(xi) and (b)(xii) an additional (W'-1) times, each time using a different subsample, using a value of w' different from the value of w' used in the previous repetition of steps (b)(xi) and (b)(xii); in: R' is an integer selected from 3 to 10, inclusive; W' is an integer selected from 3 to 10, inclusive; as well as; (xiv) incubating the subsample for said time T in the presence of a drug combination comprising said drug B and said drug C, wherein - the concentration of drug B is a concentration Y corresponding to Y obtained in the population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values H”α”,B The concentration at which the percentile value P H”α”,B Calculated by formula (E): P H”α”,B =cos(α”°)x H” in: H" corresponds to a reference percentile selected from the group consisting of:

10. and 90, where: R" is an integer selected from 2 to (R"-1), including 2 and R"-1, α" is in degrees and is calculated by the following formula: in: w" is an integer selected from 1 to W", including 1 and W"; - the concentration of drug C is a concentration Z corresponding to Z obtained in said population from each subject diagnosed with said disease 50,C The percentile value P of the distribution of values H”α”,C The concentration at which the percentile value P H”α”,C Calculated by formula (F): P H”α”,C =cos(90°–α”°)x H” (xv) repeating step (b)(xiv) an additional (R"-1) times, each time using a different subsample, using a value of H" different from the value of H" used in the previous repetition of step (b)(xiv), and using the same value of w" used in step (b)(xiv); (xvi) repeating steps (b)(xiv) and (b)(xv) an additional (W″-1) times, each time using a different subsample, using a value of w″ different from the value of w″ used in the previous repetition of steps (b)(xiv) and (b)(xv); in: R" is an integer selected from 3 to 10, inclusive; W" is an integer selected from 3 to 10, including 3 and 10; And among them: ·X 50,A is the concentration of Drug A that exerts half of its maximal activity in the subject, as estimated according to step (e)(i); ·Y 50,B is the concentration of drug B that exerts half of its maximal activity in the subject, estimated according to step (e)(i); ·Z 50,C is the concentration of drug C that exerts half of its maximal activity in the subject estimated according to step (e)(i), as follows; - The pharmacodynamic parameter value determined according to step (e) (i) optionally also includes Z 50,C Value, LCTi 0,C Value, E max,C Value and / or gamma C Values, where: ·Z 50,C Value, LCTi 0,C Value, E max,C Value and γ C The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (XI) to the experimental values ​​of LCTi counted according to the step mentioned in (d) after incubation of subsamples according to the steps mentioned in (b)(ix) and (b)(x) obtained for each concentration Z of drug C: in: Z = concentration of drug C; ·Z 50,C It is the concentration of drug C that exerts half of its maximal activity; ·LCTi 0,C is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii); ·E max,C It is LCTi caused by drug C 0,C The maximum score is reduced; γ C is the steepness of the LCTi-concentration curve of drug C; - The activity marker value determined according to step (e)(ii) optionally also includes AUC xy,C Value, α AC Value, VUS AC Value, α AC Value and / or VUS BC Values, where: - The AUC xy,C The value is calculated using formula (XII): AUC xy,C =AUC x,C -A y:10-90,C (XII) in: The AUC x,C The value is the integration between the two drug concentrations Z' and Z" of the function derived from formula (XI) for % survival after incubation of the subsamples according to the steps mentioned in (b)(ix) and (b)(x) obtained for each concentration of drug C and calculated using formula (XIII), where LCTi 0,C Considered as 100% survival rate, wherein drug concentrations Z' and Z" correspond to Z obtained in each subject diagnosed with the disease 50,C The 20th and 80th percentile concentrations of the values ​​where the Z of each subject in the population 50,C calculated according to the steps mentioned in (a) to (e)(i); as well as The A y:10-90,C Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x,C Surface, where LCTi 0,C Considered as 100% survival rate; as well as -The VUS AC The value is calculated using formula (XIV), where the VUS AC The value is the double integral between two drug concentrations X' and X" of drug A and two drug concentrations Z' and Z" of drug C of the model function of the natural logarithm of the LCTi counted after incubation of the subsamples according to steps (b)(xi) to (b)(xiii), where LCTi 0,A =LCTi 0,C , and is considered as 100% survival rate, in: Drug concentrations X' and X" correspond to the X obtained in each subject diagnosed with the disease in the population 50,A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to the steps mentioned in (a) to (e)(i); Drug concentrations Z' and Z" correspond to Z obtained in each subject diagnosed with the disease in the population 50,C The 20th and 80th percentile concentrations of the values ​​where the Z of each subject in the population 50,C calculated according to the steps mentioned in (a) to (e)(i); ·E max,A = Maximum score reduction of LPC caused by drug A; ·E max,C = Maximum score reduction of LPC caused by drug C; ·X 50,A = Play E max,A Half of the EC of drug A 50 concentration; ·Z 50,C = Play E max,C Half of the EC of drug C 50 concentration; X = concentration of drug A; Z = concentration of drug C; · in: -γA = steepness of the LCTi-concentration curve of drug A; -γC = steepness of the LCTi-concentration curve of drug C; and α AC = the synergy parameter estimated by a two-drug surface interaction model determined by fitting formula (XIV') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to steps (b)(i) and (b)(ii) obtained for each concentration x of drug A, steps (b)(ix) and (b)(x) obtained for each concentration z of drug C, and steps b(xi), b(xii) and b(xiii) obtained for each pair of concentrations of the combination of drug A and drug C: -The VUS BC The value is calculated using formula (XV), where the VUS BC The value is the double integral between two drug concentrations Y' and Y" of drug B and two drug concentrations Z' and Z" of drug C of the model function of the natural logarithm of LCTi counted after incubation of the subsamples according to steps (b)(xiv) to (b)(xvi), where LCTi 0,B =LCTi 0,C , and is considered as 100% survival rate, in: Drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease in the population 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to the steps mentioned in (a) to (e)(i); Drug concentrations Z' and Z" correspond to Z obtained in each subject diagnosed with the disease in the population 50,C The 20th and 80th percentile concentrations of the values ​​where the Z of each subject in the population 50,C calculated according to the steps mentioned in (a) to (e)(i); ·E max,B = Maximum score reduction of LPC caused by drug B; ·E max,C = Maximum score reduction of LPC caused by drug C; ·Y 50,B = Play E max,B Half of the EC of drug B 50 concentration; ·Z 50,C = Play E max,C Half of the EC of drug C 50 concentration; Y = concentration of drug B; Z = concentration of drug C; in: -γB = steepness of the LCTi-concentration curve of drug B; -γC = steepness of the LCTi-concentration curve of drug C; and α BC = the synergy parameter estimated by a two-drug surface interaction model determined by incubating a subsample of the subject according to steps (b)(iii) and (b)(iv) obtained for each concentration Y of drug B, steps (b)(ix) and (b)(x) obtained for each concentration Z of drug C, and steps b(xiv), b(xv) and b(xvi) obtained for each pair of concentrations of the combination of drug B and drug C, Fitting formula (XV') to the experimental values ​​of LCTi counted according to the procedure mentioned in (d): - The normalized marker value determined according to step (e)(ii) optionally also includes NAUC C NVUS AC and / or NVUS BC ,in: -NAUC C is the AUC calculated using formula (XVI) xy,C The normalized value of is: NAUC C =100x AUC xy,C / AUC max,C -NVUS AC is the VUS calculated using formula (XVII) AC The normalized value of is: NVUS AC =100x VUS AC / VUS max,AC -NVUS BC is the VUS calculated using formula (XVIII) BC The normalized value of is: NVUS BC =100x VUS BC / VUS max,BC in: AUC max，C = the AUC obtained in the population of each subject diagnosed with the disease xy,C The maximum value of the AUC for each subject in the population xy,C calculated according to the steps mentioned in (a) to (e)(ii); ·VUS max,AC = VUS obtained in the population per subject diagnosed with the disease AC The maximum value of the VUS for each subject in the population AC calculated according to the steps referred to in (a) to (e)(ii); and ·VUS max,BC = VUS obtained in the population per subject diagnosed with the disease BC The maximum value of the VUS for each subject in the population BC Calculate according to the steps mentioned in (a) to (e)(ii).

3. A system according to claim 1 or 2, wherein when the number of subjects diagnosed with the disease for which S / R values ​​have been calculated is equal to 20% of the size of the population used to calculate the S / R value, the subjects are included in the population and the concentration X, concentration Y, drug concentrations X' and X", drug concentrations Y' and Y", and in the case of claim 2, concentration Z and drug concentrations Z' and Z".

4. The system according to claim 1 or 2, wherein - drug A is cytarabine and drug B is idarubicin; and, in the case of claim 2 - Drug C is selected from the group consisting of fludarabine, etoposide, thioguanine and clofarabine.

5. The system according to claim 1, wherein - The steps mentioned in (c) include: (i) adding at least one conjugated antibody to each subsample incubated in step (b) to identify at least one pathological cell type therein; as well as (ii) adding at least one cell death or apoptosis marker to each subsample incubated in step (b) to identify apoptotic cells therein, as well as - The step mentioned in (d) comprises counting the number of living cells (LCTi) remaining after incubation of each subsample by counting the number of cells of the at least one pathological cell type identified according to the step mentioned in (c)(i) that were not identified as apoptotic according to the step mentioned in (c)(ii).

6. The system of claim 1, wherein the efficacy is an anti-cancer efficacy and the disease is a cancer of hematopoietic and lymphoid tissues.

7. The system of claim 1, wherein the disease is acute myeloid leukemia.

8. The system of claim 1, wherein the subject is an adult subject.

9. The system of claim 1, wherein the bone marrow cells are collected before the patient has undergone chemotherapy and / or radiation therapy.

10. The system of claim 1, wherein - the viability of the bone marrow cells is greater than or equal to 60% when incubated for 48 hours in the absence of cytarabine and / or idarubicin; and / or - The bone marrow cells are not present in the form of clumping when obtained from said subject.

11. The system of claim 1, wherein - the apparatus for carrying out the step of isolating the tissue sample comprises a microfluidic stem cell isolation device; - the apparatus for carrying out the incubation step mentioned in (b) comprises a cell culture incubator; - the means for carrying out the step of adding at least one marker comprises a pipette or an injector; - the means for carrying out the step of counting the number of viable cells comprises a cytometer; - means for carrying out the steps of determining a pharmacodynamic parameter value, an active marker value and a normalized marker value comprising at least one computer program product; - the means for performing the step of selecting comprises at least one computer program product; - the means for carrying out the step of creating a response function comprises at least one computer program product; - means for carrying out the step of calculating the threshold of said response function comprises at least one computer program product; - the means for carrying out the step of calculating the S / R value comprises at least one computer program product; and - Means for determining the efficacy of treating said disease in said subject using said drug combination comprises at least one computer program product.

12. The system of claim 1, further comprising means for prescribing a care plan for a subject, wherein the care plan prescribes the drug combination when the disease is determined to be susceptible to treatment with the drug combination in the subject.

13. A system for classifying the utility of a drug combination, each comprising drug A and drug B, in the treatment of a subject diagnosed with a disease, wherein the system comprises: (a) a device for performing the following steps: separating a tissue sample obtained from the subject into at least 20 sub-samples; (b) an apparatus for carrying out the following steps: (i) incubating the subsample in the presence of said drug A at a concentration X for a time T ranging from 2 to 168 hours; and (ii) repeating the steps mentioned in (b)(i) an additional (N-1) times, each time using a different subsample, using a different value of X from the value of X used in the previous repetition of the steps mentioned in (b)(i); as well as (iii) incubating the subsample in the presence of the drug B at a concentration Y for the time T; as well as (iv) repeating the steps mentioned in (b)(iii) an additional (M-1) times, each time using a different subsample, using a different value of Y from the value of Y used in the previous repetition of the steps mentioned in (b)(iii); wherein the values ​​of N and M are the same and are integers selected from 5 to 8; as well as (v) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug B, wherein - the concentration of drug A is a concentration X, which corresponds to X obtained in a population from each subject diagnosed with the disease 50,A The percentile value P of the distribution of values Hα,A The concentration at which the percentile value P Hα,A Calculated by formula (A): P Hα,A =cos(α°)x H in: H corresponds to a reference percentile selected from the group consisting of:

10. and 90, where: r is an integer selected from 2 to (R-1), including 2 and R-1; α is in degrees and is calculated as follows: ： w is an integer selected from 1 to W, including 1 and W; - the concentration of drug B is a concentration Y corresponding to Y obtained in a population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values Hα,B The concentration at which the percentile value P Hα,B Calculated by formula (B): P Hα,B =cos(90°–α°)x H (vi) repeating the step (b)(v) an additional (R-1) times, each time using a different subsample, using a different value of H than the value of H used in the previous repetition of the step (b)(v), and using the same value of w used in the step (b)(v); (vii) repeating the steps referenced in (b)(v) and (b)(vi) an additional (W-1) times, each time using a different subsample, using a value of w that is different from the value of w used in the previous repetition of the steps referenced in (b)(v) and (b)(vi); in; The values ​​of R and W are the same and are integers selected from 3 to 5; And among them: ·X 50,A is the concentration of Drug A that exerts half of its maximal activity in the subject estimated according to the steps mentioned in (e)(i) below; ·Y 50,B is the concentration of drug B that exerts half of its maximal activity in the subject estimated according to the steps mentioned in (e)(i) below; as well as (viii) incubating the subsample for the time T; (c) means for performing the following steps: adding at least one marker to each subsample incubated in step (b) to identify at least one cell type (CT) therein i ); (d) means for counting the number of viable cells (LCTi) of each cell type identified in step (c) that remain after incubation of each subsample according to step (b); (e) means for performing the following steps: for each cell type identified in step (c), determining: (i) pharmacodynamic parameter values, the pharmacodynamic parameter values ​​comprising X 50,A Value, LCTi 0,A Value, E max,A Value, γ A Value, Y 50,B Value, LCTi 0,B Value, E max,B Value and / or gamma B Values, where: - X of drug A 50,A Value, LCTi 0,A Value, E max,A Value and γ A Values ​​from single drug doses - a response pharmacodynamic mixed-effects nonlinear population model estimated by fitting formula (I) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(i) and (ii) obtained for each concentration X of drug A: and - the Y of drug B 50,B Value, LCTi 0,B Value, E max,B Value and γ B Values ​​from single drug doses - a response pharmacodynamic mixed-effects nonlinear population model estimated by fitting formula (II) to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(iii) and (iv) obtained for each concentration Y of drug B: wherein the population comprises the subject and other subjects diagnosed with the disease; in: X = concentration of drug A; ·X 50,A It is the concentration of drug A that exerts half of its maximal activity; ·LCTi 0,A is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii); ·E max,A It is LCTi caused by drug A 0,A The maximum score is reduced; γ A is the steepness of the LCTi-concentration curve of drug A; Y = concentration of drug B; ·Y 50,B is the concentration of drug B that exerts half of its maximal activity; ·LCTi 0,B is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii); ·E max,B It is LCTi caused by drug B 0,B The maximum score is reduced; γ B is the steepness of the LCTi-concentration curve of drug B; (ii) activity marker values, including AUC xy,A Value, AUC xy,B Value, α AB Value and / or VUS AB Values, where: - The AUC of drug A xy,A The value is calculated using formula (III): AUC xy,A =AUC x，A -A y:10-90,A in: The AUC x,A The value is the integral between two drug concentrations X' and X" after incubation of the subsamples according to steps (b)(i) and (b)(ii) obtained for each concentration x of drug A by the function of formula (I) derived from the survival rate %, where LCTi 0,A was considered as 100% survival, and the AUC x,A The value is calculated using formula (IV): wherein drug concentrations X' and X" correspond to X obtained in each subject diagnosed with the disease 50,A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to steps (a) to (e)(i); as well as The A y:10-90,A Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x,A Surface, where LCTi 0,A Considered as 100% survival rate; - The AUC of drug B xy,B The value is calculated using formula (V): AUC xy,B =AUC x,B -A y:10-90,B in: The AUC x,B The value is the integral between two drug concentrations Y' and Y" after incubation of the subsamples according to steps (b)(iii) and (b)(iv) obtained for each concentration Y of drug B by the function of formula (II) derived from the survival rate %, where LCTi 0,B was considered as 100% survival, and the AUC x,B The value is calculated using formula (VI): wherein drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to steps (a) to (e)(i); as well as The A y:10-90,B Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x,B Surface, where LCTi 0,A Considered as 100% survival rate; - The VUS of drugs A and B AB The value is calculated using formula (VII), where VUS AB The value is the double integral between two drug concentrations X' and X" of drug A and two drug concentrations Y' and Y" of drug B of the model function of the natural logarithm of the LCTi counted after incubation of the subsamples according to steps (b)(v) to (b)(vii), where LCTi 0,A =LCTi 0,B , and was considered as 100% survival rate, and the VUS AB The value is calculated using formula (VII): in: Drug concentrations X' and X" correspond to the X obtained in each subject diagnosed with the disease in the population 50,A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to steps (a) to (e)(i); Drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease in the population 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to steps (a) to (e)(i); ·E max,A = Maximum score reduction of LPC caused by drug A; ·E max,B = Maximum score reduction of LPC caused by drug B; ·X 50,A = Play E max,A Half of the EC of drug A 50 concentration; ·Y 50,B = Play E max,B Half of the EC of drug B 50 concentration; X = concentration of drug A; Y = concentration of drug B; · in: -γA = steepness of the LCTi-concentration curve of drug A; -γB = steepness of the LCTi-concentration curve of drug B; and α AB = the synergy parameter estimated by a two-drug surface interaction model determined by fitting formula (VII') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to the steps mentioned in (b)(i) and (b)(ii) obtained for each concentration X of Drug A, the steps mentioned in (b)(iii) and (b)(iv) obtained for each concentration Y of Drug B, and the steps mentioned in b(v), b(vi) and b(vii) obtained for each pair of concentrations of the combination of Drug A and Drug B: as well as (iii) normalized marker values, the normalized marker values ​​comprising NAUC A ,NAUC B and / or NVUS AB ,in: -NAUC of Drug A A is the AUC calculated using formula (VIII) xy,A The normalized value of is: NAUC A =100x AUC xy,A / AUC max,A -NAUC of Drug B B is the AUC calculated using formula (IX) xy,B The normalized value of is: NAUC B =100x AUC xy,B / AUC max,B -NVUS of Drugs A and B AB is the VUS calculated using formula (X) AB The normalized value of is: NVUS AB =100x VUS AB / VUS max,AB in: AUC max,A = AUC obtained in each population of subjects diagnosed with the disease xy,A The maximum value of the AUC for each subject in the population xy,A calculated according to steps (a) to (e)(ii); AUC max,B = the AUC obtained in the population of each subject diagnosed with the disease xy,B The maximum value of the AUC for each subject in the population xy,B calculated according to steps (a) to (e)(ii); and ·VUS max,AB = VUS obtained in the population per subject diagnosed with the disease AB The maximum value of the VUS for each subject in the population AB calculated according to steps (a) to (e)(ii); (f) means for performing the following steps: selecting: (i) a pharmacodynamic parameter value or values ​​determined according to step (e)(i) for each subject in said population of subjects; and / or (ii) the activity marker value or values ​​determined according to step (e)(ii) for each subject in said population of subjects; and / or (iii) a normalized marker value or multiple normalized marker values ​​determined according to the step mentioned in (e)(iii) for each subject in the population of subjects; and, optionally, (iv) a value of a clinical variable or values ​​of a plurality of clinical variables for each subject in said population of subjects, It depends on clinical resistance or clinical sensitivity to the drug combination, whereby the value is dependent when the probability that the value is independent of clinical resistance or clinical sensitivity is less than or equal to 0.05, (g') means for performing the following steps: calculating a score S for treating the subject with the drug A and the drug B, wherein the score is calculated using formula (XIX): in: (NAUC)d = normalized NAUC value for each drug included in the treatment; ·(NVUS) cd = the normalized NVUS for each drug combination included in the treatment; D = the number of drugs included in the treatment; C = the number of drug combinations included in the treatment; and f = compensation factor for multiple drug treatments, where: or Score S is selected from (NVUS) cd The value of NVUS; (h') means for carrying out the steps mentioned in (b) to (g') for each drug combination to be classified; and (i') means for performing the following steps: using the scores determined in steps (g') and (h') to classify each drug combination, such that: (i) Drug combinations with a score greater than 80 are classified into classification category I with a classification value of 2; (ii) drug combinations with a score less than or equal to 80 and greater than 60 were classified into classification category II with a classification value of 1; (iii) drug combinations with a score less than or equal to 60 and greater than 40 were assigned to classification category III with a classification value of 0; (iv) drug combinations with a score less than or equal to 40 and greater than 20 are classified as classification category IV with a classification value of -1; or (v) drug combinations with a score less than or equal to 20 are classified into classification category V with a classification value of -2, thereby: - each drug combination assigned to a classification category having a positive classification value or a zero classification value has the highest efficacy in the treatment of said disease in said subject; and - Each drug combination belonging to the classification category with a negative classification value has the lowest utility in the treatment of said disease in said subject.

14. The system of claim 13, wherein the drug combination is a combination of drug A and drug B and drug C, wherein: - said device for carrying out the step mentioned in (b) is additionally used for: (ix) incubating the subsample in the presence of the drug C at concentration Z for the time T; and (x) repeating step (b)(ix) an additional (L-1) times, each time using a different subsample, using a different value of Z from the value of Z used in the previous repetition of step (b)(ix); wherein L is an integer selected from 5 to 10, inclusive; as well as (xi) incubating the subsample for said time T in the presence of a drug combination comprising said drug A and said drug C, wherein - the concentration of drug A is a concentration X corresponding to X obtained in said population from each subject diagnosed with said disease 50,A The percentile value P of the distribution of values H’α’,A The concentration at which the percentile value P H’α’,A Calculated by formula (C): P H’α’,A =cos(α'°)x H' in: H' corresponds to a reference percentile selected from the group consisting of:

10. and 90, where: r' is an integer selected from 2 to (R'-1), including 2 and R'-1; α' is in degrees and is calculated by: in: w' is an integer selected from 1 to W', including 1 and W'; - the concentration of drug C is a concentration Z corresponding to Z obtained in said population from each subject diagnosed with said disease 50,C The percentile value P of the distribution of values H’α’,C The concentration at which the percentile value P H’α’,C Calculated by formula (D): P H’α’,C =cos(90°–α'°)x H' (xii) repeating step (b)(xi) an additional (R'-1) times, each time using a different subsample, using a value of H' different from the value of H' used in the previous repetition of step (b)(xi), and using the same value of w' used in step (b)(xi); and (xiii) repeating steps (b)(xi) and (b)(xii) an additional (W'-1) times, each time using a different subsample, using a value of w' different from the value of w' used in the previous repetition of steps (b)(xi) and (b)(xii); in; R' is an integer selected from 3 to 10, inclusive; W' is an integer selected from 3 to 10, inclusive; as well as; (xiv) incubating the subsample for said time T in the presence of a drug combination comprising said drug B and said drug C, wherein - the concentration of drug B is a concentration Y corresponding to Y obtained in the population from each subject diagnosed with the disease 50,B The percentile value P of the distribution of values H”α”,B The concentration at which the percentile value P H”α”,B Calculated by formula (E): P H”α”,B =cos(α”°)x H” (E) in: H" corresponds to a reference percentile selected from the group consisting of:

10. and 90, where: R" is an integer selected from 2 to (R"-1), including 2 and R"-1 α" is in degrees and is calculated by the following formula: in: w" is an integer selected from 1 to W", including 1 and W"; - the concentration of drug C is a concentration Z corresponding to Z obtained in said population from each subject diagnosed with said disease 50,C The percentile value P of the distribution of values H”α”,C The concentration at which the percentile value P H”α”,C Calculated by formula (F): P H”α”,C =cos(90°–α”°)x H” (F) (xv) repeating step (b)(xiv) an additional (R"-1) times, each time using a different subsample, using a value of H" different from the value of H" used in the previous repetition of step (b)(xiv), and using the same value of w" used in step (b)(xiv); (xvi) repeating steps (b)(xiv) and (b)(xv) an additional (W″-1) times, each time using a different subsample, using a value of w″ different from the value of w″ used in the previous repetition of steps (b)(xiv) and (b)(xv); in: R" is an integer selected from 3 to 10, inclusive; W" is an integer selected from 3 to 10, including 3 and 10; And among them: ·X 50,A is the concentration of Drug A that exerts half of its maximal activity in the subject, as estimated according to step (e)(i); ·Y 50,B is the concentration of drug B that exerts half of its maximal activity in the subject, estimated according to step (e)(i); ·Z 50,C is the concentration of drug C that exerts half of its maximal activity in the subject estimated according to step (e)(i), as follows; -(e) The pharmacodynamic parameter values ​​determined in step (i) optionally further include at least one pharmacodynamic parameter value of drug C, wherein: - Each pharmacodynamic parameter value of drug C is estimated from a single drug dose-response pharmacodynamic mixed effects nonlinear population model by fitting the formula to the experimental values ​​of LCTi counted according to the procedure mentioned in (d) after incubating a subsample of each subject in the population according to the procedures mentioned in (b)(ix) and (x): -(e)(ii) The activity marker values ​​determined in step (e) optionally further include at least one activity marker value for drug C, at least one activity marker value for drugs A and C, and / or at least one activity marker value for drugs B and C, wherein: - each activity marker value of drug C is calculated from the pharmacodynamic parameter value or pharmacodynamic parameter values ​​of drug C estimated in step (e)(i), - each activity marker value of drug A and C is calculated by a specific model developed by fitting a formula to said pharmacodynamic parameter value or values ​​of drug A and said pharmacodynamic parameter value or values ​​of drug C estimated in step (e)(i) and to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(xi) to (xiii); - each activity marker value of drug B and C is calculated by a specific model developed by fitting a formula to said pharmacodynamic parameter value or values ​​of drug B and said pharmacodynamic parameter value or values ​​of drug C estimated in step (e)(i) and to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of each subject in the population according to steps (b)(xiv) to (xvi); -(e)(iii) The normalized marker values ​​determined in the step mentioned in (e)(iii) optionally include normalized marker values ​​including at least one normalized marker value of drug C, at least one normalized marker value of drugs A and C, and / or at least one normalized marker value of drugs B and C, wherein: - each normalized marker value of drug C is calculated by the ratio of each active marker value of drug C calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​from said population; - each normalized marker value for drugs A and C is calculated by the ratio of each active marker value for drugs A and C calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​for drugs A and C from said population; - Each normalized marker value for drugs B and C is calculated by the ratio of each active marker value for drugs B and C calculated in step (e)(ii) relative to the corresponding value of the distribution of said active marker values ​​for drugs B and C from said population.

15. The system according to claim 13 or 14, wherein the combination is a combination of drug A and drug B and drug C, wherein: - the device for performing the step mentioned in (a) is used to separate the tissue sample obtained from the subject into 43 to 100 sub-samples; - the value of L in the step mentioned in (b) is the same as the value of N and M, and the values ​​of R', R", W' and W" in the step mentioned in (b) are the same as the values ​​of R and W; - The pharmacodynamic parameter value determined according to step (e) (i) optionally also includes Z 50,C Value, LCTi 0,C Value, E max,C Value and / or γC Values, where: ·Z 50,C Value, LCTi 0,C Value, E max,C Value and γ C The value is estimated by a single drug dose-response pharmacodynamic mixed effects nonlinear population model determined by fitting formula (XI) to the experimental values ​​of LCTi counted according to the step mentioned in (d) after incubation of subsamples according to the steps mentioned in (b)(ix) and (b)(x) obtained for each concentration Z of drug C: in: Z = concentration of drug C; ·Z 50,C It is the concentration of drug C that exerts half of its maximal activity; ·LCTi 0,C is the basal (pre-incubation) number of LCTi and is equal to the LCTi counted after incubation of the subsample in the absence of drug according to the step mentioned in (b)(viii); ·E max,C It is LCTi caused by drug C 0,C The maximum score is reduced; γ C is the steepness of the LCTi-concentration curve of drug C; - The activity marker value determined according to step (e)(ii) optionally also includes AUC xy,C Value, α AC Value, VUS AC Value, α BC Value and / or VUS BC Values, where: - The AUC xy,C The value is calculated using formula (XII): AUC xy,C =AUC x,C -A y:10-90,C (XII) in: The AUC x,C The value is the integration between the two drug concentrations Z' and Z" of the function derived from formula (XI) for % survival after incubation of the subsamples according to the steps mentioned in (b)(ix) and (b)(x) obtained for each concentration of drug C and calculated using formula (XIII), where LCTi 0,C Considered as 100% survival rate, wherein drug concentrations Z' and Z" correspond to Z obtained in each subject diagnosed with the disease 50,C The 20th and 80th percentile concentrations of the values ​​where the Z of each subject in the population 50,C calculated according to the steps mentioned in (a) to (e)(i); as well as The A y:10-90,C Values ​​are AUCs that fall outside the 10% and 90% limits of % survival x,C Surface, where LCTi 0,C Considered as 100% survival rate; as well as -The VUS AC The value is calculated using formula (XIV), where the VUS AC The value is the double integral between two drug concentrations X' and X" of drug A and two drug concentrations Z' and Z" of drug C of the model function of the natural logarithm of the LCTi counted after incubation of the subsamples according to steps (b)(xi) to (b)(xiii), where LCTi 0,A =LCTi 0,C , and is considered as 100% survival rate, in: Drug concentrations X' and X" correspond to the X obtained in each subject diagnosed with the disease in the population 50,A The 20th and 80th percentile concentrations of the values ​​where X for each subject in the population 50,A calculated according to the steps mentioned in (a) to (e)(i); Drug concentrations Z' and Z" correspond to Z obtained in each subject diagnosed with the disease in the population 50,C The 20th and 80th percentile concentrations of the values ​​where the Z of each subject in the population 50,C calculated according to the steps mentioned in (a) to (e)(i); ·E max,A = Maximum score reduction of LPC caused by drug A; ·E max,C = Maximum score reduction of LPC caused by drug C; ·X 50,A = Play E max,A Half of the EC of drug A 50 concentration; ·Z 50,C = Play E max,C Half of the EC of drug C 50 concentration; X = concentration of drug A; Z = concentration of drug C; · ·c int,AC = in: -γA = steepness of the LCTi-concentration curve of drug A; -γC = steepness of the LCTi-concentration curve of drug C; and α AC = the synergy parameter estimated by a two-drug surface interaction model determined by fitting formula (XIV') to the experimental values ​​of LCTi counted according to step (d) after incubating a subsample of the subject according to steps (b)(i) and (b)(ii) obtained for each concentration x of drug A, steps (b)(ix) and (b)(x) obtained for each concentration z of drug C, and steps b(xi), b(xii) and b(xiii) obtained for each pair of concentrations of the combination of drug A and drug C: -The VUS BC The value is calculated using formula (XV), where the VUS BC The value is the double integral between two drug concentrations Y' and Y" of drug B and two drug concentrations Z' and Z" of drug C of the model function of the natural logarithm of LCTi counted after incubation of the subsamples according to steps (b)(xiv) to (b)(xvi), where LCTi 0,B =LCTi 0,C , and is considered as 100% survival rate, in: Drug concentrations Y' and Y" correspond to Y obtained in each subject diagnosed with the disease in the population 50,B The 20th and 80th percentile concentrations of the values ​​where Y for each subject in the population 50,B calculated according to the steps mentioned in (a) to (e)(i); Drug concentrations Z' and Z" correspond to Z obtained in each subject diagnosed with the disease in the population 50,C The 20th and 80th percentile concentrations of the values ​​where the Z of each subject in the population 50,C calculated according to the steps mentioned in (a) to (e)(i); ·E max,B = Maximum score reduction of LPC caused by drug B; ·E max,C = Maximum score reduction of LPC caused by drug C; ·Y 50,B = Play E max,B Half of the EC of drug B 50 concentration; ·Z 50,C = Play E max,C Half of the EC of drug C 50 concentration; Y = concentration of drug B; Z = concentration of drug C; · in: -γB = steepness of the LCTi-concentration curve of drug B; -γC = steepness of the LCTi-concentration curve of drug C; and α BC = the synergy parameter estimated by a two-drug surface interaction model determined by incubating a subsample of the subject according to steps (b)(iii) and (b)(iv) obtained for each concentration Y of drug B, steps (b)(ix) and (b)(x) obtained for each concentration Z of drug C, and steps b(xiv), b(xv) and b(xvi) obtained for each pair of concentrations of the combination of drug B and drug C, Fitting formula (XV') to the experimental values ​​of LCTi counted according to the procedure mentioned in (d): - The normalized marker value determined according to step (e)(iii) optionally also includes NAUC C NVUS AC and / or NVUS BC ,in: -NAUC C is the AUC calculated using formula (XVI) xy,C The normalized value of NAUC C =100x AUC xy,C / AUC max,C (XVI) -NVUS AC is the VUS calculated using formula (XVII) AC The normalized value of NVUS AC =100x VUS AC / VUS max,AC (XVII) -NVUS BC is the VUS calculated using formula (XVIII) BC The normalized value of NVUS BC =100x VUS BC / VUS max,BC (XVIII) in: AUC max,C = the AUC obtained in the population of each subject diagnosed with the disease xy,C The maximum value of the AUC for each subject in the population xy,C calculated according to the steps mentioned in (a) to (e)(ii); ·VUS max,AC = VUS obtained in the population per subject diagnosed with the disease AC The maximum value of the VUS for each subject in the population AC calculated according to the steps referred to in (a) to (e)(ii); and ·VUS max,BC = VUS obtained in the population per subject diagnosed with the disease BC The maximum value of the VUS for each subject in the population BC Calculate according to the steps mentioned in (a) to (e)(ii).

16. A system according to claim 13 or 14, wherein when the number of subjects diagnosed with the disease for which a score S has been calculated is equal to 20% of the size of the population used to calculate the score, the subject is included in the population and the concentration X, concentration Y, drug concentrations X' and X", drug concentrations Y' and Y", and in the case of claim 14, concentration Z and drug concentrations Z' and Z". are adjusted accordingly.

17. The system of claim 13, wherein the disease is a cancer of hematopoietic and lymphoid tissues.

18. The system of claim 13, wherein the disease is acute myeloid leukemia.

19. The system of claim 13, wherein the subject is an adult subject and each subject in the population of subjects is an adult subject.

20. The system of claim 13, wherein the bone marrow cells are collected before the patient has undergone chemotherapy and / or radiation therapy.

21. The system of claim 13, wherein - when incubated for 48 hours in the absence of Drug A and / or Drug B and / or Drug C, the viability of the bone marrow cells is greater than or equal to 60%; and / or - The bone marrow cells are not present in the form of clumping when obtained from said subject.

22. The system of claim 13, wherein - the apparatus for carrying out the step of isolating the tissue sample comprises a microfluidic stem cell isolation device; - the apparatus for carrying out the incubation step mentioned in (b) comprises a cell culture incubator; - the means for carrying out the step of adding at least one marker comprises a pipette or an injector; - the means for carrying out the step of counting the number of viable cells comprises a cytometer; - means for carrying out the steps of determining a pharmacodynamic parameter value, an active marker value and a normalized marker value comprising at least one computer program product; - the means for performing the step of selecting comprises at least one computer program product; - the means for performing the step of calculating a score comprises at least one computer program product; and - the means for sorting each drug combination in order of score comprises at least one computer program product; The apparatus used to carry out the step mentioned in (h') are the same as those used in the steps mentioned in (a) to (g').

23. The system of claim 13, further comprising prescribing a care plan for the subject, wherein the care plan prescribes a drug combination selected from drug combinations classified as having the highest efficacy in the treatment of the disease in the subject.

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