Clinical Trial Phase Simulation Method and Clinical Trial Phase Simulator For Drug Trials

• US 20080256006A1
• Filed: 06/27/2005
• Published: 10/16/2008
• Est. Priority Date: 06/30/2004
• Status: Abandoned Application

• Current Assignee: Bracco Imaging S.p.A. (Bracco SPA)
• Original Assignee: Bracco Imaging S.p.A. (Bracco SPA)
• Inventors: Buscema, Paolo Massimo, Grossi, Enzo
• Application Number:
  US11/571,199
• Publication Number:
  US 20080256006A1
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A clinical trial phase simulation method for drug trials, which method allows to predict the trend of the results of a clinical trial phase of a drug with the steps of providing a database comprising for each of a certain number of individuals a predefined number of independent variables each of which corresponds to a certain clinical parameter relevant or characteristic for a disease condition against which the drug to be tested is oriented and at least a further independent variable describing the specific treatment to which the individual has been subjected between at least two different treatments one with the said drug and the second with a placebo or with another known drug, the database comprising also for each individuals one or more dependent variables describing the effects of the said treatments; carryings out an input variable selection; adding to the independent variables selected as input variables the dependent variables describing the effects of the treatments; training and validating an artificial neural network with the selected variables as input variables and with the dependent variables; interrogating the said neural network by inputting the values of the variable describing one of the treatments and obtaining as an output the variable values of the effectiveness of the treatment to which the inputted values of the variable of the treatment correspond according to the trained artificial neural network.

Claims

1. Clinical trial phase simulation method for drug trials, which method allows to predict the trend of the results of a clinical trial phase of a drug comprising the following steps:

a) providing a database comprising for each of a certain number of individuals a predefined number of independent variables each of which corresponds to a certain clinical parameter which parameters are relevant or characteristic for describing or identifying a disease condition against which the drug to be tested is oriented and at least a further independent variable describing the specific treatment to which the individual has been subjected between at least two different treatment one with the drug to be tested and the second treatment with a placebo or with another known drug, the database comprising also for each individuals one or more dependent variables describing the effects of the said treatments observed on the individuals;

b) carrying out an input variable selection by means of an input variable selection algorithm by feeding the set of independent variables of the database to the said input variable selection algorithm;

c) adding to the independent variables selected as input variables at step b) the one or more dependent variables describing the effects of the treatments;

d) training and validating an autoassociated artificial neural network with the set of selected independent variables as input variables and with the one or more dependent variables;

e) interrogating the trained and validated autoassociated artificial neural network by inputting only the values of the variable describing one of the at least two different treatments to which the individuals has been subjected and obtaining as an output the variable values of the effectiveness of the treatment to which the inputted values of the variable of the treatment correspond according to the trained artificial neural network;

f) repeating step e) for each treatment of the at least two treatments to which the individuals has been subjected;

g) comparing the values of the variables relative to the effectiveness of the different treatments to which the individuals has been subjected which values has been determined at steps e) and f).

2. A Clinical virtual trial phase according to claim 1, characterized in that it comprises a further interrogation step or an alternative interrogation step in which to the trained and validated artificial neural network the values of the dependent variables are inputted, this means the values of the effectiveness of the treatment while the network gives as an output the input variables and particularly the variables relating to the kind of treatment which according to the said network will lead to the inputted effectiveness values.

3. A clinical virtual trial phase according to claims 1 or 2, characterised in that it comprises the steps of discriminating similar output values for two different variables by determining the artificial neural network nodes dynamics of the nodes related to the said variables determining which of the two nodes reaches as first the said similar value in a stable manner and setting the output value of the variable corresponding to the said node that has reach as first one the said similar values at the said similar value, while the output value of the other variable/s is set to the value that the corresponding node/s has reached in at the time the other node has reached as the first one the said similar values.

4. A clinical virtual trial phase according to one or more of the preceding claims, characterized in that a step of carrying out a variable selection among the variables of the database is carried out.

5. A clinical virtual trial phase according to claim 4, characterized in that the variable selection is carried out on the independent variable of the database relating to the clinical parameters relevant for the disease against which the drug is destined.

6. A clinical virtual trial phase according to one or more of the preceding claims 4 to 6, characterized in that the independent variable selection step chooses a limited or reduced number of independent variables from the totality of independent variable provided in the original database.

7. A clinical virtual trial phase according to claim 6, characterised in that the independent variable selection step is stopped at a independent variable selection stage at which at least one of the variables representing at least one or at least part or all of the treatments provided in the trial is still present among the selected independent variables.

8. A clinical virtual trial phase according to one or more of the preceding claims characterized in that the variable selection step is carried out by means of a combination of a predictive algorithm and of an evolutionary algorithm.

9. A clinical virtual trial phase according to claim 8, characterized in that the variable selection is carried out by means of the following steps:

providing a starting database having a certain number of input variables, i.e. of independent variables, and a certain number of records to which the said independent and dependent variables are univocally associated;

generating a first population of artificial neural network is generated by training and testing the artificial neural network with different training and testing databases which databases are formed by distributing the records of the starting database on the said testing and on the said training database;

the records of each training database being also varied by eliminating one or more different independent variables in each different training and testing database, so that each of the artificial neural networks generated with a different training and testing database is univocally represented by the specific training database and by the validation or fitness score obtained in testing the said artificial neural network;

the said first population of artificial neural networks being successively treated as starting population for an evolutionary algorithm, particularly a genetic algorithm which uses as a genoma of each artificial neural network the corresponding specific training database;

the said evolutionary algorithm generates a new generation of artificial neural networks comprising a population of artificial neural networks each one of them has been trained and tested by means of a training and testing database which number of variables and which kind of variables are a combination of the number of variables and of the kind of variables of the corresponding training and testing databases of the two parents artificial neural networks;

for each new generated artificial neural network the fitness score is calculated by carrying out the testing phase;

repeating the said steps till a certain fitness score higher than a predetermined lower limit has been reached by a son artificial neural network which also has a minimum number of independent variables selected among the totality of independent variables which were present in the starting database.

10. A clinical virtual trial phase according to claim 9, characterised in that the evolutionary algorithm is interrupted when at least one of the independent variables representing at least one or at least part or all of the treatments provided in the trial is still present among the selected independent variables.

11. A clinical virtual trial phase according to claims 9 and 10, characterised in that the variable selection is carried out by means of the following method steps:

a) providing a database having a certain number of input variables, i.e. of independent variables, and a certain number of records to which the said independent and dependent variables are univocally associated and in which database each record relates to a known clinical or experimental case of a sample population of cases;

b) determining a selection of a reduced number of the certain predetermined number of input variables by means of mathematical tools applied to the database.

c) The said mathematical tools comprising a so called prediction algorithm such as a so called neural network;

d) dividing the database in a training and a testing dataset for training and testing the prediction algorithm;

e) defining two or more different training dataset each one having records with a reduced number of the input variables which reduced number of input variables is obtained by excluding one or more input variables from the originally defined number of input variables, while for each record the reduced number of input variables of the corresponding training set has at least one input variable which is different from the input variables of the reduced number thereof of the other training datasets.

f) training the prediction algorithm with each of the different training sets defined under point e) for generating a first population of different prediction algorithm which are divided into two groups of mother and father prediction algorithms and testing the said prediction algorithms with the associated testing set;

g) calculating a fitness score or prediction accuracy of each father and mother prediction algorithms of the said first population by means of the testing results;

i) providing a so called evolutionary algorithm such as a genetic algorithm and applying the evolutionary algorithm to the first population of mother and father prediction algorithms for achieving new generation of prediction algorithms whose training and testing dataset comprises records whose input variables selections are a combination of the input variable selections of the records of the training and of the testing datasets of the first or previous population of father and mother prediction algorithms according to the rules of the evolutionary algorithm;

j) for each generation of new prediction algorithms representing each new variant selection of input variables, the best prediction algorithm according to the best hypothesis of input variable selection is tested or validated by means of the testing dataset;

k) a fitness score is evaluated and the prediction algorithms representing the selections of input variables which have the best testing performance with the minimum number of input variables utilized are promoted for the processing of new generations.

l) repeating the steps i) to k) until a predetermined fitness score defined as best fit of the prediction algorithm and a minimum number of input variables has been reached

m) defining as the selected relevant input variables the ones related to the input variables of the selection represented by the prediction algorithm having both at least the predetermined fitness score and also the minimum number of selected input variables.

12. A clinical virtual trial phase according to one or more of the preceding claims, characterised in that it comprises a preventive step of carrying out a second phase trial of the treatment or drug on a limited number of individuals, while the database used for the simulation steps as described above is generated from the results of the second phase clinical trial.

13. A clinical virtual trial phase according to one or more of the preceding claims, characterised in that it is a simulation of a third phase clinical trial based on the data of a second phase clinical trial.

14. A clinical virtual trial phase according to one or more of the preceding claims characterised in that the second phase trial database comprises more than two alternative treatments carried out each on a group of individuals comprising part of the total number of individuals participating to a second phase trial.

15. A clinical virtual trial phase according to one or more of the preceding claims, characterised in that an interrogation is carried out by inputting the values of the variables relating to one or more of the treatments provided and by reading the output of one or more of the variables relating to the effects provided.

16. A clinical virtual trial phase according to one or more of the preceding claims characterized in that an interrogation is carried out by inputting the values of the variables relating to one or more of the effects provided and by reading the output of one or more of the variables relating to the treatments provided.

17. A clinical virtual trial phase according to one or more of the preceding claims characterized in that as a result of an interrogation also the independent variables relating to the relevant clinical parameters or of the other parameters are red in order to provide for a clinical and a anagrafic profile of the best individuals for carrying out a real third phase trial.

18. An apparatus for carrying out a simulated clinical virtual trial phase characterized in that

the said apparatus comprising a first virtual network formed by a computing machine and a program for the said computing machine which program forces the apparatus to work as a neural network of the autoassociative kind;

The said network being provided with input channels each one for a variable of a certain number of variables describing relevant clinical data of patients and variables describing the treatment to which a the said certain number of patients has been submitted;

The said network being also provided with a certain number of output channels each one relating to variables describing the effects of the treatments;

the apparatus being also provided with means for reading the variables describing the relevant clinical data of patients and the variables relating to the kind of treatment to which the said patients has been submitted and the corresponding output variables relating to the experimentally ascertained effects of the treatments on the said certain number of patients and for adjusting the network response to the input variables in order to generate the known output variables when the input variable of the said database are fed to the input channels;

Means being provided for manually inputting the variable relating to a kind of the treatment and for reading the corresponding output of the network;

And means being provided for forcing the outputs of the output channels at a value corresponding to a certain effect and reading the corresponding input values of the network.

19. An apparatus according to claim 18, characterized in that the apparatus is provided further with a program which generates different networks, by eliminating or suppressing different input channels relating to different variables describing the clinical status of the patients in each different network and which program generates combinations of the said different networks by combining the sets of the different active or eliminated or suppressed channels of each pair of networks according to combination rules following the basic biologic gene combination rules between parents;

Means being provided for evaluating the accuracy of prediction of each network

And means being provided for stopping the generation of new networks before the selection mechanism eliminates or suppresses all the variables describing the treatments.

20. An apparatus according to claims 18 or 19, characterized in that it comprises:

means for selecting one or more variable among the variables provided in the database;

means for inputting data values for the said one or more selected variable;

means for starting a computing cycle;

means for printing and/or visualizing the values of at least part or all of the other variables computed from the inputted values for one or more selected variables;

means for selection of the variables for which the values has been computed;

means for printing and/or visualizing the computed values of the selected variables among the variables for which the values has been computed.

21. An apparatus according to one or more of the preceding claims 18 to 20, characterized in that it comprises an output for visualizing or printing the nodes dynamics and for univocally highlighting the nodes corresponding to the selected variables;

22. An apparatus according to claim 21, characterised in that it comprises means for determining the computing duration for each node corresponding to a selected variable for reaching a stable computed value.

23. An apparatus according to claim 22, characterised in that it comprises means for automatically listing the nodes and the corresponding variables in and ordered manner relatively to the duration of the computation for reaching a stable computed value and means for determining the value of the resting nodes of all or of selected variables at the time one selected variable has reached as the first one a stable computed value.