FR3142593A1 - Method for determining sensitivity of a patient to a medication prescribed to treat a neurodegenerative disease - Google Patents

Abstract

Method for determining a sensitivity of a patient to a drug prescribed to treat a neurodegenerative disease A computer-implemented method for determining a sensitivity of a patient to a drug prescribed to treat a neurodegenerative disease based on 'a temporal function depending on data relating to times of taking the drug by the patient and the corresponding administration quantities and measurements relating to at least one biomarker of the patient. Abstract figure: Fig. 4

Description

Method for determining sensitivity of a patient to a medication prescribed to treat a neurodegenerative disease FIELD OF INVENTION

The present invention relates to the treatment of neurodegenerative diseases, and more particularly Parkinson's disease.

The present invention relates to a computer-implemented method for determining a sensitivity of a patient to a drug prescribed to treat a neurodegenerative disease and to a system for determining a sensitivity of a patient to a drug prescribed to treat a neurodegenerative disease. neurodegenerative disease.

STATE OF THE TECHNIQUE

Neurodegenerative diseases such as Parkinson's disease affect an increasing number of people. These diseases are characterized by the progressive disappearance of certain neurons in the brain. The main consequence of this neuronal disappearance is the reduction in the production of dopamine, a molecule which allows neurons to communicate with each other, in a region essential for the control of movements. The symptoms of Parkinson's disease are characteristic, even if they can vary from one patient to another: difficulty initiating a movement (akinesia); a slowing down of gestures; increasingly small and difficult writing; rigidity of the limbs, arms or legs (hypertonia); characteristic tremors that appear at rest, when the muscles are relaxed.

To date, there is no cure for Parkinson's disease. Only therapies exist that can limit the progression of the disease. Treatment with L-dopa, a precursor molecule of dopamine, is the most widespread, due to the more powerful and longer-lasting antiparkinsonian effect of L-Dopa compared to dopamine agonists. Transformed into dopamine after absorption by the Parkinson's patient, L-Dopa then passes from the blood to the brain. The supply of this synthetic dopamine makes up the deficiency of natural dopamine. L-Dopa acts mainly on akinesia and hypertonia but also, later after the start of treatment, on tremors.

Monitoring the reaction of a Parkinson's patient to treatment with L-Dopa is of capital importance in order to estimate the effectiveness of the treatment, to adjust the dosage prescribed by the practitioner or to consider specific interventions such as than neurostimulation.

Therapeutic treatment with L-Dopa being a long-term treatment, it could prove advantageous to take advantage of data from the daily life of Parkinson's patients for monitoring the treatment. Indeed, “real-world evidence” (RWE) makes it possible to obtain useful information complementary to clinical trial data, with the potential to fill knowledge gaps, particularly concerning the use of medications, in routine clinical practice.

The invention fits into this context.

SUMMARY

A computer-implemented method for determining a patient's sensitivity to a drug prescribed to treat a neurodegenerative disease includes the following steps:
- reception, during a temporal period, of measurements relating to at least one biomarker of the patient;
- reception of data relating to the times the medication is taken by the patient and the corresponding administration quantities, during said time period;
- determination of the sensitivity of the patient to the drug, on the basis of a temporal function depending on said data relating to the times of taking the drug by the patient and the corresponding administration quantities and of said measurements relating to at least one biomarker of the patient,
in which :
- the time function takes values over said time period and is defined, using pharmacokinetic parameters, and on the basis of previous data relating to the times of taking the drug and the corresponding administration quantities received during a previous time period prior to said time period, said time function being representative of a quantity of the drug absorbed by the patient and not yet metabolized,
- said pharmacokinetic parameters having been determined by maximizing a correlation measure between said measurements relating to at least one biomarker of the patient and the values of the temporal function over said previous time period.

By sensitivity, we mean the variation of the patient's symptoms depending on the quantity of medication absorbed but not yet metabolized. Thus, the invention makes it possible to obtain information on the patient's reaction to the treatment prescribed to him. This information can then be used by a clinician to adjust and optimize the patient’s treatment.

Advantageously, the pharmacokinetic parameters include a half-life of the drug and a duration of total absorption of the drug.

Advantageously, the correlation is a Spearman correlation. Indeed, the relationship between patient sensitivity and drug concentration is predicted to be monotonic, because the higher the patient ingests a quantity of the drug, the more their symptoms are expected to decrease. Spearman's correlation estimates how well the relationship between two variables can be described by a monotonic function.

Advantageously, the measurements relating to at least one patient biomarker are, on the one hand, discrete or continuous, and on the other hand, scattered or recorded according to an ordered schedule.

Advantageously, the neurodegenerative disease is Parkinson's disease.

Advantageously, the drug is L-dopa.

Advantageously, the temporal function is defined so that the determined sensitivity increases below a threshold of quantity absorbed but not yet metabolized of the drug. Indeed, in the case where the neurodegenerative disease is Parkinson's disease, one of the symptoms is the insufficiency of endogenous dopamine. In the case where the drug is L-dopa, the treatment thus makes it possible to provide L-dopa which is transformed into exogenous dopamine, so that the L-dopa receptor neurons gradually begin to function again. This gradual restart results in an increase in sensitivity as defined previously and corresponds to an improvement in the patient's state of health. The threshold quantity absorbed corresponds to the normal concentration, i.e. of a person without Parkinson's disease, of dopamine. Beyond this threshold, the improvement in the patient's state of health is marginal and sometimes, side effects of an overdose are observed, such as abnormal movements. Such a definition of the temporal function makes it possible to take into account this evolution of the state of health of a patient suffering from Parkinson's disease and treated with L-dopa.

Advantageously, the maximization of the correlation is carried out by a Monte Carlo algorithm or a conjugate gradient technique.

Advantageously, the determination of the pharmacokinetic parameters comprises, prior to maximizing the correlation measurement, a spectral analysis of the measurements. Indeed, very often, symptoms, such as tremors or calligraphic difficulties, present a spectral signature. A spectral analysis allows you to obtain and analyze spectral data.

Advantageously, the spectral analysis of the measurements is followed by a calculation by automatic learning. Using machine learning, the measurements can be used to identify baseline spectral signatures typical of neurodegenerative disease symptoms.

Advantageously, the temporal function is further defined using secondary parameters, said secondary parameters comprising for example a parameter representative of a difference between a daytime metabolism and a nighttime metabolism of the patient. This makes it possible to further personalize the data output from the determination process.

Advantageously, the at least one biomarker is chosen from:
- an input biomarker representative of the characteristic of an input on a keyboard such as the total typing time, the average typing speed, the maximum typing speed, the pressing time, the duration between two successive keystrokes and/ or the partial or total rhythm of typing when typing on a keyboard, preferably the pressing time and/or the typing speed when typing on a keyboard,
- a cerebral biomarker representative of the electrical activity of the brain, preferably measured by electroencephalography (EEG),
- a pronunciation biomarker representative of the accuracy of pronunciation of at least one word in a specific language,
- a voice biomarker representative of the power of the voice,
- a phonation biomarker representative of the stability of phonation,
- a tremor biomarker representative of tremors of at least one part of the body, and
- an accuracy biomarker representative of the accuracy of the gesture during calligraphy.

Advantageously, the measurements relating to the at least one biomarker are recorded by at least one accelerometer, a microphone, a touch screen, a headset dedicated to electroencephalographic measurements, a smartphone.

Another aspect of the invention relates to a system for determining a patient's sensitivity to a medication prescribed to treat a neurodegenerative disease, comprising a programmable device, said programmable device being adapted to:
- receive, over a period of time, measurements relating to at least one biomarker;
- receive data relating to the times the medication is taken by the patient and the corresponding administration quantities, during said time period;
- determine the sensitivity of the patient to the drug, on the basis of a temporal function depending on said data relating to the times of taking the drug by the patient and the corresponding administration quantities, and said measurements relating to at least one biomarker of the patient,
in which :
- the time function takes values over said time period and is defined, using pharmacokinetic parameters, on the basis of previous data relating to the times of taking the drug and the corresponding administration quantities received during a previous previous time period at said time period, said time function being representative of a quantity of the drug absorbed by the patient and not yet metabolized,
- said pharmacokinetic parameters are determined by maximizing a correlation measure between the measurements relating to at least one biomarker of the patient and the values of the temporal function over said previous time period.

Another aspect of the invention relates to a computer program product comprising instructions for implementing the following steps of a method for determining a sensitivity of a patient to a drug prescribed to treat a neurodegenerative disease when execution of the program by a processor of a programmable device:
- receive, over a period of time, measurements relating to at least one biomarker;
- receive data relating to the times the medication is taken by the patient and the corresponding administration quantities, during said time period;
- determine the sensitivity of the patient to the drug on the basis of a temporal function depending on said data relating to the times of taking the drug by the patient and the corresponding administration quantities and said measurements relating to at least one biomarker of the patient,
in which :
- the time function takes values over said time period and is defined, using pharmacokinetic parameters, and on the basis of previous data relating to the times of taking the drug and the corresponding administration quantities received during a previous time period prior to said time period, said time function being representative of a quantity of the drug absorbed by the patient and not yet metabolized,
- said pharmacokinetic parameters are determined by maximizing a correlation measure between the measurements relating to at least one biomarker of the patient and the values of the temporal function over said previous time period.

DESCRIPTION OF FIGURES

represents an example of steps implemented for carrying out a calibration phase, according to one or more embodiments.

represents an example of a monitoring sheet for recording patient data.

represents an example of steps implemented for carrying out the method for determining the sensitivity of a patient, according to one or more embodiments.

represents an example of a curve representative of the time function.

represents an example of a curve representative of a patient's sensitivity to L-Dopa.

is a schematic diagram showing the components of an example of the determination system configured to implement the method of determining a sensitivity of a patient.

DETAILED DESCRIPTION

A first aspect of the invention relates to a computer-implemented method 100 for determining a patient's sensitivity to a medication prescribed to treat a neurodegenerative disease. In the following description, the drug is L-Dopa and the neurodegenerative disease is Parkinson's disease.

By sensitivity, we mean the variation of the patient's symptoms depending on the quantity of L-Dopa absorbed but not yet metabolized. Thus, it is expected that the higher the concentration of L-Dopa absorbed, the more the patient's symptoms decrease. In other words, sensitivity to L-Dopa varies in a decreasing manner as the concentration of L-Dopa absorbed increases. Sensitivity can be described in other terms by a mathematical function having as variable the concentration of L-Dopa absorbed by the patient and being representative of the intensity of the patient's symptoms.

As will be described subsequently, this mathematical function is defined from modeling inspired by pharmacokinetic models. The modeling uses different patient-specific parameters (p _n ) with n an integer between 1 and N.

The method 100 implemented by computer to determine the sensitivity of the patient to the prescribed medication makes it possible in particular to obtain a curve representative of said variation of symptoms as a function of the quantity of L-Dopa absorbed but not yet metabolized. On the basis of this representative curve, a clinician, such as a neurologist or a referring neurologist, responsible for evaluating the patient's eligibility for neurostimulation, will be able to analyze the patient's reaction to L-Dopa and guide the treatment. of the patient. This representative curve will subsequently be called the dopaminergic sensitivity curve.

The method 100 is for example implemented by a system 1 for determining the sensitivity of a patient to a medication prescribed to treat a neurodegenerative disease.

According to one or more embodiments, the determination system 1 comprises a programmable device. By programmable device, we mean any programmable information processing system such as a business computer, a personal computer (PC), a computer (smartphone), a connected watch or a touchscreen tablet.

In a calibration phase 100a, a calibration is carried out in order to determine the patient-specific parameters (p _n ). The calibration is carried out on the basis of data collected during a first time period T _calib .

There represents an example of steps implemented for carrying out the calibration phase, according to one or more embodiments.

It is assumed here that the patient has a prescription describing, over the first time period T _calib , the prescribed times for taking medication by the patient during the first time period T _calib , as well as the corresponding quantities of medication. One of the medications corresponds to a medication comprising L-Dopa. For example, the corresponding quantities are concentrations in milligrams.

During the first time period T _calib , the patient records the different actual times t _i of taking medication M _l , with l an integer between 1 and L, as well as the corresponding quantities actually taken m _il . For example, the instruction (t _i , m _il ) is made on paper, then recorded electronically, for example, in a memory of the determination system 1. An example of a monitoring sheet where the patient records the actual moments t _i ; as well as the quantities actually taken m _it is given on the . In another example, the instruction (t _i , m _il ) is carried out electronically in a digital file by means of a computer, for example directly by means of the determination system 1.

In parallel, during the first time period T _calib , measurements relating to at least one biomarker BM _k of the patient, where k is an integer varying between 1 and K, will be recorded. These measurements are discrete or continuous. These measurements are also either scattered over the first time period T _calib , or recorded according to an ordered schedule over the first time period T _calib . All of the measurements recorded over the first time period T _calib constitute a series of signals (t _j , s _j ) _k , with t _j indicating a measurement instant and s _j , the measurement of the biomarker BM _k at time t _j .

Biomarker means a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes or responses to exposure or intervention, including therapeutic interventions. Typically, a biomarker is associated with a symptom of a pathology.

Examples of biomarkers, in the context of this description, are as follows:
- a typing biomarker representative of the characteristic of typing on a keyboard such as the total typing time, the average typing speed, the maximum typing speed for a predetermined sentence or set of words, the pressing time on a key, the duration between two successive keystrokes and/or the partial or total keystroke rhythm when typing on a keyboard, preferably the press time and/or the typing speed when typing on a keyboard ,
- a cerebral biomarker representative of the electrical activity of the brain, preferably measured by electroencephalography (EEG),
- a pronunciation biomarker representative of the accuracy of pronunciation of at least one word in a specific language,
- a voice biomarker representative of the power of the voice,
- a phonation biomarker representative of the stability of phonation,
- a tremor biomarker representative of tremors in at least one part of the body, and
- an accuracy biomarker representative of the accuracy of the gesture during calligraphy.

Generally speaking, biomarkers are measured directly or indirectly by sensors.

For example, the seizure biomarker can be determined by analyzing the data obtained by measurement when the patient uses a computer equipped with a keyboard. The computer may be the determination system 1.

For example, the brain biomarker can be measured with an EEG headset (i.e., device for measuring the electrical activity of the brain). The measurements from the EEG headset are then, for example, received by the determination system 1, said biomarker being obtained by analyzing the electrical activity of the brain. For example, the brain biomarker can be a measurement of a brain wave, such as alpha, beta, theta or delta waves.

For example, the pronunciation biomarker, the voice biomarker, the phonation biomarker can be determined by analyzing the data obtained by measurements with a microphone. The microphone can be integrated or connected, for example, to the determination system 1. For example, the amplitude of the sound signal can be used to obtain a value representative of the power of the voice and/or a sound comparison algorithm can be used. be used to calculate a numerical value of the pronunciation biomarker representative of pronunciation accuracy.

For example, the tremor biomarker can be determined by analyzing the data obtained measured with an inertial unit or an accelerometer. The inertial unit can be integrated or connected, for example, to the determination system 1.

For example, the accuracy biomarker can be determined by analyzing data recorded with a touchscreen tablet.

On the basis of the recorded setpoint (t _i , m _il ) and the sequence of signals (t _j , s _j ) _k , the patient-specific parameters will be determined as follows.

In a first step E10, a spectral analysis is carried out of each of the signals (t _j , s _j ) _k . For example, we calculate the Fourier transform of each signal (t _j ,s _j ) _k , in order to obtain a corresponding spectrum S _pk . Step E10 can be carried out by the determination system 1.

A symptom associated with a BM biomarker _k presents a characteristic spectral signature. By spectral signature, we mean the spectrum of the temporal measurement of the biomarker BM _k over a given period, when the patient presents the corresponding symptom.

Optionally, an intermediate step E10b is carried out following the first step E10. During the intermediate step E10b, each spectrum S _pk corresponding to the biomarker BM _k is identified with a reference spectrum S _refk by automatic learning. The reference spectrum S _refk is characteristic of the symptom associated with the biomarker BM _k . For example, machine learning is performed using a database of previously recorded and collected biomarker spectral signatures. Typically, the database contains 1500 spectral signatures corresponding to subjects affected by the disease and 1500 spectral signatures corresponding to healthy subjects. Step E10b can be carried out by the determination system 1.

As mentioned above, the mathematical function defining the patient's sensitivity to L-Dopa is defined from modeling inspired by pharmacokinetic models. The modeling uses the different patient-specific parameters (p _n ).

Advantageously, the patient-specific parameters (p _n ) are the following three independent parameters:
- τ _a , representing an apparent decarboxylase factor in the patient's plasma,
- g _B , representing the endogenous production of L-dopa in the patient's brain,
- Ф _B representing cerebral blood flow.

These parameters are present in the following equations:

where τ _a represents an apparent decarboxylase factor in the patient's body,
τ _W represents a decarboxylase factor in the patient's body water volume,
Ф _B represents cerebral blood flow, estimated at approximately 0.88L/minute during the day,
V _W represents the volume of water in the patient's body,
τ _B represents a decarboxylase factor in the patient's brain,
t _B represents the time required for a cerebral blood volume Ф _B dt to propagate in the patient's brain, estimated at approximately 75 seconds.

Equation (1) can be solved iteratively and has, to the knowledge of the applicant, never been previously disclosed.

The apparent decarboxylase factor τ _a varies between 75 and 120 minutes from one patient to another.

where Ma represents an apparent flow of L-Dopa,
M represents the temporal variation of the quantity of L-Dopa ingested by the patient, in milligrams per minute, which is known,
g _B represents the endogenous production of L-Dopa in the patient's brain,
V _B represents the volume of water in the patient's brain, estimated at approximately 1.1 L,
V _W represents the volume of water in the patient's body, estimated at approximately 40 L.

In particular, the expression for the parameter g _B can be deduced from equation (2).

For a given set of parameters (pn), an intermediate function f(t), having time as variable, , is defined by solving df/dt=-f/ τ _a ,+M. Thus, a time function tdLED(p _n ,t _j ) equal to f(t _j ) is defined.

In a second step E20, a step of maximizing the Spearman correlation between, on the one hand, the spectra S _pk from the first step E10 (or the spectra S _refk from the intermediate step E10b), and on the other apart the function tdLED (p _n , t _j ) is carried out. The p _{no pt} values of the patient-specific parameters maximizing the Spearman correlation between the tdLED temporal function (p _n ,t _j ) and the S _pk (or S _refk ) spectra are those used subsequently for the specific parameters. The second step E20 can be carried out by the determination system 1.

For example, a Monte Carlo algorithm can be used to determine _pnopt values. Alternatively, a conjugate gradient technique can be used. In general, any optimization technique known to those skilled in the art can be used.

Typically, the determination system 1 is a patient's smartphone, and the patient executes the first step E10, the intermediate step E10b and the second step E20 with a first dedicated application stored in his smartphone.

We now describe the computer-implemented method 100 for determining the sensitivity of the patient to L-Dopa. It is assumed that the patient-specific p _nopt parameters were determined by the calibration carried out during the calibration phase 100a presented previously.

There represents an example of steps implemented for carrying out the determination method 100, according to one or more embodiments.

It is also assumed that the patient has a prescription specifying the times at which the patient takes medication during a study period T _e , as well as the corresponding quantities of medication. One of the medications corresponds to a medication comprising L-Dopa. For example, the corresponding quantities are concentrations in milligrams. It is also assumed that the patient has the determination system 1. For example, the patient will implement the determination method with his smartphone containing a second dedicated application. The second dedicated application may correspond to the first dedicated application having executed the steps of the calibration phase.

Just as during the calibration phase 100a, during the study time period T _e , the patient records the different effective moments t _o , with o an integer between 1 and O, of taking medication M _l , as well as the quantities actually taken corresponding m _{o l} . We call study data the set of pairs (t _o , m _{o l} ). For example, the study data (t _o , m _{o l} ) are recorded on paper then recorded electronically, for example in the system memory. In another example, the study data (t _o , m _{o l} ) are computer-recorded in a digital file using a computer. For example, the study data (t _o , m _ol ) are recorded in a memory of the determination system 1. The reception of the study data by the determination system 1 is carried out during a step E30.

In parallel, during the study period T _e , measurements relating to at least one biomarker BM _p of the patient, p being an integer between 1 and P, will be recorded. At least one biomarker among the at least one BM _p biomarker is included in the set formed by the at least one BM _k biomarker used for the calibration phase 100a. These measurements are discrete or continuous. These measurements are also either scattered over the study time period T _e , or recorded according to an ordered schedule over the study time period T _e . The set of measurements recorded during the study time period T _e constitutes a series of signals (t _q , s _q ) _p , with t _q indicating a measurement instant and s _q , the measurement of the biomarker BM _p at the time t _q .
The determination system 1 receives, in a step E40, the series of signals (t _q , s _q ) _p .

In a following step E45, the programmable device of the determination system 1 calculates the time function tdLED(p _{no pt} ,t _q ). There gives an example of a curve representative of the time function tdLED(p _{no pt} ,t _q ). The points on the represent the quantity of L-dopa, in milligrams, absorbed by the patient, while the peaks and troughs of the continuous curve represent the evolution of the time function tdLED(p _nopt ,t _q ), in other words the concentration of L -Dopa in the patient's body, in mg/L.

Then, in a step E50, the programmable device calculates, by combination of the time function tdLED(p _{no pt} ,t _q ) and the sequence of signals (t _q ,s _q ) _p , and by elimination of the variables t _q , at least one representative curve C _p of the patient's sensitivity to L-Dopa. Each C _p curve has the L-Dopa concentration as abscissa and a quantity representative of the intensity of the biomarker BM _p as ordinate.

There gives an example of a curve representative of the patient's sensitivity for a biomarker C _p0 . On this curve, the gray rectangles and the ends of the vertical segments for each point of the curve represent the confidence intervals respectively with risks of 1% and 5%. It can be observed on the that the curve representative of the patient's sensitivity is decreasing. In other words, the greater the amount of L-Dopa absorbed, the less intense the patient's symptoms.

Thus, one of the advantages of the invention lies in the fact of being able to carry out very frequent monitoring of the state of symptoms in the patient, several times a day, or even continuously, thanks to devices allowing self-declaration of symptoms. by the patient or a self-test by the patient to identify symptoms.

Data recording makes it possible to obtain a curve representative of the patient's sensitivity to L-Dopa.

Thanks to monitoring, a diagnosis can be made by a practitioner making it possible to evaluate the effectiveness of the therapeutic response, and possibly modify this response by modulating the patient's treatment.

A second aspect of the invention relates to the determination system 1.

The determination system 1 is configured to implement the method for determining the sensitivity of a patient to a drug such as L-Dopa previously described.

There is a schematic diagram showing the components of an example of Determination System 1.

The determination system 1 can be implemented as a single hardware device, for example in the form of a desktop personal computer (PC), a laptop, a personal digital assistant (PDA), a smartphone, a connected watch, a server, a console or can be implemented on separate interconnected hardware devices interconnected by one or more communication links, with wired and/or wireless segments. The determination system 1 may for example be in communication with one or more cloud computing systems, one or more servers or remote devices to implement the functions described in this document for the device concerned. The determination system 1 can also be implemented itself as a cloud computing system.

As shown schematically on the , the determination system 1 may comprise at least one processor 10 configured to access at least one memory 20 comprising computer program code 70. The computer program code may comprise instructions configured to cause the determination system 1 to execute a or several or all the steps of the sensitivity determination method 100 described above.

The processor 10 may thus be configured to store, read, load, interpret, execute and/or otherwise process the computer program code 30 stored in the memory 20 such that, when the instructions encoded in the computer program code are executed by the at least one processor 10, the system 1 executes one or more steps of the determination method 100 described here.

The processor 10 may be any suitable microprocessor, microcontroller, integrated circuit or central processing unit (CPU) comprising at least one hardware-based processor or processing core.

The memory 20 may comprise a random access memory (RAM), a cache memory, a non-volatile memory, a backup memory (for example, programmable or flash memories), a read only memory (ROM), a hard disk (HDD), a solid state drive (SSD) or any combination thereof. The ROM of memory 20 may be configured to store, among other things, an operating system of the system S and/or one or more computer program codes of one or more software applications. The RAM of memory 20 can be used by processor 10 for temporary data storage.

The system 1 may further include one or more communication interfaces 40 (e.g., network interfaces for wired/wireless network access, including Ethernet interface, WIFI interface, USB interfaces, etc. The determination system 1 may include other associated hardware such as user interfaces such as a 2D visual rendering interface 30A, in our case here containing a 3D screen and 3D glasses, or haptic interfaces 30B, or any other interfaces ( for example keyboard, mouse, display screen, etc.) connected via one or more appropriate communication interfaces 40 with the processor. The determination system 1 may also include a media reader 50 for reading a computer-readable external storage medium. The processor 10 is connected to each of the other components in order to control their operation.

A third aspect of the invention relates to a computer program product comprising instructions for implementing the steps of the method for determining a sensitivity of a patient to a drug prescribed to treat a neurodegenerative disease when executing the program by a processor of a programmable device. For example, the instructions are part of the computer program code 70 stored in the memory 20 of the determination system 1.

The computer program product implements in particular steps E10, E10b, E20, on the one hand, and E30, E40, E45 and E50 on the other hand.

EXAMPLES

The present invention will be better understood on reading the following examples which illustrate the invention in a non-limiting manner.

Example 1 : Prescription of a neurostimulation intervention

This example shows the use of the method, as described above, to determine the sensitivity of a patient suffering from Parkinson's disease to a medication comprising L-Dopa, in the case where a neurologist plans to prescribe a treatment. by neurostimulation to a patient. Neurostimulation, or deep brain stimulation, is an intervention consisting of applying electrical stimulation to target structures by implanting an electrode attached to a generator. It turns out that the effectiveness of this heavy and expensive surgical procedure is correlated with the patient's sensitivity to taking L-dopa. It is assumed that the patient already has a prescription including a medication containing L-Dopa.

In a first option, the referring neurologist, responsible for assessing the patient's eligibility for neurostimulation, informs the latter that he must follow a campaign during a study period T _e equal to four weeks and consisting of:
- record study data (t _o , m _{o l} )
- record a series of signals (t _q , s _q ) _p corresponding to measurements of at least one biomarker BM _p .

In a second option, the referring neurologist, responsible for evaluating the patient's eligibility for neurostimulation, informs the patient's neurologist that it is recommended that the latter follow the campaign previously described during the study period T _e .

The patient follows the campaign during the study period T _e . It is assumed that the study period T _e includes a calibration period at the end of which the study data and the sequence of signals will be used to determine the patient-specific parameters p _{no pt} , as explained above.

Following the study period T _e , the patient's dopaminergic sensitivity curve is obtained.

The patient then consults the referring neurologist, responsible for evaluating the patient's eligibility for neurostimulation, for analysis of the dopaminergic sensitivity curve obtained. For example, the expert center finds information relating to the Spearman correlation coefficient, the presence of inflection points, the maximum slope of the sensitivity curve.

The referring neurologist, responsible for assessing the patient's eligibility for neurostimulation, uses the sensitivity curve in addition to the usual L-Dopa tests undergone by the patient. These usual tests generally consist of depriving the patient of medication for 16 hours, accepting the return of symptoms which are often painful and traumatic. The patient then receives a single dose of medication and the referring neurologist evaluates the intensity of the symptoms on a subjective scale of 0 to 4 every 15 minutes for 4 hours.

For example, sensitivity curve analysis can be used to filter or reveal dopa-sensitive patients before proceeding with routine L-Dopa testing. A significant drop in symptoms when crossing the sensitivity threshold reveals dopa-sensitive patients. A moderate or absent dropout, on the contrary, indicates low sensitivity.

For example, thanks to the results of the analysis of the sensitivity curve, the referring neurologist, responsible for evaluating the patient's eligibility for neurostimulation, can decide to reduce the usual L-Dopa tests that the patient must undergo. .

For example, if it has been decided to proceed with neurostimulation, the dopaminergic sensitivity curve can be analyzed before and after the intervention. The neurologist can thus quantify the effectiveness of the surgical intervention. It will be noted that the patient saw, for example, a 60% improvement in symptoms at a tdLED temporal function value of 100 before the intervention and an improvement, also of 60%, at a tdLED temporal function value of 30 after the intervention. 'intervention. In this case, the intervention will have made it possible to divide the drug doses by 3.

Example 2 : Use for routine patient visits to their neurologist

This example shows the use of the method for determining a sensitivity of a patient suffering from Parkinson's disease to a medication comprising L-Dopa, to assist in the collection of information relating to the patient by the neurologist during of a routine visit. It is assumed that the patient already has a prescription for L-Dopa treatment, as well as a corresponding prescription.

It is assumed that the patient and the neurologist met during a routine visit during which the neurologist informed the patient that the latter had to follow a campaign for a study period Te equal to four weeks and consisting of:
- record study data (t _o , m _{o l} )
- record a series of signals (t _q , s _q ) _p corresponding to measurements of at least one biomarker BM _p .

In this example, it is assumed that the patient has a determination system 1 as described previously and capable of receiving the study data (t _o , m _ol ) as well as the series of signals (t _q , s _q ) _p . The memory 30 of the determination system 1 includes instructions relating to the execution of steps E10, E10b and E20 on the one hand, and E30, E40, E45 and E50 on the other hand.

In this example, the patient has a synchronous pill dispenser, allowing him to enter his prescription. Thus, the pill box reminds you of the taking times. The patient records and validates in the memory 20 of the determination system S whether he took his treatment on time, or at another time, or if he did not take it.

At the same time, the patient uses the sensors to record the sequence of signals (t _q , s _q ) _p . making it possible to determine the associated biomarkers. For example, the sensors record the tremor biomarker, the phonation biomarker, the pronunciation biomarker, the accuracy biomarker, the seizure biomarker, at least three times a day, at various times of sound. choice. The signals are sent to the determination system 1 and stored in the memory 20 of the determination system 1, or alternatively in the cloud.

Following the study period T _e , the patient can print or send to the neurologist a report containing daily data similar to the monitoring sheet allowing compliance with the prescription to be consulted, a comparison of the theoretical ON-OFF curve with the conditions detected by the sensors and with the conditions reported by the patient on the monitoring sheet, graphs and tables summarized by hour, or day or week or month. The process makes it possible to calculate a theoretical level of symptoms as a function of time. We say that the patient is “off” if the symptoms exceed an “off” threshold, and that the patient is “on” if the symptoms are below an “on” threshold.

Example 3 : Helps control the total daily dose of L-Dopa during routine visits

This example shows the application of the method for determining the sensitivity of a patient suffering from Parkinson's disease to a medication comprising L-Dopa, to help the patient's neurologist control the total daily dose. of L-Dopa prescribed by the neurologist to the patient. In other words, the results of the determination method described above can be used by a neurologist to adjust the daily dose of L-Dopa prescribed to a patient.

It is assumed that the patient and the neurologist met during a routine visit during which the neurologist informed the patient that the latter had to follow a campaign for a study period Te equal to four weeks and consisting of:
- record study data (t _o , m _{o l} )
- record a series of signals (t _q , s _q ) _p corresponding to measurements of at least one biomarker BM _p .

Following the four-week study period T _e , the patient's dopaminergic sensitivity curve is obtained.

Thus, during the routine visit arriving after the end of the study period T _e , the patient can provide additional information to the neurologist, in addition to the usual daily reports, graphs and summary tables resulting from the manual and subjective reading carried out by the patient. These daily reports correspond, for example, to the monitoring sheet illustrated in .

For example, the neurologist can compare the theoretical ON-OFF curve to the states detected by the sensors measuring BMp biomarkers. The comparison can be made over a period of 24 hours, obtained by averaging over the study period T _e .

For example, the neurologist can also have the curve representative of the temporal function tdLED(p _nopt ,t _q ).

Example 4 : Help to control the distribution of the neurologist's prescription

This example shows the application of the method for determining the sensitivity of a patient suffering from Parkinson's disease to a medication comprising L-Dopa, to help the patient's neurologist control the total daily dose. of L-Dopa prescribed by the neurologist to the patient. In other words, the determination method previously described can be used by a neurologist to adjust the daily dose of L-Dopa prescribed to a patient.

It is assumed that the patient and the neurologist met during a routine visit during which the neurologist informed the patient that the latter had to follow a campaign for a study period Te equal to four weeks and consisting of:
- record study data (t _o , m _ol )
- record a series of signals (t _q , s _q ) _p corresponding to measurements of at least one biomarker BM _p .

Following the four-week study period T _e , the patient's sensitivity curve is obtained.

Thus, during the routine visit arriving after the end of the study period T _e , the patient can provide additional information to the neurologist, in addition to the usual daily reports, graphs and summary tables.

For example, the neurologist can compare the theoretical ON-OFF curve to the states detected by the sensors measuring BM _p biomarkers. The comparison can be made over a period of 24 hours, obtained by averaging over the study period T _e .

Furthermore, the neurologist can also have available the curve of the temporal function tdLED(p _nopt ,t _q ) over a period of 24 hours, averaged over one month.

The neurologist also has a system similar to the determination system 1 previously described in order to be able to implement steps E45 and E50.

Thus, in order to optimize the effectiveness of the daily dose and keep the patient in an “on” state for as long as possible, the neurologist can test a new schedule of times for taking the treatment: the new schedule of times is stored in a memory of the similar system and the neurologist can, using the similar system, calculate the values of the temporal function tdLED(p_nopt,t_q ) in order to adjust the distribution of doses throughout the day and obtain values of the tdLED temporal function always above the threshold but without excess, which will correspond to the best patient comfort for a given total daily dose.

Claims (14)

System for determining (1) a patient's sensitivity to a medication prescribed to treat a neurodegenerative disease, comprising a programmable device, said programmable device being adapted to:
- receive, during a time period (T _e ), measurements (t _q , s _q ) _p relating to at least one biomarker (BM _p );
- receive data relating to the times the medication is taken by the patient and the corresponding administration quantities (t _o , m _ol ), during said time period (T _e );
- determine the sensitivity of the patient to the drug, on the basis of a temporal function tdLED (p _nopt , t _q ) depending on said data relating to the times the drug is taken by the patient and the corresponding administration quantities (t _o , m _ol ), and said measurements (t _q , s _q ) _p relating to at least one biomarker (BM _p ) of the patient,
characterized in that:
- the temporal function tdLED(p _nopt ,t _q ) takes values over said time period (T _e ) and is defined, using pharmacokinetic parameters (p _nopt ), on the basis of previous data relating to the times of administration of the drug and to the corresponding administration quantities received during a previous time period (T _calib ) prior to said time period (T _e ), said time function tdLED(p _nopt ,t _q ) being representative of a quantity of the drug absorbed by the patient and not yet metabolized,
- said pharmacokinetic parameters (p _nopt ) are determined by maximizing a correlation measure between the measurements relating to at least one biomarker (BM _p ) of the patient and the values of the temporal function tdLED (p _n , t _j ) on said previous time period (T _calib ). System according to claim 1, characterized in that the pharmacokinetic parameters (p _nopt ) comprise a half-life of the drug and a duration of total absorption of the drug. System according to one of the preceding claims, characterized in that the correlation is a Spearman correlation. System according to one of the preceding claims, characterized in that the measurements (t _q ,s _q ) _p relating to at least one biomarker (BM _p ) of the patient are, on the one hand, discrete or continuous, and on the other hand share, scattered or recorded according to an ordered schedule. System according to one of the preceding claims, characterized in that the neurodegenerative disease is Parkinson's disease. System according to one of the preceding claims, characterized in that the drug is L-dopa. System according to one of the preceding claims, characterized in that said temporal function tdLED(p _nopt ,t _q ) is defined so that the determined sensitivity increases with an absorbed but not yet metabolized quantity of the drug. System according to claim 7, characterized in that said temporal function tdLED(p _nopt ,t _q ) is defined so that the determined sensitivity increases below a threshold for the quantity absorbed but not yet metabolized of the drug. System according to one of the preceding claims, characterized in that the maximization of the correlation is carried out by a Monte Carlo algorithm or a conjugate gradient technique. System according to one of the preceding claims, characterized in that the determination of the pharmacokinetic parameters (p _nopt ) comprises, prior to maximizing the correlation measurement, a spectral analysis of the measurements (t _j , s _j ) _k recorded during said previous time period (T _calib ). System according to claim 10, characterized in that the spectral analysis of the measurements is followed by a calculation by automatic learning. System according to one of the preceding claims, characterized in that the temporal function tdLED(p _nopt ,t _q ) is further defined using secondary parameters, said secondary parameters comprising for example a parameter representative of a difference between a daytime metabolism and a nocturnal metabolism of the patient. System according to one of the preceding claims, characterized in that the at least one biomarker (BM _p ) is chosen from:
- an input biomarker representative of the characteristic of an input on a keyboard such as the total typing time, the average typing speed, the maximum typing speed, the pressing time, the duration between two successive keystrokes and/ or the partial or total rhythm of typing when typing on a keyboard, preferably the pressing time and/or the typing speed when typing on a keyboard,
- a cerebral biomarker representative of the electrical activity of the brain, preferably measured by electroencephalography (EEG),
- a pronunciation biomarker representative of the accuracy of pronunciation of at least one word in a specific language,
- a voice biomarker representative of the power of the voice,
- a phonation biomarker representative of the stability of phonation,
- a tremor biomarker representative of tremors of at least one part of the body, and
- an accuracy biomarker representative of the accuracy of the gesture during calligraphy. System according to one of the preceding claims, characterized in that the measurements (t _q ,s _q ) _p relating to the at least one biomarker (BM _p ) are recorded by at least one accelerometer, a microphone, a touch screen, a headset dedicated to electroencephalographic measurements, a smartphone.