Method for determining a patient's sensitivity to a drug prescribed to treat a neurodegenerative disease

Abstract

Method for determining a patient's sensitivity to a drug prescribed for treating a neurodegenerative disease The invention relates to a computer-implemented method for determining a patient's sensitivity to a drug prescribed for treating a neurodegenerative disease based on a time function dependent on data relating to times of taking the drug by the patient and the corresponding administration amounts and measurements relating to at least one biomarker of the patient. Abstract figure: Fig. 4

Description

Title of the invention: Method for determining a patient's sensitivity to a drug prescribed to treat a neurodegenerative disease FIELD OF THE INVENTION

[0001] The present invention relates to the treatment of neurodegenerative diseases, and more particularly of Parkinson's disease.

[0002] The present invention relates to a computer-implemented method for determining a patient's sensitivity to a drug prescribed for treating a neurodegenerative disease and to a system for determining a patient's sensitivity to a drug prescribed for treating a neurodegenerative disease. STATE OF THE ART

[0003] 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 that 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); slowing 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.

[0004] There is currently no curative treatment 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 anti-Parkinsonian 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 for the natural dopamine deficiency. L-Dopa acts mainly on akinesia and hypertonia but also, later after the start of treatment, on tremors.

[0005] Monitoring the response of a Parkinson's patient to L-Dopa treatment 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 neurostimulation.

[0006] Since L-Dopa therapy is a long-term treatment, it may be advantageous to take advantage of data from the daily life of Parkinson's patients for treatment monitoring. Indeed, "real-world evidence" (RWE) provides useful information complementary to clinical trial data, with the potential to fill knowledge gaps, particularly regarding the use of medications, in routine clinical practice.

[0007] The invention falls within this context. SUMMARY

[0008] The invention relates to a computer-implemented method for determining a patient's sensitivity to a drug prescribed to treat a neurodegenerative disease, comprising the following steps: - receipt, over a period of time, of measurements relating to at least one patient biomarker; - receipt of data relating to the times at which the patient takes the medication and the corresponding administration quantities, during the said time period; - determination of the patient's sensitivity to the drug, on the basis of a time function depending on said data relating to the times at which the patient takes the drug 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 having been determined by maximizing a correlation measurement between said measurements relating to at least one biomarker of the patient and the values of the time function over said previous time period.

[0009] By sensitivity is meant the variation of the patient's symptoms as a function of the quantity of drug 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 in order to adjust and optimize the patient's treatment.

[0010] Advantageously, the pharmacokinetic parameters include a half-life of the drug and a total absorption time of the drug.

[0011] Advantageously, the correlation is a Spearman correlation. Indeed, the relationship between patient sensitivity and drug concentration is predicted to be monotonic, because the more the patient ingests a high quantity of the drug, the more his symptoms are expected to decrease. The Spearman correlation estimates the extent to which the relationship between two variables can be described by a monotonic function.

[0012] Advantageously, the measurements relating to at least one biomarker of the patient are, on the one hand, discrete or continuous, and on the other hand, scattered or recorded according to an ordered schedule.

[0013] Advantageously, the neurodegenerative disease is Parkinson's disease.

[0014] Advantageously, the drug is L-dopa.

[0015] Advantageously, the time function is defined so that the determined sensitivity increases below a threshold of absorbed but not yet metabolized quantity 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 start functioning again. This progressive restart results in an increase in sensitivity as defined previously and corresponds to an improvement in the patient's state of health. The threshold of absorbed quantity corresponds to the normal concentration, i.e. of a person not suffering from Parkinson's disease, of dopamine.Beyond this threshold, the improvement in the patient's health is marginal and sometimes side effects of an overdose are observed, such as abnormal movements. Such a definition of the time function makes it possible to take into account this evolution of the health of a patient suffering from Parkinson's disease and treated with L-dopa.

[0016] Advantageously, the maximization of the correlation is carried out by a Monte Carlo algorithm or a conjugate gradient technique.

[0017] 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, have a spectral signature. A spectral analysis makes it possible to obtain and analyze spectral data.

[0018] Advantageously, the spectral analysis of the measurements is followed by a calculation by machine learning. Thanks to machine learning, the measurements can be used to identify reference spectral signatures typical of symptoms of the neurodegenerative disease.

[0019] Advantageously, the time function is further defined using secondary parameters, said secondary parameters comprising for example a parameter representative of a difference between the patient's daytime and nighttime metabolism. This allows for further personalization of the output data from the determination process.

[0020] Advantageously, the at least one biomarker is chosen from: - 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, the press time, the duration between two successive keystrokes and / or the partial or total typing rhythm when typing on a keyboard, preferably the press time and / or the typing speed when typing on a keyboard, - a brain biomarker representative of the electrical activity of the brain, preferably measured by electroencephalography (EEG), - a pronunciation biomarker representative of the pronunciation accuracy of at least one word in a given language, - a voice biomarker representative of voice power, - a phonation biomarker representative of phonation stability, - 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.

[0021] 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.

[0022] Another aspect of the invention relates to a system for determining a patient's sensitivity to a drug 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 at which the patient takes the medication and the corresponding administration quantities, during the said time period; - determining the patient's sensitivity to the drug, on the basis of a time function dependent on said data relating to the times at which the patient takes the drug 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 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 time function over said previous time period.

[0023] 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 upon 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 at which the patient takes the medication and the corresponding administration quantities, during the said time period; - determining the patient's sensitivity to the drug on the basis of a time function dependent on said data relating to the times at which the patient takes the drug 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 time function over said previous time period. DESCRIPTION OF FIGURES

[0024] [Fig. 1] represents an example of steps implemented for carrying out a calibration phase, according to one or more embodiments.

[0025] [Fig.2] represents an example of a monitoring sheet for recording data of a patient.

[0026] [Fig.3] represents an example of steps implemented for carrying out the method for determining a patient's sensitivity, according to one or more embodiments.

[0027] [Fig.4] represents an example of a curve representative of the time function.

[0028] [Fig.5] represents an example of a curve representative of a sensitivity of a L-Dopa patient.

[0029] [Fig.6] 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

[0030] A first aspect of the invention relates to a computer-implemented method 100 for determining a patient's sensitivity to a drug prescribed to treat a neurodegenerative disease. In the following description, the drug is L-Dopa and the neurodegenerative disease is Parkinson's disease.

[0031] By sensitivity is meant the variation of the patient's symptoms as a function of 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, the sensitivity to L-Dopa varies in a decreasing manner as the concentration of L-Dopa absorbed increases. Sensitivity can be described in other words 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.

[0032] As will be described later, this mathematical function is defined from a modeling inspired by pharmacokinetic models. The modeling uses different patient-specific parameters (pn) with n an integer between 1 and N.

[0033] The method 100 implemented by computer to determine the patient's sensitivity to the prescribed medication makes it possible in particular to obtain a curve representative of said variation in 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 patient's treatment. This representative curve will subsequently be called the dopaminergic sensitivity curve.

[0034] The method 100 is for example implemented by a system 1 for determining a patient's sensitivity to a medication prescribed to treat a neurodegenerative disease.

[0035] According to one or more embodiments, the determination system 1 comprises a programmable device. By programmable device is meant any programmable information processing system such as a business computer, a personal computer (PC), a smartphone, a connected watch or a touch tablet.

[0036] In a calibration phase 100a, a calibration is performed in order to determine the patient-specific parameters (pn). The calibration is performed on the basis of data collected during a first time period Tcaiib.

[0037] [Fig. 1] represents an example of steps implemented for carrying out the calibration phase, according to one or more embodiments.

[0038] It is assumed here that the patient has a prescription describing, over the first time period TcaUb, the prescribed times for taking medication by the patient during the first time period Tcaiib, 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.

[0039] During the first time period TcaUb, the patient records the different actual times h of taking medication Mb with 1 an integer between 1 and L, as well as the corresponding quantities actually taken mib. For example, the instruction (f, mu) 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 times f; as well as the quantities actually taken mu is given in [Fig.2]. In another example, the instruction (ti, mu) is made electronically in a digital file by means of a computer, for example directly by means of the determination system 1.

[0040] In parallel, during the first time period Tcaiib, measurements relating to at least one biomarker BMk 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 Tcaiib, or recorded according to an ordered schedule over the first time period TcaUb. All of the measurements recorded over the first time period TcaUb constitute a sequence of signals (tj,Sj)k, with tj indicating a measurement time and Sj, the measurement of the biomarker BMk at time tj.

[0041] By biomarker is meant a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes or responses to an exposure or intervention, including interventions therapeutics. Typically, a biomarker is associated with a symptom of a pathology.

[0042] Examples of biomarkers, within the scope of the present description, are the following: - 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 time spent pressing a key, the duration between two successive keystrokes and / or the partial or total typing rhythm when typing on a keyboard, preferably the keypress time and / or the typing speed when typing on a keyboard, - a brain biomarker representative of the electrical activity of the brain, preferably measured by electroencephalography (EEG), - a pronunciation biomarker representative of the pronunciation accuracy of at least one word in a given language, - a voice biomarker representative of voice power, - a phonation biomarker representative of phonation stability, - 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.

[0043] Generally, biomarkers are measured directly or indirectly by sensors.

[0044] For example, the seizure biomarker may be determined by analyzing data obtained by the measurement during the patient's use of a computer equipped with a keyboard. The computer may be the determination system 1.

[0045] For example, the brain biomarker can be measured with an EEG headset (i.e., a 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.

[0046] For example, the pronunciation biomarker, the voice biomarker, the phonation biomarker can be determined by analyzing 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 to calculate a numerical value of the pronunciation biomarker representative of the pronunciation accuracy.

[0047] 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.

[0048] For example, the accuracy biomarker can be determined by analyzing data recorded with a touch pad.

[0049] Based on the recorded setpoint (tj, mu) and the sequence of signals (tj,Sj)k, the patient-specific parameters will be determined as follows.

[0050] In a first step E10, a spectral analysis is carried out on each of the signals (tj,Sj)k. For example, the Fourier transform of each signal (tj,Sj)k is calculated, in order to obtain a corresponding spectrum Spk. Step E10 can be carried out by the determination system 1.

[0051] A symptom associated with a BMk biomarker has a characteristic spectral signature. Spectral signature means the spectrum of the temporal measurement of the BMk biomarker over a given period, when the patient has the corresponding symptom.

[0052] Optionally, an intermediate step ElOb is carried out following the first step E10. During the intermediate step ElOb, each spectrum Spk corresponding to the biomarker BMk is identified with a reference spectrum Sie[k by machine learning. The reference spectrum Srefk is characteristic of the symptom associated with the biomarker BMk. For example, the machine learning is carried out using a database of spectral signatures of biomarkers previously recorded and collected. Typically, the database contains 1500 spectral signatures corresponding to subjects affected by the disease and 1500 spectral signatures corresponding to healthy subjects. The step ElOb can be carried out by the determination system 1.

[0053] As mentioned above, the mathematical function defining the patient's sensitivity to L-Dopa is defined from a model inspired by pharmacokinetic models. The model uses the different patient-specific parameters (pn).

[0054] Advantageously, the patient-specific parameters (pn) are the following three independent parameters: - ra, representing an apparent decarboxylase factor in the patient's plasma, - gB, representing the endogenous production of L-dopa in the patient's brain, - çpB representing cerebral blood flow.

[0055] These parameters are present in the following equations:

[0056] [Math.l] Ta1 = tç ( 1 - (1)

[0057] where ra represents an apparent decarboxylase factor in the patient's body, rw represents a decarboxylase factor in the patient's body water volume, O b represents cerebral blood flow, estimated at approximately 0.88L / minute during the day, Vw represents the patient's body water volume, rB represents a decarboxylase factor in the patient's brain, tB represents the time required for a cerebral blood volume dL dt to propagate into the patient's brain, estimated at approximately 75 seconds.

[0058] Equation (1) can be solved iteratively and has, to the best of the applicant's knowledge, never been disclosed before.

[0059] The apparent decarboxylase factor ra varies between 75 and 120 minutes from one patient to another.

[0060] [Math.2] M^ =----7—----- 2 a I ' H /

[0061] where Ma represents an apparent flux of L-Dopa, M represents the temporal variation of the amount of L-Dopa ingested by the patient, in milligrams per minute, which is known, gB represents the endogenous production of L-Dopa in the patient's brain, VB represents the volume of water in the patient's brain, estimated at approximately 1.1 L, Vw represents the volume of water in the patient's body, estimated at approximately 40 L.

[0062] In particular, the expression of the parameter gB can be deduced from equation (2).

[0063] For a given parameter set (pn), an intermediate function f(t), having for variable time,, is defined by solving df / dt=-f / ra,+M. Thus, a time function tdLED(pn,tj) equal to f(tj) is defined.

[0064] In a second step E20, a step of maximizing the Spearman correlation between, on the one hand, the Spk spectra from the first step E10 (or the Srefk spectra from the intermediate step ElOb), and on the other hand the tdLED (pn, tj) function is carried out. The pno pt values of the patient-specific parameters maximizing the Spearman correlation between the time function tdLED (pn,tj) and the Spk (or Srefk) spectra are those used subsequently for the specific parameters. The second step E20 can be carried out by the determination system 1.

[0065] For example, a Monte Carlo algorithm may be used to determine the pnopt values. Alternatively, a conjugate gradient technique may be used. Generally speaking, any optimization technique known to those skilled in the art can be used.

[0066] Typically, the determination system 1 is a smartphone of the patient, and the patient executes the first step E10, the intermediate step ElOb and the second step E20 with a first dedicated application stored in his smartphone.

[0067] The computer-implemented method 100 for determining the patient's sensitivity to L-Dopa is now described. It is assumed that the patient-specific pnopt parameters were determined by the calibration performed during the calibration phase 100a presented previously.

[0068] [Fig.3] represents an example of steps implemented for carrying out the determination method 100, according to one or more embodiments.

[0069] It is also assumed that the patient has a prescription specifying the times at which medication is taken by the patient during a study time period Te, 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.

[0070] As during the calibration phase 100a, during the study time period Te, the patient records the different actual times L, with o an integer between 1 and O, of taking medication Mb as well as the corresponding actual quantities taken mo b The study data refers to the set of pairs (L, mo J). For example, the study data (L, m„ J are recorded on paper and then recorded electronically, for example in the memory of the system. In another example, the study data (L, mo i) are recorded electronically in a digital file by means of a computer. For example, the study data (to, moi) 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.

[0071] In parallel, during the study period Te, measurements relating to at least one BMP biomarker of the patient, p being an integer between 1 and P, will be recorded. At least one biomarker among the at least one BMP biomarker is included in the set formed by the at least one BMk biomarker used for the calibration phase 100a. These measurements are discrete or continuous. These measurements are also either scattered over the study time period Te, or recorded according to an ordered calendar over the study time period Te. All measurements recorded during the study time period Te constitute a sequence of signals (tq,sq)p, with tq indicating a measurement time and sq, the measurement of the BMP biomarker at time tq. The determination system 1 receives, in a step E40, the sequence of signals (tq,sq)p.

[0072] In a following step E45, the programmable device of the determination system 1 calculates the time function tdLED(pno pt,tq). [Fig.4] gives an example of a curve representative of the time function tdLED(pno pt,tq). The points on [Fig.4] 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(pnopt,tq), in other words of the concentration of L-Dopa in the patient's body, in mg / L.

[0073] Then, in a step E50, the programmable device calculates, by combining the time function tdLED(pno pt,tq) and the sequence of signals (tq,sq)p, and by eliminating the variables tq, at least one representative curve Cp of the patient's sensitivity to L-Dopa. Each curve Cp has the L-Dopa concentration as its abscissa and a quantity representative of the intensity of the BMP biomarker as its ordinate.

[0074] [Fig.5] gives an example of a curve representing the patient's sensitivity for a Cp0 biomarker. On this curve, the gray rectangles and the ends of the vertical segments for each point of the curve represent the confidence intervals with risks of 1% and 5% respectively. It can be observed in [Fig.5] that the curve representing the patient's sensitivity is decreasing. In other words, the greater the quantity of L-Dopa absorbed, the less intense the patient's symptoms.

[0075] 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 the symptoms in the patient, several times a day, or even continuously, thanks to devices allowing self-reporting of symptoms by the patient or a self-test by the patient allowing the symptoms to be identified.

[0076] Data recording makes it possible to obtain a curve representative of the patient's sensitivity to L-Dopa.

[0077] Thanks to monitoring, a diagnosis can be made by a practitioner allowing the effectiveness of the therapeutic response to be assessed, and possibly this response to be modified by modulating the patient's treatment.

[0078] A second aspect of the invention relates to the determination system 1.

[0079] 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.

[0080] [Fig.6] is a schematic diagram showing the components of an example of the determination system 1.

[0081] The determination system 1 may be implemented as a single hardware device, for example, as a desktop personal computer (PC), a laptop computer, a personal digital assistant (PDA), a smartphone, a smartwatch, a server, a console, or may 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 remote servers or devices to implement the functions described herein for the relevant device. The determination system 1 may also be implemented itself as a cloud computing system.

[0082] As shown schematically in [Fig.6], the determination system 1 may comprise at least one processor 10 configured to access at least one memory 20 comprising a computer program code 70. The computer program code may comprise instructions configured to cause the determination system 1 to execute one or more or all of the steps of the sensitivity determination method 100 described previously.

[0083] 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 herein.

[0084] 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.

[0085] The memory 20 may include random access memory (RAM), cache memory, non-volatile memory, backup memory (e.g., programmable or flash memories), read only memory (ROM), a hard disk drive (HDD), a solid state drive (SSD), or any combination thereof. The ROM of the 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 the memory 20 may be used by the processor 10 for temporary storage of data.

[0086] The system 1 may further comprise one or more communication interfaces 40 (e.g., network interfaces for accessing a wired / wireless network, including an Ethernet interface, a WIFI interface, interfaces USB, 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 containing a 3D screen and 3D glasses, or haptic interfaces 30B, or any other interfaces (e.g. keyboard, mouse, display screen, etc.) connected via one or more suitable 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.

[0087] A third aspect of the invention relates to a computer program product comprising instructions for implementing the steps of the method for determining a patient's sensitivity to a drug prescribed for treating a neurodegenerative disease upon execution of 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.

[0088] The computer program product notably implements steps E10, E10b, E20, on the one hand, and E30, E40, E45 and E50 on the other hand. EXAMPLES

[0089] The present invention will be better understood by reading the following examples which illustrate the invention in a non-limiting manner.

[0090] Example 1: Prescription of a neurostimulation intervention

[0091] This example shows the use of the method, as described above, to determine a sensitivity of a patient suffering from Parkinson's disease, to a drug comprising L-Dopa, in the case where a neurologist is considering prescribing neurostimulation treatment 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 intervention is correlated with the patient's sensitivity to taking L-dopa. It is assumed that the patient already has a prescription including a drug comprising L-Dopa.

[0092] 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 Te equal to four weeks and consisting of: - recording study data (L, mo J - record a sequence of (tq,sq)p signals corresponding to measurements of at least one BMP biomarker.

[0093] In a second option, the referring neurologist, responsible for assessing 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 Te.

[0094] The patient follows the campaign during the study period Te. It is assumed that the study period Te includes a calibration period at the end of which the study data and the signal sequence will be used to determine the patient-specific parameters pno pt, as explained above.

[0095] Following the study period Te, the patient's dopaminergic sensitivity curve is obtained.

[0096] The patient then consults the referring neurologist, responsible for assessing the patient's eligibility for neurostimulation, for the analysis of the dopaminergic sensitivity curve obtained. For example, the expert center finds information on the Spearman correlation coefficient, the presence of inflection points, the maximum slope of the sensitivity curve.

[0097] The referring neurologist, responsible for assessing the patient's eligibility for neurostimulation, uses the sensitivity curve in addition to the patient's usual L-Dopa tests. 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 assesses the intensity of the symptoms on a subjective scale of 0 to 4 every 15 minutes for 4 hours.

[0098] For example, sensitivity curve analysis can be used to filter or reveal dopa-sensitive patients before performing the usual L-Dopa tests. A significant drop in symptoms upon crossing the sensitivity threshold reveals dopa-sensitive patients. A moderate or absent drop-off, on the contrary, indicates low sensitivity.

[0099] For example, thanks to the results of the sensitivity curve analysis, the referring neurologist, responsible for assessing the patient's eligibility for neurostimulation, may decide to reduce the usual L-Dopa tests that the patient must undergo.

[0100] 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 time function value of 100 before the intervention and an improvement, also of 60%, at a tdLED time function value of 30. after the intervention. In this case, the intervention will have made it possible to divide the doses of medication by 3.

[0101] Example 2 Use for routine visits of the patient to his neurologist

[0102] This example shows the use of the method for determining a sensitivity of a patient suffering from Parkinson's disease, to a drug comprising L-Dopa, to assist in the collection of information relating to the patient by the neurologist during a routine visit. It is assumed that the patient already has a prescription for L-Dopa treatment, as well as a corresponding prescription.

[0103] 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 during a study period Te equal to four weeks and consisting of: - record study data (to, mo J - record a sequence of (tq,sq)p signals corresponding to measurements of at least one BMP biomarker.

[0104] In this example, it is assumed that the patient has a determination system 1 as described previously and capable of receiving the study data (to, moi) as well as the sequence of signals (tq,sq)p. The memory 30 of the determination system 1 comprises 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.

[0105] In this example, the patient has a synchronous pillbox, allowing him to enter his prescription. Thus, the pillbox reminds him of the times to take it. 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 whether he did not take it.

[0106] In parallel, the patient uses the sensors to record the sequence of signals (tq,sq)p. allowing the associated biomarkers to be determined. 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 his choice. The signals are sent to the determination system 1 and recorded in the memory 20 of the determination system 1, or alternatively in the cloud.

[0107] Following the study period Te, the patient can print or send to the neurologist a report containing daily data similar to the monitoring sheet allowing to consult the compliance with the prescription, a comparison of theoretical ON-OFF curve with the states detected by the sensors and with the states reported by the patient on the monitoring sheet, graphs and tables summarized by hour, or day or week or month. The method in fact allows to calculate a level theoretical analysis of symptoms as a function of time. The patient is said to be "off" if the symptoms exceed an "off" threshold, and the patient is said to be "on" if the symptoms are below an "on" threshold.

[0108] Example 3 Aid in monitoring the total daily dose of L-Dopa during routine visits

[0109] This example shows the application of the method for determining a sensitivity of a patient suffering from Parkinson's disease, to a drug comprising L-Dopa, to aid in the control by the patient's neurologist, of the total daily dose of L-Dopa prescribed by the neurologist to the patient. In other words, the results of the determination method previously described can be used by a neurologist to adjust the daily dose of L-Dopa prescribed to a patient.

[0110] 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 during a study period Te equal to four weeks and consisting of: - record study data (to, mo J - record a sequence of (tq,sq)p signals corresponding to measurements of at least one BMP biomarker.

[0111] Following the four-week Te study period, the patient's dopaminergic sensitivity curve is obtained.

[0112] Thus, during the routine visit arriving after the end of the study period Te, 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 recording carried out by the patient. These daily reports correspond for example to the monitoring sheet illustrated in [Fig.2].

[0113] For example, the neurologist can compare the theoretical ON-OFF curve to the states detected by the sensors measuring the BMp biomarkers. The comparison can be made over a 24-hour period, obtained by averaging over the study period T xe*

[0114] For example, the neurologist can also have the representative curve of the time function tdLED(pnopt,tq).

[0115] Example 4 Aid in controlling the distribution of the neurologist's prescription

[0116] This example shows the application of the method for determining a sensitivity of a patient suffering from Parkinson's disease, to a drug comprising L-Dopa, for the aid in controlling by the patient's neurologist, the total daily dose of L-Dopa prescribed by the neurologist to the patient. In other words, the method of determining previously described can be used by a neurologist to adjust the daily dose of L-Dopa prescribed to a patient.

[0117] 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 during a study period Te equal to four weeks and consisting of: - record study data (to, me) - record a sequence of (tq,sq)p signals corresponding to measurements of at least one BMP biomarker.

[0118] Following the four-week study period Te, the patient's sensitivity curve is obtained.

[0119] Thus, during the routine visit arriving after the end of the study period Te, the patient can provide additional information to the neurologist, in addition to the usual daily reports, graphs and summary tables.

[0120] For example, the neurologist can compare the theoretical ON-OFF curve to the states detected by the sensors measuring the BMP biomarkers. The comparison can be made over a 24-hour period, obtained by averaging over the study period T J- e*

[0121] Furthermore, the neurologist can also have available the curve of the time function tdLED(pnopt,tq) over a period of 24 hours, averaged over one month.

[0122] 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.

[0123] Thus, in order to optimize the effectiveness of the daily dose and keep the patient in the "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 recorded in a memory of the similar system and the neurologist can, using the similar system, calculate the values of the time function tdLED(pnopt,tqj in order to adjust the distribution of the doses during the day and obtain values of the time function tdLED always above the threshold but without excess, which will correspond to the best comfort for the patient for a given total daily dose.

Claims

Claims

1. System for determining (1) a patient's sensitivity to a drug prescribed to treat a neurodegenerative disease, comprising a programmable device, said programmable device being adapted to: - receive, during a time period (Te), measurements (tq,sq)p relating to at least one biomarker (BMP); - receive data relating to the times at which the patient takes the drug and to the corresponding administration quantities (to, moi), during said time period (Te); - determine the patient's sensitivity to the drug, on the basis of a time function tdLED(pnopt,tq) depending on said data relating to the times at which the patient takes the drug and to the corresponding administration quantities (to, moi), and on said measurements (tq,sq)p relating to the at least one biomarker (BMP) of the patient, characterized in that: - the time function tdLED(pnopt,tq) takes values over said time period (Te) and is defined, using pharmacokinetic parameters (pnopt), 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 (Tcaiib) prior to said time period (Te), said time function tdLED(pnopt,tq) being representative of a quantity of the drug absorbed by the patient and not yet metabolized, - said pharmacokinetic parameters (pnopt) are determined by maximizing a correlation measure between the measurements relating to the at least one biomarker (BMP) of the patient and the values of the time function tdLED (pn, tj) over said previous time period (Tcaiib).,

2. System according to claim 1, characterized in that the pharmacokinetic parameters (pnopt) comprise a half-life of the drug and a total absorption time of the drug.

3. System according to one of the preceding claims, characterized in that the correlation is a Spearman correlation.

4. System according to one of the preceding claims, characterized in that the measurements (tq,sq)p relating to the at least one biomarker (BMP) of the patient are, on the one hand, discrete or continuous, and on the other hand, scattered or recorded according to an ordered schedule.

5. 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.

6. System according to one of the preceding claims, characterized in that the determination of the pharmacokinetic parameters (Pnopt) comprises, prior to the maximization of the correlation measurement, a spectral analysis of the measurements (tj,Sj)k recorded during said previous time period (Tcaiib).

7. System according to claim 6, characterized in that the spectral analysis of the measurements is followed by a calculation by automatic learning.

8. System according to one of the preceding claims, characterized in that the time function tdLED(pnopt,tq) 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.

9. System according to one of the preceding claims, characterized in that the at least one biomarker (BMP) 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 typing rhythm 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 correctness of pronunciation of at least one word in a given 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,

10. - 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 (tq,sq)p relating to the at least one biomarker (BMP) are recorded by at least one accelerometer, a microphone, a touch screen, a headset dedicated to electroencephalographic measurements, a smartphone.

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