Nanoparticles for use for treating a neuronal disorder

Abstract

The present invention relates to the medical field, in particular to the treatment of neurological disorders. More specifically the present invention relates to a nanoparticle or nanoparticles' aggregate for use in prevention or treatment of a neurological disease or at least one symptom thereof in a subject when the nanoparticle or nanoparticles' aggregate is exposed to an electric field, wherein the nanoparticle's or nanoparticles' aggregate's material is selected from a conductor material, a semiconductor material, an insulator material with a dielectric constant ε.sub.ijk equal to or above 200, and an insulator material with a dielectric constant ε.sub.ijk equal to or below 100. It further relates to compositions and kits comprising such nanoparticles and/or nanoparticles' aggregates as well as to uses thereof.

Claims

1. A method for normalizing impaired synchronization of oscillations within and/or between neuronal networks within and/or between distinct regions of the brain in a patient in need thereof, wherein the method comprises i) administering a composition to the subject, the composition comprising nanoparticles and/or nanoparticles aggregates and a pharmaceutically acceptable support, and the nanoparticle or nanoparticles aggregate material being selected from a conductor material selected from a metal having a standard reduction potential E° above 0.2 selected from Pd, Pt and Au, and an intrinsic semiconductor material with a band gap Eg below 3.0 eV selected from an element from group IVA of the Mendeleev's periodic table, a mixed composition of elements from groups III and V of the Mendeleev's periodic table, and a mixed composition of elements from group II and VI of the Mendeleev periodic table, and ii) exposing the subject to an electric field.

2. The method according to claim 1, wherein the composition comprises gold nanoparticles coated with a biocompatible hydrophilic agent having a neutral surface charge, or coated with a biocompatible agent having a negative surface charge.

3. The method according to claim 1, wherein the composition comprises silicon nanoparticles coated with a biocompatible hydrophilic agent having a neutral surface charge or silicon nanoparticles coated with a biocompatible agent having a negative surface charge.

4. The method according to claim 1, wherein the patient is suffering from a neurological disease or at least one symptom thereof selected from Parkinson disease, Alzheimer disease, autism spectrum disorder, a depression disorder, schizophrenia, dementia, or bipolar disorder.

5. The method according to claim 1, wherein the composition comprises at least two distinct nanoparticles and/or nanoparticles aggregates, each nanoparticle or nanoparticles aggregate comprising a distinct material selected from a conductor material selected from a metal having a standard reduction potential E° above 0.2 selected from Pd, Pt and Au, and an intrinsic semiconductor material with a band gap Eg below 3.0 eV, selected from an element from group IVA of the Mendeleev's periodic table, a mixed composition of elements from groups III and V of the Mendeleev's periodic table and a mixed composition of elements from group II and VI of the Mendeleev periodic table.

Description

BRIEF DESCRIPTION OF THE FIGURES

(1) FIG. 1. Schematic representation of the brain (sagittal plane).

(2) FIG. 2. Hypersynchrony and impaired synchrony between two neuronal networks.

(3) FIG. 3. Brain areas involved in various neurological diseases.

(4) FIG. 4. Effect of nanoparticles (NP1) when exposed to an electric field (E) on normalization of hypersynchrony (motor disorders).

(5) FIG. 5. Effect of nanoparticles (NP2) when exposed to an electric field (E) on normalization of impaired synchrony (psychiatric and cognitive disorders).

(6) FIG. 6. Experimental scheme of induction of Parkinson's disease with MPP.sup.+ treatment and electrical activity recording.

(7) The mouse ventral midbrain/cortex co-cultures were prepared from E14.5 NMRI mice and cultured on 48 well MEAs for 3 weeks (culture period). The cultures were treated after 7 days in culture (day 7) with the nanoparticles' suspensions (“nanoparticles” groups) or water (“control” group and “MPP.sup.+” group) and at day 8 with MPP+ (20 μM) (“nanoparticles” groups and “MPP.sup.+” group) or water (“control” group). The spontaneous activity was recorded at day 21. After the recording at day 21, the cultures were electrically stimulated on one electrode and recording of the activity was performed on the non-stimulated electrodes.

(8) FIG. 7. Scheme of two simplified bursts outlining some of the parameters that can be extracted from the electrical activity recording. Parameters describing general activity (spike, burst, inter burst interval (IBI) and burst period) and burst structure (burst duration, burst plateau, burst amplitude, burst inter spike interval (ISI) and burst area) are indicated. Standard deviations (SD) of these parameters are measures for regularity of general activity and burst structure respectively. Coefficient of variation in time (CVtime) reflects the temporal regularity of the activity pattern of each unit. CVtime is calculated by the ratio of parameter's standard deviation and mean. Coefficient of variation among the network (CVnet) reflects synchronization among neurons within the network. CVnet is calculated by the ratio of parameter's standard deviation by mean over the network. Large CVnet values imply a wide range of variation in the activity across the network, meaning less synchronization.

(9) FIG. 8. Functional effects observed in “nanoparticles” groups under electrical stimulation compared to “control” group (under electrical stimulation) and “MPP.sup.+” group (under electrical stimulation) on midbrain/cortex network activity. All MPP.sup.+-induced functional effects on network activity under electrical stimulation in the presence or not of the tested nanoparticles as well as “control” group (under electrical stimulation), were normalized to the “pre-stimulated” activity, i.e. the activity measured at day 21, set at 100% for each experiment. The data show MPP.sup.+-induced functional effects under electrical stimulation and demonstrate the prevention/rescue efficacy of the nanoparticles of the invention under electrical stimulation (i.e. ability to prevent/rescue functional effects to a level similar to that of the “control” group).

(10) FIG. 9. Effect Score analysis for the “nanoparticles” groups, “control” group (Effect Score=0) and “MPP.sup.+” group (Effect Score=1).

(11) FIG. 10. Experimental scheme of induction of Alzheimer's disease with amyloid beta 1-42 (Abeta 1-42), treatment and electrical activity recordings. After 4 weeks in cultures (culture period), Abeta 1-42 (100 nM) (“nanoparticles” group and “Abeta” group) or water (“control” group) (TO) were added to the neuronal network. Four (4) hours later, the nanoparticles suspensions (“nanoparticles” groups), or water (“control” group and “Abeta” group) were added. The spontaneous activity was recorded as follows: at T0 (prior addition of Abeta 1-42) at T0+1 h, T0+2 h, T0+3 h, T0+4 h (prior to nanoparticles or water addition), T0+5 h, and T0+6 h.

(12) FIG. 11. Functional effects observed in “nanoparticles” groups under electrical stimulation compared to “control” group (under electrical stimulation) and “Abeta 1-42” group (under electrical stimulation) on cortex network activity. All Abeta 1-42-induced functional effects on network activity under electrical stimulation in the presence or not of the tested nanoparticles, as well as “control” group (under electrical stimulation), were normalized to the “pre-stimulated” activity, i.e. the activity measured at T0+6 hours, set at 100% for each experiment.

(13) The data show Abeta 1-42 functional effects under electrical stimulation and demonstrate the rescue efficacy allowed by the nanoparticles of the invention under electrical stimulation (i.e. ability to rescue functional effects to a level similar to that of the “control” group).

(14) FIG. 12. Effect Score analysis for the “nanoparticles” groups, “control” group (Effect Score=0) and “Abeta” group (Effect Score=1).

EXAMPLES

(15) Simulation

(16) Simulation can be used to assess the effect on neuronal network(s) of nanoparticles exposed to an electrical stimulus (electric field).

(17) In Vitro Studies of Neurons

(18) At the neuron level, Patch clamp technique is very useful for detecting action potentials, as it allows simultaneous direct measurement and control of membrane potential of a neuron.

(19) This technique is used to assess the effects of nanoparticles on a single neuron.

(20) In Vitro Studies of a Network of Neurons

(21) Multi-electrode arrays (MEAs) permit stimulation and recording of a large number of neurons (neuronal network). Dissociated neuronal cultures on MEAs provide a simplified model in which network activity can be manipulated with electrical stimulation sequences through the array's multiple electrodes. This technique is very useful to assess physiologically relevant questions at the network and cellular levels leading to a better understanding of brain function and dysfunction.

(22) Dissociated neuronal cultures coupled to MEAs are indeed widely used to better understand the complexity of brain networks. In addition, the use of dissociated neuronal assemblies allows the manipulation and control of the network's connectivity. The use of dissociated neuronal cultures coupled to MEA allows the design of experiments where neurons can be extracellularly stimulated by mean of electrical pulses delivered through the same electrodes of the device. In this way, it becomes reasonable to investigate how the emerging neuronal dynamics can be modulated by the electrical stimulation, and, consequently, whether the underlying functional connectivity is modified or not (Poli D. et al, Frontiers in Neural Circuits, 2015, 9 (article 57), 1-14: Functional connectivity in in vitro neuronal assemblies).

(23) The MEA system enables non-invasive, long-lasting, simultaneous extracellular recordings from multiple sites in the neuronal network in real time, increasing spatial resolution and thereby providing a robust measure of network activity. The simultaneous gathering of action potential and field potential data over long periods of time allows the monitoring of network functions that arise from the interaction of all cellular mechanisms responsible for spatio-temporal pattern generation (Johnstone A. F. M. et al., Neurotoxicology (2010), 31: 331-350, Microelectrode arrays: a physicologically based neurotoxicity testing platform for the 21.sup.st century). Compared to patch-clamp and other single electrode recording techniques, MEA measures responses of a whole network, integrating global information on the interaction of all receptors, synapses and neuronal types which are present in the network (Novellino A. et al., Frontiers in Neuroengineering. (2011), 4(4), 1-14, Development of micro-electrode array based tests for neurotoxicity: assessment of interlaboratory reproducibility with neuroactive chemicals.). As such, MEA recordings have been employed to understand neuronal communication, information encoding, propagation, and processing in neuronal cultures (Taketani, M., and Baudry, M. (2006). Advances in Network Electrophysiology. New York, N.Y.: Springer; Obien et al., Frontiers in Neurosciences, 2015, 8(423): Revealing neuronal functions through microelectrode array recordings). The MEA technology is a sophisticated phenotypic high-content screening method to characterize functional changes in network activity in electrically active cell cultures and it is very sensitive to neurogenesis, as well as neuroregenerative and neurodegenerative aspects. Moreover, neuronal networks grown on MEAs are known as being capable of responding to neuroactive or neurotoxic compounds in approximately the same concentration ranges that alter functions of an intact mammalian nervous system (Xia et al., Alcohol, 2003, 30, 167-174: Histiotypic electrophysiological responses of cultured neuronal networks to ethanol; Gramowski et al., European Journal of Neuroscience, 2006, 24, 455-465: Functional screening of traditional antidepressants with primary cortical neuronal networks grown on multielectrode neurochips; Gramowski et al., Frontiers in Neurology, 2015, 6(158): Enhancement of cortical network activity in vitro and promotion of GABAergic neurogenesis by stimulation with an electromagnetic field with 150 MHz carrier wave pulsed with an alternating 10 and 16 Hz modulation).

(24) This technique is used to assess the effect of nanoparticles on neuronal network(s).

(25) In Vivo Studies of a Network of Neurons

(26) An appropriate animal model is considered to assess the effect on neuronal networks of animals of nanoparticles of the invention when exposed to an electrical stimulus.

(27) For instance, mouse models of Parkinson's disease are used to assess the effects of nanoparticles stimulated by tDCS (transcranial Direct Current Stimulation) on the relief of behavior impairment (motor disorders). Also, rat models of Alzheimer's disease are used to assess the effects of nanoparticles stimulated by tDCS on the spatial learning and memory dysfunction (cognitive disorders) of animals.

Example 1. Nanoparticles Prepared with a Conductor Material: Synthesis of Gold Nanoparticles Coated with a Biocompatible Coating Having a Neutral Surface Charge

(28) Gold nanoparticles were synthesized by reducing a gold chloride salt (HAuCl.sub.4) with a capping agent (sodium citrate) (protocol was adapted from G. Frens Nature Physical Science 241 (1973) 21). In a typical experiment, HAuCl.sub.4 solution was heated to boiling. Subsequently, sodium citrate solution was added. The resulting solution was maintained under boiling for an additional period of 5 minutes.

(29) A 0.22 μm filtration (filter membrane: poly(ether sulfone) (PES)) of the nanoparticles' suspension was performed and gold concentration in suspension was determined by a UV-visible spectroscopy assay at 530 nm.

(30) A surface coating was performed using α-methoxy-ω-mercaptopoly(ethylene glycol) 20 kDa (“thiol-PEG20 kDa”). A sufficient amount of “thiol-PEG 20 kDa” was added to the nanoparticles' suspension to reach at least half a monolayer coverage (2.5 molecules/nm.sup.2) on the gold nanoparticle surface. pH was adjusted between 7 and 7.2, and the nanoparticles' suspension was stirred overnight.

(31) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The hydrodynamic diameter of the so obtained biocompatible gold nanoparticles in suspension was found equal to 118 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.13.

(32) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH 7 was found equal to −1 mV.

Example 2. Nanoparticles Prepared with a Conductor Material: Synthesis of Gold Nanoparticles Coated with a Biocompatible Coating Having a Negative Surface Charge

(33) Gold nanoparticles were prepared as described in example 1 (same gold inorganic core).

(34) A 0.22 μm filtration on PES membrane filter was performed and gold concentration in suspension was determined by a UV-visible spectroscopy assay at 530 nm.

(35) A biocompatible surface coating was performed using meso-2, 3-dimercaptosuccinic acid (DMSA). A sufficient amount of DMSA was added to the nanoparticles' suspension to reach at least half a monolayer coverage (2.5 molecules/nm.sup.2) on the surface. pH was adjusted between 7 and 7.2, and the nanoparticles' suspension was stirred overnight.

(36) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The hydrodynamic diameter of the so obtained nanoparticles in suspension was equal to 76 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.46.

(37) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH 7 was found equal to −23 mV.

Example 3. Nanoparticles Prepared with an Insulator Material Having a Low Relative Dielectric Constant Equal to or Below 100: Synthesis of Zirconium Oxide Nanoparticles Coated with a Biocompatible Coating Having a Neutral Surface Charge

(38) Zirconium oxide (ZrO.sub.2) nanoparticles were synthesized by precipitation of zirconium chloride (ZrCl.sub.4) with tetramethyl ammonium hydroxide (TMAOH) at a basic pH. The resulting suspension was transferred in an autoclave and heated at a temperature above 110° C. After cooling, the suspension was washed with deionized water and acidified.

(39) A 0.22 μm filtration on PES membrane filter was performed and (ZrO.sub.2) nanoparticles' concentration was determined by drying the aqueous solution into a powder and weighing the as-obtained mass.

(40) A biocompatible coating was prepared using silane-poly(ethylene) glycol 2 kDa (“Si-PEG 2 kDa”). A sufficient amount of “Si-PEG 2 kDa” was added to the nanoparticles' suspension to reach at least half a monolayer coverage (2.5 molecules/nm.sup.2) on the surface. The nanoparticles' suspension was stirred overnight and subsequently the pH was adjusted to 7.

(41) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light

(42) Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The nanoparticles' hydrodynamic diameter was found equal to 55 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.1.

(43) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH7 was found equal to −1 mV.

Example 4. Nanoparticles Prepared with an Insulator Material Having a Low Relative Dielectric Constant Equal to or Below 100: Synthesis of Zirconium Oxide Nanoparticles Coated with a Biocompatible Coating Having a Negative Surface Charge

(44) Zirconium oxide nanoparticles were prepared as described in example 3 (same inorganic core).

(45) A 0.22 μm filtration on PES membrane filter was performed and the (ZrO.sub.2) nanoparticles' concentration was determined by drying the aqueous suspension to a powder and weighing the as-obtained mass.

(46) Surface functionalization was performed using sodium hexametaphosphate. A sufficient mass of sodium hexametaphosphate was added to the nanoparticles' suspension to reach at least half a monolayer coverage (2.5 molecules/nm.sup.2) on the surface. The nanoparticles' suspension was stirred overnight and pH was subsequently adjusted to 7.

(47) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The nanoparticles' hydrodynamic diameter was found equal to 70 nm, with a polydispersity index (dispersion of the nanoparticles population in size) of 0.11.

(48) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH 7 was found equal to −33 mV.

Example 5. Nanoparticles Prepared with a Semiconductor Material: Silicon Nanoparticles Coated with a Biocompatible Coating Having a Negative Surface Charge

(49) Silicon (Si) nanoparticles (powder) were obtained from US Research Nanomaterials Inc. They were dispersed in water at 30 g/L under sonication (with a probe).

(50) A 0.22 μm filtration on PES membrane filter was performed and the (Si) nanoparticles' concentration was determined by drying the suspension to a powder and weighing the as-obtained mass.

(51) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The nanoparticles' hydrodynamic diameter was found equal to 164 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.16.

(52) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH7 was found equal to −19 mV.

Example 6. Nanoparticles Prepared with an Insulator Material Having a High Relative Dielectric Constant Equal to or Above 200: Barium Titanate Nanoparticles Coated with a Biocompatible Coating Having a Negative Surface Charge

(53) Barium titanate (BaTiO.sub.3) nanoparticles' suspension (20% wt in water) was obtained from US Research Materials Inc. (US3835).

(54) Surface functionalization was performed using Silane-poly(ethylene) glycol 10 kDa (“Si-PEG 10 kDa”). Briefly, “Si-PEG 10 kDa” was first dissolved in an ethanol/water solution (1/3 v/v) and added to the BaTiO.sub.3 suspension (20% wt in water) to achieve a full monolayer coverage on the surface of the nanoparticles. The suspension was sonicated and subsequently stirred overnight. After a 0.22 μm filtration (filter membrane: poly(ether sulfone)), a washing step was performed in order to eliminate unreacted “Si-PEG 10 kDa” polymers.

(55) The hydrodynamic diameter (measure in intensity) was determined by Dynamic Light Scattering (DLS) with a Nano-Zetasizer (Malvern) at a scattering angle of 173° with a laser emitting at 633 nm, by diluting the nanoparticles' suspension in water (final concentration: 0.1 g/L). The nanoparticles' hydrodynamic diameter was found equal to 164 nm, with a polydispersity index (dispersion of the nanoparticles' population in size) of 0.16.

(56) The zeta potential was determined by measuring the electrophoretic mobility of the nanoparticles (Nano-Zetasizer, Malvern) by diluting the nanoparticles' suspension in a NaCl solution at 1 mM at pH 7 (final concentration: 0.1 g/L). The zeta potential at pH7 was found at −11 mV.

Example 7. Evaluation of the Prevention/Rescue Efficacy of Nanoparticles from Examples 1, 2, 5 and 6, Exposed to an Electrical Stimulation on MPP.SUP.+.-induced Neuronal Networks Using the Phenotypic MEA Screening Technology

(57) The prevention/rescue efficacy of nanoparticles of the invention was tested on MPP.sup.+-treated mouse ventral midbrain/cortex co-cultures, cultured on a 48-well MEA for 3 weeks. This model represents an in vitro Parkinson's model for screening compounds, based on the functional rescue of dopaminergic neurons using challenged midbrain/cortex cultures growing on MEAs. Midbrain is a region of the brain including the substantia nigra which is part of the basal ganglia and which contains most of the dopaminergic neurons. The evaluation of the nanoparticles' prevention/rescue effect was performed via the measurement of the extracellular electrical activity of the co-culture of neurons plated on MicroElectrode Array (MEA) chips.

(58) The induction of a parkinsonian phenotype in mouse neurons in vitro was performed with 1-methyl-4-phenyl pyridinium iodide (MPP.sup.+). There is strong evidence that mitochondrial impairment plays a role in the pathogenesis of Parkinson's disease (PD). MPP.sup.+ was found to be mitochondrial poison that inhibits cellular respiration through the blockade of the electron transport enzyme complex I (NADH: ubiquinone oxidoreductase). Several laboratories have reported that there is a selective defect in complex I of mitochondrial electron transport chain in the substantia nigra of postmortem tissue of PD patients, and there is also reduction of complex I activity in platelets of patients with early PD (Peng J. et al., Journal of Biomolecular screening, 2013, 18(5), 522-533: Using human pluripotent stem cell-derived dopaminergic neurons to evaluate candidate Parkinson's disease therapeutic agents in MPP+ and rotenone models.).

(59) Material and Methods

(60) Primary Cell Culture, Treatment Conditions and Electrical Stimulation

(61) Midbrain tissue was harvested from embryonic day 14.5 chr:NMRI mice (Charles River). Mice were sacrificed by cervical dislocation. Tissue was dissociated by enzymatic digestion (133.3 Kunitz units/ml DNase; 10 Units/ml Papain) and mechanical trituration, counted, vitality controlled, and plated in a 20 μl drop of DMEM containing laminin (10 μg/ml), 10% fetal bovine serum and 10% horse serum on MEAs. Cultures on MEAs were incubated at 37° C. in a 10% CO.sub.2 atmosphere until ready for use. Culture media were replenished two times a week with DMEM containing 10% horse serum.

(62) In the “nanoparticles” groups, wells were treated at day 7 with nanoparticles' suspension (800 μM) from examples 1, 2, 5 and with nanoparticles' suspension (2000 μM) from example 6, followed by 20 μM of MPP.sup.+ at day 8. In the “control” group, water was added to the wells at day 7, followed by water addition at day 8. In the “MPP.sup.+” group, water was added to the wells at day 7, followed by 20 μM of MPP.sup.+ at day 8. Twenty-four (24) hours following MPP.sup.+ (or water for “control” group) addition, the medium was changed to achieve wash out of MPP.sup.+. Medium was subsequently changed twice per week.

(63) At day 21, 120 minutes of neuronal activity were recorded, and 30 minutes of stable activity were analyzed. After the recording at day 21, all wells were activated at one of the actively spiking electrodes by electrical stimuli. The stimulation was performed for 30 minutes (stimulation of 1 electrode per well in 48 wells MEA, minimum stimulation duration=100 μs, artefact elimination of 2 ms after pulse, pulse 10× biphasic+/−500 mV). The response of the non-stimulated electrodes was averaged and normalized to pre-stimulation activity (FIG. 6).

(64) Microelectrode Array Neurochips

(65) The 48 wells microelectrode array neurochips were purchased from Axion Biosystems Inc. These chips have 16 passive electrodes per well. The surface was coated for 1 hour with Polyethyleneimine (PEI, 50% in Borate buffer), washed and air-dried.

(66) Multichannel Recording and Multiparametric Data Analysis

(67) For the recording, the multichannel MAESTRO recording system by Axion Biosystems (USA) was used. For extracellular recording, 48-wells MEAs were placed into the MAESTRO recording station and maintained at 37° C. Recordings were made in DMEM/10% heat inactivated horse serum. The pH was maintained at 7.4 with a continuous stream of filtered, humidified airflow with 10% CO.sub.2.

(68) Each unit represents the activity originating from one neuron recorded at one electrode. Units are separated at the beginning of the recording. For each unit, action potentials (i.e. spikes), were recorded as spike trains, which are clustered in so-called “bursts”. Bursts were quantitatively described via direct spike train analysis using the programs Spike Wrangler and NPWaveX (both NeuroProof GmbH, Rostock, Germany). Bursts were defined by the beginning and end of short spike events (FIG. 7).

(69) With a multiparametric high-content analysis of the network activity patterns, 204 activity-describing spike train parameters were extracted. These parameters allow obtaining a precise description of activity changes in the following four categories: general activity, burst structure, oscillatory behavior and synchronicity. Changes in “general activity parameters” describe the effects on action potential firing rate (spike rate), burst rate, and burst period as the time between the bursts. “Burst structure parameters” define not only the internal structure of spikes within a high-frequency spiking phase (“burst”), e.g., spike frequency in bursts, spike rate in bursts, and burst spike density, but also the overall structure of the burst, such as duration, area, and plateau. “Oscillatory parameters” quantify the regularity of occurrence or structure of bursts, which is calculated by coefficients of variation of primary activity parameters describing the variability of parameters (general activity, burst structure) within experimental episodes (Gramowski A. et al., Eur. J. Neurosci., 2004, 19, 2815-2825: Substance identification by quantitative characterization of oscillator activity in murine spinal cord networks on microelectrode arrays). Higher values indicate less regular burst structure or less regular general activity (e.g., spiking, bursting). As a measure of synchronicity in the spike trains, “CVnet parameters” reflect “synchronization” among neurons within the network (Gramowski A. et al., Eur. J. Neurosci., 2004, 19, 2815-2825: Substance identification by quantitative characterization of oscillator activity in murine spinal cord networks on microelectrode arrays). CVnet is the coefficient of variation over the network. Large CVnet values imply a wide range of variation in the activity across the network, meaning less synchronization. (Gramowski A. et al., Frontiers in Neurology, 2015, 6(158): Enhancement of cortical network activity in vitro and promotion of GABAergic neurogenesis by stimulation with an electromagnetic field with 150 MHz carrier wave pulsed with an alternating 10 and 16 Hz modulation).

(70) Functional effects induced by MPP.sup.+ on neuronal network under electrical stimulation and prevention/rescue efficacy of the nanoparticles of the invention under electrical stimulation were evaluated through the above described parameters (also recapitulated for some of them in the Table 3 below).

(71) TABLE-US-00003 TABLE 3 Activity-describing parameters from the multiparametric data analysis in the four following categories: general activity, burst structure, oscillatory behavior and synchronicity. General activity Spike rate Number of spikes per second, averaged over all spike trains recorded Burst structure Burst duration Mean lengths of bursts (ms), Oscillatory Burst area Coefficient of variation in time of area under the curve behavior CVtime after integrating the bursts, defined by burst duration, number of spikes in bursts, spike frequency in bursts. The parameter describes the variability of burst area within experimental episodes. Higher values indicate less regular structure. Burst peak Coefficient of variation in time of single unit spike peak frequency height frequency in bursts. Lower values are a measure indicating CVtime more regularity in burst peak frequency, therewith a higher degree of regular burst structure within experimental episodes. Burst duration Coefficient of variation over time of burst duration, CVtime Sum reflecting the variability of burst duration within experimental episodes. Burst period SD Standard deviation of burst period, reflecting the variation Sum of single unit distances between consecutive bursts within experimental episodes. Lower values reflect higher regularity in burst structure. Synchronicity Burst rate CVnet CVnet of burst rate, reflecting variation of burst rate over the network during experimental episodes Burst period CVnet of burst period (distance between the beginning of CVnet consecutive bursts) reflecting the variation of “burstiness” within experimental episode over the whole network. Decrease of this parameter reflects an increase in synchronization within the network. % spikes in burst CVnet of percentage of spikes in bursts, reflecting the CVnet variation of fraction of spikes within burst intervals of all spikes within experimental episode over the whole network. Decrease of this parameter reflects an increase in synchronization within the network. SynAll Average distance of bursts within a population burst from population burst center. SynAll is a measure for the strength of synchronicity of a network.

(72) MPP.sup.+-induced functional effects on network activity under electrical stimulation in the presence or not of the tested nanoparticles were normalized to the “pre-stimulated” activity, i.e. the activity measured at day 21, set at 100% for each experiment. Values related to spontaneous native activity were derived from 60 seconds bin data taken from a 30 minutes span after a 30 minutes stabilization of activity. Results (parameter values) were expressed as mean±SEM of independent networks. For each “nanoparticles” group, at least 8 active wells, for the “control” group, at least 30 active wells and for the “MPP.sup.+” group, at least 26 active wells (“active” meaning wells with a sufficient number of electrodes measuring electrical activity), were included in the analysis. The absolute parameters' distributions were tested for normality and the statistical significance between groups was assessed via one-way ANOVA.

(73) FIG. 8 presents some representative parameters from the following categories: oscillatory behavior and synchronicity. These parameters characterize MPP.sup.+-induced functional effects under electrical stimulation and the prevention/rescue efficacy allowed by the nanoparticles of the invention under electrical stimulation (i.e. the ability to prevent/rescue functional effects to a level similar to that of “control” group).

(74) To evaluate compound effects, multiparametric results of a selection of 204 parameters were projected into a single parameter termed the “Effect Score”. It is a linear combination of selected features, transforming the datasets onto a vector with “control” group exposed to an electric field at a mean value of “0” and “MPP.sup.+” group exposed to an electric field at a mean value of “1”. Calculation of the Z-factor of the Effect Score was performed through feature selection of 18 out of the 204 parameters measured, optimized to find the best discrimination between the “control” group and the “MPP.sup.+” group (Kümmel A, et al. J Biomol Screen., 2010, 15(1), 95-101: Integration of multiple readouts into the z′ factor for assay quality assessment). The Effect Score analysis is shown in FIG. 9.

(75) The prevention/rescue efficacy of the nanoparticles of the invention exposed to an electrical stimulation is shown in Table 4.

(76) TABLE-US-00004 TABLE 4 Summary of Effect Score and prevention/rescue efficacy of nanoparticles of the invention exposed to an electric field, on MPP.sup.+-induced effects on neuronal network exposed to an electric field alone. Effect Prevention/rescue Description Group Score efficacy of effects “control” group 0 Reference — (set at 100%) “MPP.sup.+” group 1  0% — “nanoparticles” group: 0.43 56% Prevention of ⅔ of biocompatible gold MPP+ effects nanoparticles from example 1 “nanoparticles” group: 0.72 28% Prevention of ⅓ of biocompatible gold MPP+ effects nanoparticles from example 2 “nanoparticles” group: 0.65 35% Prevention of ⅓ of biocompatible silicon MPP+ effects nanoparticles from example 5 “nanoparticles” group: 0.64 36% Prevention of ⅓ of biocompatible barium MPP+ effects titanate nanoparticles from example 6

(77) The treatment of Parkinson's disease symptoms by DBS is FDA-approved since 2002. The most commonly used stimulatory parameters, usable in the context of the invention in combination with the herein described nanoparticles are: 130 to 185 Hz in frequency, 60 to 210 us in pulse width and 1 to 3.5 V in voltage amplitude. In the herein described experimentations, the stimulation was performed on the neuron network co-culture for 30 minutes, with stimulus=10 biphasic pulses (pulse duration=100 μs), pulse amplitude=+/−500 mV, pulse frequency=20 Hz, and a pulse trains period=0.2 Hz.

(78) FIGS. 12, 13 and Table 4 show that pretreatment of the neuronal network with nanoparticles of the invention and exposition to an electric field, prevents/rescues MPP.sup.+ induced functional effects under electric field on the neuronal network. Interestingly, the prevention/rescue efficacy is observed for parameters in categories related to oscillatory behavior and synchronicity, and it can reach a level up to what is observed in “control” group. These oscillatory behavior and synchronization parameters are typically monitored as a measure of altered network development. These parameters can advantageously be rescued in presence of the nanoparticles of the invention exposed to an electrical stimulation.

(79) These results highlight the advantageous performances of the nanoparticles described in the present application, when exposed to an electric field, in rescuing MPP.sup.+-induced functional effects under electric field on the neuronal network.

Example 8: Evaluation of the Effects of the Nanoparticles from Examples 2, 3, 4 and 5 Exposed to an Electrical Stimulation on Amyloid Beta 1-42-Induced Functional Effects on Primary Mouse Neuronal Networks Using the Phenotypic MEA Screening Technology

(80) The rescue efficacy of nanoparticles of the invention exposed to an electrical stimulation was tested in vitro via MEAs on an amyloid beta 1-42 (Abeta 1-42)-induced model of Alzheimer's disease in frontal cortex cultures of mouse neurons.

(81) To induce an Alzheimer-related functional phenotype, synthetic HFIP (hexafluoroisopropanol)-treated Abeta 1-42 peptides (HFIP treatment produces monomers of amyloid beta) are used at a sub-toxic dose (100 nM). High levels of amyloid-beta (Abeta) reduce glutamatergic synaptic transmission and cause synaptic loss (Palop et al., Nat Neurosci., 2010, 13(7), 812-818: Amyloid-beta induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks; Hsia et al., Proc. Natl. Acad. Sci., 1999, 96, 3228-3233: Plaque-independent disruption of neural circuits in Alzheimer's disease mouse models). The production of Abeta and its secretion into the extracellular space are tightly regulated by neuronal activity in vitro and in vivo. Increased neuronal activity enhances Abeta production, and blocking neuronal activity has the opposite effect. This synaptic regulation of Abeta is mediated, at least in part, by clathrin-dependent endocytosis of surface amyloid precursor protein (APP) at presynaptic terminals, endosomal proteolytic cleavage of APP, and Abeta release at synaptic terminals (Cirrito et al., Neuron, 2005, 48, 913-922: Synaptic activity regulates interstitial fluid amyloid-beta levels in vivo).

(82) Material and Methods

(83) Primary Cell Culture

(84) Frontal cortex tissue was harvested from embryonic day 15/16 chr:NMRI mice (Charles River). Mice were sacrificed by cervical dislocation. Tissue was dissociated by enzymatic digestion (133.3 Kunitz units/ml DNase; 10 Units/ml Papain) and mechanical trituration, counted, vitality controlled, and plated in a 20 μl drop of DMEM containing laminin (10 μg/ml), 10% fetal bovine serum and 10% horse serum on MEAs. Cultures on MEAs were incubated at 37° C. in a 10% CO.sub.2 atmosphere until ready for use. Culture media were replenished two times a week with DMEM containing 10% horse serum. The developing co-cultures were treated with the mitosis inhibitors 5-fluoro-2′-deoxyuridine (25 μM) and uridine (63 μM) on day 5 after seeding to prevent further glial proliferation.

(85) In the “nanoparticles” groups, wells were first treated with Abeta 1-42 (synthetic HFIP-treated Amyloid-beta 1-42 peptides) at T0 (T0 being at the end of the 28 days-in vitro culture period). Wells were then treated at T0+4 hours with the nanoparticles' suspension from examples 2, 3, 4 and 5 (each suspension being at a concentration of 800 μM) in independent and parallel experiments. In the “Control” group, water was added to the wells at T0, and then at T0+4 hours. In the “Abeta” group, Abeta 1-42 was added to the wells at T0, and then water was added to the wells at T0+4 hours.

(86) Neuronal activity was recorded as follows: At T0, prior Abeta 1-42 addition (or water in the “control” group) At T0+1 h, T0+2 h, T0+3 h, T0+4 h (prior addition of the nanoparticles in the «nanoparticles» group or “water” in the control group), T0+5 h and T0+6 h.

(87) Values were derived from 60 seconds bin data taken from a 30 minutes span after a 30 minutes stabilization of activity.

(88) After the recording at T0+6 h, all wells were activated at one of the actively spiking electrode by electrical stimuli. The stimulation was performed for 30 minutes (stimulation of 1 electrode per well in 48 wells MEA, minimum stimulation duration=100 μs, artefact elimination of 2 ms after pulse, pulse 10× biphasic+/−500 mV). The response of the non-stimulated electrodes was averaged and normalized to pre-stimulation activity (FIG. 10).

(89) Microelectrode Array Neurochips

(90) The 48 wells microelectrode array neurochips were purchased from Axion Biosystems Inc. These chips have 16 passive electrodes per well. The surface was coated for 1 hour with Polyethyleneimine (PEI, 50% in Borate buffer), washed and air-dried.

(91) Multichannel Recording and Multiparametric Data Analysis

(92) For the recording, the multichannel MAESTRO recording system from Axion Biosystems (USA) was used. For extracellular recording, 48-wells MEAs were placed into the MAESTRO recording station and maintained at 37° C. Recordings were made in DMEM/10% heat inactivated horse serum. The pH was maintained at 7.4 with a continuous stream of filtered, humidified airflow with 10% CO.sub.2. The action potentials, or “spikes”, were recorded in spike trains and were clustered in so-called “bursts”. Bursts were quantitatively described via direct spike train analysis using the programs Spike Wrangler and NPWaveX (both NeuroProof GmbH, Rostock, Germany). Bursts were defined by the beginning and end of short spike events.

(93) With a multiparametric high-content analysis of the network activity patterns, 204 activity-describing spike train parameters were extracted. These parameters allow obtaining a precise description of activity changes in the four categories as follows: general activity, burst structure, oscillatory behavior and synchronicity.

(94) Functional effects of amyloid beta 1-42 on neuronal network exposed to electrical stimulation and rescue efficacy of functional effects of the neuronal network by the nanoparticles of the invention exposed to an electrical stimulation were evaluated through the above described parameters (also recapitulated for some of them in Table 5 below).

(95) TABLE-US-00005 TABLE 5 Activity-describing parameters from the multiparametric data analysis in the four following categories: general activity, burst structure, oscillatory behavior and synchronicity General activity Spike contrast Describes the occurrence or absence of spikes in neighboring time segments of the spike train, reflecting the variability in burstiness of units within experimental episodes Burst structure Burst duration Mean lengths of burst (ms) sum Oscillatory Burst rate SD Standard deviation of number of bursts per minute, behavior indicating the variability of burstiness of units within experimental episodes Burst period SD Standard deviation of burst period, reflecting the variation of single unit distances between consecutive bursts within experimental episodes. Low values reflect higher regularity in the burst structure Synchronicity Burst spike rate CVnet of burst spike rate, reflecting the variation of spikes CVnet within burst intervals within experimental episodes over the whole network. Decrease of this parameter reflects an increase in synchronization within the network Spike contrast CVnet of spike contrast. Higher values indicate higher CVnet variability of “burstiness” of units among the network

(96) Network activity under stimulation was normalized to the related spontaneous native activity (T0+6 hours recording), set at 100% for each experiment. Values related to spontaneous native activity were derived from 60 seconds bin data taken from a 30 minutes span after a 30 min stabilization of activity. Results (parameter values) were expressed as mean±SEM of independent networks. For each “nanoparticles” group, at least 9 active wells, for the “control” group, at least 18 active wells, and for the “Abeta” group, at least 18 active wells (“active” meaning wells with a sufficient number of electrodes measuring electrical activity), were included in the analysis. The absolute parameters' distributions were tested for normality and the statistical significance between groups was assessed via one-way ANOVA.

(97) FIG. 11 shows some representative parameters from the following categories: general activity, burst structure, oscillatory behavior and synchronicity.

(98) These parameters characterize Abeta 1-42-induced functional effects under electrical stimulation and the rescue efficacy allowed by the nanoparticles of the invention under electrical stimulation (i.e. the ability to prevent/rescue functional effects to a level similar to that of “control” group).

(99) To evaluate compound effects, multiparametric results of a selection of 204 parameters were projected into a single parameter termed the “Effect Score”. It is a linear combination of selected features, transforming the datasets onto a vector with “control” group exposed to an electric field at a mean value of “0” and “Abeta” group exposed to an electric field at a mean value of “1”. Calculation of the Z-factor of the Effect Score was performed through features' selection of 15 out of the 204 parameters measured, optimized to find the best discrimination between the “control” group and the “Abeta” group (Kümmel A, et al., J Biomol Screen., 2010, 15(495-10: Integration of multiple readouts into the z′ factor for assay quality assessment). The Effect Score analysis is shown in FIG. 12.

(100) The rescue efficacy of the nanoparticles of the invention exposed to an electrical stimulation is shown in Table 6.

(101) TABLE-US-00006 TABLE 6 Summary of Effect Score and rescue efficacy of the nanoparticles of the invention exposed to an electric field on Abeta 1-42-induced effects on the neuronal network exposed to an electric field alone. Effect Description Group Score Rescue efficacy of effects “control” group 0 Reference — (set at 100%) “Abeta” group 1  0% — “nanoparticles” group: 0.43 25% Prevention of ¼ of biocompatible gold Abeta 1-42 effects nanoparticles from example 2 “nanoparticles” group: 0.72 45% Prevention of ½ of biocompatible zirconium Abeta 1-42 effects oxide nanoparticles from example 3 “nanoparticles” group: 0.65 34% Prevention of ⅓ of biocompatible zirconium Abeta 1-42 effects oxide nanoparticles from example 4 “nanoparticles” group: 0.64 34% Prevention of ⅓ of biocompatible silicon Abeta 1-42 effects nanoparticles from example 5

(102) Clinical investigations are ongoing to evaluate the potential of DBS for the treatment of Alzheimer's disease. The stimulatory parameters, typically usable in the context of the invention in combination with the herein described nanoparticles, are: 130 Hz in frequency, 60 or 90 us in pulse width, 3 to 5 V in amplitude voltage. In the herein described experimentations, the stimulation was performed on the neuron network coculture for 30 minutes, with stimulus=10 biphasic pulses, with minimum pulse duration=100 μs, pulse amplitude=+/−500 mV, pulse frequency=20 Hz, and a pulse trains period=0.2 Hz.

(103) FIGS. 11 and 12 and Table 6 show that treatment of the neuronal network with nanoparticles of the invention, when exposed to an electric field, rescues Abeta 1-42 induced functional effects under electric field of the neuronal network. Interestingly, the rescue efficacy is observed for parameters in categories related to oscillatory behavior and synchronicity and it can reach a level up to what is observed in the “control” group.

(104) These oscillatory behavior and synchronization parameter are typically monitored as a measure of altered network development. These parameters can advantageously be rescued in presence of the nanoparticles of the invention exposed to an electrical stimulation.

(105) These results highlight the advantageous performances of the nanoparticles described in the present application, when exposed to an electric field, in rescuing Abeta 1-42 induced functional effects under electric field on the neuronal network.