Novel Nanosensor Array for Detection of Volatile Biomarkers From Skin in Multiple Sclerosis

NCT ID: NCT04074629

Last Updated: 2022-12-07

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 107 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-12-16
• Study Completion Date: 2022-08-02

Brief Summary

Multiple Sclerosis (MS) is the most common chronic neurological disease affecting young adults, with onset usually at the age 20-40 years. The disease is characterized by two main phenotypes: Relapse-Remitting MS (RR-MS) and Primary Progressive MS (PP-MS). RR-MS is the most common type of disease, for long-term management of the disease patients are treated with immunomodulatory drugs (IMD) which reduce disease activity. Response to therapy varies among patients. Presently there are no biomarkers available for diagnosis and routine follow-up of MS. Many MS patients suffer from unexpected relapsing episodes that influence dramatically their mental and physical conditions, with high stress levels, tremors, motoric disabilities, blindness and more. Therefore, early target treatment in relapse episodes is crucial, yet sufficient tools for predicting and identifying early symptoms of an upcoming relapse episode are not available. The investigators have most recently shown that breath VOCs can be used to classify among MS and non-MS patients. The major aims of the current proposal is to study the plausibility of skin based VOCs as biomarkers for MS diagnosis and To Identify and characterize skin-based VOCs as biomarkers of the clinical relapse and disease activity.

Detailed Description

BACKGROUND Multiple Sclerosis (MS) is the most common chronic neurological disease affecting young adults, with onset usually at the age 20-40 years. The disease is characterized by two main phenotypes: Relapse-Remitting MS (RR-MS) and Primary Progressive MS (PP-MS). RR-MS is the most common type of disease, for long-term management of the disease patients are treated with immunomodulatory drugs (IMD) which reduce disease activity. Response to therapy varies among patients. Presently there are no biomarkers available for diagnosis and routine follow-up of MS. Oligoclonal IgG in the CSF - which help confirm the diagnosis, require an invasive procedure and are not correlated with disease activity nor response to therapy and MRI, which allows monitoring of MS activity and its treatment, is too expensive to be routinely used. Moreover, RR-MS patients are under a significant stress as the relapse episode can appear instantly with no recognized warning sign. If relapse related biomarkers can be identified a potential prediction test could be developed, that eventually can allow a proper medical counter action provided before relapse escalates. Additionally, early diagnosis of MS and prediction of relapse would reduce the rate of accumulation of disability and save hospitalization days.

Here, the investigators propose: further explore the ability of a simple and portable sensing technology to predict and monitor MS from volatile samples. The technology that has been developed in the lab of Prof. Hossam Haick has proved potential for diagnostics of a number of medical conditions such as malignancy, infectious, and neurological diseases, including two clinical studies on MS (Broza et al 2017; Broza et al 2015; Ionescu et al 2011; Nakhleh et al 2017 and Peng et al 2009). The approach relies on the fact that cell membrane consists primarily of amphipathic phospholipids, carbohydrates and many integral membrane proteins that are distinct for different cell types. In disease processes, cells undergo structural changes that may lead to oxidative stress,, i.e. a peroxidation of the cell membrane that causes volatile organic compounds (VOCs) to be emitted. Some of these VOCs appear in distinctively different mixture compositions, depending on whether a cell is healthy or not. Oxidative stress in MS is believed to contribute to tissue injury in focal inflammatory lesions and to be involved in diffuse axonal degeneration and demyelination (Gilgun-Sherki et al 2004). What is particularly significant about this approach is that each type of disease has its own unique pattern of VOCs, and, therefore, the presence of one disease would generally not screen other disease types. These VOCs can be detected directly from different bodily fluids as exhaled breath or skin. In certain instances, breath and skin analysis offers several potential advantages: (a) breath\skin samples are noninvasive and easy to obtain; (b) breath\skin contains less complicated mixtures than either serum or urine; and (c) breath\skin testing has the potential for real-time monitoring. The NA-NOSE performs odor detection using broadly cross-reactive sensors in conjunction with pattern recognition methods (Broza et al 2017; Peng et al 2009). In contrast to the "lock-and-key" approach, each sensor in the NA-NOSE is broadly responsive to a variety of odorants. This increases the variety of compounds to which the many different sensors are sensitive to, thus enabling analyses of biomarkers in complex multi-component media.

REASERCH AIMS AND EXPECTED SIGNIFICANSE

Early target treatment in relapse episodes is crucial, yet sufficient tools for predicting and identifying early symptoms of an upcoming relapse episode are not available. The investigators have most recently shown that breath VOCs can be used to classify among MS and non-MS patients ( Broza et al 2017). The major aim of the current proposal is to follow-up this recent achievement, and test the plausibility of skin-based VOC analysis for providing complementary information regarding potential biomarkers of disease type (RR-MS and PP-MS) as well as monitoring of disease activity and specifically prediction of relapse episode in MS patients. Project aims will be accomplished by the following specific objectives:

Objective 1: To Study the plausibility of skin based VOCs as biomarkers for MS diagnosis. In this part, the investigators will 1) employ previously developed methodology for VOC skin sampling and adapt it for MS patients (RR-MS, SP-MS, PP-MS). 2) To determine the specificity of the findings the investigators will further test the difference in VOC profiles between MS and non-MS patients (healthy controls ) using sensor technology and\or Gas-Chromatography Mass-Spectrometry (GC-MS) (part of which will be planned for follow-up study). Where plausible, a sub-analysis will be performed to detect skin-based VOCs differences between RR-MS and SP-MS and PP-MS patients Based on the outcome of the first step that will identify volatile changes between MS and healthy controls from skin the investigators will evaluate the plausibility to include additional non-MS diseased population groups, such as patients with other autoimmune or other neurological diseases. If yes, than appropriate amendment will be submitted.

Objective 2: To Identify and characterize skin-based VOCs as biomarkers of the clinical relapse and disease activity by measuring and analyzing VOCs profile during relapse and remission. Based on the outcome of the first step the investigators will test the ability of the skin-based VOC test to monitor subtle changes in the metabolites that might indicate on initiation, progression and termination of the relapse episode. It will be achieved by sensor and\or GC-MS analysis work for identifying potential change in biomarkers during the relapse. In a follow-up study, the investigators intend to assess Relapse vs "Pseudo-Relapse".

Expected significance, the proposed study could have a major impact on the medical care and wellbeing of MS patients. Early diagnosis and prediction of disease activity will allow early and tailored therapeutic intervention, prevention of irreversible neurological damage and related accumulation of disability while reducing medical costs. Additionally, the project will be an important step in application of nanosensors for detection of biomarkers in medical research and practice of theranostics (therapy based on diagnosis) toward implementation of Precision Medicine.

STUDY PLAN Sample collection Skin related VOCs would be collected using Polydimethylsiloxane (PDMS) patches from different body locations for sensors and\or GC-MS analysis. The absorbing materials will be attached to the skin at different locations (arm/hand or chest or forehead), after cleaning the skin with alcohol prep pad and clean water. The patches will be covered with aluminum foil and sealed with medical adhesive tape for up to an hour. A total of 2-4 silicon patches for each patient will be collected at each time. Collected samples on PDMS will be transferred to a glass vials and be stored up to 4 ˚C until sent to the Technion for the NA-NOSE and GC-MS analysis. All the samples collected will be coded. Signal response of the sensors will be evaluated in order to match the best sensors for the sample. GC-MS will provide initial information on the average skin profile of MS and control, and will assess the need for slight adjustment is sampling procedure (e.g., sample time).

Statistics Specific patterns and predictive models for the studied MS conditions will be derived from the sensor array output, using advanced pattern recognition methods as discriminant function analysis (DFA), principal component analysis (PCA), neural networks (NN) or similar. Statistical significance of results will be determined by using parametric or a-parametric tests such as Student's T test, Man Whitney or paired analysis. Values p<0.05 will be considered to be significant.

Conditions

Multiple Sclerosis

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Skin related VOCs collected by Polydimethylsiloxane patch

The skin related VOCs will be collected by "off-line" method using Polydimethylsiloxane (PDMS) patches from different body locations for sensor system and\\or GC-MS analysis

Group Type: EXPERIMENTAL

Diagnosis, skin volatile organic compounds (VOCs)

Intervention Type: DIAGNOSTIC_TEST

The skin related VOCs will be collected by "off-line" method using Polydimethylsiloxane (PDMS) patches from different body locations for sensor system and\\or GC-MS analysis

Interventions

Diagnosis, skin volatile organic compounds (VOCs)

The skin related VOCs will be collected by "off-line" method using Polydimethylsiloxane (PDMS) patches from different body locations for sensor system and\\or GC-MS analysis

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

Individuals willing and able to give informed consent

MS group:

Male or females diagnosed with MS Age 18-75 years

Control subjects:

Healthy volunteers: individuals that do not have MS or any other condition that is defined as "autoimmune"

2. Technical problems in the performance of the tests.

Exclusion Criteria

1. Pregnant women

2. Presence of a serious infectious disease (e.g. HIV, viral hepatitis and similar contagious diseases).

Withdrawal criteria:

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Technion, Israel Institute of Technology

OTHER

Sponsor Role: collaborator

Carmel Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Ariel Miller

Director of Multiple Sclerosis & Brain Research Center

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Ariel Miller

Role: PRINCIPAL_INVESTIGATOR

Multiple Sclerosis Clinc, Carmel Medical Center

Locations

Ariel Miller

Haifa, , Israel

Countries

Israel

References

Broza YY, Har-Shai L, Jeries R, Cancilla JC, Glass-Marmor L, Lejbkowicz I, Torrecilla JS, Yao X, Feng X, Narita A, Mullen K, Miller A, Haick H. Exhaled Breath Markers for Nonimaging and Noninvasive Measures for Detection of Multiple Sclerosis. ACS Chem Neurosci. 2017 Nov 15;8(11):2402-2413. doi: 10.1021/acschemneuro.7b00181. Epub 2017 Aug 16.

Reference Type: BACKGROUND

PMID: 28768105 (View on PubMed)

Broza YY, Mochalski P, Ruzsanyi V, Amann A, Haick H. Hybrid volatolomics and disease detection. Angew Chem Int Ed Engl. 2015 Sep 14;54(38):11036-48. doi: 10.1002/anie.201500153. Epub 2015 Jul 31.

Reference Type: BACKGROUND

PMID: 26235374 (View on PubMed)

Ionescu R, Broza Y, Shaltieli H, Sadeh D, Zilberman Y, Feng X, Glass-Marmor L, Lejbkowicz I, Mullen K, Miller A, Haick H. Detection of multiple sclerosis from exhaled breath using bilayers of polycyclic aromatic hydrocarbons and single-wall carbon nanotubes. ACS Chem Neurosci. 2011 Dec 21;2(12):687-93. doi: 10.1021/cn2000603. Epub 2011 Sep 22.

Reference Type: BACKGROUND

PMID: 22860162 (View on PubMed)

Nakhleh MK, Amal H, Jeries R, Broza YY, Aboud M, Gharra A, Ivgi H, Khatib S, Badarneh S, Har-Shai L, Glass-Marmor L, Lejbkowicz I, Miller A, Badarny S, Winer R, Finberg J, Cohen-Kaminsky S, Perros F, Montani D, Girerd B, Garcia G, Simonneau G, Nakhoul F, Baram S, Salim R, Hakim M, Gruber M, Ronen O, Marshak T, Doweck I, Nativ O, Bahouth Z, Shi DY, Zhang W, Hua QL, Pan YY, Tao L, Liu H, Karban A, Koifman E, Rainis T, Skapars R, Sivins A, Ancans G, Liepniece-Karele I, Kikuste I, Lasina I, Tolmanis I, Johnson D, Millstone SZ, Fulton J, Wells JW, Wilf LH, Humbert M, Leja M, Peled N, Haick H. Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules. ACS Nano. 2017 Jan 24;11(1):112-125. doi: 10.1021/acsnano.6b04930. Epub 2016 Dec 21.

Reference Type: BACKGROUND

PMID: 28000444 (View on PubMed)

Peng G, Tisch U, Adams O, Hakim M, Shehada N, Broza YY, Billan S, Abdah-Bortnyak R, Kuten A, Haick H. Diagnosing lung cancer in exhaled breath using gold nanoparticles. Nat Nanotechnol. 2009 Oct;4(10):669-73. doi: 10.1038/nnano.2009.235. Epub 2009 Aug 30.

Reference Type: BACKGROUND

PMID: 19809459 (View on PubMed)

Gilgun-Sherki Y, Melamed E, Offen D. The role of oxidative stress in the pathogenesis of multiple sclerosis: the need for effective antioxidant therapy. J Neurol. 2004 Mar;251(3):261-8. doi: 10.1007/s00415-004-0348-9.

Reference Type: BACKGROUND

PMID: 15015004 (View on PubMed)

Other Identifiers

CMC-18-0177-CTIL

Identifier Type: -

Identifier Source: org_study_id