Methods, uses and kits for monitoring or predicting response to periodontal disease treatment

Abstract

An in vitro method for assessing or predicting the response of human patients to treatment of periodontal disease is disclosed. This method is based on insights into identifying biomarker proteins. Therefore, the concentration of a specific combination of proteins is measured in a saliva sample from the patient. One such combination is at least one of interleukin-1-β (IL-1β) and matrix metalloproteinase-8 (MMP-8), and α-1-acid glycoprotein (A1AGP). Based on the measured concentrations, at least one value reflecting the combined concentration of these proteins is determined. This at least one value can indicate the likelihood that the human patient has been or will be successfully treated for periodontitis. This at least one value can be compared to at least one threshold, which in the same manner reflects the combined concentration associated with successful treatment of periodontitis. This comparison allows assessment of whether the test value is an indicator of the patient's periodontal treatment status.

Description

Technical Field

[0001] This invention belongs to the field of oral care and relates to the evaluation of saliva-based responses to periodontal disease treatment. In particular, this invention relates to a kit, its use, and a method for assessing or predicting the response to treatment in patients with periodontal disease. Background Technology

[0002] Gingivitis, or gingivitis, is a non-destructive periodontal disease primarily caused by the adhesion of bacterial biofilms or plaque to the tooth surface. If left undetected and untreated, reversible gingivitis usually leads to inflammation of the tissues surrounding the teeth (i.e., periodontal tissues), a condition defined as periodontitis. This is irreversible and causes tissue destruction and alveolar bone loss, ultimately resulting in tooth loss. During the development of gum disease, there are usually associated clinical signs and symptoms such as swollen gums, a change in color from pink to dark red, bleeding gums, bad breath, and gums becoming more tender or painful to the touch.

[0003] Periodontitis is a chronic, multifactorial inflammatory disease caused by oral microorganisms and characterized by progressive destruction of both hard (bone) and soft (periodontal ligament) tissues, ultimately leading to tooth movement and loss. This differs from gingivitis, which is a reversible infection and inflammation of the gum tissue. Inflammatory periodontitis is one of the most common chronic diseases in humans and a leading cause of tooth loss in adults. In addition to its substantial negative impact on oral health, mounting evidence suggests that periodontitis also has systemic consequences and is a risk factor for several systemic diseases, including heart disease (such as atherosclerosis and stroke), diabetes, pregnancy complications, rheumatoid arthritis, and respiratory infections.

[0004] Therefore, early and accurate diagnosis of periodontal disease is important from both oral and overall health perspectives. Furthermore, it is desirable to accurately determine whether or not treatment for periodontal disease will be effective for a patient. In particular, it is desirable to predict the likelihood of effectiveness of treatment before it is provided to the patient or at an early stage of treatment.

[0005] In general dental practice, the diagnosis of periodontal disease remains poor, resulting in relatively low treatment intervention rates and a significant number of untreated cases. Current diagnosis relies on inaccurate, subjective clinical examinations by dental professionals based on oral tissue conditions (color, swelling, degree of bleeding on probing, pocket depth; and bone loss detected by oral X-rays). These traditional methods are very time-consuming, and some of the techniques used (pocket depth, X-rays) reflect historical events, such as past disease activity, rather than current disease activity or susceptibility to further disease.

[0006] Similarly, for patients with periodontal disease undergoing treatment, the current practice of clinically assessing treatment response post-treatment using these traditional methods leads to high costs and potentially inaccurate monitoring of the patient's condition. Furthermore, the ability to predict treatment response can be highly valuable, as treatment strategies can be tailored accordingly. Therefore, it is desirable to measure current disease activity, the subject's susceptibility to further periodontal disease, and what treatment might be successful for the patient. Consequently, a more objective, faster, more accurate, easier-to-use—ideally with predictive value—and preferably also operable by non-professionals is desired.

[0007] Saliva, or oral fluid, has long been touted as a diagnostic fluid for oral and general diseases, and with the advent of miniaturized biosensors (also known as lab-on-a-chip), point-of-care diagnostics for rapid chairside testing has gained greater scientific and clinical attention. In particular, for periodontal disease detection, inflammatory biomarkers associated with tissue inflammation and breakdown may readily terminate in saliva due to proximity, suggesting a strong potential for saliva in periodontal disease detection. Indeed, this field has received significant attention and encouraging results have been achieved. For example, Ramsier et al. (J Periodontol. 2009 Mar; 80(3):436-46) identified host- and bacterial-derived biomarkers associated with periodontal disease. However, definitive tests have yet to emerge.

[0008] Biomarkers represent biological indicators that support clinical presentation and are therefore objective indicators of clinical outcomes in diagnosing periodontal disease. Ultimately, proven biomarkers can be used to assess risk for future disease, identify disease at very early stages, identify responses to initial therapies, and allow for the implementation of preventative strategies.

[0009] Factors that have previously limited the development of point-of-care tests for saliva biomarkers include a lack of technology suitable for chairside applications and the inability to analyze multiple biomarkers in individual samples. Furthermore, the selection of which multiple biomarkers to include in such tests has not been adequately addressed in the literature or implemented in practice.

[0010] Furthermore, periodontitis can manifest itself across a wide range of severity, from mild to advanced. To facilitate assessment of disease severity, dentists often categorize patients with periodontitis into two groups—those with mild periodontitis and those with advanced periodontitis. However, available methods for performing this assessment involve labor-intensive processes, making it impossible for dentists to routinely perform this on every patient and / or at every visit, and it is not something that can be done by the user (self-diagnosis).

[0011] The aim is to provide a simpler method, particularly one that requires only the collection of a small saliva sample from the patient, and can be collected by the patient themselves. It is desired to input such samples into an in vitro diagnostic device that allows for the classification of the saliva sample based on measurements, thereby enabling the device to return an indication of the likelihood that the patient's periodontal disease is being or may be effectively treated. Summary of the Invention

[0012] To better meet the aforementioned expectations, in one aspect, the present invention relates to an in vitro method for evaluating or predicting the response of a human patient to treatment for periodontal disease, the method comprising detecting the concentration of the following proteins in a saliva sample from a human patient with periodontal disease:

[0013] At least one of interleukin-1-β (IL-1β) and matrix metalloproteinase-8 (MMP-8), and α-1-acid glycoprotein (A1AGP); or

[0014] At least one of matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), and keratin-4 (K-4), α-1-acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β); or

[0015] At least one of the following proteins: interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9); and matrix metalloproteinase-9 (MMP-9); or matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ);

[0016] At least one test value is determined that reflects the combined concentration of the protein, and the test value is compared with a threshold that reflects the combined concentration associated with successful treatment of periodontal disease in the same manner, in order to assess whether the test value is an indication of successful treatment of periodontal disease for the patient.

[0017] On the other hand, the present invention proposes the use of the aforementioned identified proteins in saliva samples from human patients as biomarkers for assessing whether a patient will respond to or has already responded to periodontal disease treatment.

[0018] Optionally, the patient's age is also used as a biomarker.

[0019] In another aspect, the present invention relates to a system for evaluating or predicting the response of a human patient to treatment for periodontal disease, the system comprising:

[0020] —A detection device capable of and adapted to detect the proteins identified in the first aspect in saliva samples from human patients; and

[0021] —A processor capable of and adapted to determine, from the determined concentration of a protein, an indication of whether a patient’s periodontal disease has been or will be successfully treated.

[0022] The system may optionally include a data connection to an interface, particularly a graphical user interface, which can present information and preferably also input information, said interface being part of the system or a remote interface.

[0023] Optionally, one or more of the foregoing items, especially the processor, are enabled to operate "in the cloud," that is, not fixed on a machine, but through an internet-based application.

[0024] In another aspect, the present invention provides a kit for detecting at least two biomarkers for periodontal disease in saliva samples from human patients, the kit comprising two or more, typically two, three or four, detectors for detecting the following proteins:

[0025] At least one of interleukin-1-β (IL-1β) and matrix metalloproteinase-8 (MMP-8), and α-1-acid glycoprotein (A1AGP); or

[0026] At least one of matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), and keratin-4 (K-4), α-1-acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β); or

[0027] At least one of the following proteins: interleukin-1β (IL-1β), hepatocyte growth factor (HGF), α-1 acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and matrix metalloproteinase-9 (MMP-9); or

[0028] Matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ).

[0029] Typically, three or more assays are used, each binding to a different biomarker. In one embodiment, a first assay is used to detect A1AGP, a second assay is used to detect IL-1β, and a third assay is used to detect one of MMP-9, K-4, and MMP-8. In another embodiment, a first assay is used to detect MMP-9, a second assay is used to detect one of the following proteins: interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and a third assay is used to detect another of the following proteins: interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9).

[0030] In another aspect, the present invention provides an in vitro method for determining changes in periodontal disease status in a human patient due to treatment of a disease over a time interval from a first time point t1 to a second time point t2, the method comprising detecting the concentrations of the following proteins in at least one saliva sample obtained from the patient at time t1 and at least one saliva sample obtained from the patient at time t2:

[0031] At least one of interleukin-1-β (IL-1β) and matrix metalloproteinase-8 (MMP-8), and α-1-acid glycoprotein (A1AGP); or

[0032] At least one of matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), and keratin-4 (K-4), α-1-acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β); or

[0033] And at least one of the following proteins: interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and matrix metalloproteinase-9 (MMP-9); or

[0034] Matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ);

[0035] And to compare concentrations, where any one, two, three, four or more differences in concentration reflect changes in state.

[0036] In another aspect, the present invention provides a method for determining whether a human patient has successfully, is successfully, or will successfully treat periodontal disease, comprising detecting proteins identified in the first aspect above in a saliva sample of the human patient, and assessing whether the human patient has successfully or will successfully treat periodontal disease based on the concentration of said proteins in said sample. Optionally, the method includes the further step of treating the patient's periodontitis.

[0037] In another aspect, the present invention provides a method for detecting the protein identified in the first aspect above in a human patient, comprising:

[0038] (a) Obtaining saliva samples from human patients; and

[0039] (b) The presence of a protein in the sample is detected by contacting the sample with two or more detection agents for binding the protein and detecting the binding of each protein to the two or more detection agents. Typically, there is a first detection agent that can bind A1AGP, a second detection agent that can bind IL-1β, and a third detection agent that can bind one of MMP-9, K-4, or MMP-8. Attached Figure Description

[0040] Figure 1 The system used in the method of this disclosure is illustrated schematically.

[0041] Figure 2 The figure shows that when using leave-one-out cross-validation (LOOCV) to assess treatment response, the number of identified biomarker proteomes with up to four protein markers is used as a function of the threshold in classification performance, based on the Receiver-Operator-Characteristic Area (Under the Curve). The figure also shows separate curves excluding age as a predictor, as well as those including age. Detailed Implementation

[0042] In a general sense, this invention is based on the insightful observation that certain combinations of protein biomarkers in saliva samples from human patients can be used to assess or predict a patient's response to treatment for periodontal disease. Combinations of saliva biomarkers can distinguish between successful and unsuccessful responses to periodontitis treatment. This insight is based at least in part on the finding that the usefulness of biomarkers used to diagnose periodontal disease (e.g., prior to treatment) does not necessarily imply their usefulness in monitoring treatment of that periodontal disease. The present disclosure proposes clinical definitions of various grades of treatment response and identifies combinations of salivary protein markers that enable the assessment or prediction of treatment response from measurements of the concentration of these salivary protein marker combinations before or after treatment (for prediction).

[0043] The biomarker proteins are α-1-acid glycoprotein (A1AGP), interleukin-1-β (IL-1β), matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), keratin-4 (K-4), hepatocyte growth factor (HGF), hemoglobin-β (Hb-β), S100 calcium-binding protein A9 (S100A9), and free light chain κ (FLC-κ). According to the present invention, the following combinations of these proteins are used to monitor or predict the treatment of periodontal disease:

[0044] At least one of matrix metalloproteinase-9 (MMP-9), matrix metalloproteinase-8 (MMP-8), and keratin 4, α-1-acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β);

[0045] Matrix metalloproteinase-9 (MMP-9), and at least one of interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin-β (Hb-β) and S100 calcium-binding protein A9 (S100A9);

[0046] A combination of α-1-acid glycoprotein (A1AGP) with at least one of matrix metalloproteinase-8 (MMP-8) or interleukin-1-β (IL-1β); or

[0047] Matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ).

[0048] The age of the subjects may optionally be included as an additional marker.

[0049] In one embodiment, the method evaluates the response of a human patient who has been previously diagnosed with periodontitis and has received treatment for that periodontitis.

[0050] In another embodiment, the method predicts the response of a human patient to treatment for periodontitis. This can be done before the patient has received treatment. Alternatively, treatment may have recently been administered to the patient, and it is desirable to know at an early stage whether the treatment is likely to be effective. Typically, treatment is administered for at least one, two, or three weeks prior to assessing the patient using the method of the present invention, for example, at least about 7 days, at least about 14 days, or at least about 21 days. Treatment can be initiated between about one week and about one month prior to assessment. Alternatively, the patient can be assessed at intervals of two, three, or four weeks after the first treatment, for example, at weeks 3, 6, and 9 after treatment, or at weeks 4, 8, and 12 after treatment. In this predictive embodiment, the concentrations of proteins MMP-8, IL-1β, and A1AGP can be detected; pyruvate kinase can also be included in this group as an additional protein biomarker.

[0051] α-1-acid glycoprotein (A1AGP) is a plasma α-globulin glycoprotein primarily synthesized by the liver. It is sometimes also called a serum mucoprotein. α-1-acid glycoprotein (A1AGP) plays a role in transporting proteins in the blood, acting as carriers of basic and neutral charged lipophilic compounds. It is also believed to regulate the interaction between blood cells and endothelial cells.

[0052] IL-1β is a member of the interleukin-1 cytokine family. This cytokine is produced as a proprotein by activated macrophages and undergoes proteolytic treatment by caspase-1 (CASP1 / ICE) to become its active form. This cytokine is an important mediator of inflammatory responses and participates in various cellular activities, including cell proliferation, differentiation, and apoptosis.

[0053] MMPs are a family of enzymes responsible for the degradation of extracellular matrix components such as collagen, proteoglycans, laminin, elastin, and fibronectin. MMPs play a crucial role in periodontal ligament (PDL) remodeling under both physiological and pathological conditions. MMP-8, also known as neutrophil collagenase or PMNL collagenase (MNL-CL), is a collagenase found in the connective tissues of most mammals. MMP-9, also known as 92kDa type IV collagenase, 92kDa gelatinase, or gelatinase B (GELB), is a matrix protein belonging to the zinc-metalloproteinase family of enzymes involved in the degradation of the extracellular matrix.

[0054] Keratin-4 (K-4), also known as cytoskeletal keratin-4 (CYK-4) or cytokeratin-4 (CK-4), is a protein encoded by the KRT4 gene in the human body. Keratin-4 is a member of the keratin gene family. Type II cytokeratins consist of basic or neutral proteins that are expressed in pairs of heterokeratin chains during the differentiation of simple and layered epithelial tissues. Type II cytokeratin CK-4 is specifically expressed in the differentiated layers of mucosal and esophageal epithelial cells containing the family member KRT13. Mutations in these genes are associated with white cavernous nevi, characterized by leukoplakia of the oral cavity, esophagus, and anus. Type II cytokeratins are clustered in the region of chromosome 12q12-q13.

[0055] Hepatocyte growth factor (HGF) is a paracrine cell growth, motility, and morphogenesis factor. Secreted by mesenchymal cells, HGF primarily targets and acts on epithelial and endothelial cells, and also on hematopoietic progenitor cells. HGF has been shown to play a crucial role in myogenesis and wound healing. Its ability to stimulate mitosis, cell motility, and matrix invasion makes it central to angiogenesis, tumorigenesis, and tissue regeneration. HGF stimulates epithelial cell growth and prevents connective tissue attachment and regeneration. HGF is a known serum marker of disease activity in various diseases.

[0056] Hemoglobin (Hb) is a ferrometallurgical metalloprotein found in the red blood cells of almost all vertebrates and in the tissues of some invertebrates. Hemoglobin-β (also known as β-globin, HBB, β-globin, and hemoglobin subunit β) is a globin that, together with α-globin (HBA), constitutes the most common form of adult hemoglobin, HbA. Hb-β is typically 146 amino acids long and has a molecular weight of 15,867 Da. Normal adult HbA is a heterotetramer composed of two α chains and two β chains. Hb-β is encoded by the HBB gene on human chromosome 11.

[0057] S100 calcium-binding protein A9 (S100A9), also known as calciprotein B, is a calcium- and zinc-binding protein that plays a significant role in the regulation of inflammatory processes and immune responses. S100 calcium-binding protein A9 can induce neutrophil chemotaxis and adhesion, promote phagocytosis through activation of SYK, PI3K / AKT, and ERK1 / 2, thereby enhancing the bactericidal activity of neutrophils, and can also induce neutrophil degranulation through a MAPK-dependent mechanism.

[0058] Free light chain proteins are immunoglobulin light chains. Free light chain proteins do not associate with immunoglobulin heavy chains. Unlike typical intact immunoglobulin molecules, free light chain proteins are not covalently linked to immunoglobulin heavy chains; for example, free light chains do not form disulfide bonds with heavy chains. Typically, a free light chain consists of approximately 220 amino acids. Free light chain proteins typically include a variable region (often called the light chain variable region, V...). L ) and the constant region (often called the light chain constant region, C L Humans produce two types of immunoglobulin light chains, named with the letters κ and λ. Each of these can be further subdivided into subgroups based on variations in the variable region, with four κ subtypes (Vκ1, Vκ2, Vκ3, and Vκ4) and six λ subtypes (Vλ1, Vλ2, Vλ3, Vλ4, Vλ5, and Vλ6). Free light chains κ are typically monomers. Free light chains λ are typically dimers linked by disulfide bonds (to another free light chain λ). Polymer forms of free light chains λ and κ have been identified. Free light chains are produced by bone marrow and lymph node cells, and locally by diffusing lymphocytes in periodontal tissues, and are rapidly cleared from the bloodstream and metabolized by the kidneys. Monomeric free light chains are cleared within 2–4 hours, while dimer free light chains are cleared within 3–6 hours.

[0059] The aforementioned proteins are known in the art. Those skilled in the art know the structure of the aforementioned proteins and methods for detecting them in aqueous samples (such as saliva samples). The following combination of protein biomarkers is collectively referred to as the "biomarker set of the present invention":

[0060] At least one of matrix metalloproteinase-9 (MMP-9), matrix metalloproteinase-8 (MMP-8), and keratin 4, α-1-acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β);

[0061] At least one of interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin-β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and matrix metalloproteinase-9 (MMP-9);

[0062] A combination of α-1-acid glycoprotein (A1AGP) with at least one of matrix metalloproteinase-8 (MMP-8) or interleukin-1-β (IL-1β); and

[0063] Matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ).

[0064] In one embodiment, the biomarker set of the present invention may consist of identified protein biomarkers. Preferably, the biomarker set of the present invention consists of no more than four protein biomarkers identified by the present invention. In addition to the biomarker set of the present invention, other biomarkers and / or data, such as demographic data (e.g., age, sex), may be included in a dataset for determining the type of periodontitis. An example of an additional protein biomarker is pyruvate kinase. An example of an extended biomarker set of the present invention is MMP-8, IL-1β, A1AGP, and pyruvate kinase.

[0065] When other biomarkers are optionally included, the total number of biomarkers (i.e., the biomarker set of the present invention plus other biomarkers) is typically 4, 5 or 6.

[0066] However, a desirable advantage of this invention is that the classification of a patient's periodontal disease can be determined by measuring preferably no more than four biomarkers, and more preferably only three, wherein a group of biomarkers consisting of at least one of α-1-acid glycoprotein (A1AGP), interleukin-1-β (IL-1β), and matrix metalloproteinase-9 (MMP-9), matrix metalloproteinase-8 (MMP-8), and keratin-4 is preferred. In particular, this determination does not require the use of other data, thus advantageously providing a simple and direct diagnostic test. The biomarker group identified herein allows for the detection of successful outcomes of periodontitis treatment.

[0067] Depending on the requirements, this method only requires obtaining a small (e.g., droplet-sized) saliva sample from the subject. The sample size is typically 0.1 μl to 2 ml, such as 1 ml to 2 ml, so smaller amounts (e.g., 0.1 μl to 100 μl) can be used for in vitro device processing, and thus, larger samples, such as up to 20 ml, such as 7.5 to 17 ml, are also possible.

[0068] The sample is fed into an in vitro diagnostic device that measures the concentration of the proteins involved and returns the results, classifying the subjects based on the likelihood of successful periodontal disease treatment.

[0069] The ease of use of this invention allows for regular (e.g., as part of a routine dental checkup or even at home) monitoring of most dental patients with periodontal disease. This particularly allows for monitoring the success or impending success of periodontitis treatment, enabling more informed treatment of periodontitis, where successful treatment can continue and unsuccessful treatment can be discontinued or not initiated. The ability to assess the success of treatment is beneficial in confirming, for example, that the patient's current treatment is satisfactory. In particular, the method is also suitable for self-diagnosis, whereby the steps of obtaining a sample and placing it into the device can be performed by the patient themselves.

[0070] In carrying out this invention, the patient is typically aware that they have periodontal disease. Specifically, the patient is typically aware that they have periodontitis. Periodontitis can be mild or advanced. Therefore, in some embodiments, this method is used to assess whether a human patient known to have periodontitis is (or will) be successfully treated for the condition.

[0071] A treatment evaluated as successful or unsuccessful can be any treatment for periodontal disease. Therapeutic agents and dental procedures, or combinations of these, are known and available. Known therapeutic agents include the application of antimicrobial agents such as mouthwash, chips, gels, or microspheres. A typical antimicrobial agent used to treat gingivitis and periodontitis is chlorhexidine. Other therapeutic agents include antibiotics, usually oral antibiotics, and enzyme inhibitors such as doxycycline. Known non-surgical procedures include: root surface instruments to disrupt the subgingival plaque biofilm; scaling and root planing (SRP); and interdental cleaning. Known surgical treatments include surgical pocket repositioning, flap surgery, gingival grafts, or bone grafts. Preferred treatments are therapeutic agents, such as antimicrobial agents, typically antimicrobial mouthwashes.

[0072] The need for and frequency of optional visits to a dentist or hygienist for treatment can vary depending on clinical needs and patient preferences. The initial phase of clinical treatment is typically completed in fewer but longer visits. In this example, the patient may visit the dental clinic twice, each visit lasting approximately 1-2 hours, for initial treatment. Subsequently, frequent follow-up visits after the initial treatment period can check the healing progress, encourage the patient, strengthen oral hygiene, and provide preventative measures for plaque reformation. This is typically achieved within 1-2 months after the initial treatment period, with short visits (e.g., 15-30 minutes) at 2-4 week intervals.

[0073] Therefore, in some embodiments, the treatment phase may include 1-2 clinical visits for root canal instrumentation. Thereafter, patients may visit their dentist or hygienist again at approximately 3, 6, and 9 weeks for follow-up appointments (for preventative, motivating, and enhanced oral hygiene). Within this timeframe, treatment plans can be implemented according to individual clinical needs.

[0074] In some embodiments, the assessment or prediction methods of the present invention can be performed at any clinical visit following the initial therapy.

[0075] The method of the present invention typically involves using one or more detection reagents to detect at least two of the proteins described above, as well as optional other biomarker proteins, that constitute the biomarker set of the present invention.

[0076] Typically, treatment is considered successful when inflammation levels significantly decrease while pocket depth remains relatively high (i.e., compared to healthy individuals or patients with gingivitis). Therefore, classifying disease based on salivary protein markers (e.g., distinguishing between healthy, gingivitis, mild periodontitis, and severe periodontitis) may not be suitable for assessing treatment response in post-treatment patients. The biomarker proteins of this invention overcome this problem and are able to specifically determine the response to treatment.

[0077] According to the present invention, the "saliva" being tested can be undiluted saliva that can be obtained by spitting or wiping, or diluted saliva that can be obtained by rinsing the mouth with a fluid. Diluted saliva can be obtained by a patient rinsing or gargling their mouth for several seconds with sterile water (e.g., 5 ml or 10 ml) or other suitable fluid and spitting it into a container. Diluted saliva may sometimes be referred to as an oral rinsing solution.

[0078] "Detection" refers to the measurement, quantification, scoring, or determination of the concentration of a biomarker protein. Methods for evaluating biological compounds, including biomarker proteins, are known in the art. It is generally accepted that methods for detecting protein biomarkers include direct and indirect measurements. Those skilled in the art can select the appropriate method for measuring a specific biomarker protein.

[0079] The term "concentration" for protein biomarkers is given its usual meaning: the abundance of protein in a volume. Protein concentration is typically measured by mass / volume, most commonly in mg / ml or μg / ml, but sometimes as low as pg / ml. Another alternative measurement is molar concentration, mol / L, or "M". Concentration can be determined by detecting the amount of protein in a known, defined, or predetermined volume of sample.

[0080] Alternatives for determining concentration include determining the absolute amount of the protein biomarker in the sample, or determining the mass fraction of the biomarker in the sample, such as the amount of the biomarker relative to the total amount of all other proteins in the sample.

[0081] A "detector" is a reagent or compound that specifically (or selectively) binds to, interacts with, or detects a protein biomarker of interest. Such detectors may include, but are not limited to, antibodies, polyclonal antibodies, or monoclonal antibodies that preferentially bind to protein biomarkers.

[0082] The term "periodontal disease" refers to gingivitis and periodontitis (mild and severe). Patients without periodontal disease are considered healthy.

[0083] When referring to an assay reagent, the phrase "specifically (or selectively) binds" or "particularly (or selectively) reacts with..." refers to a binding reaction that determines the presence of a protein biomarker in a heterogeneous population of proteins and other biological agents. Therefore, under specified immunoassay conditions, a particular assay reagent (e.g., an antibody) binds to a specific protein at least twice the background level and substantially not binds to other proteins present in the sample in significant quantities. Such specific binding under these conditions may require the selection of antibodies based on their specificity for the particular protein. Various immunoassay formats can be used to select antibodies that specifically react with a particular protein. For example, solid-phase ELISA (enzyme-linked immunosorbent assay) is routinely used to select antibodies that specifically react with a protein (see, for example, descriptions of immunoassay formats and conditions used to determine specific immune responses, Harlow & Lane, Antibodies, A Laboratory Manual (1988)). Typically, a specific or selective reaction will be at least twice the background signal or noise, and more typically 10 to 100 times or more than the background.

[0084] An "antibody" is a polypeptide ligand that is essentially encoded by one or more immunoglobulin genes or segments of immunoglobulin genes, specifically binding to and recognizing epitopes (e.g., antigens). Recognized immunoglobulin genes include κ and λ light chain constant region genes, α, γ, δ, ε, and μ heavy chain constant region genes, and a large number of immunoglobulin variable region genes. Antibodies exist, for example, as complete immunoglobulins or as several well-characterized fragments produced by digestion with various peptidases. This includes, for example, Fab' and F(ab)'2 fragments. The term "antibody" as used herein also includes modified antibody fragments produced from complete antibodies or modified antibody fragments synthesized de novo using recombinant DNA methods. Antibodies also include polyclonal antibodies, monoclonal antibodies, chimeric antibodies, humanized antibodies, or single-chain antibodies. The "Fc" portion of an antibody refers to a portion of the immunoglobulin heavy chain, including one or more heavy chain constant region domains CH1, CH2, and CH3, but excluding the heavy chain variable region. Antibodies can be bispecific antibodies, such as antibodies having a first variable region that specifically binds to a first antibody antigen and a second variable region that specifically binds to another different second antibody antigen. Using at least one bispecific antibody can reduce the number of detection reagents required.

[0085] Diagnostic methods differ in their sensitivity and specificity. The "sensitivity" of a diagnostic test refers to the percentage of diseased individuals who test positive (the "true positive" percentage). Diseased individuals who are not detected by the test are called "false negatives." Subjects who are not diseased and test negative are called "true negatives." The "specificity" of a diagnostic test is 1 minus the false positive rate, where the "false positive" rate is defined as the proportion of individuals who are not diseased but test positive. Specificity can also be referred to as the true negative rate.

[0086] One or more biomarker proteins of the present invention can be detected in a sample by any means. Preferred methods for biomarker detection include antibody-based assays, protein array assays, mass spectrometry (MS)-based assays, and (near)infrared spectroscopy-based assays. For example, immunoassays include, but are not limited to, competitive and non-competitive assay systems using techniques such as Western blotting, radioimmunoassays, ELISA, sandwich immunoassays, immunoprecipitation assays, precipitation reactions, gel diffusion precipitation reactions, immunodiffusion assays, and fluorescence immunoassays. Such assays are routine and well known in the art. Exemplary immunoassays will be briefly described below (but are not intended to be limiting).

[0087] Immunoprecipitation protocols generally involve lysing cell populations in a lysis buffer supplemented with protein phosphatases and / or protease inhibitors (e.g., EDTA, PMSF, aprotinin, sodium vanadate), such as RIPA buffer (1% NP-40 or Triton X-100, 1% sodium deoxycholate, 0.1% SDS, 0.15M NaCl, 0.01M sodium phosphate at pH 7.2, 1% succinate), adding the antibody of interest to the cell lysate, incubating at 4°C for a period of time (e.g., 1–4 hours), adding protein A and / or protein G agarose beads to the cell lysate, incubating at 4°C for about one hour or longer, washing the beads in the lysis buffer, and resuspending the beads in SDS / sample buffer. The ability of an antibody to immunoprecipitate a specific antigen can be assessed, for example, by Western blotting analysis. Parameters that can be modified to increase antibody binding to the antigen and reduce background (e.g., pre-cleaning the cell lysate with agarose beads) are well known to those skilled in the art.

[0088] Western blot analysis typically involves preparing a protein sample in a polyacrylamide gel (e.g., 8%–20% SDS-P age depending on the molecular weight of the antigen), electrophoresis of the protein sample, transferring the protein sample from the polyacrylamide gel to a membrane, such as nitrocellulose, PVDF, or nylon, blocking the membrane in a blocking solution (e.g., PBS with 3% BSA or a non-lipid emulsion), washing the membrane in a wash buffer (e.g., PBS-Tween 20), blocking the membrane with a first antibody (antibody of interest) diluted in the blocking buffer, washing the membrane in the wash buffer, blocking the membrane with a second antibody (which recognizes the first antibody, e.g., an anti-human antibody), conjugating the second antibody with an enzyme substrate (e.g., horseradish peroxidase or alkaline phosphatase) or a radioactive molecule (e.g., 32P or 125I) diluted in the blocking buffer, washing the membrane in the wash buffer, and detecting the presence of the antigen. Parameters that can be modified to increase the detection signal and reduce background are well known to those skilled in the art.

[0089] ELISA typically involves preparing an antigen (i.e., a biomarker protein or a fragment of a biomarker protein), coating the wells of a 96-well microtiter plate with the antigen, adding an antibody of interest conjugated to a detectable compound such as an enzyme substrate (e.g., horseradish peroxidase or alkaline phosphatase) to the wells, incubating for a period of time, and detecting the presence of the antigen. In ELISA, the antibody of interest does not necessarily conjugate to the detectable compound; instead, a second antibody conjugated to the detectable compound (which recognizes the antibody of interest) can be added to the wells. Furthermore, antibodies can be coated into the wells instead of the antigen. In this case, a second antibody conjugated to the detectable compound can be added after the antigen of interest has been added to the coated wells. Parameters that can be modified to increase the detection signal and other variations of ELISA known in the art are well understood by those skilled in the art.

[0090] Because multiple biomarkers are used, a threshold can be determined based on the combined concentration of these biomarkers. This threshold determines whether a patient is classified as successfully treated or unsuccessfully treated. This invention reflects the insight that measurements based on the combination of biomarkers described above can sufficiently accurately distinguish between successful and unsuccessful responses to periodontitis treatment.

[0091] This insight supports, on the other hand, the use of the protein assemblies of the present invention as biomarkers in human patient saliva samples for assessing the success or potential success of periodontitis treatment in patients. To avoid confusion, the protein assemblies of this aspect are:

[0092] (i) at least one of interleukin-1-β (IL-1β) and matrix metalloproteinase-8 (MMP-8), and α-1-acid glycoprotein (A1AGP); or

[0093] (ii) at least one of matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), and keratin-4 (K-4), α-1-acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β); or

[0094] (iii) at least one of the following proteins: interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and matrix metalloproteinase-9 (MMP-9); or

[0095] (iv) Matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ).

[0096] This application can be implemented in essentially the manner described in the context.

[0097] The method of the present invention includes determining at least one test value reflecting the combined concentration of the measured protein. The combined concentration value can be any value obtained by inputting the determined concentrations and performing arithmetic operations on these values. The combined concentration value can be, for example, a simple sum of concentrations. The combined concentration value can also involve multiplying each concentration by a factor reflecting a desired weight for that concentration and then summing the results. The combined concentration value can also involve multiplying concentrations together, or any combination of multiplication, division, subtraction, exponentiation, and addition. The combined concentration value can also involve raising the concentration to a power. Optionally, the test value reflects the combination of the determined combined concentration of the protein and the subject's age.

[0098] The obtained combined concentration values ​​can be compared to one or more thresholds, which in the same way reflect the combined concentrations associated with successful treatment of periodontitis. This comparison allows for the assessment of whether the test values ​​indicate successful treatment in the patient whose saliva was tested.

[0099] Thresholds can be, for example, combined concentration values ​​obtained in the same manner based on the concentration of the same protein determined in reference samples associated with successful periodontitis treatment (e.g., in patients previously diagnosed with periodontitis and whose disease severity has been reduced through treatment). Typically, values ​​reflecting the same or higher combined concentrations indicate successful periodontitis treatment in the tested patient. Similarly, values ​​reflecting lower combined concentrations in the saliva of the tested periodontitis patient indicate unsuccessful periodontitis treatment. However, it should be understood that thresholds can also be calculated (e.g., by using a negative multiplier) such that test values ​​indicating successful treatment are below the threshold, and test values ​​indicating unsuccessful treatment are above the threshold.

[0100] Thresholds can also be determined based on measuring the concentration of biomarker proteins present in a set of samples, including known successfully treated and unsuccessfully treated patients. Statistical analysis, potentially including machine learning, can then be performed on the measured concentration values, allowing for the differentiation of patients classified as successfully or unsuccessfully treated with desired sensitivity and specificity. This yields one or more desired thresholds. Based on these thresholds, the samples to be tested can undergo the same concentration measurements, and the concentration values ​​are then processed in the same manner as the acquisition of one or more thresholds to determine joint concentration values ​​that can be compared to the thresholds, thereby allowing the test samples to be classified as successfully treated "yes" or "no".

[0101] In one interesting embodiment, the combined concentration value is obtained as a score. A numerical value (protein concentration value, e.g., ng / ml) is assigned to each measurement, and these values ​​are used in linear or non-linear combinations to calculate a score between 0 and 1. With a threshold determined based on a set of subjects as described above, the score between 0 and 1 is typically calculated using a sigmoid function with the combined concentration as input (as further illustrated).

[0102] When the score exceeds a certain threshold, the method indicates successful treatment of periodontitis. The threshold can be selected based on the desired sensitivity and specificity.

[0103] The clinically accepted definition in this field is based on the following:

[0104] Gingival Index (GI)

[0105] The full-mouth gingival index was recorded based on the Lobene Modified Gingival Index (MGI) assessed on a scale of 0-4, where:

[0106] —0 = no inflammation.

[0107] —1 = Mild inflammation; slight change in color, but not overall, of any part of the marginal or papillary gingival unit, with almost no change in texture.

[0108] —2 = Mild inflammation; but involving the entire marginal or papillary unit,

[0109] —3 = Moderate inflammation; discoloration, redness, edema, and / or hypertrophy of the marginal or papillary units.

[0110] —4 = Severe inflammation; marked redness, edema and / or hypertrophy of the marginal or papillary gingival units, spontaneous bleeding, congestion or ulceration.

[0111] Exploration depth (PD)

[0112] Using a manual UNC-15 periodontal probe, record the probing depth to the nearest millimeter. Probing depth is the distance from the probe tip (assuming it is at the base of the pocket) to the free gingival margin.

[0113] Gingival recession (REC)

[0114] Using a manual UNC-15 periodontal probe, gingival recession was recorded to the nearest millimeter. Gingival recession is the distance from the free gingival margin to the cementoenamel junction. Gingival recession is represented by positive numbers, and gingival hyperplasia by negative numbers.

[0115] Clinical attachment loss (CAL)

[0116] Clinical attachment loss is calculated as the sum of probing depth and depressions at each site.

[0117] Bleeding on probing (BOP)

[0118] After probing, bleeding is assessed for each site. If bleeding occurs within 30 seconds of probing, the site is assigned a score of 1; otherwise, a score of 0 is assigned.

[0119] The resulting subject group (patient group) is defined as follows:

[0120] —Healthy group (H): PD ≤ 3 mm in all sites (but up to four 4 mm pockets are allowed at the distal end of the last erect molar), no sites with interproximal attachment loss, GI ≥ 2.0 in ≤ 10% of sites, and % BOP score ≤ 10%.

[0121] —Gingivitis group (G): >30% of sites had a GI ≥3.0, no sites with interproximal attachment loss, no sites with PD >4mm, and %BOP score >10%;

[0122] — Moderate periodontitis group (MP): ≥8 teeth with interproximal PD of 5mm-7mm (equivalent to approximately 2mm-4mm CAL), %BOP score >30%;

[0123] — Advanced periodontitis group (AP): ≥12 teeth with interproximal PD ≥7mm (equivalent to approximately ≥5mm CAL), %BOP score >30%.

[0124] In one embodiment, the method of the present invention utilizes, for example... Figure 1 The system is illustrated schematically. The system can be a single device with various equipment components (units) integrated therein. The system can also have various components as independent devices, or some of these components. Figure 1 The components shown are measuring equipment (A), graphical user interface (B), and computer processing unit (C).

[0125] As described above, the system of the present invention includes a data connection to an interface, whereby the interface itself may be part of the system or may be a remote interface. The latter refers to the possibility of providing the actual interface using different devices, preferably handheld devices such as smartphones or tablets. In this case, the data connection will preferably involve wireless data transmission, such as via Wi-Fi or Bluetooth, or via other technologies or standards.

[0126] The measuring device (A) is configured to receive a saliva sample, for example, by placing a drop of saliva onto a tube (A1), which can be inserted into the device (A). This device can be an existing device capable of determining the concentration of at least the biomarker protein combination of the present invention from the same saliva sample, i.e.:

[0127] At least one of matrix metalloproteinase-9 (MMP-9), matrix metalloproteinase-8 (MMP-8), and keratin 4, α-1-acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β);

[0128] At least one of interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin-β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and matrix metalloproteinase-9 (MMP-9);

[0129] A combination of α-1-acid glycoprotein (A1AGP) with at least one of matrix metalloproteinase-8 (MMP-8) or interleukin-1-β (IL-1β); or

[0130] Matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ).

[0131] The measuring device (A) should be able to receive a saliva sample, for example, by placing a drop of saliva onto a tube (A1), which can be inserted into the device (A). The device can be an existing device capable of determining the concentrations of at least two protein biomarkers of the present invention (e.g., A1AGP and IL-1β or MMP-8 and FLC-κ) from the same saliva sample.

[0132] The processing unit (C) receives a numerical value of the protein concentration from the portion (A). The unit (C) is equipped with software (typically embedded software) to allow it to calculate a score (S) between 0 and 1. The software also includes a threshold (T) value. If the calculated value (S) exceeds (T), the unit (C) outputs an indication (I) of "Periodontitis treatment successful" to the GUI (B); otherwise, the unit (C) outputs "Periodontitis treatment unsuccessful". Another embodiment can use a specific value of (S) to indicate the certainty of making the indication (I). This can be "direct" in the sense that a score S = 0.8 indicates an 80% certainty of "periodontitis treatment successful". Alternatively, this can be achieved, for example, by limiting the range R1-R2 such that when R1 < S < R2, the indication (I) displays "uncertain".

[0133] For example, a specific score calculation can be achieved using a sigmoid function and logistic regression:

[0134]

[0135] Where N is the number of proteins / biomarkers used. c0, c1, etc. are coefficients (numerical values), and B1, B2, etc. are the concentrations of each protein.

[0136] coefficient C i This can be determined through the training procedure:

[0137] — N1 subjects who have a successful response to periodontitis treatment (as determined by the dentist using current criteria) and N2 subjects who have an unsuccessful response to periodontitis treatment were selected.

[0138] —A saliva sample was obtained from each subject and the protein concentration of the combination of biomarkers as described above was measured (saliva samples may be from untreated patients [for predicting response] or treated patients [for assessing response]).

[0139] — A score S for a successful response is defined as 1, and a score S for an unsuccessful response is defined as 0.

[0140] — Fit the sigmoid function to the score and protein concentration value.

[0141] Note that, alternatively, any existing regression or machine learning method (e.g., linear regression, neural networks, support vector machines, etc.) can be used, where patients who successfully respond to treatment have a higher score S and patients who do not successfully respond to treatment have a lower score S.

[0142] Specifically, this procedure has been applied to subjects who showed successful or unsuccessful responses to periodontitis treatment in clinical studies, with successful responses identified by dental professionals through clinical evaluation based on the following criteria:

[0143]

[0144] Success Level 1:

[0145] The outcome of "Venus" treatment may be relatively impossible for most patients with periodontitis, but it can be achieved in a minority of patients.

[0146] Success Level 2:

[0147] This indicates that patients with periodontitis have a good treatment response.

[0148] Success Level 3:

[0149] Indicating a good actual treatment response, the vast majority of sites were ≤4mm, and no more than 10% of sites had a probing depth of 5mm (and no sites had a probing depth ≥6mm).

[0150] Success Level 4:

[0151] This indicates a relatively good actual treatment response, with the vast majority of sites having a probing depth of ≤5mm and no more than 10% of sites having a probing depth of 6mm (and no sites having a probing depth ≥7mm).

[0152] The term "successful treatment" as used here refers to any of the success grades from 1 to 4. Therefore, in patients previously diagnosed with periodontitis, the minimum clinical indication for "successful treatment" is 90% of sites with probe depth ≤ 5 mm, no sites with probe depth > 6 mm, and ≤ 20% bleeding on probing.

[0153] A multi-class classifier that indicates a "success" status can be used.

[0154] The performance of various biomarker combinations was evaluated by leave-one-out cross-validation, resulting in the preferred biomarker combinations of the present invention.

[0155] Referring to the above system, the present invention also provides a system for assessing whether a human patient has been or will be successfully treated for periodontitis, the system comprising:

[0156] —The detection equipment is capable of and suitable for detecting the following proteins in saliva samples from human patients:

[0157] (i) at least one of interleukin-1-β (IL-1β) and matrix metalloproteinase-8 (MMP-8), and α-1-acid glycoprotein (A1AGP); or

[0158] (ii) at least one of matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), and keratin-4 (K-4), α-1-acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β); or

[0159] (iii) at least one of the following proteins: interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and matrix metalloproteinase-9 (MMP-9); or

[0160] (iv) Matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ).

[0161] As mentioned above, such devices are known and readily available to technicians. Typically, the following are provided:

[0162] —A container for receiving oral samples from the subject, and the aforementioned testing equipment is installed inside the container;

[0163] —A processor capable of and adapted to determine an indication of whether a periodontitis treatment has been successfully administered based on the determined concentration of the protein.

[0164] Optionally, the system includes a user interface (or data connection to a remote interface) capable of presenting information, particularly a graphical user interface (GUI); a GUI is a user interface that allows users to interact with electronic devices through graphical icons and visual indicators such as auxiliary symbols, rather than text-based user interfaces, typing command labels, or text navigation (no such interface is excluded in this invention); GUIs are generally known and are typically used in handheld mobile devices such as MP3 players, portable media players, gaming devices, smartphones, and smaller home, office, and industrial control devices; as mentioned above, optionally, the interface may also be selected to be capable of inputting information such as the subject's age, gender, and BMI (body mass index).

[0165] Alone or as part of the aforementioned system, the present invention also provides a kit for detecting at least two periodontal disease biomarkers in a saliva sample from a human patient, the kit comprising one or more detection agents for detecting:

[0166] (i) at least one of interleukin-1-β (IL-1β) and matrix metalloproteinase-8 (MMP-8), and α-1-acid glycoprotein (A1AGP); or

[0167] (ii) at least one of matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), and keratin-4 (K-4), α-1-acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β); or

[0168] (iii) at least one of the following proteins: interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-10-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and matrix metalloproteinase-9 (MMP-9); or

[0169] (iv) Matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ).

[0170] Typically, a kit includes three assays, each targeting a different biomarker. More typically, a kit includes:

[0171] A first assay for detecting A1AGP, a second assay for detecting IL-1β, and a third assay for detecting one of MMP-9, K-4, and MMP-8; or

[0172] A first assay for detecting MMP-9, a second assay for detecting one of the following: protein interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and a third assay for detecting a different one of the following: protein interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9).

[0173] As discussed above regarding the method of the present invention, the kit may include additional detection agents, such as those for pyruvate kinase and / or other proteins. In a preferred embodiment, as mentioned, the detection agents available in the kit consist of those for detecting the three proteins constituting the biomarker set of the present invention.

[0174] Preferably, the kit includes a solid support, such as a chip containing the detection reagent, a microtiter plate, beads, or resin. In some embodiments, the kit includes a mass spectrometry probe, such as a ProteinChip. TM .

[0175] The kit may also provide specific wash solutions and / or detection reagents that are not bound to the detection reagent or biomarker (sandwich assay).

[0176] In one interesting aspect, the identification of the biomarker set of the present invention is applied to monitor the periodontal disease status of human patients during treatment periods. Therefore, the present invention also provides an in vitro method for determining changes in the periodontal disease status of a human patient with periodontitis due to treatment of the disease during a treatment interval from a first time point t1 to a second time point t2, the method comprising detecting the concentrations of the following proteins in at least one saliva sample obtained from the patient at t1 and at least one saliva sample obtained from the patient at t2:

[0177] (i) at least one of interleukin-1-β (IL-1β) and matrix metalloproteinase-8 (MMP-8), and α-1-acid glycoprotein (A1AGP); or

[0178] (ii) at least one of matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), and keratin-4 (K-4), α-1 acid glycoprotein (A1AGP), and interleukin-1-β (IL-1β); or

[0179] (iii) at least one of the following proteins: interleukin-1-β (IL-1β), hepatocyte growth factor (HGF), α-1-acid glycoprotein (A1AGP), hemoglobin β (Hb-β), and S100 calcium-binding protein A9 (S100A9), and matrix metalloproteinase-9 (MMP-9); or

[0180] (iv) Matrix metalloproteinase-8 (MMP-8) and free light chain κ (FLC-κ);

[0181] And by comparing concentrations, it is preferable that differences in one, two, three, four, or more concentrations reflect changes in state. Such differences can be termed concentration differences, thus allowing direct comparisons without first generating numbers between 0 and 1, or any other classification. It should be understood that measurements received at two time points can also be processed in the same manner as when determining patient state as described above.

[0182] This invention also provides a method for diagnosing whether a human patient has successfully or will successfully treat periodontal disease, including detecting the presence of the proteins of this invention in the patient's saliva. The presence of a successful treatment is assessed based on the concentration of said proteins in the sample. Optionally, this aspect of the method includes further steps in treating the patient's periodontal disease. These optional treatment steps may include the application of known therapeutic agents or dental procedures, or a combination of therapeutic agents and dental procedures. Known therapeutic agents include the application of reagents containing antimicrobial agents, such as mouthwash, chips, gels, or microspheres. A typical antimicrobial agent used to treat gingivitis and periodontitis is chlorhexidine. Other therapeutic agents include antibiotics, typically oral antibiotics, and enzyme inhibitors, such as doxycycline. Known non-surgical treatment procedures include scaling and root planing (SRP). Known surgical treatments include surgical pocket repositioning, flap surgery, gingival grafts, or bone grafts.

[0183] The present invention also provides a method for detecting the protein of the present invention in a patient, comprising:

[0184] (a) Obtaining saliva samples from human patients; and

[0185] (b) The presence of the proteins of the present invention in a saliva sample is detected by contacting the saliva sample with a protein detection agent and detecting the binding between the individual proteins and the detection agent.

[0186] The present invention will be further described with reference to the following non-limiting embodiments.

[0187] Example

[0188] In one clinical study, 106 participants underwent repeated clinical visits at two independent clinical sites to receive treatment for periodontitis, of whom:

[0189] —74 people had low / unsuccessful responses

[0190] —32 people had a high / successful response

[0191] We obtained the area under the receiver-operator characteristic curve (AUC) value >0.75 to detect successful treatment outcomes.

[0192] In statistics, the Receiver Operating Characteristic (ROC) curve is a performance curve of a binary classifier system as its discrimination threshold varies. This curve is created by plotting the true positive rate (TPR) versus the false positive rate (FPR) under various threshold settings. The true positive rate is also known as detection sensitivity, recall, or probability in machine learning. The false positive rate, also called the false alarm probability or false positive rate, can be calculated as (1 - specificity). Therefore, the ROC curve is sensitivity as a function of false alarms. Generally, if both the probability distributions used for detection and false alarms are known, an ROC curve can be generated by plotting the cumulative distribution function of the detection probability (the area under the probability distribution from negative infinity to the discrimination threshold) on the y-axis and the cumulative distribution function of the false alarm probability on the x-axis for each value of the threshold. The accuracy of a test depends on how well the test classifies the tested group into groups with or without the disease in question. Accuracy is measured by the area under the ROC curve. An area of ​​1 indicates a perfect test; an area of ​​0.5 indicates a worthless test. The guideline for classifying diagnostic test accuracy is the traditional academic point system:

[0193] — 0.90 - 1 = Excellence (A)

[0194] — 0.80 - 0.90 = Good (B)

[0195] —0.70-0.80 = General (C)

[0196] —0.60-0.70 = Difference (D)

[0197] — 0.50 - 0.60 = Failure (F)

[0198] Various biomarker combinations were evaluated using logistic regression and leave-one-out cross-validation (LOOCV) to obtain the biomarker combination of the present invention. It can be seen that the protein biomarker combination of the present invention has an AUC >0.75 to detect successful treatment outcomes.

[0199] Based on the above, in the results of the aforementioned clinical studies, a ROC AUC value greater than 0.75 is considered to represent the ideal accuracy provided by the test according to the present invention.

[0200] The protein biomarkers studied are:

[0201] MMP-8

[0202] MMP-9

[0203] ·IL-1β

[0204] HGF

[0205] Free light chains (FLC)κ

[0206] Free light chains (FLCs)

[0207] ·A1AGP

[0208] Hb-β

[0209] ·Hb-δ

[0210] ·Keratin 4

[0211] • Inhibitory protein

[0212] ·Pyruvate kinase

[0213] ·S100A8

[0214] ·S100A9

[0215] Furthermore, in the logistic regression we employ, we treat it as an additional predictor κ+λ, κ-λ, κ / λ.

[0216] In addition, we included age as a predictor.

[0217] This yielded a total of 4204 possible non-redundant groups with up to 4 protein biomarkers (excluding groups containing only age). Here, non-redundancy means that groups including, for example, κ+λ and κ-λ as predictors are not considered; as in logistic regression, non-redundancy yields the same results as the corresponding groups including κ and λ as predictors.

[0218] Note that, given the above predictors, not limiting the number of protein markers in a group yielded 98,302 possible non-redundant groups (excluding groups with only age).

[0219] Figure 2 The figure shows the number of identified groups with up to four protein markers as a function of classification performance thresholds, based on the area under the acceptor-operator-feature curve (ROC AUC LOOCV), when using leave-one-out cross-validation. The figure shows separate curves excluding age (the lower [red] line with a maximum of 700 groups) as a predictor and including age (the upper [blue] line with a maximum of approximately 1150 groups).

[0220] With an AUC LOOCV threshold of 0.75, 141 groups were found including age and 101 groups were found excluding age.

[0221] For these groups with an AUC LOOCV threshold of 0.75, the prevalence of different markers is shown in the table below:

[0222]

[0223]

[0224] In summary, these results show that MMP-9, IL-1β, A1AGP, κ, MMP-8, and HGF are the most prevalent markers.

[0225] The preferred combination of biomarker proteins covering all 141 groups is:

[0226] —One or more of MMP-9 and IL-1β, HGF, A1AGP, HB-β, and S100A9

[0227] —A1AGP and one or more of MMP-8 and IL-1β

[0228] —MMP-8 and κ

[0229] (MMP-9 and S100A9 cover a group that also includes K / L)

[0230] The number of protein markers in these 141 groups is:

[0231] —One group has only two markers: A1AGP and MMP-9 (AUC LOOCV = 0.751)

[0232] —20 groups with 3 markers

[0233] —120 groups with 4 markers

[0234] The preferred combination of biomarker proteins to cover these 20 groups with 3 markers is:

[0235] —A1AGP and / or κ, and a combination of two of MMP-8, MMP-9, IL-1β, and HGF.

[0236] —A combination of two of the following: MMP-9 and A1AGP, κ, IL-1β, HB-β, Hb-δ, keratin 4, and S100A9.

[0237] The last 14 groups with ≤4 protein markers provided AUC LOOCV > 0.8:

[0238]

[0239] Each of these combinations is a preferred combination of biomarkers according to the present invention.

[0240] It can be seen that two groups have three protein markers, and the rest have four protein markers. The protein biomarker combinations covering all 14 groups are: — (IL-1β and A1AGP) and one or two of MMP-8* and MMP-9.

[0241] *MMP-8 can be replaced by keratin 4.

[0242] It was also noted that, with the exception of one group, all groups were covered by IL-1β and A1AGP and MMP-9, thus IL-1β and A1AGP and MMP-9 are the preferred biomarker groups.

[0243] The groups identified above involve measuring biomarker levels after treatment to assess / estimate the likelihood of treatment success.

[0244] The feasibility of predicting treatment success from pre-treatment marker levels was also investigated. Based on the definition of treatment success adopted herein, the group with MMP-8, IL-1β, A1AGP, and optionally pyruvate kinase, was found to provide an AUC LOOCV > 0.75. Therefore, this is the preferred group according to the invention for predicting response to periodontitis treatment.

[0245] While the invention has been detailed and described in the accompanying drawings and the foregoing description, these descriptions and illustrations should be considered illustrative or exemplary, not restrictive; the invention is not limited to the disclosed embodiments. For example, different units may contain detection agents for different biomarkers. Alternatively, conveniently, the kit of the present invention may include a fixed set of detection agents for the protein biomarker required in the embodiments, i.e., A1AGP in some embodiments, and a flexible module that includes detection agents for other biomarkers, such as MMP-8 and / or IL-1β.

[0246] In the course of practicing the claimed invention, other variations of the disclosed embodiments can be understood and implemented by those skilled in the art through studying the accompanying drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plurality. The fact that certain features of the invention are described in mutually different dependent claims does not indicate that combinations of these features cannot be used to obtain an advantage. Any reference numerals in the claims should not be construed as limiting their scope.

[0247] In summary, we disclose an in vitro method for evaluating or predicting the response of human patients to treatment of periodontal disease. This method is based on the insight of identifying at least two biomarker proteins. Therefore, the concentrations of the proteins described herein are measured in a saliva sample from the patient. Based on the measured concentrations, a value reflecting the combined concentration of the proteins is determined. This value is typically used to calculate the probability of successful treatment and is compared with a corresponding threshold reflecting the combined concentrations associated with successful treatment of periodontitis in the same manner. This comparison allows for the assessment of whether the test value is an indicator of successful treatment of the patient's periodontitis.

Claims

1. An in vitro device for assessing or predicting a human patient's response to periodontal disease treatment, wherein the device comprises a computer processing unit configured to execute software to cause the device to: — detect concentrations of the following proteins in a saliva sample from the human patient suffering from periodontal disease: (i) interleukin-1-beta (IL-1β), matrix metalloproteinase-8 (MMP-8), keratin-4 (K-4), and alpha-1-acid glycoprotein (A1AGP); or (ii) matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), alpha-1-acid glycoprotein (A1AGP), and interleukin-1-beta (IL-1β); or (iii) matrix metalloproteinase-9 (MMP-9), alpha-1-acid glycoprotein (A1AGP), and interleukin-1-beta (IL-1β); or (iv) matrix metalloproteinase-9 (MMP-9), keratin-4 (K-4), alpha-1-acid glycoprotein (A1AGP), and interleukin-1-beta (IL-1β); or (v) alpha-1-acid glycoprotein (A1AGP) and matrix metalloproteinase-9 (MMP-9); — determine at least one test value reflecting the determined joint concentrations for the proteins; — compare the test value to a threshold value reflecting the joint concentrations in the same manner as associated with successful treatment of periodontal disease in order to assess whether the test value indicates successful treatment of periodontal disease in the patient; and — output an indication of whether the patient's periodontal disease has been or will be successfully treated based on comparing the test value to the threshold value.

2. The device of claim 1, wherein: the device assesses a human patient previously diagnosed with periodontitis and who has received treatment for the periodontitis; or the device predicts a human patient's response to periodontitis treatment.

3. The device of claim 1 or 2, wherein: the patient's age is determined and the test value in combination with the patient's age reflects the determined joint concentrations for the proteins; and / or the threshold value is based on one or more concentrations determined for the proteins in one or more reference samples, each sample associated with successful treatment of periodontitis or unsuccessful treatment of periodontitis; and / or the proteins consist of A1AGP, IL-1β, MMP-9, and K-4 or consist of A1AGP, IL-1β, MMP-9, and MMP-8.

4. The device of claim 1 or 2, wherein the determined concentration values are arithmetically processed into a number between 0 and 1.

5. The device of claim 2, wherein the treatment was proposed or applied no more than 7 days prior to assessment.

6. The device of claim 2, wherein the treatment was proposed or applied no more than 6 days prior to assessment.

7. The device of claim 2, wherein the treatment was proposed or applied no more than 5 days prior to assessment.

8. The device of claim 2, wherein the treatment was proposed or applied no more than 4 days prior to the assessment.

9. The device of claim 2, wherein the treatment was proposed or applied no more than 3 days prior to the assessment.

10. The device of claim 2, wherein the treatment was proposed or applied no more than 2 days prior to the assessment.

11. The device of claim 2, wherein the treatment was proposed or applied no more than 1 day prior to the assessment.

12. A system for assessing or predicting a human patient's response to a periodontal disease treatment, the system comprising: - a detection agent to detect in a saliva sample of the human patient the following proteins: (i) interleukin-1-beta (IL-1β), matrix metalloproteinase-8 (MMP-8), keratin-4 (K-4), and alpha-1-acid glycoprotein (A1AGP); or (ii) matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), alpha-1-acid glycoprotein (A1AGP), and interleukin-1-beta (IL-1β); or (iii) matrix metalloproteinase-9 (MMP-9), alpha-1-acid glycoprotein (A1AGP), and interleukin-1-beta (IL-1β); or (iv) matrix metalloproteinase-9 (MMP-9), keratin-4 (K-4), alpha-1-acid glycoprotein (A1AGP), and interleukin-1-beta (IL-1β); or (v) alpha-1-acid glycoprotein (A1AGP) and matrix metalloproteinase-9 (MMP-9); - a processor capable and adapted to determine from the determined concentrations of the proteins an indication of whether the patient has been or will be successfully treated for periodontal disease.

13. The system of claim 12, further comprising a receptacle for receiving an oral fluid sample.

14. The system of claim 12 or 13, further comprising: - a user interface for presenting the indication to a user; and - a data connection between the processor and the user interface for transmitting the indication from the processor to the user interface.

15. The system of claim 14, wherein: the processor is initiated by an internet-based application; and / or the interface is capable of inputting information about the patient's age, and the processor is capable and adapted to determine from the determined concentrations of the proteins an indication of whether the patient has been or will be successfully treated.

16. A kit for detecting three biomarkers for successful treatment of periodontal disease in a saliva sample of a human patient, the kit comprising detection agents consisting of: a first detection agent for detecting A1AGP, a second detection agent for detecting IL-1β, and a third detection agent for detecting MMP-9, wherein a detection agent is a reagent or a compound that specifically or selectively binds to, interacts with, or detects the biomarker. ​ 17. The kit according to claim 16, wherein the detection agent is comprised on a solid support.

18. A system for determining a change in periodontal disease status in a human patient due to treatment of the disease over a treatment time interval from a first time point ti to a second time point t2, the system comprising: - a detection agent to detect in at least one saliva sample taken from the patient at ti and in at least one saliva sample taken from the patient at t2 the concentration of: (i) interleukin-1-beta (IL-1 β), matrix metalloproteinase-8 (MMP-8), keratin-4 (K-4) and alpha-1-acid glycoprotein (A1AGP); or (ii) matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), alpha-1-acid glycoprotein (A1AGP) and interleukin-1-beta (IL-1 β); or (iii) matrix metalloproteinase-9 (MMP-9), alpha-1-acid glycoprotein (A1AGP) and interleukin-1-beta (IL-1 β); or (iv) matrix metalloproteinase-9 (MMP-9), keratin-4 (K-4), alpha-1-acid glycoprotein (A1AGP) and interleukin-1-beta (IL-1 β); or (v) alpha-1-acid glycoprotein (A1AGP) and matrix metalloproteinase-9 (MMP-9); and - a computer processing unit adapted to compare the concentration at ti with the concentration at t2, whereby a difference in the concentration reflects a change in status.

19. A system for determining whether a human patient has successfully or will successfully be treated for periodontal disease, the system comprising: - a detection agent to detect in a saliva sample of the human patient the following proteins: (i) interleukin-1-beta (IL-1 β), matrix metalloproteinase-8 (MMP-8), keratin-4 (K-4) and alpha-1-acid glycoprotein (A1AGP); or (ii) matrix metalloproteinase-8 (MMP-8), matrix metalloproteinase-9 (MMP-9), alpha-1-acid glycoprotein (A1AGP) and interleukin-1-beta (IL-1 β); or (iii) matrix metalloproteinase-9 (MMP-9), alpha-1-acid glycoprotein (A1AGP) and interleukin-1-beta (IL-1 β); or (iv) matrix metalloproteinase-9 (MMP-9), keratin-4 (K-4), alpha-1-acid glycoprotein (A1AGP) and interleukin-1-beta (IL-1 β); or (v) alpha-1-acid glycoprotein (A1AGP) and matrix metalloproteinase-9 (MMP-9); and - a computer processing unit adapted to assess whether the human patient has successfully or will successfully be treated for periodontal disease based on the concentration of the proteins in the sample.

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