A screening system for new indications of anti-osteoporosis drugs based on analysis of bone metabolism circadian rhythm

Abstract

The application discloses a new indication screening system for anti-osteoporosis drugs based on bone metabolism circadian rhythm analysis, and belongs to the field of digital medicine and bone metabolism pharmacology. The application comprises: a basal metabolic rate monitoring module, which estimates BMR based on a temperature-heart rate coupling model and constructs a 24-hour BMR circadian rhythm curve; an autonomic nervous system monitoring module, which constructs a 24-hour LF / HF circadian rhythm curve based on HRV; a temperature difference monitoring module, which constructs a core-peripheral temperature difference rhythm curve; a rhythm atlas construction module, which extracts rhythm parameters (median, amplitude, phase) and generates an etiology-specific bone metabolism rhythm atlas, including characteristic rhythm patterns of glucocorticoid-induced osteoporosis, postmenopausal osteoporosis and male osteoporosis; an efficacy prediction module, which predicts the efficacy of a drug on the etiology subtype by comparing rhythm recovery speed; and a new indication output module, which outputs new indication expansion suggestions. The application realizes rapid screening of new indications of anti-osteoporosis drugs and shortens the clinical trial period by 70%.

Description

Technical Field

[0001] This invention relates to the fields of digital healthcare and bone metabolism pharmacology, specifically to a system and method for constructing a bone metabolism rhythm map by continuously monitoring the diurnal rhythm of basal metabolic rate, heart rate variability, and core-peripheral temperature difference, for screening new indications and early prediction of efficacy of anti-osteoporosis drugs. Background Technology

[0002] The etiology of osteoporosis is highly heterogeneous, including postmenopausal osteoporosis, glucocorticoid-induced osteoporosis, and male osteoporosis. Different etiologies of osteoporosis have different pathophysiological mechanisms, and the efficacy of the same drug varies significantly across different subtypes. Traditional expansion of new indications relies on large-scale phase III clinical trials (2-3 years), which are costly and time-consuming. There is an urgent need for a biomarker tool that can rapidly distinguish etiological subtypes and predict drug efficacy at an early stage. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes a new indication screening system and method for anti-osteoporosis drugs based on circadian rhythm analysis of bone metabolism.

[0004] Technical solution

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a new indication screening system for anti-osteoporosis drugs based on circadian rhythm analysis of bone metabolism, comprising:

[0007] The data acquisition interface (L21) is used to access an acquisition system that can generate time-aligned multimodal data packets and acquire the multimodal data packets generated by it in continuous monitoring mode during osteoporosis drug treatment. The data packets include at least temperature data sub-packets, electrophysiological data sub-packets, and motion data sub-packets.

[0008] The processing module is configured to perform the following operations:

[0009] • Extract core temperature, peripheral temperature and resting heart rate from the temperature data sub-package and electrophysiological data sub-package. Estimate basal metabolic rate (BMR) based on the temperature-heart rate coupling model. Construct a 24-hour BMR diurnal rhythm curve. Extract rhythm parameters including median (MESOR), amplitude (A), and peak phase (φ) (E31, E32, E33).

[0010] • Extract heart rate variability parameters from the electrophysiological data sub-package, construct a 24-hour LF / HF diurnal rhythm curve, and extract autonomic nerve rhythm parameters;

[0011] • Extract the core-periphery temperature difference from the temperature data sub-package and construct a 24-hour temperature difference rhythm curve;

[0012] • Based on the BMR rhythm parameters, autonomic nerve rhythm parameters, and temperature difference rhythm parameters, an etiology-specific bone metabolism rhythm atlas (E4) was constructed.

[0013] • By comparing the recovery rate of rhythm parameters before and after drug intervention, the efficacy of drugs for this etiological subtype can be predicted;

[0014] • When the BMR rhythm phase recovery rate is greater than 3 times the rate of bone mineral density change, it is identified as a valid response, and a new indication expansion suggestion is output (S12).

[0015] The output module (L28) is used to output the etiology-specific bone metabolism rhythm atlas (E4) and the new indication prediction report.

[0016] Secondly, the present invention provides a method for screening new indications for anti-osteoporosis drugs based on the analysis of the diurnal rhythm of bone metabolism, including a baseline acquisition step, a drug intervention step, a post-treatment monitoring step, a rhythm recovery calculation step, an efficacy judgment step, and a new indication output step.

[0017] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0018] Beneficial effects

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. Shorten the clinical trial period for expanding new indications from 2-3 years to 6-9 months;

[0021] 2. Etiologically specific bone metabolic rhythm atlases enable precise patient stratification;

[0022] 3. Use 4-week rhythm recovery as a substitute for 12-month bone mineral density changes as a predictive indicator of efficacy;

[0023] 4. Reduce the cost of developing new indications by more than 60%. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the system structure provided in an embodiment of the present invention. The diagram shows the composition and connection relationship of the acquisition terminal (L1), the data processing and analysis platform (L2), and the output terminal (L3).

[0025] Figure 2This is a schematic diagram of the construction of a circadian rhythm map of bone metabolism provided in an embodiment of the present invention. The diagram shows the construction process of rhythm parameter extraction (E1), cosine fitting (E2), rhythm parameters (E31-E33), and etiology-specific rhythm map (E4).

[0026] Figure 3 This is a flowchart of the new indication screening process provided by an embodiment of the present invention. The flowchart shows the complete process from step 1 (S2) to step 10 (S13). Detailed Implementation

[0027] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0028] Example 1: System Structure

[0029] like Figure 1 As shown in the figure, this embodiment provides a new indication screening system for anti-osteoporosis drugs based on the analysis of the diurnal rhythm of bone metabolism.

[0030] The data acquisition terminal (L1) includes:

[0031] • Temperature sensor (L11) is used to collect core and peripheral temperatures, with a sampling rate ≥0.1Hz;

[0032] • ECG electrode (L12) is used to acquire single-lead ECG signals with a sampling rate ≥250Hz;

[0033] • Accelerometer (L13) is used to acquire triaxial acceleration signals to assist in the identification of resting state.

[0034] The data processing and analysis platform (L2) includes:

[0035] • Data acquisition interface (L21) is used to receive multimodal data packets uploaded by the acquisition terminal;

[0036] • Preprocessing module (L22) is used for signal filtering, motion artifact recognition and removal;

[0037] • The BMR estimation module (L23) is configured to estimate basal metabolic rate based on a temperature-heart rate coupling model;

[0038] • The HRV analysis module (L24) is configured to calculate heart rate variability parameters and construct the LF / HF diurnal rhythm;

[0039] • The temperature difference analysis module (L25) is configured to calculate the core-periphery temperature difference and construct the temperature difference rhythm;

[0040] • The rhythm map construction module (L26) is configured to extract rhythm parameters and construct etiology-specific bone metabolism rhythm maps (E4).

[0041] • The efficacy prediction module (L27) is configured to compare rhythm recovery speed and predict drug efficacy;

[0042] • Output module (L28) is used to output the evaluation results.

[0043] The output terminals (L3) include the clinical investigator workstation (L31) and the sponsor decision-making platform (L32).

[0044] Example 2: Construction of a circadian rhythm map of bone metabolism

[0045] like Figure 2 As shown in the figure, this embodiment details the method for constructing a circadian rhythm map of bone metabolism.

[0046] In rhythm parameter extraction (E1), the following 24-hour time series was obtained:

[0047] • 24-hour BMR time series (E11);

[0048] • 24-hour LF / HF time series (E12);

[0049] • 24-hour core-periphery temperature difference ΔT time series (E13).

[0050] Cosine fitting (E2) was used to fit each time series:

[0051] f(t) = M + A·cos(2π(t - φ) / 24)

[0052] Extract rhythm parameters (E31-E33):

[0053] •Median M (E31);

[0054] • Amplitude A (E32);

[0055] • Peak phase φ (E33).

[0056] Constructing an etiology-specific bone metabolic rhythm atlas (E4):

[0057] • Glucocorticoid-induced osteoporosis (E41): BMR rhythm amplitude A decreases by more than 50%, and the nocturnal peak of bone resorption disappears;

[0058] • Postmenopausal osteoporosis (E42): The morning peak phase φ of the BMR rhythm is advanced by 2-3 hours;

[0059] • Male osteoporosis (E43): Increased nocturnal trough of BMR rhythm, and a 30-40% decrease in diurnal rhythm amplitude (A).

[0060] Example 3: New Indication Screening Method

[0061] like Figure 3 As shown, this embodiment details the complete process for screening new indications.

[0062] Step 1 (S2): Baseline Acquisition. Individualized bone metabolic rhythm baselines are established through continuous monitoring for 7 days prior to treatment.

[0063] Step 2 (S3): Drug intervention. The patient begins anti-osteoporosis drug treatment.

[0064] Step 3 (S4): Post-treatment monitoring. Monitor continuously for 4 weeks, collecting BMR, LF / HF, and ΔT data.

[0065] Step 4 (S5): Calculate the BMR rhythm phase recovery rate:

[0066] Phase recovery rate = (φ_post-treatment - φ_baseline) / 4 weeks

[0067] Step 5 (S6): Calculate the degree of autonomic rhythm recovery:

[0068] LF / HF recovery level = (LF / HF - post-treatment amplitude) / (LF / HF - baseline amplitude)

[0069] Step 6 (S7): Calculate the degree of recovery of the temperature difference rhythm:

[0070] ΔT recovery degree = (ΔT_post-treatment amplitude) / (ΔT_baseline amplitude)

[0071] Step 7 (S8): Compare the recovery rate with the rate of change in bone density.

[0072] Step 8 (S9): Assessment of therapeutic effect:

[0073] • If the BMR rhythm phase recovery is >60% and the autonomic rhythm improves synchronously, it is considered an effective response (S10).

[0074] • Otherwise, it is considered an invalid response (S11).

[0075] Step 9 (S12): Output new indication expansion suggestions.

[0076] Step 10 (S13): Output a negative result.

[0077] Example 4: Clinical Validation

[0078] This embodiment verifies the technical effects of the present invention through clinical trials. An expanded indication study of the anti-osteoporosis drug (denosumab) in patients with glucocorticoid-induced osteoporosis was selected for verification.

[0079] The results show:

[0080] • The BMR rhythm phase recovery rate in the treatment group (n=30) (22.5° / week) was significantly higher than that in the control group (5.3° / week);

[0081] • In patients with BMR rhythm phase recovery > 60% (23 cases), bone mineral density increased by an average of 6.8% over the following 12 months;

[0082] • In patients with BMR rhythm phase recovery < 60% (7 cases), bone mineral density increased by an average of 1.2% (p < 0.01).

[0083] • The accuracy rate of predicting 12-month bone mineral density changes using 4-week BMR rhythm recovery is 91%;

[0084] • The decision-making time for expanding indications has been reduced from an average of 18 months to 4 weeks.

[0085] The clinical value of the system of the present invention in the rapid screening of new indications has been verified.

[0086] Industrial applicability

[0087] The system and method provided by this invention can be integrated into wearable health monitoring devices, remote patient management platforms, or clinical trial data systems. They are suitable for CRO companies, pharmaceutical companies' clinical research and development departments, and clinical research institutions, and have broad industrial application prospects.

[0088] The above-described embodiments are merely preferred embodiments to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention is defined by the claims.

Claims

1. A screening system for new indications of anti-osteoporosis drugs based on circadian rhythm analysis of bone metabolism, characterized in that, include: The data acquisition interface (L21) is used to access an acquisition system that can generate time-aligned multimodal data packets and acquire the multimodal data packets generated by it in continuous monitoring mode during osteoporosis drug treatment. The data packets include at least temperature data sub-packets, electrophysiological data sub-packets, and motion data sub-packets. The processing module is configured to perform the following operations: • Extract core temperature, peripheral temperature and resting heart rate from the temperature data sub-package and electrophysiological data sub-package, estimate basal metabolic rate (BMR) based on temperature-heart rate coupling model, construct a 24-hour BMR diurnal rhythm curve, and extract rhythm parameters including median (M), amplitude (A) and peak phase (φ). • Extract heart rate variability parameters from the electrophysiological data sub-package and construct a 24-hour LF / HF diurnal rhythm curve; • Extract the core-periphery temperature difference from the temperature data sub-package and construct a 24-hour temperature difference rhythm curve; • Based on the BMR rhythm parameters, autonomic nerve rhythm parameters, and temperature difference rhythm parameters, an etiology-specific bone metabolism rhythm atlas (E4) was constructed. • By comparing the recovery rate of rhythm parameters before and after drug intervention, the efficacy of drugs for this etiological subtype can be predicted; • When the BMR rhythm phase recovery rate is greater than 3 times the rate of bone mineral density change, it is identified as a valid response, and a new indication expansion suggestion is output (S12). The output module (L28) is used to output the etiology-specific bone metabolism rhythm atlas (E4) and the new indication prediction report.

2. The system according to claim 1, characterized in that, The etiology-specific bone metabolic rhythm atlas (E4) includes: • Glucocorticoid-induced osteoporosis (E41): BMR rhythm amplitude is reduced by more than 50%, and the nocturnal peak of bone resorption disappears; • Postmenopausal osteoporosis (E42): The morning peak of the BMR rhythm occurs 2-3 hours earlier; • Male osteoporosis (E43): Increased nighttime trough of BMR rhythm and a 30-40% decrease in diurnal rhythm amplitude.

3. The system according to claim 1, characterized in that, The criteria for predicting new indications are as follows: Four weeks after drug intervention, the BMR rhythm phase recovery was >60% and the autonomic nerve rhythm improved synchronously, indicating that this etiological subtype was sensitive to the drug, and it was recommended to expand the indications.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the functions of the system as described in any one of claims 1 to 3.

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