Drug random double-blind experiment system and method based on virtual human body cluster

Abstract

The invention provides a drug random double-blind experiment system and method based on a virtual human body cluster, and is characterized in that a silicon-based subject cluster is used for replacing a traditional clinical test object, a virtual experiment normal form based on multi-scale physiological modeling and AI dynamic response is constructed, and a research and development process from drug discovery to clinical transformation is remodeled. According to the system core, a virtual subject generation module, a double-blind control module, a drug effect response prediction module and a verification interface form a closed loop. The method mainly comprises the steps of generating a virtual subject cluster, performing double-blind distribution of virtual drugs and placebo, running physiological simulation for 72 hours, performing AI analysis on a drug effect response matrix, performing statistics on significant differences after blind uncovering, and outputting a dose-effect curve.

Description

Technical Field

[0001] This invention relates to the intersection of pharmaceutical research and development and artificial intelligence, and in particular to a randomized double-blind drug trial system and method based on virtual human clusters, which can replace or optimize traditional clinical trial procedures. Background Technology

[0002] 1. Bottlenecks in traditional clinical trials

[0003] Long cycle and high cost: It takes an average of 6-12 years and costs more than $2 billion to complete Phase I-III clinical trials for a new drug (according to the Tufts Center for Drug Development 2025 report).

[0004] Subject recruitment is difficult: Rare disease trials are delayed due to the scarcity of patients. For example, recruitment takes up 35% of the entire process in the spinal muscular atrophy (SMA) trial.

[0005] Ethical risks: Patients in the placebo group may experience delays in treatment, and 30% of participants in COVID-19 vaccine trials withdrew due to the risk of infection.

[0006] 2. Limitations of existing virtual technologies

[0007] Static anatomical models: cannot simulate the dynamic response of drugs, such as FEXI robots.

[0008] Single-disease drug efficacy prediction: lack of double-blind control mechanisms, such as the pharmacokinetic model of Peking University Third Hospital.

[0009] Experimental cycle simulation system: does not integrate multi-omics data, such as Haibot patent CN119400334A.

[0010] 3. The necessity of the invention

[0011] There is an urgent need to build an integrated platform that combines dynamic physiological modeling, automated double-blind control, and multi-scale efficacy verification. Summary of the Invention

[0012] (I) Technical Issues

[0013] This addresses the issues of low efficiency in traditional drug clinical trials and the inability of existing virtual technologies to achieve full-process double-blind validation.

[0014] (II) Technical Solution

[0015] The core of the system consists of a closed loop comprising a virtual subject generation module, a double-blind control module, a drug efficacy response prediction module, and a verification interface.

[0016] Core module functions:

[0017] 1. Virtual Subject Generation Module

[0018] A virtual human physiological model is constructed based on a multi-source medical database (including genomics, proteomics, and metabolomics data). The anatomical structure and physiological function differences of different age / gender / ethnic groups are simulated through dynamic parameter adjustment units. A differentiated digital human cluster is constructed by coupling generative adversarial network (GAN) and physiological engine technology.

[0019] Dynamic parameter adjustment unit: Parameter weights are set based on multi-source Chinese population genome databases (such as BGISEQ-500).

[0020] Multi-scale modeling:

[0021] Macroscale: Reconstructing the three-dimensional structure of organs based on medical images; Microscale: Coupled with cellular automata models to simulate tissue pathological responses; Dynamic response: Introducing real-time biosensor data streams to update metabolic state.

[0022] 2. Double-blind control module

[0023] A triple-blinding mechanism is employed. Drug coding: A quantum random number generator is used to allocate the drug / placebo; Model isolation: The drug efficacy prediction module cannot access the drug coding key; Dynamic interference: A physiological noise generation algorithm injects controllable noise (±5% physiological fluctuation) into physiological parameters.

[0024] Drug coding, the quantum random generator is equivalent to an absolutely fair lottery machine, ensuring the confidentiality of random grouping.

[0025] Model isolation isolates the AI ​​referee (i.e., the drug efficacy response prediction module), placing it in a "dark room." The AI ​​system analyzing drug efficacy can only see physiological data (such as heart rate and body temperature). Physically blocking the AI's access to grouped information (like sealing exam papers for grading) prevents hackers from simultaneously breaching three isolated zones (drug server / database / AI system).

[0026] Dynamic interference creates reasonable fluctuations, adding a "physiological fingerprint" to each virtual human to mimic the natural fluctuations of the real human body: for example...

[0027] Heart rate ±3 beats per minute (similar to slight panting after exercise)

[0028] Metabolic rate ±5% (like how some people flush easily after drinking alcohol)

[0029] Objective: To prevent AI from extrapolating groupings based on minute differences (e.g., finding that blood pressure fluctuations are completely consistent across all drug groups).

[0030] 3. Drug Efficacy Response Prediction Module

[0031] Integrating pharmacokinetic (PK) models with deep neural networks, this system simulates the absorption, distribution, metabolism, and excretion of drugs in a virtual human body in real time; it outputs multi-organ toxicity scores and biomarker change curves. The deep neural network employs a multi-task adversarial training architecture: a generator simulates the drug-target interaction path; a discriminator compares virtual pathological reactions with data from a real-world electronic medical record database; and a loss function jointly optimizes the organ toxicity prediction error and the biomarker curve fit.

[0032] Generator: Equivalent to an experienced virtual doctor, it simulates the drug-target interaction pathway. Discriminator: It compares the virtual pathological response with real-world electronic medical record data to determine if the simulated pathway matches reality. If not, it iteratively corrects the behavior using a loss function.

[0033] 4. Verification Interface:

[0034] The physiological response baseline of the matched population is extracted from the traditional clinical trial database; the difference in data distribution between the virtual experimental group and the real experimental group is verified by the KS test (p>0.05 is considered passing); when the deviation is >10%, parameter optimization iteration is triggered, the consistency of the virtual experimental results and the traditional clinical trial data is verified, and a deviation correction report is generated.

[0035] (III) Technical Effects

[0036] Efficiency Improvement: Virtual drug trials now take only 3 days (compared to 5-8 years for traditional trials).

[0037] Cost reduction: Reduces participant recruitment costs by more than 90%.

[0038] Predictive accuracy: Deviation from traditional clinical trial results <8% (p = 0.62 by KS test) Detailed Implementation

[0039] Example 1: Virtual Double-Blind Trial of Diabetes Drugs

[0040] Step 1: Construct a virtual subject cluster

[0041] Data source: Electronic medical records of 10,000 diabetic patients at Ruijin Hospital

[0042] Production scale: 2000 cases in the experimental group and 2000 cases in the control group.

[0043] Step 2: Implementation of double-blind intervention

[0044] Experimental group: Administered SGLT-2 inhibitor (empagliflozin 10 mg / day)

[0045] Control group: Injected with physiological noise (simulating blood glucose fluctuations of ±0.2 mmol / L after medication).

[0046] Step 3: Drug Efficacy Prediction and Verification

[0047] index Virtual Experiment Results Traditional test results deviation HbA1c decrease -1.25％ -1.37％ 8.8％ Urinary tract infection rate 5.1％ 4.9％ 4.1％

[0048] Conclusion: By verifying the interface to trigger parameter optimization (adjusting the renal metabolic weight coefficient), the bias was reduced to 5.3%.

[0049] Example 2: Prediction of progression-free survival (PFS) with tumor drugs

[0050] Model input: Molecular structure of PD-1 inhibitor (pembrolizumab)

[0051] Virtual Subjects: Models of 50 Non-Small Cell Lung Cancer Subtypes

[0052] Output results: Median PFS = 18.2 months (19.2 months in the actual KEYNOTE-189 trial).

[0053] Example 3: Virtual Trial of Antitumor Drugs

[0054] 500 virtual subjects were generated (including the EGFR L858R mutation subset).

[0055] Double-blind allocation of drug A / placebo (QRNG generated 256-bit encrypted code)

[0056] Running a 72-hour physiological simulation (GPU cluster acceleration, NVIDIA H100×8)

[0057] AI outputs a drug efficacy matrix:

[0058] Group Tumor shrinkage rate % probability of liver toxicity Drug A 62.3±5.1 17.8％ placebo 2.1±1.7 1.2％

[0059] Post-blinding statistics showed that drug A group had significant therapeutic effects (p = 3.2e-6, t-test).

[0060] Key Technical Points Statement

[0061] Dynamic parameter adjustment unit: Unlike the static modeling of FEXI robots, this system updates metabolic parameters (such as heart rate and blood oxygen) through real-time biosensor data streams.

[0062] Physiological noise generation algorithm: Breaking through the limitations of Haibot's patented periodic prediction, it achieves the first quantitative simulation of the placebo effect.

[0063] Multi-task adversarial training: Compared with the pharmacokinetic model of Peking University Third Hospital, the discriminator is trained using real-world data for supervision.

[0064] This specification fully discloses the technical solution, and all algorithm parameters involved are within a specific feasible range, which complies with the requirements of Article 26, Paragraph 3 of the Patent Law.

Claims

1. A randomized, double-blind drug trial system and method based on a virtual human cluster, characterized in that... By replacing traditional clinical trial subjects with silicon-based subject clusters, a virtual experimental paradigm based on multi-scale physiological modeling and AI dynamic response is constructed, reshaping the R&D process from drug discovery to clinical translation. The core of the system consists of a closed loop comprising a virtual subject generation module, a double-blind control module, a drug efficacy response prediction module, and a validation interface. The main steps include generating virtual subject clusters, double-blindly allocating virtual drugs and placebos, running a 72-hour physiological simulation, AI analysis of the drug efficacy response matrix, statistical analysis of significant differences after unblinding, and outputting dose-response curves.

2. The virtual subject generation module as described in claim 1, characterized in that: By integrating multi-omics data from the genome, proteome, and metabolome, and employing generative adversarial networks (GANs) coupled with a physiological engine, a differentiated digital human body cluster is constructed, with individual variability covering more than 90% of the key phenotypes in the real population.

3. The dual-blind control module as described in claim 1, characterized in that: A triple-blinding mechanism is employed. Drug coding: A quantum random number generator is used to allocate the drug / placebo; Model isolation: The drug efficacy prediction module cannot access the drug coding key; Dynamic interference: A physiological noise generation algorithm injects controllable noise (±5% physiological fluctuation) into physiological parameters.

4. The drug efficacy response prediction module as described in claim 1, characterized in that: Integrating pharmacokinetic (PK) models with deep neural networks, this system simulates the absorption, distribution, metabolism, and excretion of drugs in a virtual human body in real time; it outputs multi-organ toxicity scores and biomarker change curves. The deep neural network employs a multi-task adversarial training architecture: a generator simulates the drug-target interaction path; a discriminator compares virtual pathological reactions with data from a real-world electronic medical record database; and a loss function jointly optimizes the organ toxicity prediction error and the biomarker curve fit.

5. The verification interface as described in claim 1, characterized in that: The physiological response baseline of the matched population is extracted from the traditional clinical trial database; the difference in data distribution between the virtual experimental group and the real experimental group is verified by the KS test (p>0.05 is considered passing); when the deviation is >10%, parameter optimization iteration is triggered, the consistency of the virtual experimental results and the traditional clinical trial data is verified, and a deviation correction report is generated.