Description:

Real-time pharmacovigilance system for monitoring adverse reactions to biologic drugs in clinical trials
Field of the Invention
[0001] The present disclosure relates to pharmacovigilance systems, more particularly, to real-time systems for monitoring adverse reactions to biologic drugs during clinical trial operations.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Biologic drugs, including monoclonal antibodies, recombinant proteins, and cell-based therapies, have transformed modern therapeutics by offering targeted interventions for complex diseases. Despite clinical promise, biologics are associated with unique safety risks, including immunogenic reactions, cytokine release syndromes, and off-target immune responses. Early detection of such adverse reactions during clinical trials is critical for safeguarding patient safety, regulatory compliance, and therapeutic advancement.
[0004] Traditional pharmacovigilance in clinical trials relies heavily on investigator reporting, laboratory assessments, and periodic data reviews. These methods are limited by delayed reporting, under-detection of early safety signals, and inability to capture transient physiological changes. Manual case report forms and retrospective analyses fail to provide real-time insights, delaying recognition of serious adverse events. Moreover, adverse reaction data are often siloed across trial sites, complicating central monitoring and risk evaluation.
[0005] Recent advances in digital health technologies and data science offer potential improvements. Wearable biosensors can provide continuous patient monitoring, while machine learning can detect subtle anomalies across high-dimensional datasets. However, existing trial infrastructures have not fully integrated these capabilities into comprehensive pharmacovigilance frameworks. Standalone systems often lack interoperability with clinical trial data standards, fail to implement causality assessment with biologic drug exposure timelines, and lack adaptive alerting mechanisms aligned with regulatory thresholds.
[0006] Therefore, there exists a need for a real-time pharmacovigilance system capable of capturing multimodal patient data, applying advanced analytical models, correlating adverse signals with biologic drug administration, and generating actionable alerts for trial investigators and regulators. The disclosed system addresses these limitations by providing an integrated framework for continuous monitoring, rapid anomaly detection, causality determination, and dynamic reporting of adverse drug reactions during clinical trials.
Summary
[0007] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[0008] The following paragraphs provide additional support for the claims of the subject application.
[0009] The disclosure pertains to a real-time pharmacovigilance system for monitoring adverse reactions to biologic drugs in clinical trials is disclosed. The system comprises a patient data acquisition module configured to collect physiological, biochemical, and behavioural information from trial participants through wearable sensors, laboratory systems, and digital case reports. A data ingestion pipeline standardizes, anonymizes, and aggregates data streams from multiple sites, ensuring compliance with trial data standards. A signal detection engine applies machine learning models, including anomaly detection, clustering, and survival analysis, to identify potential adverse events in real time.
[00010] The system further includes a causality assessment module configured to correlate observed anomalies with biologic drug exposure timelines, dosing records, and concomitant medication profiles, enabling robust attribution of adverse events. A real-time alert system issues tiered notifications to investigators and regulatory authorities, while a reporting dashboard provides dynamic visualizations of individual and cohort-level safety trends. In certain embodiments, a cloud-based data warehouse supports compliance with regulatory requirements, while natural language processing modules extract safety narratives from unstructured reports.
[00011] The method of operation includes capturing continuous and episodic patient data, processing and standardizing inputs, applying anomaly detection algorithms, correlating anomalies with drug exposure, and issuing alerts and visual reports. Integration of continuous monitoring, advanced analytics, and secure communication creates a comprehensive pharmacovigilance framework optimized for biologic drug safety in clinical trials.
Brief Description of the Drawings
[00012] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00013] FIG. 1 illustrates a block diagram of the real-time pharmacovigilance system showing interconnected modules including patient data acquisition, ingestion pipeline, signal detection engine, causality assessment, alert system, and reporting dashboard, in accordance with the embodiments of the present disclosure.
[00014] FIG. 2 illustrates a sequence diagram showing the operational workflow beginning with patient data capture, continuing through ingestion, anomaly detection, causality assessment, alert generation, and dashboard reporting, in accordance with the embodiments of the present disclosure.
[00015] FIG. 3 illustrates a neural network model diagram showing how physiological and biochemical inputs are processed through input, hidden, and output layers to classify adverse reaction likelihood in real time, in accordance with the embodiments of the present disclosure.
Detailed Description
[00016] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00017] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00018] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00019] The disclosed real-time pharmacovigilance system integrates data capture, anomaly detection, causality assessment, and alerting into a unified framework designed for clinical trial safety oversight. The system is specifically configured to address the unique challenges of biologic drugs, which often elicit complex and unpredictable immunological responses.
[00020] The system begins with the patient data acquisition module. Said module captures data from multiple sources, including wearable biosensors monitoring heart rate, electrocardiography, blood oxygen levels, skin temperature, and activity metrics. Laboratory systems provide biochemical markers such as cytokine levels, liver enzymes, and hematological parameters. Electronic case report forms capture structured patient information, while patient-reported outcome tools log subjective symptoms. Each data stream is timestamped and encrypted before transmission to the central ingestion pipeline.
[00021] The data ingestion pipeline standardizes heterogeneous inputs through Clinical Data Interchange Standards Consortium (CDISC) frameworks. Raw data from wearables, laboratory instruments, and reports are normalized to uniform data structures. Identifiers are anonymized to protect patient privacy, while metadata regarding trial site and study protocol are preserved. This pipeline enables interoperability across multiple trial locations and ensures that incoming data can be analyzed in real time without preprocessing delays.
[00022] Once data is aggregated, the signal detection engine applies advanced analytics. Machine learning algorithms are deployed for anomaly detection, clustering of unusual patterns, and probabilistic survival analysis. For example, deviations in heart rate variability or cytokine levels can be flagged as potential early warnings of cytokine release syndrome. Bayesian networks integrate multi-modal data, distinguishing between transient fluctuations and clinically significant signals. The engine continuously updates models with new trial data, thereby improving detection accuracy over time.
[00023] The causality assessment module processes identified anomalies in the context of biologic drug exposure. Patient dosing records, drug infusion times, and concomitant medication data are cross-referenced with anomaly timestamps. Temporal association models and pharmacodynamic simulations determine whether observed reactions are likely attributable to the biologic under study. This module reduces false positives and strengthens the link between biologic administration and adverse reactions.
[00024] The real-time alert system escalates safety notifications proportionally. Minor deviations trigger local alerts for site investigators, while severe anomalies prompt regulatory-level notifications. Alerts are transmitted securely through encrypted protocols and may include contextual information such as patient identifiers, drug exposure history, and anomaly type.

[00022] Once data is aggregated, the signal detection engine applies advanced analytics. Machine learning algorithms are deployed for anomaly detection, clustering of unusual patterns, and probabilistic survival analysis. For example, deviations in heart rate variability or cytokine levels can be flagged as potential early warnings of cytokine release syndrome. Bayesian networks integrate multi-modal data, distinguishing between transient fluctuations and clinically significant signals. The engine continuously updates models with new trial data, thereby improving detection accuracy over time.
[00023] The causality assessment module processes identified anomalies in the context of biologic drug exposure. Patient dosing records, drug infusion times, and concomitant medication data are cross-referenced with anomaly timestamps. Temporal association models and pharmacodynamic simulations determine whether observed reactions are likely attributable to the biologic under study. This module reduces false positives and strengthens the link between biologic administration and adverse reactions.
[00024] The real-time alert system escalates safety notifications proportionally. Minor deviations trigger local alerts for site investigators, while severe anomalies prompt regulatory-level notifications. Alerts are transmitted securely through encrypted protocols and may include contextual information such as patient identifiers, drug exposure history, and anomaly type.
[00025] The reporting dashboard provides dynamic visualization of trial safety data. Investigators can view cohort-level adverse event frequencies, risk stratification heat maps, and individual patient safety timelines. Interactive visualizations allow real-time exploration of emerging trends. For regulators, dashboards can generate standardized reports suitable for compliance submission.
[00026] In a first embodiment, the system operates as a centralized monitoring hub for multi-site clinical trials. All trial sites transmit patient data to a cloud-based server where analytics and causality assessments are performed. This embodiment benefits from scalability and centralized oversight.
[00027] In a second embodiment, the system functions in a hybrid configuration. Local trial sites perform initial anomaly detection and forward filtered events to a central server for causality validation and reporting. This embodiment reduces data transfer loads while maintaining consistency in safety evaluations.
[00028] In a third embodiment, the system incorporates natural language processing modules. Investigator notes, unstructured patient narratives, and safety logs are analyzed through text-mining techniques. Extracted terms related to adverse reactions are converted into structured data for integration with physiological and biochemical signals. This embodiment provides broader coverage of adverse event reporting and captures nuances often missed in structured datasets.
[00029] Operational flows are adaptable across trial contexts. In early-phase trials with small cohorts, continuous monitoring provides granular oversight of first-in-human exposure. In large-scale phase III trials, anomaly detection algorithms scale to thousands of participants across multiple geographies. In post-marketing surveillance studies, the same framework can be extended to integrate electronic health records, creating continuity between trial and real-world pharmacovigilance.
[00030] Data processing flows are reiterated across contexts. Raw data from biosensors and laboratories undergo standardization. Machine learning engines evaluate deviations against baselines and population trends. Causality modules integrate drug timelines to validate associations. Alerts and reports are generated dynamically, creating a closed-loop safety monitoring cycle.
[00031] The disclosed system delivers technical benefits including real-time safety oversight, early detection of immunogenic and systemic reactions, robust causality attribution, and adaptive escalation protocols. Integration of multi-modal data, advanced analytics, and regulatory-compliant reporting frameworks provides a comprehensive pharmacovigilance solution for biologic drug clinical trials. By ensuring continuous monitoring and rapid response, the system enhances patient safety, accelerates regulatory evaluation, and improves trial efficiency.
[00032] Figure 1 provides a block diagram of the disclosed pharmacovigilance system. The diagram begins with the patient data acquisition module, which gathers physiological, biochemical, and behavioral data from sensors, laboratory records, and digital case report forms. Data is transferred to the ingestion pipeline, where it undergoes anonymization, normalization, and standardization according to clinical data models. The processed data is routed into the signal detection engine, where advanced algorithms identify anomalies suggestive of adverse events. Outputs from the detection engine flow into the causality assessment module, which cross-references anomaly timelines with drug exposure and dosing records. Confirmed associations are relayed to the alert system, which escalates findings based on severity. The reporting dashboard then provides investigators and regulators with real-time visualization of adverse reaction trends. This block configuration demonstrates modular integration, where each functional unit maintains autonomy yet contributes to an interconnected pharmacovigilance workflow. The technical benefit arises from structured orchestration of data flows, enabling reliability, scalability, and compliance across trial environments.
[00033] Figure 2 represents a sequence diagram showing stepwise execution of pharmacovigilance operations. The process begins when continuous patient data streams are captured and transmitted to the ingestion pipeline. The ingestion pipeline cleans, structures, and anonymizes the data, which is then delivered to the anomaly detection engine. The engine applies machine learning models to identify deviations from expected physiological baselines. Once anomalies are flagged, the causality module evaluates drug exposure timelines to confirm associations. Confirmed adverse signals are transmitted to the alert system, which issues appropriate notifications based on severity. Finally, reporting dashboards update dynamically, enabling real-time visualization of emerging safety events. The sequence representation clarifies the temporal dependencies of each step, showing that downstream modules rely on validated outputs from preceding components. The technical advantage of this arrangement is operational transparency, ensuring that adverse events are monitored and addressed without delay.
[00034] Figure 3 illustrates a neural network model diagram employed within the pharmacovigilance system’s anomaly detection engine. The model begins with an input layer receiving multimodal data including heart rate, laboratory values, and patient-reported outcomes. Hidden layers implement convolutional filters, recurrent memory units, and non-linear activation functions to capture both temporal patterns and cross-variable interactions. The output layer generates classification probabilities representing the likelihood of adverse drug reactions. The model is continuously updated with new patient data, improving sensitivity and specificity over time. This neural representation demonstrates the role of artificial intelligence in refining pharmacovigilance. The technical benefit lies in automated learning from complex clinical datasets, enabling detection of subtle adverse signals that may elude conventional statistical methods.
[00035] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00036] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A real-time pharmacovigilance system for monitoring adverse reactions to biologic drugs in clinical trials, comprising: a patient data acquisition module configured to capture physiological, biochemical, and behavioral data streams from clinical trial participants through wearable devices, laboratory systems, and electronic case report forms; a centralized data ingestion pipeline configured to aggregate, standardize, and anonymize said data streams across multiple trial sites; a signal detection engine comprising machine learning algorithms configured to identify statistical anomalies indicative of adverse drug reactions by comparing patient-specific data against baseline values and population-level trends; a causality assessment module configured to correlate detected anomalies with biologic drug exposure using temporal association analysis and pharmacodynamic modeling; a real-time alert system configured to notify trial investigators and regulatory authorities through secure communication protocols; and a reporting dashboard configured to provide dynamic visualization of adverse reaction patterns across trial cohorts.
2. The system of claim 1, wherein the patient data acquisition module comprises wearable biosensors configured to monitor heart rate, oxygen saturation, skin temperature, electrocardiographic signals, and activity levels, thereby enabling continuous capture of safety signals during trial participation.
3. The system of claim 1, wherein the data ingestion pipeline incorporates data standardization frameworks compliant with Clinical Data Interchange Standards Consortium models, thereby ensuring interoperability across heterogeneous data sources.
4. The system of claim 1, wherein the signal detection engine further comprises unsupervised clustering, Bayesian networks, and survival analysis models configured to differentiate transient variations from statistically significant adverse reaction indicators.
5. The system of claim 1, wherein the causality assessment module further integrates drug exposure timelines, dosing records, and concomitant medication data, thereby enhancing accuracy of adverse reaction attribution.
6. The system of claim 1, wherein the real-time alert system incorporates adaptive thresholding, risk stratification, and escalation protocols, thereby enabling proportional response ranging from site-level alerts to regulatory reporting triggers.
7. The system of claim 1, wherein the reporting dashboard comprises heat maps, risk timelines, and adverse event frequency charts, thereby enabling investigators to visualize cohort-level trends and individual patient trajectories.
8. The system of claim 1, wherein the system further comprises a cloud-based data warehouse configured to provide audit trails, role-based access control, and compliance with Good Clinical Practice guidelines.
9. The system of claim 1, wherein the system further comprises a natural language processing interface configured to extract adverse reaction narratives from investigator notes, patient-reported outcomes, and unstructured safety logs.
10. The system of claim 1, wherein integration of continuous patient monitoring, real-time signal detection, causality assessment, and dynamic reporting establishes a comprehensive pharmacovigilance framework optimized for biologic drug safety in clinical trials.

reactions to biologic drugs in clinical trials
Abstract
A real-time pharmacovigilance system for monitoring adverse reactions to biologic drugs in clinical trials is disclosed. The system comprises a patient data acquisition module configured to collect physiological, biochemical, and behavioral data, a standardized ingestion pipeline for multi-site aggregation, and a signal detection engine employing machine learning models for anomaly detection. A causality assessment module correlates anomalies with biologic drug exposure timelines and dosing records. A real-time alerting system issues notifications to investigators and regulators, while a reporting dashboard provides dynamic visualization of adverse event patterns. In certain embodiments, cloud-based warehouses support compliance and natural language processing modules extract safety narratives from unstructured reports. Integration of continuous monitoring, advanced anomaly detection, and dynamic reporting establishes a comprehensive pharmacovigilance framework for biologic drug safety monitoring during clinical trials.
Fig. 1

  , Claims:Claims
I/We Claim:
1. A real-time pharmacovigilance system for monitoring adverse reactions to biologic drugs in clinical trials, comprising: a patient data acquisition module configured to capture physiological, biochemical, and behavioral data streams from clinical trial participants through wearable devices, laboratory systems, and electronic case report forms; a centralized data ingestion pipeline configured to aggregate, standardize, and anonymize said data streams across multiple trial sites; a signal detection engine comprising machine learning algorithms configured to identify statistical anomalies indicative of adverse drug reactions by comparing patient-specific data against baseline values and population-level trends; a causality assessment module configured to correlate detected anomalies with biologic drug exposure using temporal association analysis and pharmacodynamic modeling; a real-time alert system configured to notify trial investigators and regulatory authorities through secure communication protocols; and a reporting dashboard configured to provide dynamic visualization of adverse reaction patterns across trial cohorts.
2. The system of claim 1, wherein the patient data acquisition module comprises wearable biosensors configured to monitor heart rate, oxygen saturation, skin temperature, electrocardiographic signals, and activity levels, thereby enabling continuous capture of safety signals during trial participation.
3. The system of claim 1, wherein the data ingestion pipeline incorporates data standardization frameworks compliant with Clinical Data Interchange Standards Consortium models, thereby ensuring interoperability across heterogeneous data sources.
4. The system of claim 1, wherein the signal detection engine further comprises unsupervised clustering, Bayesian networks, and survival analysis models configured to differentiate transient variations from statistically significant adverse reaction indicators.
5. The system of claim 1, wherein the causality assessment module further integrates drug exposure timelines, dosing records, and concomitant medication data, thereby enhancing accuracy of adverse reaction attribution.
6. The system of claim 1, wherein the real-time alert system incorporates adaptive thresholding, risk stratification, and escalation protocols, thereby enabling proportional response ranging from site-level alerts to regulatory reporting triggers.
7. The system of claim 1, wherein the reporting dashboard comprises heat maps, risk timelines, and adverse event frequency charts, thereby enabling investigators to visualize cohort-level trends and individual patient trajectories.
8. The system of claim 1, wherein the system further comprises a cloud-based data warehouse configured to provide audit trails, role-based access control, and compliance with Good Clinical Practice guidelines.
9. The system of claim 1, wherein the system further comprises a natural language processing interface configured to extract adverse reaction narratives from investigator notes, patient-reported outcomes, and unstructured safety logs.
10. The system of claim 1, wherein integration of continuous patient monitoring, real-time signal detection, causality assessment, and dynamic reporting establishes a comprehensive pharmacovigilance framework optimized for biologic drug safety in clinical trials.