FDA Monoclonal Antibody Regulatory Dataset

About the Dataset

This dataset aggregates comprehensive regulatory documentation and resources from the U.S. Food and Drug Administration (FDA), specifically related to monoclonal antibodies (mAbs). It provides structured access to critical FDA filings, clinical trial documentation, and drug labels, serving as an essential resource for regulatory analysis, clinical research, and AI-driven applications.

Contents

The dataset comprises:

• FDA Documentation

  • New Drug Applications (NDA) submissions and approval summaries.
  • Investigational New Drug (IND) filings, including clinical and preclinical data.
  • International Council for Harmonisation (ICH) guidance documents relevant to monoclonal antibody regulation.

• Clinical Trial Documentation

  • Protocols, study designs, and outcome reports from clinical trials.
  • Regulatory correspondence and approval notices.

• Drug Labels

  • Structured drug labeling information for 180 approved monoclonal antibodies, detailing indications, dosages, adverse reactions, warnings, and clinical pharmacology.

Potential Use Cases

This dataset supports various research and analytical tasks, including:

• Regulatory compliance analysis: Identify key elements and benchmarks for successful FDA approvals.
• Clinical trial design optimization: Inform trial protocols using historical approval data.
• Natural Language Processing (NLP) applications: Enable text classification, information extraction, summarization, and entity recognition tasks.
• Safety and efficacy research: Facilitate comparative analysis of drug labels and clinical outcomes.

Intended Audience

• Regulatory professionals and pharmaceutical industry researchers.
• Biomedical data scientists and informaticians.
• NLP and machine learning practitioners focused on biomedical applications.

Data Format

• All documents and labels are provided in machine-readable PDF format that can be parsed using PyPDF, but some drug labels may be a faxed document in a PDF, which may require OCR to parse via Tesseract.

Acknowledgments

This dataset utilizes publicly available information provided by the FDA and other regulatory bodies.

Citation

If you use this dataset in your research or applications, please provide an appropriate citation referencing this dataset.

The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data. A public–private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER) was established in part to develop and publicize novel methods to facilitate cross-study analysis of SEND datasets. As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses. The sendigR package also includes an integrated Python package, xptcleaner, which can be used to harmonize the terminology used in SEND datasets by mapping to CDISC controlled terminologies. The sendigR R package is freely available on the comprehensive R Archive Network (CRAN) and at https://github.com/phuse-org/sendigR. An R Shiny web application was included in the R package to enable toxicologists with no coding experience to perform historical control analyses. Experienced R programmers will be able to integrate the package functions into their own custom scripts/packages and potentially contribute improvements to the functionality of sendigR.sendigR reference manual: https://phuse-org.github.io/sendigR/.sendigR R Shiny demo app: https://phuse-org.shinyapps.io/sendigR/.

The FDA Device Dataset by Dataplex provides comprehensive access to over 24 million rows of detailed information, covering 9 key data types essential for anyone involved in the medical device industry. Sourced directly from the U.S. Food and Drug Administration (FDA), this dataset is a critical resource for regulatory compliance, market analysis, and product safety assessment regarding.

Dataset Overview:

This dataset includes data on medical device registrations, approvals, recalls, and adverse events, among other crucial aspects. The dataset is meticulously cleaned and structured to ensure that it meets the needs of researchers, regulatory professionals, and market analysts.

24 Million Rows of Data:

With over 24 million rows, this dataset offers an extensive view of the regulatory landscape for medical devices. It includes data types such as classification, event, enforcement, 510k, registration listings, recall, PMA, UDI, and covid19 serology. This wide range of data types allows users to perform granular analysis on a broad spectrum of device-related topics.

Sourced from the FDA:

All data in this dataset is sourced directly from the FDA, ensuring that it is accurate, up-to-date, and reliable. Regular updates ensure that the dataset remains current, reflecting the latest in device approvals, clearances, and safety reports.

Key Features:

• Comprehensive Coverage: Includes 9 key device data types, such as 510(k) clearances, premarket approvals, device classifications, and adverse event reports.

• Regulatory Compliance: Provides detailed information necessary for tracking compliance with FDA regulations, including device recalls and enforcement actions.

• Market Analysis: Analysts can utilize the dataset to assess market trends, monitor competitor activities, and track the introduction of new devices.

• Product Safety Analysis: Researchers can analyze adverse event reports and device recalls to evaluate the safety and performance of medical devices.

Use Cases: - Regulatory Compliance: Ensure your devices meet FDA standards, monitor compliance trends, and stay informed about regulatory changes.

• Market Research: Identify trends in the medical device market, track new device approvals, and analyze competitive landscapes with up-to-date and historical data.

• Product Safety: Assess the safety and performance of medical devices by examining detailed adverse event reports and recall data.

Data Quality and Reliability:

The FDA Device Dataset prioritizes data quality and reliability. Each record is meticulously sourced from the FDA's official databases, ensuring that the information is both accurate and up-to-date. This makes the dataset a trusted resource for critical applications, where data accuracy is vital.

Integration and Usability:

The dataset is provided in CSV format, making it compatible with most data analysis tools and platforms. Users can easily import, analyze, and utilize the data for various applications, from regulatory reporting to market analysis.

User-Friendly Structure and Metadata:

The data is organized for easy navigation, with clear metadata files included to help users identify relevant records. The dataset is structured by device type, approval and clearance processes, and adverse event reports, allowing for efficient data retrieval and analysis.

Ideal For:

• Regulatory Professionals: Monitor FDA compliance, track regulatory changes, and prepare for audits with comprehensive and up-to-date product data.

• Market Analysts: Conduct detailed research on market trends, assess new device entries, and analyze competitive dynamics with extensive FDA data.

• Healthcare Researchers: Evaluate the safety and efficacy of medical devices product data, identify potential risks, and contribute to improved patient outcomes through detailed analysis.

This dataset is an indispensable resource for anyone involved in the medical device industry, providing the data and insights necessary to drive informed decisions and ensure compliance with FDA regulations.

Objectives: To develop and pilot a tool to measure and improve pharmaceutical companies’ clinical trial data sharing policies and practices. Design: Cross sectional descriptive analysis. Setting: Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015. Data sources: Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies. Main outcome measures: Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices. Results: Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval. Conclusions: It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study’s measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.

Clinical Trial Data Repository Market Outlook

The global clinical trial data repository market size was estimated to be approximately $1.8 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 9.5% to reach around $4.1 billion by 2032. The primary growth factors include the increasing volume and complexity of clinical trials, rising need for efficient data management systems, and stringent regulatory requirements for data accuracy and integrity. The advent of advanced technologies such as artificial intelligence and big data analytics further drives market expansion by enhancing data processing capabilities and providing actionable insights.

The growth of the clinical trial data repository market is significantly influenced by the increasing number of clinical trials being conducted globally. With the rise in chronic diseases, the need for innovative treatments and therapies has surged, leading to an upsurge in clinical trials. This increase in clinical trials necessitates robust data management systems to handle vast amounts of data generated, thereby propelling the demand for clinical trial data repositories. Moreover, the complexity of modern clinical trials, which often involve multiple sites and diverse patient populations, further amplifies the need for sophisticated data management solutions.

Another critical driver for the market is the stringent regulatory landscape governing clinical trial data. Regulatory bodies such as the FDA, EMA, and other local authorities mandate rigorous data management standards to ensure data integrity, accuracy, and accessibility. These regulations necessitate the adoption of advanced data repository systems that can comply with regulatory requirements, thereby fueling market growth. Additionally, regulatory frameworks are becoming increasingly stringent, prompting pharmaceutical and biotechnology companies to invest in state-of-the-art data management systems to avoid compliance issues and potential financial penalties.

Technological advancements play a pivotal role in the market's growth. The integration of artificial intelligence, machine learning, and big data analytics into data repository systems enhances data processing and analysis capabilities. These technologies enable real-time data monitoring, predictive analytics, and improved decision-making, thereby improving the efficiency of clinical trials. Furthermore, the shift towards cloud-based solutions offers scalability, flexibility, and cost-effectiveness, making advanced data management systems accessible to even small and medium-sized enterprises.

Regionally, North America dominates the clinical trial data repository market owing to its robust healthcare infrastructure, high R&D investments, and presence of major pharmaceutical and biotechnology companies. Europe follows closely due to stringent regulatory standards and a strong focus on clinical research. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to increasing clinical trial activities, growing healthcare expenditure, and the rising adoption of advanced technologies. Latin America and the Middle East & Africa are also likely to experience growth, albeit at a slower pace, driven by improving healthcare systems and increasing focus on clinical research.

Component Analysis

The clinical trial data repository market is segmented by components into software and services. The software segment is anticipated to hold a significant share of the market due to the essential role software plays in data management. Advanced software solutions offer capabilities such as data storage, management, retrieval, and analysis, which are critical for effective clinical trial management. The integration of AI and machine learning algorithms into these software systems further enhances their efficiency by enabling predictive analytics and real-time monitoring, thus driving the software segment's growth.

Software solutions in clinical trial data repositories also offer interoperability, enabling seamless integration with other clinical trial management systems (CTMS) and electronic data capture (EDC) systems. This interoperability is crucial for ensuring data consistency and accuracy across different platforms, thereby enhancing overall data management. Additionally, the increasing adoption of cloud-based software solutions provides scalability, cost-effectiveness, and remote acce

US FDA Emergency Use Authorization Masks Marketing Data

(Ellie Lockhart, Ph.D.)

During the COVID-19 crisis, the United States FDA has issued a series of emergency authorizations for masks of both domestic and import origin. These masks conform to various sets of safety standards established by the FDA during this time as defined in Emergency Use Authorizations (EUAs), and can have their use authorizations revoked if they fail to meet standards. There are a number of regulatory standards and burdens, and it has become fairly simple for companies to have compliant products added to the FDA's list. Despite this, masks are still sold in the United States which do not meet the FDA's EUA standards, or whose manufacturers/importers/sellers have not taken the steps to verify that they meet these standards. This is not a violation of the law, as masks which are registered with the FDA may also be sold; however, as a mask consumer, I was disappointed to learn the difference between a product being "FDA registered," which simply means that the company is not violating FDA regulations and having an EUA meant that the US FDA had been doing no particular testing on a brand of KF95 (Korean Filter 95) masks which I and my family have been using.

As these products were listed on Amazon.com (and likely elsewhere) as being "FDA registered," I believe a large number of online distributors may be exploiting the difference between having FDA authorization and simply having registered. Utilizing ScrapeHero Cloud to quickly obtain all masks that appeared in search results for "FDA mask" on Amazon.com on February 8, 2021, I then manually compiled the brand names of all FDA-authorized masks that have been authorized as a result of the COVID crisis under EUAs from the FDA list linked in this document, as well as their list of masks which are no longer authorized for various reasons (such as having been decontaminated). Using PostgreSQL to quickly link together tables and compile them as .csv files, I then created this data set, with two tables: all_authorized_masks.csv and unauthorized_masks.csv - NOTE: the data set does not go down to the level of specific authorized products, which means uncertainty exists for manufacturers who have both authorized masks and masks which were authorized but are no longer authorized. The goal of this project is to use standard notebook data analysis methods and fuzzy language recognition to compile some statistics and visuals on the extent to which "FDA masks" being sold online are in fact FDA EUA authorized masks.

Some caveats:

1. I am not asserting any manufacturer in particular is committing misconduct, but rather attempting to determine the extent to which terminology confusion may be leading US consumers to purchase masks which have not been tested by the US FDA. (I, personally, am fine with using KFDA aka Korean FDA approved masks, but the lack of clarity is still less than ideal for consumers.)
2. Many manufacturers produce some EUA approved masks and some unapproved masks. This list is only able to determine definite negatives in terms of comparing who has an EUA and has "FDA mask" as part of their Amazon search results; it can also be used to suggest cases where the approval status of masks is unclear.
3. The dataset of all registered masks is extremely large. A seller is likely operating legally and not doing anything wrong as long as they are registered, and collecting the full dataset of registered FDA masks is not feasible as the current publicly available bulk dataset on this topic is from before the outbreak of COVID-19 and I am not comfortable engaging in scraping of government websites.
4. Nothing about this dataset is to imply specific products may be unsafe, but simply to make US mask consumers aware of the rate at which given masks have been granted US FDA Emergency Use Authorization when they use the term "FDA" in their marketing on (in this case, as the largest US online retailer) Amazon's US website.

Wearing any mask is better than wearing none. However, the use of "FDA" to simply indicate that a product can be legally sold in the US is misleading; customers expect that "FDA" in a product description includes some form of approval. The analysis that comes out of this dataset should show an approximate estimate, as of February 8, 2021, of how many masks claiming FDA compliance are indeed EUA authorized.

FDA Cybersecurity Compliance Solutions Market Outlook

According to our latest research, the global FDA Cybersecurity Compliance Solutions market size reached USD 2.1 billion in 2024, with a robust compound annual growth rate (CAGR) of 12.5%. This growth is primarily driven by the increasing regulatory scrutiny on healthcare cybersecurity and the rising sophistication of cyber threats targeting medical devices and healthcare infrastructure. As per our projections, the market is set to expand significantly, reaching USD 6.1 billion by 2033. The implementation of stringent FDA guidelines and ongoing digital transformation in healthcare are pivotal factors fueling this marketÂ’s expansion.

The primary growth factor propelling the FDA Cybersecurity Compliance Solutions market is the rapidly evolving threat landscape targeting healthcare data and connected medical devices. Cyberattacks on healthcare institutions have surged, exposing vulnerabilities in medical devices and critical infrastructure. The FDAÂ’s proactive stance, including the introduction of premarket and postmarket cybersecurity guidance, has compelled medical device manufacturers, healthcare providers, and pharmaceutical companies to adopt comprehensive compliance solutions. These solutions encompass risk assessment, vulnerability management, and incident response, ensuring adherence to regulatory requirements and safeguarding patient safety. The increasing integration of Internet of Medical Things (IoMT) devices amplifies the need for robust cybersecurity frameworks, further driving market demand.

Another significant driver is the digital transformation sweeping across the healthcare sector. The adoption of electronic health records (EHRs), telemedicine, and cloud-based healthcare solutions has exponentially increased the volume of sensitive data managed by healthcare organizations. This digital shift, while enhancing operational efficiency, also creates new attack vectors for cybercriminals. To mitigate these risks, organizations are investing heavily in FDA-compliant cybersecurity solutions that offer end-to-end protection. The market is witnessing heightened demand for advanced threat intelligence, automated compliance management, and real-time monitoring tools, as stakeholders prioritize patient data integrity and regulatory compliance.

Additionally, the evolving regulatory landscape is a crucial growth catalyst for the FDA Cybersecurity Compliance Solutions market. The FDAÂ’s ongoing updates to cybersecurity guidelines, alongside parallel regulations in other regions such as the European UnionÂ’s Medical Device Regulation (MDR), are compelling stakeholders to adopt proactive compliance strategies. The growing collaboration between regulatory bodies and industry players is fostering the development of standardized cybersecurity frameworks, accelerating the deployment of compliance solutions. Furthermore, the increased frequency of regulatory audits and the potential for hefty penalties in the event of non-compliance are incentivizing organizations to invest in robust solutions, ensuring sustained market growth.

In the realm of healthcare cybersecurity, Connected Medical Device Security has emerged as a critical focus area. As medical devices become increasingly interconnected through the Internet of Medical Things (IoMT), the potential attack surface for cyber threats expands significantly. Ensuring the security of these devices is paramount, as they often handle sensitive patient data and are integral to patient care. The FDA's guidelines emphasize the need for robust security measures to protect these devices from unauthorized access and potential cyberattacks. Manufacturers are thus prioritizing the integration of advanced security features into their devices, including encryption, secure boot processes, and regular security updates. This proactive approach not only safeguards patient safety but also ensures compliance with stringent regulatory standards, thereby fostering trust in digital healthcare solutions.

From a regional perspective, North America remains the dominant market for FDA Cybersecurity Compliance Solutions, accounting for a significant share of global revenue. This leadership is attributed to the presence of advanced healthcare infrastructure, high regulatory awareness, and a lar

Electronic Data Capture Software Market Outlook

According to our latest research, the global Electronic Data Capture (EDC) Software market size reached USD 1.63 billion in 2024, registering a robust growth trajectory. The market is set to expand at a CAGR of 12.1% from 2025 to 2033, driven by the increasing digitization of clinical trials and the growing demand for efficient data management solutions in the healthcare sector. By 2033, the EDC Software market is forecasted to achieve a value of USD 4.54 billion, reflecting the sector’s dynamic evolution and the rising emphasis on regulatory compliance and patient safety. This growth is propelled by the integration of advanced technologies, such as artificial intelligence and cloud computing, which are transforming the landscape of clinical data management.

The primary growth driver for the Electronic Data Capture Software market is the escalating complexity and volume of clinical trials worldwide. As pharmaceutical and biotechnology companies accelerate the development of new therapeutics, especially in the wake of global health challenges, there is a heightened need for robust, scalable, and compliant data capture solutions. EDC software enables real-time data collection, validation, and monitoring, which significantly enhances the efficiency and accuracy of clinical trials. The increasing adoption of decentralized and remote trials further emphasizes the necessity for digital platforms that can seamlessly manage multi-site and multi-phase research studies. The ability of EDC software to standardize and streamline data across diverse geographies and regulatory environments is a critical factor fueling market expansion.

Another significant growth factor is the rising emphasis on regulatory compliance and data integrity in clinical research. Regulatory bodies such as the FDA and EMA have instituted stringent guidelines for electronic records and signatures, compelling organizations to adopt advanced EDC systems that ensure compliance and auditability. The integration of features like automated data validation, query management, and real-time reporting within EDC platforms helps organizations minimize errors, reduce risks, and maintain high-quality data standards. Furthermore, the increasing need for rapid decision-making in clinical development, particularly in oncology and rare diseases, has intensified the demand for sophisticated EDC solutions that facilitate timely data access and analysis.

Technological advancements are also playing a pivotal role in shaping the EDC Software market. The incorporation of artificial intelligence, machine learning, and analytics into EDC platforms is enabling predictive insights, automated data cleaning, and anomaly detection, thereby enhancing the overall value proposition of these solutions. The shift towards cloud-based deployment models is further contributing to market growth by offering scalability, cost-effectiveness, and remote accessibility. Additionally, the integration of EDC with other eClinical tools, such as electronic patient-reported outcomes (ePRO) and clinical trial management systems (CTMS), is driving the development of comprehensive digital ecosystems for clinical research. This interconnected approach is helping organizations optimize workflows, improve collaboration, and accelerate the drug development lifecycle.

From a regional perspective, North America continues to dominate the Electronic Data Capture Software market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of a well-established pharmaceutical industry, high clinical trial activity, and favorable regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing region, supported by increasing investments in healthcare infrastructure, rising clinical research activities, and the rapid adoption of digital health technologies. Europe also holds a significant share, driven by strong government initiatives and a growing focus on personalized medicine. The market outlook remains positive across all regions, with ongoing innovations and collaborations expected to further stimulate growth during the forecast period.

Large scale analysis of in vivo toxicology studies has been hindered by the lack of a standardized digital format for data analysis. The SEND standard enables the analysis of data from multiple studies performed by different laboratories. The objective of this work is to develop methods to transform, sort, and analyze data to automate cross study analysis of toxicology studies. Cross study analysis can be applied to use cases such as understanding a single compound’s toxicity profile across all studies performed and/or evaluating on- versus off-target toxicity for multiple compounds intended for the same pharmacological target. This collaborative work between BioCelerate and FDA involved development of data harmonization/transformation strategies and analytic techniques to enable cross-study analysis of both numerical and categorical SEND data. Four de-identified SEND data sets from the BioCelerate Toxicology Data Sharing module of DataCelerate® were used for the analyses. Toxicity prof..., Deidentified SEND data was donated by companies participating in BioCelerate’s Toxicology Data Sharing Initiative (TDS module in DataCelerate®).The data included 1-Month Rat and 1-Month Dog SEND datasets for two different compounds intended for the same pharmacological target. To facilitate cross-study analysis of toxicology studies, it is practical to categorize findings within organ systems to provide insights into target organ toxicity. In the proof-of-concept for this application, we focused on the target organs with compound-related effects, namely the kidney, liver, hematopoietic system, endocrine system, and reproductive tract (male). The body weights (BW), food and water consumption (FW), laboratory test results (LB), organ measurements (OM), and microscopic findings (MI) SEND domains were included in the analysis. Each parameter was then assigned to the relevant organ system(s) (Table 1) based on veterinary literature (Faqi 2017) (Stockham 2008), scientific literature on ..., , # Dataset for Cross Study Analyses of SEND Data: Toxicity Profile Classification

https://doi.org/10.5061/dryad.s1rn8pkgr

The data included 1-Month Rat and 1-Month Dog SEND datasets for two different compounds (Compound A and Compound B) intended for the same pharmacological target.Â

Description of the data and file structure

The files contain data from toxicology studies performed in rats and dogs to support clinical development for two different drugs intended for the same pharmacological target. The studies were donated by the pharmaceutical companies involved in development of the compounds. All proprietary and identifying information has been removed and deidentified. Â

The toxicology data is organized based on the CDISC - Standard for Exchange of Nonclinical Data (SEND) data standard (https://www.cdisc.org/standards/foundational/send/sendig-v3-1) and stored in .json a...,

Clinical Data Management Cloud Market Outlook

According to our latest research, the global clinical data management cloud market size reached USD 2.19 billion in 2024, reflecting a robust demand for advanced data solutions in the healthcare and life sciences sectors. The market is poised for strong expansion, with a projected compound annual growth rate (CAGR) of 13.2% from 2025 to 2033. By the end of 2033, the market is anticipated to attain a value of USD 6.25 billion, driven by increasing clinical trial complexity, regulatory requirements, and the shift toward decentralized and virtual clinical trials. As per our latest research, the rapid adoption of cloud-based technologies and the growing emphasis on data-driven decision-making are acting as primary catalysts for this sustained growth trajectory.

The surge in the clinical data management cloud market is primarily attributed to the escalating volume and complexity of clinical trials across the globe. Pharmaceutical and biotechnology companies are increasingly seeking scalable, secure, and efficient data management solutions to handle the vast amounts of data generated during multi-phase clinical studies. The transition from traditional paper-based systems to digital, cloud-enabled platforms has streamlined data capture, validation, and reporting, significantly reducing errors and accelerating timelines. Furthermore, the integration of artificial intelligence and machine learning within cloud platforms has enabled real-time analytics, predictive modeling, and automated data cleaning, all of which contribute to higher data quality and faster regulatory submissions. These advancements are vital for sponsors and contract research organizations (CROs) aiming to maintain competitive advantage and meet stringent compliance standards.

Another significant growth driver for the clinical data management cloud market is the rising regulatory scrutiny and the need for enhanced patient safety. Regulatory bodies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have introduced rigorous guidelines for data integrity, traceability, and transparency in clinical research. Cloud-based clinical data management systems offer robust audit trails, centralized data repositories, and advanced encryption, ensuring compliance with global standards such as GxP, HIPAA, and GDPR. The ability to facilitate remote monitoring and real-time data access has become particularly critical in the wake of the COVID-19 pandemic, where decentralized and hybrid trial models have gained traction. This shift has further cemented the role of cloud solutions as indispensable tools for sponsors and CROs navigating the evolving clinical research landscape.

The growing adoption of cloud-based clinical data management platforms is also fueled by the need for collaboration and interoperability among diverse stakeholders. Multi-center and multinational studies require seamless data sharing across geographies, which is efficiently supported by cloud infrastructure. The scalability and flexibility offered by these platforms accommodate the dynamic requirements of various trial phases and therapeutic areas. Additionally, cloud solutions enable integration with electronic health records (EHRs), laboratory information management systems (LIMS), and other digital health tools, facilitating a holistic approach to data management. The resulting efficiencies in workflow, cost savings, and accelerated time-to-market for new therapies are compelling factors driving investment in cloud-based clinical data management systems.

Regionally, North America continues to dominate the clinical data management cloud market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The presence of leading pharmaceutical companies, advanced healthcare infrastructure, and a favorable regulatory environment have positioned North America at the forefront of technological adoption. Europe is witnessing increased uptake due to stringent data protection regulations and a strong focus on clinical research innovation. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by expanding clinical trial activity, rising healthcare investments, and supportive government initiatives. Latin America and the Middle East & Africa are gradually catching up, driven by the globalization of clinical trials and the need for cost-effective, scalable data management solutions.

Component Analysis<

ALS Community GuidanceALS Community Guidance for Industry: Drug Development for Amyotrophic Lateral Sclerosis, 2018.Community Guidance.pdf

eSource Platforms Market Outlook

According to our latest research, the Global eSource Platforms market size was valued at $1.8 billion in 2024 and is projected to reach $7.2 billion by 2033, expanding at a CAGR of 16.5% during 2024–2033. The primary factor fueling this robust growth is the accelerating digital transformation across the clinical research ecosystem, particularly the widespread adoption of electronic data capture (EDC) and remote monitoring technologies in clinical trials. As pharmaceutical companies, contract research organizations (CROs), and academic institutions increasingly prioritize data integrity, real-time accessibility, and regulatory compliance, eSource platforms are becoming indispensable for streamlining clinical workflows and ensuring high-quality, audit-ready data. This surge in demand is further amplified by the rising complexity of clinical studies, heightened regulatory scrutiny, and the growing emphasis on decentralized and hybrid trial models, all of which necessitate advanced, interoperable digital solutions.

Regional Outlook

North America commands the largest share of the eSource Platforms market, accounting for approximately 45% of the global market value in 2024. This dominance stems from the region’s mature healthcare infrastructure, high adoption rates of advanced clinical technologies, and a well-established regulatory framework that actively encourages digital innovation. The United States, in particular, is home to a dense concentration of pharmaceutical giants, CROs, and technology providers, all of whom are early adopters of eSource solutions. Additionally, the presence of major regulatory authorities such as the FDA, which has issued clear guidelines on electronic source data in clinical investigations, has further propelled market growth. The region’s focus on patient-centric research and the rapid deployment of decentralized clinical trials (DCTs) have also driven substantial investments in eSource technologies, solidifying North America’s leadership position in this evolving market.

The Asia Pacific region is emerging as the fastest-growing market for eSource Platforms, with a projected CAGR of 19.2% from 2024 to 2033. This impressive growth trajectory is fueled by increasing R&D expenditure, expanding clinical trial activity, and the rapid digitalization of healthcare systems across countries like China, India, South Korea, and Japan. Governments in these regions are rolling out supportive policies and incentives to attract global clinical trials, while local pharmaceutical and biotech firms are investing heavily in state-of-the-art data management solutions to meet international quality standards. The region’s large, diverse patient populations and growing expertise in data-driven research are further catalyzing the adoption of eSource platforms. Moreover, the rise of cloud-based deployment models and mobile health technologies is making these solutions more accessible and cost-effective, particularly for smaller research entities and academic institutes.

In contrast, emerging economies in Latin America, the Middle East, and Africa are experiencing a more gradual uptake of eSource Platforms due to infrastructural constraints, limited digital literacy, and varying regulatory landscapes. While there is growing interest in modernizing clinical research practices, challenges such as fragmented healthcare systems, inconsistent data standards, and budgetary limitations hinder rapid adoption. However, international collaborations, capacity-building initiatives, and increasing participation in global clinical trials are gradually driving market penetration. Localized demand is also rising as governments and private stakeholders recognize the value of eSource platforms in improving data accuracy, reducing trial timelines, and enhancing compliance. As these regions continue to invest in digital health infrastructure and regulatory harmonization, the long-term outlook for eSource adoption remains positive, albeit at a measured pace compared to more developed markets.

Report Scope

Clinical Data Management Market Outlook

According to our latest research, the global clinical data management market size stands at USD 4.1 billion in 2024, reflecting robust growth driven by the increasing digitization of healthcare and clinical research processes. The market is projected to expand at a CAGR of 11.2% from 2025 to 2033, reaching an estimated USD 10.7 billion by 2033. This substantial growth is primarily attributed to the rising volume of clinical trials, stringent regulatory requirements, and the escalating adoption of advanced data management solutions across pharmaceutical, biotechnology, and healthcare sectors. As per our latest research, the industry is witnessing a paradigm shift towards cloud-based platforms and AI-driven data analytics, further accelerating market expansion.

One of the most significant growth factors for the clinical data management market is the exponential increase in clinical trials worldwide. The surge in new drug development, personalized medicine, and the need for real-world evidence have compelled pharmaceutical and biotechnology companies to implement robust clinical data management systems. These systems streamline data collection, validation, and analysis, ensuring high data quality and regulatory compliance. Furthermore, the COVID-19 pandemic has underscored the importance of efficient data management in accelerating vaccine and therapeutic development, thereby reinforcing the value proposition of clinical data management solutions. The ongoing digital transformation in healthcare, including the integration of electronic health records (EHRs) and wearable devices, is also contributing to the marketÂ’s upward trajectory by generating large volumes of structured and unstructured data that require sophisticated management tools.

Another pivotal driver is the evolving regulatory landscape governing clinical research. Regulatory bodies such as the FDA, EMA, and ICH have imposed stringent guidelines for data integrity, patient safety, and trial transparency. Compliance with these regulations necessitates the implementation of advanced clinical data management platforms capable of providing audit trails, data traceability, and secure data storage. The growing emphasis on data privacy, especially with regulations like GDPR and HIPAA, has further fueled the demand for secure, compliant, and interoperable data management solutions. Additionally, the increasing complexity of clinical trials, including multi-center and global studies, has amplified the need for centralized data management and real-time data access, driving the adoption of cloud-based and AI-powered platforms.

Technological advancements are playing a critical role in shaping the clinical data management market. The integration of artificial intelligence, machine learning, and blockchain technologies is revolutionizing data validation, anomaly detection, and patient recruitment processes. These innovations are enhancing data accuracy, reducing manual errors, and accelerating decision-making in clinical research. Moreover, the proliferation of mobile health (mHealth) applications and remote monitoring devices is generating a wealth of patient data, necessitating scalable and interoperable data management systems. The shift towards decentralized and virtual trials is also prompting the adoption of flexible, cloud-based solutions that support remote data capture and real-time analytics, thereby driving market growth.

The advent of Big Data Analytics for Clinical Research is revolutionizing the way data is harnessed and utilized in the healthcare industry. By leveraging vast datasets, researchers can uncover patterns and insights that were previously inaccessible, leading to more informed decision-making and enhanced clinical outcomes. This technology enables the integration of diverse data sources, including electronic health records, genomic data, and real-world evidence, facilitating a comprehensive understanding of patient populations and treatment efficacy. As the volume of clinical data continues to grow, the application of big data analytics is becoming increasingly crucial in optimizing clinical trial design, patient recruitment, and monitoring processes. This not only accelerates the drug development timeline but also enhances the precision and personalization of therapeutic interventions.

The ability of drugs to induce phospholipidosis (PLD) is linked directly to their molecular substructures: hydrophobic, cyclic moieties with hydrophilic, peripheral amine groups. These structural properties can be captured and coded into SMILES arbitrary target specification (SMARTS) patterns. Such structural alerts, which are capable of identifying potential PLD inducers, should ideally be developed on a relatively large but reliable data set. We had previously developed a model based on SMARTS patterns consisting of 32 structural fragments using information from 450 chemicals. In the present study, additional PLD structural alerts have been developed based on a newer and larger data set combining two data sets published recently by the United States Food and Drug Administration (US FDA). To assess the predictive performance of the updated SMARTS model, two publicly available data sets were considered. These data sets were constructed using different criteria and hence represent different standards for overall quality. In the first data set high quality was assured as all negative chemicals were confirmed by the gold standard method for the detection of PLDtransmission electron microscopy (EM). The second data set was constructed from seven previously published data sets and then curated by removing compounds where conflicting results were found for PLD activity. Evaluation of the updated SMARTS model showed a strong, positive correlation between predictive performance of the alerts and the quality of the data set used for the assessment. The results of this study confirm the importance of using high quality data for modeling and evaluation, especially in the case of PLD, where species, tissue, and dose dependence of results are additional confounding factors.

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population (CNP) at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Unit (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the CNP at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the CNP at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This "second replenishment sample" was combined for estimation and analysis purposes with the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the CNP at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Dataset 0002 (DS0002) contains the data from the State Design Data. This file contains 7 variables and 82,139 cases. The state identifier in the State Design file reflects the participant's state of residence at the time of selection and recruitment for the PATH Study. Dataset 1011 (DS1011) contains the data from the Wave 1 Adult Questionnaire. This data file contains 2,021 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1012 (DS1012) contains the data from the Wave 1 Youth and Parent Questionnaire. This file contains 1,431 variables and 13,651 cases. Dataset 1411 (DS1411) contains the Wave 1 State Identifier data for Adults and has 5 variables and 32,320 cases. Dataset 1412 (DS1412) contains the Wave 1 State Identifier data for Youth (and Parents) and has 5 variables and 13,651 cases. The same 5 variables are in each State Identifier dataset, including PERSONID for linking the State Identifier to the questionnaire and biomarker data and 3 variables designating the state (state Federal Information Processing System (FIPS), state abbreviation, and full name of the state). The State Identifier values in these datasets represent participants' state of residence at the time of Wave 1, which is also their state of residence at the time of recruitment. Dataset 1611 (DS1611) contains the Tobacco Universal Product Code (UPC) data from Wave 1. This data file contains 32 variables and 8,601 cases. This file contains UPC values on the packages of tobacco products used or in the possession of adult respondents at the time of Wave 1. The UPC values can be used to identify and validate the specific products used by respondents and augment the analyses of the characteristics of tobacco products used

Pay-Per-Use Features for SDV Market Outlook

According to our latest research, the Global Pay-Per-Use Features for SDV market size was valued at $1.2 billion in 2024 and is projected to reach $5.8 billion by 2033, expanding at a robust CAGR of 19.2% during 2024–2033. This remarkable growth trajectory is primarily fueled by the increasing adoption of decentralized clinical trials and the growing demand for scalable, cost-effective data validation solutions across the pharmaceutical and life sciences sectors. As the industry shifts toward digital transformation, the flexibility and efficiency offered by pay-per-use models for Source Data Verification (SDV) are proving indispensable for organizations aiming to optimize operational costs while maintaining rigorous compliance and data integrity standards.

Regional Outlook

North America currently holds the largest share of the Pay-Per-Use Features for SDV market, accounting for approximately 42% of the global revenue in 2024. The region’s dominance is attributed to its mature healthcare infrastructure, advanced technological ecosystem, and the presence of leading pharmaceutical and contract research organizations. Regulatory frameworks such as the FDA’s guidelines on electronic records and clinical trial data integrity have accelerated the adoption of digital SDV solutions. Moreover, the prevalence of large-scale clinical trials and significant investments in research and development activities further bolster market growth. The United States, in particular, stands out for its early adoption of innovative data management platforms and its robust ecosystem of software and service providers catering to the life sciences industry.

The Asia Pacific region is projected to be the fastest-growing market for pay-per-use SDV features, with a forecasted CAGR of 23.4% between 2024 and 2033. This rapid expansion is driven by increasing investments in healthcare IT infrastructure, the proliferation of clinical trial activities in emerging economies such as China and India, and favorable government policies promoting digital health solutions. The region’s large patient pool and cost advantages have attracted multinational pharmaceutical companies and contract research organizations to establish regional hubs. Additionally, the growing focus on remote monitoring and decentralized trials, especially in response to the COVID-19 pandemic, has accelerated the adoption of cloud-based SDV solutions in Asia Pacific, positioning it as a critical growth engine for the global market.

Emerging economies in Latin America and the Middle East & Africa are gradually increasing their adoption of pay-per-use SDV features, though they face unique challenges such as limited digital infrastructure, regulatory complexities, and workforce skill gaps. Despite these hurdles, localized demand for efficient clinical data management and compliance solutions is rising, particularly as governments and private sectors invest in healthcare modernization. Efforts to harmonize regulatory standards and implement pilot digital health projects are paving the way for broader adoption. However, the pace of growth in these regions is somewhat tempered by the need for further policy reforms, capacity building, and the establishment of robust data security frameworks.

Report Scope

Clinical Trial Data Security Market Outlook

The global clinical trial data security market size is projected to grow from USD 1.5 billion in 2023 to USD 3.6 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 12.3% during the forecast period. This significant growth is primarily driven by the increasing volume of clinical trials and the growing emphasis on data privacy and security. The rising cases of cyber-attacks and data breaches in the healthcare sector are further propelling the demand for robust security solutions in clinical trials. Additionally, regulatory requirements mandating stringent data protection measures are contributing to the market expansion.

The escalating volume of clinical trials, propelled by the burgeoning pharmaceutical industry, represents one of the crucial growth factors for the clinical trial data security market. With the advent of personalized medicine and the influx of innovative therapies, the number of clinical trials is on a steep rise. Each trial generates a considerable amount of sensitive data that must be protected against potential breaches. Consequently, there is an increasing demand for advanced data security solutions that can safeguard this valuable information, ensuring compliance with regulatory standards and maintaining the integrity of the research data.

Another significant growth driver is the rising incidence of cyber-attacks targeting the healthcare sector. Clinical trials, which involve the collection and storage of sensitive patient data, have become prime targets for cybercriminals. These attacks can lead to substantial financial losses, reputational damage, and, most critically, jeopardize patient safety. As a result, pharmaceutical companies and research organizations are investing heavily in sophisticated security technologies to mitigate these risks. The integration of artificial intelligence and machine learning in security solutions is also enhancing their effectiveness, thereby boosting market growth.

The evolving regulatory landscape is further accelerating the adoption of data security solutions in clinical trials. Regulatory bodies across the globe, such as the FDA in the United States and the EMA in Europe, have established stringent guidelines for data protection in clinical research. Compliance with these regulations is mandatory and requires robust security protocols to ensure the confidentiality, integrity, and availability of clinical trial data. This regulatory pressure is compelling organizations to adopt advanced security measures, contributing to the market's upward trajectory.

From a regional perspective, North America holds the largest share in the clinical trial data security market, owing to the presence of major pharmaceutical companies and advanced healthcare infrastructure. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by the increasing number of clinical trials and rising awareness about data security. Europe also represents a significant market, supported by stringent data protection regulations and a strong focus on research and development. In contrast, Latin America and the Middle East & Africa are expected to showcase moderate growth due to developing healthcare facilities and regulatory frameworks.

Component Analysis

The component segment of the clinical trial data security market is categorized into software, hardware, and services. The software segment dominates the market, largely attributed to the critical role of cybersecurity software in protecting sensitive clinical trial data. Software solutions offer a comprehensive approach to data security, encompassing encryption, access control, intrusion detection, and endpoint protection. These capabilities are essential in securing the vast amounts of data generated during clinical trials against unauthorized access and cyber threats.

Hardware components, though not as dominant as software, play a pivotal role in the overall data security infrastructure. Hardware-based security solutions, such as secure servers and dedicated security appliances, provide an additional layer of protection. They ensure that data is processed and stored in a secure environment, reducing the risk of physical tampering and unauthorized access. Furthermore, the integration of hardware security modules (HSMs) helps in safeguarding cryptographic keys, which are vital for data encryption and decryption processes.

The services segment is also witnessing significant growth, driven by the increasing reliance on managed security

Users of the V-safe data are required to adhere to the following standards for the analysis and reporting of research data. All research results must be presented and/or published in a manner that protects the confidentiality of participants. V-safe data will not be presented and/or published in any way in which an individual can be identified.

Therefore, users will:

1. Not attempt to link or permit others to link the data with individually identified records in another database.
2. Not attempt to learn the identity of any participant in the data and will not deliberately combine these data with other CDC or non-CDC data for the purpose of matching records to identify individuals. If you should inadvertently discover the identity of any participant, you will ensure the identity of any participant is kept confidential, and will not be used in any publications and/or presentations.
3. Not imply or state, either in written or oral form, that interpretations based on analysis of the data reflect official CDC policies or positions.
4. Understand that sub-national analyses are not appropriate for this national sample and will not be conducted.
5. Understand that V-safe is a voluntary self-enrollment program requiring smartphone access; therefore, information from V-safe might not be representative or generalizable to the US population.

V-safe is an active surveillance program to monitor the safety of COVID-19 vaccines that are authorized for use under U.S. Food and Drug Administration (FDA) Emergency Use Authorization (EUA) and after FDA licensure.

These data include registrant information (deidentified), health check-in, and vaccination data collected through V-safe from 12/13/2020 to 06/30/2023. Please review the V-safe data user agreement before analyzing any V-safe data. Updated on September 5, 2025, to comply with the President's Executive Order 14168.

Clinical Data Management System Market Outlook

According to our latest research, the global Clinical Data Management System (CDMS) market size reached USD 2.47 billion in 2024, with a robust compound annual growth rate (CAGR) of 11.2% projected from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a valuation of USD 6.43 billion. This strong growth trajectory is primarily driven by the increasing adoption of digital health solutions, the surge in clinical trials, and the imperative need for efficient data management to comply with stringent regulatory requirements.

The expansion of the Clinical Data Management System market is underpinned by the exponential increase in clinical trials globally, particularly in the fields of oncology, rare diseases, and infectious diseases. As the pharmaceutical and biotechnology industries accelerate drug development pipelines, the volume and complexity of clinical data being generated have surged. This necessitates advanced CDMS solutions capable of ensuring data accuracy, integrity, and regulatory compliance. Furthermore, the growing trend toward decentralized and virtual clinical trials, fueled by the COVID-19 pandemic and technological advancements, has heightened the demand for scalable and interoperable data management platforms. These factors collectively contribute to the rapid adoption of CDMS across diverse healthcare settings.

Another significant growth factor is the escalating focus on regulatory compliance and data security. With authorities such as the FDA, EMA, and other global regulatory bodies enforcing strict guidelines on clinical data management, organizations are compelled to invest in sophisticated CDMS platforms. These systems not only streamline the data capture and validation process but also provide robust audit trails, real-time data monitoring, and advanced analytics capabilities. The integration of artificial intelligence and machine learning into CDMS further enhances data quality and accelerates decision-making. As a result, clinical research organizations, pharmaceutical companies, and medical device manufacturers are increasingly prioritizing digital transformation in their data management strategies to maintain a competitive edge and ensure successful product approvals.

Moreover, the proliferation of cloud-based deployment models and the advent of interoperable, user-friendly CDMS solutions have made these systems more accessible to a broader range of end users, including small and medium-sized enterprises. The ability to seamlessly integrate CDMS with electronic health records, laboratory information systems, and other clinical research tools has significantly improved workflow efficiency and data transparency. Additionally, the growing emphasis on patient-centric trials and real-world evidence generation is driving the adoption of advanced CDMS platforms capable of capturing and managing diverse data types. These trends, combined with increasing investments in healthcare IT infrastructure, are expected to sustain the marketÂ’s momentum over the forecast period.

In the realm of clinical data management, the integration of Scientific Data Management Systems (SDMS) is becoming increasingly pivotal. These systems are designed to handle the vast amounts of data generated in clinical trials, particularly in complex fields such as genomics and proteomics. SDMS provide a framework for storing, retrieving, and analyzing scientific data, ensuring that it is both accessible and secure. By incorporating SDMS into clinical data workflows, organizations can enhance their data management capabilities, leading to more efficient data processing and improved compliance with regulatory standards. This integration is particularly beneficial in managing the lifecycle of scientific data, from initial collection through to analysis and reporting, thereby supporting the overall objectives of clinical research.

Regionally, North America continues to dominate the Clinical Data Management System market, accounting for the largest share in 2024 due to the presence of major pharmaceutical companies, a well-established clinical research ecosystem, and strong regulatory frameworks. However, the Asia Pacific region is witnessing the fastest growth, supported by expanding clinical trial activities, favorable government initiatives

The recent global pandemic associated with the highly contagious novel coronavirus (SARS-CoV-2) has led to an unpredictable loss of life and economy worldwide, and the discovery of antiviral drugs is an urgent necessity. For the discovery of new drug leads and for the treatment of various diseases, natural products and purified photochemical from medicinal plants are used. The RNA cap was methylated by two S-adenosyl-L-methionine (SAM)-dependent methyltransferases of SARS coronavirus (SARS-CoV-2), catalyzed by NSP16 2′-O-Mtase. Natural substrate SAM, 128 Phytocompounds retrieved from the Phytocompounds database, and 11 standard FDA-approved HIV drugs reclaimed from the PubChem database are subjected to docking analysis. The docking study was done using AutoDock Vina. Further, admetSAR and DruLiTO servers are used to analyze the drug-likeness properties. The NSP16/10 structure and natural substrate SAM, Phytocompounds Withanolide (WTL), and HIV standard drug Dolutegravir (DLT) as hit compounds were identified by molecular dynamics using the Gromacs GPU-enabled package. To examine the effectiveness of the identified drugs versus COVID-19, further in vitro and in vivo studies are required. Communicated by Ramaswamy H. Sarma