We are passionate about curating the world's clinical development data to elevate accessibility, transparency, and raise the standard of clinical trials.
We see insight retrieval as a cornerstone of data curation. ZETTAI, powered by GenAI technology, unlocks the hidden potential of clinical development data. It enables effortless navigation through vast datasets, performs advanced data analysis, and retrieves deep, meaningful insights, while ensuring exceptional accessibility.
Clinical development data represents a vital knowledge base of fundamental clinical research on the human body, crucial for benefiting all of humanity. We are building robust data models, enhancing data quality, and cleaning, organizing, enriching, and contextualizing this invaluable information.
Supporting it all is our cloud-based big data platform: CLINICA. It organizes and processes clinical development data on a previously unprecedented scale, leveraging cutting-edge technologies. CLINICA is capable of storing and retrieving vast amounts of related documents quickly and efficiently.
Unlocking the future of clinical development data with GenAI
Clinical development data has the potential to uncover crucial insights, significantly reducing risk and increasing trial success. ZettAI unlocks this hidden potential, especially as it is supported by a robust data platform and high-quality, high-volume clinical development data. With ZettAI, navigating large, diverse datasets becomes effortless, allowing users to derive valuable insights from extensive and complex trial documents.
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CLINICA is a cutting-edge, cloud-based data platform designed to seamlessly integrate complex clinical development datasets such as clinical trial data, regulatory approvals, disease or hospital data - amongst others.
By utilizing advanced data models and state-of-the-art technologies such as GenAl, CLINICA harnesses the power of big data and non-linear knowledge graphs to unlock the full potential of clinical trial data.
Our innovative platform enables the efficient organization, processing, and analysis of vast amounts of information, providing valuable insights to drive trial success and improve outcomes.
Smart data and insights
drive clinical trial success
Clinical development data is essential throughout the drug development process, providing valuable insights and strategic advantages.
Data fuels
successful
clinical development
Long before a new medical intervention is tested in a clinical trial, the manufacturer makes strategic decisions on the clinical conditions, markets and drug types to focus on. They thoroughly investigate the latest advancements in biomolecular research but also business opportunities, market conditions, and competitor activities.
After that, hundreds of compounds are screened to identify a subset of promising candidates, which are then thoroughly characterized in in-vitro and in-vivo studies to ensure basic safety.
High-quality clinical development data offers a comprehensive overview of current and planned competitor activities, helping companies assess future market conditions, anticipate market launches, and gauge market competitiveness.
By analyzing past, current, and planned clinical research activities across the industry, companies can uncover promising opportunities in the development of new medical interventions.
Utilizing clinical development data for competitive benchmarking provides valuable insights into efficiency and performance metrics, such as study duration, number of arms, and participant count. This enables companies to quantify their strengths and weaknesses, as well as to build and leverage competitive advantages.
Clinical study design is a critical phase at the outset of the clinical development process, where the framework and operations for conducting a clinical trial are meticulously planned. This phase involves defining the objectives, methodology, and logistics of the study to ensure it meets regulatory standards and scientific rigor. Key documents developed during this phase include the study protocol, which outlines the trial's objectives, design, methodology, statistical considerations, and organization. Additionally, the informed consent forms, case report forms (CRFs), and data management plans are prepared to ensure proper documentation and ethical management of participant data.
A well-constructed study design not only lays the groundwork for successful trial execution but also plays a pivotal role in the reliability and credibility of the trial's findings. By anticipating potential challenges and incorporating risk management strategies, this phase helps to safeguard the integrity and validity of the entire clinical development process
In the clinical trial design phase, data plays a pivotal role in shaping a robust and effective study. Historic trial data from previous studies can be utilized to identify risks, establish benchmarks, measure trial complexity and feasibility and identify similar past studies for comparisons. Historical data and documents, such as protocols and forms, and relevant scientific literature are analyzed to inform the selection of endpoints, inclusion and exclusion criteria, principal investigators and optimal dosing strategies as well as develop new protocols and consent forms and identify clinical sites for participation.
Statistical data guides the calculation of sample sizes needed to achieve adequate power and detect clinically significant effects. Additionally, demographic and epidemiological data help identify suitable populations for the study, ensuring diversity and generalizability of results. Data on past recruitment rates of clinical sites and dropout patterns are leveraged to design realistic timelines and mitigate potential challenges.
Site selection and preparation are critical phases in the setup of clinical studies, directly influencing the trial's success. The importance of site selection lies in identifying locations that not only meet the logistical and regulatory requirements but also have the capability to recruit and retain the target patient population. Selecting the right sites involves evaluating their infrastructure, access to suitable participants, experience in conducting similar trials, and ability to adhere to the study protocol. Effective site preparation includes training the site staff, setting up necessary equipment, and ensuring compliance with regulatory and ethical standards.
Thorough site selection and preparation are crucial for minimizing trial delays and ensuring that data collected is both reliable and of high quality. By fostering strong communication and collaboration with site teams, sponsors can enhance trial efficiency and optimize the likelihood of achieving successful outcomes.
Clinical study data is invaluable in the site selection process. Historical performance data from previous trials can highlight sites with strong track records in patient recruitment, retention, and data accuracy. Leveraging historic participant recruitment rates helps ensure targets are met, while data also provides insights into site experience with desired medical conditions. Additionally, identifying planned and ongoing competing studies helps avoid or mitigate recruitment risks.
Data analytics can assess the demographic and epidemiological characteristics of potential sites to ensure they match the study's requirements. Predictive modeling will forecast site performance and identify potential challenges, enabling proactive mitigation strategies. Additionally, data from feasibility studies can provide insights into the operational capabilities of sites, ensuring that only those with the necessary expertise and infrastructure are chosen.
The phase of participant recruitment is pivotal in the clinical trial process, directly impacting the validity and success of the study. This phase involves identifying, screening, and enrolling participants who meet the study's eligibility criteria.
Ensuring informed consent is a crucial step, where potential participants are thoroughly educated about the study's purpose, procedures, risks, and benefits, allowing them to make a well-informed decision about their participation. Once participants are enrolled, randomization is employed to assign them to different study groups in an unbiased manner, ensuring the integrity of the study and reducing selection bias. Effective patient recruitment is essential for achieving the required sample size, maintaining study timelines, and ensuring the reliability of the study outcomes.
By leveraging clinical study data, sponsors can improve patient recruitment and enrollment efficiency, enhance participant retention, and ensure ethical and effective randomization processes.
Historical recruitment rates and site enrollment performance metrics help identify candidate sites and set expectations for the pace of patient enrollment in the study. If recruitment proves challenging, this data can assist in identifying additional sites with promising recruitment rates.
Participant demographics can guide targeted recruitment strategies and pinpoint regions with higher prevalence of the medical condition under study.
Data analytics can assess and predict recruitment challenges and participant dropout rates, enabling proactive adjustments. Monitoring competing recruitment activities by other sponsors and potentially interfering competitor studies helps mitigate recruitment risks.
Predictive modeling can forecast site performance, while GenAI-based technologies can streamline the generation of informed consent documents based on previous trials.
Once a clinical study is underway, the focus shifts to the operational phases of clinical trial monitoring, data collection, and quality assurance. Clinical trial monitoring involves regular oversight of the trial's progress to ensure compliance with the study protocol, regulatory requirements, and ethical standards. Monitors conduct site visits to verify the accuracy and completeness of the data, ensuring that the trial is conducted according to the approved plan. This process includes checking informed consent forms, reviewing source documents, and confirming that adverse events are reported accurately. Effective monitoring helps identify and address any issues early, ensuring the integrity of the trial and the safety of participants.
Additionally, robust data collection processes are vital for maintaining the credibility of the trial's outcomes, as they ensure that the information gathered is reliable and can withstand regulatory scrutiny. Quality assurance measures, such as audits and independent reviews, further safeguard the trial's validity, helping to build confidence in the final results.
Clinical study data aids in monitoring ongoing or planned competitor activities that might directly impact participant recruitment. This data can help foresee when a competitor trial may be launched at a participating hospital, potentially posing challenges for patient recruitment. Additionally, it enables the creation of participant enrollment projections to establish benchmarks, making it clear early on if a study falls behind recruitment expectations. Furthermore, the data helps identify potential competitive situations within a competitor's portfolio.
Data collection and quality assurance are fundamental to the success of ongoing clinical trials. Accurate and timely data collection is achieved through the use of electronic data capture (EDC) systems, which streamline the process and reduce the risk of errors. Rigorous quality assurance procedures, such as data validation and audit trails, are implemented to maintain data integrity. Regular data reviews and interim analyses are conducted to ensure that the collected data meets predefined quality standards and to make any necessary adjustments to the trial. These processes are crucial for generating reliable and valid results, which are essential for regulatory submissions and for advancing medical knowledge.
To obtain marketing authorization, new drugs and medical devices require a New Drug Application (NDA). Generic drugs use an abbreviated NDA. For devices, a different type of submission is required. This can either be a 510(k) submission if there is a similar device that has already received marketing authorization, a De Novo submission for low to medium risk devices, or a Pre-Market Authorization (PMA) for Class III, high-risk devices.
Once a compound is validated through clinical studies, the approved drug must be monitored as it is used by a significantly broader population. Post-market surveillance ensures ongoing drug safety and efficacy through robust pharmacovigilance and proactive risk management. This includes adverse event reporting, additional studies, and comprehensive data analysis to identify and mitigate risks, thereby safeguarding public health and ensuring market success. Ongoing monitoring of critical data and engagement with regulatory agencies and healthcare professionals helps to promptly address any emerging safety concerns and adjust safety protocols as needed.
Clinical study data plays a crucial role in the regulatory submission process for new drugs and medical devices by providing evidence required for New Drug Applications (NDAs) and various device submissions such as 510(k), De Novo, or Pre-Market Authorization (PMA). This data supports the demonstration of safety, efficacy, and performance, which is essential for obtaining marketing authorization. Additionally, once a compound is approved, clinical study data aids in post-market surveillance by facilitating ongoing monitoring of drug safety and effectiveness through robust pharmacovigilance, adverse event reporting, and risk management.
GenAI can significantly enhance this process by automating the drafting of essential documents for regulatory submissions, such as NDAs and device applications. It can generate comprehensive and accurate reports based on clinical study data, streamline the preparation of post-market surveillance documentation, and assist in developing risk management plans. By leveraging advanced algorithms, GenAI ensures that the documentation is both thorough and compliant with regulatory requirements, ultimately supporting a smoother and more efficient submission and monitoring process.
Our team consists of seasoned professionals with a wealth of experience in the life-science and pharmaceutical sectors. With decades of collective knowledge under our belts, we bring a diverse skill set and a passion for driving innovation in the industry.
Dr. Philipp Diesinger is a Data and AI enthusiast with a career dedicated to serving clients in the life science sector. He is driven by his passion for leveraging data-driven decision-making to deliver tangible results with real-world impact.
Stefan Stefanov is a seasoned software engineer with over 7 years of experience in leading and developing Data and AI solutions for the life science sector. He is passionate about transforming intricate data into user-friendly, insightful visualizations.
Dr. Andreas Thomik is a data scientist with nearly a decade of experience in leveraging data and AI to generate business value across multiple industries, with a particular focus on the life sciences field. He is dedicated to advancing innovative data-driven strategies within the sector.
Works as a data and technology expert in the pharma industry, solving problems across the pharma value chain.
Dr. Peter Roche is a data expert in the life science sector, specializing in data systems architecture, and backend development. He has 10 years of experience as a quantitative analyst in capital markets and asset management, and an additional 8 years as a senior data engineer and data scientist.
Experienced Data and Backend Engineer with deep expertise in cloud technologies, automation, system integration, end-to-end data pipelines.
Dr. Christian Schilling is a Managing Partner at AVANTECA Partners AG, a privately held asset management company based in Basel, Switzerland. He is a former senior executive of Boehringer Ingelheim and has over 25 years of experience in Big Pharma, including strategic and operational roles in research, clinical development, marketing and sales as well as country management. He also held executive roles in business development and M&A.
Simone Menne, current President of the American Chamber of Commerce is a German business leader. She was CFO of Lufthansa from 2012 to 2016. From September 2016 until end of 2017, she was a member of the management board of Boehringer Ingelheim, responsible for its finance division. Simone is currently a supervisory board member and non-executive director at several companies and runs an art gallery in Kiel, Germany.
Ning Li is a successful entrepreneur best known as the founder of Made.com, an online furniture retailer that transformed the industry with its direct-to-consumer model. He co-founded Made.com after launching MyFab.com to enhance online furniture shopping. Following this success, Ning founded Typology, a skincare brand focused on transparency and affordability. With a proven track record in identifying market opportunities and scaling startups, Ning continues to innovate in the consumer goods space.
Experienced Data and Backend Engineer with deep expertise in cloud technologies, automation, system integration, end-to-end data pipelines.
Sep 16, 2024
We are excited to share that our team has published a new article in Issue 8 of the esteemed Data & AI Magazine. Titled “Can large data reveal how sponsor competition affects participant recruitment in clinical trials?", the article investigates the dynamics between sponsor competition and its influence on the recruitment of participants for clinical trials.
In this insightful piece, we analyze how varying levels of competition among sponsors can impact recruitment strategies and participant engagement. By leveraging large datasets, we uncover patterns and insights that can help optimize recruitment processes, ultimately leading to more successful clinical outcomes.
This article embodies our commitment to driving innovation and thought leadership in the field of clinical data analysis. We invite you to read the full article in Issue 8 of the Data & AI Magazine to gain valuable insights into the intricate relationship between sponsor strategies and participant recruitment.
Stay tuned for more updates and thank you for following our journey in transforming clinical research!
Jul 5, 2024
We are thrilled to announce that GoTrial is now forming strategic collaborations with select industry partners in the life sciences sector, with a particular focus on clinical development. This initiative aims to foster innovation and mutual learning by working closely with carefully chosen organizations.
Our goal is to explore new frontiers in clinical development through these early partnerships, leveraging our expertise and insights while gaining valuable perspectives from our collaborators. We believe that by working together, we can drive advancements, address complex challenges, and create impactful solutions in the field.
While we are excited about the prospect of these collaborations, we will be selectively partnering with a limited number of organizations to ensure a focused and productive engagement. If your organization is interested in exploring a partnership that promotes innovation and growth, we would be delighted to discuss potential opportunities with you.
We look forward to building meaningful relationships and achieving great outcomes together.
For more information on how to get involved, please contact us.
Jun 10, 2024
We are thrilled to announce that our team has published a new article in the esteemed magazine The Data Scientist. Titled “Challenges and Opportunities in Clinical Trial Registry Data”, the article delves into the complexities and potential of clinical trial registries in the data science landscape.
In this insightful piece, we explore the multifaceted challenges faced when working with clinical trial registry data, including issues related to data quality, accessibility, and standardization. We also highlight the exciting opportunities that this rich data source presents for advancing research and improving trial outcomes.
Our article reflects our commitment to driving innovation and thought leadership in the field of clinical data analysis. We invite you to read the full article in The Data Scientist to gain a deeper understanding of how leveraging registry data can transform clinical trials and foster new advancements in medical research.
Stay tuned for more updates and insights from our team!
Apr 22, 2024
We are excited to share that we have entered into a strategic partnership with Rewire, a leading data science consulting company. This collaboration represents a powerful synergy between our data expertise and Rewire’s exceptional consulting capabilities. Rewire is renowned for its expertise in data science, data engineering, and large-scale AI transformations.
In this partnership, we bring our life science expertise, advanced data platform, and (Gen)AI tools to the table. Rewire complements this with their top-notch data science, data engineering, and AI transformation talent. Together, we offer a comprehensive and unique value proposition that combines our clinical development insights and analytics with Rewire’s consulting prowess.
This integrated approach is designed to enhance clinical development processes, drive innovation, and deliver actionable insights to optimize trial outcomes.
We are thrilled about the potential of this partnership to create value and advance the field of life sciences.
For more information about Rewire and their impressive capabilities, visit Rewire's website.
Stay tuned for more updates as we embark on this exciting journey together!
If you are interested in a demonstration or wish to get in touch, please use the online form or the email provided below. We always try to respond in a timely fashion.
