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Medidata

Medidata have been redefining what’s possible in clinical trials for over 20 years.

Only Medidata combines the wealth of data, AI-powered insights, and patient-centric solutions required to bring tomorrow’s breakthrough therapies and devices to life, and into the hands of patients.

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6 Data Linkage Use Cases to Future-Proof Your Clinical Trial

Clinical trials remain the gold standard for regulatory decision making in medicine. However, patients who participate in clinical trials continue generating a wealth of real-world data (RWD) in their interactions with the larger healthcare ecosystem before, during, and after a trial. Trial patients’ RWD, such as medical claims, electronic medical records (EMR), registry data, and wearable device data, that are routinely collected in the background can supplement active collection of clinical trial data (CTD) and provide a substantially deeper insight on benefits, risks, and cost of treatments

Integrated Evidence Success Stories and Partnerships

Teams know there is real world data that could generate evidence to inform study design, ease recruitment, or drive medical engagement. However, finding the relevant data and generating insights can be challenging. Until now. Integrated Evidence connects historical data from more than 20,000 cross-sponsor clinical trials and 6 million patient volunteers, with real world data, to deliver powerful insights and the necessary evidence clinical development leaders need to increase the probability of trial success. In this e-book, you will find out how Integrated Evidence can resolve the challenges of clinical trial data availability and ensure that the data is of regulatory-approved quality.

The Digital Model for Clinical Research

The biopharma industry constantly innovates to bring crucial therapies to patients, and this spirit of innovation has propelled the adoption of digital technologies for clinical trials. Large sponsors, during the past twenty years, have implemented digital technologies to connect clinical trial workflows, derive superior value from clinical and real-world data, and manage critical processes like study planning and regulatory submission.

FAQs to Guide Your Decentralized Clinical Trials Strategy

Decentralized trials, also known as hybrid trials, virtual(ized) clinical trials, remote trials, and directto-patient trials, employ a method of conducting clinical trials where parts or all of the trial happen outside a traditional physical clinic or trial site.

Five Quick Tips to Accelerate Your Study Build

Accelerating study build times became an imperative during COVID-19. Delivering faster study builds has since propelled the adoption of new technology, processes, and resources. Study build teams are required to simplify and future proof design to save downstream time and costs. They also have an opportunity to gain greater control of study data and execution, optimize the entire trial lifecycle, and accelerate time-to-market. While speed prevails, study build teams need to ensure study integrity, patient safety, and regulatory requirements. The technology strategy they choose, the approach they take, and the specialists they partner with drive the pace, data integrity, and successful outcomes of their trials. This eBook outlines five specific tips you can apply to accelerate and optimize your study builds today, and into the future.

Synthetic Control Arm®: The Regulatory Grade External Control Arm to Power your Clinical Trials

Randomized Control Trials (RCTs) are the gold standard for evaluating the safety and efficacy of new treatments. However, in some circumstances, maintaining a concurrent control arm is not feasible. External Control Arms (ECAs) can help sponsors overcome recruitment challenges in trials with small patient populations. They can also provide supplementary data, beyond what a clinical trial itself can produce, to bolster trial results when necessary. This eBook provides guidance on the increasing role of ECAs, the differences between control groups built using real world data (RWD) and a Synthetic Control Arm (SCA® ) containing historical clinical trial data (HCTD), and the successful impact of SCA on clinical trial design decisions and regulatory conversations.

8 Strategic Questions to Help Determine Your Path to Decentralization

In this eBook, we’ve compiled eight strategic questions that will help jumpstart your path to decentralized clinical trials (DCTs), explaining what qualifies as a decentralized clinical trial, what decentralizing technology means for study teams, and how decentralization benefits patients, sites, and sponsors. Let’s start with the basics.

Unlock the Value of Your Clinical Trial Data and Content with Big Data Discovery

From leisure activities, to our own health and wellness, to the industries in which we work — Big Data has transformed our world. Subscription-based content providers, like Netflix and Amazon Prime, are changing television programming by using detailed customer segmentation and viewing habits to rethink how new programming is funded, produced, and released to the market. Everyday items like Nest are transforming home heating and cooling by collecting and aggregating sensor data to automate thermostat changes. In the clinical research industry, the creation and availability of many more data points is changing everything too. Historically, data came from a single source: a patient visiting a clinic, whose information would be entered into an electronic data capture (EDC) system. Today, clinical trials are accommodating an incredible variety of data and content sources: from traditional clinical data, to high resolution images, to genomic and wearable sensor data, investigator files, consent forms, and much more. This data explosion brings new and transformative opportunities, but it also comes with additional risks

Synthetic Control Arm® in Clinical Trials

While randomized controlled trials (RCTs) are the gold standard for evaluating the safety and efficacy of new medical treatments, maintaining a concurrent control arm is sometimes not feasible and can lead to increased patient burden and threaten the completion of a trial. Such uncontrolled trials are commonly conducted in rare, orphan, or very serious drug indications, when there is a shortage of patients or investigational drug, when there are scientific concerns about treatment switching/crossover, or for ethical concerns. In such cases, sponsors rely on study designs that deviate from the traditional RCT, such as single-arm trials, which can yield important safety and efficacy data that can support a regulatory submission and have recognized benefits, such as smaller sample sizes, the ability to end quickly if a drug has low activity, and that all (or at least most) patients receive the investigational drug (Grayling, 2016). However, uncontrolled trials also risk generating biased data because of a lack of randomization. To overcome these challenges, sponsors sometimes employ external controls; these improve the interpretation of single-arm trials, by providing supportive evidence that is highly contextual and would otherwise be absent, and also allow sponsors to better understand their trial population if patients were not on therapy. While there are several available external control options, the accumulation of vast amounts of patient-level data is enabling higher-quality and more informative external control arms. This white paper discusses the concept of the Synthetic Control Arm® (SCA®),1 which is a type of external control that is generated using patient-level data from patients external to the trial with the goal of improving the interpretation of uncontrolled trials, which can enable better product development decisions. A series of case studies are provided to highlight the different ways an SCA has been used.

Clinical trials are better, faster, cheaper with big data

Clinical trials have never been more in the public eye than in the past year, as the world watched the development of vaccines against covid-19, the disease at the center of the 2020 coronavirus pandemic. Discussions of study phases, efficacy, and side effects dominated the news. The most distinctive feature of the vaccine trials was their speed. Because the vaccines are meant for universal distribution, the study population is, basically, everyone. That unique feature means that recruiting enough people for the trials has not been the obstacle that it commonly is. “One of the most difficult parts of my job is enrolling patients into studies,” says Nicholas Borys, chief medical officer for Lawrenceville, N.J., biotechnology company Celsion, which develops next-generation chemotherapy and immunotherapy agents for liver and ovarian cancers and certain types of brain tumors. Borys estimates that fewer than 10% of cancer patients are enrolled in clinical trials. “If we could get that up to 20% or 30%, we probably could have had several cancers conquered by now.” Clinical trials test new drugs, devices, and procedures to determine whether they’re safe and effective before they’re approved for general use. But the path from study design to approval is long, winding, and expensive. Today, researchers are using artificial intelligence and advanced data analytics to speed up the process, reduce costs, and get effective treatments more swiftly to those who need them. And they’re tapping into an underused but rapidly growing resource: data on patients from past trials.

Direct-to-Patient Clinical Trials

Drug development is a risky endeavour that is expensive, complex, and lengthy. A significant portion of resources earmarked for drug development programmes are consumed by screening, recruiting, enrolling, engaging, monitoring, and retaining patients in clinical trials. The pharmaceutical industry is continually exploring novel trial designs and methods that may alleviate the high costs and shorten study times, which can ultimately expedite new medicines to market. In clinical trials, the Direct-to-Patient (DtP) model allows for study medications to be received and administered in a patient’s home rather than a clinical site. The DtP drug supply model has gained momentum in recent years and continues to evolve since it can confer valuable benefits to sponsors and patients. This white paper provides an overview of the factors that drive the rapidly evolving clinical trial design landscape and demonstrates how DtP trials can solve some of the difficulties facing drug sponsors today, including common pitfalls to consider when embarking on a DtP trial. Download this white paper today.

Synthetic Control Arm® in Clinical Trials

While randomized controlled trials (RCTs) are the gold standard for evaluating the safety and efficacy of new medical treatments, maintaining a concurrent control arm is sometimes not feasible and can lead to increased patient burden and threaten the completion of a trial. Such uncontrolled trials are commonly conducted in rare, orphan, or very serious drug indications, when there is a shortage of patients or investigational drug, when there are scientific concerns about treatment switching/crossover, or for ethical concerns. In such cases, sponsors rely on study designs that deviate from the traditional RCT, such as single-arm trials, which can yield important safety and efficacy data that can support a regulatory submission and have recognized benefits, such as smaller sample sizes, the ability to end quickly if a drug has low activity, and that all (or at least most) patients receive the investigational drug (Grayling, 2016). However, uncontrolled trials also risk generating biased data because of a lack of randomization. To overcome these challenges, sponsors sometimes employ external controls; these improve the interpretation of single-arm trials, by providing supportive evidence that is highly contextual and would otherwise be absent, and also allow sponsors to better understand their trial population if patients were not on therapy. While there are several available external control options, the accumulation of vast amounts of patient-level data is enabling higher-quality and more informative external control arms. This white paper discusses the concept of the Synthetic Control Arm® (SCA®),1 which is a type of external control that is generated using patient-level data from patients external to the trial with the goal of improving the interpretation of uncontrolled trials, which can enable better product development decisions. A series of case studies are provided to highlight the different ways an SCA has been used.

Electronic Informed Consent in Clinical Research

Medidata is conducting a study to understand the regulatory positions, adoption and the variability regarding electronic informed consent (eConsent) around the world. This exercise has come about due to the extensive number of regulatory relevant enquires Medidata gets from sponsors and organisations managing trials in research. These organisations are keen to have the option to leverage electronic means for consenting trial participants but are uncertain of the regulatory positions on the topic. The only way to seek clarity on this topic was to directly engage with the relevant bodies. The study initially focused on the countries in the European geographic region countries but has evolved to other regions including Asia Pacific and the Americas. The study prioritized countries where there was an aspiration to run electronic informed consent by organisations running clinical trials. Throughout the study, Medidata has directly engaged with regulators, health authorities and ethics groups to assess the eConsent landscape. Overall, the Medidata study team has collected meaningful feedback and engaged in positive and encouraging dialogues with these organisations. However, almost half of the bodies Medidata contacted have not responded or did not fully respond to the question being asked. We deduce this may be due to a lack time to consider the topic and/or a lack of general position. The patient-centric benefits of electronic informed consent technologies have been well documented.1,2 Electronic informed consent has led to improved patient engagement and the ability to obtain trial patient signatures electronically whilst personally identifiable information (PII) remains safe and securely stored. The US Food and Drug Administration (FDA) provides non binging recommendations on the subject and the US has seen accelerated adoption of eConsent solutions. Subsequently, the UK regulator, the MHRA (Medicines and Healthcare Regulatory Agency) has also issued a very comprehensive guidance and is exemplary in the thought leadership in terms of the interplay of eConsent and the consideration of the multiple EU regulations and UK laws. The EU has seen variable adoption, mostly due to a lack of regulatory clarity and operational reasons. It is good to note that new EU eSystems guidance is being written to include electronic informed consent.

Decentralized Clinical Trials: The Future of Clinical Research Is Here

Improvements in technologies and methods to drive clinical trial innovations have focused on the incorporation of decentralised clinical trials (DCTs) - also sometimes referred to as ‘remote’, ‘hybrid’, ‘virtual’ or ‘patient-centric’ trials. The COVID-19 pandemic has significantly accelerated the adoption of DCTs, which allowed sponsors to quickly virtualise many trial activities, including remote monitoring, telehealth visits, electronic consent (eConsent), electronic patient reported outcomes (ePRO), electronic clinical outcome assessments (eCOA), and use of wearables/sensors. The uptake in DCTs is expected to continue in a post-COVID-19 world due to the experience gained during the pandemic The DCT trend is being driven by several headwinds that have converged, including the COVID-19 pandemic, ballooning drug development costs, and a greater focus on the patient-centricity and value demonstration of new therapies. Clinical trials are currently evolving and becoming modernised as they continue to integrate more and more virtual elements. Sponsors must prepare themselves for this increasingly digital future, which will demand scalable, flexible, interoperable, unified, and intelligent software platforms to synthesise patient-centric data into real-time insights that will offer benefits to stakeholders across the healthcare spectrum. This white paper provides an introduction to DCTs and summarises the state of the industry from the perspective of different stakeholders, including the pharmaceutical and medical device industries, regulators across the world, and patients.

Decentralized Clinical Trials

With more data being collected directly from patients, either via wearable devices and sensors, electronic clinical outcome assessment (eCOA), and/or telehealth platforms, there is an urgent need to shift our thinking from reactive strategies into proactive planning and technology-based solutions to replace the query and listing-based reviews of trials past. The challenge for those in Data Management leadership and those working directly on trials themselves is how best to incorporate an ever-increasing list of data sources, novel data types, and analytic tools executing the core function of Data Management - ensuring that the clinical data collected is fit for purpose. This white paper explores how Data Management can be better equipped to provide engagement from protocol design, to data visualisation development and patient-centric technology solutions in any phase of a decentralised trial.

Modernizing Clinical Trial Oversight: The Path to Clinical Operations Excellence

The rising complexity of clinical trials, combined with pressures resulting from the COVID-19 pandemic, have forced sites, sponsors, and clinical research organizations (CROs) to adopt remote and risk-based approaches for clinical trial execution to ensure the safety of trial participants, maintain compliance with good clinical practice, and minimize risks to trial integrity. With the increasing prevalence of decentralized clinical trials (DCTs), the industry is now poised to fully embrace and implement risk-based quality management approaches to trial execution and oversight. Despite a decade’s worth of industry dialogue and widespread regulatory acceptance, Risk-Based Monitoring (RBM) and Risk-Based Quality Management (RBQM) have not been widely adopted by clinical trial sponsors and CROs. But the rising complexity of clinical trial protocols, the increase in the types and volume of patient-centric data, and the challenges of the COVID-19 pandemic - limits to on-site activities, in particular - have brought renewed attention and interest to these approaches. Now that risk-based approaches to clinical trial oversight are of greater importance, it is time to renew the conversation around RBQM. Many sponsors and CROs recognized operational efficiencies and improvements in trial execution as a result of the risk-based approaches they took in 2020, and these benefits could continue to accrue long after the pandemic is over. In this paper, Medidata outlines the current state of RBQM approaches to virtualizing clinical oversight, and the value that adopting these approaches brings to sponsors, CROs, sites, and ultimately patients.

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