PhaseForward: Web-based vs. Web-enabled Clinical Trial Solutions

Discovering and developing new drugs/ medicines is very crucial for the pharmaceutical industry. The increasing number of drugs approved in recent years demonstrates the impact of modern drug discovery approaches, digital technologies, and automated drug development methodologies. Drug development is a systematic and methodological process of developing a new pharmaceutical drug once the process of Drug discovery has identified the prime pharmacological component. The structured sequence of steps followed for drug development aims to ensure the safety and efficacy of the drug being developed. It includes pre-clinical research on microorganisms and animals, preparation of detailed data with respect to pharmacology, pharmacokinetics and toxicology details, application and approval by regulatory authorities and conduction of clinical trials. The conduction of clinical trials is an expensive affair as it needs a collaborative effort by multiple stakeholders along with a high level of monitoring and regulation. The data generated during the lifecycle of clinical trials is very critical for pharmacological scientific publications, regulatory approval for the target drug and post-marketing surveillance that ultimately leads to the development of better decision support systems for drug development. Hence, the data integrity of such data is of prime importance. Several Clinical data management (CDM) systems have been developed to ensure seamless collection and management of clinical trial data. These CDM systems enable useful analysis and decisions supported by authentic data. However, such systems face several security challenges with respect to privacy, integrity and authenticity of the clinical data. Another major challenge in conducting the clinical trials is finding the appropriate willing candidate who is physically and clinically suitable for the study. In view of the above, it is highly desirable to have a technology component that can address the above-mentioned issues. In this chapter, the technologies like blockchain and cloud computing have been introduced to address the challenges posed by clinical trial data management. The paper also proposes a blockchain based secure clinical data management system. The proposed system intends to help the data security issues like data integrity, privacy, ease and quick access to immutable clinical trial data with thorough access control enabling greater transparency and accountability.

<p>ABSTRACT<br />Over the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for their<br />new drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for their<br />medical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examine<br />and evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the most<br />valuable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reduces<br />the duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitates<br />data management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up the<br />clinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importance<br />of data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concluded<br />that data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials and<br />facilitates the drug discovery and development.<br />Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture.</p>

Treating the Black Hypertensive in 2010: Achieve Lower Targets While Awaiting More Definitive Evidence

This paper is a presentation of some of the results of modeling web-based clinical trial electronic data collection process. Clinical trial data collection is usually a paper-based process. Here we propose a new process of electronic data collection adjusted for the utilization of the web-based technology. Two models that we present here are the basis for the development of the electronic data collection software to support clinical trials. The first one defines use cases of the actors using the system. The second one is a model of the structure of the electronic case report form (eCRF) document. Descriptions of these two models are presented at a high level of abstraction using the standard UML (unified modeling language) version 2.0.

: The Cooperative International Neuromuscular Research Group (CINRG) is a consortium of medical and scientific investigators and centers focused on improving quality of life of neuromuscular disease (NMD) patients by cooperatively conducting clinical research studies in NMD. The CINRG coordinating center (CC) is an infrastructure to support the network in conducting study protocols, standardizing clinical trial methodology, data collection and quality assurance, and performing data analyses. In this award period the electronic database capture system was revamped and implemented for two studies in Duchenne Muscular Dystrophy (DMD). CINRG's public website has been revamped. One measurement clinical trial was completed, one large observational study is in follow-up and two studies are in active recruitment. New subcommittees were formed; three new sites were added to the network. Eleven presentations were given at national and international scientific and parent advocacy groups. Connections with DMD external groups were strengthened and formalized. With a strong ability to assess which outcomes are more sensitive and reliable to measure clinical change, as this CINRG CC supports, therapeutic approaches can be assessed and either adopted or abandoned quickly. This infrastructure support yields better overall results in research for improving care in DMD in the largest network of institutions for this disease worldwide.

Despite the extensive use of real-world data for retrospective, observational clinical research, our understanding of how real-world data might increase the efficiency of data collection in patient-level randomized clinical trials is largely unknown. The structure of real-world data is inherently heterogeneous, with each source electronic health record and claims database different from the next. Their fitness-for-use as data sources for multisite trials in the United States has not been established. For a subset of participants in the HARMONY Outcomes Trial, we obtained electronic health record data from recruiting sites or Medicare claims data from the Centers for Medicare & Medicaid Services. For baseline characteristics and follow-up events, we assessed the level of agreement between these real-world data and data documented in the trial database. Real-world data-derived demographic information tended to agree with trial-reported demographic information, although real-world data were less accurate in identifying medical history. The ability of real-world data to identify baseline medication usage differed by real-world data source, with claims data demonstrating substantially better performance than electronic health record data. The limited number of lab results in the collected electronic health record data matched closely with values in the trial database. There were not enough follow-up events in the ancillary study population to draw meaningful conclusions about the performance of real-world data for identification of events. Based on the conduct of this ancillary study, the challenges and opportunities of using real-world data within clinical trials are discussed. Based on a subset of participants from the HARMONY Outcomes Trial, our results suggest that electronic health record or claims data, as currently available, are unlikely to be a complete substitute for trial data collection of medical history or baseline lab results, but that Medicare claims were able to identify most medications. The limited size of the study population prevents us from drawing strong conclusions based on these results, and other studies are clearly needed to confirm or refute these findings.

Prescribing doctors are, increasingly, using clinical trial data as a major source of information for evidence-based medicine for the treatment of infectious diseases, as in other clinical disciplines. However, it may be difficult to extract from these data the information that is needed for the management of the individual patient. At the same time, clinical trial data have been used, apparently satisfactorily, in the process of drug registration, and the pharmaceutical industry has spent increasingly large sums of money to satisfy the needs of this process. This paradox— more money spent, but an outcome of limited usefulness to the prescriber—was the subject of the report of a group with expertise in all aspects of antibiotic clinical trials, meeting as the Witley Park Symposium. 1 Their objective was to identify the strengths and weaknesses of the current antibiotic clinical trial procedure for all its potential users, and to suggest means that would improve the utility of the results for doctors and their individual patients. The group identified current users of clinical trial data as: the pharmaceutical company developing, promoting and marketing the drug; the regulatory agencies responsible for drug registration for marketing; a group, defined as politicians, responsible for the well-being of the public; prescribers; and patients, who are taking an increasingly vociferous interest in the way in which their illnesses are managed. From this list alone it is clear that the needs are diverse, and that not all data are of value to all users. The established validity of the comparative, randomized, blinded, prospective clinical trial, with its means of avoiding bias and its statistically valid outcome, was fully recognized. It was also recognized that its full potential was not always realized in practice. A major disadvantage of such trials was identified as their reliance on historical results that established the efficacy of what is usually referred to as the comparator drug: this drug may never have been satisfactorily demonstrated to be effective in comparison with placebo, and may, furthermore, have lost efficacy because of an increasing incidence of acquired resistance. As a result of the common requirement for the demonstration of equivalent efficacy, patients infected with bacteria that are resistant in vitro are often excluded from clinical trials, so that the clinician will have no guidance on how to manage such infections, and the microbiologist no guidance on the clinical importance of the antimicrobial resistance that he recognizes in the laboratory. There is an added problem that antibiotics have to be prescribed for serious infection before the specific pathogen has been identified: even in the best designed clinical trial, patients are treated for, e.g., community-acquired pneumonia, rather than for pneumococcal pnuemonia, let alone penicillin-resistant pneumococcal pneumonia. At the very least this will result in randomization problems when subsets of patients are eventually analysed. The conclusion of the Witley Park group was that these and other factors result in failures in external validity—that is, the results cannot be applied to individual patients who rarely correspond to the ‘average’ patient for whom results are necessarily presented in such trials. This is the nub of the problem for the would-be rational antibiotic prescriber. The problem was not left there. The group recognized that rare, adverse effects on the individual patient would not necessarily be recognized, even in the large clinical trials that appear to have become the norm. Furthermore, the effects of prescribing a given drug on the environment and on society at large, whether this is an increase in antibiotic resistance or the cost burden, are scarcely considered at all in clinical trials. Finally, little use is made of the extensive databases of medically relevant data that are becoming generally available. 2 In the face of all these problems, changes in the way antibiotic clinical trials are designed and performed are clearly necessary, although this must not tip the balance so far as to render them less useful for those who currently derive greatest benefit from them. In coming up with sug

Clinical trials help pharmaceutical/biomedical organisations to bring the drug/treatment/procedure from the development stage to the end user stage. Normally clinical trials consists of four stages Phase I – IV. During the Phase-III which involves human beings as the subject of trial, requires significant amount of data to be captured, cleaned, transformed, compiled, audited, analysed and reported. Traditional data management techniques may not be sufficient to manage the current clinical trials as they are conducted at different location results in high cost of collecting, storing and processing of data. Hence there is need for advanced technologies such as online/web based data management techniques to increase the efficiency in collecting, storing and processing of data and also to reduce the cost related to the clinical trial data. This paper starts with a brief overview of clinical trials, regulatory requirement, guidelines, and its different phases. It further discusses the traditional tools, techniques and standards to handle clinical trials data, software available for clinical trial data management and recent trends.

Evidence from clinical trials should form the foundation of healthcare advice. Comparatively little has been investigated about the implementation of evidence in over-the-counter (OTC) counselling in community pharmacies. The objective of this study was to explore the current impact of clinical trials on OTC counselling from the perspective of pharmaceutical staff. Pharmaceutical staff in German community pharmacies were invited to participate in a nationwide cross-sectional survey using SoSci Survey. Within the questionnaire, the following items were queried: (i) attitudes towards influencing factors on OTC drug recommendation, (ii) practical procedure of OTC drug recommendations, (iii) experiences and challenges in handling clinical trials, and (iv) current and desired continuing education on self-medication. Multiple logistic regression was employed to identify predictors for OTC recommendations based on clinical trials in routine. In total, 1068 participants completed the survey. (i) Clinical trial data were rated as 'very important' for drug recommendations by 33% and as 'rather important' by 50%. (ii) 8% reported that they base their recommendations on clinical trial data 'in almost all consultations', 40% 'in most consultations'. (iii) 69% faced difficulties in including clinical trial data into their counselling. (iv) 7% read original clinical trials at least once a month. Predictors for routine OTC recommendations based on clinical trials were as follows: male gender, university degree, clinical trials considered to be important for recommendations, inclusion of clinical trials in counselling assessed as easy, higher frequency of reading specialized literature per year, increasing number of working pharmacists in the pharmacy, and more casual than regular customers in the pharmacy. Most of the pharmaceutical staff recognize the importance of clinical studies for self-medication advice. Based on self-report, however, clinical trial data are not taken routinely into account in practice. Only a minor proportion of staff routinely deal with studies. Therefore, appropriate instruments are required to make knowledge from clinical trials available in community pharmacies.

Prospective Clinical Trial Registration: A Prerequisite for Publishing Your Results.

1568 Background: Traditional data collection for clinical trials can be cumbersome and inefficient. FDA guidance now promotes capturing clinical trial source data in electronic form and transmitting it to the EDC, recognizing that this should reduce double data entry and improve data quality and timeliness. Electronic data originators can include the EHR, which can be used during routine clinical care and for gathering data during a clinical investigation. We evaluated a pilot of structured EHR data transfer to EDC in a clinical trial setting. Methods: IDC capability in the EHR was developed and applied to a subset of patients for an open label prospective Phase II study. EHR variables, that include free text, standard dictionaries (eg CTCAEv5, CDISC, First Databank), and derived responses, were mapped to the electronic case report form (eCRF). Bulk transmission of IDC clinical data (adverse events, AEs; concomitant medications, CMs; past medical history, PMH) from the EHR to EDC was executed at a designated time point during study conduct. We assessed the quantity (records) and quality (queries) of data captured and transferred, and timeliness of entry. Data to be used for the production analysis of the trial was collected via eCRF in an EDC system in parallel per usual standards. Results: Data for 102 patients across 8 sites of care by 99 different users was captured in the EHR via IDC from 3/1/2021 to 9/9/2022 and transferred from EHR to EDC in bulk on 9/9/22. 3,958 data records were transferred (852 AEs; 1,581 CMs; 1,525 PMHs). Of the AEs, 99% had an associated grade. Of the CMs and PMHs, none were copy-forwarded (all were entered de novo). Regarding queries in the EDC for transcription error validation among matching records, EHR data produced 205 opened edit check queries from 12 edit checks across all domains (149 AEs, 0 CMs, and 56 PMHs). Regarding timeliness, 95% of matched records were entered in IDC prior to entry in the production EDC. Conclusions: For an open label Phase II study, clinical trial source data was captured electronically through IDC using the EHR as the data originator, and directly bulk-transmitted to EDC. This may enable the elimination of transcription of source data entry into eCRF and result in more timely access to the data in the EDC. Additional investigation into how IDC with EHR-to-EDC compares in quality to traditional EDC is warranted. Planned follow up research includes interviewing site users to evaluate their experiences with IDC data entry.

Background and rationale for mechanism of action, efficacy, and safety of fingolimod (Gilenya), the first oral therapy for remitting-relapsing multiple sclerosis

Sharing Clinical Trial Data: A Proposal from the International Committee of Medical Journal Editors

Cancer medicine continues to evolve into a more molecularly based discipline with many new treatments being developed aimed at specific cancer-related pathways and genetic alterations. In order to meet the challenges of the evolving science, new clinical trial designs and approaches for treatment trials are needed to evaluate the promise of these potential therapies. The National Cancer Institute (NCI) with the national NCI Clinical Trials Network (NCTN) Groups will launch two clinical research initiatives in lung cancer in 2014 that demonstrate innovative approaches to the design and conduct of clinical trials. The first initiative is the ALCHEMIST trial (Adjuvant Lung Cancer Enrichment Marker Identification and Sequencing Trial) which is aimed at patients with lung adenocarcinoma that has been entirely removed by surgery. In this study, samples of tumor tissue from thousands of patients will be tested for two specific genetic alterations, one in the EGFR gene, and the other in the ALK gene. These alterations are not common; about 10% of the lung cancer patient population in the US will have tumors with alterations in the EGFR gene and 5% will have alterations in the ALK gene. Rather than conduct a single trial for each alteration, the screening component of the ALCHEMIST trial will screen patients for both alterations at the same time. The ALCHEMIST is connected to 2 specific NCI-supported NCTN Group clinical treatment trials that are each testing a targeted agent specific for one of the alterations in conjunction with adjuvant therapy. Patients found to have tumors containing one of the alterations will be offered enrollment onto the appropriate clinical treatment trial. Every patient in the ALCHEMIST trial, including those without an EGFR or ALK alteration, will also be studied for cancer risk characteristics and their tumor tissue will be analyzed in a research genomics initiative conducted by the NCI Center for Cancer Genomics (CCG). This effort will employ next-generation sequencing technology to capitalize on the foundation of the CCG's earlier effort, the Cancer Genome Atlas. The second initiative, the Advanced Squamous Cell Carcinoma Lung Master Protocol, is a public-private effort made possible through collaboration with stakeholders including the NCI, NCTN Groups, FDA, NIH Foundation, the Friends of Cancer patient advocacy group, and the pharmaceutical industry. By pooling resources to screen tumor samples from about a thousand patients per year with advanced squamous cell carcinoma for multiple genetic alterations, this master protocol will enable several, randomized, phase II clinical trials with promising new agents to take place in parallel within a single study management framework. Agents will be evaluated rapidly and either be further evaluated in a phase III clinical trial or replaced by other new agents for phase II evaluation, without having to develop individual trials de novo each time. Compared with the usual process of developing clinical trials, these two new design approaches will hopefully allow more patients to be enrolled in trials with agents targeted for important molecular characteristics specific for their tumors and the trials will yield clinically useful results more rapidly. Citation Format: Margaret M. Mooney, Jack Welch, Jeffrey S. Abrams. Clinical trial design and master protocols in NCI clinical treatment trials. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr IA08.

Clinical Trial Inclusion of Therapy-Related Myelodysplastic Syndromes (MDS) over the Past 20 Years