Clinical Trial Task

A Clinical Trial Task is a domain-specific task for the delivery of a clinical trial.

• Context:
  • It can be supported by a Clinical Trial Management Task (supported by a clinical trial management system).
  • It can (typically) be associated to a Clinical Trial Sub-Stage:
    • Clinical Trial Protocol Design Task.
    • Clinical Trial Regulatory Review Task.
    • Clinical Trial Start-up Task.
    • Clinical Trial Site Selection and Activation/Initiation Task.
    • Clinical Trial Participant Recruitment and Enrollment Task.
    • Clinical Trial Implementation Task.
    • Clinical Trial Intervention/Data Collection Task.
    • Clinical Trial Data Analysis Task.
    • Clinical Trial Report Writing Task.
    • Clinical Trial Closeout Task.
    • Clinical Trial Followup Task.
    • Clinical Trial Dissemination of Results Task.
  • …
  • It can integrate a Project Management Task.
  • …
• Example(s):
  • Clincail Trial Patient Matching.
  • Clinical Trial Audit Trail Task.
  • Clinical Trial Consent Task.
  • Clinical Trial Data Management Task.
    • Clinical Trial Data Capture Task.
    • Clinical Trial Data Quality Management Task.
  • Clinical Trial Patient Monitoring Task / Patient Monitoring Task.
  • Clinical Trial Patient Recruitment Task.
  • Clinical Trial Medical Coding Task.
  • Clinical Research Validation Task.
  • Clinical Outcome Assessment Task.
  • …
• Counter-Example(s):
  • Clinical Quality Management Task,
  • Clinical Endpoint Detection Task,
  • Patient Monitoring System Task,
  • Patient Recruitment Task,
  • Patient Retention Task.
• See: Clinical Research, Decentralized Clinical Trial, Clinical Trial Protocol, Clinical Trial Participant, Clinical Trial Phase, Clinical Trial Design.

---

---

References

2022

• (Liu et al., 2022) ⇒ Qi Liu, Ruihao Huang, Julie Hsieh, Hao Zhu, Mo Tiwari, Guansheng Liu, Daphney Jean et al. (2022). “Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021.” In: Clinical Pharmacology and Therapeutics. https://doi.org/10.1002/cpt.2668
  • ABSTRACT: An analysis of regulatory submissions of drug and biological products to the US Food and Drug Administration from 2016 to 2021 demonstrated an increasing number of submissions that included artificial intelligence/machine learning (AI/ML). AI/ML was used to perform a variety of tasks, such as informing drug discovery/repurposing, enhancing clinical trial design elements, dose optimization, enhancing adherence to drug regimen, end-point/biomarker assessment, and postmarketing surveillance. AI/ML is being increasingly explored to facilitate drug development.
  • QUOTE: ...

    ... We summarized common analysis types of AI/ML applications in these submissions (Figure 2).

    • Outcome prediction: AI/ML was often used for prediction of clinical outcome, including disease prognosis and treatment response (for both efficacy and safety) based on characteristics of patients as well as treatments (e.g., drug and dose). This is not surprising since one of the most mature areas where AI/ML has shown significant promise generally is its predictive capabilities and its ability to handle a large number of input variables.
    • Covariate selection/confounding adjustment: AI/ML was also commonly used for covariate selection and confounding adjustment. For example, in some submissions, AI algorithms, such as decision tree–based algorithms, were used to screen through a large amount of baseline information (e.g., patient demographic data and lab values) to find important factors that impact patients' prognosis as well as exposure or response to a drug. In some cases, a simpler model trained with the factors selected by the AI/ML algorithm was built to guide treatment or monitoring of participants.
    • Pharmacometric modeling: AI/ML has been used for pharmacometric modeling, consistent with predictive capabilities of AI/ML algorithms and the increasing interest in AI/ML among the pharmacometrics community.2-4
    • Anomaly detection: There were a number of submissions that described the use of AI/ML for anomaly detection. In fact, for high-dimensional data, AI/ML techniques, such as the Isolation Forest Algorithm, are being proposed as more powerful than traditional statistical analyses or graphical methods in identifying potential outliers or anomalies.
    • Imaging, video, and voice analysis: Another area where AI/ML has long shown promise is the analyses and evaluation of imaging data. We identified some submissions that included AI/ML, usually deep learning, for the analyses of imaging data,5, 6 videos, or voices.
    • RWD phenotyping/Natural Language Processing: Some submissions explored the use of AI/ML (natural language processing) to support the phenotyping of real-world data from certain sources.
  • In addition, we summarized the common objectives for the use of AI/ML in these submissions (Figure 2).
    • Drug discovery/repurposing: AI/ML has been demonstrated to be a useful tool for drug discovery and repurposing. Although information about the discovery phase may not be typically in a submission to the FDA, we have seen submissions where sponsors stated that the selection of the therapeutic targets or drug candidates was supported by AI/ML.
    • Drug toxicity prediction: In some submissions, AI/ML was used to predict the potential safety risk of a drug based on its structure, physiochemical properties, or affinity for targets.7
    • Enrichment design: One common application of AI/ML in drug development is to facilitate enrichment trial design. Enrichment is the prospective use of any patient characteristic to select a study population in which detection of a drug effect is more likely than it would be in an unselected population, if there is in fact a drug effect.8 Generally speaking, there are three categories of enrichment strategies: (i) decreasing variability, (ii) prognostic enrichment strategies, and (iii) predictive enrichment strategies.8 AI/ML has been used for all three enrichment strategies. For example, a sponsor proposed to use an AI/ML–based clinical trial inclusion/exclusion criterion to select canonical patients whose symptoms are characterized by greater similarity to the typical patients in the target population in order to decrease intersubject variability. Another sponsor proposed to use AI/ML to generate a prognostic score to identify patients for primary efficacy analyses for a phase III trial.
    • Patient risk stratification and management: Certain submissions explored the use of AI/ML for patient risk stratification and management, as well as treatment or dose selection/optimization. For example, a sponsor proposed AI/ML to predict patients' risk for a specific severe adverse event based on patient baseline information, and then they proposed to use this prediction to help determine the need of inpatient or outpatient monitoring for each patient.
    • Dose selection/optimization: AI/ML was proposed for the selection/optimization of the drug or dose based on patient characteristics.
    • Adherence to drug regimen: AI/ML–based monitoring platforms were used in trials to aid and confirm adherence to an investigational drug regimen. It is worth mentioning that this kind of approach has been proposed in trials across phases (i.e., phases I, II, and III trials).
    • Synthetic Control: AI/ML has been proposed to generate synthetic controls. One example is to use ML to create digital twins to predict what would happen to a specific participant in a clinical trial if he or she had received a placebo.9
    • End-point/biomarker assessment: AI/ML was proposed as components of drug development tools, such as clinical outcome assessments and biomarker identification. For example, a sponsor proposed that an AI/ML algorithm be used as part of a digital health technology tool to serve as a clinical outcome assessment to support the evaluation of the effectiveness of therapeutic interventions in participants with a skin condition. Another sponsor proposed to use AI/ML analysis of video and audio recordings of patients to calculate visual and auditory markers of schizophrenia symptomatology and use them as exploratory efficacy end points to measure change from baseline in disease severity. In another submission, a sponsor proposed using AI/ML to discover radiographic biomarkers that correlate with survival and adverse events after the cancer treatment. In addition, a recent biomarker qualification-related submission proposed a biomarker consisting of the features comprising the Non-Alcoholic Fatty Liver Disease Activity Score and fibrosis staging, assessed on liver biopsy as interpreted by a convolutional neural network.5, 6
    • Postmarketing Surveillance: AI/ML has also been used in postmarketing surveillance. For example, a sponsor proposed using AI/ML on real-world data for a postmarketing requirement pregnancy outcomes study.

2015

• (Gupta et al., 2015) ⇒ Anjali Gupta, Karen J. Calfas, Simon J. Marshall, Thomas N. Robinson, Cheryl L. Rock, Jeannie S. Huang, Melanie Epstein-Corbin et al. (2015). “Clinical Trial Management of Participant Recruitment, Enrollment, Engagement, and Retention in the SMART Study Using a Marketing and Information Technology (MARKIT) Model.” Contemporary Clinical Trials, 42
  • ABSTRACT: Advances in information technology and near ubiquity of the Internet have spawned novel modes of communication and unprecedented insights into human behavior via the digital footprint. Health behavior randomized controlled trials (RCTs), especially technology-based, can leverage these advances to improve the overall clinical trials management process and benefit from improvements at every stage, from recruitment and enrollment to engagement and retention. In this paper, we report the results for recruitment and retention of participants in the SMART study and introduce a new model for clinical trials management that is a result of interdisciplinary team science. The MARKIT model brings together best practices from information technology, marketing, and clinical research into a single framework to maximize efforts for recruitment, enrollment, engagement, and retention of participants into a RCT. These practices may have contributed to the study's on-time recruitment that was within budget, 86% retention at 24 months, and a minimum of 57% engagement with the intervention over the 2-year RCT. Use of technology in combination with marketing practices may enable investigators to reach a larger and more diverse community of participants to take part in technology-based clinical trials, help maximize limited resources, and lead to more cost-effective and efficient clinical trial management of study participants as modes of communication evolve among the target population of participants.

---