Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021

Liu, Qi; Huang, Ruihao; Hsieh, Julie; Zhu, Hao; Tiwari, Mo; Liu, Guansheng; Jean, Daphney; ElZarrad, M. Khair; Fakhouri, Tala; Berman, Steven; Dunn, Billy; Diamond, Matthew C; Huang, Shiew‐Mei

doi:10.1002/cpt.2668

Cited by 44 publications

(35 citation statements)

References 4 publications

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“…An outgrowth of this interest has been the submission of some of these efforts to regulatory authorities. As recent work suggests (Liu et al, 2023), the number of these submissions has increased dramatically recently allowing authorities to gain an understanding of the diversity of applications that might support drug development and judge the usefulness of the approach for regulatory decision making. Some positive outcomes from this early experience includes the generation of early thoughts on guiding principles and the development of consortiums and shared resources.…”

Section: Discussionmentioning

confidence: 99%

Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development

Barrett

Oskoui²,

Russell³

et al. 2023

Front. Pharmacol.

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Early-stage drug discovery is highly dependent upon drug target evaluation, understanding of disease progression and identification of patient characteristics linked to disease progression overlaid upon chemical libraries of potential drug candidates. Artificial intelligence (AI) has become a credible approach towards dealing with the diversity and volume of data in the modern drug development phase. There are a growing number of services and solutions available to pharmaceutical sponsors though most prefer to constrain their own data to closed solutions given the intellectual property considerations. Newer platforms offer an alternative, outsourced solution leveraging sponsors data with other, external open-source data to anchor predictions (often proprietary algorithms) which are refined given data indexed upon the sponsor’s own chemical libraries. Digital research environments (DREs) provide a mechanism to ingest, curate, integrate and otherwise manage the diverse data types relevant for drug discovery activities and also provide workspace services from which target sharing and collaboration can occur providing yet another alternative with sponsors being in control of the platform, data and predictive algorithms. Regulatory engagement will be essential in the operationalizing of the various solutions and alternatives; current treatment of drug discovery data may not be adequate with respect to both quality and useability in the future. More sophisticated AI/ML algorithms are likely based on current performance metrics and diverse data types (e.g., imaging and genomic data) will certainly be a more consistent part of the myriad of data types that fuel future AI-based algorithms. This favors a dynamic DRE-enabled environment to support drug discovery.

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Section: Discussionmentioning

confidence: 99%

Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development

Barrett

Oskoui²,

Russell³

et al. 2023

Front. Pharmacol.

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“…Other quantitative approaches, for example, mechanistic modeling, machine learning, and modeling of real‐world data, are potential emerging tools that can tackle the challenges in rare disease drug development. Liu et al 34 reviewed the application of artificial intelligence and machine learning (ML) in regulatory submissions for drug development based on the FDA's experience from 2016 to 2021, providing a comprehensive illustration on a variety of tasks for which artificial intelligence/ML was used, including clinical trial design, dose optimization, biomarker assessment, and so on 43 . In the rare disease setting, we are expecting more submissions using ML across different aspects of the drug development cycle.…”

Section: Future Directions and Considerationsmentioning

confidence: 99%

Model‐Informed Approach Supporting Drug Development and Regulatory Evaluation for Rare Diseases

et al. 2022

The Journal of Clinical Pharma

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A rare disease is defined as a condition affecting fewer than 200 000 people in the United States by the Orphan Drug Act. For rare diseases, it is challenging to enroll a large number of patients and obtain all critical information to support drug approval through traditional clinical trial approaches. In addition, over half of the population affected by rare diseases are children, which presents additional drug development challenges. Thus, maximizing the use of all available data is in the interest of drug developers and regulators in rare diseases. This brings opportunities for model-informed drug development to use and integrate all available sources and knowledge to quantitatively assess the benefit/risk of a new product under development and to inform dosing. This review article provides an overview of 4 broad categories of use of model-informed drug development in drug development and regulatory decision making in rare diseases: optimizing dose regimen, supporting pediatric extrapolation, informing clinical trial design, and providing confirmatory evidence for effectiveness. The totality of evidence based on population pharmacokinetic simulation as well as exposure-response relationships for efficacy and safety, provides the regulatory ground for the approval of an unstudied dosing regimen in rare diseases without the need for additional clinical data. Given the practical and ethical challenges in drug development in rare diseases, model-informed approaches using all collective information (eg, disease, drug, placebo effect, exposure-response in nonclinical and clinical settings) are powerful and can be applied throughout the drug development stages to facilitate decision making.

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“…Indeed, these methods were used in the FDA regulatory submissions for purposes of end point/ biomarker assessment to evaluate the effectiveness of therapeutic interventions across different indications. 69…”

Section: Late-stage Clinical Developmentmentioning

confidence: 99%

“…The gap between existing statistical methodologies and data complexity requires alternative approaches that include machine learning and artificial intelligence. Indeed, these methods were used in the FDA regulatory submissions for purposes of end point/biomarker assessment to evaluate the effectiveness of therapeutic interventions across different indications 69 …”

Section: Case Studiesmentioning

confidence: 99%

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

Izmailova¹,

Maguire

McCarthy

et al. 2022

Clinical Translational Sci

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The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor‐based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross‐validating algorithms. Data standardization and creation of shared databases will be vital. Pre‐competitive consortia (public‐private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.

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Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021

Cited by 44 publications

References 4 publications

Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development

Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development

Model‐Informed Approach Supporting Drug Development and Regulatory Evaluation for Rare Diseases

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

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