Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 12 appraisals
Journal of computer-aided molecular design
Artificial intelligence in impurity prediction: current landscape, challenges, and future directions
Earlier identification, regulation of impurities in pharmaceutical products is critical throughout the medication development process because they have a high impact on drug quality, safety, and regulatory approval. Traditional analytical methods like LC-MS and NMR are frequently used, but they are labour intensive, time consuming and have limited capacity to detect unknown or trace level impurities. New opportunities to forecast impurity formation before experimental observation have been enabled by developments in cheminformatics and computer modelling. This study covers advanced chemical representation techniques, data generation, data curation, and model validation with highlighting recent advances in machine learning and deep learning algorithms for impurity prediction. In association with forecasting synthetic by products, degradation products, and harmful contaminants, traditional QSAR techniques, and contemporary deep learning models such as graph neural networks, transformer based architectures, and generative frameworks are examined. Additionally, discussed the increasing significance of explainable models for regulatory body acceptability. Lastly, newly developed advanced techniques like digital twins, automated impurity profiling, integrated reaction degradation modelling, and real-time monitoring are examined, demonstrating how computational methods are transforming impurity assessment from a reactive effort into a predictive and preventive procedure.
23 July 2026
Read appraisal →Journal of medical Internet research
Co-Lifecycle Governance for Learning Medical AI: A Hybrid Convergence Framework for Adaptive Regulatory Oversight
Artificial intelligence (AI) in health care is increasingly defined not by static algorithms but by adaptive intelligence-systems that evolve over time through interactions with data, clinicians, and clinical environments. This adaptive capacity creates a structural mismatch with regulatory frameworks built for technologies whose behavior remains static. As AI models drift, recalibrate, or degrade in real-world contexts, they dissolve the linear boundaries between design, deployment, and clinical interpretation. These temporal, epistemic, and organizational frictions expose responsibility gaps that cannot be resolved through incremental modifications to legacy oversight structures. Regulators across major jurisdictions are beginning to respond to these challenges, though with differing orientations. The United States advances mechanisms for predictable adaptation, including Predetermined Change Control Plans, real-world evidence frameworks, and life cycle-oriented quality management reforms. The European Union emphasizes precautionary, rights-based governance through the European Union Artificial Intelligence Act (AI Act) and modernized liability rules. South Korea, operating within a hyperconnected digital health ecosystem, has introduced the Digital Medical Products Act (DMPA), one of the world's first comprehensive statutory frameworks for learning medical AI. Despite philosophical differences, these regulatory trajectories converge on a shared insight: learning AI systems cannot be governed by static rules or episodic evaluation. This viewpoint proposes Co-Lifecycle Governance as a conceptual framework to synchronize regulatory oversight with adaptive intelligence. Rather than treating oversight as a discrete event, Co-Lifecycle Governance frames regulation as a continuous, synchronized process grounded in 4 pillars: continuous validation, agile change management, proactive performance surveillance, and distributed accountability. Each pillar functions as a structural antidote to the responsibility frictions that arise when AI systems evolve faster than expectations surrounding them. Together, these pillars provide a governance grammar capable of supporting safe, iterative model improvement while maintaining system-level trust. Drawing from the strengths of US predictability, European Union accountability, and Korean scalability, this paper outlines a hybrid convergence pathway that synthesizes predictability, accountability, and operational feasibility. Learning AI will not wait for governance to catch up; oversight must evolve in lockstep with adaptive intelligence. Co-Lifecycle Governance offers a foundation for regulatory systems that not only regulate learning AI but also learn with it-at the speed at which adaptive intelligence actually changes.
20 May 2026
Read appraisal →Drug discoveries & therapeutics
Artificial intelligence (AI)-assisted ultrasound in clinical trials: Endpoint automation, decentralized monitoring, and regulatory readiness
Ultrasound is among the most widely used imaging modalities in clinical trials, and yet its dependence on operator skill and equipment settings has historically limited the reproducibility of ultrasound-based endpoints in multi-center studies. Artificial intelligence (AI) now addresses this limitation across two complementary dimensions: automated measurement algorithms that quantify cardiac function, organ volume, and vascular parameters with reproducibility approaching or, in select settings, exceeding that of trained human readers and real-time acquisition guidance systems that enable clinicians with no formal sonography training to perform diagnostic-level examinations, making remote and decentralized assessment increasingly feasible. This narrative review synthesizes current evidence and regulatory developments across three interconnected domains. First, use of automated ultrasound to ascertain endpoints has advanced from single-institution validation to prospective and randomized evidence, with deep learning measurement of the left ventricular ejection fraction demonstrating formal equivalence to expert readers across multiple echocardiographic parameters and AI-first workflows shortening the time to diagnosis in a blinded non-inferiority trial. Second, AI-guided ultrasound acquisition by nurses and other non-expert operators has achieved a high rate of diagnostic acceptability in cardiac and pulmonary ultrasound, laying the groundwork for use of ultrasound-based endpoints in decentralized clinical trial designs, as reflected in Food and Drug Administration (FDA) guidance on decentralized trial elements. Third, the regulatory frameworks governing AI-enabled medical devices - including the U.S. FDA's Predetermined Change Control Plan guidance, the EU Artificial Intelligence Act, and internationally harmonized good machine learning practice relevant to Japan and other jurisdictions - increasingly emphasize overlapping principles such as specification of prospective performance, post-marketing oversight, and transparent reporting. Addressing remaining challenges in domain generalization across vendors, subgroup fairness, and algorithm change management during ongoing trials will be essential for AI-assisted ultrasound to fulfill its potential as a robust, scalable endpoint in clinical research worldwide.
18 May 2026
Read appraisal →Therapeutic innovation & regulatory science
Applications and Challenges of Artificial Intelligence and Big Data in Drug Regulation
Artificial intelligence (AI) and big data are increasingly applied in drug regulation and have demonstrated significant potential worldwide. The U.S. Food and Drug Administration (FDA) has developed a relatively comprehensive approach through strategic frameworks, regulatory guidelines, and pilot programs. The European Medicines Agency (EMA) has promoted AI adoption via the Big Data Task Force, DARWIN EU®, and a multi-annual work plan, while Japan, Canada, and other countries have also advanced relevant initiatives and strengthened international cooperation. In China, smart regulation has been incorporated into the "14th Five-Year Plan" and subsequent strategies, with progress in establishing national regulatory data platforms, pharmaceutical traceability systems, and pilot AI applications. Nevertheless, AI in drug regulation remains at an exploratory stage, facing challenges such as limited model reliability and interpretability, insufficient data standards and interoperability, regulatory gaps, and ethical as well as public trust concerns. Future progress will depend on strengthening regulatory standards, enhancing data governance, improving regulatory capacity, and deepening international collaboration to achieve more scientific, intelligent, and efficient drug regulation.
9 May 2026
Read appraisal →Drug development research
Artificial Intelligence in Drug Discovery and Development: Transforming Pharmaceutical Innovation
Drug discovery remains a lengthy, costly, and high-risk endeavor, often requiring over a decade from target identification to clinical translation. Artificial intelligence (AI) is reshaping this paradigm by enabling more efficient and accurate decision-making across the discovery and development pipeline. Advances in machine learning, deep learning, and natural language processing now support target identification, hit finding, lead optimization, and drug repurposing with unprecedented speed and precision. AI-driven insilico platforms further enhance early-stage predictability by forecasting toxicity, pharmacokinetics, and developability, thereby reducing late-stage attrition. This review critically examines the evolving role of AI in modern drug discovery and its expanding impact on pharmaceutical formulation development and personalized medicine. Collaborative models between AI developers and the pharmaceutical industries, essential for accelerating translational outcomes, are also highlighted. Finally, key challenges, including algorithmic transparency, data quality, interoperability, and regulatory acceptance, are discussed, along with future directions for harnessing AI's full potential in pharmaceutics.
6 May 2026
Read appraisal →Journal of the American College of Cardiology
Extension Reports in Clinical Trials: A Systematic Review and Consequent Guidance
BACKGROUND: Following the primary publication of a clinical trial, questions of longer-term efficacy and safety remain unanswered. Investigators often have sequel publications based on extended follow-up; yet, no guidelines exist for such extension reports. OBJECTIVES: The purpose of this analysis was to assess the characteristics of extension reports of cardiovascular trials published in 7 high-impact medical journals. METHODS: We searched MEDLINE to identify cardiovascular trial extension papers published between January 2019 and October 2023 in 7 high-impact medical journals. Extension reports were defined as those presenting additional follow-up data after publication of the primary results. Papers were screened by 2 reviewers before extracting data using a standard proforma. RESULTS: After screening 1,388 papers, 69 extension reports across 64 trials were identified. A variety of cardiovascular topics were covered, coronary heart disease (n = 20) being the most common. Interventions were medical devices, drugs, surgery, or treatment strategies. Duration of follow-up varied considerably, most common being an original 1-year report (n = 20) with extension at 2, 3, 5, or 10 years. Nearly all extension reports had a superiority hypothesis (n = 68). Most extensions (n = 53) examined the same primary outcome as the original publication. There were also 7 nonrandomized open-label extensions, which suffered from the lack of a concurrent control arm. In 18 reports, treatment crossovers were permitted during the extension, and in 16 cases, alternatives to analysis by intention to treat were utilized. One-half of the extension studies were prespecified (n = 38), and only 10 had dedicated protocols or statistical analysis plans. No study used multiplicity corrections to control type I error across the totality of evidence. Landmark analyses were common (n = 39) which, while descriptively useful, were not based on the original randomized groups. Considerable loss to follow-up (>10%) occurred in 10 of 50 extension reports with clear reporting of this issue. For some trials, the conclusions regarding treatment effects were altered by the extended follow-up. In a previous survey of cardiovascular trials published in the New England Journal of Medicine, Lancet, and Journal of the American Medical Association in 2019, we identified a total of 23 of 84 trials involving extensions in some form. CONCLUSIONS: This first systematic investigation into the reporting of extension studies found a great diversity in practice, noting several issues that need more adequate consideration. Considerations are provided on what trialists should consider when planning, conducting, and reporting extension studies.
25 Apr 2026
Read appraisal →PDA journal of pharmaceutical science and technology
The European Qualified Person: Legal Accountability, Quality Leadership, and AI Integration in Modern Pharmaceutical Manufacturing: Navigating Complex Therapies and AI-Supported Oversight to Safeguard Patient Safety
The European Qualified Person (QP) holds unique legal responsibility for certifying batch release, ensuring patient safety, and maintaining regulatory compliance. Modern pharmaceutical manufacturing-especially in biologics, advanced therapy medicinal products (ATMPs), and personalized medicines-generates complex, multi-site, data-rich environments that challenge traditional oversight. Artificial Intelligence (AI) offers predictive analytics, anomaly detection, and trend recognition to support decision-making but cannot currently replace the QP's scientific judgment or legal accountability. The forthcoming Annex 22 introduces Human-in-the-Loop (HITL) frameworks that embed human oversight within AI-supported processes, aligning technological advancement with regulatory control. This review explores evolving QP responsibilities, core and emerging competencies, AI integration within Quality Risk Management (QRM) and Good Manufacturing Practice (GMP) frameworks, and ethical considerations, illustrated with practical case studies. By developing AI literacy and applying HITL oversight, QPs can more effectively translate technological potential into transparent, science-based, and patient-centered batch-release decisions.
12 Apr 2026
Read appraisal →PDA journal of pharmaceutical science and technology
Integrating Real-Time Viable Biofluorescent Particle Counters within Robotic Gloveless Isolators
Advanced technologies in both aseptic filling and environmental monitoring are coming together to improve the resilience and sterility assurance of aseptic processing. Continuous, real-time environmental monitoring using biofluorescent particle counters (BFPCs) to detect viable and nonviable particles during the aseptic filling process for injectable drugs is gaining traction as an accepted alternative to conventional methods such as active air sampling instruments. Evolving regulatory guidance, including EU Annex 1 guidelines, are increasingly recommending the adoption of isolator technology as well as continuous environmental monitoring during GMP processes, including technologies that offer real-time feedback during drug product manufacturing. Computational flow dynamics and airflow visualization studies are additional tools that support the design of equipment and determination of locations for environmental monitoring. The current nutrient culture media growth-based environmental monitoring, rooted in science from 150 years ago, is unable to keep pace with recent technological advances and the ability to immediately react to an out-of-control state. Here we examine the design of an isolator that eliminates human intervention and indirect product contact parts during aseptic fill finish operations and present computational fluid dynamics (CFD) studies verified by airflow visualization, along with the incorporation of BFPCs at critical areas. Also, we report the results of the interference study characterizing the baseline results for detection of total particles (nonviable plus viable) using a BFPC within a robotic gloveless isolator during dynamic and static operating conditions. The results of our study using BFPCs demonstrate that airflow in the robotic gloveless isolator provides protection of critical areas from contamination during the tub peeling process, and that the stoppering process in this environment does not generate detectable particles. During dynamic conditions and material transfer, the study demonstrates the design of the robotic gloveless isolator prevents false positives from interfering with materials during normal operations.
12 Apr 2026
Read appraisal →Trials
The use of complex clinical trials: a regulatory review.
BACKGROUND: Complex clinical trials offer flexibility in evaluating multiple treatments or diseases simultaneously. These trials often feature adaptive designs, common controls, and the potential to add new arms. However, the increased complexity raises methodological challenges particularly regarding multiple testing or the adequate choice of control groups. Despite guidance from regulatory bodies like the European Medicines Agency and the U.S. Food and Drug Administration, uncertainties remain about the regulatory acceptance of these trials. METHODS: This systematic review examines scientific advice procedures of products in the remit of the Paul-Ehrlich-Institut for complex clinical trials to highlight key concerns and regulatory feedback. We identified 30 scientific advice procedures corresponding to 29 different complex clinical trials. RESULTS: Our findings reveal an increasing number of complex trial designs proposed by applicants. A lack of multiplicity control due to multiple arms was generally considered acceptable by regulators in exploratory trials and if cohorts can be considered independent. Additionally, the use of common control groups is frequently proposed by applicants. The review underscores the importance of pre-planning for new treatment arms, appropriate multiplicity control, and the definition of control groups in trial designs. CONCLUSION: Overall, our findings suggest that regulatory concerns regarding complex trials largely align with those in traditional trial designs, though their complexity requires careful, case-by-case consideration. Early engagement with regulatory agencies can be crucial to ensure the successful design and implementation of these trials.
12 Apr 2026
Read appraisal →PDA journal of pharmaceutical science and technology
Bridging Guidance and Regulation: Interpreting the Draft Annex 22 on Artificial Intelligence in GMP Manufacturing
This paper builds upon Stassen et al. (2025), "Recommendations for Artificial Intelligence Application in Continued Process Verification," to provide a detailed interpretation of the European Medicines Agency (EMA) Draft Annex 22-Artificial Intelligence (July 2025 draft) within the context of GMP-regulated pharmaceutical manufacturing. The draft annex introduces a structured, risk-based framework for artificial intelligence (AI) adoption, defining expectations for intended use, validation, life cycle management, explainability, and human-in-the-loop (HITL) oversight. This manuscript examines the alignment between Annex 22 provisions and continued process verification (CPV) recommendations, identifies convergence, operational challenges, and areas for future development, and proposes a roadmap for compliant AI implementation. Conceptual frameworks and workflow illustrations depict the harmonization of CPV principles, life cycle management, and emerging regulatory expectations. This review supports harmonized AI governance and readiness for Annex 22 compliance across the pharmaceutical sector.
12 Apr 2026
Read appraisal →International journal of pharmaceutics
Beyond spectroscopy: Machine vision as the future of non-destructive testing in 3D-printed pharmaceuticals
3D Printing (3DP) has revolutionized personalized medicine by enabling the production of patient-specific dosage forms and adaptable drug delivery systems. However, despite these advances, 3DP presents significant challenges in quality control (QC), particularly as traditional methods may not be suitable for small-scale or on-demand manufacturing. To address these challenges, it is essential to develop flexible, precise, and efficient QC strategies that ensure product integrity. This review focuses on non-destructive spectroscopic- and imaging-based testing techniques that can bridge this gap. Techniques such as Near-Infrared Spectroscopy, Raman Spectroscopy, Terahertz Spectroscopy, Optical Coherence Tomography, and Hyperspectral Imaging provide robust alternatives to conventional QC approaches. Moreover, Machine Vision (MV) is emerging as a promising tool for real-time defect detection and visual inspection. When integrated with advanced algorithms and artificial intelligence, MV enables automated decision-making and defect identification in 3DP pharmaceuticals. Recent advancements in deep learning, especially through Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO)-based models, have further enhanced the potential of MV in this domain, offering improved accuracy, speed, and adaptability under varying conditions. This review also includes a comparative evaluation of these techniques, highlighting the increasing importance of non-destructive tools, particularly MV, in supporting innovation and ensuring quality within an increasingly personalized pharmaceutical landscape. Furthermore, it also discusses existing gaps, regulatory challenges, and the need for standardization to ensure the safe and reliable integration of these technologies into pharmaceutical manufacturing workflows.
10 Apr 2026
Read appraisal →Pharmacology research & perspectives
Improving Population Pharmacokinetic Modelling with Artificial Patients using Generative Artificial Intelligence
In population pharmacokinetics (PopPK), non-linear mixed effects (NLME) models are used to simultaneously describe a drug's pharmacokinetics (PK) and dynamics (PD) in a patient population using systems of ordinary differential equations. In this field, machine learning is mainly used for data preparation, hypothesis generation, predictive modeling, and model validation. Some approaches to integrate artificially generated information have already been explored, but real-world application is still limited. We therefore conducted a proof-of-concept study to analyze the ability of generative artificial intelligence (AI) to create artificial patient profiles to augment PopPK data sets and assess their influence on parameter estimates. We defined the pharmacokinetic parameters of a hypothetical drug and simulated the concentration curves of 20 patients. We then trained Wasserstein Generative Adversarial Networks (WGANs) with gradient penalty (GP) to generate artificial patients. The data distribution of original and artificial patients was statistically indistinguishable as shown by the Maximum Mean Discrepancy test. Therefore, the WGAN-GP is neither overfitted, that is producing only single instances of artificial patients, nor underfitted, that is producing unrealistic artificial patients. We then combined different shares of original and artificial patients in separate data sets to build and compare PopPK model estimates. Addition of artificial patients led to narrower confidence intervals, indicating more robust parameter estimates, and accentuated the allometric effect of weight on the volume of distribution. In conclusion, we provide a proof-of-concept that generative AI can be used to augment pharmacokinetic data sets, with preliminary evidence suggesting improved parameter estimation.
4 Apr 2026
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