Application of machine learning methods in clinical trials for precision medicine

Wang, Yizhuo; Carter, Bing Z.; Li, Ziyi; Huang, Xuelin

doi:10.1093/jamiaopen/ooab107

Cited by 17 publications

(12 citation statements)

References 58 publications

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“…Importantly, the models created by Gerdes et al [ 141 ], also predicted drug responses in an independent study focusing on cholangiocarcinoma cell lines and primary samples. These proof-of-principle studies support the integration of machine learning-based precision medicine [ 142 ], although more work is needed for the confident roll out of these methods in the clinic. Despite the remaining challenges, it is clear that the phosphoproteome will be an invaluable source of information to guide the next generation of targeted and personalised therapies.…”

Section: Applications Of Phosphoproteomics In Cancer Researchmentioning

confidence: 84%

Principles of phosphoproteomics and applications in cancer research

Higgins

Gerdes

Cutillas

2023

Biochemical Journal

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Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

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Section: Applications Of Phosphoproteomics In Cancer Researchmentioning

confidence: 84%

Principles of phosphoproteomics and applications in cancer research

Higgins

Gerdes

Cutillas

2023

Biochemical Journal

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“…36 Despite their promise, this trial design is not applicable to large, phase III trials powered for major clinical endpoints. Similar concepts also apply to response adaptive randomization, 37 or sequential multiple assignment adaptive randomized trials (SMART), 38 which allow patients who do not respond to an initial treatment to be re-randomized.…”

Section: Discussionmentioning

confidence: 99%

“…Despite their promise, this trial design is not applicable to large, phase III trials powered against hard clinical endpoints, such as mortality. Similarly, response adaptive randomization, 37 or sequential multiphase adaptive randomized trials (SMART), which allow patients who do not respond to an initial course of treatment to be re-randomized to a separate arm, 38 interfere with the random assignment of treatment, a hallmark of RCTs.…”

Section: Discussionmentioning

confidence: 99%

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials

Oikonomou

Thangaraj

Bhatt

et al. 2023

Preprint

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Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource intensive. We propose and evaluate a novel machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke or IRIS), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in SPRINT), we constructed dynamic phenotypic representations to infer profiles of patients benefiting from the intervention versus control during interim trial analyses and examined their association with study outcomes. Across three interim analyses, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit in each arm. By conditioning a prospective candidate’s probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across five simulations (IRIS: -18 ± 4.7%,p=0.008; SPRINT: -27.4 ± 3.4%,p=0.002), while preserving the original average treatment effect (IRIS: hazard ratio of 0.71 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all comparisons withp<0.01). This adaptive framework has the potential to maximize RCT enrollment efficiency.

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“…Using these AI/ML algorithms for patient screening and selection before randomization may reduce variability and increase study power. 245 For example, if an AI/ML model could predict the likelihood of a severe adverse event before administering an investigational treatment, participants could be divided into different groups and subsequently monitored (or excluded depending on the predicted severity of the adverse event). Such predictive models can also be used for participant stratification and enrichment strategies.…”

Section: Trial Participants' Selection and Stratificationmentioning

confidence: 99%

“…Using these AI/ML algorithms for patient screening and selection before randomization may reduce variability and increase study power. 245 …”

Section: Clinical Studiesmentioning

confidence: 99%

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Niazi

2023

DDDT

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Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

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Application of machine learning methods in clinical trials for precision medicine

Cited by 17 publications

References 58 publications

Principles of phosphoproteomics and applications in cancer research

Principles of phosphoproteomics and applications in cancer research

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

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