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Nene L, Flepisi BT, Brand SJ, Basson C, Balmith M. Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence. Clin Ther 2024:S0149-2918(24)00138-3. [PMID: 38981791 DOI: 10.1016/j.clinthera.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes. METHODS A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes. FINDINGS The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs. IMPLICATIONS AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.
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Affiliation(s)
- Linda Nene
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Brian Thabile Flepisi
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Sarel Jacobus Brand
- Center of Excellence for Pharmaceutical Sciences, Department of Pharmacology, North-West University, Potchefstroom, South Africa
| | - Charlise Basson
- Department of Physiology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Marissa Balmith
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.
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2
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Kim SH, Kim Y. Big Data Research on Severe Asthma. Tuberc Respir Dis (Seoul) 2024; 87:213-220. [PMID: 38443148 PMCID: PMC11222096 DOI: 10.4046/trd.2023.0186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 01/11/2024] [Accepted: 02/29/2024] [Indexed: 03/07/2024] Open
Abstract
The continuously increasing prevalence of severe asthma has imposed an increasing burden worldwide. Despite the emergence of novel therapeutic agents, management of severe asthma remains challenging. Insights garnered from big data may be helpful in the effort to determine the complex nature of severe asthma. In the field of asthma research, a vast amount of big data from various sources, including electronic health records, national claims data, and international cohorts, is now available. However, understanding of the strengths and limitations is required for proper utilization of specific datasets. Use of big data, along with advancements in artificial intelligence techniques, could potentially facilitate the practice of precision medicine in management of severe asthma.
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Affiliation(s)
- Sang Hyuk Kim
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Dongguk University Gyeongju Hospital, Dongguk University College of Medicine, Gyeongju, Republic of Korea
| | - Youlim Kim
- Division of Pulmonary and Allergy, Department of Internal Medicine, Konkuk University Hospital, Konkuk University School of Medicine, Seoul, Republic of Korea
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3
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Seng LL, Hai Kiat TP, Bee YM, Jafar TH. Real-World Systolic and Diastolic Blood Pressure Levels and Cardiovascular Mortality in Patients With Type 2 Diabetes-Results From a Large Registry Cohort in Asia. J Am Heart Assoc 2023; 12:e030772. [PMID: 37930066 PMCID: PMC10727329 DOI: 10.1161/jaha.123.030772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 10/25/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Elevated blood pressure (BP) is associated with increased risk of cardiovascular mortality. However, there is ongoing debate whether intensive BP lowering may paradoxically increase the risk of cardiovascular disease (CVD), especially in patients with type 2 diabetes (T2D). We investigated the association of BP with risk of CVD mortality in patients with T2D. METHODS AND RESULTS We used data on 83 721 patients with T2D from a multi-institutional diabetes registry in Singapore from 2013 to 2019. BP was analyzed as categories and restricted cubic splines using Cox multivariable regression analysis stratified by preexisting CVD and age (<65 years versus ≥65 years). The primary outcome was CVD mortality, determined via linkage with the national registry. Among 83 721 patients with T2D (mean age 65.3 years, 50.6% women, 78.9% taking antihypertensive medications), 7.6 per 1000 person-years experienced the primary outcome. Systolic BP had a graded relationship with a significant increase in CVD mortality at levels >120 to 129 mm Hg. Diastolic BP levels >90 mm Hg were significantly associated with CVD mortality in those aged ≥65 years. In addition, diastolic BP <70 mm Hg was associated with a significantly higher risk of CVD mortality in all patients. CONCLUSIONS In patients with T2D, clinic systolic BP levels ≥130 mm Hg or diastolic BP levels ≥90 mm Hg are associated with higher risk of CVD mortality. Diastolic BP <70 mm Hg is also associated with the risk of adverse CVD outcomes, although reverse causality cannot be ruled out.
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Affiliation(s)
- Loraine Liping Seng
- Program in Health Services and Systems ResearchDuke‐NUS Medical SchoolSingapore
| | | | - Yong Mong Bee
- Department of EndocrinologySingapore General HospitalSingapore
| | - Tazeen H. Jafar
- Program in Health Services and Systems ResearchDuke‐NUS Medical SchoolSingapore
- Department of Renal MedicineSingapore General HospitalSingapore
- Duke Global Health Institute, Duke UniversityDurhamNCUSA
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Yuan Q, Zhao WL, Qin B. Big data and variceal rebleeding prediction in cirrhosis patients. Artif Intell Gastroenterol 2023; 4:1-9. [DOI: 10.35712/aig.v4.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/03/2023] [Accepted: 03/10/2023] [Indexed: 06/08/2023] Open
Abstract
Big data has convincing merits in developing risk stratification strategies for diseases. The 6 “V”s of big data, namely, volume, velocity, variety, veracity, value, and variability, have shown promise for real-world scenarios. Big data can be applied to analyze health data and advance research in preclinical biology, medicine, and especially disease initiation, development, and control. A study design comprises data selection, inclusion and exclusion criteria, standard confirmation and cohort establishment, follow-up strategy, and events of interest. The development and efficiency verification of a prognosis model consists of deciding the data source, taking previous models as references while selecting candidate predictors, assessing model performance, choosing appropriate statistical methods, and model optimization. The model should be able to inform disease development and outcomes, such as predicting variceal rebleeding in patients with cirrhosis. Our work has merits beyond those of other colleagues with respect to cirrhosis patient screening and data source regarding variceal bleeding.
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Affiliation(s)
- Quan Yuan
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
| | - Wen-Long Zhao
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
- Medical Data Science Academy, Chongqing 400016, China
- Chongqing Engineering Research Centre for Clinical Big-data and Drug Evaluation, Chongqing 400016, China
| | - Bo Qin
- Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
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Feng Y, Qi L, Tian W. PhenoBERT: A Combined Deep Learning Method for Automated Recognition of Human Phenotype Ontology. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1269-1277. [PMID: 35471885 DOI: 10.1109/tcbb.2022.3170301] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Automated recognition of Human Phenotype Ontology (HPO) terms from clinical texts is of significant interest to the field of clinical data mining. In this study, we develop a combined deep learning method named PhenoBERT for this purpose. PhenoBERT uses BERT, currently the state-of-the-art NLP model, as its core model for evaluating whether a clinically relevant text segment (CTS) could be represented by an HPO term. However, to avoid unnecessary comparison of a CTS with each of ∼14,000 HPO terms using BERT, we introduce a two-levels CNN module consisting of a series of CNN models organized at two levels in PhenoBERT. For a given CTS, the CNN module produces only a short list of candidate HPO terms for BERT to evaluate, significantly improving the computational efficiency. In addition, BERT is able to assign an ancestor HPO term to a CTS when recognition of the direct HPO term is not successful, mimicking the process of HPO term assignment by human. In two benchmarks, PhenoBERT outperforms four traditional dictionary-based methods and two recently developed deep learning-based methods in two benchmark tests, and its advantage is more obvious when the recognition task is more challenging. As such, PhenoBERT is of great use for assisting in the mining of clinical text data.
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Lareyre F, Behrendt CA, Chaudhuri A, Ayache N, Delingette H, Raffort J. Big Data and Artificial Intelligence in Vascular Surgery: Time for Multidisciplinary Cross-Border Collaboration. Angiology 2022; 73:697-700. [PMID: 35815537 DOI: 10.1177/00033197221113146] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, 70607Hospital of Antibes Juan-les-Pins, Antibes, France.,Université Côte d'Azur, Inserm U1065, C3M, Nice, France
| | - Christian-Alexander Behrendt
- Research Group GermanVasc, Department of Vascular Medicine, University Heart and Vascular Centre UKE Hamburg, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Arindam Chaudhuri
- Bedfordshire - Milton Keynes Vascular Centre, 575329Bedfordshire Hospitals NHS Foundation Trust, Bedford, UK
| | - Nicholas Ayache
- Université Côte d'Azur84436 Inria, EPIONE Team, Sophia Antipolis, France.,Université Côte d'Azur 3IA Institute, France
| | - Hervé Delingette
- Université Côte d'Azur84436 Inria, EPIONE Team, Sophia Antipolis, France.,Université Côte d'Azur 3IA Institute, France
| | - Juliette Raffort
- Université Côte d'Azur, Inserm U1065, C3M, Nice, France.,Université Côte d'Azur 3IA Institute, France.,Department of clinical Biochemistry, 37045University Hospital of Nice, Nice, France
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A comparative study assessing the incidence and degree of hyperkalemia in patients on angiotensin-converting enzyme inhibitors versus angiotensin-receptor blockers. J Hum Hypertens 2022; 36:485-487. [PMID: 34650213 DOI: 10.1038/s41371-021-00625-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 09/23/2021] [Accepted: 10/04/2021] [Indexed: 11/08/2022]
Abstract
OVERVIEW Angiotensin-converting enzyme inhibitors (ACEI) and angiotensin-receptor blockers (ARB) are the most commonly prescribed anti-hypertensive medications in the United States, yet whether ACEI or ARB use is associated with a greater risk of hyperkalemia remains uncertain. Using real-world evidence from electronic health records, our study demonstrates that treatment with ACEI is associated with both a higher incidence and greater degree of hyperkalemia than treatment with ARB in adjusted models, especially in patients with chronic kidney disease. Providers should therefore consider this possible difference in hyperkalemia risk when choosing between ACEI and ARB therapy.
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Charvériat M, Darmoni SJ, Lafon V, Moore N, Bordet R, Veys J, Mouthon F. Use of real-world evidence in translational pharmacology research. Fundam Clin Pharmacol 2021; 36:230-236. [PMID: 34676579 DOI: 10.1111/fcp.12734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 10/01/2021] [Accepted: 10/18/2021] [Indexed: 12/01/2022]
Abstract
Real-world evidence (RWE) refers to observational data gathered outside the formalism of randomized controlled trials, in real life situations, on marketed drugs. While clinical trials are the gold standards to demonstrate the efficacy and tolerability of a medicinal product, the generalizability of their results to actual use in real-life is limited by the biases induced by the very nature of clinical trials; indeed, the patients included in the trials may differ from actual users because of their concomitant diseases or treatments, or other factors excluding them from the trials. Clinical researchers and pharmaceutical industries have hence become increasingly interested in expanding and integrating RWE into clinical research, by capitalizing on the exponential growth in access to data from electronic health records, claims databases, electronic devices, software or mobile applications, registries embedded in clinical practice and social media. Meanwhile, applications of RWE may also be used for drug discovery and repurposing, for clinical developments and post-marketing studies. The aim of this review is to provide our opinion regarding the use of RWE in translational research, including non-clinical and clinical pharmacology research, at the different step of drugs development use.
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Affiliation(s)
| | - Stephan J Darmoni
- Department of BioMedical Informatics, Rouen University Hospital & LIMICS U1142 INSERM, Sorbonne University, Paris, France
| | | | | | - Régis Bordet
- INSERM, CHU Lille, University of Lille, Lille, France
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Gilyarevsky SR. Approaches to Assessing the Quality of Observational Studies of Clinical Practice Based on the Big Data Analysis. RATIONAL PHARMACOTHERAPY IN CARDIOLOGY 2021. [DOI: 10.20996/1819-6446-2021-08-01] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The article is devoted to the discussion of the problems of assessing the quality of observational studies in real clinical practice and determining their place in the hierarchy of evidence-based information. The concept of “big data” and the acceptability of using such a term to refer to large observational studies is being discussed. Data on the limitations of administrative and claims databases when performing observational studies to assess the effects of interventions are presented. The concept of confounding factors influencing the results of observational studies is discussed. Modern approaches to reducing the severity of bias in real-life clinical practice studies are presented. The criteria for assessing the quality of observational pharmacoepidemiological studies and the fundamental differences between such studies and randomized clinical trials are presented. The results of systematic reviews of real-life clinical trials to assess the effects of direct oral anticoagulants are discussed.
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Long E, Wu X, Ding X, Yang Y, Wang X, Guo C, Zhang X, Chen K, Yu T, Wu D, Zhao X, Liu Z, Liu Y, Lin H. Real-world big data demonstrates prevalence trends and developmental patterns of myopia in China: a retrospective, multicenter study. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:554. [PMID: 33987252 DOI: 10.21037/atm-20-6663] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Myopia is a complex disease caused by a combination of multiple pathogenic factors. Prevalence trends and developmental patterns of myopia exhibit substantial variability that cannot be clearly assessed using limited sample sizes. This study aims to determine the myopia prevalence over the past 60 years and trace the myopia development in a school-aged population using medical big data. Methods The refraction data from electronic medical records in eight hospitals in South China were collected from January 2005 to October 2018; including patients' year of birth, refraction status, and age at the exam. All optometry tests were performed in accordance with standard procedures by qualified senior optometrists. The cross-sectional datasets (individuals with a single examination) and longitudinal datasets (individuals with multiple examinations) were analyzed respectively. SAS statistical software was used to extract and statistically analyse all target data and to identify prevalence trends and developmental patterns related to myopia. Results In total, 1,112,054 cross-sectional individual refraction records and 774,645 longitudinal records of 273,006 individuals were collected. The myopia prevalence significantly increased among individuals who were born after the 1960s and showed a steep rise until reaching a peak of 80% at the 1980s. Regarding developmental patterns, the cross-sectional data demonstrated that the myopia prevalence increased dramatically from 23.13% to 82.83% aging from 5 to 11, and the prevalence stabilized at the age of 20. The longitudinal data confirmed the results that the age of myopic onset was 7.47±1.67 years, the age of myopia stabilized at 17.14±2.61 years, and the degree of myopia stabilized at -4.35±3.81 D. Conclusions The medical big data used in this study demonstrated prevalence trends of myopia over the past 60 years and revealed developmental patterns in the onset, progression and stability of myopia in China.
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Affiliation(s)
- Erping Long
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaohu Ding
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yahan Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xun Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Chong Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiayin Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Kexin Chen
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Tongyong Yu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Dongxuan Wu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xutu Zhao
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Zhenzhen Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
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