1
|
Chen Z, Zhang H, Guo Y, George TJ, Prosperi M, Hogan WR, He Z, Shenkman EA, Wang F, Bian J. Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer's disease. NPJ Digit Med 2021; 4:84. [PMID: 33990663 PMCID: PMC8121837 DOI: 10.1038/s41746-021-00452-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 04/06/2021] [Indexed: 02/06/2023] Open
Abstract
In this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimer’s disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trial’s study protocol to identify the study population, treatment regimen assignments and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care (SOC) arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher serious adverse event (SAE) rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23 mg arm than in the 10 mg SOC arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials’ design. Nevertheless, such an approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted.
Collapse
Affiliation(s)
- Zhaoyi Chen
- Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Hansi Zhang
- Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Yi Guo
- Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Thomas J George
- Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, FL, USA
| | - William R Hogan
- Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Zhe He
- School of Information, Florida State University, Tallahassee, FL, USA
| | - Elizabeth A Shenkman
- Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Fei Wang
- Department of Healthcare Policy and Research, Cornell University, New York, NY, USA
| | - Jiang Bian
- Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA.
| |
Collapse
|
2
|
Alsultan A, Alghamdi WA, Alghamdi J, Alharbi AF, Aljutayli A, Albassam A, Almazroo O, Alqahtani S. Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia. Saudi Pharm J 2020; 28:1217-1227. [PMID: 33132716 PMCID: PMC7584801 DOI: 10.1016/j.jsps.2020.08.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 08/14/2020] [Indexed: 01/10/2023] Open
Abstract
Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.
Collapse
Affiliation(s)
- Abdullah Alsultan
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.,Clinical Pharmacokinetics and Pharmacodynamics Unit, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Wael A Alghamdi
- Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia
| | - Jahad Alghamdi
- The Saudi Biobank, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abeer F Alharbi
- College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | | | - Ahmed Albassam
- Department of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | | - Saeed Alqahtani
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.,Clinical Pharmacokinetics and Pharmacodynamics Unit, King Saud University Medical City, Riyadh, Saudi Arabia
| |
Collapse
|
3
|
Gal J, Bailleux C, Chardin D, Pourcher T, Gilhodes J, Jing L, Guigonis JM, Ferrero JM, Milano G, Mograbi B, Brest P, Chateau Y, Humbert O, Chamorey E. Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer. Comput Struct Biotechnol J 2020; 18:1509-1524. [PMID: 32637048 PMCID: PMC7327012 DOI: 10.1016/j.csbj.2020.05.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 05/15/2020] [Accepted: 05/16/2020] [Indexed: 02/08/2023] Open
Abstract
Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering. In-silico survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.
Collapse
Affiliation(s)
- Jocelyn Gal
- University Côte d’Azur, Epidemiology and Biostatistics Department, Centre Antoine Lacassagne, Nice F-06189, France
| | - Caroline Bailleux
- University Côte d’Azur, Medical Oncology Department Centre Antoine Lacassagne, Nice F-06189, France
| | - David Chardin
- University Côte d’Azur, Nuclear Medicine Department, Centre Antoine Lacassagne, Nice F-06189, France
- University Côte d’Azur, Commissariat à l’Energie Atomique, Institut de Biosciences et Biotechnologies d'Aix-Marseille, Laboratory Transporters in Imaging and Radiotherapy in Oncology, Faculty of Medicine, Nice F-06100, France
| | - Thierry Pourcher
- University Côte d’Azur, Commissariat à l’Energie Atomique, Institut de Biosciences et Biotechnologies d'Aix-Marseille, Laboratory Transporters in Imaging and Radiotherapy in Oncology, Faculty of Medicine, Nice F-06100, France
| | - Julia Gilhodes
- Department of Biostatistics, Institut Claudius Regaud, IUCT-O Toulouse, France
| | - Lun Jing
- University Côte d’Azur, Commissariat à l’Energie Atomique, Institut de Biosciences et Biotechnologies d'Aix-Marseille, Laboratory Transporters in Imaging and Radiotherapy in Oncology, Faculty of Medicine, Nice F-06100, France
| | - Jean-Marie Guigonis
- University Côte d’Azur, Commissariat à l’Energie Atomique, Institut de Biosciences et Biotechnologies d'Aix-Marseille, Laboratory Transporters in Imaging and Radiotherapy in Oncology, Faculty of Medicine, Nice F-06100, France
| | - Jean-Marc Ferrero
- University Côte d’Azur, Medical Oncology Department Centre Antoine Lacassagne, Nice F-06189, France
| | - Gerard Milano
- University Côte d’Azur, Centre Antoine Lacassagne, Oncopharmacology Unit, Nice F-06189, France
| | - Baharia Mograbi
- University Côte d’Azur, CNRS UMR7284, INSERM U1081, IRCAN TEAM4 Centre Antoine Lacassagne FHU-Oncoage, Nice F-06189, France
| | - Patrick Brest
- University Côte d’Azur, CNRS UMR7284, INSERM U1081, IRCAN TEAM4 Centre Antoine Lacassagne FHU-Oncoage, Nice F-06189, France
| | - Yann Chateau
- University Côte d’Azur, Epidemiology and Biostatistics Department, Centre Antoine Lacassagne, Nice F-06189, France
| | - Olivier Humbert
- University Côte d’Azur, Nuclear Medicine Department, Centre Antoine Lacassagne, Nice F-06189, France
- University Côte d’Azur, Commissariat à l’Energie Atomique, Institut de Biosciences et Biotechnologies d'Aix-Marseille, Laboratory Transporters in Imaging and Radiotherapy in Oncology, Faculty of Medicine, Nice F-06100, France
| | - Emmanuel Chamorey
- University Côte d’Azur, Epidemiology and Biostatistics Department, Centre Antoine Lacassagne, Nice F-06189, France
| |
Collapse
|
4
|
Artificial Intelligence Transforms the Future of Health Care. Am J Med 2019; 132:795-801. [PMID: 30710543 PMCID: PMC6669105 DOI: 10.1016/j.amjmed.2019.01.017] [Citation(s) in RCA: 158] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 01/16/2019] [Accepted: 01/17/2019] [Indexed: 02/06/2023]
Abstract
Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. AI focuses on how computers learn from data and mimic human thought processes. AI increases learning capacity and provides decision support system at scales that are transforming the future of health care. This article is a review of applications for machine learning in health care with a focus on clinical, translational, and public health applications with an overview of the important role of privacy, data sharing, and genetic information.
Collapse
|