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Wang P, Leong QY, Lau NY, Ng WY, Kwek SP, Tan L, Song SW, You K, Chong LM, Zhuang I, Ong YH, Foo N, Tadeo X, Kumar KS, Vijayakumar S, Sapanel Y, Raczkowska MN, Remus A, Blasiak A, Ho D. N-of-1 medicine. Singapore Med J 2024; 65:167-175. [PMID: 38527301 PMCID: PMC11060644 DOI: 10.4103/singaporemedj.smj-2023-243] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/19/2024] [Indexed: 03/27/2024]
Abstract
ABSTRACT The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.
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Affiliation(s)
- Peter Wang
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Qiao Ying Leong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Ni Yin Lau
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Wei Ying Ng
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Siong Peng Kwek
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Lester Tan
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Shang-Wei Song
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Kui You
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Li Ming Chong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Isaiah Zhuang
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Yoong Hun Ong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Nigel Foo
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Xavier Tadeo
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Kirthika Senthil Kumar
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Smrithi Vijayakumar
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Yoann Sapanel
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Singapore’s Health District @ Queenstown, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Marlena Natalia Raczkowska
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
| | - Alexandria Remus
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Heat Resilience Performance Centre (HRPC), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Agata Blasiak
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Dean Ho
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Singapore’s Health District @ Queenstown, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The Bia-Echo Asia Centre for Reproductive Longevity and Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Remus A, Tadeo X, Kai GNS, Blasiak A, Kee T, Vijayakumar S, Nguyen L, Raczkowska MN, Chai QY, Aliyah F, Rusalovski Y, Teo K, Yeo TT, Wong ALA, Chia D, Asplund CL, Ho D, Vellayappan BA. CURATE.AI COR-Tx platform as a digital therapy and digital diagnostic for cognitive function in patients with brain tumour postradiotherapy treatment: protocol for a prospective mixed-methods feasibility clinical trial. BMJ Open 2023; 13:e077219. [PMID: 37879700 PMCID: PMC10603439 DOI: 10.1136/bmjopen-2023-077219] [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: 06/28/2023] [Accepted: 09/29/2023] [Indexed: 10/27/2023] Open
Abstract
INTRODUCTION Conventional interventional modalities for preserving or improving cognitive function in patients with brain tumour undergoing radiotherapy usually involve pharmacological and/or cognitive rehabilitation therapy administered at fixed doses or intensities, often resulting in suboptimal or no response, due to the dynamically evolving patient state over the course of disease. The personalisation of interventions may result in more effective results for this population. We have developed the CURATE.AI COR-Tx platform, which combines a previously validated, artificial intelligence-derived personalised dosing technology with digital cognitive training. METHODS AND ANALYSIS This is a prospective, single-centre, single-arm, mixed-methods feasibility clinical trial with the primary objective of testing the feasibility of the CURATE.AI COR-Tx platform intervention as both a digital intervention and digital diagnostic for cognitive function. Fifteen patient participants diagnosed with a brain tumour requiring radiotherapy will be recruited. Participants will undergo a remote, home-based 10-week personalised digital intervention using the CURATE.AI COR-Tx platform three times a week. Cognitive function will be assessed via a combined non-digital cognitive evaluation and a digital diagnostic session at five time points: preradiotherapy, preintervention and postintervention and 16-weeks and 32-weeks postintervention. Feasibility outcomes relating to acceptability, demand, implementation, practicality and limited efficacy testing as well as usability and user experience will be assessed at the end of the intervention through semistructured patient interviews and a study team focus group discussion at study completion. All outcomes will be analysed quantitatively and qualitatively. ETHICS AND DISSEMINATION This study has been approved by the National Healthcare Group (NHG) DSRB (DSRB2020/00249). We will report our findings at scientific conferences and/or in peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT04848935.
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Affiliation(s)
- Alexandria Remus
- Heat Resilence and Performance Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Xavier Tadeo
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
| | - Grady Ng Shi Kai
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Social Sciences, Yale-NUS College, Singapore
| | - Agata Blasiak
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Theodore Kee
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Smrithi Vijayakumar
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
| | - Le Nguyen
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Marlena N Raczkowska
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
| | - Qian Yee Chai
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
| | - Fatin Aliyah
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
| | - Yaromir Rusalovski
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
| | - Kejia Teo
- Department of Surgery, Division of Neurosurgery, National University Hospital, Singapore
| | - Tseng Tsai Yeo
- Department of Surgery, Division of Neurosurgery, National University Hospital, Singapore
| | - Andrea Li Ann Wong
- Department of Hematology-Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
| | - David Chia
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Christopher L Asplund
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Social Sciences, Yale-NUS College, Singapore
| | - Dean Ho
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The Bia-Echo Asia Centre for Reproductive Longevity and Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Balamurugan A Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Shan W, Shen C, Luo L, Ding P. Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs. iScience 2023; 26:108020. [PMID: 37854693 PMCID: PMC10579440 DOI: 10.1016/j.isci.2023.108020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/26/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023] Open
Abstract
Combinatorial drug therapy is a promising approach for treating complex diseases by combining drugs with synergistic effects. However, predicting effective drug combinations is challenging due to the complexity of biological systems and the limited understanding of pathophysiological mechanisms and drug targets. In this paper, we proposed a computational framework called VGAETF (Variational Graph Autoencoder Tensor Decomposition), which leveraged multi-relational graph to model complex relationships between entities in biological systems and predicted disease-related synergistic drug combinations in an end-to-end manner. In the computational experiments, VGAETF achieved high performances (AUROC [the area under receiver operating characteristic] = 0.9767, AUPR [the area under precision-recall] = 0.9660), outperforming other compared methods. Moreover, case studies further demonstrated the effectiveness of VGAETF in identifying potential disease-related synergistic drug combinations.
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Affiliation(s)
- Wenyu Shan
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China
| | - Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China
- Hunan Medical Big Data International Science and Technology Innovation Cooperation Base, Hengyang, Hunan 421001, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China
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4
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Tan SB, Kumar KS, Truong ATL, Tan LWJ, Chong LM, Gan TRX, Mali VP, Aw MM, Blasiak A, Ho D. Comparing the Performance of Multiple Small-Data Personalized Tacrolimus Dosing Models for Pediatric Liver Transplant: A Retrospective Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083591 DOI: 10.1109/embc40787.2023.10341002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Tacrolimus is a potent immunosuppressant used after pediatric liver transplant. However, tacrolimus's narrow therapeutic window, reliance on physicians' experience for the dose titration, and intra- and inter-patient variability result in liver transplant patients falling out of the target tacrolimus trough levels frequently. Existing personalized dosing models based on the area-under-the-concentration over time curves require a higher frequency of blood draws than the current standard of care and may not be practically feasible. We present a small-data artificial intelligence-derived platform, CURATE.AI, that uses data from individual patients obtained once daily to model the dose and response relationship and identify suitable doses dynamically. Retrospective optimization using 6 models of CURATE.AI and data from 16 patients demonstrated good predictive performance and identified a suitable model for further investigations.Clinical Relevance- This study established and compared the predictive performance of 6 personalized tacrolimus dosing models for pediatric liver transplant patients and identified a suitable model with consistently good predictive performance based on data from pediatric liver transplant patients.
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Hany D, Zoetemelk M, Bhattacharya K, Nowak-Sliwinska P, Picard D. Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer. Cell Mol Life Sci 2023; 80:80. [PMID: 36869202 PMCID: PMC10032341 DOI: 10.1007/s00018-023-04730-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/20/2023] [Accepted: 02/19/2023] [Indexed: 03/05/2023]
Abstract
Breast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on the estrogen receptor α (ERα) for tumor progression. Therefore, targeting ERα with antagonists, such as tamoxifen, or estrogen deprivation by aromatase inhibitors remain standard therapies for ERα + breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, decrease toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERα + breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERα + /HER2-/PI3Kα-mutant subtype of breast cancer. The 3-drug combination targets ERα in combination with PI3Kα and cyclin-dependent kinase inhibitor 1 (p21). In addition, the 4-drug combination contains an inhibitor for poly (ADP-ribose) polymerase 1 (PARP1), which showed benefits in long-term treatments. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.
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Affiliation(s)
- Dina Hany
- Département de Biologie Moléculaire et Cellulaire, Université de Genève, Sciences III, Quai Ernest-Ansermet 30, 1211, Genève 4, Switzerland
- On leave from: Department of Pharmacology and Therapeutics, Faculty of Pharmacy, Pharos University in Alexandria, Alexandria, 21311, Egypt
| | - Marloes Zoetemelk
- Groupe de Pharmacologie Moléculaire, Section des Sciences Pharmaceutiques, Université de Genève, Genève, Switzerland
- Institut des Sciences Pharmaceutiques de Suisse Occidentale, Université de Genève, Genève, Switzerland
- Centre de Recherche Translationnelle en Onco-hématologie, Université de Genève, Genève, Switzerland
| | - Kaushik Bhattacharya
- Département de Biologie Moléculaire et Cellulaire, Université de Genève, Sciences III, Quai Ernest-Ansermet 30, 1211, Genève 4, Switzerland
| | - Patrycja Nowak-Sliwinska
- Groupe de Pharmacologie Moléculaire, Section des Sciences Pharmaceutiques, Université de Genève, Genève, Switzerland
- Institut des Sciences Pharmaceutiques de Suisse Occidentale, Université de Genève, Genève, Switzerland
- Centre de Recherche Translationnelle en Onco-hématologie, Université de Genève, Genève, Switzerland
| | - Didier Picard
- Département de Biologie Moléculaire et Cellulaire, Université de Genève, Sciences III, Quai Ernest-Ansermet 30, 1211, Genève 4, Switzerland.
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Tan P, Chen X, Zhang H, Wei Q, Luo K. Artificial intelligence aids in development of nanomedicines for cancer management. Semin Cancer Biol 2023; 89:61-75. [PMID: 36682438 DOI: 10.1016/j.semcancer.2023.01.005] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/28/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023]
Abstract
Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.
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Affiliation(s)
- Ping Tan
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiaoting Chen
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hu Zhang
- Amgen Bioprocessing Centre, Keck Graduate Institute, Claremont, CA 91711, USA
| | - Qiang Wei
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Kui Luo
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
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7
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Qureshi SA, Hsiao WWW, Hussain L, Aman H, Le TN, Rafique M. Recent Development of Fluorescent Nanodiamonds for Optical Biosensing and Disease Diagnosis. BIOSENSORS 2022; 12:bios12121181. [PMID: 36551148 PMCID: PMC9775945 DOI: 10.3390/bios12121181] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/07/2022] [Accepted: 12/16/2022] [Indexed: 05/24/2023]
Abstract
The ability to precisely monitor the intracellular temperature directly contributes to the essential understanding of biological metabolism, intracellular signaling, thermogenesis, and respiration. The intracellular heat generation and its measurement can also assist in the prediction of the pathogenesis of chronic diseases. However, intracellular thermometry without altering the biochemical reactions and cellular membrane damage is challenging, requiring appropriately biocompatible, nontoxic, and efficient biosensors. Bright, photostable, and functionalized fluorescent nanodiamonds (FNDs) have emerged as excellent probes for intracellular thermometry and magnetometry with the spatial resolution on a nanometer scale. The temperature and magnetic field-dependent luminescence of naturally occurring defects in diamonds are key to high-sensitivity biosensing applications. Alterations in the surface chemistry of FNDs and conjugation with polymer, metallic, and magnetic nanoparticles have opened vast possibilities for drug delivery, diagnosis, nanomedicine, and magnetic hyperthermia. This study covers some recently reported research focusing on intracellular thermometry, magnetic sensing, and emerging applications of artificial intelligence (AI) in biomedical imaging. We extend the application of FNDs as biosensors toward disease diagnosis by using intracellular, stationary, and time-dependent information. Furthermore, the potential of machine learning (ML) and AI algorithms for developing biosensors can revolutionize any future outbreak.
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Affiliation(s)
- Shahzad Ahmad Qureshi
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan
| | - Wesley Wei-Wen Hsiao
- Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
| | - Lal Hussain
- Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
- Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam 13230, Pakistan
| | - Haroon Aman
- School of Mathematics and Physics, The University of Queensland, St Lucia, QLD 4072, Australia
- National Institute of Lasers and Optronics College, PIEAS, Islamabad 45650, Pakistan
| | - Trong-Nghia Le
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei 106, Taiwan
| | - Muhammad Rafique
- Department of Physics, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
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8
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Goh J, De Mel S, Hoppe MM, Mohd Abdul Rashid MB, Zhang XY, Jaynes P, Ka Yan Ng E, Rahmat NDB, Jayalakshmi, Liu CX, Poon L, Chan E, Lee J, Chee YL, Koh LP, Tan LK, Soh TG, Yuen YC, Loi HY, Ng SB, Goh X, Eu D, Loh S, Ng S, Tan D, Cheah DMZ, Pang WL, Huang D, Ong SY, Nagarajan C, Chan JY, Ha JCH, Khoo LP, Somasundaram N, Tang T, Ong CK, Chng WJ, Lim ST, Chow EK, Jeyasekharan AD. An ex vivo platform to guide drug combination treatment in relapsed/refractory lymphoma. Sci Transl Med 2022; 14:eabn7824. [PMID: 36260690 DOI: 10.1126/scitranslmed.abn7824] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Although combination therapy is the standard of care for relapsed/refractory non-Hodgkin's lymphoma (RR-NHL), combination treatment chosen for an individual patient is empirical, and response rates remain poor in individuals with chemotherapy-resistant disease. Here, we evaluate an experimental-analytic method, quadratic phenotypic optimization platform (QPOP), for prediction of patient-specific drug combination efficacy from a limited quantity of biopsied tumor samples. In this prospective study, we enrolled 71 patients with RR-NHL (39 B cell NHL and 32 NK/T cell NHL) with a median of two prior lines of treatment, at two academic hospitals in Singapore from November 2017 to August 2021. Fresh biopsies underwent ex vivo testing using a panel of 12 drugs with known efficacy against NHL to identify effective single and combination treatments. Individualized QPOP reports were generated for 67 of 75 patient samples, with a median turnaround time of 6 days from sample collection to report generation. Doublet drug combinations containing copanlisib or romidepsin were most effective against B cell NHL and NK/T cell NHL samples, respectively. Off-label QPOP-guided therapy offered at physician discretion in the absence of standard options (n = 17) resulted in five complete responses. Among patients with more than two prior lines of therapy, the rates of progressive disease were lower with QPOP-guided treatments than with conventional chemotherapy. Overall, this study shows that the identification of patient-specific drug combinations through ex vivo analysis was achievable for RR-NHL in a clinically applicable time frame. These data provide the basis for a prospective clinical trial evaluating ex vivo-guided combination therapy in RR-NHL.
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Affiliation(s)
- Jasmine Goh
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore
| | - Sanjay De Mel
- NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore 117599, Singapore.,Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119074, Singapore
| | - Michal M Hoppe
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore
| | | | - Xi Yun Zhang
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore
| | - Patrick Jaynes
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore
| | - Esther Ka Yan Ng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore
| | | | - Jayalakshmi
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore
| | - Clementine Xin Liu
- Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119074, Singapore
| | - Limei Poon
- NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore 117599, Singapore.,Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119074, Singapore
| | - Esther Chan
- NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore 117599, Singapore.,Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119074, Singapore
| | - Joanne Lee
- NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore 117599, Singapore.,Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119074, Singapore
| | - Yen Lin Chee
- NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore 117599, Singapore.,Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119074, Singapore
| | - Liang Piu Koh
- Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119074, Singapore
| | - Lip Kun Tan
- Department of Laboratory Medicine, National University Hospital, Singapore 119074, Singapore
| | - Teck Guan Soh
- Department of Laboratory Medicine, National University Hospital, Singapore 119074, Singapore
| | - Yi Ching Yuen
- Department of Pharmacy, National University Health System, Singapore 119074, Singapore
| | - Hoi-Yin Loi
- Department of Diagnostic Imaging, National University Hospital, Singapore 119074, Singapore
| | - Siok-Bian Ng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore.,NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore 117599, Singapore.,Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Xueying Goh
- Department of Otolaryngology, National University Hospital, Singapore 119074, Singapore
| | - Donovan Eu
- Department of Otolaryngology, National University Hospital, Singapore 119074, Singapore
| | - Stanley Loh
- Department of Diagnostic Imaging, National University Hospital, Singapore 119074, Singapore
| | - Sheldon Ng
- Department of Diagnostic Imaging, National University Hospital, Singapore 119074, Singapore
| | - Daryl Tan
- Mount Elizabeth Novena Hospital, Singapore 329563, Singapore.,Department of Haematology, Singapore General Hospital, Singapore 169608, Singapore
| | - Daryl Ming Zhe Cheah
- Lymphoma Genomic Translational Research Laboratory, Division of Cellular and Molecular Research, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Wan Lu Pang
- Lymphoma Genomic Translational Research Laboratory, Division of Cellular and Molecular Research, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Dachuan Huang
- Lymphoma Genomic Translational Research Laboratory, Division of Cellular and Molecular Research, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Shin Yeu Ong
- Department of Haematology, Singapore General Hospital, Singapore 169608, Singapore
| | | | - Jason Yongsheng Chan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore.,SingHealth Duke-NUS Blood Cancer Centre, Singapore 168582, Singapore
| | - Jeslin Chian Hung Ha
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Lay Poh Khoo
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Nagavalli Somasundaram
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Tiffany Tang
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Choon Kiat Ong
- Lymphoma Genomic Translational Research Laboratory, Division of Cellular and Molecular Research, National Cancer Centre Singapore, Singapore 169610, Singapore.,Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore.,Genome Institute of Singapore, A*STAR, Singapore 138672, Singapore
| | - Wee-Joo Chng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore.,NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore 117599, Singapore.,Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119074, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Soon Thye Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore.,SingHealth Duke-NUS Blood Cancer Centre, Singapore 168582, Singapore.,Office of Education, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Edward K Chow
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore.,NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore 117599, Singapore.,Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.,N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore.,Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Anand D Jeyasekharan
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore.,NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore 117599, Singapore.,Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Health System, Singapore 119074, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
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9
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Owh C, Ow V, Lin Q, Wong JHM, Ho D, Loh XJ, Xue K. Bottom-up design of hydrogels for programmable drug release. BIOMATERIALS ADVANCES 2022; 141:213100. [PMID: 36096077 DOI: 10.1016/j.bioadv.2022.213100] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/22/2022] [Accepted: 08/27/2022] [Indexed: 06/15/2023]
Abstract
Hydrogels are a promising drug delivery system for biomedical applications due to their biocompatibility and similarity to native tissue. Programming the release rate from hydrogels is critical to ensure release of desired dosage over specified durations, particularly with the advent of more complicated medical regimens such as combinatorial drug therapy. While it is known how hydrogel structure affects release, the parameters that can be explicitly controlled to modulate release ab initio could be useful for hydrogel design. In this review, we first survey common physical models of hydrogel release. We then extensively go through the various input parameters that we can exercise direct control over, at the levels of synthesis, formulation, fabrication and environment. We also illustrate some examples where hydrogels can be programmed with the input parameters for temporally and spatially defined release. Finally, we discuss the exciting potential and challenges for programming release, and potential implications with the advent of machine learning.
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Affiliation(s)
- Cally Owh
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore (NUS), 21 Lower Kent Ridge Rd, Singapore 119077, Singapore
| | - Valerie Ow
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Singapore
| | - Qianyu Lin
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore (NUS), 21 Lower Kent Ridge Rd, Singapore 119077, Singapore
| | - Joey Hui Min Wong
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Singapore
| | - Dean Ho
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Engineering Block 4, Singapore 117583, Singapore
| | - Xian Jun Loh
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Singapore; Department of Materials Science and Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore; School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, #01-30 General Office, Block N4.1, Singapore 639798, Singapore.
| | - Kun Xue
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Singapore.
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10
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Blasiak A, Truong ATL, Wang P, Hooi L, Chye DH, Tan SB, You K, Remus A, Allen DM, Chai LYA, Chan CEZ, Lye DCB, Tan GYG, Seah SGK, Chow EKH, Ho D. IDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention. ACS NANO 2022; 16:15141-15154. [PMID: 35977379 DOI: 10.1021/acsnano.2c06366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Nanomedicine-based and unmodified drug interventions to address COVID-19 have evolved over the course of the pandemic as more information is gleaned and virus variants continue to emerge. For example, some early therapies (e.g., antibodies) have experienced markedly decreased efficacy. Due to a growing concern of future drug resistant variants, current drug development strategies are seeking to find effective drug combinations. In this study, we used IDentif.AI, an artificial intelligence-derived platform, to investigate the drug-drug and drug-dose interaction space of six promising experimental or currently deployed therapies at various concentrations: EIDD-1931, YH-53, nirmatrelvir, AT-511, favipiravir, and auranofin. The drugs were tested in vitro against a live B.1.1.529 (Omicron) virus first in monotherapy and then in 50 strategic combinations designed to interrogate the interaction space of 729 possible combinations. Key findings and interactions were then further explored and validated in an additional experimental round using an expanded concentration range. Overall, we found that few of the tested drugs showed moderate efficacy as monotherapies in the actionable concentration range, but combinatorial drug testing revealed significant dose-dependent drug-drug interactions, specifically between EIDD-1931 and YH-53, as well as nirmatrelvir and YH-53. Checkerboard validation analysis confirmed these synergistic interactions and also identified an interaction between EIDD-1931 and favipiravir in an expanded range. Based on the platform nature of IDentif.AI, these findings may support further explorations of the dose-dependent drug interactions between different drug classes in further pre-clinical and clinical trials as possible combinatorial therapies consisting of unmodified and nanomedicine-enabled drugs, to combat current and future COVID-19 strains and other emerging pathogens.
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Affiliation(s)
- Agata Blasiak
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117583, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
| | - Anh T L Truong
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117583, Singapore
| | - Peter Wang
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117583, Singapore
| | - Lissa Hooi
- Cancer Science Institute of Singapore, National University of Singapore, 117599, Singapore
| | - De Hoe Chye
- Defence Medical and Environmental Research Institute, DSO National Laboratories, 117510, Singapore
| | - Shi-Bei Tan
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117583, Singapore
| | - Kui You
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117583, Singapore
| | - Alexandria Remus
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117583, Singapore
| | - David Michael Allen
- Infectious Diseases Translational Research Program, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 117545, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore
- Division of Infectious Disease, Department of Medicine, National University Hospital, 119074, Singapore
| | - Louis Yi Ann Chai
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore
- Division of Infectious Disease, Department of Medicine, National University Hospital, 119074, Singapore
| | - Conrad E Z Chan
- Defence Medical and Environmental Research Institute, DSO National Laboratories, 117510, Singapore
- National Centre for Infectious Diseases (NCID), Jalan Tan Tock Seng, 308442, Singapore
| | - David C B Lye
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore
- National Centre for Infectious Diseases (NCID), Jalan Tan Tock Seng, 308442, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, 308232, Singapore
- Department of Infectious Diseases, Tan Tock Seng Hospital, 308433, Singapore
| | - Gek-Yen G Tan
- Defence Medical and Environmental Research Institute, DSO National Laboratories, 117510, Singapore
| | - Shirley G K Seah
- Defence Medical and Environmental Research Institute, DSO National Laboratories, 117510, Singapore
| | - Edward Kai-Hua Chow
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117583, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, 117599, Singapore
| | - Dean Ho
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 117583, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
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11
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Thng DKH, Toh TB, Pigini P, Hooi L, Dan YY, Chow PK, Bonney GK, Rashid MBMA, Guccione E, Wee DKB, Chow EK. Splice-switch oligonucleotide-based combinatorial platform prioritizes synthetic lethal targets CHK1 and BRD4 against MYC-driven hepatocellular carcinoma. Bioeng Transl Med 2022; 8:e10363. [PMID: 36684069 PMCID: PMC9842033 DOI: 10.1002/btm2.10363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/29/2022] [Accepted: 06/12/2022] [Indexed: 01/25/2023] Open
Abstract
Deregulation of MYC is among the most frequent oncogenic drivers in hepatocellular carcinoma (HCC). Unfortunately, the clinical success of MYC-targeted therapies is limited. Synthetic lethality offers an alternative therapeutic strategy by leveraging on vulnerabilities in tumors with MYC deregulation. While several synthetic lethal targets of MYC have been identified in HCC, the need to prioritize targets with the greatest therapeutic potential has been unmet. Here, we demonstrate that by pairing splice-switch oligonucleotide (SSO) technologies with our phenotypic-analytical hybrid multidrug interrogation platform, quadratic phenotypic optimization platform (QPOP), we can disrupt the functional expression of these targets in specific combinatorial tests to rapidly determine target-target interactions and rank synthetic lethality targets. Our SSO-QPOP analyses revealed that simultaneous attenuation of CHK1 and BRD4 function is an effective combination specific in MYC-deregulated HCC, successfully suppressing HCC progression in vitro. Pharmacological inhibitors of CHK1 and BRD4 further demonstrated its translational value by exhibiting synergistic interactions in patient-derived xenograft organoid models of HCC harboring high levels of MYC deregulation. Collectively, our work demonstrates the capacity of SSO-QPOP as a target prioritization tool in the drug development pipeline, as well as the therapeutic potential of CHK1 and BRD4 in MYC-driven HCC.
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Affiliation(s)
- Dexter Kai Hao Thng
- Cancer Science Institute of Singapore, National University of SingaporeSingaporeSingapore
| | - Tan Boon Toh
- The N.1 Institute for Health, National University of SingaporeSingaporeSingapore,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of SingaporeSingapore
| | - Paolo Pigini
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR)SingaporeSingapore
| | - Lissa Hooi
- Cancer Science Institute of Singapore, National University of SingaporeSingaporeSingapore,NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore
| | - Yock Young Dan
- Cancer Science Institute of Singapore, National University of SingaporeSingaporeSingapore,Division of Gastroenterology and HepatologyNational University Health SystemSingaporeSingapore,Department of Medicine, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore
| | - Pierce Kah‐Hoe Chow
- Division of Surgical OncologyNational Cancer Centre SingaporeSingaporeSingapore,Department of Hepato‐Pancreato‐Biliary and Transplant SurgerySingapore General HospitalSingaporeSingapore,Duke‐NUS Medical SchoolSingaporeSingapore
| | - Glenn Kunnath Bonney
- NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore,Division of Hepatobiliary and Liver Transplantation SurgeryNational University Health SystemSingaporeSingapore
| | | | - Ernesto Guccione
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR)SingaporeSingapore,Department of Oncological SciencesTisch Cancer Institute, Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA,Mount Sinai Center for Therapeutics Discovery, Department of Oncological and Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Dave Keng Boon Wee
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR)SingaporeSingapore
| | - Edward Kai‐Hua Chow
- Cancer Science Institute of Singapore, National University of SingaporeSingaporeSingapore,The N.1 Institute for Health, National University of SingaporeSingaporeSingapore,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of SingaporeSingapore,NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore,Department of Pharmacology, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore,Department of Biomedical Engineering, College of Design and EngineeringNational University of SingaporeSingaporeSingapore
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12
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Ding P, Pan Y, Wang Q, Xu R. Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records. J Biomed Inform 2022; 133:104164. [PMID: 35985621 DOI: 10.1016/j.jbi.2022.104164] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 11/18/2022]
Abstract
Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large amounts of biomedical data. Existing computational approaches are often underpowered due to their reliance on our limited understanding of disease mechanisms. On the other hand, observable phenotypic inter-relationships among thousands of diseases often reflect their underlying shared genetic and molecular underpinnings, therefore can offer unique opportunities to design computational models to discover novel combinational therapies by automatically transferring knowledge among phenotypically related diseases. We developed a novel phenome-driven drug discovery system, named TuSDC, which leverages knowledge of existing drug combinations, disease comorbidities, and disease treatments of thousands of disease and drug entities extracted from over 31.5 million biomedical research articles using natural language processing techniques. TuSDC predicts combination pharmacotherapy by extracting representations of diseases and drugs using tensor factorization approaches. In external validation, TuSDC achieved an average precision of 0.77 for top ranked candidates, outperforming a state of art mechanism-based method for discovering drug combinations in treating hypertension. We evaluated top ranked anti-hypertension drug combinations using electronic health records of 84.7 million unique patients and showed that a novel drug combination hydrochlorothiazide-digoxin was associated with significantly lower hazards of subsequent hypertension as compared to the monotherapy hydrochlorothiazide alone (HR: 0.769, 95% CI [0.732, 0.807]) and digoxin alone (0.857, 95% CI [0.785, 0.936]). Data-driven informatics analyses reveal that the renin-angiotensin system is involved in the synergistical interactions of hydrochlorothiazide and digoxin on regulating hypertension. The prediction model's code with PyTorch version 1.5 is available at http://nlp.case.edu/public/data/TuSDC/.
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Affiliation(s)
- Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Yiheng Pan
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Quanqiu Wang
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
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13
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Lim JJ, Hooi L, Dan YY, Bonney GK, Zhou L, Chow PKH, Chee CE, Toh TB, Chow EKH. Rational drug combination design in patient-derived avatars reveals effective inhibition of hepatocellular carcinoma with proteasome and CDK inhibitors. J Exp Clin Cancer Res 2022; 41:249. [PMID: 35971164 PMCID: PMC9377092 DOI: 10.1186/s13046-022-02436-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Hepatocellular carcinoma (HCC) remains difficult to treat due to limited effective treatment options. While the proteasome inhibitor bortezomib has shown promising preclinical activity in HCC, clinical trials of bortezomib showed no advantage over the standard-of-care treatment sorafenib, highlighting the need for more clinically relevant therapeutic strategies. Here, we propose that rational drug combination design and validation in patient-derived HCC avatar models such as patient-derived xenografts (PDXs) and organoids can improve proteasome inhibitor-based therapeutic efficacy and clinical potential.
Methods
HCC PDXs and the corresponding PDX-derived organoids (PDXOs) were generated from primary patient samples for drug screening and efficacy studies. To identify effective proteasome inhibitor-based drug combinations, we applied a hybrid experimental-computational approach, Quadratic Phenotypic Optimization Platform (QPOP) on a pool of nine drugs comprising proteasome inhibitors, kinase inhibitors and chemotherapy agents. QPOP utilizes small experimental drug response datasets to accurately identify globally optimal drug combinations.
Results
Preliminary drug screening highlighted the increased susceptibility of HCC PDXOs towards proteasome inhibitors. Through QPOP, the combination of second-generation proteasome inhibitor ixazomib (Ixa) and CDK inhibitor dinaciclib (Dina) was identified to be effective against HCC. In vitro and in vivo studies demonstrated the synergistic pro-apoptotic and anti-proliferative activity of Ixa + Dina against HCC PDXs and PDXOs. Furthermore, Ixa + Dina outperformed sorafenib in mitigating tumor formation in mice. Mechanistically, increased activation of JNK signaling mediates the combined anti-tumor effects of Ixa + Dina in HCC tumor cells.
Conclusions
Rational drug combination design in patient-derived avatars highlights the therapeutic potential of proteasome and CDK inhibitors and represents a feasible approach towards developing more clinically relevant treatment strategies for HCC.
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14
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The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens. NPJ Digit Med 2022; 5:83. [PMID: 35773329 PMCID: PMC9244889 DOI: 10.1038/s41746-022-00627-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/01/2022] [Indexed: 12/15/2022] Open
Abstract
IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.
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15
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Yung MMH, Siu MKY, Ngan HYS, Chan DW, Chan KKL. Orchestrated Action of AMPK Activation and Combined VEGF/PD-1 Blockade with Lipid Metabolic Tunning as Multi-Target Therapeutics against Ovarian Cancers. Int J Mol Sci 2022; 23:ijms23126857. [PMID: 35743298 PMCID: PMC9224484 DOI: 10.3390/ijms23126857] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/06/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
Ovarian cancer is one of the most lethal gynecological malignancies worldwide, and chemoresistance is a critical obstacle in the clinical management of the disease. Recent studies have suggested that exploiting cancer cell metabolism by applying AMP-activated protein kinase (AMPK)-activating agents and distinctive adjuvant targeted therapies can be a plausible alternative approach in cancer treatment. Therefore, the perspectives about the combination of AMPK activators together with VEGF/PD-1 blockade as a dual-targeted therapy against ovarian cancer were discussed herein. Additionally, ferroptosis, a non-apoptotic regulated cell death triggered by the availability of redox-active iron, have been proposed to be governed by multiple layers of metabolic signalings and can be synergized with immunotherapies. To this end, ferroptosis initiating therapies (FITs) and metabolic rewiring and immunotherapeutic approaches may have substantial clinical potential in combating ovarian cancer development and progression. It is hoped that the viewpoints deliberated in this review would accelerate the translation of remedial concepts into clinical trials and improve the effectiveness of ovarian cancer treatment.
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Affiliation(s)
- Mingo M. H. Yung
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
| | - Michelle K. Y. Siu
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
| | - Hextan Y. S. Ngan
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
| | - David W. Chan
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Correspondence: or (D.W.C.); (K.K.L.C.); Tel.: +852-3917-9367 or +852-3943-6053 (D.W.C.); +852-2255-4260 (K.K.L.C.); Fax: +852-2816-1947 or +852-2603-5123 (D.W.C.); +852-2255-0947 (K.K.L.C.)
| | - Karen K. L. Chan
- Department of Obstetrics & Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; (M.M.H.Y.); (M.K.Y.S.); (H.Y.S.N.)
- Correspondence: or (D.W.C.); (K.K.L.C.); Tel.: +852-3917-9367 or +852-3943-6053 (D.W.C.); +852-2255-4260 (K.K.L.C.); Fax: +852-2816-1947 or +852-2603-5123 (D.W.C.); +852-2255-0947 (K.K.L.C.)
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16
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Xie J, Tian S, Liu J, Cao R, Yue P, Cai X, Shang Q, Yang M, Han L, Zhang DK. Dual role of the nasal microbiota in neurological diseases—An unignorable risk factor or a potential therapy carrier. Pharmacol Res 2022; 179:106189. [DOI: 10.1016/j.phrs.2022.106189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/06/2022] [Accepted: 03/17/2022] [Indexed: 12/11/2022]
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17
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Villa Nova M, Lin TP, Shanehsazzadeh S, Jain K, Ng SCY, Wacker R, Chichakly K, Wacker MG. Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence. Front Digit Health 2022; 4:799341. [PMID: 35252958 PMCID: PMC8894322 DOI: 10.3389/fdgth.2022.799341] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/26/2022] [Indexed: 12/12/2022] Open
Abstract
Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.
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Affiliation(s)
- Mônica Villa Nova
- Department of Pharmacy, State University of Maringá, Maringá, Brazil
| | - Tzu Ping Lin
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Saeed Shanehsazzadeh
- Biological Resources Imaging Laboratory, Mark Wainwright Analytical Centre, UNSW Sydney, Sydney, NSW, Australia
| | - Kinjal Jain
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Samuel Cheng Yong Ng
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
| | | | | | - Matthias G. Wacker
- Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore
- *Correspondence: Matthias G. Wacker
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18
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REDDY HB, CRENA.M J, PSG P, SUBRAMANİAN S, APPUKUTTAN D. AN EXPLORATORY REVIEW OF CURRENT TRENDS IN NANODENTISTRY. CUMHURIYET DENTAL JOURNAL 2022. [DOI: 10.7126/cumudj.974945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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19
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Tan BKJ, Teo CB, Tadeo X, Peng S, Soh HPL, Du SDX, Luo VWY, Bandla A, Sundar R, Ho D, Kee TW, Blasiak A. Personalised, Rational, Efficacy-Driven Cancer Drug Dosing via an Artificial Intelligence SystEm (PRECISE): A Protocol for the PRECISE CURATE.AI Pilot Clinical Trial. Front Digit Health 2021; 3:635524. [PMID: 34713106 PMCID: PMC8521832 DOI: 10.3389/fdgth.2021.635524] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/04/2021] [Indexed: 01/02/2023] Open
Abstract
Introduction: Oncologists have traditionally administered the maximum tolerated doses of drugs in chemotherapy. However, these toxicity-guided doses may lead to suboptimal efficacy. CURATE.AI is an indication-agnostic, mechanism-independent and efficacy-driven personalised dosing platform that may offer a more optimal solution. While CURATE.AI has already been applied in a variety of clinical settings, there are no prior randomised controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumours. Therefore, we aim to assess the technical and logistical feasibility of a future RCT for CURATE.AI-guided solid tumour chemotherapy dosing. We will also collect exploratory data on efficacy and toxicity, which will inform RCT power calculations. Methods and analysis: This is an open-label, single-arm, two-centre, prospective pilot clinical trial, recruiting adults with metastatic solid tumours and raised baseline tumour marker levels who are planned for palliative-intent, capecitabine-based chemotherapy. As CURATE.AI is a small data platform, it will guide drug dosing for each participant based only on their own tumour marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied to provide efficacy-driven personalised dosing, as judged based on predefined considerations. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. We aim to initially enrol 10 participants from two hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or an RCT. Recruitment began in August 2020. This pilot clinical trial will provide key data for a future RCT of CURATE.AI-guided personalised dosing for precision oncology. Ethics and dissemination: The National Healthcare Group (NHG) Domain Specific Review Board has granted ethical approval for this study (DSRB 2020/00334). We will distribute our findings at scientific conferences and publish them in peer-reviewed journals. Trial registration number: NCT04522284
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Affiliation(s)
- Benjamin Kye Jyn Tan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Chong Boon Teo
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xavier Tadeo
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore, Singapore
| | - Siyu Peng
- Department of Medicine, National University Health System, Singapore, Singapore
| | - Hazel Pei Lin Soh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Sherry De Xuan Du
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Vilianty Wen Ya Luo
- Haematology-Oncology Research Group, National University Cancer Institute, Singapore (NCIS), Singapore, Singapore
| | - Aishwarya Bandla
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore
| | - Raghav Sundar
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Haematology-Oncology Research Group, National University Cancer Institute, Singapore (NCIS), Singapore, Singapore.,Department of Haematology-Oncology, National University Health System, Singapore, Singapore
| | - Dean Ho
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore, Singapore.,Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Smart Systems Institute, National University of Singapore, Singapore, Singapore
| | - Theodore Wonpeum Kee
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore, Singapore
| | - Agata Blasiak
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore, Singapore
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20
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Ducrozet F, Girard HA, Leroy J, Larquet E, Florea I, Brun E, Sicard-Roselli C, Arnault JC. New Insights into the Reactivity of Detonation Nanodiamonds during the First Stages of Graphitization. NANOMATERIALS 2021; 11:nano11102671. [PMID: 34685112 PMCID: PMC8537936 DOI: 10.3390/nano11102671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/06/2021] [Accepted: 10/07/2021] [Indexed: 12/15/2022]
Abstract
The present study aims to compare the early stages of graphitization of the same DND source for two annealing atmospheres (primary vacuum, argon at atmospheric pressure) in an identical set-up. DND samples are finely characterized by a combination of complementary techniques (FTIR, Raman, XPS, HR-TEM) to highlight the induced modifications for temperature up to 1100 °C. The annealing atmosphere has a significant impact on the graphitization kinetics with a higher fraction of sp2-C formed under vacuum compared to argon for the same temperature. Whatever the annealing atmosphere, carbon hydrogen bonds are created at the DND surface during annealing according to FTIR. A “nano effect”, specific to the <10 nm size of DND, exalts the extreme surface chemistry in XPS analysis. According to HR-TEM images, the graphitization is limited to the first outer shell even for DND annealed at 1100 °C under vacuum.
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Affiliation(s)
- Florent Ducrozet
- Université Paris-Saclay, CEA, CNRS, NIMBE, CEDEX, 91191 Gif sur Yvette, France; (F.D.); (J.L.)
- Institut de Chimie Physique, UMR 8000, CNRS, Université Paris-Saclay, 91405 Orsay, France; (E.B.); (C.S.-R.)
| | - Hugues A. Girard
- Université Paris-Saclay, CEA, CNRS, NIMBE, CEDEX, 91191 Gif sur Yvette, France; (F.D.); (J.L.)
- Correspondence: (H.A.G.); (J.-C.A.)
| | - Jocelyne Leroy
- Université Paris-Saclay, CEA, CNRS, NIMBE, CEDEX, 91191 Gif sur Yvette, France; (F.D.); (J.L.)
| | - Eric Larquet
- Condensed Matter Physics Laboratory (PMC), UMR CNRS 7643, Ecole Polytechnique, IP-Paris, 91228 Palaiseau, France;
| | - Ileana Florea
- Laboratory of Physics of Interfaces and Thin Films (LPICM), UMR CNRS 7647, Ecole Polytechnique, IP-Paris, 91228 Palaiseau, France;
| | - Emilie Brun
- Institut de Chimie Physique, UMR 8000, CNRS, Université Paris-Saclay, 91405 Orsay, France; (E.B.); (C.S.-R.)
| | - Cécile Sicard-Roselli
- Institut de Chimie Physique, UMR 8000, CNRS, Université Paris-Saclay, 91405 Orsay, France; (E.B.); (C.S.-R.)
| | - Jean-Charles Arnault
- Université Paris-Saclay, CEA, CNRS, NIMBE, CEDEX, 91191 Gif sur Yvette, France; (F.D.); (J.L.)
- Correspondence: (H.A.G.); (J.-C.A.)
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21
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Xu J, Gu M, Hooi L, Toh TB, Thng DKH, Lim JJ, Chow EKH. Enhanced penetrative siRNA delivery by a nanodiamond drug delivery platform against hepatocellular carcinoma 3D models. NANOSCALE 2021; 13:16131-16145. [PMID: 34542130 DOI: 10.1039/d1nr03502a] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Small interfering RNA (siRNA) can cause specific gene silencing and is considered promising for treating a variety of cancers, including hepatocellular carcinoma (HCC). However, siRNA has many undesirable physicochemical properties that limit its application. Additionally, conventional methods for delivering siRNA are limited in their ability to penetrate solid tumors. In this study, nanodiamonds (NDs) were evaluated as a nanoparticle drug delivery platform for improved siRNA delivery into tumor cells. Our results demonstrated that ND-siRNA complexes could effectively be formed through electrostatic interactions. The ND-siRNA complexes allowed for efficient cellular uptake and endosomal escape that protects siRNA from degradation. Moreover, ND delivery of siRNA was more effective at penetrating tumor spheroids compared to liposomal formulations. This enhanced penetration capacity makes NDs ideal vehicles to deliver siRNA against solid tumor masses as efficient gene knockdown and decreased tumor cell proliferation were observed in tumor spheroids. Evaluation of ND-siRNA complexes within the context of a 3D cancer disease model demonstrates the potential of NDs as a promising gene delivery platform against solid tumors, such as HCC.
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Affiliation(s)
- Jingru Xu
- Cancer Science Institute of Singapore, National University of Singapore, 117599, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
| | - Mengjie Gu
- Cancer Science Institute of Singapore, National University of Singapore, 117599, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
| | - Lissa Hooi
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
| | - Tan Boon Toh
- The N.1 Institute for Health, National University of Singapore, 117456, Singapore
| | - Dexter Kai Hao Thng
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
| | - Jhin Jieh Lim
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
| | - Edward Kai-Hua Chow
- Cancer Science Institute of Singapore, National University of Singapore, 117599, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
- The N.1 Institute for Health, National University of Singapore, 117456, Singapore
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
- NUS Center for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, 117456, Singapore
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22
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Wang B, Warden AR, Ding X. The optimization of combinatorial drug therapies: Strategies and laboratorial platforms. Drug Discov Today 2021; 26:2646-2659. [PMID: 34332097 DOI: 10.1016/j.drudis.2021.07.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/19/2021] [Accepted: 07/14/2021] [Indexed: 12/26/2022]
Abstract
Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.
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Affiliation(s)
- Boqian Wang
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China
| | - Antony R Warden
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China
| | - Xianting Ding
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China.
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23
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Truong ATL, Tan LWJ, Chew KA, Villaraza S, Siongco P, Blasiak A, Chen C, Ho D. Harnessing CURATE.AI for N‐of‐1 Optimization Analysis of Combination Therapy in Hypertension Patients: A Retrospective Case Series. ADVANCED THERAPEUTICS 2021. [DOI: 10.1002/adtp.202100091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Anh T. L. Truong
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine National University of Singapore Singapore 117456
- Department of Biomedical Engineering, NUS Engineering National University of Singapore Singapore 117583
| | - Lester W. J. Tan
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine National University of Singapore Singapore 117456
- Department of Biomedical Engineering, NUS Engineering National University of Singapore Singapore 117583
| | - Kimberly A. Chew
- Memory, Ageing and Cognition Center (MACC), Department of Pharmacology, Yong Loo Lin School of Medicine National University of Singapore Singapore 117600
| | - Steven Villaraza
- Memory, Ageing and Cognition Center (MACC), Department of Psychological Medicine National University Hospital Singapore 119074
| | - Paula Siongco
- Memory, Ageing and Cognition Center (MACC), Department of Psychological Medicine National University Hospital Singapore 119074
| | - Agata Blasiak
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine National University of Singapore Singapore 117456
- Department of Biomedical Engineering, NUS Engineering National University of Singapore Singapore 117583
| | - Christopher Chen
- Memory, Ageing and Cognition Center (MACC), Department of Pharmacology, Yong Loo Lin School of Medicine National University of Singapore Singapore 117600
- Memory, Ageing and Cognition Center (MACC), Department of Psychological Medicine National University Hospital Singapore 119074
| | - Dean Ho
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine National University of Singapore Singapore 117456
- Department of Biomedical Engineering, NUS Engineering National University of Singapore Singapore 117583
- Department of Pharmacology, Yong Loo Lin School of Medicine National University of Singapore Singapore 117600
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24
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Soltani M, Moradi Kashkooli F, Souri M, Zare Harofte S, Harati T, Khadem A, Haeri Pour M, Raahemifar K. Enhancing Clinical Translation of Cancer Using Nanoinformatics. Cancers (Basel) 2021; 13:2481. [PMID: 34069606 PMCID: PMC8161319 DOI: 10.3390/cancers13102481] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/08/2021] [Accepted: 05/16/2021] [Indexed: 12/14/2022] Open
Abstract
Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients.
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Affiliation(s)
- Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Faculty of Science, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi Univesity of Technology, Tehran 14176-14411, Iran
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Farshad Moradi Kashkooli
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Mohammad Souri
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Samaneh Zare Harofte
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Tina Harati
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Atefeh Khadem
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Mohammad Haeri Pour
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran; (F.M.K.); (M.S.); (S.Z.H.); (T.H.); (A.K.); (M.H.P.)
| | - Kaamran Raahemifar
- Faculty of Science, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), State College, Penn State University, Pennsylvania, PA 16801, USA
- Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada
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25
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Tjo K, Varamini P. Nanodiamonds and their potential applications in breast cancer therapy: a narrative review. Drug Deliv Transl Res 2021; 12:1017-1028. [PMID: 33970463 DOI: 10.1007/s13346-021-00996-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/26/2021] [Indexed: 12/24/2022]
Abstract
Breast cancer remains the most commonly diagnosed cancer and the leading cause of cancer-related death among women worldwide. With the projected increase in breast cancer cases in recent years, optimising treatment becomes increasingly important. Current treatment modalities in breast cancer present major limitations, including chemoresistance, dose-limiting adverse effects and lack of selectivity in aggressive subtypes of breast cancers such as triple-negative breast cancer. Nanodiamonds have demonstrated promising outcomes in preclinical models from their unique surface characteristics allowing optimised delivery of various therapeutic agents, overcoming some of the significant hurdles in conventional treatment modalities. This review will present an update on preclinical findings of nanodiamond-based drug delivery systems for breast cancer therapy to date, challenges with the use of nanodiamonds along with considerations for future research.
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Affiliation(s)
- Kenny Tjo
- Sydney Pharmacy School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, 2016, Australia
| | - Pegah Varamini
- Sydney Pharmacy School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, 2016, Australia. .,Sydney Nano Institute, The University of Sydney, Sydney, NSW, 2006, Australia.
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26
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Du X, Li L, Wei S, Wang S, Li Y. A tumor-targeted, intracellular activatable and theranostic nanodiamond drug platform for strongly enhanced in vivo antitumor therapy. J Mater Chem B 2021; 8:1660-1671. [PMID: 32011619 DOI: 10.1039/c9tb02259g] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Enhancing tumor homing and improving the efficacy of drugs are urgent needs for cancer treatment. Herein a novel targeted, intracellularly activatable fluorescence and cytotoxicity nanodiamond (ND) drug system (ND-PEG-HYD-FA/DOX, NPHF/D) was successfully prepared based on doxorubicin (DOX) and folate (FA) covalently bound to PEGylated NDs, in which the DOX was covalently coupled via an intracellularly hydrolyzable hydrazone bond that was stable in the physiological environment to ensure minimal drug release in circulation. Cell uptake studies demonstrated the selective internalization of NPHF/D by folate receptor (FR) mediated endocytosis in the order MCF-7 > HeLa > HepG2 ≫ CHO, using confocal laser scanning microscopy (CLSM) and flow cytometry. Interestingly, the DOX fluorescence of NPHF/D was significantly quenched, while the fluorescence recovery and cytotoxicity took place by low pH regulation in intracellular lysosomes, which made NPHF/D act as a fluorescence OFF-ON messenger for activatable imaging and cancer therapy. Of note, NPHF/D significantly inhibited the growth of tumors. Simultaneously, it was demonstrated that the introduction of FA and the cleavability of the hydrazone greatly enhanced the therapeutic performance of NPHF/D. In addition, toxicity studies in mice verified that the composites were devoid of any detected hepatotoxicity, cardiotoxicity, and nephrotoxicity using histopathology and blood biochemistry studies. Our work provides a novel strategy for cancer therapy, using ND-conjugated cancer drugs, and the exploration of theranostic drug-delivery systems.
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Affiliation(s)
- Xiangbin Du
- Department of Chemistry, College of Chemistry and Chemical Engineering, Shanxi University, Taiyuan 030006, P. R. China
| | - Lin Li
- Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Institute of Molecular Science, Shanxi University, Taiyuan 030006, P. R. China. and Department of Chemistry, Taiyuan Normal University, Jinzhong, 030619, P. R. China
| | - Shiguo Wei
- Department of Chemistry, College of Chemistry and Chemical Engineering, Shanxi University, Taiyuan 030006, P. R. China
| | - Songbai Wang
- Department of Chemistry, College of Chemistry and Chemical Engineering, Shanxi University, Taiyuan 030006, P. R. China
| | - Yingqi Li
- Department of Chemistry, College of Chemistry and Chemical Engineering, Shanxi University, Taiyuan 030006, P. R. China and Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Institute of Molecular Science, Shanxi University, Taiyuan 030006, P. R. China.
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27
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Saw WS, Anasamy T, Foo YY, Kwa YC, Kue CS, Yeong CH, Kiew LV, Lee HB, Chung LY. Delivery of Nanoconstructs in Cancer Therapy: Challenges and Therapeutic Opportunities. ADVANCED THERAPEUTICS 2021. [DOI: 10.1002/adtp.202000206] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Wen Shang Saw
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
| | - Theebaa Anasamy
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
| | - Yiing Yee Foo
- Department of Pharmacology Faculty of Medicine University of Malaya Kuala Lumpur 50603 Malaysia
| | - Yee Chu Kwa
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
| | - Chin Siang Kue
- Department of Diagnostic and Allied Health Sciences Faculty of Health and Life Sciences Management and Science University Shah Alam Selangor 40100 Malaysia
| | - Chai Hong Yeong
- School of Medicine Faculty of Health and Medical Sciences Taylor's University Subang Jaya Selangor 47500 Malaysia
| | - Lik Voon Kiew
- Department of Pharmacology Faculty of Medicine University of Malaya Kuala Lumpur 50603 Malaysia
| | - Hong Boon Lee
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
- School of Biosciences Faculty of Health and Medical Sciences Taylor's University Subang Jaya Selangor 47500 Malaysia
| | - Lip Yong Chung
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
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Govula K, Prasad G, Anumula L, Kumar P. Evaluation of the biocompatibility of silver nanoparticles, ascertaining their safety in the field of endodontic therapy. JOURNAL OF THE INTERNATIONAL CLINICAL DENTAL RESEARCH ORGANIZATION 2021. [DOI: 10.4103/jicdro.jicdro_22_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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29
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Iftikhar MS, Talha GM, Aleem M, Shamim A. Bioinformatics–computer programming. NANOTECHNOLOGY IN CANCER MANAGEMENT 2021:125-148. [DOI: 10.1016/b978-0-12-818154-6.00009-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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30
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Hejmady S, Pradhan R, Alexander A, Agrawal M, Singhvi G, Gorain B, Tiwari S, Kesharwani P, Dubey SK. Recent advances in targeted nanomedicine as promising antitumor therapeutics. Drug Discov Today 2020; 25:2227-2244. [DOI: 10.1016/j.drudis.2020.09.031] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 05/29/2020] [Accepted: 09/26/2020] [Indexed: 12/18/2022]
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31
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Guo P, Huang J, Moses MA. Cancer Nanomedicines in an Evolving Oncology Landscape. Trends Pharmacol Sci 2020; 41:730-742. [PMID: 32873407 DOI: 10.1016/j.tips.2020.08.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/21/2020] [Accepted: 08/02/2020] [Indexed: 12/12/2022]
Abstract
Nanomedicine represents an important class of cancer therapy. Clinical translation of cancer nanomedicine has significantly reduced the toxicity and adverse consequences of standard-of-care chemotherapy. Recent advances in new cancer treatment modalities (e.g., gene and immune therapies) are profoundly changing the oncology landscape, bringing with them new requirements and challenges for next-generation cancer nanomedicines. We present an overview of cancer nanomedicines in four emerging oncology-associated fields: (i) gene therapy, (ii) immunotherapy, (iii) extracellular vesicle (EV) therapy, and (iv) machine learning-assisted therapy. We discuss the incorporation of nanomedicine into these emerging disciplines, present prominent examples, and evaluate their advantages and challenges. Finally, we discuss future opportunities for next-generation cancer nanomedicines.
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Affiliation(s)
- Peng Guo
- Vascular Biology Program, Boston Children's Hospital, Boston, MA, USA; Department of Surgery, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Jing Huang
- Vascular Biology Program, Boston Children's Hospital, Boston, MA, USA; Department of Surgery, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Marsha A Moses
- Vascular Biology Program, Boston Children's Hospital, Boston, MA, USA; Department of Surgery, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
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Ho D, Quake SR, McCabe ERB, Chng WJ, Chow EK, Ding X, Gelb BD, Ginsburg GS, Hassenstab J, Ho CM, Mobley WC, Nolan GP, Rosen ST, Tan P, Yen Y, Zarrinpar A. Enabling Technologies for Personalized and Precision Medicine. Trends Biotechnol 2020; 38:497-518. [PMID: 31980301 PMCID: PMC7924935 DOI: 10.1016/j.tibtech.2019.12.021] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 02/06/2023]
Abstract
Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care.
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Affiliation(s)
- Dean Ho
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore; The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Stephen R Quake
- Department of Bioengineering, Stanford University, CA, USA; Department of Applied Physics, Stanford University, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA
| | | | - Wee Joo Chng
- Department of Haematology and Oncology, National University Cancer Institute, National University Health System, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Edward K Chow
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Xianting Ding
- Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bruce D Gelb
- Mindich Child Health and Development Institute, Departments of Pediatrics and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, USA
| | - Geoffrey S Ginsburg
- Center for Applied Genomics and Precision Medicine, Duke University, NC, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University in St. Louis, MO, USA; Psychological & Brain Sciences, Washington University in St. Louis, MO, USA
| | - Chih-Ming Ho
- Department of Mechanical Engineering, University of California, Los Angeles, CA, USA
| | - William C Mobley
- Department of Neurosciences, University of California, San Diego, CA, USA
| | - Garry P Nolan
- Department of Microbiology & Immunology, Stanford University, CA, USA
| | - Steven T Rosen
- Comprehensive Cancer Center and Beckman Research Institute, City of Hope, CA, USA
| | - Patrick Tan
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Yun Yen
- College of Medical Technology, Center of Cancer Translational Research, Taipei Cancer Center of Taipei Medical University, Taipei, Taiwan
| | - Ali Zarrinpar
- Department of Surgery, Division of Transplantation & Hepatobiliary Surgery, University of Florida, FL, USA
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Affiliation(s)
- Dean Ho
- The N.1 Institute for Health (N.1)National University of Singapore 117456 Singapore
- The Institute for Digital Medicine (WisDM)Yong Loo Lin School of MedicineNational University of Singapore 117597 Singapore
- Department of Biomedical EngineeringNUS EngineeringNational University of Singapore 117583 Singapore
- Department of PharmacologyYong Loo Lin School of MedicineNational University of Singapore 117600 Singapore
| | - Gavin Teo
- B Capital Group 94111 San Francisco
- Straits Venture Capital 169813 Singapore
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Abdulla A, Wang B, Qian F, Kee T, Blasiak A, Ong YH, Hooi L, Parekh F, Soriano R, Olinger GG, Keppo J, Hardesty CL, Chow EK, Ho D, Ding X. Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention. ADVANCED THERAPEUTICS 2020; 3:2000034. [PMID: 32838027 PMCID: PMC7235487 DOI: 10.1002/adtp.202000034] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Indexed: 12/24/2022]
Abstract
In 2019/2020, the emergence of coronavirus disease 2019 (COVID‐19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID‐19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi‐drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose‐finding to achieve drug synergy. This approach is widely‐used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI‐based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top‐ranked combination being optimally and sub‐optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism‐agnostic, and potentially applicable to the systematic N‐of‐1 and population‐wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID‐19 and future pandemics.
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Affiliation(s)
- Aynur Abdulla
- Institute for Personalized Medicine School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 China
| | - Boqian Wang
- Institute for Personalized Medicine School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 China
| | - Feng Qian
- Ministry of Education Key Laboratory of Contemporary Anthropology Human Phenome Institute School of Life Sciences Fudan University Shanghai 200438 China
| | - Theodore Kee
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore.,The Institute for Digital Medicine (WisDM) Yong Loo Lin School of Medicine National University of Singapore Singapore 11756 Singapore.,Department of Biomedical Engineering NUS Engineering National University of Singapore Singapore 117583 Singapore
| | - Agata Blasiak
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore.,The Institute for Digital Medicine (WisDM) Yong Loo Lin School of Medicine National University of Singapore Singapore 11756 Singapore.,Department of Biomedical Engineering NUS Engineering National University of Singapore Singapore 117583 Singapore
| | - Yoong Hun Ong
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore
| | - Lissa Hooi
- Cancer Science Institute of Singapore National University of Singapore Singapore 117599 Singapore
| | | | | | - Gene G Olinger
- Global Health Surveillance and Diagnostic Division MRIGlobal Gaithersburg MD 20878 USA.,Boston University School of Medicine Division of Infectious Diseases Boston MA 02118 USA
| | - Jussi Keppo
- NUS Business School and Institute of Operations Research and Analytics National University of Singapore Singapore 119245 Singapore
| | - Chris L Hardesty
- KPMG Global Health and Life Sciences Centre of Excellence Singapore 048581 Singapore
| | - Edward K Chow
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore.,Cancer Science Institute of Singapore National University of Singapore Singapore 117599 Singapore.,Department of Pharmacology Yong Loo Lin School of Medicine National University of Singapore Singapore 117600 Singapore
| | - Dean Ho
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore.,The Institute for Digital Medicine (WisDM) Yong Loo Lin School of Medicine National University of Singapore Singapore 11756 Singapore.,Department of Biomedical Engineering NUS Engineering National University of Singapore Singapore 117583 Singapore.,Department of Pharmacology Yong Loo Lin School of Medicine National University of Singapore Singapore 117600 Singapore
| | - Xianting Ding
- Institute for Personalized Medicine School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 China
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35
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Wilson B, KM G. Artificial intelligence and related technologies enabled nanomedicine for advanced cancer treatment. Nanomedicine (Lond) 2020; 15:433-435. [DOI: 10.2217/nnm-2019-0366] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
- Barnabas Wilson
- Department of Pharmaceutics, College of Pharmaceutical Sciences, Dayananda Sagar University, Kumaraswamy Layout, Bangalore, 560078, India
| | - Geetha KM
- Department of Pharmacology, College of Pharmaceutical Sciences, Dayananda Sagar University, Kumaraswamy Layout, Bangalore, 560078, India
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Lim JJ, Goh J, Rashid MBMA, Chow EK. Maximizing Efficiency of Artificial Intelligence‐Driven Drug Combination Optimization through Minimal Resolution Experimental Design. ADVANCED THERAPEUTICS 2019. [DOI: 10.1002/adtp.201900122] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Jhin Jieh Lim
- Cancer Science Institute of SingaporeYong Loo Lin School of MedicineNational University of Singapore Singapore 117599 Singapore
| | - Jasmine Goh
- Cancer Science Institute of SingaporeYong Loo Lin School of MedicineNational University of Singapore Singapore 117599 Singapore
| | | | - Edward Kai‐Hua Chow
- Cancer Science Institute of Singapore, Yong Loo Lin School of MedicineNational University of Singapore Singapore 117599 Singapore
- Department of Pharmacology, Yong Loo Lin School of MedicineNational University of Singapore Singapore 117599 Singapore
- N.1 Institute for HealthNational University of Singapore Singapore 117599 Singapore
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37
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Horwitz MA, Clemens DL, Lee B. AI‐Enabled Parabolic Response Surface Approach Identifies Ultra Short‐Course Near‐Universal TB Drug Regimens. ADVANCED THERAPEUTICS 2019. [PMCID: PMC6988120 DOI: 10.1002/adtp.201900086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Tuberculosis (TB) is a major health problem that causes more deaths worldwide than any other single infectious disease. Current multidrug therapy for tuberculosis is exceedingly lengthy, leading to poor drug adherence, and consequently the emergence of drug resistance. Hence, much more rapid treatments are needed. Experimentally identifying the most synergistic drug combinations among available drugs is complicated by the astronomical number of possible drug-dose combinations. This problem is dealt with by the use of an artificial-intelligence-enabled parabolic response surface platform in conjunction with an in vitro Mycobacterium tuberculosis–infected macrophage cell culture assay amenable to high-throughput screening. This strategy allows rapid identification of the most effective drug-dose combinations by testing only a small fraction of the total drug-dose efficacy response surface. The same platform is then used to optimize the in vivo doses of each drug in the most potent regimens. Thus, regimens are identified that are dramatically more effective than the Standard Regimen in treating TB in a mouse model—a model broadly predictive of drug efficacy in humans. The most effective regimens reported herein shorten the duration of treatment required to achieve relapse-free cure by 80% and are suitable for treating both drug-sensitive and most drug-resistant cases of tuberculosis.
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Affiliation(s)
- Marcus A. Horwitz
- Department of MedicineUCLA School of Medicine, University of California–Los Angeles, CHS 37‐121 Los Angeles CA 90095 USA
| | - Daniel L. Clemens
- Department of MedicineUCLA School of Medicine, University of California–Los Angeles, CHS 37‐121 Los Angeles CA 90095 USA
| | - Bai‐Yu Lee
- Department of MedicineUCLA School of Medicine, University of California–Los Angeles, CHS 37‐121 Los Angeles CA 90095 USA
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Tangeretin-Assisted Platinum Nanoparticles Enhance the Apoptotic Properties of Doxorubicin: Combination Therapy for Osteosarcoma Treatment. NANOMATERIALS 2019; 9:nano9081089. [PMID: 31362420 PMCID: PMC6723885 DOI: 10.3390/nano9081089] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 07/25/2019] [Accepted: 07/28/2019] [Indexed: 12/12/2022]
Abstract
Osteosarcoma (OS) is the most common type of cancer and the most frequent malignant bone tumor in childhood and adolescence. Nanomedicine has become an indispensable field in biomedical and clinical research, with nanoparticles (NPs) promising to increase the therapeutic efficacy of anticancer drugs. Doxorubicin (DOX) is a commonly used chemotherapeutic drug against OS; however, it causes severe side effects that restrict its clinical applications. Here, we investigated whether combining platinum NPs (PtNPs) and DOX could increase their anticancer activity in human bone OS epithelial cells (U2OS). PtNPs with nontoxic, effective, thermally stable, and thermoplasmonic properties were synthesized and characterized using tangeretin. We examined the combined effects of PtNPs and DOX on cell viability, proliferation, and morphology, reactive oxygen species (ROS) generation, lipid peroxidation, nitric oxide, protein carbonyl content, antioxidants, mitochondrial membrane potential (MMP), adenosine tri phosphate (ATP) level, apoptotic and antiapoptotic gene expression, oxidative stress-induced DNA damage, and DNA repair genes. PtNPs and DOX significantly inhibited U2OS viability and proliferation in a dose-dependent manner, increasing lactate dehydrogenase leakage, ROS generation, and malondialdehyde, nitric oxide, and carbonylated protein levels. Mitochondrial dysfunction was confirmed by reduced MMP, decreased ATP levels, and upregulated apoptotic/downregulated antiapoptotic gene expression. Oxidative stress was a major cause of cytotoxicity and genotoxicity, confirmed by decreased levels of various antioxidants. Furthermore, PtNPs and DOX increased 8-oxo-dG and 8-oxo-G levels and induced DNA damage and repair gene expression. Combination of cisplatin and DOX potentially induce apoptosis comparable to PtNPs and DOX. To the best of our knowledge, this is the first report to describe the combined effects of PtNPs and DOX in OS.
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Kee T, Weiyan C, Blasiak A, Wang P, Chong JK, Chen J, Yeo BTT, Ho D, Asplund CL. Harnessing CURATE.AI as a Digital Therapeutics Platform by Identifying N‐of‐1 Learning Trajectory Profiles. ADVANCED THERAPEUTICS 2019. [DOI: 10.1002/adtp.201900023] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Theodore Kee
- Department of Biomedical EngineeringNational University of Singapore Singapore 117583
| | - Chee Weiyan
- The N.1 Institute for Health (N.1)National University of Singapore Singapore 117456
| | - Agata Blasiak
- Department of Biomedical EngineeringNational University of Singapore Singapore 117583
| | - Peter Wang
- The N.1 Institute for Health (N.1)National University of Singapore Singapore 117456
| | - Jordan K. Chong
- Department of Biomedical EngineeringNational University of Singapore Singapore 117583
| | - Jonna Chen
- The N.1 Institute for Health (N.1)National University of Singapore Singapore 117456
| | - B. T. Thomas Yeo
- The N.1 Institute for Health (N.1)National University of Singapore Singapore 117456
- Clinical Imaging Research CentreYong Loo Lin School of MedicineNational University of Singapore Singapore 117599
- Centre for Cognitive NeuroscienceDuke‐NUS Medical SchoolNational University of Singapore Singapore 169857
- Institute for Application of Learning Science and Educational TechnologyNational University of Singapore Singapore 119077
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalHarvard Medical School 149 13th St Charlestown MA 02129 USA
| | - Dean Ho
- The N.1 Institute for Health (N.1)National University of Singapore Singapore 117456
- Department of Biomedical EngineeringNational University of Singapore Singapore 117583
- Department of PharmacologyYong Loo Lin School of MedicineBioengineering Institute for Global Health Research and TechnologyNational University of Singapore Singapore 117600
| | - Christopher L. Asplund
- The N.1 Institute for Health (N.1)National University of Singapore Singapore 117456
- Clinical Imaging Research CentreYong Loo Lin School of MedicineNational University of Singapore Singapore 117599
- Centre for Cognitive NeuroscienceDuke‐NUS Medical SchoolNational University of Singapore Singapore 169857
- Institute for Application of Learning Science and Educational TechnologyNational University of Singapore Singapore 119077
- Division of Social SciencesYale‐NUS CollegeNational University of Singapore Singapore 138533
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Abstract
The field of nanomedicine has made substantial strides in the areas of therapeutic and diagnostic development. For example, nanoparticle-modified drug compounds and imaging agents have resulted in markedly enhanced treatment outcomes and contrast efficiency. In recent years, investigational nanomedicine platforms have also been taken into the clinic, with regulatory approval for Abraxane® and other products being awarded. As the nanomedicine field has continued to evolve, multifunctional approaches have been explored to simultaneously integrate therapeutic and diagnostic agents onto a single particle, or deliver multiple nanomedicine-functionalized therapies in unison. Similar to the objectives of conventional combination therapy, these strategies may further improve treatment outcomes through targeted, multi-agent delivery that preserves drug synergy. Also, similar to conventional/unmodified combination therapy, nanomedicine-based drug delivery is often explored at fixed doses. A persistent challenge in all forms of drug administration is that drug synergy is time-dependent, dose-dependent and patient-specific at any given point of treatment. To overcome this challenge, the evolution towards nanomedicine-mediated co-delivery of multiple therapies has made the potential of interfacing artificial intelligence (AI) with nanomedicine to sustain optimization in combinatorial nanotherapy a reality. Specifically, optimizing drug and dose parameters in combinatorial nanomedicine administration is a specific area where AI can actionably realize the full potential of nanomedicine. To this end, this review will examine the role that AI can have in substantially improving nanomedicine-based treatment outcomes, particularly in the context of combination nanotherapy for both N-of-1 and population-optimized treatment.
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Affiliation(s)
- Dean Ho
- Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore.
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Rizvi SMD, Alshammari AAA, Almawkaa WA, Ahmed ABF, Katamesh A, Alafnan A, Almutairi TJ, Alshammari RF. An oncoinformatics study to predict the inhibitory potential of recent FDA-approved anti-cancer drugs against human Polo-like kinase 1 enzyme: a step towards dual-target cancer medication. 3 Biotech 2019; 9:70. [PMID: 30800581 DOI: 10.1007/s13205-019-1594-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 01/23/2019] [Indexed: 10/27/2022] Open
Abstract
Cancer prevalence has increased at an alarming rate worldwide. Complexity, resistance mechanism and multiple compensatory survival pathways of cancer cells have abated the response of currently available cancer medications. Therefore, multi-target agents rather than single target might provide a better solution to these cancer therapy issues. In the present study, anti-PLK1 (Polo-like kinase 1) potential of the eight FDA-approved (2017) anti-cancer drugs have been explored using molecular docking approach. Out of all the tested drugs, brigatinib, niraparib and ribociclib showed better binding affinity towards the 'kinase domain' of PLK1. The Gibbs free binding energy (ΔG) and inhibition constant (K i) values for brigatinib, niraparib and ribociclib interaction with the kinase domain of PLK1 were '- 8.05 kcal/mol and 1.26 µM', '- 8.35 kcal/mol and 0.729 µM' and '- 7.29 kcal/mol and 4.52 µM', respectively. Interestingly, the docking results of these three drugs were better than the known PLK1 inhibitors (BI-2536 and rigosertib). The ΔG and K i values for BI-2536 and rigosertib interaction with the kinase domain of PLK1 were '- 6.8 kcal/mol and 10.38 µM' and '- 6.6 kcal/mol and 14.51 µM', respectively. Brigatinib, niraparib and ribociclib have been approved by FDA for the treatment of non-small cell lung cancer, ovarian/fallopian tube cancer and breast cancer, respectively. PLK1 is regarded as a potential cancer target, and it is specifically over-expressed in different types of cancer cells, including aforementioned cancers. Actually, the target enzymes for anti-cancer action of brigatinib, niraparib and ribociclib are tyrosine kinase, poly(ADP-ribose) polymerase and cyclin-dependent kinase 4/6, respectively. However, based on our outcomes, we could safely state that PLK1 might plausibly emerge as an add-on target for each of these three anti-cancer drugs. We strongly believe that this study would assist in the development of better dual-targeting cancer therapeutic agent in the near future.
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Ali MS, Metwally AA, Fahmy RH, Osman R. Nanodiamonds: Minuscule gems that ferry antineoplastic drugs to resistant tumors. Int J Pharm 2019; 558:165-176. [PMID: 30641180 DOI: 10.1016/j.ijpharm.2018.12.090] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 12/21/2018] [Accepted: 12/27/2018] [Indexed: 10/27/2022]
Abstract
Remarkable efforts are currently devoted to the area of nanodiamonds (NDs) research due to their superior properties viz: biocompatibility, minute size, inert core, and tunable surface chemistry. The use of NDs for the delivery of anticancer drugs has been at the forefront of NDs applications owing to their ability to increase chemosensitivity, sustain drug release, and minimize drug side effects. Accelerated steps towards the move of NDs from bench side to bedside have been recently witnessed. In this review, the effects of NDs production and purification techniques on NDs' final properties are discussed. Special concern is given to studies focusing on NDs use for anticancer drug delivery, stability enhancement and mediated targeted delivery. The aim of this review is to put the results of studies oriented towards NDs-mediated anticancer drug delivery side by side such that the reader can assess the potential use of NDs in clinics and follow up the upcoming results of clinical testing of NDs on animals and humans.
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Affiliation(s)
- Moustafa S Ali
- Department of Pharmaceutics, Faculty of Pharmacy, Ahram Canadian University, 6th of October City, Giza, Egypt.
| | - Abdelkader A Metwally
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt; Department of Pharmaceutics, Faculty of Pharmacy, Health Sciences Center, Kuwait University, Kuwait
| | - Rania H Fahmy
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Cairo University, Egypt; Department of Pharmaceutics, Faculty of Pharmacy, Ahram Canadian University, 6th of October City, Giza, Egypt
| | - Rihab Osman
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt
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Loh KP, Ho D, Chiu GNC, Leong DT, Pastorin G, Chow EKH. Clinical Applications of Carbon Nanomaterials in Diagnostics and Therapy. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2018; 30:e1802368. [PMID: 30133035 DOI: 10.1002/adma.201802368] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 05/28/2018] [Indexed: 06/08/2023]
Abstract
Nanomaterials have the potential to improve how patients are clinically treated and diagnosed. While there are a number of nanomaterials that can be used toward improved drug delivery and imaging, how these nanomaterials confer an advantage over other nanomaterials, as well as current clinical approaches is often application or disease specific. How the unique properties of carbon nanomaterials, such as nanodiamonds, carbon nanotubes, carbon nanofibers, graphene, and graphene oxides, make them promising nanomaterials for a wide range of clinical applications are discussed herein, including treating chemoresistant cancer, enhancing magnetic resonance imaging, and improving tissue regeneration and stem cell banking, among others. Additionally, the strategies for further improving drug delivery and imaging by carbon nanomaterials are reviewed, such as inducing endothelial leakiness as well as applying artificial intelligence toward designing optimal nanoparticle-based drug combination delivery. While the clinical application of carbon nanomaterials is still an emerging field of research, there is substantial preclinical evidence of the translational potential of carbon nanomaterials. Early clinically trial studies are highlighted, further supporting the use of carbon nanomaterials in clinical applications for both drug delivery and imaging.
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Affiliation(s)
- Kian Ping Loh
- Department of Chemistry and Centre for Advanced 2D Materials (CA2DM), National University of Singapore, Singapore, 117543, Singapore
| | - Dean Ho
- Department of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
- Singapore Institute for Neurotechnology (SINAPSE), Singapore, 117456, Singapore
- Biomedical Institute for Global Health Research and Technology (BIGHEART), Singapore, 117599, Singapore
| | - Gigi Ngar Chee Chiu
- Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore
| | - David Tai Leong
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Giorgia Pastorin
- Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore
| | - Edward Kai-Hua Chow
- Cancer Science Institute of Singapore, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
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Tekin E, Yeh PJ, Savage VM. General Form for Interaction Measures and Framework for Deriving Higher-Order Emergent Effects. Front Ecol Evol 2018. [DOI: 10.3389/fevo.2018.00166] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
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Tekin E, White C, Kang TM, Singh N, Cruz-Loya M, Damoiseaux R, Savage VM, Yeh PJ. Prevalence and patterns of higher-order drug interactions in Escherichia coli. NPJ Syst Biol Appl 2018; 4:31. [PMID: 30181902 PMCID: PMC6119685 DOI: 10.1038/s41540-018-0069-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 07/02/2018] [Accepted: 07/03/2018] [Indexed: 01/23/2023] Open
Abstract
Interactions and emergent processes are essential for research on complex systems involving many components. Most studies focus solely on pairwise interactions and ignore higher-order interactions among three or more components. To gain deeper insights into higher-order interactions and complex environments, we study antibiotic combinations applied to pathogenic Escherichia coli and obtain unprecedented amounts of detailed data (251 two-drug combinations, 1512 three-drug combinations, 5670 four-drug combinations, and 13608 five-drug combinations). Directly opposite to previous assumptions and reports, we find higher-order interactions increase in frequency with the number of drugs in the bacteria’s environment. Specifically, as more drugs are added, we observe an elevated frequency of net synergy (effect greater than expected based on independent individual effects) and also increased instances of emergent antagonism (effect less than expected based on lower-order interaction effects). These findings have implications for the potential efficacy of drug combinations and are crucial for better navigating problems associated with the combinatorial complexity of multi-component systems. Interactions play an important role in determining the dynamics of complex systems yet higher-order interactions that involve more than two components are poorly understood. A research team from University of California, Los Angeles led by Pamela Yeh, use a bacteria system to show that higher-order interactions among antibiotics are prevalent and also that there are systematic patterns in how they occur: the frequency of higher-order interactions increases with the number of components, net interactions tend to be more synergistic, and emergent interactions—arising at specific higher-order levels—tend toward antagonism. By detecting patterns in interactions as the number of drugs increases, they provide a method to handle the combinatorial complexity that results from higher-order interactions, yielding a solid foundation for exploring the patterns and consequences of emergent phenomena in other research areas.
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Affiliation(s)
- Elif Tekin
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA.,2Department of Biomathematics, University of California, David Geffen School of Medicine, Los Angeles, CA 90095 USA
| | - Cynthia White
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA
| | - Tina Manzhu Kang
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA
| | - Nina Singh
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA
| | - Mauricio Cruz-Loya
- 2Department of Biomathematics, University of California, David Geffen School of Medicine, Los Angeles, CA 90095 USA
| | - Robert Damoiseaux
- 3California NanoSystems Institute, University of California, Los Angeles, 570 Westwood Plaza, Los Angeles, CA 90095 USA
| | - Van M Savage
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA.,2Department of Biomathematics, University of California, David Geffen School of Medicine, Los Angeles, CA 90095 USA.,4Santa Fe Institute, Santa Fe, NM 87501 USA
| | - Pamela J Yeh
- 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095 USA.,4Santa Fe Institute, Santa Fe, NM 87501 USA
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Balek L, Buchtova M, Kunova Bosakova M, Varecha M, Foldynova-Trantirkova S, Gudernova I, Vesela I, Havlik J, Neburkova J, Turner S, Krzyscik MA, Zakrzewska M, Klimaschewski L, Claus P, Trantirek L, Cigler P, Krejci P. Nanodiamonds as “artificial proteins”: Regulation of a cell signalling system using low nanomolar solutions of inorganic nanocrystals. Biomaterials 2018; 176:106-121. [DOI: 10.1016/j.biomaterials.2018.05.030] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 03/31/2018] [Accepted: 05/19/2018] [Indexed: 12/14/2022]
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Pantuck AJ, Lee DK, Kee T, Wang P, Lakhotia S, Silverman MH, Mathis C, Drakaki A, Belldegrun AS, Ho CM, Ho D. Modulating BET Bromodomain Inhibitor ZEN-3694 and Enzalutamide Combination Dosing in a Metastatic Prostate Cancer Patient Using CURATE.AI, an Artificial Intelligence Platform. ADVANCED THERAPEUTICS 2018. [DOI: 10.1002/adtp.201800104] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Allan J. Pantuck
- Ronald Reagan UCLA Medical Center; Department of Urology; David Geffen School of Medicine; Institute of Urologic Oncology; University of California; 757 Westwood Plaza Los Angeles CA 90095 USA
- Jonsson Comprehensive Cancer Center; University of California; 10833 Le Conte Ave Los Angeles CA 90095 USA
| | - Dong-Keun Lee
- Department of Bioengineering; Henry Samueli School of Engineering and Applied Science; University of California; 410 Westwood Plaza Los Angeles CA 90095 USA
| | - Theodore Kee
- Department of Biomedical Engineering; National University of Singapore; Singapore 117583 Singapore
- Department of Bioengineering; Henry Samueli School of Engineering and Applied Science; University of California; 410 Westwood Plaza Los Angeles CA 90095 USA
| | - Peter Wang
- Department of Chemical and Biomolecular Engineering; Henry Samueli School of Engineering and Applied Science; University of California; 5531 Boelter Hall Los Angeles CA 90095 USA
| | - Sanjay Lakhotia
- Zenith Epigenetics; Suite 4010, 44 Montgomery Street San Francisco CA 94104 USA
- Zenith Epigenetics; 300, 4820 Richard Road SW Calgary AB T3E 6L1 Canada
| | - Michael H. Silverman
- Zenith Epigenetics; Suite 4010, 44 Montgomery Street San Francisco CA 94104 USA
- Zenith Epigenetics; 300, 4820 Richard Road SW Calgary AB T3E 6L1 Canada
| | - Colleen Mathis
- Ronald Reagan UCLA Medical Center; Department of Urology; David Geffen School of Medicine; Institute of Urologic Oncology; University of California; 757 Westwood Plaza Los Angeles CA 90095 USA
| | - Alexandra Drakaki
- Ronald Reagan UCLA Medical Center; Department of Urology; David Geffen School of Medicine; Institute of Urologic Oncology; University of California; 757 Westwood Plaza Los Angeles CA 90095 USA
- Department of Medicine; Division of Hematology & Oncology; David Geffen School of Medicine; University of California; 10833 Le Conte Ave. 11-934 Factor Bldg. Los Angeles CA 90095 USA
| | - Arie S. Belldegrun
- Ronald Reagan UCLA Medical Center; Department of Urology; David Geffen School of Medicine; Institute of Urologic Oncology; University of California; 757 Westwood Plaza Los Angeles CA 90095 USA
| | - Chih-Ming Ho
- Jonsson Comprehensive Cancer Center; University of California; 10833 Le Conte Ave Los Angeles CA 90095 USA
- Department of Bioengineering; Henry Samueli School of Engineering and Applied Science; University of California; 410 Westwood Plaza Los Angeles CA 90095 USA
- Department of Mechanical and Aerospace Engineering; Henry Samueli School of Engineering and Applied Science; University of California; 420 Westwood Plaza Los Angeles CA 90095 USA
| | - Dean Ho
- Department of Biomedical Engineering; National University of Singapore; Singapore 117583 Singapore
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Rashid MBMA, Toh TB, Hooi L, Silva A, Zhang Y, Tan PF, Teh AL, Karnani N, Jha S, Ho CM, Chng WJ, Ho D, Chow EKH. Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP). Sci Transl Med 2018; 10:10/453/eaan0941. [DOI: 10.1126/scitranslmed.aan0941] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 03/29/2018] [Accepted: 07/20/2018] [Indexed: 12/12/2022]
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Abstract
The development of novel nanoparticles consisting of both diagnostic and therapeutic components has increased over the past decade. These "theranostic" nanoparticles have been tailored toward one or more types of imaging modalities and have been developed for optical imaging, magnetic resonance imaging, ultrasound, computed tomography, and nuclear imaging comprising both single-photon computed tomography and positron emission tomography. In this review, we focus on state-of-the-art theranostic nanoparticles that are capable of both delivering therapy and self-reporting/tracking disease through imaging. We discuss challenges and the opportunity to rapidly adjust treatment for individualized medicine.
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Affiliation(s)
- Cristina Zavaleta
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Dean Ho
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA, USA
- Weintraub Center for Reconstructive Biotechnology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eun Ji Chung
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
- Department of Stem Cell Biology and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA
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