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Blasiak A, Tan LWJ, Chong LM, Tadeo X, Truong ATL, Senthil Kumar K, Sapanel Y, Poon M, Sundar R, de Mel S, Ho D. Personalized dose selection for the first Waldenström macroglobulinemia patient on the PRECISE CURATE.AI trial. NPJ Digit Med 2024; 7:223. [PMID: 39191913 DOI: 10.1038/s41746-024-01195-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 07/12/2024] [Indexed: 08/29/2024] Open
Abstract
The digital revolution in healthcare, amplified by the COVID-19 pandemic and artificial intelligence (AI) advances, has led to a surge in the development of digital technologies. However, integrating digital health solutions, especially AI-based ones, in rare diseases like Waldenström macroglobulinemia (WM) remains challenging due to limited data, among other factors. CURATE.AI, a clinical decision support system, offers an alternative to big data approaches by calibrating individual treatment profiles based on that individual's data alone. We present a case study from the PRECISE CURATE.AI trial with a WM patient, where, over two years, CURATE.AI provided dynamic Ibrutinib dose recommendations to clinicians (users) aimed at achieving optimal IgM levels. An 80-year-old male with newly diagnosed WM requiring treatment due to anemia was recruited to the trial for CURATE.AI-based dosing of the Bruton tyrosine kinase inhibitor Ibrutinib. The primary and secondary outcome measures were focused on scientific and logistical feasibility. Preliminary results underscore the platform's potential in enhancing user and patient engagement, in addition to clinical efficacy. Based on a two-year-long patient enrollment into the CURATE.AI-augmented treatment, this study showcases how AI-enabled tools can support the management of rare diseases, emphasizing the integration of AI to enhance personalized therapy.
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Affiliation(s)
- Agata Blasiak
- The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
- Roche Information Solutions, Santa Clara, California, USA.
| | - Lester W J Tan
- The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Li Ming Chong
- The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Xavier Tadeo
- The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
| | - Anh T L Truong
- The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Kirthika Senthil Kumar
- The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
| | - Yoann Sapanel
- The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore
| | - Michelle Poon
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Department of Haematology-Oncology, National University Cancer Institute (NCIS), National University Hospital, Singapore, 119228, Singapore
| | - Raghav Sundar
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Department of Haematology-Oncology, National University Cancer Institute (NCIS), National University Hospital, Singapore, 119228, Singapore
- Singapore Gastric Cancer Consortium, Department of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Sanjay de Mel
- Department of Haematology-Oncology, National University Cancer Institute (NCIS), National University Hospital, Singapore, 119228, Singapore.
| | - Dean Ho
- The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
- Singapore Gastric Cancer Consortium, Department of Medicine, National University of Singapore, Singapore, 119228, Singapore.
- The Bia-Echo Asia Centre for Reproductive Longevity and Equality (ACRLE), National University of Singapore, Singapore, 117456, Singapore.
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2
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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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3
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Glycosylated triptolide affords a potent in vivo therapeutic activity to hepatocellular carcinoma in mouse model. Med Chem Res 2022. [DOI: 10.1007/s00044-022-03008-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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4
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Blasiak A, Truong ATL, Remus A, Hooi L, Seah SGK, Wang P, Chye DH, Lim APC, Ng KT, Teo ST, Tan YJ, Allen DM, Chai LYA, Chng WJ, Lin RTP, Lye DCB, Wong JEL, Tan GYG, Chan CEZ, Chow EKH, Ho D. 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] [Key Words] [Grants] [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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Affiliation(s)
- Agata Blasiak
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
| | - Anh T L Truong
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Alexandria Remus
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Lissa Hooi
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore
| | - Shirley Gek Kheng Seah
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Peter Wang
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - De Hoe Chye
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Angeline Pei Chiew Lim
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Kim Tien Ng
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Swee Teng Teo
- Infectious Diseases Translational Research Program, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore
| | - Yee-Joo Tan
- Infectious Diseases Translational Research Program, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117545, Singapore
- Institute of Molecular and Cell Biology (IMCB), A*STAR, Singapore, 138673, Singapore
| | - David Michael Allen
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Division of Infectious Diseases, National University Hospital, Singapore, 119074, Singapore
| | - Louis Yi Ann Chai
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Division of Infectious Diseases, National University Hospital, Singapore, 119074, Singapore
| | - Wee Joo Chng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Hospital, Singapore, 119074, Singapore
- NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
| | - Raymond T P Lin
- National Centre for Infectious Diseases (NCID), Jalan Tan Tock Seng, Singapore, 308442, Singapore
- Department of Laboratory Medicine, National University Hospital, Singapore, 119074, Singapore
| | - David C B Lye
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- National Centre for Infectious Diseases (NCID), Jalan Tan Tock Seng, Singapore, 308442, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- Department of Infectious Diseases, Tan Tock Seng Hospital, Singapore, 308433, Singapore
| | - John Eu-Li Wong
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Department of Haematology-Oncology, National University Cancer Institute, Singapore, National University Hospital, Singapore, 119074, Singapore
| | - Gek-Yen Gladys Tan
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore
| | - Conrad En Zuo Chan
- Defence Medical and Environmental Research Institute, DSO National Laboratories, Singapore, 117510, Singapore.
- National Centre for Infectious Diseases (NCID), Jalan Tan Tock Seng, Singapore, 308442, Singapore.
| | - Edward Kai-Hua Chow
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore.
- NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
| | - Dean Ho
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
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Szablewski L. Glucose transporters as markers of diagnosis and prognosis in cancer diseases. Oncol Rev 2022; 16:561. [PMID: 35340885 PMCID: PMC8941341 DOI: 10.4081/oncol.2022.561] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 11/24/2021] [Indexed: 11/22/2022] Open
Abstract
The primary metabolic substrate for cells is glucose, which acts as both a source of energy and a substrate in several processes. However, being lipophilic, the cell membrane is impermeable to glucose and specific carrier proteins are needed to allow transport. In contrast to normal cells, cancer cells are more likely to generate energy by glycolysis; as this process generates fewer molecules of adenosine triphosphate (ATP) than complete oxidative breakdown, more glucose molecules are needed. The increased demand for glucose in cancer cells is satisfied by overexpression of a number of glucose transporters, and decreased levels of others. As specific correlations have been observed between the occurrence of cancer and the expression of glucose carrier proteins, the presence of changes in expression of glucose transporters may be treated as a marker of diagnosis and/or prognosis for cancer patients.
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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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7
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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] [Grants] [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 MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
| | - Boqian Wang
- Institute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
| | - Feng Qian
- Ministry of Education Key Laboratory of Contemporary AnthropologyHuman Phenome InstituteSchool of Life SciencesFudan UniversityShanghai200438China
| | - Theodore Kee
- The N.1 Institute for Health (N.1)National University of SingaporeSingapore117456Singapore
- The Institute for Digital Medicine (WisDM)Yong Loo Lin School of MedicineNational University of SingaporeSingapore11756Singapore
- Department of Biomedical EngineeringNUS EngineeringNational University of SingaporeSingapore117583Singapore
| | - Agata Blasiak
- The N.1 Institute for Health (N.1)National University of SingaporeSingapore117456Singapore
- The Institute for Digital Medicine (WisDM)Yong Loo Lin School of MedicineNational University of SingaporeSingapore11756Singapore
- Department of Biomedical EngineeringNUS EngineeringNational University of SingaporeSingapore117583Singapore
| | - Yoong Hun Ong
- The N.1 Institute for Health (N.1)National University of SingaporeSingapore117456Singapore
| | - Lissa Hooi
- Cancer Science Institute of SingaporeNational University of SingaporeSingapore117599Singapore
| | | | | | - Gene G. Olinger
- Global Health Surveillance and Diagnostic DivisionMRIGlobalGaithersburgMD20878USA
- Boston University School of MedicineDivision of Infectious DiseasesBostonMA02118USA
| | - Jussi Keppo
- NUS Business School and Institute of Operations Research and AnalyticsNational University of SingaporeSingapore119245Singapore
| | - Chris L. Hardesty
- KPMG Global Health and Life Sciences Centre of ExcellenceSingapore048581Singapore
| | - Edward K. Chow
- The N.1 Institute for Health (N.1)National University of SingaporeSingapore117456Singapore
- Cancer Science Institute of SingaporeNational University of SingaporeSingapore117599Singapore
- Department of PharmacologyYong Loo Lin School of MedicineNational University of SingaporeSingapore117600Singapore
| | - Dean Ho
- The N.1 Institute for Health (N.1)National University of SingaporeSingapore117456Singapore
- The Institute for Digital Medicine (WisDM)Yong Loo Lin School of MedicineNational University of SingaporeSingapore11756Singapore
- Department of Biomedical EngineeringNUS EngineeringNational University of SingaporeSingapore117583Singapore
- Department of PharmacologyYong Loo Lin School of MedicineNational University of SingaporeSingapore117600Singapore
| | - Xianting Ding
- Institute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200030China
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8
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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: 126] [Impact Index Per Article: 31.5] [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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9
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Zarrinpar A, Kim UB, Boominathan V. Phenotypic Response and Personalized Medicine in Liver Cancer and Transplantation: Approaches to Complex Systems. ADVANCED THERAPEUTICS 2020. [DOI: 10.1002/adtp.201900167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Ali Zarrinpar
- Department of Surgery, College of MedicineUniversity of Florida Gainesville FL 32610 USA
- Department of Biochemistry and Molecular Biology, College of MedicineUniversity of Florida Gainesville FL 32610 USA
- Department of Bioengineering, Herbert Wertheim College of EngineeringUniversity of Florida Gainesville FL 32610 USA
| | - Un Bi Kim
- Department of Surgery, College of MedicineUniversity of Florida Gainesville FL 32610 USA
| | - Vijay Boominathan
- Department of Surgery, College of MedicineUniversity of Florida Gainesville FL 32610 USA
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10
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Application of an ex-vivo drug sensitivity platform towards achieving complete remission in a refractory T-cell lymphoma. Blood Cancer J 2020; 10:9. [PMID: 31988286 PMCID: PMC6985240 DOI: 10.1038/s41408-020-0276-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 01/05/2020] [Accepted: 01/10/2020] [Indexed: 01/02/2023] Open
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11
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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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12
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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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13
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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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14
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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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15
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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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16
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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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17
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Yin Z, Deng Z, Zhao W, Cao Z. Searching Synergistic Dose Combinations for Anticancer Drugs. Front Pharmacol 2018; 9:535. [PMID: 29872399 PMCID: PMC5972206 DOI: 10.3389/fphar.2018.00535] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Accepted: 05/03/2018] [Indexed: 01/01/2023] Open
Abstract
Recent development has enabled synergistic drugs in treating a wide range of cancers. Being highly context-dependent, however, identification of successful ones often requires screening of combinational dose on different testing platforms in order to gain the best anticancer effects. To facilitate the development of effective computational models, we reviewed the latest strategy in searching optimal dose combination from three perspectives: (1) mainly experimental-based approach; (2) Computational-guided experimental approach; and (3) mainly computational-based approach. In addition to the introduction of each strategy, critical discussion of their advantages and disadvantages were also included, with a strong focus on the current applications and future improvements.
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Affiliation(s)
- Zuojing Yin
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Zeliang Deng
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Wenyan Zhao
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Zhiwei Cao
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
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18
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Wang X, Gu M, Toh TB, Abdullah NLB, Chow EKH. Stimuli-Responsive Nanodiamond-Based Biosensor for Enhanced Metastatic Tumor Site Detection. SLAS Technol 2017; 23:44-56. [PMID: 29020497 DOI: 10.1177/2472630317735497] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Metastasis is often critical to cancer progression and linked to poor survival and drug resistance. Early detection of metastasis, as well as identification of metastatic tumor sites, can improve cancer patient survival. Thus, developing technology to improve the detection of cancer metastasis biomarkers can improve both diagnosis and treatment. In this study, we investigated the use of nanodiamonds to develop a stimuli-responsive metastasis detection complex that utilizes matrix metalloproteinase 9 (MMP9) as a metastasis biomarker, as MMP9 increased expression has been shown to be indicative of metastasis. The nanodiamond-MMP9 biosensor complex consists of nanodiamonds functionalized with MMP9-specific fluorescent-labeled substrate peptides. Using this design, protease activity of MMP9 can be accurately measured and correlated to MMP9 expression. The nanodiamond-MMP9 biosensor also demonstrated an enhanced ability to protect the base sensor peptide from nonspecific serum protease cleavage. This enhanced peptide stability, combined with a quantitative stimuli-responsive output function, provides strong evidence for the further development of a nanodiamond-MMP9 biosensor for metastasis site detection. More importantly, this work provides the foundation for use of nanodiamonds as a platform for stimuli-responsive biosensors and theranostic complexes that can be implemented across a wide range of biomedical applications.
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Affiliation(s)
- Xin Wang
- 1 Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mengjie Gu
- 1 Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tan Boon Toh
- 2 Cancer Science Institute of Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Nurrul Lissa Binti Abdullah
- 2 Cancer Science Institute of Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Edward Kai-Hua Chow
- 1 Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,2 Cancer Science Institute of Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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19
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Bioactive constituents from the whole plants of Leontopodium leontopodioides (Wild.) Beauv. J Nat Med 2017; 72:202-210. [DOI: 10.1007/s11418-017-1132-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Accepted: 09/12/2017] [Indexed: 10/18/2022]
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20
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Wong I, Liu W, Ho CM, Ding X. Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization. SLAS Technol 2017; 22:289-305. [PMID: 28378610 DOI: 10.1177/2472630317690318] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction-based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.
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Affiliation(s)
- Ieong Wong
- 1 School of Biomedical Engineering, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China.,2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Wenjia Liu
- 2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Chih-Ming Ho
- 1 School of Biomedical Engineering, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China.,2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Xianting Ding
- 2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
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21
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Abstract
Liver cancer is fundamentally physiologically different from the surrounding liver tissue. Despite multiple efforts to target the altered signaling pathways created by oncogenic mutations, not many have focused on targeting the altered metabolism that allows liver cancer to develop and grow. Still to be resolved is the question of whether the altered metabolic pathways in this cancer differ enough from the surrounding noncancerous cells to allow for the development of potent and specific compounds. Clinical studies of metabolic modulators would provide some more information with regard to the feasibility of this approach. Furthermore, as it appears that oncogenic signaling is essential to this cancer's altered metabolism, it stands to reason that targeting this altered signaling may allow the exploitation of specific metabolic vulnerabilities in combination with other drugs for enhanced efficacy. The identification of biomarkers of metabolic sensitivity will also be essential to determine whether these drugs will have the desired effect.
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Affiliation(s)
- Ali Zarrinpar
- 1 Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
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22
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Fong ELS, Toh TB, Yu H, Chow EKH. 3D Culture as a Clinically Relevant Model for Personalized Medicine. SLAS Technol 2017; 22:245-253. [PMID: 28277923 DOI: 10.1177/2472630317697251] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Advances in understanding many of the fundamental mechanisms of cancer progression have led to the development of molecular targeted therapies. While molecular targeted therapeutics continue to improve the outcome for cancer patients, tumor heterogeneity among patients, as well as intratumoral heterogeneity, limits the efficacy of these drugs to specific patient subtypes, as well as contributes to relapse. Thus, there is a need for a more personalized approach toward drug development and diagnosis that takes into account the diversity of cancer patients, as well as the complex milieu of tumor cells within a single patient. Three-dimensional (3D) culture systems paired with patient-derived xenografts or patient-derived organoids may provide a more clinically relevant system to address issues presented by personalized or precision medical approaches. In this review, we cover the current methods available for applying 3D culture systems toward personalized cancer research and drug development, as well as key challenges that must be addressed in order to fully realize the potential of 3D patient-derived culture systems for cancer drug development. Greater implementation of 3D patient-derived culture systems in the cancer research field should accelerate the development of truly personalized medical therapies for cancer patients.
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Affiliation(s)
- Eliza Li Shan Fong
- 1 Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tan Boon Toh
- 2 Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Hanry Yu
- 1 Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,3 Institute of Bioengineering and Nanotechnology, A*STAR, Singapore.,6 Mechanobiology Institute, National University of Singapore, Singapore
| | - Edward Kai-Hua Chow
- 2 Cancer Science Institute of Singapore, National University of Singapore, Singapore.,8 Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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23
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Lee BY, Clemens DL, Silva A, Dillon BJ, Masleša-Galić S, Nava S, Ding X, Ho CM, Horwitz MA. Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time. Nat Commun 2017; 8:14183. [PMID: 28117835 PMCID: PMC5287291 DOI: 10.1038/ncomms14183] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 12/05/2016] [Indexed: 12/12/2022] Open
Abstract
The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we identified several experimental drug regimens among billions of possible drug-dose combinations that outperform the current standard regimen. Here we use this platform to optimize the in vivo drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill M. tuberculosis much more rapidly than the standard regimen and reduce treatment time to relapse-free cure by 75%. Thus, these regimens have the potential to provide a markedly shorter course of treatment for tuberculosis in humans. As these regimens omit isoniazid, rifampicin, fluoroquinolones and injectable aminoglycosides, they would be suitable for treating many cases of multidrug and extensively drug-resistant tuberculosis. Current antibiotic therapies for tuberculosis are lengthy and onerous. Here, the authors use an output-driven approach to optimize drug doses for two experimental drug regimens in a mouse model of tuberculosis, leading to improved regimens that reduce treatment time by 75%.
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Affiliation(s)
- Bai-Yu Lee
- Division of Infectious Diseases, Department of Medicine, University of California, Los Angeles, California 90095, USA
| | - Daniel L Clemens
- Division of Infectious Diseases, Department of Medicine, University of California, Los Angeles, California 90095, USA
| | - Aleidy Silva
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, California 90095, USA
| | - Barbara Jane Dillon
- Division of Infectious Diseases, Department of Medicine, University of California, Los Angeles, California 90095, USA
| | - Saša Masleša-Galić
- Division of Infectious Diseases, Department of Medicine, University of California, Los Angeles, California 90095, USA
| | - Susana Nava
- Division of Infectious Diseases, Department of Medicine, University of California, Los Angeles, California 90095, USA
| | - Xianting Ding
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Chih-Ming Ho
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, California 90095, USA.,Department of Bioengineering, University of California, Los Angeles, California 90095, USA
| | - Marcus A Horwitz
- Division of Infectious Diseases, Department of Medicine, University of California, Los Angeles, California 90095, USA
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24
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Lee DK, Chang VY, Kee T, Ho CM, Ho D. Optimizing Combination Therapy for Acute Lymphoblastic Leukemia Using a Phenotypic Personalized Medicine Digital Health Platform: Retrospective Optimization Individualizes Patient Regimens to Maximize Efficacy and Safety. SLAS Technol 2016; 22:276-288. [PMID: 27920397 DOI: 10.1177/2211068216681979] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Acute lymphoblastic leukemia (ALL) is a blood cancer that is characterized by the overproduction of lymphoblasts in the bone marrow. Treatment for pediatric ALL typically uses combination chemotherapy in phases, including a prolonged maintenance phase with oral methotrexate and 6-mercaptopurine, which often requires dose adjustment to balance side effects and efficacy. However, a major challenge confronting combination therapy for virtually every disease indication is the inability to pinpoint drug doses that are optimized for each patient, and the ability to adaptively and continuously optimize these doses to address comorbidities and other patient-specific physiological changes. To address this challenge, we developed a powerful digital health technology platform based on phenotypic personalized medicine (PPM). PPM identifies patient-specific maps that parabolically correlate drug inputs with phenotypic outputs. In a disease mechanism-independent fashion, we individualized drug ratios/dosages for two pediatric patients with standard-risk ALL in this study via PPM-mediated retrospective optimization. PPM optimization demonstrated that dynamically adjusted dosing of combination chemotherapy could enhance treatment outcomes while also substantially reducing the amount of chemotherapy administered. This may lead to more effective maintenance therapy, with the potential for shortening duration and reducing the risk of serious side effects.
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Affiliation(s)
- Dong-Keun Lee
- 1 Division of Oral Biology and Medicine, School of Dentistry, UCLA, Los Angeles, CA, USA
| | - Vivian Y Chang
- 2 Division of Pediatric Hematology and Oncology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.,3 Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA
| | - Theodore Kee
- 4 Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, UCLA, Los Angeles, CA, USA
| | - Chih-Ming Ho
- 3 Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA.,4 Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, UCLA, Los Angeles, CA, USA.,5 Department of Mechanical and Aerospace Engineering, Henry Samueli School of Engineering and Applied Science, UCLA, Los Angeles, CA, USA
| | - Dean Ho
- 1 Division of Oral Biology and Medicine, School of Dentistry, UCLA, Los Angeles, CA, USA.,3 Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA.,4 Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, UCLA, Los Angeles, CA, USA.,6 Jane and Jerry Weintraub Center for Reconstructive Biotechnology, UCLA, Los Angeles, CA, USA.,7 California NanoSystems Institute, UCLA, Los Angeles, CA, USA
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Ho D, Zarrinpar A, Chow EKH. Diamonds, Digital Health, and Drug Development: Optimizing Combinatorial Nanomedicine. ACS NANO 2016; 10:9087-9092. [PMID: 27682869 DOI: 10.1021/acsnano.6b06174] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The field of nanomedicine has already seen substantial progress in the clinic, with multiple formulations being evaluated through clinical studies. From poly(lactic-co-glycolic acid) and cyclodextrin-based drug-delivery platforms to metallic nanoparticles for photothermal treatment and imaging, nanotechnology has enabled versatile strategies to treat and to diagnose a wide range of disorders. However, as the field as a whole pushes forward, barriers that have always challenged conventional drug development have already started to impact nanomedicine translation. These include exorbitant costs, substantial time to development, and the uncertainty of achieving major improvements in efficacy or safety. Of note, there has been, until recent advances, a virtual inability to identify optimal drug doses either as monotherapies or components of combination therapy. In this Nano Focus, we assess how the impact of nanotechnology in the clinic can be optimized through systematically designed combinatorial nanotherapy. In addition, we provide a clinical perspective on how a recently unveiled technology platform can substantially alter the landscape of combinatorial nanomedicine, drug development, as well as conventional drug development.
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Affiliation(s)
- Dean Ho
- Division of Oral Biology and Medicine, School of Dentistry, ‡The Jane and Jerry Weintraub Center for Reconstructive Biotechnology, §Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, ∥California NanoSystems Institute, ⊥Jonsson Comprehensive Cancer Center, #Department of Surgery, Division of Liver and Pancreas Transplantation, David Geffen School of Medicine, and ¶Dumont-UCLA Liver Transplant and Cancer Center, University of California , Los Angeles, California 90095, United States
- Cancer Science Institute of Singapore and ▽Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 117599
| | - Ali Zarrinpar
- Division of Oral Biology and Medicine, School of Dentistry, ‡The Jane and Jerry Weintraub Center for Reconstructive Biotechnology, §Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, ∥California NanoSystems Institute, ⊥Jonsson Comprehensive Cancer Center, #Department of Surgery, Division of Liver and Pancreas Transplantation, David Geffen School of Medicine, and ¶Dumont-UCLA Liver Transplant and Cancer Center, University of California , Los Angeles, California 90095, United States
- Cancer Science Institute of Singapore and ▽Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 117599
| | - Edward Kai-Hua Chow
- Division of Oral Biology and Medicine, School of Dentistry, ‡The Jane and Jerry Weintraub Center for Reconstructive Biotechnology, §Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, ∥California NanoSystems Institute, ⊥Jonsson Comprehensive Cancer Center, #Department of Surgery, Division of Liver and Pancreas Transplantation, David Geffen School of Medicine, and ¶Dumont-UCLA Liver Transplant and Cancer Center, University of California , Los Angeles, California 90095, United States
- Cancer Science Institute of Singapore and ▽Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 117599
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Moore L, Yang J, Lan TTH, Osawa E, Lee DK, Johnson WD, Xi J, Chow EKH, Ho D. Biocompatibility Assessment of Detonation Nanodiamond in Non-Human Primates and Rats Using Histological, Hematologic, and Urine Analysis. ACS NANO 2016; 10:7385-400. [PMID: 27439019 DOI: 10.1021/acsnano.6b00839] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Detonation nanodiamonds (DNDs) have been widely explored for biomedical applications ranging from cancer therapy to magnetic resonance imaging due to several promising properties. These include faceted surfaces that mediate potent drug binding and water coordination that have resulted in marked enhancements to the efficacy and safety of drug delivery and imaging. In addition, scalable processing of DNDs yields uniform particles. Furthermore, a broad spectrum of biocompatibility studies has shown that DNDs appear to be well-tolerated. Prior to the clinical translation of DNDs for indications that are addressed via intravenous administration, comprehensive assessment of DND safety in both small and large animal preclinical models is needed. This article reports the results of a DND biocompatibility study in both non-human primates and rats. The rat study was performed as a multiple dose subacute investigation in two cohorts that lasted for 2 weeks and included histological, serum, and urine analysis. The non-human primate study was performed as a dual gender, multiple dose, and long-term investigation in both standard/clinically relevant and elevated dosing cohorts that lasted for 6 months and included comprehensive serum, urine, histological, and body weight analysis. The results from these studies indicate that NDs are well-tolerated at clinically relevant doses. Examination of dose-dependent changes in biomarker levels provides important guidance for the downstream in-human validation of DNDs for clinical drug delivery and imaging.
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Affiliation(s)
- Laura Moore
- Department of Biomedical Engineering, Northwestern University , Evanston, Illinois 60208, United States
| | - Junyu Yang
- Department of Biomedical Engineering, Peking University , Beijing, China 100871
| | - Thanh T Ha Lan
- Alverno Clinical Laboratories , Hammond, Indiana 46324, United States
| | - Eiji Osawa
- NanoCarbon Research Institute, Asama Research Extension Centre, Shinshu University , Ueda, Nagano 386-8567, Japan
| | | | - William D Johnson
- Life Sciences Group, IIT Research Institute , Chicago, Illinois 60616, United States
| | - Jianzhong Xi
- Department of Biomedical Engineering, Peking University , Beijing, China 100871
| | - Edward Kai-Hua Chow
- Cancer Science Institute of Singapore, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 117599
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore , Singapore 117600
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Affiliation(s)
- Xianting Ding
- School of Biomedical Engineering, Institute for Personalized Medicine, Shanghai Jiao Tong University, Med-X Research Institute, Shanghai, China
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Zarrinpar A, Lee DK, Silva A, Datta N, Kee T, Eriksen C, Weigle K, Agopian V, Kaldas F, Farmer D, Wang SE, Busuttil R, Ho CM, Ho D. Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform. Sci Transl Med 2016; 8. [DOI: 10.1126/scitranslmed.aac5954] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Abstract
Postoperative liver transplant immunosuppression was personalized using a phenotypic, disease mechanism–independent and indication-agnostic approach.
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Affiliation(s)
- Ali Zarrinpar
- Division of Liver and Pancreas Transplantation, Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Dong-Keun Lee
- Division of Oral Biology and Medicine and the Jane and Jerry Weintraub Center for Reconstructive Biotechnology, School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Aleidy Silva
- Department of Mechanical Engineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nakul Datta
- Division of Liver and Pancreas Transplantation, Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Theodore Kee
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Calvin Eriksen
- Division of Liver and Pancreas Transplantation, Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Keri Weigle
- Division of Liver and Pancreas Transplantation, Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Vatche Agopian
- Division of Liver and Pancreas Transplantation, Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Fady Kaldas
- Division of Liver and Pancreas Transplantation, Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Douglas Farmer
- Division of Liver and Pancreas Transplantation, Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Sean E. Wang
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ronald Busuttil
- Division of Liver and Pancreas Transplantation, Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Chih-Ming Ho
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Mechanical Engineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Dean Ho
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Division of Oral Biology and Medicine and the Jane and Jerry Weintraub Center for Reconstructive Biotechnology, School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
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Chow EKH. JALA Special Issue: High-Throughput Imaging. ACTA ACUST UNITED AC 2016; 21:234-7. [PMID: 26887980 DOI: 10.1177/2211068216629734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2016] [Indexed: 11/17/2022]
Affiliation(s)
- Edward Kai-Hua Chow
- Cancer Science Institute of Singapore, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Ho D, Wang CHK, Chow EKH. Nanodiamonds: The intersection of nanotechnology, drug development, and personalized medicine. SCIENCE ADVANCES 2015; 1:e1500439. [PMID: 26601235 PMCID: PMC4643796 DOI: 10.1126/sciadv.1500439] [Citation(s) in RCA: 109] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 07/20/2015] [Indexed: 05/07/2023]
Abstract
The implementation of nanomedicine in cellular, preclinical, and clinical studies has led to exciting advances ranging from fundamental to translational, particularly in the field of cancer. Many of the current barriers in cancer treatment are being successfully addressed using nanotechnology-modified compounds. These barriers include drug resistance leading to suboptimal intratumoral retention, poor circulation times resulting in decreased efficacy, and off-target toxicity, among others. The first clinical nanomedicine advances to overcome these issues were based on monotherapy, where small-molecule and nucleic acid delivery demonstrated substantial improvements over unmodified drug administration. Recent preclinical studies have shown that combination nanotherapies, composed of either multiple classes of nanomaterials or a single nanoplatform functionalized with several therapeutic agents, can image and treat tumors with improved efficacy over single-compound delivery. Among the many promising nanomaterials that are being developed, nanodiamonds have received increasing attention because of the unique chemical-mechanical properties on their faceted surfaces. More recently, nanodiamond-based drug delivery has been included in the rational and systematic design of optimal therapeutic combinations using an implicitly de-risked drug development platform technology, termed Phenotypic Personalized Medicine-Drug Development (PPM-DD). The application of PPM-DD to rapidly identify globally optimized drug combinations successfully addressed a pervasive challenge confronting all aspects of drug development, both nano and non-nano. This review will examine various nanomaterials and the use of PPM-DD to optimize the efficacy and safety of current and future cancer treatment. How this platform can accelerate combinatorial nanomedicine and the broader pharmaceutical industry toward unprecedented clinical impact will also be discussed.
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Affiliation(s)
- Dean Ho
- Division of Oral Biology and Medicine, University of California, Los Angeles (UCLA) School of Dentistry, Los Angeles, CA 90095, USA
- Department of Bioengineering, UCLA School of Engineering and Applied Science, Los Angeles, CA 90095, USA
- The Jane and Jerry Weintraub Center for Reconstructive Biotechnology, UCLA School of Dentistry, Los Angeles, CA 90095, USA
- California NanoSystems Institute, UCLA, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90095, USA
- Corresponding author. E-mail: (D. H.); (E. K.-H. C.)
| | | | - Edward Kai-Hua Chow
- Cancer Science Institute of Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 177599, Singapore
- National University Cancer Institute, Singapore, Singapore 119082, Singapore
- Corresponding author. E-mail: (D. H.); (E. K.-H. C.)
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